Data cleaning and EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_2018 = pd.read_csv(\"Data/marathon_cologne_2018.csv\").iloc[:,1:]\n",
    "marathon_cologne_2019 = pd.read_csv(\"Data/marathon_cologne_2019.csv\").iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First combine 2018 with 2019 results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_2018['year']=2018\n",
    "marathon_cologne_2019['year']=2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_df = marathon_cologne_2018.append(marathon_cologne_2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall_place</th>\n",
       "      <th>bib</th>\n",
       "      <th>name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>gender_place</th>\n",
       "      <th>age_group_place</th>\n",
       "      <th>netto_time</th>\n",
       "      <th>brutto_time</th>\n",
       "      <th>5km</th>\n",
       "      <th>10km</th>\n",
       "      <th>15km</th>\n",
       "      <th>hm</th>\n",
       "      <th>25km</th>\n",
       "      <th>30km</th>\n",
       "      <th>35km</th>\n",
       "      <th>40km</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.</td>\n",
       "      <td>1</td>\n",
       "      <td>Tobias BLUM*</td>\n",
       "      <td>1995</td>\n",
       "      <td>GER</td>\n",
       "      <td>LC Rehlingen</td>\n",
       "      <td>1. M</td>\n",
       "      <td>1. Elite</td>\n",
       "      <td>2:16:55</td>\n",
       "      <td>2:16:57</td>\n",
       "      <td>15:44</td>\n",
       "      <td>31:44</td>\n",
       "      <td>47:42</td>\n",
       "      <td>1:07:20</td>\n",
       "      <td>1:19:40</td>\n",
       "      <td>1:35:43</td>\n",
       "      <td>1:51:41</td>\n",
       "      <td>2:09:11</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.</td>\n",
       "      <td>4</td>\n",
       "      <td>Marcin BLAZINSKI*</td>\n",
       "      <td>1988</td>\n",
       "      <td>GER</td>\n",
       "      <td>LG farbtex Nordschwarzwald</td>\n",
       "      <td>2. M</td>\n",
       "      <td>2. Elite</td>\n",
       "      <td>2:20:04</td>\n",
       "      <td>2:20:06</td>\n",
       "      <td>15:40</td>\n",
       "      <td>31:39</td>\n",
       "      <td>47:34</td>\n",
       "      <td>1:07:14</td>\n",
       "      <td>1:19:44</td>\n",
       "      <td>1:36:46</td>\n",
       "      <td>1:53:55</td>\n",
       "      <td>2:12:18</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.</td>\n",
       "      <td>2</td>\n",
       "      <td>Dominik FABIANOWSKI*</td>\n",
       "      <td>1989</td>\n",
       "      <td>GER</td>\n",
       "      <td>ASV Köln</td>\n",
       "      <td>3. M</td>\n",
       "      <td>3. Elite</td>\n",
       "      <td>2:21:17</td>\n",
       "      <td>2:21:19</td>\n",
       "      <td>16:37</td>\n",
       "      <td>33:10</td>\n",
       "      <td>49:43</td>\n",
       "      <td>1:10:11</td>\n",
       "      <td>1:23:12</td>\n",
       "      <td>1:40:09</td>\n",
       "      <td>1:56:26</td>\n",
       "      <td>2:13:42</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.</td>\n",
       "      <td>3</td>\n",
       "      <td>Christian SCHREINER*</td>\n",
       "      <td>1986</td>\n",
       "      <td>GER</td>\n",
       "      <td>LAZ Puma Rhein-Sieg</td>\n",
       "      <td>4. M</td>\n",
       "      <td>4. Elite</td>\n",
       "      <td>2:23:46</td>\n",
       "      <td>2:23:48</td>\n",
       "      <td>16:38</td>\n",
       "      <td>33:10</td>\n",
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       "      <td>1:40:09</td>\n",
       "      <td>1:57:06</td>\n",
       "      <td>2:15:46</td>\n",
       "      <td>2018.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5.</td>\n",
       "      <td>7</td>\n",
       "      <td>Nikki JOHNSTONE*</td>\n",
       "      <td>1984</td>\n",
       "      <td>GBR</td>\n",
       "      <td>ART-Düsseldorf</td>\n",
       "      <td>5. M</td>\n",
       "      <td>5. Elite</td>\n",
       "      <td>2:25:43</td>\n",
       "      <td>2:25:46</td>\n",
       "      <td>17:13</td>\n",
       "      <td>34:47</td>\n",
       "      <td>52:21</td>\n",
       "      <td>1:13:41</td>\n",
       "      <td>1:26:57</td>\n",
       "      <td>1:44:09</td>\n",
       "      <td>2:00:44</td>\n",
       "      <td>2:18:18</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  overall_place bib                  name birth_year nationality  \\\n",
       "0            1.   1          Tobias BLUM*       1995         GER   \n",
       "1            2.   4     Marcin BLAZINSKI*       1988         GER   \n",
       "2            3.   2  Dominik FABIANOWSKI*       1989         GER   \n",
       "3            4.   3  Christian SCHREINER*       1986         GER   \n",
       "4            5.   7      Nikki JOHNSTONE*       1984         GBR   \n",
       "\n",
       "                         club gender_place age_group_place netto_time  \\\n",
       "0                LC Rehlingen         1. M        1. Elite    2:16:55   \n",
       "1  LG farbtex Nordschwarzwald         2. M        2. Elite    2:20:04   \n",
       "2                    ASV Köln         3. M        3. Elite    2:21:17   \n",
       "3         LAZ Puma Rhein-Sieg         4. M        4. Elite    2:23:46   \n",
       "4              ART-Düsseldorf         5. M        5. Elite    2:25:43   \n",
       "\n",
       "  brutto_time    5km   10km   15km       hm     25km     30km     35km  \\\n",
       "0     2:16:57  15:44  31:44  47:42  1:07:20  1:19:40  1:35:43  1:51:41   \n",
       "1     2:20:06  15:40  31:39  47:34  1:07:14  1:19:44  1:36:46  1:53:55   \n",
       "2     2:21:19  16:37  33:10  49:43  1:10:11  1:23:12  1:40:09  1:56:26   \n",
       "3     2:23:48  16:38  33:10  49:43  1:10:11  1:23:12  1:40:09  1:57:06   \n",
       "4     2:25:46  17:13  34:47  52:21  1:13:41  1:26:57  1:44:09  2:00:44   \n",
       "\n",
       "      40km    year  \n",
       "0  2:09:11  2018.0  \n",
       "1  2:12:18  2018.0  \n",
       "2  2:13:42  2018.0  \n",
       "3  2:15:46  2018.0  \n",
       "4  2:18:18  2018.0  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marathon_cologne_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data cleaning: Set empty strings as missing values and remove disqualified runners from the results. Further, introduce a DNF dummy variable if the runner did not finish the marathon as well as converting all timings to seconds for easier processing and creating a gender variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_df = marathon_cologne_df.replace(r'^\\s*$', np.nan, regex=True) #replace empty strings with nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_df = marathon_cologne_df[marathon_cologne_df['overall_place'] != \"DSQ\"] #remove DSQ's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_df['dnf'] = np.where(marathon_cologne_df['overall_place']=='DNF', '1', '0')\n",
    "marathon_cologne_df['overall_place'] = marathon_cologne_df['overall_place'].replace('DNF', np.nan) #replace DNF strings with nan\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_seconds(x):\n",
    "    x = np.where(x.str.len() < 6,'0:' + x.astype(str), x)\n",
    "    x = pd.Series(x)\n",
    "    return pd.to_timedelta(x).dt.total_seconds()\n",
    "\n",
    "time_cols = ['netto_time', 'brutto_time', '5km',\n",
    "       '10km', '15km', 'hm', '25km', '30km', '35km', '40km']\n",
    "\n",
    "marathon_cologne_df[time_cols] = marathon_cologne_df[time_cols].apply(convert_to_seconds, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_df['gender'] = marathon_cologne_df['gender_place'].str[-1:]\n",
    "marathon_cologne_df['gender_place'] = marathon_cologne_df['gender_place'].str[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>Marcin BLAZINSKI*</td>\n",
       "      <td>1988</td>\n",
       "      <td>GER</td>\n",
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       "      <th>2</th>\n",
       "      <td>3.</td>\n",
       "      <td>2</td>\n",
       "      <td>Dominik FABIANOWSKI*</td>\n",
       "      <td>1989</td>\n",
       "      <td>GER</td>\n",
       "      <td>ASV Köln</td>\n",
       "      <td>3.</td>\n",
       "      <td>3. Elite</td>\n",
       "      <td>8477.0</td>\n",
       "      <td>8479.0</td>\n",
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       "      <td>4211.0</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>6009.0</td>\n",
       "      <td>6986.0</td>\n",
       "      <td>8022.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
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       "      <th>3</th>\n",
       "      <td>4.</td>\n",
       "      <td>3</td>\n",
       "      <td>Christian SCHREINER*</td>\n",
       "      <td>1986</td>\n",
       "      <td>GER</td>\n",
       "      <td>LAZ Puma Rhein-Sieg</td>\n",
       "      <td>4.</td>\n",
       "      <td>4. Elite</td>\n",
       "      <td>8626.0</td>\n",
       "      <td>8628.0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5.</td>\n",
       "      <td>7</td>\n",
       "      <td>Nikki JOHNSTONE*</td>\n",
       "      <td>1984</td>\n",
       "      <td>GBR</td>\n",
       "      <td>ART-Düsseldorf</td>\n",
       "      <td>5.</td>\n",
       "      <td>5. Elite</td>\n",
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       "      <td>4421.0</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>6249.0</td>\n",
       "      <td>7244.0</td>\n",
       "      <td>8298.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>M</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4812</th>\n",
       "      <td>NaN</td>\n",
       "      <td>213</td>\n",
       "      <td>Rene GRASS</td>\n",
       "      <td>1967</td>\n",
       "      <td>GER</td>\n",
       "      <td>Rhein-Berg Runners</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF M50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>4813</th>\n",
       "      <td>NaN</td>\n",
       "      <td>F203</td>\n",
       "      <td>Anja BERGTE</td>\n",
       "      <td>1966</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Nieste)</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF W50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4788.0</td>\n",
       "      <td>7484.0</td>\n",
       "      <td>10858.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
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       "    <tr>\n",
       "      <th>4814</th>\n",
       "      <td>NaN</td>\n",
       "      <td>F171</td>\n",
       "      <td>Karin MARKERT</td>\n",
       "      <td>1974</td>\n",
       "      <td>GER</td>\n",
       "      <td>Team Klinikum Nürnberg</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF W45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3497.0</td>\n",
       "      <td>5344.0</td>\n",
       "      <td>7561.0</td>\n",
       "      <td>9231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4815</th>\n",
       "      <td>NaN</td>\n",
       "      <td>610</td>\n",
       "      <td>Jörg VOGEL</td>\n",
       "      <td>1969</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Köln)</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF M50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3512.0</td>\n",
       "      <td>5338.0</td>\n",
       "      <td>7715.0</td>\n",
       "      <td>9420.0</td>\n",
       "      <td>11804.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4816</th>\n",
       "      <td>NaN</td>\n",
       "      <td>609</td>\n",
       "      <td>Dennis KUNZ</td>\n",
       "      <td>1990</td>\n",
       "      <td>GER</td>\n",
       "      <td>Musketiere</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF MHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3398.0</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>7052.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9744 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     overall_place   bib                  name birth_year nationality  \\\n",
       "0               1.     1          Tobias BLUM*       1995         GER   \n",
       "1               2.     4     Marcin BLAZINSKI*       1988         GER   \n",
       "2               3.     2  Dominik FABIANOWSKI*       1989         GER   \n",
       "3               4.     3  Christian SCHREINER*       1986         GER   \n",
       "4               5.     7      Nikki JOHNSTONE*       1984         GBR   \n",
       "...            ...   ...                   ...        ...         ...   \n",
       "4812           NaN   213            Rene GRASS       1967         GER   \n",
       "4813           NaN  F203           Anja BERGTE       1966         GER   \n",
       "4814           NaN  F171         Karin MARKERT       1974         GER   \n",
       "4815           NaN   610            Jörg VOGEL       1969         GER   \n",
       "4816           NaN   609           Dennis KUNZ       1990         GER   \n",
       "\n",
       "                            club gender_place age_group_place  netto_time  \\\n",
       "0                   LC Rehlingen           1.        1. Elite      8215.0   \n",
       "1     LG farbtex Nordschwarzwald           2.        2. Elite      8404.0   \n",
       "2                       ASV Köln           3.        3. Elite      8477.0   \n",
       "3            LAZ Puma Rhein-Sieg           4.        4. Elite      8626.0   \n",
       "4                 ART-Düsseldorf           5.        5. Elite      8743.0   \n",
       "...                          ...          ...             ...         ...   \n",
       "4812          Rhein-Berg Runners          DNF         DNF M50         NaN   \n",
       "4813                    (Nieste)          DNF         DNF W50         NaN   \n",
       "4814      Team Klinikum Nürnberg          DNF         DNF W45         NaN   \n",
       "4815                      (Köln)          DNF         DNF M50         NaN   \n",
       "4816                  Musketiere          DNF         DNF MHK         NaN   \n",
       "\n",
       "      brutto_time  ...    10km    15km       hm    25km     30km    35km  \\\n",
       "0          8217.0  ...  1904.0  2862.0   4040.0  4780.0   5743.0  6701.0   \n",
       "1          8406.0  ...  1899.0  2854.0   4034.0  4784.0   5806.0  6835.0   \n",
       "2          8479.0  ...  1990.0  2983.0   4211.0  4992.0   6009.0  6986.0   \n",
       "3          8628.0  ...  1990.0  2983.0   4211.0  4992.0   6009.0  7026.0   \n",
       "4          8746.0  ...  2087.0  3141.0   4421.0  5217.0   6249.0  7244.0   \n",
       "...           ...  ...     ...     ...      ...     ...      ...     ...   \n",
       "4812          NaN  ...  2577.0  3878.0   5686.0     NaN      NaN     NaN   \n",
       "4813          NaN  ...  4788.0  7484.0  10858.0     NaN      NaN     NaN   \n",
       "4814          NaN  ...  3497.0  5344.0   7561.0  9231.0      NaN     NaN   \n",
       "4815          NaN  ...  3512.0  5338.0   7715.0  9420.0  11804.0     NaN   \n",
       "4816          NaN  ...  3398.0  5040.0   7052.0     NaN      NaN     NaN   \n",
       "\n",
       "        40km    year  dnf gender  \n",
       "0     7751.0  2018.0    0      M  \n",
       "1     7938.0  2018.0    0      M  \n",
       "2     8022.0  2018.0    0      M  \n",
       "3     8146.0  2018.0    0      M  \n",
       "4     8298.0  2018.0    0      M  \n",
       "...      ...     ...  ...    ...  \n",
       "4812     NaN     NaN    1      M  \n",
       "4813     NaN     NaN    1      W  \n",
       "4814     NaN     NaN    1      W  \n",
       "4815     NaN     NaN    1      M  \n",
       "4816     NaN     NaN    1      M  \n",
       "\n",
       "[9744 rows x 21 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marathon_cologne_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the number of DNFs and also see where runners stopped (including missing timings, eg. see higher share of missing values for 5km than 10km):  \n",
    "\n",
    "It seems like there is a strong in crease of runners stopping after the half-way mark!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_missing = marathon_cologne_df[time_cols].isnull().sum() * 100 / len(marathon_cologne_df)\n",
    "missing_value_df = pd.DataFrame({'column_name': time_cols,\n",
    "                                 'percent_missing': percent_missing})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a674150>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_value_df.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigate the missing timings of runner who did finish the marathon but don't have a timing for the 40km checkpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>overall_place</th>\n",
       "      <th>bib</th>\n",
       "      <th>name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>gender_place</th>\n",
       "      <th>age_group_place</th>\n",
       "      <th>netto_time</th>\n",
       "      <th>brutto_time</th>\n",
       "      <th>...</th>\n",
       "      <th>10km</th>\n",
       "      <th>15km</th>\n",
       "      <th>hm</th>\n",
       "      <th>25km</th>\n",
       "      <th>30km</th>\n",
       "      <th>35km</th>\n",
       "      <th>40km</th>\n",
       "      <th>year</th>\n",
       "      <th>dnf</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>307.</td>\n",
       "      <td>23898</td>\n",
       "      <td>Dennis SCHÄFER</td>\n",
       "      <td>1992</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Aachen)</td>\n",
       "      <td>288.</td>\n",
       "      <td>41. MHK</td>\n",
       "      <td>11376.0</td>\n",
       "      <td>11399.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>723.</td>\n",
       "      <td>21799</td>\n",
       "      <td>Jonas EICKMANN</td>\n",
       "      <td>1986</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Frechen)</td>\n",
       "      <td>684.</td>\n",
       "      <td>140. M30</td>\n",
       "      <td>12391.0</td>\n",
       "      <td>12443.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>801.</td>\n",
       "      <td>16452</td>\n",
       "      <td>Markus KERSTJENS</td>\n",
       "      <td>1979</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Goch)</td>\n",
       "      <td>757.</td>\n",
       "      <td>137. M35</td>\n",
       "      <td>12482.0</td>\n",
       "      <td>12559.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>1270.</td>\n",
       "      <td>3932</td>\n",
       "      <td>Mark MCGINLEY</td>\n",
       "      <td>1973</td>\n",
       "      <td>GBR</td>\n",
       "      <td>(Belfast)</td>\n",
       "      <td>1158.</td>\n",
       "      <td>176. M45</td>\n",
       "      <td>13199.0</td>\n",
       "      <td>13425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3203.0</td>\n",
       "      <td>4791.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7957.0</td>\n",
       "      <td>9510.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>1367.</td>\n",
       "      <td>12358</td>\n",
       "      <td>Guido STEINMETZ</td>\n",
       "      <td>1962</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Stolberg)</td>\n",
       "      <td>1240.</td>\n",
       "      <td>69. M55</td>\n",
       "      <td>13337.0</td>\n",
       "      <td>13694.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>2059.</td>\n",
       "      <td>4129</td>\n",
       "      <td>Volker KADLER</td>\n",
       "      <td>1965</td>\n",
       "      <td>GER</td>\n",
       "      <td>Osendorfer Wölfe</td>\n",
       "      <td>1782.</td>\n",
       "      <td>232. M50</td>\n",
       "      <td>14101.0</td>\n",
       "      <td>14155.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3256.0</td>\n",
       "      <td>4835.0</td>\n",
       "      <td>6743.0</td>\n",
       "      <td>7958.0</td>\n",
       "      <td>12208.0</td>\n",
       "      <td>11283.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3797</th>\n",
       "      <td>3798.</td>\n",
       "      <td>1145</td>\n",
       "      <td>Marcus IBALD</td>\n",
       "      <td>1968</td>\n",
       "      <td>GER</td>\n",
       "      <td>Roadrunners Heimbach-Weis</td>\n",
       "      <td>3060.</td>\n",
       "      <td>437. M50</td>\n",
       "      <td>16458.0</td>\n",
       "      <td>17213.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>5714.0</td>\n",
       "      <td>8051.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11383.0</td>\n",
       "      <td>13408.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4557</th>\n",
       "      <td>4558.</td>\n",
       "      <td>F25031</td>\n",
       "      <td>Anke FRANKEN</td>\n",
       "      <td>1976</td>\n",
       "      <td>GER</td>\n",
       "      <td>Irrecrew</td>\n",
       "      <td>1011.</td>\n",
       "      <td>155. W40</td>\n",
       "      <td>18708.0</td>\n",
       "      <td>19468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>1869.</td>\n",
       "      <td>4268</td>\n",
       "      <td>Wolfgang BLAESER</td>\n",
       "      <td>1968</td>\n",
       "      <td>GER</td>\n",
       "      <td>MST Laufpoint</td>\n",
       "      <td>1579.</td>\n",
       "      <td>197. M50</td>\n",
       "      <td>14703.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3351.0</td>\n",
       "      <td>5001.0</td>\n",
       "      <td>7069.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10125.0</td>\n",
       "      <td>11857.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>1960.</td>\n",
       "      <td>8192</td>\n",
       "      <td>Alex SHESTEROV</td>\n",
       "      <td>1982</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Köln)</td>\n",
       "      <td>1647.</td>\n",
       "      <td>268. M35</td>\n",
       "      <td>14862.0</td>\n",
       "      <td>15232.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>4380.0</td>\n",
       "      <td>6313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>11712.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2438</th>\n",
       "      <td>2439.</td>\n",
       "      <td>1143</td>\n",
       "      <td>Lukas SEIFERT</td>\n",
       "      <td>1993</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Köln)</td>\n",
       "      <td>2014.</td>\n",
       "      <td>273. MHK</td>\n",
       "      <td>15563.0</td>\n",
       "      <td>15785.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>4726.0</td>\n",
       "      <td>6739.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10054.0</td>\n",
       "      <td>12095.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2748</th>\n",
       "      <td>2749.</td>\n",
       "      <td>2688</td>\n",
       "      <td>Manuel DIETSCH</td>\n",
       "      <td>1979</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Unterleinleiter)</td>\n",
       "      <td>2230.</td>\n",
       "      <td>344. M40</td>\n",
       "      <td>16053.0</td>\n",
       "      <td>16673.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3434.0</td>\n",
       "      <td>5192.0</td>\n",
       "      <td>7394.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10770.0</td>\n",
       "      <td>12809.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>3082.</td>\n",
       "      <td>448</td>\n",
       "      <td>Patrick BÖHM</td>\n",
       "      <td>1989</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Köln)</td>\n",
       "      <td>2464.</td>\n",
       "      <td>406. M30</td>\n",
       "      <td>16605.0</td>\n",
       "      <td>16999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3185.0</td>\n",
       "      <td>4801.0</td>\n",
       "      <td>6918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10797.0</td>\n",
       "      <td>13248.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3161</th>\n",
       "      <td>3162.</td>\n",
       "      <td>3432</td>\n",
       "      <td>Laurent WIJNANTS</td>\n",
       "      <td>1948</td>\n",
       "      <td>BEL</td>\n",
       "      <td>(Zutendaal)</td>\n",
       "      <td>2530.</td>\n",
       "      <td>9. M70</td>\n",
       "      <td>16761.0</td>\n",
       "      <td>17212.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3655.0</td>\n",
       "      <td>5466.0</td>\n",
       "      <td>7923.0</td>\n",
       "      <td>9535.0</td>\n",
       "      <td>11555.0</td>\n",
       "      <td>13715.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3646</th>\n",
       "      <td>3647.</td>\n",
       "      <td>1948</td>\n",
       "      <td>Uli SCHARRENBERG</td>\n",
       "      <td>1948</td>\n",
       "      <td>GER</td>\n",
       "      <td>Fortuna Düsseldorf Lauf e.V.</td>\n",
       "      <td>2853.</td>\n",
       "      <td>11. M70</td>\n",
       "      <td>17747.0</td>\n",
       "      <td>17766.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3121.0</td>\n",
       "      <td>4645.0</td>\n",
       "      <td>6536.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10149.0</td>\n",
       "      <td>12723.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3673</th>\n",
       "      <td>3674.</td>\n",
       "      <td>4970</td>\n",
       "      <td>Jörg EßER</td>\n",
       "      <td>1970</td>\n",
       "      <td>GER</td>\n",
       "      <td>(Mechernich)</td>\n",
       "      <td>2872.</td>\n",
       "      <td>441. M45</td>\n",
       "      <td>17809.0</td>\n",
       "      <td>18502.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4015.0</td>\n",
       "      <td>6073.0</td>\n",
       "      <td>8892.0</td>\n",
       "      <td>10819.0</td>\n",
       "      <td>13526.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     overall_place     bib              name birth_year nationality  \\\n",
       "306           307.   23898    Dennis SCHÄFER       1992         GER   \n",
       "722           723.   21799    Jonas EICKMANN       1986         GER   \n",
       "800           801.   16452  Markus KERSTJENS       1979         GER   \n",
       "1269         1270.    3932     Mark MCGINLEY       1973         GBR   \n",
       "1366         1367.   12358   Guido STEINMETZ       1962         GER   \n",
       "2058         2059.    4129     Volker KADLER       1965         GER   \n",
       "3797         3798.    1145      Marcus IBALD       1968         GER   \n",
       "4557         4558.  F25031      Anke FRANKEN       1976         GER   \n",
       "1868         1869.    4268  Wolfgang BLAESER       1968         GER   \n",
       "1959         1960.    8192    Alex SHESTEROV       1982         GER   \n",
       "2438         2439.    1143     Lukas SEIFERT       1993         GER   \n",
       "2748         2749.    2688    Manuel DIETSCH       1979         GER   \n",
       "3081         3082.     448      Patrick BÖHM       1989         GER   \n",
       "3161         3162.    3432  Laurent WIJNANTS       1948         BEL   \n",
       "3646         3647.    1948  Uli SCHARRENBERG       1948         GER   \n",
       "3673         3674.    4970         Jörg EßER       1970         GER   \n",
       "\n",
       "                              club gender_place age_group_place  netto_time  \\\n",
       "306                       (Aachen)         288.         41. MHK     11376.0   \n",
       "722                      (Frechen)         684.        140. M30     12391.0   \n",
       "800                         (Goch)         757.        137. M35     12482.0   \n",
       "1269                     (Belfast)        1158.        176. M45     13199.0   \n",
       "1366                    (Stolberg)        1240.         69. M55     13337.0   \n",
       "2058              Osendorfer Wölfe        1782.        232. M50     14101.0   \n",
       "3797     Roadrunners Heimbach-Weis        3060.        437. M50     16458.0   \n",
       "4557                      Irrecrew        1011.        155. W40     18708.0   \n",
       "1868                 MST Laufpoint        1579.        197. M50     14703.0   \n",
       "1959                        (Köln)        1647.        268. M35     14862.0   \n",
       "2438                        (Köln)        2014.        273. MHK     15563.0   \n",
       "2748             (Unterleinleiter)        2230.        344. M40     16053.0   \n",
       "3081                        (Köln)        2464.        406. M30     16605.0   \n",
       "3161                   (Zutendaal)        2530.          9. M70     16761.0   \n",
       "3646  Fortuna Düsseldorf Lauf e.V.        2853.         11. M70     17747.0   \n",
       "3673                  (Mechernich)        2872.        441. M45     17809.0   \n",
       "\n",
       "      brutto_time  ...    10km    15km      hm     25km     30km     35km  \\\n",
       "306       11399.0  ...     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "722       12443.0  ...     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "800       12559.0  ...     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "1269      13425.0  ...  3203.0  4791.0     NaN   7957.0   9510.0  11000.0   \n",
       "1366      13694.0  ...     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "2058      14155.0  ...  3256.0  4835.0  6743.0   7958.0  12208.0  11283.0   \n",
       "3797      17213.0  ...  3810.0  5714.0  8051.0      NaN  11383.0  13408.0   \n",
       "4557      19468.0  ...     NaN     NaN     NaN      NaN      NaN      NaN   \n",
       "1868      15088.0  ...  3351.0  5001.0  7069.0      NaN  10125.0  11857.0   \n",
       "1959      15232.0  ...  2907.0  4380.0  6313.0      NaN   9700.0  11712.0   \n",
       "2438      15785.0  ...  3147.0  4726.0  6739.0      NaN  10054.0  12095.0   \n",
       "2748      16673.0  ...  3434.0  5192.0  7394.0      NaN  10770.0  12809.0   \n",
       "3081      16999.0  ...  3185.0  4801.0  6918.0      NaN  10797.0  13248.0   \n",
       "3161      17212.0  ...  3655.0  5466.0  7923.0   9535.0  11555.0  13715.0   \n",
       "3646      17766.0  ...  3121.0  4645.0  6536.0      NaN  10149.0  12723.0   \n",
       "3673      18502.0  ...  4015.0  6073.0  8892.0  10819.0  13526.0      NaN   \n",
       "\n",
       "      40km    year  dnf gender  \n",
       "306    NaN  2018.0    0      M  \n",
       "722    NaN  2018.0    0      M  \n",
       "800    NaN  2018.0    0      M  \n",
       "1269   NaN  2018.0    0      M  \n",
       "1366   NaN  2018.0    0      M  \n",
       "2058   NaN  2018.0    0      M  \n",
       "3797   NaN  2018.0    0      M  \n",
       "4557   NaN  2018.0    0      W  \n",
       "1868   NaN     NaN    0      M  \n",
       "1959   NaN     NaN    0      M  \n",
       "2438   NaN     NaN    0      M  \n",
       "2748   NaN     NaN    0      M  \n",
       "3081   NaN     NaN    0      M  \n",
       "3161   NaN     NaN    0      M  \n",
       "3646   NaN     NaN    0      M  \n",
       "3673   NaN     NaN    0      M  \n",
       "\n",
       "[16 rows x 21 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marathon_cologne_df[(pd.isnull(marathon_cologne_df['netto_time']) == False) &\n",
    "                    (pd.isnull(marathon_cologne_df['40km']) == True)] #runners with missing timings at 40km, despite finishing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "marathon_cologne_df.to_csv(\"marathon_cologne_complete.csv\")"
   ]
  }
 ],
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