Web_Scrapping_test_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://www.hubertiming.com/results/2017GPTR10K\"\n",
    "#url = \"https://tradingeconomics.com/commodity/copper\"\n",
    "html = urlopen(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bs4.BeautifulSoup"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup = BeautifulSoup(html, 'lxml')\n",
    "type(soup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<title>2017 Intel Great Place to Run 10K \\ Urban Clash Games Race Results</title>\n"
     ]
    }
   ],
   "source": [
    "title = soup.title\n",
    "print(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = soup.get_text()\n",
    "#print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find_all = soup.find_all('a')\n",
    "#print(find_all)\n",
    "#soup.find_all('tr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/results/2017GPTR\n",
      "https://www.hubertiming.com/\n",
      "#individual\n",
      "#team\n",
      "mailto:timing@hubertiming.com\n",
      "#tabs-1\n",
      "None\n",
      "None\n",
      "https://www.hubertiming.com/\n",
      "https://facebook.com/hubertiming/\n"
     ]
    }
   ],
   "source": [
    "'''As you can see from the output above, html tags sometimes come with attributes such as class, src, etc. \n",
    "These attributes provide additional information about html elements. You can use a for loop and the get('\"href\")\n",
    "method to extract and print out only hyperlinks.'''\n",
    "\n",
    "all_links = soup.find_all(\"a\")\n",
    "for link in all_links:\n",
    "    print(link.get(\"href\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tr><td>Finishers:</td><td>577</td></tr>, <tr><td>Male:</td><td>414</td></tr>, <tr><td>Female:</td><td>163</td></tr>, <tr class=\"header\">\n",
      "<th>Place</th>\n",
      "<th>Bib</th>\n",
      "<th>Name</th>\n",
      "<th>Gender</th>\n",
      "<th>City</th>\n",
      "<th>State</th>\n",
      "<th>Chip Time</th>\n",
      "<th>Chip Pace</th>\n",
      "<th>Gender Place</th>\n",
      "<th>Age Group</th>\n",
      "<th>Age Group Place</th>\n",
      "<th>Time to Start</th>\n",
      "<th>Gun Time</th>\n",
      "<th>Team</th>\n",
      "</tr>, <tr>\n",
      "<td>1</td>\n",
      "<td>814</td>\n",
      "<td>JARED WILSON</td>\n",
      "<td>M</td>\n",
      "<td>TIGARD</td>\n",
      "<td>OR</td>\n",
      "<td>00:36:21</td>\n",
      "<td>05:51</td>\n",
      "<td>1 of 414</td>\n",
      "<td>M 36-45</td>\n",
      "<td>1 of 152</td>\n",
      "<td>00:00:03</td>\n",
      "<td>00:36:24</td>\n",
      "<td></td>\n",
      "</tr>, <tr>\n",
      "<td>2</td>\n",
      "<td>573</td>\n",
      "<td>NATHAN A SUSTERSIC</td>\n",
      "<td>M</td>\n",
      "<td>PORTLAND</td>\n",
      "<td>OR</td>\n",
      "<td>00:36:42</td>\n",
      "<td>05:55</td>\n",
      "<td>2 of 414</td>\n",
      "<td>M 26-35</td>\n",
      "<td>1 of 154</td>\n",
      "<td>00:00:03</td>\n",
      "<td>00:36:45</td>\n",
      "<td>INTEL TEAM F</td>\n",
      "</tr>, <tr>\n",
      "<td>3</td>\n",
      "<td>687</td>\n",
      "<td>FRANCISCO MAYA</td>\n",
      "<td>M</td>\n",
      "<td>PORTLAND</td>\n",
      "<td>OR</td>\n",
      "<td>00:37:44</td>\n",
      "<td>06:05</td>\n",
      "<td>3 of 414</td>\n",
      "<td>M 46-55</td>\n",
      "<td>1 of 64</td>\n",
      "<td>00:00:04</td>\n",
      "<td>00:37:48</td>\n",
      "<td></td>\n",
      "</tr>, <tr>\n",
      "<td>4</td>\n",
      "<td>623</td>\n",
      "<td>PAUL MORROW</td>\n",
      "<td>M</td>\n",
      "<td>BEAVERTON</td>\n",
      "<td>OR</td>\n",
      "<td>00:38:34</td>\n",
      "<td>06:13</td>\n",
      "<td>4 of 414</td>\n",
      "<td>M 36-45</td>\n",
      "<td>2 of 152</td>\n",
      "<td>00:00:03</td>\n",
      "<td>00:38:37</td>\n",
      "<td></td>\n",
      "</tr>, <tr>\n",
      "<td>5</td>\n",
      "<td>569</td>\n",
      "<td>DEREK G OSBORNE</td>\n",
      "<td>M</td>\n",
      "<td>HILLSBORO</td>\n",
      "<td>OR</td>\n",
      "<td>00:39:21</td>\n",
      "<td>06:20</td>\n",
      "<td>5 of 414</td>\n",
      "<td>M 26-35</td>\n",
      "<td>2 of 154</td>\n",
      "<td>00:00:03</td>\n",
      "<td>00:39:24</td>\n",
      "<td>INTEL TEAM F</td>\n",
      "</tr>, <tr>\n",
      "<td>6</td>\n",
      "<td>642</td>\n",
      "<td>JONATHON TRAN</td>\n",
      "<td>M</td>\n",
      "<td>PORTLAND</td>\n",
      "<td>OR</td>\n",
      "<td>00:39:49</td>\n",
      "<td>06:25</td>\n",
      "<td>6 of 414</td>\n",
      "<td>M 18-25</td>\n",
      "<td>1 of 34</td>\n",
      "<td>00:00:06</td>\n",
      "<td>00:39:55</td>\n",
      "<td></td>\n",
      "</tr>]\n"
     ]
    }
   ],
   "source": [
    "# Print the first 10 rows for sanity check\n",
    "rows = soup.find_all('tr')\n",
    "print(rows[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<td>14TH</td>, <td>INTEL TEAM M</td>, <td>04:43:23</td>, <td>00:58:59 - DANIELLE CASILLAS</td>, <td>01:02:06 - RAMYA MERUVA</td>, <td>01:17:06 - PALLAVI J SHINDE</td>, <td>01:25:11 - NALINI MURARI</td>]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "bs4.element.ResultSet"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for row in rows:\n",
    "    row_td = row.find_all('td')\n",
    "print(row_td)\n",
    "type(row_td)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14TH, INTEL TEAM M, 04:43:23, 00:58:59 - DANIELLE CASILLAS, 01:02:06 - RAMYA MERUVA, 01:17:06 - PALLAVI J SHINDE, 01:25:11 - NALINI MURARI]\n"
     ]
    }
   ],
   "source": [
    "'''The output above shows that each row is printed with html tags embedded in each row. This is not what you want.\n",
    "You can use remove the html tags using Beautiful Soup or regular expressions.\n",
    "The easiest way to remove html tags is to use Beautiful Soup, and it takes just one line of code to do this.\n",
    "Pass the string of interest into BeautifulSoup() and use the get_text() method to extract the text without html tags.'''\n",
    "\n",
    "str_cells = str(row_td)\n",
    "cleantext = BeautifulSoup(str_cells, \"lxml\").get_text()\n",
    "print(cleantext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14TH, INTEL TEAM M, 04:43:23, 00:58:59 - DANIELLE CASILLAS, 01:02:06 - RAMYA MERUVA, 01:17:06 - PALLAVI J SHINDE, 01:25:11 - NALINI MURARI]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "list_rows = []\n",
    "for row in rows:\n",
    "    cells = row.find_all('td')\n",
    "    str_cells = str(cells)\n",
    "    clean = re.compile('<.*?>')\n",
    "    clean2 = (re.sub(clean, '', str_cells))\n",
    "    list_rows.append(clean2)\n",
    "print(clean2)\n",
    "type(clean2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Finishers:, 577]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Male:, 414]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Female:, 163]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1, 814, JARED WILSON, M, TIGARD, OR, 00:36:21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[2, 573, NATHAN A SUSTERSIC, M, PORTLAND, OR, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[3, 687, FRANCISCO MAYA, M, PORTLAND, OR, 00:3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[4, 623, PAUL MORROW, M, BEAVERTON, OR, 00:38:...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[5, 569, DEREK G OSBORNE, M, HILLSBORO, OR, 00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[6, 642, JONATHON TRAN, M, PORTLAND, OR, 00:39...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0\n",
       "0                                  [Finishers:, 577]\n",
       "1                                       [Male:, 414]\n",
       "2                                     [Female:, 163]\n",
       "3                                                 []\n",
       "4  [1, 814, JARED WILSON, M, TIGARD, OR, 00:36:21...\n",
       "5  [2, 573, NATHAN A SUSTERSIC, M, PORTLAND, OR, ...\n",
       "6  [3, 687, FRANCISCO MAYA, M, PORTLAND, OR, 00:3...\n",
       "7  [4, 623, PAUL MORROW, M, BEAVERTON, OR, 00:38:...\n",
       "8  [5, 569, DEREK G OSBORNE, M, HILLSBORO, OR, 00...\n",
       "9  [6, 642, JONATHON TRAN, M, PORTLAND, OR, 00:39..."
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''The next step is to convert the list into a dataframe and get a quick view of the first 10 rows using Pandas.'''\n",
    "\n",
    "df = pd.DataFrame(list_rows)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Finishers:</td>\n",
       "      <td>577]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Male:</td>\n",
       "      <td>414]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1</td>\n",
       "      <td>814</td>\n",
       "      <td>JARED WILSON</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:21</td>\n",
       "      <td>05:51</td>\n",
       "      <td>1 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>1 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[2</td>\n",
       "      <td>573</td>\n",
       "      <td>NATHAN A SUSTERSIC</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:42</td>\n",
       "      <td>05:55</td>\n",
       "      <td>2 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>1 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[3</td>\n",
       "      <td>687</td>\n",
       "      <td>FRANCISCO MAYA</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:37:44</td>\n",
       "      <td>06:05</td>\n",
       "      <td>3 of 414</td>\n",
       "      <td>M 46-55</td>\n",
       "      <td>1 of 64</td>\n",
       "      <td>00:00:04</td>\n",
       "      <td>00:37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[4</td>\n",
       "      <td>623</td>\n",
       "      <td>PAUL MORROW</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:38:34</td>\n",
       "      <td>06:13</td>\n",
       "      <td>4 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>2 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[5</td>\n",
       "      <td>569</td>\n",
       "      <td>DEREK G OSBORNE</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:21</td>\n",
       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>2 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:39:24</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[6</td>\n",
       "      <td>642</td>\n",
       "      <td>JONATHON TRAN</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:49</td>\n",
       "      <td>06:25</td>\n",
       "      <td>6 of 414</td>\n",
       "      <td>M 18-25</td>\n",
       "      <td>1 of 34</td>\n",
       "      <td>00:00:06</td>\n",
       "      <td>00:39:55</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1                    2     3           4     5          6   \\\n",
       "0  [Finishers:   577]                 None  None        None  None       None   \n",
       "1       [Male:   414]                 None  None        None  None       None   \n",
       "2     [Female:   163]                 None  None        None  None       None   \n",
       "3           []   None                 None  None        None  None       None   \n",
       "4           [1    814         JARED WILSON     M      TIGARD    OR   00:36:21   \n",
       "5           [2    573   NATHAN A SUSTERSIC     M    PORTLAND    OR   00:36:42   \n",
       "6           [3    687       FRANCISCO MAYA     M    PORTLAND    OR   00:37:44   \n",
       "7           [4    623          PAUL MORROW     M   BEAVERTON    OR   00:38:34   \n",
       "8           [5    569      DEREK G OSBORNE     M   HILLSBORO    OR   00:39:21   \n",
       "9           [6    642        JONATHON TRAN     M    PORTLAND    OR   00:39:49   \n",
       "\n",
       "       7          8         9          10         11         12  \\\n",
       "0    None       None      None       None       None       None   \n",
       "1    None       None      None       None       None       None   \n",
       "2    None       None      None       None       None       None   \n",
       "3    None       None      None       None       None       None   \n",
       "4   05:51   1 of 414   M 36-45   1 of 152   00:00:03   00:36:24   \n",
       "5   05:55   2 of 414   M 26-35   1 of 154   00:00:03   00:36:45   \n",
       "6   06:05   3 of 414   M 46-55    1 of 64   00:00:04   00:37:48   \n",
       "7   06:13   4 of 414   M 36-45   2 of 152   00:00:03   00:38:37   \n",
       "8   06:20   5 of 414   M 26-35   2 of 154   00:00:03   00:39:24   \n",
       "9   06:25   6 of 414   M 18-25    1 of 34   00:00:06   00:39:55   \n",
       "\n",
       "               13  \n",
       "0            None  \n",
       "1            None  \n",
       "2            None  \n",
       "3            None  \n",
       "4               ]  \n",
       "5   INTEL TEAM F]  \n",
       "6               ]  \n",
       "7               ]  \n",
       "8   INTEL TEAM F]  \n",
       "9               ]  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''The dataframe is not in the format we want. To clean it up, you should split the \"0\" column into \n",
    "multiple columns at the comma position. This is accomplished by using the str.split() method.'''\n",
    "df1 = df[0].str.split(',', expand=True)\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finishers:</td>\n",
       "      <td>577]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
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       "      <td>Male:</td>\n",
       "      <td>414]</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>JARED WILSON</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:21</td>\n",
       "      <td>05:51</td>\n",
       "      <td>1 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>1 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>NATHAN A SUSTERSIC</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:42</td>\n",
       "      <td>05:55</td>\n",
       "      <td>2 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>1 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>FRANCISCO MAYA</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:37:44</td>\n",
       "      <td>06:05</td>\n",
       "      <td>3 of 414</td>\n",
       "      <td>M 46-55</td>\n",
       "      <td>1 of 64</td>\n",
       "      <td>00:00:04</td>\n",
       "      <td>00:37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>PAUL MORROW</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:38:34</td>\n",
       "      <td>06:13</td>\n",
       "      <td>4 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>2 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>DEREK G OSBORNE</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:21</td>\n",
       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>2 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:39:24</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>642</td>\n",
       "      <td>JONATHON TRAN</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:49</td>\n",
       "      <td>06:25</td>\n",
       "      <td>6 of 414</td>\n",
       "      <td>M 18-25</td>\n",
       "      <td>1 of 34</td>\n",
       "      <td>00:00:06</td>\n",
       "      <td>00:39:55</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1                    2     3           4     5          6   \\\n",
       "0  Finishers:   577]                 None  None        None  None       None   \n",
       "1       Male:   414]                 None  None        None  None       None   \n",
       "2     Female:   163]                 None  None        None  None       None   \n",
       "3           ]   None                 None  None        None  None       None   \n",
       "4           1    814         JARED WILSON     M      TIGARD    OR   00:36:21   \n",
       "5           2    573   NATHAN A SUSTERSIC     M    PORTLAND    OR   00:36:42   \n",
       "6           3    687       FRANCISCO MAYA     M    PORTLAND    OR   00:37:44   \n",
       "7           4    623          PAUL MORROW     M   BEAVERTON    OR   00:38:34   \n",
       "8           5    569      DEREK G OSBORNE     M   HILLSBORO    OR   00:39:21   \n",
       "9           6    642        JONATHON TRAN     M    PORTLAND    OR   00:39:49   \n",
       "\n",
       "       7          8         9          10         11         12  \\\n",
       "0    None       None      None       None       None       None   \n",
       "1    None       None      None       None       None       None   \n",
       "2    None       None      None       None       None       None   \n",
       "3    None       None      None       None       None       None   \n",
       "4   05:51   1 of 414   M 36-45   1 of 152   00:00:03   00:36:24   \n",
       "5   05:55   2 of 414   M 26-35   1 of 154   00:00:03   00:36:45   \n",
       "6   06:05   3 of 414   M 46-55    1 of 64   00:00:04   00:37:48   \n",
       "7   06:13   4 of 414   M 36-45   2 of 152   00:00:03   00:38:37   \n",
       "8   06:20   5 of 414   M 26-35   2 of 154   00:00:03   00:39:24   \n",
       "9   06:25   6 of 414   M 18-25    1 of 34   00:00:06   00:39:55   \n",
       "\n",
       "               13  \n",
       "0            None  \n",
       "1            None  \n",
       "2            None  \n",
       "3            None  \n",
       "4               ]  \n",
       "5   INTEL TEAM F]  \n",
       "6               ]  \n",
       "7               ]  \n",
       "8   INTEL TEAM F]  \n",
       "9               ]  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[0] = df1[0].str.strip('[')\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_labels = soup.find_all('th')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[Place, Bib, Name, Gender, City, State, Chip Time, Chip Pace, Gender Place, Age Group, Age Group Place, Time to Start, Gun Time, Team]']\n"
     ]
    }
   ],
   "source": [
    "#Similar to table rows, you can use Beautiful Soup to extract text in between html tags for table headers.\n",
    "all_header = []\n",
    "col_str = str(col_labels)\n",
    "cleantext2 = BeautifulSoup(col_str, \"lxml\").get_text()\n",
    "all_header.append(cleantext2)\n",
    "print(all_header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
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       "                                                   0\n",
       "0  [Place, Bib, Name, Gender, City, State, Chip T..."
      ]
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     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#You can then convert the list of headers into a pandas dataframe.\n",
    "df2 = pd.DataFrame(all_header)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>Chip Time</td>\n",
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       "      <td>Gender Place</td>\n",
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      ],
      "text/plain": [
       "       0     1      2        3      4       5           6           7   \\\n",
       "0  [Place   Bib   Name   Gender   City   State   Chip Time   Chip Pace   \n",
       "\n",
       "              8           9                 10              11         12  \\\n",
       "0   Gender Place   Age Group   Age Group Place   Time to Start   Gun Time   \n",
       "\n",
       "       13  \n",
       "0   Team]  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Similarly, you can split column \"0\" into multiple columns at the comma position for all rows.\n",
    "df3 = df2[0].str.split(',', expand=True)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>JARED WILSON</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:21</td>\n",
       "      <td>05:51</td>\n",
       "      <td>1 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>1 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>NATHAN A SUSTERSIC</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:42</td>\n",
       "      <td>05:55</td>\n",
       "      <td>2 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>1 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>FRANCISCO MAYA</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:37:44</td>\n",
       "      <td>06:05</td>\n",
       "      <td>3 of 414</td>\n",
       "      <td>M 46-55</td>\n",
       "      <td>1 of 64</td>\n",
       "      <td>00:00:04</td>\n",
       "      <td>00:37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>PAUL MORROW</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:38:34</td>\n",
       "      <td>06:13</td>\n",
       "      <td>4 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>2 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>DEREK G OSBORNE</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:21</td>\n",
       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>2 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:39:24</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1                    2        3           4       5   \\\n",
       "0      [Place    Bib                 Name   Gender        City   State   \n",
       "0  Finishers:   577]                 None     None        None    None   \n",
       "1       Male:   414]                 None     None        None    None   \n",
       "2     Female:   163]                 None     None        None    None   \n",
       "3           ]   None                 None     None        None    None   \n",
       "4           1    814         JARED WILSON        M      TIGARD      OR   \n",
       "5           2    573   NATHAN A SUSTERSIC        M    PORTLAND      OR   \n",
       "6           3    687       FRANCISCO MAYA        M    PORTLAND      OR   \n",
       "7           4    623          PAUL MORROW        M   BEAVERTON      OR   \n",
       "8           5    569      DEREK G OSBORNE        M   HILLSBORO      OR   \n",
       "\n",
       "           6           7              8           9                 10  \\\n",
       "0   Chip Time   Chip Pace   Gender Place   Age Group   Age Group Place   \n",
       "0        None        None           None        None              None   \n",
       "1        None        None           None        None              None   \n",
       "2        None        None           None        None              None   \n",
       "3        None        None           None        None              None   \n",
       "4    00:36:21       05:51       1 of 414     M 36-45          1 of 152   \n",
       "5    00:36:42       05:55       2 of 414     M 26-35          1 of 154   \n",
       "6    00:37:44       06:05       3 of 414     M 46-55           1 of 64   \n",
       "7    00:38:34       06:13       4 of 414     M 36-45          2 of 152   \n",
       "8    00:39:21       06:20       5 of 414     M 26-35          2 of 154   \n",
       "\n",
       "               11         12              13  \n",
       "0   Time to Start   Gun Time           Team]  \n",
       "0            None       None            None  \n",
       "1            None       None            None  \n",
       "2            None       None            None  \n",
       "3            None       None            None  \n",
       "4        00:00:03   00:36:24               ]  \n",
       "5        00:00:03   00:36:45   INTEL TEAM F]  \n",
       "6        00:00:04   00:37:48               ]  \n",
       "7        00:00:03   00:38:37               ]  \n",
       "8        00:00:03   00:39:24   INTEL TEAM F]  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The two dataframes can be concatenated into one using the concat() method as illustrated below.\n",
    "\n",
    "frames = [df3,df1]\n",
    "\n",
    "df4 = pd.concat(frames)\n",
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>[Place</th>\n",
       "      <th>Bib</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Chip Time</th>\n",
       "      <th>Chip Pace</th>\n",
       "      <th>Gender Place</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age Group Place</th>\n",
       "      <th>Time to Start</th>\n",
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       "      <th>0</th>\n",
       "      <td>[Place</td>\n",
       "      <td>Bib</td>\n",
       "      <td>Name</td>\n",
       "      <td>Gender</td>\n",
       "      <td>City</td>\n",
       "      <td>State</td>\n",
       "      <td>Chip Time</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male:</td>\n",
       "      <td>414]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       [Place    Bib   Name   Gender   City   State   Chip Time   Chip Pace  \\\n",
       "0      [Place    Bib   Name   Gender   City   State   Chip Time   Chip Pace   \n",
       "0  Finishers:   577]   None     None   None    None        None        None   \n",
       "1       Male:   414]   None     None   None    None        None        None   \n",
       "2     Female:   163]   None     None   None    None        None        None   \n",
       "3           ]   None   None     None   None    None        None        None   \n",
       "\n",
       "    Gender Place   Age Group   Age Group Place   Time to Start   Gun Time  \\\n",
       "0   Gender Place   Age Group   Age Group Place   Time to Start   Gun Time   \n",
       "0           None        None              None            None       None   \n",
       "1           None        None              None            None       None   \n",
       "2           None        None              None            None       None   \n",
       "3           None        None              None            None       None   \n",
       "\n",
       "    Team]  \n",
       "0   Team]  \n",
       "0    None  \n",
       "1    None  \n",
       "2    None  \n",
       "3    None  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Below shows how to assign the first row to be the table header.\n",
    "df5 = df4.rename(columns=df4.iloc[0])\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#At this point, the table is almost properly formatted. For analysis, you can start by getting an overview of the data as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 597 entries, 0 to 595\n",
      "Data columns (total 14 columns):\n",
      "[Place              597 non-null object\n",
      " Bib                596 non-null object\n",
      " Name               593 non-null object\n",
      " Gender             593 non-null object\n",
      " City               593 non-null object\n",
      " State              593 non-null object\n",
      " Chip Time          593 non-null object\n",
      " Chip Pace          578 non-null object\n",
      " Gender Place       578 non-null object\n",
      " Age Group          578 non-null object\n",
      " Age Group Place    578 non-null object\n",
      " Time to Start      578 non-null object\n",
      " Gun Time           578 non-null object\n",
      " Team]              578 non-null object\n",
      "dtypes: object(14)\n",
      "memory usage: 70.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(597, 14)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.info()\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The table has 597 rows and 14 columns. You can drop all rows with any missing values.\n",
    "df6 = df5.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>[Place</th>\n",
       "      <th>Bib</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Chip Time</th>\n",
       "      <th>Chip Pace</th>\n",
       "      <th>Gender Place</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age Group Place</th>\n",
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       "      <th>Gun Time</th>\n",
       "      <th>Team]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>JARED WILSON</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:21</td>\n",
       "      <td>05:51</td>\n",
       "      <td>1 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>1 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>NATHAN A SUSTERSIC</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:42</td>\n",
       "      <td>05:55</td>\n",
       "      <td>2 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>1 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>FRANCISCO MAYA</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:37:44</td>\n",
       "      <td>06:05</td>\n",
       "      <td>3 of 414</td>\n",
       "      <td>M 46-55</td>\n",
       "      <td>1 of 64</td>\n",
       "      <td>00:00:04</td>\n",
       "      <td>00:37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>PAUL MORROW</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:38:34</td>\n",
       "      <td>06:13</td>\n",
       "      <td>4 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>2 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>DEREK G OSBORNE</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:21</td>\n",
       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>2 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:39:24</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  [Place   Bib                 Name  Gender        City  State  Chip Time  \\\n",
       "4      1   814         JARED WILSON       M      TIGARD     OR   00:36:21   \n",
       "5      2   573   NATHAN A SUSTERSIC       M    PORTLAND     OR   00:36:42   \n",
       "6      3   687       FRANCISCO MAYA       M    PORTLAND     OR   00:37:44   \n",
       "7      4   623          PAUL MORROW       M   BEAVERTON     OR   00:38:34   \n",
       "8      5   569      DEREK G OSBORNE       M   HILLSBORO     OR   00:39:21   \n",
       "\n",
       "   Chip Pace  Gender Place  Age Group  Age Group Place  Time to Start  \\\n",
       "4      05:51      1 of 414    M 36-45         1 of 152       00:00:03   \n",
       "5      05:55      2 of 414    M 26-35         1 of 154       00:00:03   \n",
       "6      06:05      3 of 414    M 46-55          1 of 64       00:00:04   \n",
       "7      06:13      4 of 414    M 36-45         2 of 152       00:00:03   \n",
       "8      06:20      5 of 414    M 26-35         2 of 154       00:00:03   \n",
       "\n",
       "    Gun Time           Team]  \n",
       "4   00:36:24               ]  \n",
       "5   00:36:45   INTEL TEAM F]  \n",
       "6   00:37:48               ]  \n",
       "7   00:38:37               ]  \n",
       "8   00:39:24   INTEL TEAM F]  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df6.drop(df6.index[0])\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Bib</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Chip Time</th>\n",
       "      <th>Chip Pace</th>\n",
       "      <th>Gender Place</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age Group Place</th>\n",
       "      <th>Time to Start</th>\n",
       "      <th>Gun Time</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>JARED WILSON</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:21</td>\n",
       "      <td>05:51</td>\n",
       "      <td>1 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>1 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>NATHAN A SUSTERSIC</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:36:42</td>\n",
       "      <td>05:55</td>\n",
       "      <td>2 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>1 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>FRANCISCO MAYA</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:37:44</td>\n",
       "      <td>06:05</td>\n",
       "      <td>3 of 414</td>\n",
       "      <td>M 46-55</td>\n",
       "      <td>1 of 64</td>\n",
       "      <td>00:00:04</td>\n",
       "      <td>00:37:48</td>\n",
       "      <td>]</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>PAUL MORROW</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:38:34</td>\n",
       "      <td>06:13</td>\n",
       "      <td>4 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>2 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>DEREK G OSBORNE</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:21</td>\n",
       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>2 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:39:24</td>\n",
       "      <td>INTEL TEAM F]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Place   Bib                 Name  Gender        City  State  Chip Time  \\\n",
       "4     1   814         JARED WILSON       M      TIGARD     OR   00:36:21   \n",
       "5     2   573   NATHAN A SUSTERSIC       M    PORTLAND     OR   00:36:42   \n",
       "6     3   687       FRANCISCO MAYA       M    PORTLAND     OR   00:37:44   \n",
       "7     4   623          PAUL MORROW       M   BEAVERTON     OR   00:38:34   \n",
       "8     5   569      DEREK G OSBORNE       M   HILLSBORO     OR   00:39:21   \n",
       "\n",
       "   Chip Pace  Gender Place  Age Group  Age Group Place  Time to Start  \\\n",
       "4      05:51      1 of 414    M 36-45         1 of 152       00:00:03   \n",
       "5      05:55      2 of 414    M 26-35         1 of 154       00:00:03   \n",
       "6      06:05      3 of 414    M 46-55          1 of 64       00:00:04   \n",
       "7      06:13      4 of 414    M 36-45         2 of 152       00:00:03   \n",
       "8      06:20      5 of 414    M 26-35         2 of 154       00:00:03   \n",
       "\n",
       "    Gun Time            Team  \n",
       "4   00:36:24               ]  \n",
       "5   00:36:45   INTEL TEAM F]  \n",
       "6   00:37:48               ]  \n",
       "7   00:38:37               ]  \n",
       "8   00:39:24   INTEL TEAM F]  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''You can perform more data cleaning by renaming the '[Place' and ' Team]' columns.\n",
    "Python is very picky about space. Make sure you include space after the quotation mark in ' Team]'.'''\n",
    "df7.rename(columns={'[Place':'Place'}, inplace=True)\n",
    "df7.rename(columns={' Team]':'Team'}, inplace=True)\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>INTEL TEAM F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>FRANCISCO MAYA</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
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       "      <td>06:05</td>\n",
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       "      <td>00:37:48</td>\n",
       "      <td></td>\n",
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       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>PAUL MORROW</td>\n",
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       "      <td>4 of 414</td>\n",
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       "      <td>2 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:38:37</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>DEREK G OSBORNE</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:21</td>\n",
       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>2 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:39:24</td>\n",
       "      <td>INTEL TEAM F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Place   Bib                 Name  Gender        City  State  Chip Time  \\\n",
       "4     1   814         JARED WILSON       M      TIGARD     OR   00:36:21   \n",
       "5     2   573   NATHAN A SUSTERSIC       M    PORTLAND     OR   00:36:42   \n",
       "6     3   687       FRANCISCO MAYA       M    PORTLAND     OR   00:37:44   \n",
       "7     4   623          PAUL MORROW       M   BEAVERTON     OR   00:38:34   \n",
       "8     5   569      DEREK G OSBORNE       M   HILLSBORO     OR   00:39:21   \n",
       "\n",
       "   Chip Pace  Gender Place  Age Group  Age Group Place  Time to Start  \\\n",
       "4      05:51      1 of 414    M 36-45         1 of 152       00:00:03   \n",
       "5      05:55      2 of 414    M 26-35         1 of 154       00:00:03   \n",
       "6      06:05      3 of 414    M 46-55          1 of 64       00:00:04   \n",
       "7      06:13      4 of 414    M 36-45         2 of 152       00:00:03   \n",
       "8      06:20      5 of 414    M 26-35         2 of 154       00:00:03   \n",
       "\n",
       "    Gun Time           Team  \n",
       "4   00:36:24                 \n",
       "5   00:36:45   INTEL TEAM F  \n",
       "6   00:37:48                 \n",
       "7   00:38:37                 \n",
       "8   00:39:24   INTEL TEAM F  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The final data cleaning step involves removing the closing bracket for cells in the \"Team\" column.\n",
    "df7['Team'] = df7['Team'].str.strip(']')\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_list = df7[' Chip Time'].tolist()\n",
    "\n",
    "#Use for loop to convert 'Chip Time' to minutes\n",
    "\n",
    "time_mins = []\n",
    "for i in time_list:\n",
    "    h,m,s = i.split(':')\n",
    "    math = (int(h) * 3600 +int(m) * 60 +int(s))/60\n",
    "    \n",
    "    time_mins.append(math)\n",
    "#print(time_mins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
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       "      <th>Name</th>\n",
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       "      <th>Gun Time</th>\n",
       "      <th>Team</th>\n",
       "      <th>Runner_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>JARED WILSON</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
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       "      <td></td>\n",
       "      <td>36.350000</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
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       "      <td>M</td>\n",
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       "      <td>OR</td>\n",
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       "      <td>1 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>INTEL TEAM F</td>\n",
       "      <td>36.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>FRANCISCO MAYA</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:37:44</td>\n",
       "      <td>06:05</td>\n",
       "      <td>3 of 414</td>\n",
       "      <td>M 46-55</td>\n",
       "      <td>1 of 64</td>\n",
       "      <td>00:00:04</td>\n",
       "      <td>00:37:48</td>\n",
       "      <td></td>\n",
       "      <td>37.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>PAUL MORROW</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:38:34</td>\n",
       "      <td>06:13</td>\n",
       "      <td>4 of 414</td>\n",
       "      <td>M 36-45</td>\n",
       "      <td>2 of 152</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:38:37</td>\n",
       "      <td></td>\n",
       "      <td>38.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>DEREK G OSBORNE</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>00:39:21</td>\n",
       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
       "      <td>M 26-35</td>\n",
       "      <td>2 of 154</td>\n",
       "      <td>00:00:03</td>\n",
       "      <td>00:39:24</td>\n",
       "      <td>INTEL TEAM F</td>\n",
       "      <td>39.350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Place   Bib                 Name  Gender        City  State  Chip Time  \\\n",
       "4     1   814         JARED WILSON       M      TIGARD     OR   00:36:21   \n",
       "5     2   573   NATHAN A SUSTERSIC       M    PORTLAND     OR   00:36:42   \n",
       "6     3   687       FRANCISCO MAYA       M    PORTLAND     OR   00:37:44   \n",
       "7     4   623          PAUL MORROW       M   BEAVERTON     OR   00:38:34   \n",
       "8     5   569      DEREK G OSBORNE       M   HILLSBORO     OR   00:39:21   \n",
       "\n",
       "   Chip Pace  Gender Place  Age Group  Age Group Place  Time to Start  \\\n",
       "4      05:51      1 of 414    M 36-45         1 of 152       00:00:03   \n",
       "5      05:55      2 of 414    M 26-35         1 of 154       00:00:03   \n",
       "6      06:05      3 of 414    M 46-55          1 of 64       00:00:04   \n",
       "7      06:13      4 of 414    M 36-45         2 of 152       00:00:03   \n",
       "8      06:20      5 of 414    M 26-35         2 of 154       00:00:03   \n",
       "\n",
       "    Gun Time           Team  Runner_min  \n",
       "4   00:36:24                  36.350000  \n",
       "5   00:36:45   INTEL TEAM F   36.700000  \n",
       "6   00:37:48                  37.733333  \n",
       "7   00:38:37                  38.566667  \n",
       "8   00:39:24   INTEL TEAM F   39.350000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The next step is to convert the list back into a dataframe\n",
    "#and make a new column (\"Runner_mins\") for runner chip times expressed in just minutes.\n",
    "\n",
    "\n",
    "df7['Runner_min'] = time_mins\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below shows how to calculate statistics for numeric columns only in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Runner_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>577.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.035933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.970623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>36.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Runner_min\n",
       "count  577.000000\n",
       "mean    60.035933\n",
       "std     11.970623\n",
       "min     36.350000\n",
       "25%     51.000000\n",
       "50%     59.016667\n",
       "75%     67.266667\n",
       "max    101.300000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.describe(include=[np.number])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, the average chip time for all runners was ~60 mins. The fastest 10K runner finished in 36.35 mins, and the slowest runner finished in 101.30 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A boxplot is another useful tool to visualize summary statistics (maximum, minimum, medium, first quartile, third quartile, including outliers). Below are data summary statistics for the runners shown in a boxplot. For data visualization, it is convenient to first import parameters from the pylab module that comes with matplotlib and set the same size for all figures to avoid doing it for each figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15,5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x186f3374518>],\n",
       " <a list of 1 Text xticklabel objects>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''df7.boxplot(column='Runner_mins')\n",
    "plt.grid(True, axis='y')\n",
    "plt.ylabel(' Chip Time')\n",
    "plt.xticks([1], ['Runners'])'''\n",
    "\n",
    "df7.boxplot(column='Runner_min')\n",
    "plt.grid(True, axis='y')\n",
    "plt.ylabel('Chip Time')\n",
    "plt.xticks([1], ['Runners'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second question to answer is: Did the runners' finish times follow a normal distribution?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Below is a distribution plot of runners' chip times plotted using the seaborn library. The distribution looks almost normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df7['Runner_min']\n",
    "ax = sns.distplot(x, hist=True, kde=True, rug=False, color='m', bins=25, hist_kws={'edgecolor':'black'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third question deals with whether there were any performance differences between males and females of various age groups. Below is a distribution plot of chip times for males and females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x186f2f9cc18>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_fuko = df7.loc[df7[' Gender']==' F']['Runner_min']\n",
    "m_fuko = df7.loc[df7[' Gender']==' M']['Runner_min']\n",
    "sns.distplot(f_fuko, hist=True, kde=True, rug=False, hist_kws={'edgecolor':'black'}, label='Female')\n",
    "sns.distplot(m_fuko, hist=False, kde=True, rug=False, hist_kws={'edgecolor':'black'}, label='Male')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution indicates that females were slower than males on average. You can use the groupby() method to compute summary statistics for males and females separately as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Runner_min                                                         \\\n",
      "             count       mean        std        min        25%        50%   \n",
      " Gender                                                                     \n",
      " F           163.0  66.119223  12.184440  43.766667  58.758333  64.616667   \n",
      " M           414.0  57.640821  11.011857  36.350000  49.395833  55.791667   \n",
      "\n",
      "                                \n",
      "               75%         max  \n",
      " Gender                         \n",
      " F       72.058333  101.300000  \n",
      " M       64.804167   98.516667  \n"
     ]
    }
   ],
   "source": [
    "g_stats = df7.groupby(\" Gender\", as_index=True).describe()\n",
    "print(g_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average chip time for all females and males was ~66 mins and ~58 mins, respectively. Below is a side-by-side boxplot comparison of male and female finish times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7.boxplot(column='Runner_min', by=' Gender')\n",
    "plt.ylabel('Chip Time')\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}