Web Scraping Practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "% matplotlib inline\n",
    "\n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"http://www.hubertiming.com/results/2017GPTR10K\"\n",
    "html = urlopen(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bs4.BeautifulSoup"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup = BeautifulSoup(html, 'lxml')\n",
    "type(soup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<title>2017 Intel Great Place to Run 10K \\ Urban Clash Games Race Results</title>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the title of the html\n",
    "soup.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n\\n\\n\\n\\n\\n2017 Intel Great Place to Run 10K \\\\ Urban Clash Games Race Results\\n\\n\\n\\n\\n\\n\\r\\n        @media print {\\r\\n            .noprint {\\r\\n                display: none;\\r\\n            }\\r\\n\\r\\n            .dataTables_filter {\\r\\n                display: none;\\r\\n            }\\r\\n        }\\r\\n        #individualResults tbody tr {\\r\\n            cursor: pointer;\\r\\n        }\\r\\n        .video-container {\\r\\n            position: relative;\\r\\n            padding-bottom: 56.25%; /* 16:9 */\\r\\n            padding-top: 25px;\\r\\n            height: 0;\\r\\n        }\\r\\n        .video-container iframe {\\r\\n            position: absolute;\\r\\n            top: 0;\\r\\n            left: 0;\\r\\n            width: 100%;\\r\\n            height: 100%;\\r\\n        }\\r\\n        .dataTables_wrapper .dataTables_paginate {\\r\\n            float: right;\\r\\n            text-align: right;\\r\\n            padding-top: 0.25em;\\r\\n        }\\r\\n        .dataTables_wrapper .dataTables_paginate .paginate_button {\\r\\n                box-sizing: border-box;\\r\\n                display: inline-block;\\r\\n                min-width: 1.5em;\\r\\n                padding: 0.5em 1em;\\r\\n                margin-left: 2px;\\r\\n                text-align: center;\\r\\n                text-decoration: none !important;\\r\\n                cursor: pointer;\\r\\n                *cursor: hand;\\r\\n                color: #333333 !important;\\r\\n                border: 1px solid transparent;\\r\\n            }\\r\\n\\r\\n        .dataTables_wrapper .dataTables_paginate .paginate_button.current, .dataTables_wrapper .dataTables_paginate .paginate_button.current:hover {\\r\\n                color: #333333 !important;\\r\\n                border: 1px solid #cacaca;\\r\\n                background-color: white;\\r\\n                background: -webkit-gradient(linear, left top, left bottom, color-stop(0%, white), color-stop(100%, gainsboro));\\r\\n                /* Chrome,Safari4+ */\\r\\n                background: -webkit-linear-gradient(top, white 0%, gainsboro 100%);\\r\\n                /* Chrome10+,Safari5.1+ */\\r\\n                background: -moz-linear-gradient(top, white 0%, gainsboro 100%);\\r\\n                /* FF3.6+ */\\r\\n                background: -ms-linear-gradient(top, white 0%, gainsboro 100%);\\r\\n                /* IE10+ */\\r\\n                background: -o-linear-gradient(top, white 0%, gainsboro 100%);\\r\\n                /* Opera 11.10+ */\\r\\n                background: linear-gradient(to bottom, white 0%, gainsboro 100%);\\r\\n                /* W3C */\\r\\n            }\\r\\n\\r\\n            .dataTables_wrapper .dataTables_paginate .paginate_button.disabled, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled:hover, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled:active {\\r\\n                cursor: default;\\r\\n                color: #666 !important;\\r\\n                border: 1px solid transparent;\\r\\n                background: transparent;\\r\\n                box-shadow: none;\\r\\n            }\\r\\n\\r\\n            .dataTables_wrapper .dataTables_paginate .paginate_button:hover {\\r\\n                color: white !important;\\r\\n                border: 1px solid #111111;\\r\\n                background-color: #585858;\\r\\n                background: -webkit-gradient(linear, left top, left bottom, color-stop(0%, #585858), color-stop(100%, #111111));\\r\\n                /* Chrome,Safari4+ */\\r\\n                background: -webkit-linear-gradient(top, #585858 0%, #111111 100%);\\r\\n                /* Chrome10+,Safari5.1+ */\\r\\n                background: -moz-linear-gradient(top, #585858 0%, #111111 100%);\\r\\n                /* FF3.6+ */\\r\\n                background: -ms-linear-gradient(top, #585858 0%, #111111 100%);\\r\\n                /* IE10+ */\\r\\n                background: -o-linear-gradient(top, #585858 0%, #111111 100%);\\r\\n                /* Opera 11.10+ */\\r\\n                background: linear-gradient(to bottom, #585858 0%, #111111 100%);\\r\\n                /* W3C */\\r\\n            }\\r\\n\\r\\n            .dataTables_wrapper .dataTables_paginate .paginate_button:active {\\r\\n                outline: none;\\r\\n                background-color: #2b2b2b;\\r\\n                background: -webkit-gradient(linear, left top, left bottom, color-stop(0%, #2b2b2b), color-stop(100%, #0c0c0c));\\r\\n                /* Chrome,Safari4+ */\\r\\n                background: -webkit-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);\\r\\n                /* Chrome10+,Safari5.1+ */\\r\\n                background: -moz-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);\\r\\n                /* FF3.6+ */\\r\\n                background: -ms-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);\\r\\n                /* IE10+ */\\r\\n                background: -o-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);\\r\\n                /* Opera 11.10+ */\\r\\n                background: linear-gradient(to bottom, #2b2b2b 0%, #0c0c0c 100%);\\r\\n                /* W3C */\\r\\n                box-shadow: inset 0 0 3px #111;\\r\\n            }\\r\\n\\r\\n        .dataTables_wrapper .dataTables_processing {\\r\\n            position: absolute;\\r\\n            top: 50%;\\r\\n            left: 50%;\\r\\n            width: 100%;\\r\\n            height: 40px;\\r\\n            margin-left: -50%;\\r\\n            margin-top: -25px;\\r\\n            padding-top: 20px;\\r\\n            text-align: center;\\r\\n            font-size: 1.2em;\\r\\n            background-color: white;\\r\\n            background: -webkit-gradient(linear, left top, right top, color-stop(0%, rgba(255, 255, 255, 0)), color-stop(25%, rgba(255, 255, 255, 0.9)), color-stop(75%, rgba(255, 255, 255, 0.9)), color-stop(100%, rgba(255, 255, 255, 0)));\\r\\n            /* Chrome,Safari4+ */\\r\\n            background: -webkit-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%);\\r\\n            /* Chrome10+,Safari5.1+ */\\r\\n            background: -moz-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%);\\r\\n            /* FF3.6+ */\\r\\n            background: -ms-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%);\\r\\n            /* IE10+ */\\r\\n            background: -o-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%);\\r\\n            /* Opera 11.10+ */\\r\\n            background: linear-gradient(to right, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%);\\r\\n            /* W3C */\\r\\n        }\\r\\n\\r\\n        .dataTables_wrapper .dataTables_length,\\r\\n        .dataTables_wrapper .dataTables_filter,\\r\\n        .dataTables_wrapper .dataTables_info,\\r\\n        .dataTables_wrapper .dataTables_processing,\\r\\n        .dataTables_wrapper .dataTables_paginate {\\r\\n            color: #333333;\\r\\n        }\\r\\n\\r\\n    \\n\\n\\r\\n        body {\\r\\n            margin: 0;\\r\\n            font-family: \"Helvetica Neue\",Helvetica,Arial,sans-serif;\\r\\n            font-size: 16px;\\r\\n            line-height: 18px;\\r\\n            color: #333;\\r\\n            background-color: #fff;\\r\\n        }\\r\\n\\r\\n\\r\\n        .ui-widget {\\r\\n            font-family: \"Helvetica Neue\",Helvetica,Arial,sans-serif;\\r\\n            font-size: 16px;\\r\\n        }\\r\\n\\r\\n        /* Should consider switching to font awesome icons so they appear larger */\\r\\n        #tabs li .ui-icon-close {\\r\\n            float: left;\\r\\n            margin: 10px 0px 0px 0px;\\r\\n            cursor: pointer;\\r\\n        }\\r\\n\\r\\n        .ui-state-default, .ui-widget-content .ui-state-default, .ui-widget-header .ui-state-default {\\r\\n            font-weight: bold;\\r\\n            color: #ffffff;\\r\\n        }\\r\\n\\r\\n        /* Remove border. Make this work a little better on mobile devices. Not true responsive though.*/\\r\\n        .ui-widget-content {\\r\\n            border: none;\\r\\n        }\\r\\n\\r\\n        .alert-info {\\r\\n            margin-bottom:0;\\r\\n            width: 100%;\\r\\n        }\\r\\n        .table td {\\r\\n            padding: .5rem;\\r\\n        }\\r\\n    \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n5K\\n10K\\n\\n\\n\\n\\n 2017 Intel Great Place to Run 10K \\\\ Urban Clash Games\\n Hillsboro Stadium, Hillsboro, OR\\n June 2nd, 2017\\r\\n\\r\\n                        \\n\\n\\n\\n\\n\\nHuber Timing Home\\n\\n\\nFinishers:577\\nMale:414\\nFemale:163\\n\\n\\n\\n\\n\\n\\n\\r\\n            [Individual Results]\\r\\n            [Team Results] \\n\\n\\r\\n                Email timing@hubertiming.com with any questions or corrections. Please include your bib number if you have it.\\n\\n\\n\\n\\n\\nResults\\n\\n\\n\\n\\n\\n\\n\\nPlace\\nBib\\nName\\nGender\\nCity\\nState\\nChip Time\\nChip Pace\\nGender Place\\nAge Group\\nAge Group Place\\nTime to Start\\nGun Time\\nTeam\\n\\n\\n\\n\\n1\\n814\\nJARED WILSON\\nM\\nTIGARD\\nOR\\n00:36:21\\n05:51\\n1 of 414\\nM 36-45\\n1 of 152\\n00:00:03\\n00:36:24\\n\\n\\n\\n2\\n573\\nNATHAN A SUSTERSIC\\nM\\nPORTLAND\\nOR\\n00:36:42\\n05:55\\n2 of 414\\nM 26-35\\n1 of 154\\n00:00:03\\n00:36:45\\nINTEL TEAM F\\n\\n\\n3\\n687\\nFRANCISCO MAYA\\nM\\nPORTLAND\\nOR\\n00:37:44\\n06:05\\n3 of 414\\nM 46-55\\n1 of 64\\n00:00:04\\n00:37:48\\n\\n\\n\\n4\\n623\\nPAUL MORROW\\nM\\nBEAVERTON\\nOR\\n00:38:34\\n06:13\\n4 of 414\\nM 36-45\\n2 of 152\\n00:00:03\\n00:38:37\\n\\n\\n\\n5\\n569\\nDEREK G OSBORNE\\nM\\nHILLSBORO\\nOR\\n00:39:21\\n06:20\\n5 of 414\\nM 26-35\\n2 of 154\\n00:00:03\\n00:39:24\\nINTEL TEAM F\\n\\n\\n6\\n642\\nJONATHON TRAN\\nM\\nPORTLAND\\nOR\\n00:39:49\\n06:25\\n6 of 414\\nM 18-25\\n1 of 34\\n00:00:06\\n00:39:55\\n\\n\\n\\n7\\n144\\nGEORGE TOTONCHY\\nM\\nPORTLAND\\nOR\\n00:40:04\\n06:27\\n7 of 414\\nM 36-45\\n3 of 152\\n00:00:13\\n00:40:17\\n\\n\\n\\n8\\n395\\nBENJAMIN C CHAFFIN\\nM\\nPORTLAND\\nOR\\n00:40:05\\n06:27\\n8 of 414\\nM 36-45\\n4 of 152\\n00:00:04\\n00:40:09\\n\\n\\n\\n9\\n7\\nBRANDON THOMAS\\nM\\n\\n\\n00:40:17\\n06:29\\n9 of 414\\nM 26-35\\n3 of 154\\n00:00:07\\n00:40:24\\nCOLUMBIA TEAM B\\n\\n\\n10\\n3\\nERIK BJORNSTAD\\nM\\n\\n\\n00:40:21\\n06:30\\n10 of 414\\nM 36-45\\n5 of 152\\n00:00:04\\n00:40:25\\nCOLUMBIA TEAM A\\n\\n\\n11\\n68\\nWITALI SPULING\\nM\\nPORTLAND\\nOR\\n00:40:28\\n06:31\\n11 of 414\\nM 26-35\\n4 of 154\\n00:00:12\\n00:40:40\\nDTNA1\\n\\n\\n12\\n788\\nANDY WEDAM\\nM\\nPORTLAND\\nOR\\n00:40:36\\n06:33\\n12 of 414\\nM 36-45\\n6 of 152\\n00:00:06\\n00:40:42\\nDTNA2\\n\\n\\n13\\n729\\nMICHAEL P GEORGE\\nM\\nPORTLAND\\nOR\\n00:40:43\\n06:34\\n13 of 414\\nM 18-25\\n2 of 34\\n00:00:07\\n00:40:50\\n\\n\\n\\n14\\n151\\nETHAN JORDAN\\nM\\nPORTLAND\\nOR\\n00:41:01\\n06:37\\n14 of 414\\nM 36-45\\n7 of 152\\n00:00:12\\n00:41:13\\n\\n\\n\\n15\\n758\\nFLAVIO GRIGGIO\\nM\\nPORTLAND\\nOR\\n00:41:19\\n06:39\\n15 of 414\\nM 26-35\\n5 of 154\\n00:00:05\\n00:41:24\\n\\n\\n\\n16\\n868\\nWILLIAM BARRY\\nM\\nBEAVERTON\\nOR\\n00:41:43\\n06:43\\n16 of 414\\nM 46-55\\n2 of 64\\n00:00:06\\n00:41:49\\nINTEL TEAM K\\n\\n\\n17\\n2\\nJIM DROZDOWSKI\\nM\\n\\n\\n00:41:59\\n06:46\\n17 of 414\\nM 46-55\\n3 of 64\\n00:00:03\\n00:42:02\\nCOLUMBIA TEAM A\\n\\n\\n18\\n362\\nBRITON BARKER\\nM\\nHILLSBORO\\nOR\\n00:42:23\\n06:50\\n18 of 414\\nM 36-45\\n8 of 152\\n00:00:05\\n00:42:28\\n\\n\\n\\n19\\n755\\nNEILANJAN DUTTA\\nM\\nHILLSBORO\\nOR\\n00:42:29\\n06:51\\n19 of 414\\nM 26-35\\n6 of 154\\n00:00:05\\n00:42:34\\n\\n\\n\\n20\\n543\\nKENNETH KLOTZ\\nM\\nEUGENE\\nOR\\n00:42:35\\n06:52\\n20 of 414\\nM 26-35\\n7 of 154\\n00:00:13\\n00:42:48\\n\\n\\n\\n21\\n401\\nEDDIE J FRIE\\nM\\nHILLSBORO\\nOR\\n00:42:36\\n06:52\\n21 of 414\\nM 46-55\\n4 of 64\\n00:00:16\\n00:42:52\\nINTEL TEAM K\\n\\n\\n22\\n155\\nJARED PAGER\\nM\\nPORTLAND\\nOR\\n00:42:37\\n06:52\\n22 of 414\\nM 26-35\\n8 of 154\\n00:00:21\\n00:42:58\\n\\n\\n\\n23\\n781\\nDAVID HERRON\\nM\\nPORTLAND\\nOR\\n00:42:56\\n06:55\\n23 of 414\\nM 26-35\\n9 of 154\\n00:00:08\\n00:43:04\\nFXG1\\n\\n\\n24\\n468\\nTIMOTHY WESLEY\\nM\\nNOT SPECIFIED\\nOR\\n00:43:26\\n07:00\\n24 of 414\\nM 18-25\\n3 of 34\\n00:00:10\\n00:43:36\\n\\n\\n\\n25\\n882\\nMONROE SULLIVAN\\nM\\nPORTLAND\\nOR\\n00:43:27\\n07:00\\n25 of 414\\nM 46-55\\n5 of 64\\n00:00:03\\n00:43:30\\n\\n\\n\\n26\\n236\\nVINCENT OLSEN\\nM\\nBOISE\\nID\\n00:43:40\\n07:02\\n26 of 414\\nM 36-45\\n9 of 152\\n00:00:03\\n00:43:43\\n\\n\\n\\n27\\n742\\nLEONARD NEIBERG\\nM\\nPORTLAND\\nOR\\n00:43:42\\n07:02\\n27 of 414\\nM 46-55\\n6 of 64\\n00:00:19\\n00:44:01\\n\\n\\n\\n28\\n344\\nBENJAMIN JANN\\nM\\nHILLSBORO\\nOR\\n00:43:43\\n07:03\\n28 of 414\\nM 36-45\\n10 of 152\\n00:00:09\\n00:43:52\\n\\n\\n\\n29\\n732\\nANDREW HUNTER\\nM\\nBEAVERTON\\nOR\\n00:43:46\\n07:03\\n29 of 414\\nM 36-45\\n11 of 152\\n00:00:05\\n00:43:51\\n\\n\\n\\n30\\n253\\nSAMANTHA BAGLEY\\nF\\nHILLSBORO\\nOR\\n00:43:46\\n07:03\\n1 of 163\\nF 18-25\\n1 of 21\\n00:00:07\\n00:43:53\\nINTEL TEAM K\\n\\n\\n31\\n674\\nTAYSEER MAHDI\\nF\\nHILLSBORO\\nOR\\n00:43:47\\n07:03\\n2 of 163\\nF 26-35\\n1 of 59\\n00:00:05\\n00:43:52\\n\\n\\n\\n32\\n761\\nCHEN-GUAN LEE\\nM\\nPORTLAND\\nOR\\n00:43:47\\n07:03\\n30 of 414\\nM 26-35\\n10 of 154\\n00:00:05\\n00:43:52\\n\\n\\n\\n33\\n560\\nLEONARDO PEREZ\\nM\\nHILLSBORO\\nOR\\n00:44:04\\n07:06\\n31 of 414\\nM 26-35\\n11 of 154\\n00:00:06\\n00:44:10\\n\\n\\n\\n34\\n789\\nVASILIOS TSIRIMIAGOS\\nM\\nPORTLAND\\nOR\\n00:44:05\\n07:06\\n32 of 414\\nM 36-45\\n12 of 152\\n00:00:07\\n00:44:12\\nDTNA2\\n\\n\\n35\\n807\\nDAN CAMPBELL\\nM\\nHILLSBORO\\nOR\\n00:44:06\\n07:06\\n33 of 414\\nM 36-45\\n13 of 152\\n00:00:12\\n00:44:18\\n\\n\\n\\n36\\n646\\nJANELLE M KLASER\\nF\\nPORTLAND\\nOR\\n00:44:07\\n07:06\\n3 of 163\\nF 36-45\\n1 of 56\\n00:00:10\\n00:44:17\\n\\n\\n\\n37\\n740\\nYOSHIYASU YAMAKAWA\\nM\\nPORTLAND\\nOR\\n00:44:23\\n07:09\\n34 of 414\\nM 36-45\\n14 of 152\\n00:00:06\\n00:44:29\\n\\n\\n\\n38\\n874\\nJONATHAN M MARTIN\\nM\\nPORTLAND\\nOR\\n00:44:38\\n07:12\\n35 of 414\\nM 18-25\\n4 of 34\\n00:00:31\\n00:45:09\\n\\n\\n\\n39\\n361\\nRICHARD FLORES\\nM\\nPORTLAND\\nOR\\n00:44:41\\n07:12\\n36 of 414\\nM 36-45\\n15 of 152\\n00:00:29\\n00:45:10\\n\\n\\n\\n40\\n710\\nDAVID W GAIBLER\\nM\\nPORTLAND\\nOR\\n00:44:42\\n07:12\\n37 of 414\\nM 36-45\\n16 of 152\\n00:00:04\\n00:44:46\\n\\n\\n\\n41\\n791\\nANDRE RAUCH\\nM\\nPORTLAND\\nOR\\n00:44:48\\n07:13\\n38 of 414\\nM 26-35\\n12 of 154\\n00:00:13\\n00:45:01\\nDTNA2\\n\\n\\n42\\n867\\nJOSHUA FEDDERLY\\nM\\nBEAVERTON\\nOR\\n00:45:03\\n07:15\\n39 of 414\\nM 36-45\\n17 of 152\\n00:00:43\\n00:45:46\\n\\n\\n\\n43\\n142\\nTONDERAI NEMARUNDWE\\nM\\nBEAVERTON\\nOR\\n00:45:05\\n07:16\\n40 of 414\\nM 26-35\\n13 of 154\\n00:00:04\\n00:45:09\\n\\n\\n\\n44\\n787\\nJONATHAN PARSONS\\nM\\nPORTLAND\\nOR\\n00:45:10\\n07:17\\n41 of 414\\nM 18-25\\n5 of 34\\n00:00:12\\n00:45:22\\nDTNA2\\n\\n\\n45\\n673\\nPIETER ROWLETTE\\nM\\nPORTLAND\\nOR\\n00:45:11\\n07:17\\n42 of 414\\nM 36-45\\n18 of 152\\n00:00:19\\n00:45:30\\n\\n\\n\\n46\\n746\\nRAMON CALDERER\\nM\\nPORTLAND\\nOR\\n00:45:11\\n07:17\\n43 of 414\\nM 26-35\\n14 of 154\\n00:00:07\\n00:45:18\\n\\n\\n\\n47\\n770\\nJONATHAN M HATFIELD\\nM\\nPORTLAND\\nOR\\n00:45:13\\n07:17\\n44 of 414\\nM 36-45\\n19 of 152\\n00:00:22\\n00:45:35\\n\\n\\n\\n48\\n622\\nDAVID N GOLDSTEIN\\nM\\nBEAVERTON\\nOR\\n00:45:14\\n07:17\\n45 of 414\\nM 36-45\\n20 of 152\\n00:00:18\\n00:45:32\\n\\n\\n\\n49\\n625\\nGREGORY M RUTTER\\nM\\nHILLSBORO\\nOR\\n00:45:15\\n07:17\\n46 of 414\\nM 26-35\\n15 of 154\\n00:00:11\\n00:45:26\\n\\n\\n\\n50\\n635\\nSTEFAN MEISTER\\nM\\nNOT SPECIFIED\\nOR\\n00:45:17\\n07:18\\n47 of 414\\nM 36-45\\n21 of 152\\n00:00:07\\n00:45:24\\n\\n\\n\\n51\\n765\\nMATT TRAVERSO\\nM\\nHILLSBORO\\nOR\\n00:45:27\\n07:19\\n48 of 414\\nM 26-35\\n16 of 154\\n00:00:26\\n00:45:53\\n\\n\\n\\n52\\n438\\nKEN W GILDERSLEEVE\\nM\\nPORTLAND\\nOR\\n00:45:28\\n07:20\\n49 of 414\\nM 26-35\\n17 of 154\\n00:00:06\\n00:45:34\\n\\n\\n\\n53\\n582\\nANDREW L HERTEL\\nM\\nHILLSBORO\\nOR\\n00:45:29\\n07:20\\n50 of 414\\nM 26-35\\n18 of 154\\n00:00:13\\n00:45:42\\nINTEL TEAM A\\n\\n\\n54\\n696\\nCHRISTOPHER L QUAM\\nM\\nPORTLAND\\nOR\\n00:45:36\\n07:21\\n51 of 414\\nM 26-35\\n19 of 154\\n00:00:55\\n00:46:31\\n\\n\\n\\n55\\n580\\nSICELO MASANGO\\nM\\nHILLSBORO\\nOR\\n00:45:36\\n07:21\\n52 of 414\\nM 26-35\\n20 of 154\\n00:00:41\\n00:46:17\\n\\n\\n\\n56\\n558\\nCHAD MUSZYNSKI\\nM\\nHILLSBORO\\nOR\\n00:45:40\\n07:22\\n53 of 414\\nM 36-45\\n22 of 152\\n00:00:19\\n00:45:59\\n\\n\\n\\n57\\n47\\nFUMIE K WEIBY\\nF\\nPORTLAND\\nOR\\n00:45:40\\n07:22\\n4 of 163\\nF 36-45\\n2 of 56\\n00:00:22\\n00:46:02\\n\\n\\n\\n58\\n303\\nAURELIEN T MOZIPO\\nM\\nBEAVERTON\\nOR\\n00:45:49\\n07:23\\n54 of 414\\nM 46-55\\n7 of 64\\n00:00:08\\n00:45:57\\n\\n\\n\\n59\\n271\\nKYLE H AMBERT\\nM\\nTIGARD\\nOR\\n00:45:55\\n07:24\\n55 of 414\\nM 36-45\\n23 of 152\\n00:00:10\\n00:46:05\\nINTEL TEAM I\\n\\n\\n60\\n39\\nBRANDON LADICK\\nM\\nPORTLAND\\nOR\\n00:46:02\\n07:25\\n56 of 414\\nM 26-35\\n21 of 154\\n00:00:05\\n00:46:07\\n\\n\\n\\n61\\n223\\nADRIAN POP\\nM\\nWEST LINN\\nOR\\n00:46:13\\n07:27\\n57 of 414\\nM 36-45\\n24 of 152\\n00:00:03\\n00:46:16\\n\\n\\n\\n62\\n671\\nAARON G JOHNSON\\nM\\nHILLSBORO\\nOR\\n00:46:20\\n07:28\\n58 of 414\\nM 26-35\\n22 of 154\\n00:00:19\\n00:46:39\\n\\n\\n\\n63\\n574\\nMATTHEW BROWN\\nM\\nALOHA\\nOR\\n00:46:23\\n07:28\\n59 of 414\\nM 36-45\\n25 of 152\\n00:00:05\\n00:46:28\\n\\n\\n\\n64\\n349\\nEDMONDO MAZZULLI\\nM\\nHILLSBORO\\nOR\\n00:46:26\\n07:29\\n60 of 414\\nM 26-35\\n23 of 154\\n00:00:07\\n00:46:33\\nINTEL TEAM B\\n\\n\\n65\\n423\\nMILES F SCHWARTZ\\nM\\nPORTLAND\\nOR\\n00:46:33\\n07:30\\n61 of 414\\nM 36-45\\n26 of 152\\n00:00:25\\n00:46:58\\n\\n\\n\\n66\\n783\\nLEO SOTO\\nM\\nPORTLAND\\nOR\\n00:46:35\\n07:30\\n62 of 414\\nM 26-35\\n24 of 154\\n00:01:26\\n00:48:01\\nFXG1\\n\\n\\n67\\n13\\nNICHOLAS FISCHER\\nM\\n\\n\\n00:46:41\\n07:31\\n63 of 414\\nM 36-45\\n27 of 152\\n00:00:21\\n00:47:02\\nCOLUMBIA TEAM D\\n\\n\\n68\\n327\\nCHASE MCCLOSKEY\\nM\\nHILLSBORO\\nOR\\n00:46:42\\n07:32\\n64 of 414\\nM 18-25\\n6 of 34\\n00:00:11\\n00:46:53\\n\\n\\n\\n69\\n67\\nINGA ANDREYEVA\\nF\\nPORTLAND\\nOR\\n00:46:45\\n07:32\\n5 of 163\\nF 18-25\\n2 of 21\\n00:00:12\\n00:46:57\\nDTNA1\\n\\n\\n70\\n283\\nTHOR SEWELL\\nM\\nBEAVERTON\\nOR\\n00:46:46\\n07:32\\n65 of 414\\nM 46-55\\n8 of 64\\n00:00:20\\n00:47:06\\n\\n\\n\\n71\\n153\\nTODD HARRIS\\nM\\nPORTLAND\\nOR\\n00:46:47\\n07:32\\n66 of 414\\nM 46-55\\n9 of 64\\n00:00:13\\n00:47:00\\n\\n\\n\\n72\\n606\\nABHIRAM MURALIDHAR\\nM\\nHILLSBORO\\nOR\\n00:46:49\\n07:33\\n67 of 414\\nM 26-35\\n25 of 154\\n00:01:00\\n00:47:49\\n\\n\\n\\n73\\n841\\nJACK E LUBY\\nM\\nPORTLAND\\nOR\\n00:46:49\\n07:33\\n68 of 414\\nM 18-25\\n7 of 34\\n00:00:07\\n00:46:56\\n\\n\\n\\n74\\n49\\nAURORE LARIVEN\\nF\\nPORTLAND\\nCA\\n00:46:52\\n07:33\\n6 of 163\\nF 36-45\\n3 of 56\\n00:00:17\\n00:47:09\\n\\n\\n\\n75\\n704\\nRYAN MANIS\\nM\\nBEAVERTON\\nOR\\n00:46:55\\n07:34\\n69 of 414\\nM 26-35\\n26 of 154\\n00:01:59\\n00:48:54\\n\\n\\n\\n76\\n393\\nLUCAS M JENISON\\nM\\nBEAVERTON\\nOR\\n00:46:56\\n07:34\\n70 of 414\\nM 36-45\\n28 of 152\\n00:00:36\\n00:47:32\\n\\n\\n\\n77\\n858\\nRICHARD M STEGER\\nM\\nBEAVERTON\\nOR\\n00:47:00\\n07:34\\n71 of 414\\nM 46-55\\n10 of 64\\n00:00:21\\n00:47:21\\n\\n\\n\\n78\\n536\\nGERMAN VILLALOBOS\\nM\\nHILLSBORO\\nOR\\n00:47:02\\n07:35\\n72 of 414\\nM 36-45\\n29 of 152\\n00:00:08\\n00:47:10\\n\\n\\n\\n79\\n811\\nMICHAEL BEALE\\nM\\nHILLSBORO\\nOR\\n00:47:05\\n07:35\\n73 of 414\\nM 36-45\\n30 of 152\\n00:00:32\\n00:47:37\\n\\n\\n\\n80\\n6\\nRANIER EVANS\\nM\\n\\n\\n00:47:05\\n07:35\\n74 of 414\\nM 18-25\\n8 of 34\\n00:00:08\\n00:47:13\\nCOLUMBIA TEAM B\\n\\n\\n81\\n893\\nBRAD STEFANELLI\\nM\\nPORTLAND\\nOR\\n00:47:08\\n07:36\\n75 of 414\\nM 36-45\\n31 of 152\\n00:00:33\\n00:47:41\\n\\n\\n\\n82\\n656\\nMALLORY TYLER\\nF\\nPORTLAND\\nOR\\n00:47:09\\n07:36\\n7 of 163\\nF 18-25\\n3 of 21\\n00:00:21\\n00:47:30\\n\\n\\n\\n83\\n697\\nANIRUDH MODI\\nM\\nPORTLAND\\nOR\\n00:47:27\\n07:39\\n76 of 414\\nM 36-45\\n32 of 152\\n00:01:01\\n00:48:28\\n\\n\\n\\n84\\n256\\nBRAD CHADDICK\\nM\\nPORTLAND\\nOR\\n00:47:29\\n07:39\\n77 of 414\\nM 46-55\\n11 of 64\\n00:00:16\\n00:47:45\\n\\n\\n\\n85\\n227\\nLYNNETTE NGUYEN\\nF\\nPORTLAND\\nOR\\n00:47:31\\n07:39\\n8 of 163\\nF 36-45\\n4 of 56\\n00:00:05\\n00:47:36\\n\\n\\n\\n86\\n764\\nREBECCA JENSEN\\nF\\nHILLSBORO\\nOR\\n00:47:35\\n07:40\\n9 of 163\\nF 26-35\\n2 of 59\\n00:00:24\\n00:47:59\\n\\n\\n\\n87\\n333\\nJIMMY E CASSIS\\nM\\nHILLSBORO\\nOR\\n00:47:42\\n07:41\\n78 of 414\\nM 26-35\\n27 of 154\\n00:00:13\\n00:47:55\\n\\n\\n\\n88\\n17\\nBENJAMIN HODGDON\\nM\\n\\n\\n00:47:45\\n07:42\\n79 of 414\\nM 36-45\\n33 of 152\\n00:00:27\\n00:48:12\\nCOLUMBIA TEAM D\\n\\n\\n89\\n230\\nJONATHAN THACH\\nM\\nHILLSBORO\\nOR\\n00:47:49\\n07:42\\n80 of 414\\nM 18-25\\n9 of 34\\n00:00:12\\n00:48:01\\n\\n\\n\\n90\\n548\\nDOUGLAS GRESHAM\\nM\\nPORTLAND\\nOR\\n00:47:50\\n07:42\\n81 of 414\\nM 36-45\\n34 of 152\\n00:00:13\\n00:48:03\\n\\n\\n\\n91\\n862\\nCARLOS M VIVAS\\nM\\nPORTLAND\\nOR\\n00:47:55\\n07:43\\n82 of 414\\nM 36-45\\n35 of 152\\n00:00:14\\n00:48:09\\n\\n\\n\\n92\\n347\\nTEAL HAND\\nM\\nOTHER\\nOR\\n00:47:56\\n07:43\\n83 of 414\\nM 18-25\\n10 of 34\\n00:00:13\\n00:48:09\\nINTEL TEAM D\\n\\n\\n93\\n359\\nAI SSA CHAI\\nF\\nPORTLAND\\nOR\\n00:47:56\\n07:43\\n10 of 163\\nF 36-45\\n5 of 56\\n00:00:13\\n00:48:09\\n\\n\\n\\n94\\n31\\nSHAHAR TSAMERET\\nF\\nPORTLAND\\nOR\\n00:47:58\\n07:44\\n11 of 163\\nF Under 18\\n1 of 3\\n00:00:08\\n00:48:06\\n\\n\\n\\n95\\n715\\nSHAOPENG WANG\\nM\\nHILLSBORO\\nOR\\n00:47:59\\n07:44\\n84 of 414\\nM 46-55\\n12 of 64\\n00:01:04\\n00:49:03\\n\\n\\n\\n96\\n323\\nCONNOR SMITH\\nM\\nHILLSBORO\\nOR\\n00:48:09\\n07:45\\n85 of 414\\nM 18-25\\n11 of 34\\n00:00:11\\n00:48:20\\n\\n\\n\\n97\\n415\\nCHRISTOPHER MOZAK\\nM\\nPORTLAND\\nOR\\n00:48:13\\n07:46\\n86 of 414\\nM 36-45\\n36 of 152\\n00:00:14\\n00:48:27\\n\\n\\n\\n98\\n435\\nTODD BAIRD\\nM\\nBEAVERTON\\nOR\\n00:48:18\\n07:47\\n87 of 414\\nM 46-55\\n13 of 64\\n00:00:48\\n00:49:06\\n\\n\\n\\n99\\n541\\nWENDY HEUSSNER\\nF\\nPORTLAND\\nOR\\n00:48:18\\n07:47\\n12 of 163\\nF 46-55\\n1 of 22\\n00:00:38\\n00:48:56\\n\\n\\n\\n100\\n538\\nASSAF SHAIM\\nM\\nHILLSBORO\\nOR\\n00:48:20\\n07:47\\n88 of 414\\nM 36-45\\n37 of 152\\n00:00:20\\n00:48:40\\n\\n\\n\\n101\\n62\\nMICHAEL E TURK\\nM\\nHILLSBORO\\nOR\\n00:48:21\\n07:48\\n89 of 414\\nM 26-35\\n28 of 154\\n00:00:37\\n00:48:58\\n\\n\\n\\n102\\n852\\nCHRISTOPHER M ROMAN\\nM\\nHILLSBORO\\nOR\\n00:48:29\\n07:49\\n90 of 414\\n\\n1 of 6\\n00:00:34\\n00:49:03\\n\\n\\n\\n103\\n601\\nILAN TSAMERET\\nM\\nPORTLAND\\nOR\\n00:48:30\\n07:49\\n91 of 414\\nM 46-55\\n14 of 64\\n00:00:08\\n00:48:38\\n\\n\\n\\n104\\n820\\nTRAVIS FURRER\\nM\\nOTHER\\nOR\\n00:48:37\\n07:50\\n92 of 414\\nM 36-45\\n38 of 152\\n00:00:59\\n00:49:36\\n\\n\\n\\n105\\n587\\nBRIAN WIESENAUER\\nM\\nPORTLAND\\nOR\\n00:48:38\\n07:50\\n93 of 414\\nM 26-35\\n29 of 154\\n00:00:16\\n00:48:54\\n\\n\\n\\n106\\n591\\nERIN F WIESENAUER\\nF\\nPORTLAND\\nOR\\n00:48:39\\n07:50\\n13 of 163\\nF 26-35\\n3 of 59\\n00:00:17\\n00:48:56\\nINTEL TEAM N\\n\\n\\n107\\n849\\nPAUL H HONEYMAN-COLVIN\\nM\\nPORTLAND\\nOR\\n00:48:40\\n07:51\\n94 of 414\\nM 46-55\\n15 of 64\\n00:00:34\\n00:49:14\\n\\n\\n\\n108\\n458\\nSATOSHI SUZUKI\\nM\\nHILLSBORO\\nOR\\n00:48:42\\n07:51\\n95 of 414\\nM 26-35\\n30 of 154\\n00:00:30\\n00:49:12\\n\\n\\n\\n109\\n728\\nJORGE MUNOZ\\nM\\nBEAVERTON\\nOR\\n00:48:43\\n07:51\\n96 of 414\\nM 26-35\\n31 of 154\\n00:00:32\\n00:49:15\\n\\n\\n\\n110\\n572\\nJUN TAKEI\\nM\\nPORTLAND\\nOR\\n00:48:44\\n07:51\\n97 of 414\\nM 46-55\\n16 of 64\\n00:00:21\\n00:49:05\\nINTEL TEAM B\\n\\n\\n111\\n461\\nREAGAN LOPEZ\\nM\\nHILLSBORO\\nOR\\n00:48:49\\n07:52\\n98 of 414\\nM 26-35\\n32 of 154\\n00:00:04\\n00:48:53\\nINTEL TEAM A\\n\\n\\n112\\n880\\nSTEVEN R CECH\\nM\\nPORTLAND\\nOR\\n00:48:56\\n07:53\\n99 of 414\\nM 36-45\\n39 of 152\\n00:00:58\\n00:49:54\\n\\n\\n\\n113\\n126\\nBENJAMIN R PORTER\\nM\\nHILLSBORO\\nOR\\n00:49:03\\n07:54\\n100 of 414\\nM 26-35\\n33 of 154\\n00:00:19\\n00:49:22\\nINTEL TEAM D\\n\\n\\n114\\n339\\nBEN BORGSTEDE\\nM\\nHILLSBORO\\nOR\\n00:49:09\\n07:55\\n101 of 414\\nM 18-25\\n12 of 34\\n00:00:52\\n00:50:01\\n\\n\\n\\n115\\n270\\nKISHOR KHARBAS\\nM\\nHILLSBORO\\nOR\\n00:49:12\\n07:56\\n102 of 414\\nM 26-35\\n34 of 154\\n00:00:25\\n00:49:37\\nINTEL TEAM E\\n\\n\\n116\\n752\\nTERENCE LEE\\nM\\nPORTLAND\\nOR\\n00:49:18\\n07:57\\n103 of 414\\nM 26-35\\n35 of 154\\n00:00:08\\n00:49:26\\n\\n\\n\\n117\\n660\\nOCTAVIO RAMOS\\nM\\nPORTLAND\\nOR\\n00:49:23\\n07:57\\n104 of 414\\nM 36-45\\n40 of 152\\n00:00:06\\n00:49:29\\n\\n\\n\\n118\\n255\\nMARK SAVOY\\nM\\nHILLSBORO\\nOR\\n00:49:26\\n07:58\\n105 of 414\\nM 36-45\\n41 of 152\\n00:00:17\\n00:49:43\\n\\n\\n\\n119\\n373\\nTONY SALAZAR\\nM\\nBEAVERTON\\nOR\\n00:49:30\\n07:59\\n106 of 414\\nM 46-55\\n17 of 64\\n00:00:33\\n00:50:03\\n\\n\\n\\n120\\n42\\nMEGAN M GOODSON\\nF\\nPORTLAND\\nOR\\n00:49:32\\n07:59\\n14 of 163\\nF 46-55\\n2 of 22\\n00:00:20\\n00:49:52\\nINTEL TEAM A\\n\\n\\n121\\n331\\nVIVEKANANTHAN SANJEEPAN\\nM\\nHILLSBORO\\nOR\\n00:49:35\\n07:59\\n107 of 414\\nM 36-45\\n42 of 152\\n00:00:58\\n00:50:33\\n\\n\\n\\n122\\n550\\nTREY GUNNELL\\nM\\nSALT LAKE CITY\\nUT\\n00:49:42\\n08:01\\n108 of 414\\nM 26-35\\n36 of 154\\n00:00:15\\n00:49:57\\n\\n\\n\\n123\\n298\\nBENJAMIN BRADEA\\nM\\nHILLSBORO\\nOR\\n00:49:44\\n08:01\\n109 of 414\\nM 26-35\\n37 of 154\\n00:00:01\\n00:49:45\\n\\n\\n\\n124\\n449\\nRAHULDEVA GHOSH\\nM\\nPORTLAND\\nOR\\n00:49:44\\n08:01\\n110 of 414\\nM 36-45\\n43 of 152\\n00:00:20\\n00:50:04\\n\\n\\n\\n125\\n676\\nTSUAN-CHUNG CHANG\\nM\\nPORTLAND\\nOR\\n00:49:51\\n08:02\\n111 of 414\\nM 46-55\\n18 of 64\\n00:00:59\\n00:50:50\\n\\n\\n\\n126\\n855\\nRODNEY B STEWART\\nM\\nTIGARD\\nOR\\n00:49:54\\n08:03\\n112 of 414\\nM 46-55\\n19 of 64\\n00:00:40\\n00:50:34\\n\\n\\n\\n127\\n683\\nMYRON LEE\\nM\\nBEAVERTON\\nOR\\n00:49:58\\n08:03\\n113 of 414\\nM 26-35\\n38 of 154\\n00:01:24\\n00:51:22\\n\\n\\n\\n128\\n798\\nJEFF BENDER\\nM\\nBEAVERTON\\nOR\\n00:50:07\\n08:05\\n114 of 414\\nM 36-45\\n44 of 152\\n00:01:22\\n00:51:29\\n\\n\\n\\n129\\n711\\nADAM J SCHAFER\\nM\\nPORTLAND\\nOR\\n00:50:12\\n08:05\\n115 of 414\\nM 36-45\\n45 of 152\\n00:00:53\\n00:51:05\\n\\n\\n\\n130\\n598\\nDRAGOS SEGHETE\\nM\\nPORTLAND\\nOR\\n00:50:15\\n08:06\\n116 of 414\\nM 36-45\\n46 of 152\\n00:01:14\\n00:51:29\\n\\n\\n\\n131\\n16\\nJEN KELLER\\nF\\n\\n\\n00:50:18\\n08:06\\n15 of 163\\nF 36-45\\n6 of 56\\n00:00:27\\n00:50:45\\nCOLUMBIA TEAM D\\n\\n\\n132\\n18\\nGREG LAWRENCE\\nM\\n\\n\\n00:50:19\\n08:07\\n117 of 414\\nM 46-55\\n20 of 64\\n00:00:26\\n00:50:45\\nCOLUMBIA TEAM E\\n\\n\\n133\\n249\\nDUSTIN WILLIAMS\\nM\\nNOT SPECIFIED\\nOR\\n00:50:22\\n08:07\\n118 of 414\\nM 36-45\\n47 of 152\\n00:00:21\\n00:50:43\\n\\n\\n\\n134\\n265\\nRICHARD DORRANCE\\nM\\nPORTLAND\\nOR\\n00:50:24\\n08:07\\n119 of 414\\nM 26-35\\n39 of 154\\n00:00:05\\n00:50:29\\n\\n\\n\\n135\\n226\\nVINA HOANG\\nF\\nBEAVERTON\\nOR\\n00:50:26\\n08:08\\n16 of 163\\nF 36-45\\n7 of 56\\n00:00:13\\n00:50:39\\n\\n\\n\\n136\\n790\\nAUSTIN WIPF\\nM\\nPORTLAND\\nOR\\n00:50:29\\n08:08\\n120 of 414\\nM 26-35\\n40 of 154\\n00:00:16\\n00:50:45\\nDTNA3\\n\\n\\n137\\n654\\nJEREMY GUTTMAN\\nM\\nPORTLAND\\nOR\\n00:50:36\\n08:09\\n121 of 414\\nM 18-25\\n13 of 34\\n00:00:25\\n00:51:01\\n\\n\\n\\n138\\n167\\nTERRY C CUTLER\\nM\\nBEAVERTON\\nOR\\n00:50:38\\n08:10\\n122 of 414\\nM 36-45\\n48 of 152\\n00:00:49\\n00:51:27\\n\\n\\n\\n139\\n355\\nSTEPHEN P BRUSS\\nM\\nPORTLAND\\nOR\\n00:50:39\\n08:10\\n123 of 414\\nM 46-55\\n21 of 64\\n00:02:11\\n00:52:50\\n\\n\\n\\n140\\n592\\nAVIV SHAI\\nM\\nPORTLAND\\nOR\\n00:50:40\\n08:10\\n124 of 414\\nM 46-55\\n22 of 64\\n00:00:17\\n00:50:57\\n\\n\\n\\n141\\n260\\nAARON P LINDSEY\\nM\\nHILLSBORO\\nOR\\n00:50:41\\n08:10\\n125 of 414\\nM 18-25\\n14 of 34\\n00:00:36\\n00:51:17\\n\\n\\n\\n142\\n263\\nTIM NGUYEN\\nM\\nBEAVERTON\\nOR\\n00:50:43\\n08:10\\n126 of 414\\nM 46-55\\n23 of 64\\n00:00:15\\n00:50:58\\n\\n\\n\\n143\\n857\\nALISON M FRIDAY\\nF\\nPORTLAND\\nOR\\n00:50:46\\n08:11\\n17 of 163\\nF 36-45\\n8 of 56\\n00:00:11\\n00:50:57\\n\\n\\n\\n144\\n883\\nBRANDON PARSIANI\\nM\\nPORTLAND\\nOR\\n00:50:51\\n08:12\\n127 of 414\\nM 18-25\\n15 of 34\\n00:00:07\\n00:50:58\\n\\n\\n\\n145\\n620\\nSANGHYUN SEO\\nM\\nPORTLAND\\nOR\\n00:51:00\\n08:13\\n128 of 414\\nM 36-45\\n49 of 152\\n00:00:41\\n00:51:41\\n\\n\\n\\n146\\n738\\nCORY E WEBER\\nM\\nHILLSBORO\\nOR\\n00:51:02\\n08:14\\n129 of 414\\nM 36-45\\n50 of 152\\n00:00:23\\n00:51:25\\n\\n\\n\\n147\\n372\\nSRINIDHI N KAUSHIK\\nM\\nBEAVERTON\\nOR\\n00:51:05\\n08:14\\n130 of 414\\nM 26-35\\n41 of 154\\n00:00:17\\n00:51:22\\n\\n\\n\\n148\\n685\\nERIC CHEN\\nM\\nPORTLAND\\nOR\\n00:51:06\\n08:14\\n131 of 414\\nM 46-55\\n24 of 64\\n00:01:00\\n00:52:06\\n\\n\\n\\n149\\n383\\nKEALOHA OYER\\nM\\nHILLSBORO\\nOR\\n00:51:08\\n08:14\\n132 of 414\\nM 36-45\\n51 of 152\\n00:00:10\\n00:51:18\\n\\n\\n\\n150\\n563\\nCAMERON R WAGNER\\nM\\nBEAVERTON\\nOR\\n00:51:08\\n08:14\\n133 of 414\\nM 36-45\\n52 of 152\\n00:00:07\\n00:51:15\\n\\n\\n\\n151\\n261\\nJUSTON LI\\nM\\nOTHER\\nOR\\n00:51:10\\n08:15\\n134 of 414\\nM 18-25\\n16 of 34\\n00:00:34\\n00:51:44\\nINTEL TEAM D\\n\\n\\n152\\n161\\nJAIMIEY I SEARS\\nM\\nHILLSBORO\\nOR\\n00:51:12\\n08:15\\n135 of 414\\nM 18-25\\n17 of 34\\n00:00:16\\n00:51:28\\n\\n\\n\\n153\\n799\\nMANOJ ATHAWALE\\nM\\nHILLSBORO\\nOR\\n00:51:18\\n08:16\\n136 of 414\\nM 36-45\\n53 of 152\\n00:01:01\\n00:52:19\\n\\n\\n\\n154\\n277\\nMALLIKARJUNA CHILAKALA\\nM\\nHILLSBORO\\nOR\\n00:51:20\\n08:16\\n137 of 414\\nM 36-45\\n54 of 152\\n00:00:32\\n00:51:52\\nINTEL TEAM I\\n\\n\\n155\\n368\\nJAYADEV MUNIMALLAPPA\\nM\\nPORTLAND\\nOR\\n00:51:22\\n08:17\\n138 of 414\\nM 36-45\\n55 of 152\\n00:00:31\\n00:51:53\\n\\n\\n\\n156\\n176\\nANDREW WENDLANDT\\nM\\nHILLSBORO\\nOR\\n00:51:24\\n08:17\\n139 of 414\\nM 26-35\\n42 of 154\\n00:00:42\\n00:52:06\\n\\n\\n\\n157\\n422\\nTRISTAN WOEHRLE\\nM\\nBEAVERTON\\nOR\\n00:51:34\\n08:19\\n140 of 414\\nM 26-35\\n43 of 154\\n00:00:07\\n00:51:41\\n\\n\\n\\n158\\n276\\nPAUL HACK\\nM\\nPORTLAND\\nOR\\n00:51:35\\n08:19\\n141 of 414\\nM 46-55\\n25 of 64\\n00:00:39\\n00:52:14\\n\\n\\n\\n159\\n595\\nJIANREN TAI\\nM\\nPORTLAND\\nOR\\n00:51:38\\n08:19\\n142 of 414\\nM 36-45\\n56 of 152\\n00:00:22\\n00:52:00\\n\\n\\n\\n160\\n159\\nEUGENE LUHAVY\\nM\\nPORTLAND\\nOR\\n00:51:41\\n08:20\\n143 of 414\\nM 26-35\\n44 of 154\\n00:00:50\\n00:52:31\\n\\n\\n\\n161\\n221\\nREET CHATTERJEE\\nM\\nBEAVERTON\\nOR\\n00:51:43\\n08:20\\n144 of 414\\nM Under 18\\n1 of 2\\n00:00:27\\n00:52:10\\n\\n\\n\\n162\\n4\\nBHASKAR MANDALA\\nM\\n\\n\\n00:51:49\\n08:21\\n145 of 414\\nM 36-45\\n57 of 152\\n00:00:21\\n00:52:10\\nCOLUMBIA TEAM A\\n\\n\\n163\\n757\\nJOSE MAURICIO MARULANDA\\nM\\nHILLSBORO\\nOR\\n00:51:52\\n08:21\\n146 of 414\\nM 26-35\\n45 of 154\\n00:00:18\\n00:52:10\\n\\n\\n\\n164\\n571\\nMARC STEVENSON SO\\nM\\nBEAVERTON\\nOR\\n00:51:54\\n08:22\\n147 of 414\\nM 36-45\\n58 of 152\\n00:00:17\\n00:52:11\\n\\n\\n\\n165\\n805\\nTRISTAN HANSON\\nM\\nFOREST GROVE\\nOR\\n00:52:15\\n08:25\\n148 of 414\\nM 26-35\\n46 of 154\\n00:00:30\\n00:52:45\\n\\n\\n\\n166\\n398\\nDEREK R COLLIER\\nM\\nPORTLAND\\nOR\\n00:52:17\\n08:25\\n149 of 414\\nM 46-55\\n26 of 64\\n00:00:24\\n00:52:41\\n\\n\\n\\n167\\n389\\nDANIEL P GARUBBA\\nM\\nHILLSBORO\\nOR\\n00:52:25\\n08:27\\n150 of 414\\nM 26-35\\n47 of 154\\n00:01:08\\n00:53:33\\n\\n\\n\\n168\\n856\\nAARON J WOODS\\nM\\nALOHA\\nOR\\n00:52:26\\n08:27\\n151 of 414\\nM 36-45\\n59 of 152\\n00:00:08\\n00:52:34\\n\\n\\n\\n169\\n748\\nJAMES GREALISH\\nM\\nHILLSBORO\\nOR\\n00:52:34\\n08:28\\n152 of 414\\nM 55+\\n1 of 4\\n00:00:44\\n00:53:18\\n\\n\\n\\n170\\n627\\nLUCAS P BEVERLIN\\nM\\nHILLSBORO\\nOR\\n00:52:34\\n08:28\\n153 of 414\\nM 36-45\\n60 of 152\\n00:00:20\\n00:52:54\\n\\n\\n\\n171\\n885\\nBRIAN HINTZMAN\\nM\\nPORTLAND\\nOR\\n00:52:35\\n08:29\\n154 of 414\\nM 46-55\\n27 of 64\\n00:00:07\\n00:52:42\\n\\n\\n\\n172\\n542\\nJUSTINE BONNER\\nF\\nPORTLAND\\nOR\\n00:52:35\\n08:29\\n18 of 163\\nF 46-55\\n3 of 22\\n00:00:34\\n00:53:09\\n\\n\\n\\n173\\n304\\nVANESSA R BRITT\\nF\\nPORTLAND\\nOR\\n00:52:36\\n08:29\\n19 of 163\\nF 26-35\\n4 of 59\\n00:00:25\\n00:53:01\\n\\n\\n\\n174\\n709\\nRICHMOND B STURGILL\\nM\\nTIGARD\\nOR\\n00:52:38\\n08:29\\n155 of 414\\nM 36-45\\n61 of 152\\n00:00:17\\n00:52:55\\n\\n\\n\\n175\\n9\\nBETH UNGER\\nF\\n\\n\\n00:52:38\\n08:29\\n20 of 163\\nF 26-35\\n5 of 59\\n00:00:30\\n00:53:08\\nCOLUMBIA TEAM C\\n\\n\\n176\\n44\\nBINAY SINGH\\nM\\nHILLSBORO\\nOR\\n00:52:41\\n08:29\\n156 of 414\\nM 36-45\\n62 of 152\\n00:00:23\\n00:53:04\\n\\n\\n\\n177\\n332\\nVASUDEV RUPANAGUNTLA\\nM\\nHILLSBORO\\nOR\\n00:52:43\\n08:30\\n157 of 414\\nM 18-25\\n18 of 34\\n00:00:08\\n00:52:51\\n\\n\\n\\n178\\n584\\nCODY L COVEY\\nM\\nHILLSBORO\\nOR\\n00:52:47\\n08:30\\n158 of 414\\nM 26-35\\n48 of 154\\n00:00:05\\n00:52:52\\nINTEL TEAM F\\n\\n\\n179\\n21\\nVINOTHKUMAR RADHAKRISHNAN\\nM\\nPORTLAND\\nOR\\n00:52:48\\n08:31\\n159 of 414\\nM 26-35\\n49 of 154\\n00:00:14\\n00:53:02\\n\\n\\n\\n180\\n337\\nDEEPAK RAJENDRAKUMARAN\\nM\\nHILLSBORO\\nOR\\n00:52:50\\n08:31\\n160 of 414\\nM 26-35\\n50 of 154\\n00:00:32\\n00:53:22\\n\\n\\n\\n181\\n459\\nJOHN HAMILTON\\nM\\nPORTLAND\\nOR\\n00:52:55\\n08:32\\n161 of 414\\nM 46-55\\n28 of 64\\n00:00:29\\n00:53:24\\nINTEL TEAM K\\n\\n\\n182\\n444\\nVARUN K SETLUR\\nM\\nHILLSBORO\\nOR\\n00:52:55\\n08:32\\n162 of 414\\nM 26-35\\n51 of 154\\n00:00:56\\n00:53:51\\n\\n\\n\\n183\\n211\\nNAHOM YEMANE\\nM\\nBEAVERTON\\nOR\\n00:52:58\\n08:32\\n163 of 414\\nM 18-25\\n19 of 34\\n00:00:32\\n00:53:30\\n\\n\\n\\n184\\n55\\nERNIE KHAW\\nM\\nHILLSBORO\\nOR\\n00:53:02\\n08:33\\n164 of 414\\nM 46-55\\n29 of 64\\n00:00:16\\n00:53:18\\n\\n\\n\\n185\\n613\\nANTON RODRIGUEZ-DMITRIEV\\nM\\nPORTLAND\\nOR\\n00:53:05\\n08:33\\n165 of 414\\nM 26-35\\n52 of 154\\n00:01:06\\n00:54:11\\n\\n\\n\\n186\\n129\\nKARL H AMSPACHER\\nM\\nHILLSBORO\\nOR\\n00:53:06\\n08:34\\n166 of 414\\nM 26-35\\n53 of 154\\n00:00:38\\n00:53:44\\n\\n\\n\\n187\\n599\\nXIAODONG1 YANG\\nM\\nPORTLAND\\nOR\\n00:53:09\\n08:34\\n167 of 414\\nM 36-45\\n63 of 152\\n00:01:02\\n00:54:11\\nINTEL TEAM J\\n\\n\\n188\\n652\\nCURTIS J HOFFMAN\\nM\\nALOHA\\nOR\\n00:53:12\\n08:34\\n168 of 414\\nM 36-45\\n64 of 152\\n00:00:55\\n00:54:07\\n\\n\\n\\n189\\n731\\nROBERT HEUSSNER\\nM\\nPORTLAND\\nOR\\n00:53:16\\n08:35\\n169 of 414\\nM 46-55\\n30 of 64\\n00:00:38\\n00:53:54\\n\\n\\n\\n190\\n767\\nROBERT EHLERT\\nM\\nPORTLAND\\nOR\\n00:53:23\\n08:36\\n170 of 414\\nM 36-45\\n65 of 152\\n00:00:30\\n00:53:53\\n\\n\\n\\n191\\n170\\nJAYCEE SLACK\\nF\\nBEAVERTON\\nOR\\n00:53:26\\n08:37\\n21 of 163\\nF 18-25\\n4 of 21\\n00:00:34\\n00:54:00\\n\\n\\n\\n192\\n610\\nYATHIN PISSAY\\nM\\nHILLSBORO\\nOR\\n00:53:31\\n08:38\\n171 of 414\\nM 26-35\\n54 of 154\\n00:00:21\\n00:53:52\\n\\n\\n\\n193\\n36\\nAJITHPRASADJAIN ADALAGEREJWALANNA\\nM\\nBEAVERTON\\nOR\\n00:53:32\\n08:38\\n172 of 414\\nM 26-35\\n55 of 154\\n00:00:20\\n00:53:52\\n\\n\\n\\n194\\n707\\nCANDICE L YUCA\\nF\\nBEAVERTON\\nOR\\n00:53:33\\n08:38\\n22 of 163\\nF 36-45\\n9 of 56\\n00:00:31\\n00:54:04\\n\\n\\n\\n195\\n763\\nTHIERRY TAMBE\\nM\\nHILLSBORO\\nOR\\n00:53:37\\n08:38\\n173 of 414\\nM 26-35\\n56 of 154\\n00:00:38\\n00:54:15\\n\\n\\n\\n196\\n817\\nERIC GREGORY\\nM\\nPORTLAND\\nOR\\n00:53:44\\n08:40\\n174 of 414\\nM 46-55\\n31 of 64\\n00:01:01\\n00:54:45\\n\\n\\n\\n197\\n881\\nMICHELLE G DINSMOOR\\nF\\nPORTLAND\\nOR\\n00:53:45\\n08:40\\n23 of 163\\nF 36-45\\n10 of 56\\n00:00:13\\n00:53:58\\n\\n\\n\\n198\\n280\\nNIKHIL TALPALLIKAR\\nM\\nPORTLAND\\nOR\\n00:53:49\\n08:40\\n175 of 414\\nM 26-35\\n57 of 154\\n00:00:29\\n00:54:18\\nINTEL TEAM B\\n\\n\\n199\\n760\\nPAUL S MCCARTHY\\nM\\nHILLSBORO\\nOR\\n00:53:53\\n08:41\\n176 of 414\\nM 26-35\\n58 of 154\\n00:00:59\\n00:54:52\\n\\n\\n\\n200\\n875\\nKRISTINA M HIERSCHE\\nF\\nBEAVERTON\\nOR\\n00:53:56\\n08:42\\n24 of 163\\nF 26-35\\n6 of 59\\n00:00:19\\n00:54:15\\n\\n\\n\\n201\\n10\\nMIKE HIRT\\nM\\n\\n\\n00:53:58\\n08:42\\n177 of 414\\nM 46-55\\n32 of 64\\n00:00:25\\n00:54:23\\nCOLUMBIA TEAM C\\n\\n\\n202\\n589\\nBUM SOO KIM\\nM\\nNOT SPECIFIED\\nOR\\n00:53:59\\n08:42\\n178 of 414\\nM 36-45\\n66 of 152\\n00:00:10\\n00:54:09\\n\\n\\n\\n203\\n780\\nTONY GONZALEZ\\nM\\nPORTLAND\\nOR\\n00:53:59\\n08:42\\n179 of 414\\nM 36-45\\n67 of 152\\n00:00:56\\n00:54:55\\nFXG1\\n\\n\\n204\\n741\\nBLAKE ADKINS\\nM\\nPORTLAND\\nOR\\n00:54:05\\n08:43\\n180 of 414\\nM 26-35\\n59 of 154\\n00:01:06\\n00:55:11\\n\\n\\n\\n205\\n69\\nKEATON WEISENBORN\\nM\\nPORTLAND\\nOR\\n00:54:09\\n08:44\\n181 of 414\\nM 26-35\\n60 of 154\\n00:00:16\\n00:54:25\\nDTNA1\\n\\n\\n206\\n70\\nMAISIE WEISENBORN\\nF\\nPORTLAND\\nOR\\n00:54:10\\n08:44\\n25 of 163\\nF 26-35\\n7 of 59\\n00:00:15\\n00:54:25\\nDTNA1\\n\\n\\n207\\n48\\nLOUISE R DUNNE\\nF\\nPORTLAND\\nOR\\n00:54:16\\n08:45\\n26 of 163\\nF 26-35\\n8 of 59\\n00:00:36\\n00:54:52\\n\\n\\n\\n208\\n714\\nCLAYTON A BROWN\\nM\\nHILLSBORO\\nOR\\n00:54:17\\n08:45\\n182 of 414\\nM 46-55\\n33 of 64\\n00:00:47\\n00:55:04\\n\\n\\n\\n209\\n851\\nHONG NHUNG T TRAN\\nF\\nBEAVERTON\\nOR\\n00:54:17\\n08:45\\n27 of 163\\nF 26-35\\n9 of 59\\n00:00:49\\n00:55:06\\n\\n\\n\\n210\\n586\\nJOHN S SUMMERS\\nM\\nPORTLAND\\nOR\\n00:54:20\\n08:45\\n183 of 414\\nM 26-35\\n61 of 154\\n00:01:21\\n00:55:41\\n\\n\\n\\n211\\n289\\nHAREESH KHATTRI\\nM\\nNOT SPECIFIED\\nOR\\n00:54:21\\n08:45\\n184 of 414\\nM 26-35\\n62 of 154\\n00:00:34\\n00:54:55\\n\\n\\n\\n212\\n664\\nABHISHEK JAIN\\nM\\nHILLSBORO\\nOR\\n00:54:29\\n08:47\\n185 of 414\\nM 36-45\\n68 of 152\\n00:01:00\\n00:55:29\\n\\n\\n\\n213\\n386\\nBENJAMIN D MOORE\\nM\\nBEAVERTON\\nOR\\n00:54:38\\n08:48\\n186 of 414\\nM 46-55\\n34 of 64\\n00:00:33\\n00:55:11\\n\\n\\n\\n214\\n643\\nMEAGEN A GILLISPIE\\nF\\nHILLSBORO\\nOR\\n00:54:41\\n08:49\\n28 of 163\\nF 36-45\\n11 of 56\\n00:01:24\\n00:56:05\\n\\n\\n\\n215\\n428\\nCHRIS LORD\\nM\\nPORTLAND\\nOR\\n00:54:41\\n08:49\\n187 of 414\\nM 46-55\\n35 of 64\\n00:01:48\\n00:56:29\\n\\n\\n\\n216\\n242\\nSARAT V DASAKA\\nM\\nPORTLAND\\nOR\\n00:54:43\\n08:49\\n188 of 414\\nM 36-45\\n69 of 152\\n00:00:39\\n00:55:22\\n\\n\\n\\n217\\n845\\nSUMANT PATANKAR\\nM\\nBEAVERTON\\nOR\\n00:54:44\\n08:49\\n189 of 414\\nM 26-35\\n63 of 154\\n00:00:33\\n00:55:17\\n\\n\\n\\n218\\n41\\nVIVEK PARANJAPE\\nM\\nPORTLAND\\nOR\\n00:54:46\\n08:50\\n190 of 414\\nM 46-55\\n36 of 64\\n00:00:12\\n00:54:58\\n\\n\\n\\n219\\n425\\nHOWARD HECK\\nM\\nBEAVERTON\\nOR\\n00:54:50\\n08:50\\n191 of 414\\nM 46-55\\n37 of 64\\n00:00:18\\n00:55:08\\n\\n\\n\\n220\\n267\\nDANIEL NGUYEN\\nM\\nHILLSBORO\\nOR\\n00:54:53\\n08:51\\n192 of 414\\nM 26-35\\n64 of 154\\n00:01:12\\n00:56:05\\n\\n\\n\\n221\\n873\\nHAYDEN LOCKHART\\nM\\nHILLSBORO\\nOR\\n00:55:00\\n08:52\\n193 of 414\\nM 18-25\\n20 of 34\\n00:00:51\\n00:55:51\\n\\n\\n\\n222\\n698\\nJEYASEKAR MARIMUTHU\\nM\\nPORTLAND\\nOR\\n00:55:00\\n08:52\\n194 of 414\\nM 36-45\\n70 of 152\\n00:00:11\\n00:55:11\\n\\n\\n\\n223\\n802\\nNICK LINDERT\\nM\\nPORTLAND\\nOR\\n00:55:00\\n08:52\\n195 of 414\\nM 36-45\\n71 of 152\\n00:01:02\\n00:56:02\\n\\n\\n\\n224\\n713\\nRAVI K SARIPALLI\\nM\\nPORTLAND\\nOR\\n00:55:00\\n08:52\\n196 of 414\\nM 46-55\\n38 of 64\\n00:00:22\\n00:55:22\\n\\n\\n\\n225\\n750\\nRAJKUMAR GOVINDARAJAN\\nM\\nHILLSBORO\\nOR\\n00:55:00\\n08:52\\n197 of 414\\nM 26-35\\n65 of 154\\n00:00:46\\n00:55:46\\n\\n\\n\\n226\\n35\\nDAMON MAPES\\nM\\nHILLSBORO\\nOR\\n00:55:05\\n08:53\\n198 of 414\\nM 46-55\\n39 of 64\\n00:00:38\\n00:55:43\\n\\n\\n\\n227\\n701\\nJAINAVEEN SUNDARAM\\nM\\nBEAVERTON\\nOR\\n00:55:12\\n08:54\\n199 of 414\\nM 26-35\\n66 of 154\\n00:00:24\\n00:55:36\\n\\n\\n\\n228\\n134\\nZACHARY L NESBITT\\nM\\nPORTLAND\\nOR\\n00:55:16\\n08:54\\n200 of 414\\nM 26-35\\n67 of 154\\n00:00:27\\n00:55:43\\n\\n\\n\\n229\\n246\\nJOHN V KIM\\nM\\nBEAVERTON\\nOR\\n00:55:19\\n08:55\\n201 of 414\\nM 18-25\\n21 of 34\\n00:00:07\\n00:55:26\\nINTEL TEAM C\\n\\n\\n230\\n675\\nBAMIDELE ADISA\\nM\\nPORTLAND\\nOR\\n00:55:23\\n08:56\\n202 of 414\\nM 36-45\\n72 of 152\\n00:00:20\\n00:55:43\\n\\n\\n\\n231\\n371\\nPAUL A JONES\\nM\\nOTHER\\nOR\\n00:55:28\\n08:56\\n203 of 414\\nM 26-35\\n68 of 154\\n00:00:36\\n00:56:04\\n\\n\\n\\n232\\n243\\nSHANE R MARTIN\\nM\\nHILLSBORO\\nOR\\n00:55:29\\n08:57\\n204 of 414\\nM 36-45\\n73 of 152\\n00:00:54\\n00:56:23\\n\\n\\n\\n233\\n769\\nSIVAKUMAR P MUDANAI\\nM\\nLAKE OSWEGO\\nOR\\n00:55:33\\n08:57\\n205 of 414\\nM 36-45\\n74 of 152\\n00:01:01\\n00:56:34\\n\\n\\n\\n234\\n753\\nXIAOSHU ZHAO\\nM\\nLAKE OSWEGO\\nOR\\n00:55:42\\n08:59\\n206 of 414\\nM 46-55\\n40 of 64\\n00:00:15\\n00:55:57\\n\\n\\n\\n235\\n650\\nPAVEL GHOSH\\nM\\nBEAVERTON\\nOR\\n00:55:45\\n08:59\\n207 of 414\\nM 36-45\\n75 of 152\\n00:01:04\\n00:56:49\\n\\n\\n\\n236\\n605\\nERIC POZZI\\nM\\nHILLSBORO\\nOR\\n00:55:50\\n09:00\\n208 of 414\\nM 26-35\\n69 of 154\\n00:00:41\\n00:56:31\\nINTEL TEAM G\\n\\n\\n237\\n464\\nISHAAN BISWAS\\nM\\nHILLSBORO\\nOR\\n00:55:56\\n09:01\\n209 of 414\\nM 26-35\\n70 of 154\\n00:00:08\\n00:56:04\\n\\n\\n\\n238\\n655\\nANNE HAO\\nF\\nPORTLAND\\nOR\\n00:56:00\\n09:02\\n29 of 163\\nF 36-45\\n12 of 56\\n00:00:16\\n00:56:16\\n\\n\\n\\n239\\n784\\nYUNFENG PI\\nM\\nPORTLAND\\nOR\\n00:56:06\\n09:02\\n210 of 414\\nM 36-45\\n76 of 152\\n00:00:22\\n00:56:28\\nDTNA3\\n\\n\\n240\\n342\\nPRAMOD PESARA\\nM\\nNOT SPECIFIED\\nOR\\n00:56:13\\n09:04\\n211 of 414\\nM 36-45\\n77 of 152\\n00:01:02\\n00:57:15\\n\\n\\n\\n241\\n53\\nJOHN R AYERS\\nM\\nPORTLAND\\nOR\\n00:56:19\\n09:05\\n212 of 414\\nM 46-55\\n41 of 64\\n00:00:09\\n00:56:28\\n\\n\\n\\n242\\n32\\nKYLE DEGENHARDT\\nM\\nPORTLAND\\nOR\\n00:56:20\\n09:05\\n213 of 414\\nM 36-45\\n78 of 152\\n00:01:08\\n00:57:28\\n\\n\\n\\n243\\n564\\nJARED M SCHULZ\\nM\\nHILLSBORO\\nOR\\n00:56:26\\n09:06\\n214 of 414\\nM 26-35\\n71 of 154\\n00:00:38\\n00:57:04\\n\\n\\n\\n244\\n25\\nMINDEE HARDIN\\nF\\nBEAVERTON\\nOR\\n00:56:30\\n09:06\\n30 of 163\\nF 36-45\\n13 of 56\\n00:00:40\\n00:57:10\\n\\n\\n\\n245\\n315\\nROY MUNOZ\\nM\\nHILLSBORO\\nOR\\n00:56:31\\n09:06\\n215 of 414\\nM 26-35\\n72 of 154\\n00:00:32\\n00:57:03\\n\\n\\n\\n246\\n175\\nSCOTT HARDIN\\nM\\nBEAVERTON\\nOR\\n00:56:33\\n09:07\\n216 of 414\\nM 36-45\\n79 of 152\\n00:00:39\\n00:57:12\\n\\n\\n\\n247\\n177\\nCHRIS K HART\\nF\\nHILLSBORO\\nOR\\n00:56:38\\n09:08\\n31 of 163\\nF 46-55\\n4 of 22\\n00:00:36\\n00:57:14\\n\\n\\n\\n248\\n597\\nCODY G XIU\\nM\\nPORTLAND\\nOR\\n00:56:48\\n09:09\\n217 of 414\\nM 26-35\\n73 of 154\\n00:00:46\\n00:57:34\\n\\n\\n\\n249\\n436\\nFRED COOPER\\nM\\nPORTLAND\\nOR\\n00:56:55\\n09:10\\n218 of 414\\nM 46-55\\n42 of 64\\n00:00:34\\n00:57:29\\n\\n\\n\\n250\\n456\\nKARTHIK RAMAN\\nM\\nBEAVERTON\\nOR\\n00:56:56\\n09:11\\n219 of 414\\nM 26-35\\n74 of 154\\n00:00:17\\n00:57:13\\n\\n\\n\\n251\\n203\\nMARIAH JEFFERY\\nF\\nHILLSBORO\\nOR\\n00:56:58\\n09:11\\n32 of 163\\nF 36-45\\n14 of 56\\n00:01:05\\n00:58:03\\n\\n\\n\\n252\\n381\\nCASEY M JEFFERY\\nM\\nHILLSBORO\\nOR\\n00:56:58\\n09:11\\n220 of 414\\nM 36-45\\n80 of 152\\n00:01:06\\n00:58:04\\n\\n\\n\\n253\\n399\\nJAY ESPENIDA\\nM\\nHILLSBORO\\nOR\\n00:57:03\\n09:12\\n221 of 414\\nM 36-45\\n81 of 152\\n00:01:13\\n00:58:16\\n\\n\\n\\n254\\n835\\nKEVIN KURSCHNER\\nM\\nBEAVERTON\\nOR\\n00:57:05\\n09:12\\n222 of 414\\nM 18-25\\n22 of 34\\n00:00:07\\n00:57:12\\n\\n\\n\\n255\\n888\\nHALADY ARPIT RAO\\nM\\nHILLSBORO\\nOR\\n00:57:05\\n09:12\\n223 of 414\\nM 18-25\\n23 of 34\\n00:00:41\\n00:57:46\\n\\n\\n\\n256\\n410\\nKIRTI DEVI\\nF\\nHILLSBORO\\nOR\\n00:57:05\\n09:12\\n33 of 163\\nF 46-55\\n5 of 22\\n00:00:27\\n00:57:32\\n\\n\\n\\n257\\n356\\nLUIS D CONEJO ALPIZAR\\nM\\nHILLSBORO\\nOR\\n00:57:13\\n09:13\\n224 of 414\\nM 36-45\\n82 of 152\\n00:01:12\\n00:58:25\\n\\n\\n\\n258\\n575\\nVIRAJ P MODAK\\nM\\nNOT SPECIFIED\\nOR\\n00:57:16\\n09:14\\n225 of 414\\nM 26-35\\n75 of 154\\n00:00:34\\n00:57:50\\nINTEL TEAM F\\n\\n\\n259\\n137\\nDON P BADIE\\nM\\nBEAVERTON\\nOR\\n00:57:17\\n09:14\\n226 of 414\\nM 46-55\\n43 of 64\\n00:00:44\\n00:58:01\\n\\n\\n\\n260\\n397\\nSTEWART CHANG\\nM\\nPORTLAND\\nOR\\n00:57:31\\n09:16\\n227 of 414\\nM 36-45\\n83 of 152\\n00:00:33\\n00:58:04\\n\\n\\n\\n261\\n385\\nKAYLA SEAGER\\nF\\nOTHER\\nOR\\n00:57:31\\n09:16\\n34 of 163\\nF 26-35\\n10 of 59\\n00:00:41\\n00:58:12\\n\\n\\n\\n262\\n690\\nSTEPHANIE H MAYA\\nF\\nPORTLAND\\nOR\\n00:57:33\\n09:17\\n35 of 163\\nF 36-45\\n15 of 56\\n00:00:34\\n00:58:07\\n\\n\\n\\n263\\n340\\nTIMOTHY L KIRKHAM\\nM\\nTIGARD\\nOR\\n00:57:33\\n09:17\\n228 of 414\\nM 26-35\\n76 of 154\\n00:00:23\\n00:57:56\\n\\n\\n\\n264\\n556\\nTOD PAULY\\nM\\nWINLOCK\\nWA\\n00:57:36\\n09:17\\n229 of 414\\nM 36-45\\n84 of 152\\n00:01:16\\n00:58:52\\n\\n\\n\\n265\\n637\\nPRANAV NAKATE\\nM\\nHILLSBORO\\nOR\\n00:57:38\\n09:17\\n230 of 414\\nM 26-35\\n77 of 154\\n00:00:25\\n00:58:03\\nINTEL TEAM B\\n\\n\\n266\\n872\\nGABRIEL A WILLARD\\nM\\nTIGARD\\nOR\\n00:57:42\\n09:18\\n231 of 414\\nM 26-35\\n78 of 154\\n00:00:57\\n00:58:39\\n\\n\\n\\n267\\n466\\nHIMANSHU JAIN\\nM\\nHILLSBORO\\nOR\\n00:57:43\\n09:18\\n232 of 414\\nM 26-35\\n79 of 154\\n00:00:15\\n00:57:58\\n\\n\\n\\n268\\n782\\nARINDA SCHRUM\\nF\\nPORTLAND\\nOR\\n00:57:45\\n09:18\\n36 of 163\\nF 26-35\\n11 of 59\\n00:00:56\\n00:58:41\\nFXG1\\n\\n\\n269\\n404\\nNEETA DAMAHE\\nF\\nHILLSBORO\\nOR\\n00:57:51\\n09:19\\n37 of 163\\nF 36-45\\n16 of 56\\n00:00:05\\n00:57:56\\n\\n\\n\\n270\\n745\\nROSS GIDDINGS\\nM\\nNOT SPECIFIED\\nOR\\n00:58:01\\n09:21\\n233 of 414\\nM 46-55\\n44 of 64\\n00:01:02\\n00:59:03\\n\\n\\n\\n271\\n348\\nRAQUEL KUSTERS\\nF\\nNOT SPECIFIED\\nOR\\n00:58:02\\n09:21\\n38 of 163\\nF 18-25\\n5 of 21\\n00:00:32\\n00:58:34\\n\\n\\n\\n272\\n445\\nPRAVEEN BALAJI THANGAMANI BALASUBRAMANIAN\\nM\\nHILLSBORO\\nOR\\n00:58:13\\n09:23\\n234 of 414\\nM 26-35\\n80 of 154\\n00:00:18\\n00:58:31\\n\\n\\n\\n273\\n712\\nATUL J SHAH\\nM\\nHILLSBORO\\nOR\\n00:58:15\\n09:23\\n235 of 414\\nM 36-45\\n85 of 152\\n00:00:31\\n00:58:46\\n\\n\\n\\n274\\n733\\nMARK A BYRAM\\nM\\nPORTLAND\\nOR\\n00:58:22\\n09:24\\n236 of 414\\nM 36-45\\n86 of 152\\n00:00:15\\n00:58:37\\n\\n\\n\\n275\\n215\\nRENE LORIA\\nM\\nPORTLAND\\nOR\\n00:58:28\\n09:25\\n237 of 414\\nM 46-55\\n45 of 64\\n00:00:19\\n00:58:47\\n\\n\\n\\n276\\n274\\nRENE LORIA BARRIENTOS\\nM\\nBEAVERTON\\nOR\\n00:58:28\\n09:25\\n238 of 414\\nM 46-55\\n46 of 64\\n00:00:19\\n00:58:47\\n\\n\\n\\n277\\n632\\nRUDRAJIT DATTA\\nM\\nPORTLAND\\nOR\\n00:58:30\\n09:26\\n239 of 414\\nM 26-35\\n81 of 154\\n00:01:04\\n00:59:34\\n\\n\\n\\n278\\n281\\nJEFF V TRAN\\nM\\nHILLSBORO\\nOR\\n00:58:33\\n09:26\\n240 of 414\\nM 36-45\\n87 of 152\\n00:00:13\\n00:58:46\\n\\n\\n\\n279\\n287\\nMANASI D NAVARE\\nF\\nPORTLAND\\nOR\\n00:58:35\\n09:27\\n39 of 163\\nF 26-35\\n12 of 59\\n00:00:09\\n00:58:44\\nINTEL TEAM L\\n\\n\\n280\\n294\\nLIZA PMD CAD JONES\\nF\\nPORTLAND\\nOR\\n00:58:39\\n09:27\\n40 of 163\\nF 46-55\\n6 of 22\\n00:01:10\\n00:59:49\\n\\n\\n\\n281\\n11\\nMICHELLE MIDDLETON\\nF\\n\\n\\n00:58:40\\n09:27\\n41 of 163\\nF 36-45\\n17 of 56\\n00:00:31\\n00:59:11\\nCOLUMBIA TEAM C\\n\\n\\n282\\n166\\nBRIAN N TALBOTT\\nM\\nPORTLAND\\nOR\\n00:58:45\\n09:28\\n241 of 414\\nM 46-55\\n47 of 64\\n00:00:24\\n00:59:09\\n\\n\\n\\n283\\n609\\nJONATHAN J BURK\\nM\\nPORTLAND\\nOR\\n00:58:47\\n09:28\\n242 of 414\\nM 26-35\\n82 of 154\\n00:01:28\\n01:00:15\\n\\n\\n\\n284\\n317\\nAJAY CHINTALAPATI\\nM\\nHILLSBORO\\nOR\\n00:58:51\\n09:29\\n243 of 414\\nM 18-25\\n24 of 34\\n00:00:22\\n00:59:13\\n\\n\\n\\n285\\n768\\nBIBICHE M GEUSKENS\\nF\\nBEAVERTON\\nOR\\n00:58:51\\n09:29\\n42 of 163\\nF 46-55\\n7 of 22\\n00:00:24\\n00:59:15\\nINTEL TEAM H\\n\\n\\n286\\n657\\nKALEIGH OBRIEN\\nF\\nPORTLAND\\nOR\\n00:58:58\\n09:30\\n43 of 163\\nF 18-25\\n6 of 21\\n00:00:21\\n00:59:19\\n\\n\\n\\n287\\n258\\nPEI-FANG SUNG\\nF\\nHILLSBORO\\nOR\\n00:58:59\\n09:30\\n44 of 163\\nF 26-35\\n13 of 59\\n00:01:29\\n01:00:28\\n\\n\\n\\n288\\n583\\nDANIELLE CASILLAS\\nF\\nPORTLAND\\nOR\\n00:58:59\\n09:30\\n45 of 163\\nF 26-35\\n14 of 59\\n00:01:28\\n01:00:27\\nINTEL TEAM M\\n\\n\\n289\\n354\\nJOHNNIE L BIRCH JR\\nM\\nNOT SPECIFIED\\nOR\\n00:59:01\\n09:31\\n244 of 414\\nM 36-45\\n88 of 152\\n00:00:36\\n00:59:37\\n\\n\\n\\n290\\n705\\nANDRES MALDONADO\\nM\\nBEAVERTON\\nOR\\n00:59:02\\n09:31\\n245 of 414\\nM 26-35\\n83 of 154\\n00:00:23\\n00:59:25\\n\\n\\n\\n291\\n668\\nJIE HUANG1\\nF\\nPORTLAND\\nOR\\n00:59:11\\n09:32\\n46 of 163\\nF 46-55\\n8 of 22\\n00:00:57\\n01:00:08\\n\\n\\n\\n292\\n621\\nZEENATH R TADISINA\\nM\\nHILLSBORO\\nOR\\n00:59:19\\n09:34\\n246 of 414\\nM 36-45\\n89 of 152\\n00:00:27\\n00:59:46\\n\\n\\n\\n293\\n143\\nNICOLAS LANGLEY\\nM\\nPORTLAND\\nOR\\n00:59:27\\n09:35\\n247 of 414\\nM 18-25\\n25 of 34\\n00:00:54\\n01:00:21\\n\\n\\n\\n294\\n470\\nALLYSON HEILINGER\\nF\\nHILLSBORO\\nOR\\n00:59:27\\n09:35\\n47 of 163\\nF 26-35\\n15 of 59\\n00:01:09\\n01:00:36\\n\\n\\n\\n295\\n860\\nPAUL STEARNS\\nM\\nOTHER\\nOR\\n00:59:29\\n09:35\\n248 of 414\\nM 36-45\\n90 of 152\\n00:00:43\\n01:00:12\\n\\n\\n\\n296\\n248\\nJAMES A HEILINGER\\nM\\nHILLSBORO\\nOR\\n00:59:29\\n09:35\\n249 of 414\\nM 26-35\\n84 of 154\\n00:01:09\\n01:00:38\\n\\n\\n\\n297\\n819\\nALAN HATFIELD\\nM\\nBEAVERTON\\nOR\\n00:59:30\\n09:35\\n250 of 414\\nM 46-55\\n48 of 64\\n00:00:45\\n01:00:15\\n\\n\\n\\n298\\n171\\nVENU KARUMURU\\nM\\nPORTLAND\\nOR\\n00:59:31\\n09:36\\n251 of 414\\nM 46-55\\n49 of 64\\n00:00:35\\n01:00:06\\n\\n\\n\\n299\\n329\\nKELLY DEFACCI\\nF\\nPORTLAND\\nOR\\n00:59:32\\n09:36\\n48 of 163\\nF 18-25\\n7 of 21\\n00:00:40\\n01:00:12\\n\\n\\n\\n300\\n380\\nSRIKANTH RAGAM\\nM\\nPORTLAND\\nOR\\n00:59:40\\n09:37\\n252 of 414\\nM 36-45\\n91 of 152\\n00:00:14\\n00:59:54\\n\\n\\n\\n301\\n803\\nSYDNEY WHALEY\\nF\\nBRIDGEWATER\\nNJ\\n00:59:41\\n09:37\\n49 of 163\\nF 18-25\\n8 of 21\\n00:00:40\\n01:00:21\\n\\n\\n\\n302\\n451\\nDANIEL S TOZAKI\\nM\\nHILLSBORO\\nOR\\n00:59:43\\n09:38\\n253 of 414\\nM 26-35\\n85 of 154\\n00:00:57\\n01:00:40\\n\\n\\n\\n303\\n328\\nRISHANKA PRABHU\\nF\\nHILLSBORO\\nOR\\n00:59:47\\n09:38\\n50 of 163\\nF 26-35\\n16 of 59\\n00:00:20\\n01:00:07\\n\\n\\n\\n304\\n26\\nKOUNG CHHIM\\nF\\nHILLSBORO\\nOR\\n00:59:52\\n09:39\\n51 of 163\\nF 46-55\\n9 of 22\\n00:00:15\\n01:00:07\\n\\n\\n\\n305\\n578\\nDAVID J DESROCHER\\nM\\nPORTLAND\\nOR\\n00:59:53\\n09:39\\n254 of 414\\nM 36-45\\n92 of 152\\n00:00:50\\n01:00:43\\n\\n\\n\\n306\\n71\\nCHRISTINA HUNTER\\nF\\nBEAVERTON\\nOR\\n00:59:55\\n09:39\\n52 of 163\\nF 46-55\\n10 of 22\\n00:00:14\\n01:00:09\\n\\n\\n\\n307\\n716\\nPRASANNA A GOWDA\\nM\\nPORTLAND\\nOR\\n01:00:01\\n09:40\\n255 of 414\\nM 36-45\\n93 of 152\\n00:01:06\\n01:01:07\\n\\n\\n\\n308\\n269\\nSTANSLAUS W MWAKALEBELA\\nM\\nPORTLAND\\nOR\\n01:00:03\\n09:41\\n256 of 414\\nM 36-45\\n94 of 152\\n00:01:14\\n01:01:17\\n\\n\\n\\n309\\n640\\nANIL KAZA\\nM\\nHILLSBORO\\nOR\\n01:00:12\\n09:42\\n257 of 414\\nM 36-45\\n95 of 152\\n00:00:29\\n01:00:41\\n\\n\\n\\n310\\n561\\nMARIA PATLA\\nF\\nFOREST GROVE\\nOR\\n01:00:12\\n09:42\\n53 of 163\\nF 36-45\\n18 of 56\\n00:00:37\\n01:00:49\\n\\n\\n\\n311\\n747\\nLAWRENCE HEYWOOD\\nM\\nPORTLAND\\nOR\\n01:00:13\\n09:42\\n258 of 414\\nM 46-55\\n50 of 64\\n00:00:55\\n01:01:08\\n\\n\\n\\n312\\n718\\nSETH PATLA\\nM\\nNOT SPECIFIED\\nOR\\n01:00:13\\n09:42\\n259 of 414\\nM 36-45\\n96 of 152\\n00:00:38\\n01:00:51\\n\\n\\n\\n313\\n163\\nALEXIS GREGERSON\\nF\\nPORTLAND\\nOR\\n01:00:18\\n09:43\\n54 of 163\\nF 18-25\\n9 of 21\\n00:00:53\\n01:01:11\\n\\n\\n\\n314\\n775\\nJASKARAN KHURANA\\nM\\nHILLSBORO\\nOR\\n01:00:18\\n09:43\\n260 of 414\\nM 26-35\\n86 of 154\\n00:00:11\\n01:00:29\\n\\n\\n\\n315\\n884\\nYOSHIKAZU KAWASE\\nM\\nBEAVERTON\\nOR\\n01:00:18\\n09:43\\n261 of 414\\nM 26-35\\n87 of 154\\n00:01:13\\n01:01:31\\n\\n\\n\\n316\\n653\\nRAGHUVIR RAMACHANDRAN\\nM\\nPORTLAND\\nOR\\n01:00:19\\n09:43\\n262 of 414\\nM 36-45\\n97 of 152\\n00:00:42\\n01:01:01\\n\\n\\n\\n317\\n130\\nBRENDAN MORTON\\nM\\nHILLSBORO\\nOR\\n01:00:20\\n09:43\\n263 of 414\\nM 36-45\\n98 of 152\\n00:00:59\\n01:01:19\\n\\n\\n\\n318\\n629\\nRANJITH KUMAR\\nM\\nBEAVERTON\\nOR\\n01:00:23\\n09:44\\n264 of 414\\nM 26-35\\n88 of 154\\n00:00:52\\n01:01:15\\n\\n\\n\\n319\\n346\\nJULIE MAAS\\nF\\nBEAVERTON\\nOR\\n01:00:24\\n09:44\\n55 of 163\\nF 36-45\\n19 of 56\\n00:01:08\\n01:01:32\\nINTEL TEAM N\\n\\n\\n320\\n210\\nCAROLINA DE AGUIAR MARTINS\\nF\\nHILLSBORO\\nOR\\n01:00:39\\n09:46\\n56 of 163\\nF 26-35\\n17 of 59\\n00:01:08\\n01:01:47\\n\\n\\n\\n321\\n350\\nRAFAEL MISOCZKI\\nM\\nHILLSBORO\\nOR\\n01:00:41\\n09:47\\n265 of 414\\nM 26-35\\n89 of 154\\n00:01:10\\n01:01:51\\n\\n\\n\\n322\\n455\\nHABTEAB L YEMANE\\nM\\nBEAVERTON\\nOR\\n01:00:42\\n09:47\\n266 of 414\\nM 36-45\\n99 of 152\\n00:00:31\\n01:01:13\\n\\n\\n\\n323\\n577\\nJOHN H EPPLE\\nM\\nNOT SPECIFIED\\nOR\\n01:00:47\\n09:48\\n267 of 414\\nM 36-45\\n100 of 152\\n00:00:50\\n01:01:37\\n\\n\\n\\n324\\n147\\nMEETA A BHATE\\nF\\nHILLSBORO\\nOR\\n01:00:49\\n09:48\\n57 of 163\\nF 36-45\\n20 of 56\\n00:00:27\\n01:01:16\\nINTEL TEAM N\\n\\n\\n325\\n876\\nRUTH NEILL-JONES\\nF\\nBEAVERTON\\nOR\\n01:00:56\\n09:49\\n58 of 163\\nF 36-45\\n21 of 56\\n00:00:28\\n01:01:24\\n\\n\\n\\n326\\n321\\nBILLY FLEMING\\nM\\nBEAVERTON\\nOR\\n01:00:59\\n09:50\\n268 of 414\\nM 26-35\\n90 of 154\\n00:01:14\\n01:02:13\\n\\n\\n\\n327\\n437\\nTHOMAS A BUSSELL\\nM\\nPORTLAND\\nOR\\n01:01:01\\n09:50\\n269 of 414\\nM 26-35\\n91 of 154\\n00:00:29\\n01:01:30\\n\\n\\n\\n328\\n450\\nJAY JUSTICE\\nM\\nBEAVERTON\\nOR\\n01:01:10\\n09:52\\n270 of 414\\nM 36-45\\n101 of 152\\n00:01:11\\n01:02:21\\n\\n\\n\\n329\\n27\\nTHAMILSELVAN NADARAJAH\\nM\\nFOREST GROVE\\nOR\\n01:01:18\\n09:53\\n271 of 414\\nM 36-45\\n102 of 152\\n00:00:09\\n01:01:27\\n\\n\\n\\n330\\n209\\nSERIN BUSSELL\\nF\\nPORTLAND\\nOR\\n01:01:19\\n09:53\\n59 of 163\\nF 26-35\\n18 of 59\\n00:00:28\\n01:01:47\\n\\n\\n\\n331\\n446\\nHRISHIKESH KANITKAR\\nM\\nPORTLAND\\nOR\\n01:01:21\\n09:53\\n272 of 414\\nM 26-35\\n92 of 154\\n00:00:45\\n01:02:06\\n\\n\\n\\n332\\n140\\nKSHITZ GUPTA\\nM\\nPORTLAND\\nOR\\n01:01:22\\n09:53\\n273 of 414\\nM 18-25\\n26 of 34\\n00:00:49\\n01:02:11\\n\\n\\n\\n333\\n45\\nKRISHNA DRONAVAJJALA\\nM\\nNOT SPECIFIED\\nOR\\n01:01:22\\n09:54\\n274 of 414\\nM 36-45\\n103 of 152\\n00:00:19\\n01:01:41\\n\\n\\n\\n334\\n65\\nAMANDA K SHARP\\nF\\nPORTLAND\\nOR\\n01:01:27\\n09:54\\n60 of 163\\nF 46-55\\n11 of 22\\n00:00:55\\n01:02:22\\n\\n\\n\\n335\\n568\\nMICHELLE R MARTIN\\nF\\nHILLSBORO\\nOR\\n01:01:30\\n09:55\\n61 of 163\\nF 46-55\\n12 of 22\\n00:00:54\\n01:02:24\\n\\n\\n\\n336\\n462\\nTHOMAS V TRIMELONI\\nM\\nNOT SPECIFIED\\nOR\\n01:01:30\\n09:55\\n275 of 414\\nM 26-35\\n93 of 154\\n00:00:34\\n01:02:04\\n\\n\\n\\n337\\n469\\nJOSEPH BROUSSARD\\nM\\nHILLSBORO\\nOR\\n01:01:32\\n09:55\\n276 of 414\\nM 26-35\\n94 of 154\\n00:00:31\\n01:02:03\\n\\n\\n\\n338\\n847\\nHIMANSHU SARODE\\nM\\nHILLSBORO\\nOR\\n01:01:35\\n09:55\\n277 of 414\\nM 26-35\\n95 of 154\\n00:01:11\\n01:02:46\\n\\n\\n\\n339\\n262\\nAARON C TERSTEEG\\nM\\nPORTLAND\\nOR\\n01:01:48\\n09:58\\n278 of 414\\nM 46-55\\n51 of 64\\n00:00:53\\n01:02:41\\n\\n\\n\\n340\\n375\\nSHANTESH R PINGE\\nM\\nBEAVERTON\\nOR\\n01:01:48\\n09:58\\n279 of 414\\nM 26-35\\n96 of 154\\n00:01:45\\n01:03:33\\n\\n\\n\\n341\\n206\\nCESAR MARTINEZ SPESSOT\\nM\\nHILLSBORO\\nOR\\n01:01:48\\n09:58\\n280 of 414\\nM 36-45\\n104 of 152\\n00:00:53\\n01:02:41\\n\\n\\n\\n342\\n292\\nLINJIA CHANG\\nF\\nHILLSBORO\\nOR\\n01:01:49\\n09:58\\n62 of 163\\nF 18-25\\n10 of 21\\n00:00:31\\n01:02:20\\nINTEL TEAM L\\n\\n\\n343\\n590\\nEILEEN M HASSETT\\nF\\nNOT SPECIFIED\\nOR\\n01:02:04\\n10:00\\n63 of 163\\nF 26-35\\n19 of 59\\n00:00:15\\n01:02:19\\n\\n\\n\\n344\\n454\\nLIDIA A KUSNADI\\nF\\nPORTLAND\\nOR\\n01:02:05\\n10:00\\n64 of 163\\nF 26-35\\n20 of 59\\n00:00:41\\n01:02:46\\n\\n\\n\\n345\\n628\\nSAGAR SUTHRAM\\nM\\nPORTLAND\\nOR\\n01:02:05\\n10:00\\n281 of 414\\nM 26-35\\n97 of 154\\n00:00:41\\n01:02:46\\n\\n\\n\\n346\\n367\\nRAMYA MERUVA\\nF\\nHILLSBORO\\nOR\\n01:02:06\\n10:01\\n65 of 163\\nF 26-35\\n21 of 59\\n00:00:23\\n01:02:29\\nINTEL TEAM M\\n\\n\\n347\\n546\\nTYLER GERDIN\\nM\\nTIGARD\\nOR\\n01:02:10\\n10:01\\n282 of 414\\nM 26-35\\n98 of 154\\n00:00:16\\n01:02:26\\n\\n\\n\\n348\\n806\\nBRANDON VANCE\\nM\\nTIGARD\\nOR\\n01:02:18\\n10:03\\n283 of 414\\nM 26-35\\n99 of 154\\n00:00:14\\n01:02:32\\n\\n\\n\\n349\\n773\\nFRANK CHIOU\\nM\\nBEAVERTON\\nOR\\n01:02:21\\n10:03\\n284 of 414\\nM 18-25\\n27 of 34\\n00:00:12\\n01:02:33\\n\\n\\n\\n350\\n665\\nERIC S HIPONIA\\nM\\nHILLSBORO\\nOR\\n01:02:22\\n10:03\\n285 of 414\\nM 36-45\\n105 of 152\\n00:01:23\\n01:03:45\\n\\n\\n\\n351\\n326\\nAVINASH K GUDAGI\\nM\\nHILLSBORO\\nOR\\n01:02:25\\n10:04\\n286 of 414\\nM 26-35\\n100 of 154\\n00:00:40\\n01:03:05\\n\\n\\n\\n352\\n661\\nCARINA E HAHN\\nF\\nHILLSBORO\\nOR\\n01:02:29\\n10:04\\n66 of 163\\nF 18-25\\n11 of 21\\n00:00:14\\n01:02:43\\n\\n\\n\\n353\\n244\\nSANDEEP CHIPPADA\\nM\\nPORTLAND\\nOR\\n01:02:30\\n10:04\\n287 of 414\\nM 26-35\\n101 of 154\\n00:01:07\\n01:03:37\\n\\n\\n\\n354\\n756\\nVEERESH A HONGAL\\nM\\nHILLSBORO\\nOR\\n01:02:36\\n10:05\\n288 of 414\\nM 26-35\\n102 of 154\\n00:01:06\\n01:03:42\\n\\n\\n\\n355\\n658\\nRAVINDRA HOSKOTE\\nM\\nPORTLAND\\nOR\\n01:02:39\\n10:06\\n289 of 414\\nM 36-45\\n106 of 152\\n00:01:03\\n01:03:42\\n\\n\\n\\n356\\n850\\nSHASHIKIRAN KONNUR SAMPATHKUMAR\\nM\\nHILLSBORO\\nOR\\n01:02:41\\n10:06\\n290 of 414\\nM 26-35\\n103 of 154\\n00:00:50\\n01:03:31\\n\\n\\n\\n357\\n608\\nMAITHREYI GOPALAKRISHNAN\\nF\\nHILLSBORO\\nOR\\n01:02:43\\n10:07\\n67 of 163\\nF 18-25\\n12 of 21\\n00:01:11\\n01:03:54\\n\\n\\n\\n358\\n376\\nSREENIVAS KASTURI\\nM\\nTIGARD\\nOR\\n01:02:44\\n10:07\\n291 of 414\\nM 36-45\\n107 of 152\\n00:01:07\\n01:03:51\\n\\n\\n\\n359\\n406\\nJEFFREY K SCIPIO\\nM\\nBEAVERTON\\nOR\\n01:02:47\\n10:07\\n292 of 414\\nM 46-55\\n52 of 64\\n00:00:31\\n01:03:18\\n\\n\\n\\n360\\n229\\nMEENAKSHI MAMUNURU\\nF\\nTIGARD\\nOR\\n01:02:48\\n10:07\\n68 of 163\\nF 26-35\\n22 of 59\\n00:01:08\\n01:03:56\\n\\n\\n\\n361\\n699\\nLEE L THAMMAVONG\\nM\\nPORTLAND\\nOR\\n01:02:48\\n10:07\\n293 of 414\\nM 36-45\\n108 of 152\\n00:00:47\\n01:03:35\\n\\n\\n\\n362\\n314\\nAMANDA DEVASTO\\nF\\nPORTLAND\\nOR\\n01:02:48\\n10:07\\n69 of 163\\nF 26-35\\n23 of 59\\n00:00:32\\n01:03:20\\n\\n\\n\\n363\\n679\\nHSIAO-AN SAMMY WANG\\nM\\nHILLSBORO\\nOR\\n01:03:02\\n10:10\\n294 of 414\\nM 36-45\\n109 of 152\\n00:01:00\\n01:04:02\\n\\n\\n\\n364\\n818\\nHAO SHANG\\nM\\nPORTLAND\\nOR\\n01:03:03\\n10:10\\n295 of 414\\nM 36-45\\n110 of 152\\n00:00:56\\n01:03:59\\n\\n\\n\\n365\\n800\\nERIN MORLAN\\nF\\nPORTLAND\\nOR\\n01:03:08\\n10:11\\n70 of 163\\nF 26-35\\n24 of 59\\n00:00:57\\n01:04:05\\n\\n\\n\\n366\\n432\\nBRIAN SCHENCK\\nM\\nNOT SPECIFIED\\nOR\\n01:03:17\\n10:12\\n296 of 414\\nM 46-55\\n53 of 64\\n00:01:05\\n01:04:22\\n\\n\\n\\n367\\n570\\nLOICE CHINGOZHA\\nF\\nHILLSBORO\\nOR\\n01:03:19\\n10:12\\n71 of 163\\nF 26-35\\n25 of 59\\n00:00:26\\n01:03:45\\n\\n\\n\\n368\\n61\\nAMANDA L HINSDALE\\nF\\nPORTLAND\\nOR\\n01:03:34\\n10:15\\n72 of 163\\nF 36-45\\n22 of 56\\n00:00:49\\n01:04:23\\n\\n\\n\\n369\\n737\\nHEIDI N BARNABY\\nF\\nOTHER\\nOR\\n01:03:34\\n10:15\\n73 of 163\\nF 36-45\\n23 of 56\\n00:00:49\\n01:04:23\\n\\n\\n\\n370\\n615\\nSEAN P DELCAMBRE\\nM\\nPORTLAND\\nOR\\n01:03:36\\n10:15\\n297 of 414\\nM 26-35\\n104 of 154\\n00:00:32\\n01:04:08\\n\\n\\n\\n371\\n859\\nERIN MCKALIP\\nF\\nPORTLAND\\nOR\\n01:03:44\\n10:16\\n74 of 163\\nF 26-35\\n26 of 59\\n00:00:19\\n01:04:03\\n\\n\\n\\n372\\n309\\nBHARADWAJ THANDRA\\nM\\nHILLSBORO\\nOR\\n01:03:44\\n10:16\\n298 of 414\\nM 18-25\\n28 of 34\\n00:00:08\\n01:03:52\\n\\n\\n\\n373\\n725\\nGENE H LANDREVILLE\\nM\\nHILLSBORO\\nOR\\n01:03:49\\n10:17\\n299 of 414\\nM 46-55\\n54 of 64\\n00:01:07\\n01:04:56\\n\\n\\n\\n374\\n678\\nRITU ARORA\\nF\\nHILLSBORO\\nOR\\n01:03:51\\n10:17\\n75 of 163\\nF 26-35\\n27 of 59\\n00:00:29\\n01:04:20\\n\\n\\n\\n375\\n343\\nJAYAKUMARAN RAVI\\nM\\nHILLSBORO\\nOR\\n01:03:51\\n10:18\\n300 of 414\\nM 26-35\\n105 of 154\\n00:00:43\\n01:04:34\\n\\n\\n\\n376\\n131\\nASHLEY DURHAM\\nF\\nHILLSBORO\\nOR\\n01:03:53\\n10:18\\n76 of 163\\nF 36-45\\n24 of 56\\n00:00:45\\n01:04:38\\n\\n\\n\\n377\\n854\\nJESSE T HARRIS\\nM\\nLAKE OSWEGO\\nOR\\n01:04:07\\n10:20\\n301 of 414\\nM 36-45\\n111 of 152\\n00:00:43\\n01:04:50\\n\\n\\n\\n378\\n638\\nGAGAN D MALLARAPU\\nM\\nHILLSBORO\\nOR\\n01:04:09\\n10:20\\n302 of 414\\nM 26-35\\n106 of 154\\n00:00:17\\n01:04:26\\n\\n\\n\\n379\\n565\\nCHANDRAMOULI V KASHYAP\\nM\\nPORTLAND\\nOR\\n01:04:09\\n10:20\\n303 of 414\\nM 46-55\\n55 of 64\\n00:01:02\\n01:05:11\\n\\n\\n\\n380\\n566\\nNANDAKISHORE V KURUGANTI\\nM\\nHILLSBORO\\nOR\\n01:04:10\\n10:21\\n304 of 414\\nM 36-45\\n112 of 152\\n00:01:01\\n01:05:11\\n\\n\\n\\n381\\n228\\nMATTHEW MAAS\\nM\\nBEAVERTON\\nOR\\n01:04:11\\n10:21\\n305 of 414\\nM 36-45\\n113 of 152\\n00:01:09\\n01:05:20\\n\\n\\n\\n382\\n836\\nDAWN HARRIS\\nF\\nLAKE OSWEGO\\nOR\\n01:04:16\\n10:22\\n77 of 163\\nF 36-45\\n25 of 56\\n00:00:43\\n01:04:59\\n\\n\\n\\n383\\n644\\nJIAYIN LIN\\nF\\nHILLSBORO\\nOR\\n01:04:19\\n10:22\\n78 of 163\\nF 26-35\\n28 of 59\\n00:00:56\\n01:05:15\\n\\n\\n\\n384\\n5\\nKENDRA DONLIN\\nF\\n\\n\\n01:04:19\\n10:22\\n79 of 163\\nF 26-35\\n29 of 59\\n00:00:32\\n01:04:51\\nCOLUMBIA TEAM B\\n\\n\\n385\\n60\\nLILI WEI\\nF\\nPORTLAND\\nOR\\n01:04:22\\n10:22\\n80 of 163\\nF 36-45\\n26 of 56\\n00:00:09\\n01:04:31\\n\\n\\n\\n386\\n325\\nPARKER RIDD\\nM\\nHILLSBORO\\nOR\\n01:04:25\\n10:23\\n306 of 414\\nM 18-25\\n29 of 34\\n00:00:14\\n01:04:39\\n\\n\\n\\n387\\n413\\nPAUL C GEORGE\\nM\\nBEAVERTON\\nOR\\n01:04:28\\n10:23\\n307 of 414\\nM 36-45\\n114 of 152\\n00:01:07\\n01:05:35\\n\\n\\n\\n388\\n581\\nJAVIER E SALVATIERRA\\nM\\nNOT SPECIFIED\\nOR\\n01:04:30\\n10:24\\n308 of 414\\nM 36-45\\n115 of 152\\n00:00:55\\n01:05:25\\n\\n\\n\\n389\\n603\\nKELLY M HERWICK\\nF\\nOTHER\\nOR\\n01:04:33\\n10:24\\n81 of 163\\nF 36-45\\n27 of 56\\n00:00:12\\n01:04:45\\n\\n\\n\\n390\\n869\\nPRIYARANGA KOSWATTA\\nM\\nBEAVERTON\\nOR\\n01:04:35\\n10:25\\n309 of 414\\nM 26-35\\n107 of 154\\n00:00:47\\n01:05:22\\nINTEL TEAM H\\n\\n\\n391\\n202\\nGAURI KAMBHATLA\\nF\\nPORTLAND\\nOR\\n01:04:37\\n10:25\\n82 of 163\\nF 18-25\\n13 of 21\\n00:00:35\\n01:05:12\\n\\n\\n\\n392\\n706\\nROHIT KULKARNI\\nM\\nHILLSBORO\\nOR\\n01:04:40\\n10:25\\n310 of 414\\nM 26-35\\n108 of 154\\n00:00:29\\n01:05:09\\n\\n\\n\\n393\\n63\\nERIN M MCDONNELL\\nF\\nALOHA\\nOR\\n01:04:44\\n10:26\\n83 of 163\\nF 26-35\\n30 of 59\\n00:00:20\\n01:05:04\\n\\n\\n\\n394\\n808\\nROSINA ZEVALLOS\\nF\\nPORTLAND\\nOR\\n01:04:48\\n10:27\\n84 of 163\\nF 36-45\\n28 of 56\\n00:00:36\\n01:05:24\\n\\n\\n\\n395\\n54\\nMICHAEL J JACKSON\\nM\\nPORTLAND\\nOR\\n01:04:51\\n10:27\\n311 of 414\\nM 26-35\\n109 of 154\\n00:00:30\\n01:05:21\\n\\n\\n\\n396\\n391\\nSARAH M GORE\\nF\\nHILLSBORO\\nOR\\n01:04:55\\n10:28\\n85 of 163\\nF 36-45\\n29 of 56\\n00:00:45\\n01:05:40\\n\\n\\n\\n397\\n40\\nDENISE HERNANDEZ\\nF\\nBEAVERTON\\nOR\\n01:04:56\\n10:28\\n86 of 163\\nF 26-35\\n31 of 59\\n00:00:45\\n01:05:41\\n\\n\\n\\n398\\n567\\nSTACIE L COLLINS\\nF\\nPORTLAND\\nOR\\n01:04:57\\n10:28\\n87 of 163\\nF 26-35\\n32 of 59\\n00:00:46\\n01:05:43\\n\\n\\n\\n399\\n30\\nBAPPADITYA SAMANTA\\nM\\nPORTLAND\\nOR\\n01:04:59\\n10:28\\n312 of 414\\nM 26-35\\n110 of 154\\n00:00:27\\n01:05:26\\n\\n\\n\\n400\\n463\\nSAMATHA GAVVA\\nF\\nHILLSBORO\\nOR\\n01:05:12\\n10:31\\n88 of 163\\nF 18-25\\n14 of 21\\n00:00:25\\n01:05:37\\n\\n\\n\\n401\\n290\\nCHINNA PRUDVI\\nM\\nPORTLAND\\nOR\\n01:05:13\\n10:31\\n313 of 414\\nM 46-55\\n56 of 64\\n00:00:49\\n01:06:02\\nINTEL TEAM C\\n\\n\\n402\\n222\\nVEENA CHANDRANNA\\nF\\nPORTLAND\\nOR\\n01:05:14\\n10:31\\n89 of 163\\nF 36-45\\n30 of 56\\n00:00:41\\n01:05:55\\n\\n\\n\\n403\\n724\\nTAYLOR EVANS\\nM\\nTUALATIN\\nOR\\n01:05:16\\n10:31\\n314 of 414\\nM 26-35\\n111 of 154\\n00:00:30\\n01:05:46\\n\\n\\n\\n404\\n224\\nKATIE SCARLETT KIMBALL\\nF\\nPORTLAND\\nOR\\n01:05:16\\n10:31\\n90 of 163\\nF 26-35\\n33 of 59\\n00:01:14\\n01:06:30\\n\\n\\n\\n405\\n700\\nRAGHUVEER DEVULAPALLI\\nM\\nHILLSBORO\\nOR\\n01:05:16\\n10:31\\n315 of 414\\nM 26-35\\n112 of 154\\n00:00:59\\n01:06:15\\n\\n\\n\\n406\\n693\\nCALEIGH J FEENY\\nF\\nPORTLAND\\nOR\\n01:05:19\\n10:32\\n91 of 163\\nF 18-25\\n15 of 21\\n00:00:21\\n01:05:40\\n\\n\\n\\n407\\n299\\nVIJAYA C YADAV\\nM\\nHILLSBORO\\nOR\\n01:05:21\\n10:32\\n316 of 414\\nM 26-35\\n113 of 154\\n00:00:18\\n01:05:39\\n\\n\\n\\n408\\n430\\nKENT C LUSTED\\nM\\nBEAVERTON\\nOR\\n01:05:27\\n10:33\\n317 of 414\\nM 36-45\\n116 of 152\\n00:01:17\\n01:06:44\\n\\n\\n\\n409\\n771\\nPATRICK GOTTSACKER\\nM\\nPORTLAND\\nOR\\n01:05:33\\n10:34\\n318 of 414\\nM 36-45\\n117 of 152\\n00:00:22\\n01:05:55\\nINTEL TEAM J\\n\\n\\n410\\n408\\nLYLE LU\\nM\\nHILLSBORO\\nOR\\n01:05:37\\n10:35\\n319 of 414\\nM 55+\\n2 of 4\\n00:00:50\\n01:06:27\\n\\n\\n\\n411\\n801\\nMUSTAFA JAMA\\nM\\nPORTLAND\\nOR\\n01:05:44\\n10:36\\n320 of 414\\nM 46-55\\n57 of 64\\n00:00:37\\n01:06:21\\n\\n\\n\\n412\\n285\\nFRED BARBEE\\nM\\nTIGARD\\nOR\\n01:05:48\\n10:36\\n321 of 414\\nM 46-55\\n58 of 64\\n00:01:17\\n01:07:05\\n\\n\\n\\n413\\n682\\nHADAR SHAIM\\nF\\nHILLSBORO\\nOR\\n01:05:53\\n10:37\\n92 of 163\\nF 36-45\\n31 of 56\\n00:00:23\\n01:06:16\\n\\n\\n\\n414\\n537\\nJEEVAN MADDALA\\nM\\n20746 SW MABEL ST\\nOR\\n01:06:04\\n10:39\\n322 of 414\\nM 26-35\\n114 of 154\\n00:00:59\\n01:07:03\\n\\n\\n\\n415\\n305\\nNIKET D CHOKSI\\nM\\nHILLSBORO\\nOR\\n01:06:14\\n10:41\\n323 of 414\\nM 26-35\\n115 of 154\\n00:01:06\\n01:07:20\\n\\n\\n\\n416\\n23\\nDANIEL LOSEKE\\nM\\nHILLSBORO\\nOR\\n01:06:19\\n10:41\\n324 of 414\\nM 26-35\\n116 of 154\\n00:01:17\\n01:07:36\\n\\n\\n\\n417\\n720\\nAMANDA S BRACKEN\\nF\\nBEAVERTON\\nOR\\n01:06:28\\n10:43\\n93 of 163\\nF 36-45\\n32 of 56\\n00:01:23\\n01:07:51\\n\\n\\n\\n418\\n419\\nAJAY JAGARLAMUDI\\nM\\nHILLSBORO\\nOR\\n01:06:32\\n10:44\\n325 of 414\\nM 26-35\\n117 of 154\\n00:00:38\\n01:07:10\\n\\n\\n\\n419\\n721\\nKARIN SUNG\\nF\\nNOT SPECIFIED\\nOR\\n01:06:38\\n10:44\\n94 of 163\\nF 26-35\\n34 of 59\\n00:00:46\\n01:07:24\\n\\n\\n\\n420\\n666\\nVIVEK THIRTHA\\nM\\nBEAVERTON\\nOR\\n01:06:41\\n10:45\\n326 of 414\\nM 36-45\\n118 of 152\\n00:00:33\\n01:07:14\\n\\n\\n\\n421\\n433\\nCHRIS NORMAN\\nM\\nHILLSBORO\\nOR\\n01:06:43\\n10:45\\n327 of 414\\nM 46-55\\n59 of 64\\n00:00:51\\n01:07:34\\n\\n\\n\\n422\\n636\\nROHAN R PATIL\\nM\\nHILLSBORO\\nOR\\n01:06:45\\n10:46\\n328 of 414\\nM 26-35\\n118 of 154\\n00:01:12\\n01:07:57\\n\\n\\n\\n423\\n726\\nMARK C GIOVANNOZZI\\nM\\nOUT OF STATE\\nWA\\n01:06:46\\n10:46\\n329 of 414\\nM 46-55\\n60 of 64\\n00:01:14\\n01:08:00\\n\\n\\n\\n424\\n297\\nHUANG JIN\\nM\\nNOT SPECIFIED\\nOR\\n01:06:50\\n10:46\\n330 of 414\\nM 36-45\\n119 of 152\\n00:00:32\\n01:07:22\\n\\n\\n\\n425\\n8\\nMARIE-PIERRE VIGNE\\nF\\n\\n\\n01:06:51\\n10:47\\n95 of 163\\nF 36-45\\n33 of 56\\n00:00:28\\n01:07:19\\nCOLUMBIA TEAM B\\n\\n\\n426\\n604\\nINDRI ADISOEMARTA\\nF\\nPORTLAND\\nOR\\n01:06:52\\n10:47\\n96 of 163\\nF 18-25\\n16 of 21\\n00:01:10\\n01:08:02\\n\\n\\n\\n427\\n148\\nFRAZER TEE\\nM\\nHILLSBORO\\nOR\\n01:06:53\\n10:47\\n331 of 414\\nM 18-25\\n30 of 34\\n00:00:36\\n01:07:29\\n\\n\\n\\n428\\n842\\nSHIVARAMAN RAMACHANDRAN\\nM\\nHILLSBORO\\nOR\\n01:06:55\\n10:47\\n332 of 414\\nM 36-45\\n120 of 152\\n00:00:32\\n01:07:27\\nINTEL TEAM J\\n\\n\\n429\\n641\\nVENKATA ADITYA ADDEPALLI\\nM\\nHILLSBORO\\nOR\\n01:07:04\\n10:49\\n333 of 414\\nM 18-25\\n31 of 34\\n00:00:26\\n01:07:30\\n\\n\\n\\n430\\n669\\nCHAITANYA YADDANAPUDI VENKATA\\nM\\nHILLSBORO\\nOR\\n01:07:14\\n10:50\\n334 of 414\\nM 26-35\\n119 of 154\\n00:00:25\\n01:07:39\\n\\n\\n\\n431\\n33\\nROBERT ROBINSON\\nM\\nHILLSBORO\\nOR\\n01:07:14\\n10:50\\n335 of 414\\nM 18-25\\n32 of 34\\n00:00:26\\n01:07:40\\n\\n\\n\\n432\\n611\\nADITYA KASUKURTI\\nM\\nHILLSBORO\\nOR\\n01:07:16\\n10:51\\n336 of 414\\nM 26-35\\n120 of 154\\n00:01:00\\n01:08:16\\n\\n\\n\\n433\\n34\\nREBECCA LATIOLAIT\\nF\\nHILLSBORO\\nOR\\n01:07:16\\n10:51\\n97 of 163\\nF 26-35\\n35 of 59\\n00:00:24\\n01:07:40\\n\\n\\n\\n434\\n663\\nVINEET V NAIR\\nM\\nHILLSBORO\\nOR\\n01:07:16\\n10:51\\n337 of 414\\nM 26-35\\n121 of 154\\n00:00:38\\n01:07:54\\n\\n\\n\\n435\\n369\\nANNE C MCEWAN\\nF\\nPORTLAND\\nOR\\n01:07:18\\n10:51\\n98 of 163\\nF 46-55\\n13 of 22\\n00:00:54\\n01:08:12\\n\\n\\n\\n436\\n400\\nKERI A CARKEEK\\nF\\nPORTLAND\\nOR\\n01:07:18\\n10:51\\n99 of 163\\nF 46-55\\n14 of 22\\n00:00:54\\n01:08:12\\n\\n\\n\\n437\\n877\\nVICKY L ESPIASSE\\nF\\nHILLSBORO\\nOR\\n01:07:23\\n10:52\\n100 of 163\\nF 46-55\\n15 of 22\\n00:00:05\\n01:07:28\\n\\n\\n\\n438\\n379\\nEDBERG LIONEL STEPHEN CHARLES\\nM\\nPORTLAND\\nOR\\n01:07:25\\n10:52\\n338 of 414\\nM 26-35\\n122 of 154\\n00:00:50\\n01:08:15\\n\\n\\n\\n439\\n878\\nDAVID N ARBOW\\nM\\nBEAVERTON\\nOR\\n01:07:28\\n10:52\\n339 of 414\\nM 36-45\\n121 of 152\\n00:00:51\\n01:08:19\\n\\n\\n\\n440\\n301\\nSUDHEER MOGILAPPAGARI\\nM\\nHILLSBORO\\nOR\\n01:07:35\\n10:54\\n340 of 414\\nM 36-45\\n122 of 152\\n00:00:53\\n01:08:28\\n\\n\\n\\n441\\n302\\nKARTHIKK SRIDHARAN\\nM\\nPORTLAND\\nOR\\n01:07:41\\n10:55\\n341 of 414\\nM 26-35\\n123 of 154\\n00:00:45\\n01:08:26\\n\\n\\n\\n442\\n252\\nSKYLAR BROWN\\nM\\nHILLSBORO\\nOR\\n01:07:46\\n10:55\\n342 of 414\\nM 26-35\\n124 of 154\\n00:00:36\\n01:08:22\\n\\n\\n\\n443\\n254\\nKIRSTIN M BROWN\\nF\\nHILLSBORO\\nOR\\n01:07:51\\n10:56\\n101 of 163\\nF 18-25\\n17 of 21\\n00:00:35\\n01:08:26\\n\\n\\n\\n444\\n405\\nABHISHEK PILLAI\\nM\\nBEAVERTON\\nOR\\n01:07:52\\n10:56\\n343 of 414\\nM 36-45\\n123 of 152\\n00:00:57\\n01:08:49\\n\\n\\n\\n445\\n616\\nOMKAR R PARAJULI\\nM\\nBEAVERTON\\nOR\\n01:07:56\\n10:57\\n344 of 414\\nM 36-45\\n124 of 152\\n00:00:51\\n01:08:47\\n\\n\\n\\n446\\n588\\nJINPYO HONG\\nM\\nBEAVERTON\\nOR\\n01:08:07\\n10:59\\n345 of 414\\nM 26-35\\n125 of 154\\n00:00:10\\n01:08:17\\nINTEL TEAM G\\n\\n\\n447\\n659\\nGOUTHAM BATTULA\\nM\\nHILLSBORO\\nOR\\n01:08:11\\n10:59\\n346 of 414\\nM 18-25\\n33 of 34\\n00:00:23\\n01:08:34\\n\\n\\n\\n448\\n318\\nPADMA M CHILAKALA\\nF\\nTIGARD\\nOR\\n01:08:12\\n11:00\\n102 of 163\\n\\n2 of 6\\n00:00:41\\n01:08:53\\n\\n\\n\\n449\\n259\\nVISHNU V RAVI\\nM\\nHILLSBORO\\nOR\\n01:08:16\\n11:00\\n347 of 414\\nM 26-35\\n126 of 154\\n00:00:30\\n01:08:46\\n\\n\\n\\n450\\n667\\nCHANAKYA KOPPOLU\\nM\\nHILLSBORO\\nOR\\n01:08:17\\n11:00\\n348 of 414\\nM 36-45\\n125 of 152\\n00:01:03\\n01:09:20\\n\\n\\n\\n451\\n551\\nSTACIE HYMAS\\nF\\nTUALATIN\\nOR\\n01:08:18\\n11:00\\n103 of 163\\nF 26-35\\n36 of 59\\n00:00:29\\n01:08:47\\n\\n\\n\\n452\\n363\\nKEITH OSOWSKI\\nM\\nBEAVERTON\\nOR\\n01:08:22\\n11:01\\n349 of 414\\nM 36-45\\n126 of 152\\n00:01:07\\n01:09:29\\n\\n\\n\\n453\\n421\\nKUSHAL SREEDHAR\\nM\\nHILLSBORO\\nOR\\n01:08:33\\n11:03\\n350 of 414\\nM 26-35\\n127 of 154\\n00:01:09\\n01:09:42\\n\\n\\n\\n454\\n22\\nNAOMI HOFFMAN\\nF\\nBEAVERTON\\nOR\\n01:08:42\\n11:04\\n104 of 163\\nF 26-35\\n37 of 59\\n00:00:55\\n01:09:37\\n\\n\\n\\n455\\n452\\nVIJAY KASTURI\\nM\\nHILLSBORO\\nOR\\n01:08:47\\n11:05\\n351 of 414\\nM 26-35\\n128 of 154\\n00:00:04\\n01:08:51\\n\\n\\n\\n456\\n66\\nSONIKA JOHRI\\nF\\nPORTLAND\\nOR\\n01:08:52\\n11:06\\n105 of 163\\nF 26-35\\n38 of 59\\n00:00:39\\n01:09:31\\n\\n\\n\\n457\\n424\\nKONDAL R PURMA\\nM\\nPORTLAND\\nOR\\n01:08:54\\n11:06\\n352 of 414\\nM 36-45\\n127 of 152\\n00:00:28\\n01:09:22\\n\\n\\n\\n458\\n306\\nPHANI VADALI\\nF\\nHILLSBORO\\nOR\\n01:08:56\\n11:07\\n106 of 163\\nF 26-35\\n39 of 59\\n00:00:28\\n01:09:24\\n\\n\\n\\n459\\n320\\nSRI HARISHA SAI KASTURI\\nF\\nHILLSBORO\\nOR\\n01:08:57\\n11:07\\n107 of 163\\nF 26-35\\n40 of 59\\n00:00:04\\n01:09:01\\n\\n\\n\\n460\\n651\\nSRI PAVANI MEDAPURAM L N\\nF\\nHILLSBORO\\nOR\\n01:08:58\\n11:07\\n108 of 163\\nF 26-35\\n41 of 59\\n00:00:59\\n01:09:57\\n\\n\\n\\n461\\n846\\nSOMA V KANDULA\\nM\\nHILLSBORO\\nOR\\n01:09:04\\n11:08\\n353 of 414\\nM 36-45\\n128 of 152\\n00:00:18\\n01:09:22\\n\\n\\n\\n462\\n217\\nRAHUL PENDYALA\\nM\\nBEAVERTON\\nOR\\n01:09:06\\n11:08\\n354 of 414\\nM 26-35\\n129 of 154\\n00:01:09\\n01:10:15\\n\\n\\n\\n463\\n365\\nLIZ STAMPS\\nF\\nNOT SPECIFIED\\nOR\\n01:09:06\\n11:08\\n109 of 163\\nF 36-45\\n34 of 56\\n00:01:12\\n01:10:18\\n\\n\\n\\n464\\n785\\nADRIAN ONG\\nM\\nPORTLAND\\nOR\\n01:09:14\\n11:10\\n355 of 414\\nM 36-45\\n129 of 152\\n00:00:24\\n01:09:38\\nDTNA3\\n\\n\\n465\\n554\\nSHALINI GUPTA\\nF\\nHILLSBORO\\nOR\\n01:09:15\\n11:10\\n110 of 163\\nF 36-45\\n35 of 56\\n00:01:00\\n01:10:15\\n\\n\\n\\n466\\n840\\nJEANNIE L MCGUIRE\\nF\\nPORTLAND\\nOR\\n01:09:16\\n11:10\\n111 of 163\\nF 36-45\\n36 of 56\\n00:00:42\\n01:09:58\\n\\n\\n\\n467\\n396\\nASHOK RAJ\\nM\\nPORTLAND\\nOR\\n01:09:18\\n11:10\\n356 of 414\\nM 46-55\\n61 of 64\\n00:00:09\\n01:09:27\\n\\n\\n\\n468\\n786\\nRICHARD LEE\\nM\\nPORTLAND\\nOR\\n01:09:18\\n11:10\\n357 of 414\\nM 26-35\\n130 of 154\\n00:00:20\\n01:09:38\\nDTNA3\\n\\n\\n469\\n266\\nMITCH SIKAPIZYE\\nM\\nPORTLAND\\nOR\\n01:09:19\\n11:10\\n358 of 414\\nM 26-35\\n131 of 154\\n00:00:26\\n01:09:45\\nINTEL TEAM E\\n\\n\\n470\\n596\\nARUN GANGOPADHYAY\\nM\\nPORTLAND\\nOR\\n01:09:20\\n11:11\\n359 of 414\\nM 46-55\\n62 of 64\\n00:00:14\\n01:09:34\\n\\n\\n\\n471\\n37\\nCURTIS KELLEM\\nM\\nHILLSBORO\\nOR\\n01:09:27\\n11:12\\n360 of 414\\nM 36-45\\n130 of 152\\n00:00:39\\n01:10:06\\n\\n\\n\\n472\\n617\\nCOREY KUKIS\\nM\\nHILLSBORO\\nOR\\n01:09:29\\n11:12\\n361 of 414\\nM 26-35\\n132 of 154\\n00:00:38\\n01:10:07\\n\\n\\n\\n473\\n844\\nCLEMENT JOSEPH\\nM\\nBEAVERTON\\nOR\\n01:09:31\\n11:12\\n362 of 414\\nM 36-45\\n131 of 152\\n00:00:56\\n01:10:27\\n\\n\\n\\n474\\n417\\nHEMALATHA GURUMOORTHY\\nF\\nHILLSBORO\\nOR\\n01:09:49\\n11:15\\n112 of 163\\nF 26-35\\n42 of 59\\n00:00:57\\n01:10:46\\n\\n\\n\\n475\\n162\\nKARTHIK VADLA\\nM\\nHILLSBORO\\nOR\\n01:09:59\\n11:17\\n363 of 414\\nM 26-35\\n133 of 154\\n00:00:13\\n01:10:12\\n\\n\\n\\n476\\n43\\nQUANEISHA JENKINS-PENHA\\nF\\nPORTLAND\\nOR\\n01:10:01\\n11:17\\n113 of 163\\nF 26-35\\n43 of 59\\n00:01:12\\n01:11:13\\n\\n\\n\\n477\\n677\\nPIYUSH VIVEK DESHPANDE\\nM\\nHILLSBORO\\nOR\\n01:10:04\\n11:18\\n364 of 414\\nM 26-35\\n134 of 154\\n00:00:24\\n01:10:28\\n\\n\\n\\n478\\n336\\nPRIYANKA SHANKARAN\\nF\\nHILLSBORO\\nOR\\n01:10:06\\n11:18\\n114 of 163\\nF 26-35\\n44 of 59\\n00:00:23\\n01:10:29\\n\\n\\n\\n479\\n377\\nANNAPURNA KAMBHATLA\\nF\\nPORTLAND\\nOR\\n01:10:13\\n11:19\\n115 of 163\\nF 46-55\\n16 of 22\\n00:00:37\\n01:10:50\\n\\n\\n\\n480\\n284\\nNACHIKET DESAI\\nM\\nPORTLAND\\nOR\\n01:10:37\\n11:23\\n365 of 414\\nM 26-35\\n135 of 154\\n00:00:40\\n01:11:17\\n\\n\\n\\n481\\n774\\nHARSH SHAH\\nM\\nHILLSBORO\\nOR\\n01:10:50\\n11:25\\n366 of 414\\nM 26-35\\n136 of 154\\n00:00:26\\n01:11:16\\nINTEL TEAM G\\n\\n\\n482\\n871\\nERIC WELLINGTON\\nM\\nBEAVERTON\\nOR\\n01:10:51\\n11:25\\n367 of 414\\nM 26-35\\n137 of 154\\n00:00:57\\n01:11:48\\n\\n\\n\\n483\\n870\\nRICHARD L DAVIS\\nM\\nBEAVERTON\\nOR\\n01:10:55\\n11:26\\n368 of 414\\nM 55+\\n3 of 4\\n00:00:56\\n01:11:51\\n\\n\\n\\n484\\n127\\nALLEN NG\\nM\\nHILLSBORO\\nOR\\n01:11:00\\n11:27\\n369 of 414\\nM 55+\\n4 of 4\\n00:00:06\\n01:11:06\\n\\n\\n\\n485\\n214\\nKARLA REYES\\nF\\nHILLSBORO\\nOR\\n01:11:01\\n11:27\\n116 of 163\\nF 36-45\\n37 of 56\\n00:01:12\\n01:12:13\\n\\n\\n\\n486\\n312\\nDIVYA SHREE VEMPARALA\\nF\\nHILLSBORO\\nOR\\n01:11:07\\n11:28\\n117 of 163\\nF 18-25\\n18 of 21\\n00:00:40\\n01:11:47\\n\\n\\n\\n487\\n759\\nJUN CHAO GOH\\nM\\nHILLSBORO\\nOR\\n01:11:09\\n11:28\\n370 of 414\\nM 26-35\\n138 of 154\\n00:00:12\\n01:11:21\\n\\n\\n\\n488\\n429\\nMELISSA BEEGLE\\nF\\nHILLSBORO\\nOR\\n01:11:10\\n11:28\\n118 of 163\\nF 46-55\\n17 of 22\\n00:00:46\\n01:11:56\\n\\n\\n\\n489\\n804\\nSTEVEN BEEGLE\\nM\\nHILLSBORO\\nOR\\n01:11:10\\n11:28\\n371 of 414\\nM 46-55\\n63 of 64\\n00:00:44\\n01:11:54\\n\\n\\n\\n490\\n633\\nJHANSI PILLI\\nF\\nHILLSBORO\\nOR\\n01:11:12\\n11:29\\n119 of 163\\nF 36-45\\n38 of 56\\n00:00:17\\n01:11:29\\n\\n\\n\\n491\\n686\\nSATISH SHRIMALI\\nM\\nPORTLAND\\nOR\\n01:11:21\\n11:30\\n372 of 414\\nM 36-45\\n132 of 152\\n00:00:42\\n01:12:03\\n\\n\\n\\n492\\n160\\nJEFF NGUYEN\\nM\\nHILLSBORO\\nOR\\n01:11:31\\n11:32\\n373 of 414\\nM 36-45\\n133 of 152\\n00:00:44\\n01:12:15\\n\\n\\n\\n493\\n138\\nVIKRAM MAILARAM\\nM\\nPORTLAND\\nOR\\n01:11:39\\n11:33\\n374 of 414\\nM 26-35\\n139 of 154\\n00:00:18\\n01:11:57\\n\\n\\n\\n494\\n887\\nMARIA KHAW\\nF\\nHILLSBORO\\nOR\\n01:11:40\\n11:33\\n120 of 163\\nF 46-55\\n18 of 22\\n00:00:19\\n01:11:59\\n\\n\\n\\n495\\n823\\nNO NAME PLS EMAIL TIMER \\nM\\n\\n\\n01:11:54\\n11:35\\n375 of 414\\n\\n3 of 6\\n00:00:04\\n01:11:58\\n\\n\\n\\n496\\n121\\nSHYAMALA PADALA\\nF\\nPORTLAND\\nOR\\n01:11:56\\n11:36\\n121 of 163\\n\\n4 of 6\\n00:00:00\\n01:11:56\\n\\n\\n\\n497\\n205\\nPRABHA VISWANATHAN\\nF\\nHILLSBORO\\nOR\\n01:11:59\\n11:36\\n122 of 163\\nF 26-35\\n45 of 59\\n00:00:09\\n01:12:08\\n\\n\\n\\n498\\n559\\nDEEPA RAO KOPPA VENKATESH\\nM\\nPORTLAND\\nAK\\n01:12:02\\n11:37\\n376 of 414\\nM 36-45\\n134 of 152\\n00:01:04\\n01:13:06\\n\\n\\n\\n499\\n553\\nJENNIFER HOWARD\\nF\\nPORTLAND\\nOR\\n01:12:08\\n11:38\\n123 of 163\\nF 36-45\\n39 of 56\\n00:01:02\\n01:13:10\\n\\n\\n\\n500\\n692\\nJOSHUA M HOWARD\\nM\\nPORTLAND\\nOR\\n01:12:09\\n11:38\\n377 of 414\\nM 36-45\\n135 of 152\\n00:01:01\\n01:13:10\\n\\n\\n\\n501\\n272\\nJESSICA Y LI\\nF\\nOUT OF STATE\\nCA\\n01:12:14\\n11:39\\n124 of 163\\nF 18-25\\n19 of 21\\n00:00:16\\n01:12:30\\n\\n\\n\\n502\\n282\\nJANE M JACKSON\\nF\\nBEAVERTON\\nOR\\n01:12:19\\n11:39\\n125 of 163\\nF 46-55\\n19 of 22\\n00:00:45\\n01:13:04\\nINTEL TEAM N\\n\\n\\n503\\n279\\nKRISTIN VANDERSCHICK\\nF\\nPORTLAND\\nOR\\n01:12:20\\n11:40\\n126 of 163\\nF 26-35\\n46 of 59\\n00:00:44\\n01:13:04\\n\\n\\n\\n504\\n245\\nCHANG-TSUNG FU\\nM\\nPORTLAND\\nOR\\n01:12:32\\n11:41\\n378 of 414\\nM 36-45\\n136 of 152\\n00:00:26\\n01:12:58\\n\\n\\n\\n505\\n848\\nDAVID SALAS\\nM\\nPORTLAND\\nOR\\n01:12:42\\n11:43\\n379 of 414\\nM 36-45\\n137 of 152\\n00:00:11\\n01:12:53\\n\\n\\n\\n506\\n833\\nMARIA CARDENAS\\nF\\nPORTLAND\\nOR\\n01:12:43\\n11:43\\n127 of 163\\nF 36-45\\n40 of 56\\n00:00:10\\n01:12:53\\n\\n\\n\\n507\\n313\\nYI1 ZHANG\\nF\\nPORTLAND\\nOR\\n01:12:53\\n11:45\\n128 of 163\\nF 36-45\\n41 of 56\\n00:00:10\\n01:13:03\\n\\n\\n\\n508\\n345\\nMANASA M KALBURGI\\nF\\nHILLSBORO\\nOR\\n01:12:55\\n11:45\\n129 of 163\\nF 18-25\\n20 of 21\\n00:00:25\\n01:13:20\\n\\n\\n\\n509\\n157\\nLAURA M HOOVER\\nF\\nHILLSBORO\\nOR\\n01:13:02\\n11:46\\n130 of 163\\nF 26-35\\n47 of 59\\n00:00:43\\n01:13:45\\n\\n\\n\\n510\\n390\\nKETKI H KANITKAR\\nF\\nPORTLAND\\nOR\\n01:13:08\\n11:47\\n131 of 163\\nF 26-35\\n48 of 59\\n00:00:45\\n01:13:53\\n\\n\\n\\n511\\n618\\nBADRI RANGARAJAN\\nM\\nHILLSBORO\\nOR\\n01:13:23\\n11:50\\n380 of 414\\nM 36-45\\n138 of 152\\n00:00:20\\n01:13:43\\n\\n\\n\\n512\\n168\\nJAMES C RAUPP\\nM\\nPORTLAND\\nOR\\n01:13:25\\n11:50\\n381 of 414\\nM 36-45\\n139 of 152\\n00:00:28\\n01:13:53\\n\\n\\n\\n513\\n448\\nMAYUR B MAHAJAN\\nM\\nHILLSBORO\\nOR\\n01:13:30\\n11:51\\n382 of 414\\nM 26-35\\n140 of 154\\n00:01:05\\n01:14:35\\n\\n\\n\\n514\\n366\\nDEVDATTA P KULKARNI\\nM\\nPORTLAND\\nOR\\n01:13:44\\n11:53\\n383 of 414\\nM 36-45\\n140 of 152\\n00:00:15\\n01:13:59\\n\\n\\n\\n515\\n251\\nDHAVAL V SHAH\\nM\\nHILLSBORO\\nOR\\n01:13:49\\n11:54\\n384 of 414\\nM 18-25\\n34 of 34\\n00:00:25\\n01:14:14\\nINTEL TEAM D\\n\\n\\n516\\n829\\nNO NAME PLS EMAIL TIMER \\nM\\n\\n\\n01:13:49\\n11:54\\n385 of 414\\n\\n5 of 6\\n00:01:17\\n01:15:06\\n\\n\\n\\n517\\n322\\nSARJU RAJBHANDARI\\nM\\nHILLSBORO\\nOR\\n01:14:01\\n11:56\\n386 of 414\\nM 36-45\\n141 of 152\\n00:00:51\\n01:14:52\\n\\n\\n\\n518\\n169\\nAJAY GARG\\nM\\nPORTLAND\\nOR\\n01:14:10\\n11:57\\n387 of 414\\nM 36-45\\n142 of 152\\n00:00:49\\n01:14:59\\n\\n\\n\\n519\\n607\\nSWATHI DHANAVANTHRI\\nF\\nHILLSBORO\\nOR\\n01:14:13\\n11:58\\n132 of 163\\nF 26-35\\n49 of 59\\n00:00:57\\n01:15:10\\nINTEL TEAM N\\n\\n\\n520\\n152\\nAMIT CHUTANI\\nM\\nNOT SPECIFIED\\nOR\\n01:14:18\\n11:59\\n388 of 414\\nM 26-35\\n141 of 154\\n00:00:49\\n01:15:07\\n\\n\\n\\n521\\n257\\nCHRISTIAN AMOAH-KUSI\\nM\\nNOT SPECIFIED\\nOR\\n01:14:19\\n11:59\\n389 of 414\\nM 26-35\\n142 of 154\\n00:01:30\\n01:15:49\\n\\n\\n\\n522\\n235\\nEMILY RALSTON\\nF\\nHILLSBORO\\nOR\\n01:14:22\\n11:59\\n133 of 163\\nF 26-35\\n50 of 59\\n00:01:20\\n01:15:42\\n\\n\\n\\n523\\n154\\nDAVID W MENDIES\\nM\\nHILLSBORO\\nOR\\n01:14:32\\n12:01\\n390 of 414\\nM 36-45\\n143 of 152\\n00:00:37\\n01:15:09\\nINTEL TEAM H\\n\\n\\n524\\n295\\nBRUCE CHHUON\\nM\\nOTHER\\nOR\\n01:14:40\\n12:02\\n391 of 414\\nM 26-35\\n143 of 154\\n00:01:11\\n01:15:51\\n\\n\\n\\n525\\n680\\nLUIS F CEPEDA\\nM\\nPORTLAND\\nOR\\n01:14:54\\n12:04\\n392 of 414\\nM 36-45\\n144 of 152\\n00:01:14\\n01:16:08\\n\\n\\n\\n526\\n20\\nSAYALI KULKARNI\\nF\\nPORTLAND\\nOR\\n01:15:13\\n12:07\\n134 of 163\\nF 26-35\\n51 of 59\\n00:00:16\\n01:15:29\\n\\n\\n\\n527\\n440\\nVIVEK R DESHPANDE\\nM\\nHILLSBORO\\nOR\\n01:15:34\\n12:11\\n393 of 414\\nM 26-35\\n144 of 154\\n00:00:28\\n01:16:02\\n\\n\\n\\n528\\n639\\nRAYMOND NAZARIO\\nM\\nHILLSBORO\\nOR\\n01:15:38\\n12:11\\n394 of 414\\nM 26-35\\n145 of 154\\n00:00:44\\n01:16:22\\n\\n\\n\\n529\\n879\\nHOLLI K SHENFIELD\\nF\\nPORTLAND\\nOR\\n01:15:48\\n12:13\\n135 of 163\\nF 36-45\\n42 of 56\\n00:01:10\\n01:16:58\\n\\n\\n\\n530\\n864\\nAUDREY SCHAAB\\nF\\nBEAVERTON\\nOR\\n01:15:51\\n12:14\\n136 of 163\\nF 36-45\\n43 of 56\\n00:01:10\\n01:17:01\\n\\n\\n\\n531\\n434\\nSUMIT K SONI\\nM\\nPORTLAND\\nOR\\n01:15:58\\n12:15\\n395 of 414\\nM 26-35\\n146 of 154\\n00:00:23\\n01:16:21\\n\\n\\n\\n532\\n360\\nSUSHMA CHENNU\\nF\\nBEAVERTON\\nOR\\n01:16:00\\n12:15\\n137 of 163\\nF 36-45\\n44 of 56\\n00:00:05\\n01:16:05\\n\\n\\n\\n533\\n247\\nRAJESH POORNACHANDRAN\\nM\\nPORTLAND\\nOR\\n01:16:19\\n12:18\\n396 of 414\\nM 36-45\\n145 of 152\\n00:00:54\\n01:17:13\\nINTEL TEAM H\\n\\n\\n534\\n534\\nCHARLENE DE LA PAZ\\nF\\nHILLSBORO\\nOR\\n01:16:25\\n12:19\\n138 of 163\\nF Under 18\\n2 of 3\\n00:00:55\\n01:17:20\\n\\n\\n\\n535\\n387\\nGREESHMA PISHARODY\\nF\\nPORTLAND\\nOR\\n01:16:46\\n12:23\\n139 of 163\\nF 36-45\\n45 of 56\\n00:00:44\\n01:17:30\\n\\n\\n\\n536\\n240\\nMATEO GUZMAN\\nM\\nHILLSBORO\\nOR\\n01:16:48\\n12:23\\n397 of 414\\nM 26-35\\n147 of 154\\n00:00:54\\n01:17:42\\nINTEL TEAM E\\n\\n\\n537\\n708\\nRAVIRAJ M KOKIL\\nM\\nHILLSBORO\\nOR\\n01:16:52\\n12:23\\n398 of 414\\nM 26-35\\n148 of 154\\n00:00:14\\n01:17:06\\n\\n\\n\\n538\\n447\\nPALLAVI J SHINDE\\nF\\nHILLSBORO\\nOR\\n01:17:06\\n12:26\\n140 of 163\\nF 26-35\\n52 of 59\\n00:00:30\\n01:17:36\\nINTEL TEAM M\\n\\n\\n539\\n352\\nBALA NADIKATLA\\nM\\nTIGARD\\nOR\\n01:17:14\\n12:27\\n399 of 414\\nM 26-35\\n149 of 154\\n00:00:40\\n01:17:54\\n\\n\\n\\n540\\n838\\nMIKE GAULT\\nM\\nBEAVERTON\\nOR\\n01:18:08\\n12:36\\n400 of 414\\nM 36-45\\n146 of 152\\n00:00:48\\n01:18:56\\n\\n\\n\\n541\\n843\\nKRISTINA M GAULT\\nF\\nBEAVERTON\\nOR\\n01:18:09\\n12:36\\n141 of 163\\nF 46-55\\n20 of 22\\n00:00:47\\n01:18:56\\n\\n\\n\\n542\\n555\\nJANE SHU\\nF\\nHILLSBORO\\nOR\\n01:18:56\\n12:43\\n142 of 163\\nF 36-45\\n46 of 56\\n00:01:01\\n01:19:57\\n\\n\\n\\n543\\n834\\nKABILA JAYARAMAN\\nF\\nBEAVERTON\\nOR\\n01:20:46\\n13:01\\n143 of 163\\nF 36-45\\n47 of 56\\n00:00:56\\n01:21:42\\n\\n\\n\\n544\\n38\\nDEEPA CHETTY\\nF\\nBEAVERTON\\nOR\\n01:23:08\\n13:24\\n144 of 163\\nF 36-45\\n48 of 56\\n00:01:05\\n01:24:13\\n\\n\\n\\n545\\n557\\nADITHYA CHETTY\\nM\\nBEAVERTON\\nOR\\n01:23:09\\n13:24\\n401 of 414\\nM Under 18\\n2 of 2\\n00:01:04\\n01:24:13\\n\\n\\n\\n546\\n839\\nGEORGE E MATTHEW\\nM\\nALOHA\\nOR\\n01:23:10\\n13:24\\n402 of 414\\nM 26-35\\n150 of 154\\n00:00:11\\n01:23:21\\nINTEL TEAM G\\n\\n\\n547\\n695\\nAPURVA LONKAR\\nF\\nHILLSBORO\\nOR\\n01:23:12\\n13:25\\n145 of 163\\nF 36-45\\n49 of 56\\n00:00:12\\n01:23:24\\n\\n\\n\\n548\\n539\\nJESS NICOLLS\\nF\\nHILLSBORO\\nOR\\n01:23:30\\n13:28\\n146 of 163\\nF 26-35\\n53 of 59\\n00:00:40\\n01:24:10\\n\\n\\n\\n549\\n58\\nRAMA CHAKRI\\nM\\nHILLSBORO\\nOR\\n01:23:30\\n13:28\\n403 of 414\\nM 26-35\\n151 of 154\\n00:00:10\\n01:23:40\\n\\n\\n\\n550\\n353\\nRAJYALAKSHMI BOMMARAJU\\nF\\nPORTLAND\\nOR\\n01:23:45\\n13:30\\n147 of 163\\nF 36-45\\n50 of 56\\n00:00:37\\n01:24:22\\n\\n\\n\\n551\\n672\\nSANDEEP K DEY\\nM\\nHILLSBORO\\nOR\\n01:23:54\\n13:31\\n404 of 414\\nM 36-45\\n147 of 152\\n00:00:57\\n01:24:51\\n\\n\\n\\n552\\n216\\nLAKSHMI BHUPALAM\\nF\\nPORTLAND\\nOR\\n01:24:09\\n13:34\\n148 of 163\\nF 36-45\\n51 of 56\\n00:00:45\\n01:24:54\\n\\n\\n\\n553\\n634\\nPAVAN KUMAR KOTA\\nM\\nHILLSBORO\\nOR\\n01:24:19\\n13:36\\n405 of 414\\nM 26-35\\n152 of 154\\n00:00:09\\n01:24:28\\n\\n\\n\\n554\\n465\\nNALINI MURARI\\nF\\nHILLSBORO\\nOR\\n01:25:11\\n13:44\\n149 of 163\\nF 26-35\\n54 of 59\\n00:00:39\\n01:25:50\\nINTEL TEAM M\\n\\n\\n555\\n231\\nTANISHA CHETTY\\nF\\nBEAVERTON\\nOR\\n01:25:49\\n13:50\\n150 of 163\\nF Under 18\\n3 of 3\\n00:01:02\\n01:26:51\\n\\n\\n\\n556\\n409\\nJAY CHETTY\\nM\\nBEAVERTON\\nOR\\n01:25:49\\n13:50\\n406 of 414\\n\\n6 of 6\\n00:01:05\\n01:26:54\\n\\n\\n\\n557\\n124\\nAVANTIKA DHARMAGADDA\\nF\\nPORTLAND\\nOR\\n01:26:22\\n13:55\\n151 of 163\\nF 26-35\\n55 of 59\\n00:00:20\\n01:26:42\\n\\n\\n\\n558\\n208\\nSHUNMU PONNA\\nF\\nPORTLAND\\nOR\\n01:27:08\\n14:03\\n152 of 163\\nF 36-45\\n52 of 56\\n00:00:44\\n01:27:52\\n\\n\\n\\n559\\n540\\nRUPAL SHAH\\nF\\nPORTLAND\\nOR\\n01:28:19\\n14:14\\n153 of 163\\nF 36-45\\n53 of 56\\n00:00:30\\n01:28:49\\n\\n\\n\\n560\\n319\\nRAGHA KHANDENAHALLY\\nM\\nHILLSBORO\\nOR\\n01:28:33\\n14:17\\n407 of 414\\nM 26-35\\n153 of 154\\n00:00:26\\n01:28:59\\n\\n\\n\\n561\\n735\\nWENCESLAO DE LA PAZ\\nM\\nHILLSBORO\\nOR\\n01:29:04\\n14:21\\n408 of 414\\nM 36-45\\n148 of 152\\n00:00:55\\n01:29:59\\n\\n\\n\\n562\\n645\\nCHAREL DE LA PAZ\\nF\\nHILLSBORO\\nOR\\n01:29:04\\n14:21\\n154 of 163\\nF 36-45\\n54 of 56\\n00:00:55\\n01:29:59\\n\\n\\n\\n563\\n886\\nGARGI SANT\\nF\\nROCKVILLE\\nMD\\n01:31:20\\n14:43\\n155 of 163\\nF 26-35\\n56 of 59\\n00:00:34\\n01:31:54\\n\\n\\n\\n564\\n1\\nSURESH GAUJULA\\nM\\n\\n\\n01:32:10\\n14:51\\n409 of 414\\nM 36-45\\n149 of 152\\n00:00:32\\n01:32:42\\nCOLUMBIA TEAM A\\n\\n\\n565\\n411\\nABHILASH AKULA\\nM\\nBEAVERTON\\nOR\\n01:32:18\\n14:53\\n410 of 414\\nM 26-35\\n154 of 154\\n00:01:35\\n01:33:53\\n\\n\\n\\n566\\n219\\nBHARAT ADDALA\\nM\\nPORTLAND\\nOR\\n01:32:20\\n14:53\\n411 of 414\\nM 36-45\\n150 of 152\\n00:01:31\\n01:33:51\\n\\n\\n\\n567\\n133\\nLOLA ABDUL-JABBAR\\nF\\nOUT OF STATE\\nOR\\n01:32:28\\n14:54\\n156 of 163\\nF 26-35\\n57 of 59\\n00:00:37\\n01:33:05\\n\\n\\n\\n568\\n51\\nAIMEE TONEY-LOVINGS\\nF\\nBEAVERTON\\nOR\\n01:32:30\\n14:55\\n157 of 163\\nF 46-55\\n21 of 22\\n00:00:31\\n01:33:01\\n\\n\\n\\n569\\n158\\nNARENDER MUDUGANTI\\nM\\nHILLSBORO\\nOR\\n01:33:50\\n15:08\\n412 of 414\\nM 36-45\\n151 of 152\\n00:00:04\\n01:33:54\\n\\n\\n\\n570\\n670\\nHEMA VIJWANI\\nF\\nHILLSBORO\\nOR\\n01:34:45\\n15:17\\n158 of 163\\nF 26-35\\n58 of 59\\n00:00:08\\n01:34:53\\n\\n\\n\\n571\\n357\\nUSHA K KETINENI\\nF\\nPORTLAND\\nOR\\n01:34:48\\n15:17\\n159 of 163\\nF 36-45\\n55 of 56\\n00:00:39\\n01:35:27\\n\\n\\n\\n572\\n293\\nSPASS O STOIANTSCHEWSKY\\nM\\nPORTLAND\\nOR\\n01:37:10\\n15:40\\n413 of 414\\nM 46-55\\n64 of 64\\n00:01:23\\n01:38:33\\n\\n\\n\\n573\\n273\\nRACHEL L VANEY\\nF\\nOTHER\\nOR\\n01:38:17\\n15:51\\n160 of 163\\nF 18-25\\n21 of 21\\n00:00:17\\n01:38:34\\n\\n\\n\\n574\\n467\\nROHIT B DSOUZA\\nM\\nPORTLAND\\nOR\\n01:38:31\\n15:53\\n414 of 414\\nM 36-45\\n152 of 152\\n00:02:01\\n01:40:32\\nINTEL TEAM I\\n\\n\\n575\\n471\\nCENITA D\\'SOUZA\\nF\\nPORTLAND\\nOR\\n01:38:32\\n15:53\\n161 of 163\\nF 46-55\\n22 of 22\\n00:02:02\\n01:40:34\\n\\n\\n\\n576\\n338\\nPRANAVI APPANA\\nF\\nHILLSBORO\\nOR\\n01:40:47\\n16:15\\n162 of 163\\nF 26-35\\n59 of 59\\n00:01:14\\n01:42:01\\n\\n\\n\\n577\\n443\\nLIBBY B MITCHELL\\nF\\nHILLSBORO\\nOR\\n01:41:18\\n16:20\\n163 of 163\\nF 36-45\\n56 of 56\\n00:00:52\\n01:42:10\\n\\n\\n\\n\\n\\nTeam Results\\n\\n\\n\\n\\nAward\\nName\\nCombined Time\\n12341ST\\nDTNA2\\n02:54:41\\n00:40:36 - ANDY WEDAM00:44:05 - VASILIOS TSIRIMIAGOS00:44:48 - ANDRE RAUCH00:45:10 - JONATHAN PARSONS2ND\\nINTEL TEAM K\\n03:01:02\\n00:41:43 - WILLIAM BARRY00:42:36 - EDDIE J FRIE00:43:46 - SAMANTHA BAGLEY00:52:55 - JOHN HAMILTON3RD\\nINTEL TEAM F\\n03:06:06\\n00:36:42 - NATHAN A SUSTERSIC00:39:21 - DEREK G OSBORNE00:52:47 - CODY L COVEY00:57:16 - VIRAJ P MODAK4TH\\nDTNA1\\n03:15:33\\n00:40:28 - WITALI SPULING00:46:45 - INGA ANDREYEVA00:54:09 - KEATON WEISENBORN00:54:10 - MAISIE WEISENBORN5TH\\nFXG1\\n03:21:16\\n00:42:56 - DAVID HERRON00:46:35 - LEO SOTO00:53:59 - TONY GONZALEZ00:57:45 - ARINDA SCHRUM6TH\\nINTEL TEAM B\\n03:26:38\\n00:46:26 - EDMONDO MAZZULLI00:48:44 - JUN TAKEI00:53:49 - NIKHIL TALPALLIKAR00:57:38 - PRANAV NAKATE7TH\\nCOLUMBIA TEAM B\\n03:38:34\\n00:40:17 - BRANDON THOMAS00:47:05 - RANIER EVANS01:04:19 - KENDRA DONLIN01:06:51 - MARIE-PIERRE VIGNE8TH\\nINTEL TEAM D\\n03:41:59\\n00:47:56 - TEAL HAND00:49:03 - BENJAMIN R PORTER00:51:10 - JUSTON LI01:13:49 - DHAVAL V SHAH9TH\\nCOLUMBIA TEAM A\\n03:46:19\\n00:40:21 - ERIK BJORNSTAD00:41:59 - JIM DROZDOWSKI00:51:49 - BHASKAR MANDALA01:32:10 - SURESH GAUJULA10TH\\nINTEL TEAM N\\n04:02:13\\n00:48:39 - ERIN F WIESENAUER01:00:24 - JULIE MAAS01:00:49 - MEETA A BHATE01:12:19 - JANE M JACKSON11TH\\nDTNA3\\n04:05:09\\n00:50:29 - AUSTIN WIPF00:56:06 - YUNFENG PI01:09:14 - ADRIAN ONG01:09:18 - RICHARD LEE12TH\\nINTEL TEAM H\\n04:34:19\\n00:58:51 - BIBICHE M GEUSKENS01:04:35 - PRIYARANGA KOSWATTA01:14:32 - DAVID W MENDIES01:16:19 - RAJESH POORNACHANDRAN13TH\\nINTEL TEAM G\\n04:37:58\\n00:55:50 - ERIC POZZI01:08:07 - JINPYO HONG01:10:50 - HARSH SHAH01:23:10 - GEORGE E MATTHEW14TH\\nINTEL TEAM M\\n04:43:23\\n00:58:59 - DANIELLE CASILLAS01:02:06 - RAMYA MERUVA01:17:06 - PALLAVI J SHINDE01:25:11 - NALINI MURARI\\n\\n\\n\\n\\n\\n\\nHuber Timing\\n\\n\\n\\n\\r\\n        <table class=\\'table table-striped table-bordered table-condensed\\'>\\r\\n            <thead>\\r\\n                <tr class=\\'header\\'>\\r\\n                                <th>Place</th>\\r\\n                                <th>Bib</th>\\r\\n                                <th>Name</th>\\r\\n                                <th>Gender</th>\\r\\n                                <th>City</th>\\r\\n                                <th>State</th>\\r\\n                                <th>Chip Time</th>\\r\\n                                <th>Chip Pace</th>\\r\\n                                <th>Gender Place</th>\\r\\n                                <th>Age Group</th>\\r\\n                                <th>Age Group Place</th>\\r\\n                                <th>Time to Start</th>\\r\\n                                <th>Gun Time</th>\\r\\n                                <th>Team</th>\\r\\n\\r\\n                </tr>\\r\\n            </thead>\\r\\n            <tbody>\\r\\n                <tr class=\\'odd\\'>\\r\\n                                <td>{{_Place}}</td>\\r\\n                                <td>{{_Bib}}</td>\\r\\n                                <td>{{_Name}}</td>\\r\\n                                <td>{{_Gender}}</td>\\r\\n                                <td>{{_City}}</td>\\r\\n                                <td>{{_State}}</td>\\r\\n                                <td>{{_Chip_Time}}</td>\\r\\n                                <td>{{_Chip_Pace}}</td>\\r\\n                                <td>{{_Gender_Place}}</td>\\r\\n                                <td>{{_Age_Group}}</td>\\r\\n                                <td>{{_Age_Group_Place}}</td>\\r\\n                                <td>{{_Time_to_Start}}</td>\\r\\n                                <td>{{_Gun_Time}}</td>\\r\\n                                <td>{{_Team}}</td>\\r\\n\\r\\n                </tr>\\r\\n            </tbody>\\r\\n        </table>\\r\\n\\r\\n\\r\\n        <span id=\"certDisplay\" style=\"display: none\">\\r\\n                            <img style=\"display: block; margin-right: auto; margin-left: auto;\" src=\"http://www.hubertiming.com/finisherCert/cert?name={{_Name}}&Time={{_Chip_Time}}&CertName=2017GPTR10Kv1\" width=\"750\" alt=\"Finisher Certificate\" />\\r\\n\\r\\n            \\r\\n            <br />\\r\\n            <span style=\"display: block; margin-right: auto; margin-left: auto; text-align: center;\">\\r\\n                \\r\\n                <button id=\"printCert\" class=\"btn btn-large btn-primary\">Print Finisher Certificate</button>\\r\\n            </span>\\r\\n        </span>\\r\\n    \\n\\n\\n\\n\\n\\n\\n\\n\\n\\r\\n    \\r\\n\\r\\n\\r\\n    $(document).ready(function () {\\r\\n        var eventName = \\'2017 Intel Great Place to Run 10K \\\\ Urban Clash Games\\';\\r\\n        var certName = \\'2017GPTR10Kv1\\';\\r\\n        var resultsURL = \\'http://www.hubertiming.com/results/2017GPTR10K\\';\\r\\n\\r\\n                 var splitData = {};\\r\\n\\r\\n\\r\\n\\r\\n        var individualResults = $(\\'#individualResults\\').dataTable({\\r\\n                \"bPaginate\": false,\\r\\n            \"sDom\": \"f\",\\r\\n            \\r\\n\\r\\n            \"bLengthChange\": false,\\r\\n                    \"bFilter\": true,\\r\\n\\r\\n            \"bSort\": false,\\r\\n            \"bInfo\": false,\\r\\n            \"bAutoWidth\": false,\\r\\n            \"orderClasses\": false,\\r\\n            \\r\\n        });\\r\\n\\r\\n\\r\\n        $(\"#individualResults tbody tr\").on(\"click\", function (event) {\\r\\n            var bib = individualResults.fnGetData(this)[1];\\r\\n                    splitData[bib] = [];\\r\\n                    splitData[bib]._Place = individualResults.fnGetData(this)[0];\\r\\n                    splitData[bib]._Bib = individualResults.fnGetData(this)[1];\\r\\n                    splitData[bib]._Name = individualResults.fnGetData(this)[2];\\r\\n                    splitData[bib]._Gender = individualResults.fnGetData(this)[3];\\r\\n                    splitData[bib]._City = individualResults.fnGetData(this)[4];\\r\\n                    splitData[bib]._State = individualResults.fnGetData(this)[5];\\r\\n                    splitData[bib]._Chip_Time = individualResults.fnGetData(this)[6];\\r\\n                    splitData[bib]._Chip_Pace = individualResults.fnGetData(this)[7];\\r\\n                    splitData[bib]._Gender_Place = individualResults.fnGetData(this)[8];\\r\\n                    splitData[bib]._Age_Group = individualResults.fnGetData(this)[9];\\r\\n                    splitData[bib]._Age_Group_Place = individualResults.fnGetData(this)[10];\\r\\n                    splitData[bib]._Time_to_Start = individualResults.fnGetData(this)[11];\\r\\n                    splitData[bib]._Gun_Time = individualResults.fnGetData(this)[12];\\r\\n                    splitData[bib]._Team = individualResults.fnGetData(this)[13];\\r\\n\\r\\n\\r\\n            addTab(bib);\\r\\n        });\\r\\n\\r\\n\\r\\n        var tabTemplate = \"<li><a style=\\'font-size: 18px\\' href=\\'#{href}\\'>#{label}</a> <span class=\\'ui-icon ui-icon-close\\' role=\\'presentation\\'>Remove Tab</span></li>\";\\r\\n        var tabCounter = 2;\\r\\n\\r\\n        var tabs = $(\"#tabs\").tabs({\\r\\n            cache:true,\\r\\n            load: function (e, ui) {\\r\\n                $(ui.panel).find(\".tab-loading\").remove();\\r\\n            },\\r\\n            beforeLoad: function (e, ui) {\\r\\n                var $panel = $(ui.panel);\\r\\n\\r\\n                if ($panel.is(\":empty\")) {\\r\\n                    $panel.append(\"<div class=\\'tab-loading\\'>Loading...</div>\")\\r\\n                }\\r\\n            }\\r\\n        });\\r\\n\\r\\n        // close icon: removing the tab on click\\r\\n        tabs.delegate(\"span.ui-icon-close\", \"click\", function () {\\r\\n            var panelId = $(this).closest(\"li\").remove().attr(\"aria-controls\");\\r\\n            $(\"#\" + panelId).remove();\\r\\n            tabBeingAdded--;\\r\\n            tabs.tabs(\"refresh\");\\r\\n        });\\r\\n\\r\\n\\r\\n\\r\\n        var tabBeingAdded = 1;\\r\\n        function addTab(bib) {\\r\\n\\r\\n\\r\\n            var data = splitData[bib];\\r\\n\\r\\n                        var certURL = \\'http://www.hubertiming.com/finisherCert/cert?name=\\' + data._Name + \\'&Time=\\' + data._Chip_Time + \\'&CertName=\\' + certName;\\r\\n\\r\\n\\r\\n            var label = data._Name,\\r\\n            id = \"tabs-\" + tabCounter,\\r\\n\\r\\n                    li = $(tabTemplate.replace(/#\\\\{href\\\\}/g, \"#\" + id).replace(/#\\\\{label\\\\}/g, label));\\r\\n            tabs.find(\".ui-tabs-nav\").append(li);\\r\\n            tabs.append(\"<div id=\\'\" + id + \"\\'>\" + Mustache.to_html($(\\'#tabTemplate\\').html(), data) + \"</div>\");\\r\\n            tabs.tabs(\"refresh\");\\r\\n            tabs.tabs({ active: tabBeingAdded });\\r\\n\\r\\n\\r\\n            tabBeingAdded++;\\r\\n            tabCounter++;\\r\\n\\r\\n            // Hide the certificate if the participant hasn\\'t finished\\r\\n            if (data._Chip_Time != \"\")\\r\\n                $(\\'#\\' + id + \\' #certDisplay\\').show();\\r\\n            else\\r\\n                $(\\'#\\' + id + \\' #certDisplay\\').hide();\\r\\n\\r\\n            $(\\'#\\' + id + \\' #printCert\\').on(\"click\", function (event) {\\r\\n                var myWindow = window.open(\"\", \"CertWindow\", \"width=1000,height=800\");\\r\\n                myWindow.document.write(\\'<html><style type=\"text/css\">@media print{ @page {size: landscape;} .noprint {display: none !important;} }</style><body><button style=\"display: block; margin-right: auto; margin-left: auto;\" class=\"noprint\" onClick=\"window.print();\">Print</button><img style=\"margin-right: auto; margin-left: auto;\" src=\"\\' + certURL + \\'\"></body></html>\\');\\r\\n            });\\r\\n\\r\\n            function removeLeadingZerosFromTime(time) {\\r\\n                if (time.indexOf(\"00:\") == 0)\\r\\n                    return time.substring(3);\\r\\n                else\\r\\n                    return time;\\r\\n            }\\r\\n\\r\\n            /*\\r\\n                FB.ui({\\r\\n        method: \\'feed\\',\\r\\n        name: \\'\\',\\r\\n        link: window.location.href,\\r\\n        caption: \\'Check out my results\\',\\r\\n        picture: imgUrl,\\r\\n        description: \\'\\'\\r\\n    }, function (response) {\\r\\n        if (response && response.post_id) { }\\r\\n        else { }\\r\\n    });\\r\\n        */\\r\\n\\r\\n            // Overall place: \\'+data.Place+\\'. Gender place: \\'+data.Gender_Place+\\'. Age Group Place: \\'+data.Age_Group_Place\\r\\n            $(\\'#\\' + id + \\' #shareCert\\').on(\"click\", function (event) {\\r\\n                data._Chip_Time = removeLeadingZerosFromTime(data._Chip_Time);\\r\\n                data._Chip_Pace = removeLeadingZerosFromTime(data._Chip_Pace);\\r\\n\\r\\n                FB.ui({\\r\\n                    method: \\'feed\\',\\r\\n                    name: eventName,\\r\\n                    caption: data._Name + \\' finished the \\' + eventName + \\' in \\' + data._Chip_Time + \\' with a pace of \\' + data._Chip_Pace + \\' per mile.\\',\\r\\n                    description: data._Name + \\' finished the \\' + eventName + \\' in \\' + data._Chip_Time + \\' with a pace of \\' + data._Chip_Pace + \\' per mile.\\',\\r\\n                    link: resultsURL,\\r\\n                    picture: certURL,\\r\\n                    display: \\'popup\\'\\r\\n                },\\r\\n                function (response) { });\\r\\n            });\\r\\n        }\\r\\n    });\\r\\n\\r\\n    var _gaq = _gaq || [];\\r\\n    _gaq.push([\\'_setAccount\\', \\'UA-38679873-1\\']);\\r\\n    _gaq.push([\\'_trackPageview\\']);\\r\\n    (function () {\\r\\n        var ga = document.createElement(\\'script\\'); ga.type = \\'text/javascript\\'; ga.async = true;\\r\\n        ga.src = (\\'https:\\' == document.location.protocol ? \\'https://ssl\\' : \\'http://www\\') + \\'.google-analytics.com/ga.js\\';\\r\\n        var s = document.getElementsByTagName(\\'script\\')[0]; s.parentNode.insertBefore(ga, s);\\r\\n    })();\\r\\n\\n\\n\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the text\n",
    "soup.get_text()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a class=\"btn btn-primary btn-lg\" href=\"/results/2017GPTR\" role=\"button\" style=\"margin: 0px 0px 5px 5px\">5K</a>,\n",
       " <a href=\"http://hubertiming.com/\">Huber Timing Home</a>,\n",
       " <a href=\"#individual\">Individual Results</a>,\n",
       " <a href=\"#team\">Team Results</a>,\n",
       " <a href=\"mailto:timing@hubertiming.com\">timing@hubertiming.com</a>,\n",
       " <a href=\"#tabs-1\" style=\"font-size: 18px\">Results</a>,\n",
       " <a name=\"individual\"></a>,\n",
       " <a name=\"team\"></a>,\n",
       " <a href=\"http://www.hubertiming.com/\"><img height=\"65\" src=\"/sites/all/themes/hubertiming/images/clockWithFinishSign_small.png\" width=\"50\"/>Huber Timing</a>,\n",
       " <a href=\"http://facebook.com/hubertiming/\"><img src=\"/results/FB-f-Logo__blue_50.png\"/></a>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find hyperlinks\n",
    "soup.find_all('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/results/2017GPTR\n",
      "http://hubertiming.com/\n",
      "#individual\n",
      "#team\n",
      "mailto:timing@hubertiming.com\n",
      "#tabs-1\n",
      "None\n",
      "None\n",
      "http://www.hubertiming.com/\n",
      "http://facebook.com/hubertiming/\n"
     ]
    }
   ],
   "source": [
    "links = soup.find_all('a')\n",
    "for link in links:\n",
    "    print(link.get('href'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = soup.find_all('tr')\n",
    "#rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_rows = []\n",
    "for row in rows:\n",
    "    row_td = row.find_all('td')\n",
    "    str_cells = str(row_td)\n",
    "    cleantext = BeautifulSoup(str_cells, 'lxml').get_text()\n",
    "    list_rows.append(cleantext.split(', '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0     1             2     3       4     5         6      7   \\\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",
       "4           [1   814  JARED WILSON     M  TIGARD    OR  00:36:21  05:51   \n",
       "\n",
       "         8        9         10        11        12    13  \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  1 of 414  M 36-45  1 of 152  00:00:03  00:36:24     ]  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(list_rows)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<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>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_labels = soup.find_all('th')\n",
    "col_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[Place',\n",
       " 'Bib',\n",
       " 'Name',\n",
       " 'Gender',\n",
       " 'City',\n",
       " 'State',\n",
       " 'Chip Time',\n",
       " 'Chip Pace',\n",
       " 'Gender Place',\n",
       " 'Age Group',\n",
       " 'Age Group Place',\n",
       " 'Time to Start',\n",
       " 'Gun Time',\n",
       " 'Team]']"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_labels = BeautifulSoup(str(col_labels), 'lxml').get_text().split(', ')\n",
    "col_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>[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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        [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",
       "4           [1   814  JARED WILSON      M  TIGARD    OR  00:36:21     05:51   \n",
       "\n",
       "  Gender Place Age Group Age Group Place Time to Start  Gun Time Team]  \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     1 of 414   M 36-45        1 of 152      00:00:03  00:36:24     ]  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = col_labels\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 596 entries, 0 to 595\n",
      "Data columns (total 14 columns):\n",
      "[Place             596 non-null object\n",
      "Bib                595 non-null object\n",
      "Name               592 non-null object\n",
      "Gender             592 non-null object\n",
      "City               592 non-null object\n",
      "State              592 non-null object\n",
      "Chip Time          592 non-null object\n",
      "Chip Pace          577 non-null object\n",
      "Gender Place       577 non-null object\n",
      "Age Group          577 non-null object\n",
      "Age Group Place    577 non-null object\n",
      "Time to Start      577 non-null object\n",
      "Gun Time           577 non-null object\n",
      "Team]              577 non-null object\n",
      "dtypes: object(14)\n",
      "memory usage: 65.3+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(596, 14)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df.info())\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>00:36:45</td>\n",
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       "      <th>6</th>\n",
       "      <td>[3</td>\n",
       "      <td>687</td>\n",
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       "      <td>M</td>\n",
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       "      <th>7</th>\n",
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       "      <td>]</td>\n",
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       "      <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 Chip Pace  \\\n",
       "4     [1  814        JARED WILSON      M     TIGARD    OR  00:36:21     05:51   \n",
       "5     [2  573  NATHAN A SUSTERSIC      M   PORTLAND    OR  00:36:42     05:55   \n",
       "6     [3  687      FRANCISCO MAYA      M   PORTLAND    OR  00:37:44     06:05   \n",
       "7     [4  623         PAUL MORROW      M  BEAVERTON    OR  00:38:34     06:13   \n",
       "8     [5  569     DEREK G OSBORNE      M  HILLSBORO    OR  00:39:21     06:20   \n",
       "\n",
       "  Gender Place Age Group Age Group Place Time to Start  Gun Time  \\\n",
       "4     1 of 414   M 36-45        1 of 152      00:00:03  00:36:24   \n",
       "5     2 of 414   M 26-35        1 of 154      00:00:03  00:36:45   \n",
       "6     3 of 414   M 46-55         1 of 64      00:00:04  00:37:48   \n",
       "7     4 of 414   M 36-45        2 of 152      00:00:03  00:38:37   \n",
       "8     5 of 414   M 26-35        2 of 154      00:00:03  00:39:24   \n",
       "\n",
       "           Team]  \n",
       "4              ]  \n",
       "5  INTEL TEAM F]  \n",
       "6              ]  \n",
       "7              ]  \n",
       "8  INTEL TEAM F]  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.dropna(axis=0, how='any')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['[Place', 'Bib', 'Name', 'Gender', 'City', 'State', 'Chip Time',\n",
       "       'Chip Pace', 'Gender Place', 'Age Group', 'Age Group Place',\n",
       "       'Time to Start', 'Gun Time', 'Team]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Noah\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3781: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  return super(DataFrame, self).rename(**kwargs)\n"
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       "      <td>INTEL TEAM F]</td>\n",
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       "      <td>M</td>\n",
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       "      <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",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Place  Bib                Name Gender       City State Chip Time Chip Pace  \\\n",
       "4    [1  814        JARED WILSON      M     TIGARD    OR  00:36:21     05:51   \n",
       "5    [2  573  NATHAN A SUSTERSIC      M   PORTLAND    OR  00:36:42     05:55   \n",
       "6    [3  687      FRANCISCO MAYA      M   PORTLAND    OR  00:37:44     06:05   \n",
       "7    [4  623         PAUL MORROW      M  BEAVERTON    OR  00:38:34     06:13   \n",
       "8    [5  569     DEREK G OSBORNE      M  HILLSBORO    OR  00:39:21     06:20   \n",
       "\n",
       "  Gender Place Age Group Age Group Place Time to Start  Gun Time  \\\n",
       "4     1 of 414   M 36-45        1 of 152      00:00:03  00:36:24   \n",
       "5     2 of 414   M 26-35        1 of 154      00:00:03  00:36:45   \n",
       "6     3 of 414   M 46-55         1 of 64      00:00:04  00:37:48   \n",
       "7     4 of 414   M 36-45        2 of 152      00:00:03  00:38:37   \n",
       "8     5 of 414   M 26-35        2 of 154      00:00:03  00:39:24   \n",
       "\n",
       "            Team  \n",
       "4              ]  \n",
       "5  INTEL TEAM F]  \n",
       "6              ]  \n",
       "7              ]  \n",
       "8  INTEL TEAM F]  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.rename(columns={'[Place':'Place'}, inplace=True)\n",
    "df2.rename(columns={'Team]':'Team'}, inplace=True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Noah\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Noah\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4405: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
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       "      <td>06:20</td>\n",
       "      <td>5 of 414</td>\n",
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       "      <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",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Place  Bib                Name Gender       City State Chip Time Chip Pace  \\\n",
       "0     1  814        JARED WILSON      M     TIGARD    OR  00:36:21     05:51   \n",
       "1     2  573  NATHAN A SUSTERSIC      M   PORTLAND    OR  00:36:42     05:55   \n",
       "2     3  687      FRANCISCO MAYA      M   PORTLAND    OR  00:37:44     06:05   \n",
       "3     4  623         PAUL MORROW      M  BEAVERTON    OR  00:38:34     06:13   \n",
       "4     5  569     DEREK G OSBORNE      M  HILLSBORO    OR  00:39:21     06:20   \n",
       "\n",
       "  Gender Place Age Group Age Group Place Time to Start  Gun Time          Team  \n",
       "0     1 of 414   M 36-45        1 of 152      00:00:03  00:36:24                \n",
       "1     2 of 414   M 26-35        1 of 154      00:00:03  00:36:45  INTEL TEAM F  \n",
       "2     3 of 414   M 46-55         1 of 64      00:00:04  00:37:48                \n",
       "3     4 of 414   M 36-45        2 of 152      00:00:03  00:38:37                \n",
       "4     5 of 414   M 26-35        2 of 154      00:00:03  00:39:24  INTEL TEAM F  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Place']  = df2['Place'].str.strip('[')\n",
    "df2.Team = df2.Team.str.strip(']')\n",
    "df2 = df2.reset_index(drop=True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Chip Time'] = pd.to_datetime(df2['Chip Time'], format='%H:%M:%S').dt.time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_list = df2['Chip Time'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " datetime.time(1, 12, 2),\n",
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       " datetime.time(1, 12, 14),\n",
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       " datetime.time(1, 13, 49),\n",
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       " datetime.time(1, 16),\n",
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       " datetime.time(1, 16, 25),\n",
       " datetime.time(1, 16, 46),\n",
       " datetime.time(1, 16, 48),\n",
       " datetime.time(1, 16, 52),\n",
       " datetime.time(1, 17, 6),\n",
       " datetime.time(1, 17, 14),\n",
       " datetime.time(1, 18, 8),\n",
       " datetime.time(1, 18, 9),\n",
       " datetime.time(1, 18, 56),\n",
       " datetime.time(1, 20, 46),\n",
       " datetime.time(1, 23, 8),\n",
       " datetime.time(1, 23, 9),\n",
       " datetime.time(1, 23, 10),\n",
       " datetime.time(1, 23, 12),\n",
       " datetime.time(1, 23, 30),\n",
       " datetime.time(1, 23, 30),\n",
       " datetime.time(1, 23, 45),\n",
       " datetime.time(1, 23, 54),\n",
       " datetime.time(1, 24, 9),\n",
       " datetime.time(1, 24, 19),\n",
       " datetime.time(1, 25, 11),\n",
       " datetime.time(1, 25, 49),\n",
       " datetime.time(1, 25, 49),\n",
       " datetime.time(1, 26, 22),\n",
       " datetime.time(1, 27, 8),\n",
       " datetime.time(1, 28, 19),\n",
       " datetime.time(1, 28, 33),\n",
       " datetime.time(1, 29, 4),\n",
       " datetime.time(1, 29, 4),\n",
       " datetime.time(1, 31, 20),\n",
       " datetime.time(1, 32, 10),\n",
       " datetime.time(1, 32, 18),\n",
       " datetime.time(1, 32, 20),\n",
       " datetime.time(1, 32, 28),\n",
       " datetime.time(1, 32, 30),\n",
       " datetime.time(1, 33, 50),\n",
       " datetime.time(1, 34, 45),\n",
       " datetime.time(1, 34, 48),\n",
       " datetime.time(1, 37, 10),\n",
       " datetime.time(1, 38, 17),\n",
       " datetime.time(1, 38, 31),\n",
       " datetime.time(1, 38, 32),\n",
       " datetime.time(1, 40, 47),\n",
       " datetime.time(1, 41, 18)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_min = []\n",
    "for i in time_list:\n",
    "    time_min.append(i.hour*60+i.minute+i.second/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       " 74.53333333333333,\n",
       " 74.66666666666667,\n",
       " 74.9,\n",
       " 75.21666666666667,\n",
       " 75.56666666666666,\n",
       " 75.63333333333334,\n",
       " 75.8,\n",
       " 75.85,\n",
       " 75.96666666666667,\n",
       " 76.0,\n",
       " 76.31666666666666,\n",
       " 76.41666666666667,\n",
       " 76.76666666666667,\n",
       " 76.8,\n",
       " 76.86666666666666,\n",
       " 77.1,\n",
       " 77.23333333333333,\n",
       " 78.13333333333334,\n",
       " 78.15,\n",
       " 78.93333333333334,\n",
       " 80.76666666666667,\n",
       " 83.13333333333334,\n",
       " 83.15,\n",
       " 83.16666666666667,\n",
       " 83.2,\n",
       " 83.5,\n",
       " 83.5,\n",
       " 83.75,\n",
       " 83.9,\n",
       " 84.15,\n",
       " 84.31666666666666,\n",
       " 85.18333333333334,\n",
       " 85.81666666666666,\n",
       " 85.81666666666666,\n",
       " 86.36666666666666,\n",
       " 87.13333333333334,\n",
       " 88.31666666666666,\n",
       " 88.55,\n",
       " 89.06666666666666,\n",
       " 89.06666666666666,\n",
       " 91.33333333333333,\n",
       " 92.16666666666667,\n",
       " 92.3,\n",
       " 92.33333333333333,\n",
       " 92.46666666666667,\n",
       " 92.5,\n",
       " 93.83333333333333,\n",
       " 94.75,\n",
       " 94.8,\n",
       " 97.16666666666667,\n",
       " 98.28333333333333,\n",
       " 98.51666666666667,\n",
       " 98.53333333333333,\n",
       " 100.78333333333333,\n",
       " 101.3]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>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",
       "      <th>mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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",
       "      <td>36.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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",
       "      <td>36.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>3</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>4</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 Chip Pace  \\\n",
       "0     1  814        JARED WILSON      M     TIGARD    OR  00:36:21     05:51   \n",
       "1     2  573  NATHAN A SUSTERSIC      M   PORTLAND    OR  00:36:42     05:55   \n",
       "2     3  687      FRANCISCO MAYA      M   PORTLAND    OR  00:37:44     06:05   \n",
       "3     4  623         PAUL MORROW      M  BEAVERTON    OR  00:38:34     06:13   \n",
       "4     5  569     DEREK G OSBORNE      M  HILLSBORO    OR  00:39:21     06:20   \n",
       "\n",
       "  Gender Place Age Group Age Group Place Time to Start  Gun Time  \\\n",
       "0     1 of 414   M 36-45        1 of 152      00:00:03  00:36:24   \n",
       "1     2 of 414   M 26-35        1 of 154      00:00:03  00:36:45   \n",
       "2     3 of 414   M 46-55         1 of 64      00:00:04  00:37:48   \n",
       "3     4 of 414   M 36-45        2 of 152      00:00:03  00:38:37   \n",
       "4     5 of 414   M 26-35        2 of 154      00:00:03  00:39:24   \n",
       "\n",
       "           Team       mins  \n",
       "0                36.350000  \n",
       "1  INTEL TEAM F  36.700000  \n",
       "2                37.733333  \n",
       "3                38.566667  \n",
       "4  INTEL TEAM F  39.350000  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['mins'] = time_min\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>mins</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": [
       "             mins\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": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate statistics for numeric columns only\n",
    "df2.describe(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15,5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1e526a730f0>],\n",
       " <a list of 1 Text xticklabel objects>)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.boxplot(column='mins')\n",
    "plt.grid(True, axis ='y')\n",
    "plt.ylabel('Chip Time')\n",
    "plt.xticks([1], ['Runners'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df2['mins']\n",
    "ax=sns.distplot(x, hist =True, kde=True, rug=False, color='m', bins=50, hist_kws={'edgecolor':'black'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_fuko = df2.query('Gender ==\"F\"').mins\n",
    "m_fuko = df2.query('Gender == \"M\"').mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(f_fuko, hist=True, kde=True, rug=False, hist_kws={'edgecolor': 'black'}, label='Female')\n",
    "sns.distplot(m_fuko, hist=True, kde=True, rug=False, hist_kws={'edgecolor': 'black'}, label='Male')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">mins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>163.0</td>\n",
       "      <td>66.119223</td>\n",
       "      <td>12.184440</td>\n",
       "      <td>43.766667</td>\n",
       "      <td>58.758333</td>\n",
       "      <td>64.616667</td>\n",
       "      <td>72.058333</td>\n",
       "      <td>101.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>414.0</td>\n",
       "      <td>57.640821</td>\n",
       "      <td>11.011857</td>\n",
       "      <td>36.350000</td>\n",
       "      <td>49.395833</td>\n",
       "      <td>55.791667</td>\n",
       "      <td>64.804167</td>\n",
       "      <td>98.516667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mins                                                         \\\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  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_stats = df2.groupby('Gender', as_index=True).describe()\n",
    "g_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.boxplot(column='mins', by='Gender')\n",
    "plt.ylabel('Chip Time')\n",
    "plt.suptitle(\"\");"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}