iiSeymour/NBA MVP 13-14

## NBA MVP 13-14
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1><center>NBA MVP 13/14 By The Numbers</center></h1>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from mpltools import style\n",
      "from __future__ import division\n",
      "style.use('ggplot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**NBA statistics for the 13/14 season** *([source](http://www.dougstats.com/13-14.html))*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head player_stats team_stats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "==> player_stats <==\r\n",
        "Player           Team PS GP  Min  FGM  FGA  3M  3A FTM  FTA  OR   TR   AS  ST  TO  BK  PF  DQ   PTS  TC  EJ  FF Sta +/-\r\n",
        "acy,quincy        sac SF 63  852   66  141   4  15  35   53  71  215   28  23  30  26 122   1   171   5   0   0   0 -134\r\n",
        "adams,steven      okl  C 81 1200   93  185   0   0  79  136 142  332   44  40  71  57 203   3   265   1   0   0  20   57\r\n",
        "adrien,jeff       mil SF 53  963  143  275   0   0  76  119 102  306   38  24  39  36 108   0   362   2   0   0  12  -72\r\n",
        "afflalo,arron     orl SG 73 2550  464 1011 128 300 274  336  32  262  248  35 146   3 136   0  1330   4   0   0  73 -365\r\n",
        "ajinca,alexis     nor  C 56  952  136  250   0   1  56   67  94  277   40  23  63  46 187   3   328   0   0   0  30  -99\r\n",
        "aldrich,cole      nyk  C 46  336   33   61   0   0  26   30  37  129   14   8  18  30  40   0    92   0   0   0   2  -15\r\n",
        "aldridge,lamarcu  por PF 69 2496  652 1423   3  15 296  360 166  766  178  64 122  69 147   1  1603   2   0   0  69  367\r\n",
        "allen,lavoy       ind PF 65 1068  134  299   2  13  33   50 119  311   71  24  45  33 126   1   303   0   0   0   2 -209\r\n",
        "allen,ray         mia SG 73 1937  240  543 116 309 105  116  23  205  143  54  83   8 115   0   701   0   0   0   9  162\r\n",
        "\r\n",
        "==> team_stats <==\r\n",
        "team                 won lost min  fgm  fga   3m   3a  ftm  fta   or   tr   as   st   to   bk   pf   pts  tc  ej  ff\r\n",
        "AtlantaHawks         38   44 19857 3061 6688  768 2116 1392 1782  713 3278 2041  679 1189  326 1577  8282  10   0   0\r\n",
        "BostonCeltics        25   57 19730 2996 6881  575 1730 1325 1706  980 3485 1726  584 1183  344 1743  7892  16   0   0\r\n",
        "CharlotteBobcats     43   39 19915 2976 6730  516 1471 1474 2000  776 3500 1778  499  954  421 1493  7942  16   0   0\r\n",
        "ChicagoBulls         48   34 19957 2843 6577  508 1459 1486 1908  937 3621 1860  594 1148  423 1565  7680  38   0   0\r\n",
        "ClevelandCavaliers   33   49 19947 3036 6954  584 1638 1398 1861  988 3617 1739  579 1105  304 1640  8054  13   0   0\r\n",
        "DallasMavericks      49   33 19855 3249 6858  721 1877 1378 1733  840 3354 1935  704 1082  356 1636  8597  22   0   0\r\n",
        "DenverNuggets        36   46 19766 3147 7041  702 1959 1563 2154 1008 3725 1839  615 1260  459 1890  8559  29   0   0\r\n",
        "DetroitPistons       29   53 19780 3182 7124  507 1580 1415 2111 1196 3721 1714  687 1143  395 1666  8286  48   0   0\r\n",
        "GSWarriors           51   31 19837 3236 7002  774 2037 1303 1731  895 3713 1913  642 1226  406 1784  8549  30   0   0\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Read statistics into a DataFrame and remove columns we are not interested in**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = pd.read_table('player_stats', sep=' +')\n",
      "stats = stats[stats.Team != 'na']\n",
      "del stats['PS'], stats['PF'], stats['DQ'], stats['TC'], stats['EJ'], stats['FF']\n",
      "stats.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>Player</th>\n",
        "      <th>Team</th>\n",
        "      <th>GP</th>\n",
        "      <th>Min</th>\n",
        "      <th>FGM</th>\n",
        "      <th>FGA</th>\n",
        "      <th>3M</th>\n",
        "      <th>3A</th>\n",
        "      <th>FTM</th>\n",
        "      <th>FTA</th>\n",
        "      <th>OR</th>\n",
        "      <th>TR</th>\n",
        "      <th>AS</th>\n",
        "      <th>ST</th>\n",
        "      <th>TO</th>\n",
        "      <th>BK</th>\n",
        "      <th>PTS</th>\n",
        "      <th>Sta</th>\n",
        "      <th>+/-</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    acy,quincy</td>\n",
        "      <td> sac</td>\n",
        "      <td> 63</td>\n",
        "      <td>  852</td>\n",
        "      <td>  66</td>\n",
        "      <td>  141</td>\n",
        "      <td>   4</td>\n",
        "      <td>  15</td>\n",
        "      <td>  35</td>\n",
        "      <td>  53</td>\n",
        "      <td>  71</td>\n",
        "      <td> 215</td>\n",
        "      <td>  28</td>\n",
        "      <td> 23</td>\n",
        "      <td>  30</td>\n",
        "      <td> 26</td>\n",
        "      <td>  171</td>\n",
        "      <td>  0</td>\n",
        "      <td>-134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  adams,steven</td>\n",
        "      <td> okl</td>\n",
        "      <td> 81</td>\n",
        "      <td> 1200</td>\n",
        "      <td>  93</td>\n",
        "      <td>  185</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>  79</td>\n",
        "      <td> 136</td>\n",
        "      <td> 142</td>\n",
        "      <td> 332</td>\n",
        "      <td>  44</td>\n",
        "      <td> 40</td>\n",
        "      <td>  71</td>\n",
        "      <td> 57</td>\n",
        "      <td>  265</td>\n",
        "      <td> 20</td>\n",
        "      <td>  57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>   adrien,jeff</td>\n",
        "      <td> mil</td>\n",
        "      <td> 53</td>\n",
        "      <td>  963</td>\n",
        "      <td> 143</td>\n",
        "      <td>  275</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>  76</td>\n",
        "      <td> 119</td>\n",
        "      <td> 102</td>\n",
        "      <td> 306</td>\n",
        "      <td>  38</td>\n",
        "      <td> 24</td>\n",
        "      <td>  39</td>\n",
        "      <td> 36</td>\n",
        "      <td>  362</td>\n",
        "      <td> 12</td>\n",
        "      <td> -72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> afflalo,arron</td>\n",
        "      <td> orl</td>\n",
        "      <td> 73</td>\n",
        "      <td> 2550</td>\n",
        "      <td> 464</td>\n",
        "      <td> 1011</td>\n",
        "      <td> 128</td>\n",
        "      <td> 300</td>\n",
        "      <td> 274</td>\n",
        "      <td> 336</td>\n",
        "      <td>  32</td>\n",
        "      <td> 262</td>\n",
        "      <td> 248</td>\n",
        "      <td> 35</td>\n",
        "      <td> 146</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1330</td>\n",
        "      <td> 73</td>\n",
        "      <td>-365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> ajinca,alexis</td>\n",
        "      <td> nor</td>\n",
        "      <td> 56</td>\n",
        "      <td>  952</td>\n",
        "      <td> 136</td>\n",
        "      <td>  250</td>\n",
        "      <td>   0</td>\n",
        "      <td>   1</td>\n",
        "      <td>  56</td>\n",
        "      <td>  67</td>\n",
        "      <td>  94</td>\n",
        "      <td> 277</td>\n",
        "      <td>  40</td>\n",
        "      <td> 23</td>\n",
        "      <td>  63</td>\n",
        "      <td> 46</td>\n",
        "      <td>  328</td>\n",
        "      <td> 30</td>\n",
        "      <td> -99</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 19 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "          Player Team  GP   Min  FGM   FGA   3M   3A  FTM  FTA   OR   TR   AS  \\\n",
        "0     acy,quincy  sac  63   852   66   141    4   15   35   53   71  215   28   \n",
        "1   adams,steven  okl  81  1200   93   185    0    0   79  136  142  332   44   \n",
        "2    adrien,jeff  mil  53   963  143   275    0    0   76  119  102  306   38   \n",
        "3  afflalo,arron  orl  73  2550  464  1011  128  300  274  336   32  262  248   \n",
        "4  ajinca,alexis  nor  56   952  136   250    0    1   56   67   94  277   40   \n",
        "\n",
        "   ST   TO  BK   PTS  Sta  +/-  \n",
        "0  23   30  26   171    0 -134  \n",
        "1  40   71  57   265   20   57  \n",
        "2  24   39  36   362   12  -72  \n",
        "3  35  146   3  1330   73 -365  \n",
        "4  23   63  46   328   30  -99  \n",
        "\n",
        "[5 rows x 19 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "team_stats = pd.read_table('team_stats', sep=' +', usecols=['team', 'won', 'lost'])\n",
      "team_stats.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>team</th>\n",
        "      <th>won</th>\n",
        "      <th>lost</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>       AtlantaHawks</td>\n",
        "      <td> 38</td>\n",
        "      <td> 44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      BostonCeltics</td>\n",
        "      <td> 25</td>\n",
        "      <td> 57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>   CharlotteBobcats</td>\n",
        "      <td> 43</td>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>       ChicagoBulls</td>\n",
        "      <td> 48</td>\n",
        "      <td> 34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> ClevelandCavaliers</td>\n",
        "      <td> 33</td>\n",
        "      <td> 49</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                 team  won  lost\n",
        "0        AtlantaHawks   38    44\n",
        "1       BostonCeltics   25    57\n",
        "2    CharlotteBobcats   43    39\n",
        "3        ChicagoBulls   48    34\n",
        "4  ClevelandCavaliers   33    49\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Merge Team Win Percentage**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "team_stats['Win%'] = team_stats.won / 82 * 100 \n",
      "team_stats.sort('team', inplace=True)\n",
      "team_stats['Team_short'] = sorted(stats.Team.unique())\n",
      "stats = stats.merge(team_stats[['Team_short', 'Win%']], right_on='Team_short', left_on='Team')\n",
      "stats.Team = stats.Team.apply(str.upper)\n",
      "teams = stats.Team.unique()\n",
      "del stats['Team_short']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Calculate Per Game Statisics**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats['MPG'] = stats.Min / stats.GP\n",
      "stats['PPG'] = stats.PTS / stats.GP\n",
      "stats['RPG'] = stats.TR / stats.GP\n",
      "stats['APG'] = stats.AS / stats.GP\n",
      "stats['SPG'] = stats.ST / stats.GP\n",
      "stats['BPG'] = stats.BK / stats.GP\n",
      "stats['TPG'] = stats.TO / stats.GP\n",
      "stats['FGMPG'] = stats.FGM / stats.GP\n",
      "stats['FTMPG'] = stats.FTM / stats.GP\n",
      "stats['StaPer'] = stats.Sta / stats.GP \n",
      "\n",
      "stats['FGP'] = stats.FGM / stats.FGA * 100\n",
      "stats['FTP'] = stats.FTM / stats.FTA * 100\n",
      "stats['3PP'] = stats['3M'] / stats['3A'] * 100\n",
      "\n",
      "stats['COMB'] = stats.PPG + stats.RPG + stats.APG + stats.SPG + stats.BPG - stats.TPG\n",
      "stats['Player'] = stats.Player.apply(lambda x: ' '.join(map(str.title, x.split(',')[::-1])))\n",
      "\n",
      "stats.reset_index(drop=True, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**MVP should be in top half for GP and MPG **"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(stats.GP, stats.MPG)\n",
      "plt.ylim([0, stats.MPG.max() + 2])\n",
      "plt.xlim([0, stats.GP.max() + 2])\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])\n",
      "gca().add_patch(Rectangle((stats.GP.median(), stats.MPG.median()), \n",
      "                           stats.GP.max() - stats.GP.median() + 1,\n",
      "                           stats.MPG.max() - stats.MPG.median() + 1, alpha=0.3))\n",
      "plt.xlabel('Games Played')\n",
      "plt.ylabel('Minutes Per Game')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2bZrg4hARERERjYxbxlpH9ACA7J2m0bgpGaWoNmBG9IrI9hUMqqivz96Djh8v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\norU1yWuQiIiIiMhmLM+5YFdtbUEsWhTFokVRtLUFrS4OERF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       "text": [
        "<matplotlib.figure.Figure at 0x10acd4710>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = stats[(stats.GP > stats.GP.median()) & (stats.MPG > stats.MPG.median())]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**MVP's are starters**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(stats.GP, stats.StaPer)\n",
      "plt.ylim([stats.StaPer.min() - 0.05, stats.StaPer.max() + 0.05])\n",
      "plt.xlim([stats.GP.min() - 1, stats.GP.max() + 1])\n",
      "plt.hlines(0.49, stats.GP.min() - 1, stats.GP.max() + 1, linestyle=':', lw=2, color='red')\n",
      "plt.xlabel('Games Played')\n",
      "plt.ylabel('Percentage Of Games Started')\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Eq+pcmbxGubm1MY7o2GhPsDK7tSnaU3iZplMSs9KEm8vlUn5+fpf1ISUG3n33Xf3yl7/U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\nAACIY/8PaeL8y8FtBE8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10acf02d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = stats[stats.StaPer >= 0.5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**MVP's have postive +/-**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats['+/-'].hist(bins=30)\n",
      "plt.xlim(stats['+/-'].min(), stats['+/-'].max())\n",
      "plt.vlines(0, 0, plt.ylim()[1], linestyle='--', lw=3, color='steelblue')\n",
      "plt.xlabel('Player +/-')\n",
      "plt.ylabel('Number of Players')\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mTp2q2NhY03EAAAAAAIgYRk8lkCSPx6Nly5Zp5MiRGjJkyE2PFxUVqaioyHc/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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10acd18d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = stats[stats['+/-'] > 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**MVP's are winners**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats['Win%'].hist(bins=range(40,80,5))\n",
      "plt.xlim(stats['Win%'].min(), stats['Win%'].max())\n",
      "plt.vlines(50, 0, plt.ylim()[1], linestyle='--', lw=3, color='steelblue')\n",
      "plt.xlabel('Win Percentage')\n",
      "plt.ylabel('Number of Players')\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10acf88d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = stats[stats['Win%'] > 50.0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Number Of Candidates Per Team**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.groupby('Team').size().plot(kind='bar')\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])\n",
      "plt.xlabel('')\n",
      "plt.ylabel('Number of Candidates')\n",
      "plt.tick_params(axis='x', labelsize=14)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CVL9+fTVv3twLVXlORkaGtm3bphMnTshisSgiIkLx8fE+N0iSs7Kzs7V69Wr1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\n+KOSkpL0ww8/6MYbb1SfPn3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       "text": [
        "<matplotlib.figure.Figure at 0x10ace0810>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Teams that didn't cut it**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print ' '.join([team for team in teams if team not in stats.Team.unique()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SAC MIL ORL NOR NYK PHI BOS ATL DEN MIN LAL CLE UTA DET\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Top MVP Candidate By Team**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "candidates = stats.groupby('Team', as_index=False).apply(lambda player: player[player.COMB==player.COMB.max()])\n",
      "\n",
      "for candidate in candidates.iterrows():\n",
      "    print candidate[1].Team, candidate[1].Player"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BRO Deron Williams\n",
        "CHA Al Jefferson\n",
        "CHI Joakim Noah\n",
        "DAL Dirk Nowitzki\n",
        "GSW Stephen Curry\n",
        "HOU James Harden\n",
        "IND Paul George\n",
        "LAC Blake Griffin\n",
        "MEM Zach Randolph\n",
        "MIA Lebron James\n",
        "OKL Kevin Durant\n",
        "PHO Goran Dragic\n",
        "POR Lamarcu Aldridge\n",
        "SAN Tim Duncan\n",
        "TOR Demar Derozan\n",
        "WAS John Wall\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**The Top 10**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top10 = candidates.sort('COMB', ascending=False).head(10).reset_index(drop=True)\n",
      "\n",
      "for candidate in top10.iterrows():\n",
      "    print candidate[0] + 1, candidate[1].Player"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 Kevin Durant\n",
        "2 Lebron James\n",
        "3 Lamarcu Aldridge\n",
        "4 Blake Griffin\n",
        "5 Al Jefferson\n",
        "6 Stephen Curry\n",
        "7 James Harden\n",
        "8 Paul George\n",
        "9 John Wall\n",
        "10 Dirk Nowitzki\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Stats Profile**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top10['TPG'] = top10['TPG'].apply(lambda x: -abs(x))\n",
      "top10[['TPG', 'BPG', 'SPG', 'APG', 'RPG', 'PPG']].head(5).plot(kind='bar')\n",
      "plt.xticks([x + 0.65 for x in range(5)], top10.Player.head(5), rotation=45)\n",
      "plt.ylim([int(5 * round(top10.TPG.min()/5)), int(5 * round(top10.PPG.max()/5)) + 5])\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])\n",
      "\n",
      "top10[['TPG', 'BPG', 'SPG', 'APG', 'RPG', 'PPG']].tail(5).plot(kind='bar')\n",
      "plt.xticks([x + 0.65 for x in range(5)], top10.Player.tail(5).reset_index(drop=True), rotation=45)\n",
      "plt.ylim([int(5 * round(top10.TPG.min()/5)), int(5 * round(top10.PPG.max()/5)) + 5])\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Dq1NR4GKKiork7//Tyv1+fn4qKiqyW1DO4lIf8B999FG9/fbbWrp0qXJzcyVJ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\nPv74Y3bs2IHT6SQ8PJyAgAD6+/vdNXezZs1SnZ1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       "text": [
        "<matplotlib.figure.Figure at 0x10acb0f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ujhqw3+k5vn//fq1YsUIRERFKS0tTXl6eIiMjdc0112jHjh16+eWX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ad32990>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Shooting Percentage**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top10[['3PP', 'FGP', 'FTP']].plot(kind='bar')\n",
      "plt.ylabel('Shooting Percentage')\n",
      "plt.xticks([x + 0.5 for x in range(10)], top10.Player, rotation=45)\n",
      "plt.tight_layout(rect=[0, 0, 2.4, 1.0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SxsXFeTosAAAAAACaPY8XBSSpX79+6tevn6fDAAAAAACgRTHPmDFj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       "text": [
        "<matplotlib.figure.Figure at 0x10ac55250>"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}