{
 "metadata": {
  "name": "",
  "signature": "sha256:0350ecdf81327697c728d70794ee100561e7ed49a11bc91b283bce648c6d7052"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from goodies import as_table\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sqlite3\n",
      "\n",
      "sql_query = \"\"\"\n",
      "SELECT country1, teams.title as country2, winner  \n",
      "FROM ( SELECT teams.title as country1, team2_id, winner \n",
      "       FROM (SELECT team1_id, team2_id, winner \n",
      "             FROM games \n",
      "             INNER JOIN rounds on games.round_id=rounds.id \n",
      "             WHERE rounds.title='Final') t1 \n",
      "       INNER JOIN teams on teams.id=t1.team1_id) t2 \n",
      "INNER JOIN teams on teams.id=t2.team2_id;\n",
      "\"\"\"\n",
      "\n",
      "db = sqlite3.connect('../data/worldcup.db')\n",
      "cur = db.execute(sql_query)\n",
      "\n",
      "arr = np.fromiter(cur, dtype=[('country1', 'S30'),\n",
      "                              ('country2', 'S30'),\n",
      "                              ('winner', 'i2')])\n",
      "\n",
      "arr['country1'] = np.char.replace(arr['country1'], 'West Germany (-1989)', 'Germany')\n",
      "arr['country2'] = np.char.replace(arr['country2'], 'West Germany (-1989)', 'Germany')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "as_table(arr, maxrows=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>country1</th><th>country2</th><th>winner</th></tr><tr><td>Uruguay</td><td>Argentina</td><td>1</td></tr><tr><td>Italy</td><td>Czechoslovakia (-1992)</td><td>1</td></tr><tr><td>Hungary</td><td>Italy</td><td>2</td></tr><tr><td>Hungary</td><td>Germany</td><td>2</td></tr><tr><td>Sweden</td><td>Brazil</td><td>2</td></tr><tr><td>Brazil</td><td>Czechoslovakia (-1992)</td><td>1</td></tr><tr><td>England</td><td>Germany</td><td>1</td></tr><tr><td>Brazil</td><td>Italy</td><td>1</td></tr><tr><td>Netherlands</td><td>Germany</td><td>2</td></tr><tr><td>Netherlands</td><td>Argentina</td><td>2</td></tr><tr><td>Italy</td><td>Germany</td><td>1</td></tr><tr><td>Argentina</td><td>Germany</td><td>1</td></tr><tr><td>Germany</td><td>Argentina</td><td>1</td></tr><tr><td>Brazil</td><td>Italy</td><td>1</td></tr><tr><td>Brazil</td><td>France</td><td>2</td></tr><tr><td>Germany</td><td>Brazil</td><td>2</td></tr><tr><td>Italy</td><td>France</td><td>1</td></tr><tr><td>Netherlands</td><td>Spain</td><td>2</td></tr><tr><td>Germany</td><td>Argentina</td><td>1</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.HTML at 0x10cfabc90>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "countries = np.unique(np.concatenate([arr['country1'], arr['country2']]))\n",
      "winners = np.where(arr['winner']==1, arr['country1'], arr['country2'])\n",
      "loosers = np.where(arr['winner']==2, arr['country1'], arr['country2'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wins = [np.sum(winners==c) for c in countries]\n",
      "losses = [np.sum(loosers==c) for c in countries]\n",
      "\n",
      "stats = np.rec.fromarrays((countries, wins, losses),\n",
      "                          names='country,wins,losses')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = np.argsort(stats, order=('wins', 'losses', 'country'))[::-1]\n",
      "stats = stats[i]\n",
      "x = np.arange(len(stats))\n",
      "plt.bar(x, stats['wins'])\n",
      "plt.xticks(x+0.5, stats['country'], rotation=90)\n",
      "plt.ylabel('wins in\\nworld cup', rotation=0, labelpad=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x10d07cf90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OVatWISMjQ6Wjx6l0nJpWi/VYr2bXi4tjvZpcq84GTlJSEhwdHeHg4ABLS0u8+OKL2Lx5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\nEbU63AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bee0810>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "plt.bar(x, stats['wins']+stats['losses'], fc='w', ec='k')\n",
      "plt.bar(x, stats['wins'], ec='none', fc='k')\n",
      "plt.xticks(x+0.5, stats['country'], rotation=90)\n",
      "ax = plt.gca()\n",
      "[ax.spines[s].set_visible(False) for s in ['top', 'left', 'right']]\n",
      "#ax.spines['left'].set_position(('outward',20))\n",
      "ax.xaxis.set_ticks_position('none')\n",
      "ax.yaxis.set_ticks_position('left')\n",
      "plt.ylabel('wins vs\\nfinals', rotation=0, labelpad=30)\n",
      "ax.set_axis_bgcolor('0.9')\n",
      "ax.set_axisbelow(True)\n",
      "plt.grid(axis='y', ls='-', color='w',zorder=200)\n",
      "plt.title('FIFA World Cup (1930-2014)')\n",
      "plt.figtext(0.15,-0.35, 'Data source: openfootball.github.io', size=9)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x10d1d3b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Xr17ovpiYGBw+fFi67efnBz8/P32mJxszMzO1UyCit9xbU1hWr16NPn36FHmfp6cnPD09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\njIzQsWNHLFq0SOruq1+/vmJrTJFhKGo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VlWw2SzabLZnnpi9fvlBfX2+2tfkbG79Z6RTL\nrVTfbI/1d9temUyGwcFBAFpaWnj58uWu8QuxX8hW2P+QTqf5+PEjFy5c4PHjxwQCAV6/fk1vb69Z\n47BYLORyOQBevHjB+fPnefPmDcPDwzvqIPl8nmAwyPPnz2lrayMSieByuUilUgDMz89TVVUFgM1m\nM4ve79+/N9vYOqhpmkYikTA/G4aBpmncu3ePRCJBIpEgHo+XzM/pdPL27Vtg46S7bDaLw+HAMAzz\nme/evcPpdOJwOKipqSEej5NIJEin04RCIQzD2LW2UCpHq9VqHs86OzuL2+3GYrGQz+cLctoey9ev\nX4lGoySTSV69eoXVav3t8zfV1tYWtFVsQtnan1C8bmKz2VhcXAQ2ztYoFavL5SoZi8vlMgv9qVQK\nt9v9r3IQYj+QFcsebe7vr62tceLECWKxGBaLhc7OTm7fvk0sFuPUqVPmgHPlyhVu3bpFa2sr169f\n58aNGySTSc6ePbtjUMpms/j9flRVJZfLMTY2xrlz54jH47S1tfHz50/Gx8cBGBwcpLu7m5qaGvMf\nMxQOdPfv3ycUCjE5OYmqqsRiMR48eEB/fz/j4+MYhoHP5+Pu3buEw2F8Ph+appn319XV4fV6aWlp\n4devX4yOjqIoCoqi8O3bNy5fvszy8jLRaBTYqH+0t7djGAYHDhwgEolw7NixokXzzXYAOjo6iuY4\nNjZGIBBAVVU0TTPbrqys5OrVqwwMDBTEous6z5494/jx45w5c4bW1lY8Hg8Oh2PHM7fGEgwGmZyc\npLOzk6mpKS5evEhDQwOHDx/ecd/W/gyFQgX9t3nNtWvX6OjoIJlMUl1dXZDz3Nwcly5dYm1tjVgs\nBsCdO3d4+PAhdrvdvHZoaIibN2/y9OlTjh49WvDCghD7nZzHIvZsZGSE2tpaAoHAnw6l7PL5PAcP\nHmR2dpZwOMzMzMyfDkmIv46sWMR/spfXdf8mfr8fXddZX18veha5EGJ3smIRQghRVlK8F0IIUVYy\nsQghhCgrmViEEEKUlUwsQgghykomFiGEEGUlE4sQQoiy+geiB7VswekIgQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b50df90>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}