StumpJump.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import ggplot as gg\n",
    "import ultrasignup as us\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = us.event_results(299)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>agegroup</th>\n",
       "      <th>bib</th>\n",
       "      <th>city</th>\n",
       "      <th>firstname</th>\n",
       "      <th>formattime</th>\n",
       "      <th>gender</th>\n",
       "      <th>gender_place</th>\n",
       "      <th>lastname</th>\n",
       "      <th>participant_id</th>\n",
       "      <th>place</th>\n",
       "      <th>runner_rank</th>\n",
       "      <th>state</th>\n",
       "      <th>status</th>\n",
       "      <th>time</th>\n",
       "      <th>time_hour</th>\n",
       "      <th>year</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 26</td>\n",
       "      <td> 20-29</td>\n",
       "      <td> </td>\n",
       "      <td> Chattanooga</td>\n",
       "      <td>  Daniel</td>\n",
       "      <td> 4:25:28</td>\n",
       "      <td> M</td>\n",
       "      <td> 1</td>\n",
       "      <td> Hamilton</td>\n",
       "      <td> 274774</td>\n",
       "      <td> 1</td>\n",
       "      <td> 93.18</td>\n",
       "      <td> TN</td>\n",
       "      <td> 1</td>\n",
       "      <td> 15928</td>\n",
       "      <td> 4.424444</td>\n",
       "      <td> 2014</td>\n",
       "      <td> 50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 27</td>\n",
       "      <td> 20-29</td>\n",
       "      <td> </td>\n",
       "      <td> Clarksville</td>\n",
       "      <td>    Kyle</td>\n",
       "      <td> 4:29:22</td>\n",
       "      <td> M</td>\n",
       "      <td> 2</td>\n",
       "      <td>   Curtin</td>\n",
       "      <td> 512120</td>\n",
       "      <td> 2</td>\n",
       "      <td> 90.56</td>\n",
       "      <td> TN</td>\n",
       "      <td> 1</td>\n",
       "      <td> 16162</td>\n",
       "      <td> 4.489444</td>\n",
       "      <td> 2014</td>\n",
       "      <td> 50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 33</td>\n",
       "      <td> 30-39</td>\n",
       "      <td> </td>\n",
       "      <td>       Ogden</td>\n",
       "      <td>  Joshua</td>\n",
       "      <td> 4:32:01</td>\n",
       "      <td> M</td>\n",
       "      <td> 3</td>\n",
       "      <td>     Korn</td>\n",
       "      <td> 418055</td>\n",
       "      <td> 3</td>\n",
       "      <td> 87.56</td>\n",
       "      <td> UT</td>\n",
       "      <td> 1</td>\n",
       "      <td> 16321</td>\n",
       "      <td> 4.533611</td>\n",
       "      <td> 2014</td>\n",
       "      <td> 50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 20</td>\n",
       "      <td> 20-29</td>\n",
       "      <td> </td>\n",
       "      <td>     Roswell</td>\n",
       "      <td>    Alec</td>\n",
       "      <td> 4:39:55</td>\n",
       "      <td> M</td>\n",
       "      <td> 4</td>\n",
       "      <td>   Blenis</td>\n",
       "      <td> 380104</td>\n",
       "      <td> 4</td>\n",
       "      <td> 89.18</td>\n",
       "      <td> GA</td>\n",
       "      <td> 1</td>\n",
       "      <td> 16795</td>\n",
       "      <td> 4.665278</td>\n",
       "      <td> 2014</td>\n",
       "      <td> 50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 28</td>\n",
       "      <td> 20-29</td>\n",
       "      <td> </td>\n",
       "      <td>      Auburn</td>\n",
       "      <td> William</td>\n",
       "      <td> 4:41:59</td>\n",
       "      <td> M</td>\n",
       "      <td> 5</td>\n",
       "      <td>   Ansick</td>\n",
       "      <td>   1561</td>\n",
       "      <td> 5</td>\n",
       "      <td> 80.48</td>\n",
       "      <td> AL</td>\n",
       "      <td> 1</td>\n",
       "      <td> 16919</td>\n",
       "      <td> 4.699722</td>\n",
       "      <td> 2014</td>\n",
       "      <td> 50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age agegroup bib         city firstname formattime gender  gender_place  \\\n",
       "0   26    20-29      Chattanooga    Daniel    4:25:28      M             1   \n",
       "1   27    20-29      Clarksville      Kyle    4:29:22      M             2   \n",
       "2   33    30-39            Ogden    Joshua    4:32:01      M             3   \n",
       "3   20    20-29          Roswell      Alec    4:39:55      M             4   \n",
       "4   28    20-29           Auburn   William    4:41:59      M             5   \n",
       "\n",
       "   lastname  participant_id  place  runner_rank state  status   time  \\\n",
       "0  Hamilton          274774      1        93.18    TN       1  15928   \n",
       "1    Curtin          512120      2        90.56    TN       1  16162   \n",
       "2      Korn          418055      3        87.56    UT       1  16321   \n",
       "3    Blenis          380104      4        89.18    GA       1  16795   \n",
       "4    Ansick            1561      5        80.48    AL       1  16919   \n",
       "\n",
       "   time_hour  year distance  \n",
       "0   4.424444  2014      50K  \n",
       "1   4.489444  2014      50K  \n",
       "2   4.533611  2014      50K  \n",
       "3   4.665278  2014      50K  \n",
       "4   4.699722  2014      50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeffrey/anaconda3/lib/python3.4/site-packages/ggplot/ggplot.py:200: RuntimeWarning: Facetting is currently not supported with geom_bar. See\n",
      "                    https://github.com/yhat/ggplot/issues/196 for more information\n",
      "  warnings.warn(msg, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
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N05w5c5wuBwC6LY4MA8Ax4Pnnn1dKSopOPfVU7d69W3fddZdSUlIcGzICAKYgDAPAMaCu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\nyjAAAACMRRkGAACAsf4PEvmhv1DLzgoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96a3db0b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (-9223363269044096922)>\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "p1 = gg.ggplot(\n",
    "  gg.aes(x='time_hour',fill='gender'),d[(d.distance=='50K')&(d.time_hour>1.0)]) + \\\n",
    "  gg.facet_grid(x='gender') + \\\n",
    "  gg.geom_bar(stat=\"bin\",binwidth=.5,position=\"dodge\",colour=\"black\") + \\\n",
    "  gg.xlab(\"Time (hours)\") + gg.ylab(\"Number of Finishers\") + \\\n",
    "  gg.ggtitle(\"50K Finishing Times for All Years\")\n",
    "print(p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeffrey/anaconda3/lib/python3.4/site-packages/ggplot/ggplot.py:200: RuntimeWarning: Facetting is currently not supported with geom_bar. See\n",
      "                    https://github.com/yhat/ggplot/issues/196 for more information\n",
      "  warnings.warn(msg, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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jEYYBAABgLMIwAAAAjEUYBgAAgLEIwwAAADAWYRgAAADGIgwDAADAWIRhAAAAGIswDAAA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\npyhceqEPil8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96a3c71f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (-9223363269014736103)>\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "p2 = gg.ggplot(\n",
    "  gg.aes(x='time_hour',fill='gender'),d[(d.distance=='11 Miler')&(d.time_hour>1.0)]) + \\\n",
    "  gg.facet_grid(x='gender') + \\\n",
    "  gg.geom_bar(stat=\"bin\",binwidth=.5,position=\"dodge\",colour=\"black\") + \\\n",
    "  gg.xlab(\"Time (hours)\") + gg.ylab(\"Number of Finishers\") + \\\n",
    "  gg.ggtitle(\"11M Finishing Times for All Years\")\n",
    "print(p2)"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}