kokes/koubsky.ipynb

## koubsky.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Koubského linka"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<blockquote class=\"twitter-tweet\" lang=\"en\"><p lang=\"sk\" dir=\"ltr\">Přemýšlím o definici nejlepšího věku: když a dokud umíte uběhnout 10 km za tolik minut, kolik je vám let.</p>&mdash; Petr Koubský (@petrkou) <a href=\"https://twitter.com/petrkou/status/647406779983360000\">September 25, 2015</a></blockquote> <script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ale to by snad šlo i nějak dopočítat. Pohled do statistik nachází [pražskej Night Run](http://www.night-run.cz/vysledky/2015/s-night-run-10km/z-praha). Pražská Grand Prix má víc dat, ale nemaj roky narození.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pyquery import PyQuery as pq\n",
    "import re\n",
    "from datetime import datetime\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 10\n",
    "rcParams['font.size'] = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rurl = 'http://www.night-run.cz/vysledky/2015/s-night-run-10km/z-praha'\n",
    "p = pq(rurl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res = zip(p.find('table.res-zavod td.c-jm'), p.find('table.res-zavod td.c-cas'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Máme jen rok narození, tak čistě frekvenčně losujem číslo a podle počtu dnů v roce určujeme, jestli už měl danej holomek letos narozeniny."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def parsuj(jm, cas):\n",
    "    rok = int(re.search('([0-9]{4})\\)', pq(jm).text()).group(1))\n",
    "    \n",
    "    trs = datetime(2015, 9, 19).timetuple().tm_yday / 365.0\n",
    "    vek = (2014-rok) + (np.random.rand() < trs)*1\n",
    "\n",
    "    hr, mn, sec = map(float, pq(cas).text().split(':'))\n",
    "    minut = 60*hr + mn + sec/60\n",
    "    \n",
    "    return (vek, minut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vek</th>\n",
       "      <th>cas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>34.547450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>36.351583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>36.885933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>37.090217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>37.407417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vek        cas\n",
       "0   29  34.547450\n",
       "1   26  36.351583\n",
       "2   29  36.885933\n",
       "3   27  37.090217\n",
       "4   26  37.407417"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(map(lambda x: parsuj(x[0], x[1]), res), columns=['vek', 'cas'])\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Šťastnejch bylo 24 z 973.\n"
     ]
    }
   ],
   "source": [
    "print 'Šťastnejch bylo %s z %s.' % ((df['cas'] < df['vek']).value_counts()[True], len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prvních pět\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vek</th>\n",
       "      <th>cas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>46</td>\n",
       "      <td>38.852900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>49</td>\n",
       "      <td>38.970067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44</td>\n",
       "      <td>39.261900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>45</td>\n",
       "      <td>40.214817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>54</td>\n",
       "      <td>40.716900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    vek        cas\n",
       "9    46  38.852900\n",
       "10   49  38.970067\n",
       "11   44  39.261900\n",
       "18   45  40.214817\n",
       "22   54  40.716900"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'Prvních pět'\n",
    "(df[df['cas'] < df ['vek']]).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10579e2d0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10579e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='vek', y='cas', title='Vek versus doba ubehnuti 10 km')\n",
    "plt.plot([30, 70], [30, 70], 'k-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stěžujte si [na twitteru](http://twitter.com/kondrej)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}