06-10-2023-run-for-women-results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28dc4809",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da78371",
   "metadata": {},
   "source": [
    "## 1. Collect data from website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02e76d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set params\n",
    "urls = [\"https://startlinetiming.com/en/races/2023/rfw_toronto/event/10K\",\n",
    "        \"https://startlinetiming.com/en/races/2023/rfw_toronto/event/5K\"]\n",
    "\n",
    "ua = {\n",
    "    \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2fdb2805",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(url):\n",
    "    response = requests.get(url, headers=ua)\n",
    "    soup = BeautifulSoup(response.content, \"html.parser\")\n",
    "\n",
    "    table = soup.find(\"table\")\n",
    "\n",
    "    headers = []\n",
    "    for row in table.find_all(\"th\"):\n",
    "        headers.append(row.text.strip())\n",
    "\n",
    "    data = []\n",
    "\n",
    "    for row in table.find_all(\"tr\"):\n",
    "        row_data = [cell.text.strip() for cell in row.find_all(\"td\")]\n",
    "        row_data = [re.sub(r\"\\n+\", lambda match: \"\\n\" if match.start() != 0 else \"\", l) for l in row_data]\n",
    "        row_data = [l.replace(\"\\n\", \"; \") for l in row_data]\n",
    "\n",
    "        data.append(row_data)\n",
    "\n",
    "    return headers, data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2c86d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "headers = []\n",
    "\n",
    "for url in urls:\n",
    "    h, d = get_data(url)\n",
    "    headers.append(h)\n",
    "    data.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a39cbb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df10k = pd.DataFrame(data[0], columns=headers[0])\n",
    "df5k = pd.DataFrame(data[1], columns=headers[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7432fb77",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df10k,df5k])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5ee41c",
   "metadata": {},
   "source": [
    "## 2. Clean and prep data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d757bd84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get location from name\n",
    "df['Location'] = df['Name'].apply(lambda x: x.split(\"; \")[1])\n",
    "\n",
    "# get city from location\n",
    "df['City'] = df['Location'].apply(lambda x: x.split(',')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0165b63e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# get gender and age from division\n",
    "df['Gender'] = df['Division'].apply(lambda x: x[0])\n",
    "df['Age'] = df['Division'].apply(lambda x: x[1:3]+'-'+x[3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6de5d21b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Name','Location'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c78ef49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove rows w/o age\n",
    "df = df[df['Age'] != '99-99'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e358b49",
   "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>Event</th>\n",
       "      <th>Place</th>\n",
       "      <th>Gun Time</th>\n",
       "      <th>Race #</th>\n",
       "      <th>Division</th>\n",
       "      <th>Place/Gender</th>\n",
       "      <th>Place/Division</th>\n",
       "      <th>Chip Time</th>\n",
       "      <th>City</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10K</td>\n",
       "      <td>1/374</td>\n",
       "      <td>0:39:03</td>\n",
       "      <td>15946</td>\n",
       "      <td>M3039</td>\n",
       "      <td>1/108</td>\n",
       "      <td>1/49</td>\n",
       "      <td>0:39:00</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10K</td>\n",
       "      <td>2/374</td>\n",
       "      <td>0:39:13</td>\n",
       "      <td>16066</td>\n",
       "      <td>F3039</td>\n",
       "      <td>1/263</td>\n",
       "      <td>1/98</td>\n",
       "      <td>0:38:29</td>\n",
       "      <td>Markham</td>\n",
       "      <td>F</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10K</td>\n",
       "      <td>3/374</td>\n",
       "      <td>0:39:14</td>\n",
       "      <td>16065</td>\n",
       "      <td>F4049</td>\n",
       "      <td>2/263</td>\n",
       "      <td>1/58</td>\n",
       "      <td>0:38:28</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>F</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10K</td>\n",
       "      <td>4/374</td>\n",
       "      <td>0:40:18</td>\n",
       "      <td>16126</td>\n",
       "      <td>M3039</td>\n",
       "      <td>2/108</td>\n",
       "      <td>2/49</td>\n",
       "      <td>0:40:15</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10K</td>\n",
       "      <td>5/374</td>\n",
       "      <td>0:40:24</td>\n",
       "      <td>15342</td>\n",
       "      <td>M2029</td>\n",
       "      <td>3/108</td>\n",
       "      <td>1/25</td>\n",
       "      <td>0:40:15</td>\n",
       "      <td>North York</td>\n",
       "      <td>M</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Event  Place Gun Time Race # Division Place/Gender Place/Division Chip Time  \\\n",
       "0   10K  1/374  0:39:03  15946    M3039        1/108           1/49   0:39:00   \n",
       "1   10K  2/374  0:39:13  16066    F3039        1/263           1/98   0:38:29   \n",
       "2   10K  3/374  0:39:14  16065    F4049        2/263           1/58   0:38:28   \n",
       "3   10K  4/374  0:40:18  16126    M3039        2/108           2/49   0:40:15   \n",
       "4   10K  5/374  0:40:24  15342    M2029        3/108           1/25   0:40:15   \n",
       "\n",
       "         City Gender    Age  \n",
       "0     Toronto      M  30-39  \n",
       "1     Markham      F  30-39  \n",
       "2     Toronto      F  40-49  \n",
       "3     Toronto      M  30-39  \n",
       "4  North York      M  20-29  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b975fd25",
   "metadata": {},
   "source": [
    "## 3. Explore and find insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc464824",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d9ce40",
   "metadata": {},
   "source": [
    "### The 10K skewed younger than the 5K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2c7e7bf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': '10K'}>, <Axes: title={'center': '5K'}>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sort_values(by='Age').hist(column='Age', by='Event')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db944ff4",
   "metadata": {},
   "source": [
    "### There was a similar distribution of males and females in each race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6fc44087",
   "metadata": {
    "scrolled": false
   },
   "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>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10K</th>\n",
       "      <td>34.3</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5K</th>\n",
       "      <td>35.2</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender     F     M\n",
       "Event             \n",
       "10K     34.3  14.1\n",
       "5K      35.2  16.3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Event', 'Gender'])['Gender'].count().transform(lambda x: round(x/x.sum() * 100, 1)).rename\\\n",
    "('perc').reset_index().pivot(index='Event', columns='Gender', values='perc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac200288",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gun Time_dt'] = pd.to_datetime(df['Gun Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb49741d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gun Time_m'] = round((df['Gun Time_dt'].dt.hour*3600 + df['Gun Time_dt'].dt.minute*60 + df['Gun Time_dt'].dt.second)/60, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b61488b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed414e60",
   "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>Event</th>\n",
       "      <th>Place</th>\n",
       "      <th>Gun Time</th>\n",
       "      <th>Race #</th>\n",
       "      <th>Division</th>\n",
       "      <th>Place/Gender</th>\n",
       "      <th>Place/Division</th>\n",
       "      <th>Chip Time</th>\n",
       "      <th>City</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gun Time_dt</th>\n",
       "      <th>Gun Time_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10K</td>\n",
       "      <td>1/374</td>\n",
       "      <td>0:39:03</td>\n",
       "      <td>15946</td>\n",
       "      <td>M3039</td>\n",
       "      <td>1/108</td>\n",
       "      <td>1/49</td>\n",
       "      <td>0:39:00</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M</td>\n",
       "      <td>30-39</td>\n",
       "      <td>2023-06-11 00:39:03</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10K</td>\n",
       "      <td>2/374</td>\n",
       "      <td>0:39:13</td>\n",
       "      <td>16066</td>\n",
       "      <td>F3039</td>\n",
       "      <td>1/263</td>\n",
       "      <td>1/98</td>\n",
       "      <td>0:38:29</td>\n",
       "      <td>Markham</td>\n",
       "      <td>F</td>\n",
       "      <td>30-39</td>\n",
       "      <td>2023-06-11 00:39:13</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10K</td>\n",
       "      <td>3/374</td>\n",
       "      <td>0:39:14</td>\n",
       "      <td>16065</td>\n",
       "      <td>F4049</td>\n",
       "      <td>2/263</td>\n",
       "      <td>1/58</td>\n",
       "      <td>0:38:28</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>F</td>\n",
       "      <td>40-49</td>\n",
       "      <td>2023-06-11 00:39:14</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10K</td>\n",
       "      <td>4/374</td>\n",
       "      <td>0:40:18</td>\n",
       "      <td>16126</td>\n",
       "      <td>M3039</td>\n",
       "      <td>2/108</td>\n",
       "      <td>2/49</td>\n",
       "      <td>0:40:15</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M</td>\n",
       "      <td>30-39</td>\n",
       "      <td>2023-06-11 00:40:18</td>\n",
       "      <td>40.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10K</td>\n",
       "      <td>5/374</td>\n",
       "      <td>0:40:24</td>\n",
       "      <td>15342</td>\n",
       "      <td>M2029</td>\n",
       "      <td>3/108</td>\n",
       "      <td>1/25</td>\n",
       "      <td>0:40:15</td>\n",
       "      <td>North York</td>\n",
       "      <td>M</td>\n",
       "      <td>20-29</td>\n",
       "      <td>2023-06-11 00:40:24</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Event  Place Gun Time Race # Division Place/Gender Place/Division Chip Time  \\\n",
       "0   10K  1/374  0:39:03  15946    M3039        1/108           1/49   0:39:00   \n",
       "1   10K  2/374  0:39:13  16066    F3039        1/263           1/98   0:38:29   \n",
       "2   10K  3/374  0:39:14  16065    F4049        2/263           1/58   0:38:28   \n",
       "3   10K  4/374  0:40:18  16126    M3039        2/108           2/49   0:40:15   \n",
       "4   10K  5/374  0:40:24  15342    M2029        3/108           1/25   0:40:15   \n",
       "\n",
       "         City Gender    Age         Gun Time_dt  Gun Time_m  \n",
       "0     Toronto      M  30-39 2023-06-11 00:39:03        39.0  \n",
       "1     Markham      F  30-39 2023-06-11 00:39:13        39.2  \n",
       "2     Toronto      F  40-49 2023-06-11 00:39:14        39.2  \n",
       "3     Toronto      M  30-39 2023-06-11 00:40:18        40.3  \n",
       "4  North York      M  20-29 2023-06-11 00:40:24        40.4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83306534",
   "metadata": {},
   "source": [
    "### The 5K was finished in 20 to 40 mins, whereas the 10K took between 40 and 90 mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16d1a7dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gun Time_m', ylabel='Count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='Gun Time_m', data=df, hue='Event', binwidth=5, binrange=[0,100])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef31783e",
   "metadata": {},
   "source": [
    "### Overall, Male runners finished earlier than female runners in the 5K\n",
    "\n",
    "### There were two groups of Male and Female runners respectively – those who finished before or after 35 mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5127b048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gun Time_m', ylabel='Count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='Gun Time_m', data=df[df['Event']=='5K'], hue='Gender', binwidth=1, binrange=[10,50])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03bcb6f4",
   "metadata": {},
   "source": [
    "### Male runners finished earlier than Female runners in the 10K\n",
    "\n",
    "### However, there were plenty of outliers, with more female runners finishing before male runners for the earliest times (< 45 mins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa95a720",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gun Time_m', ylabel='Count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='Gun Time_m', data=df[df['Event']=='10K'], hue='Gender', binwidth=5, binrange=[20,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "89ec8090",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_gap_o = df.groupby(['Event','Age','Gender'])['Gun Time_m'].mean().round(1).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d4652ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "gender_gap_o.sort_values(by=['Event','Age'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "921a8051",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# drop 70-79 age group\n",
    "gender_gap_o.drop(index=[12,13],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad55b50e",
   "metadata": {},
   "source": [
    "### The gender gap in times was the most for the 50-59 (\\~3 mins) and 60-69 (~6 mins) age groups in the 5K\n",
    "\n",
    "### The gender gap in times was the most for the 30-39 (\\~6 mins) and 40-49 (~7 mins) age groups in the 10K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "094726cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Event  Age  \n",
       "10K    -U-20    -3.0\n",
       "       20-29     3.8\n",
       "       30-39     6.3\n",
       "       40-49     6.7\n",
       "       50-59     4.7\n",
       "       60-69    -0.7\n",
       "       70-79    63.5\n",
       "5K     -U-20     2.4\n",
       "       20-29     2.2\n",
       "       30-39     1.2\n",
       "       40-49     0.2\n",
       "       50-59     3.4\n",
       "       60-69     6.4\n",
       "Name: Gun Time_m, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Gender'] == 'F'].groupby(['Event','Age'])['Gun Time_m'].mean().round(1) - df[df['Gender'] == 'M'].groupby(['Event','Age'])['Gun Time_m'].mean().round(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bacdf9c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Chip Time_dt'] = pd.to_datetime(df['Chip Time']) - pd.Timedelta('1 days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "002aa6dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['adv'] = (df['Gun Time_dt'] - df['Chip Time_dt']).dt.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "87050009",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['adv_m'] = df['adv']/60"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7294380a",
   "metadata": {},
   "source": [
    "### The mean advantage of going earlier was 2mins for the 5K, suggesting that some runners' times were adversely affected by starting later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c862969d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    371.000000\n",
       "mean       1.144924\n",
       "std        2.924843\n",
       "min        0.000000\n",
       "25%        0.166667\n",
       "50%        0.366667\n",
       "75%        0.616667\n",
       "max       16.416667\n",
       "Name: adv_m, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Event'] == '10K']['adv_m'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "10cb7577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     395.000000\n",
       "mean       19.784599\n",
       "std       160.183391\n",
       "min         0.000000\n",
       "25%         0.233333\n",
       "50%         0.616667\n",
       "75%         3.225000\n",
       "max      1439.966667\n",
       "Name: adv_m, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Event'] == '5K']['adv_m'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6500f275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    390.000000\n",
       "mean       1.672009\n",
       "std        2.123988\n",
       "min        0.000000\n",
       "25%        0.233333\n",
       "50%        0.600000\n",
       "75%        3.183333\n",
       "max        7.783333\n",
       "Name: adv_m, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Event'] == '5K') & (df['adv_m'] < 9)].describe().loc[:, 'adv_m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "01e936b1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'adv_m'}>]], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['Event'] == '5K') & (df['adv_m'] < 9)].hist(column='adv_m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0a45302",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}