NBA-Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如何获取NBA数据\n",
    "- Stats.nba提供的api来抓取数据\n",
    "- pip install nbashots来获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone20091652/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/Users/stone20091652/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import nbashots as nba # this will throw a warning if using matplotlib 1.5\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用nba-shots抓取NBA球员的player_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201939"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curry_id = nba.get_player_id(\"Curry, Stephen\")[0]\n",
    "curry_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "库里的player_id是 201939"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PERSON_ID</th>\n",
       "      <th>DISPLAY_LAST_COMMA_FIRST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1505</td>\n",
       "      <td>Abdul-Wahad, Tariq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>949</td>\n",
       "      <td>Abdur-Rahim, Shareef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101165</td>\n",
       "      <td>Acker, Alex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203112</td>\n",
       "      <td>Acy, Quincy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200801</td>\n",
       "      <td>Adams, Hassan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PERSON_ID DISPLAY_LAST_COMMA_FIRST\n",
       "0       1505       Abdul-Wahad, Tariq\n",
       "1        949     Abdur-Rahim, Shareef\n",
       "2     101165              Acker, Alex\n",
       "3     203112              Acy, Quincy\n",
       "4     200801            Adams, Hassan"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 获取所有人的数据\n",
    "players_with_shots = nba.get_all_player_ids(\"shots\")\n",
    "players_with_shots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1498, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_with_shots.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1505\n",
       "1           949\n",
       "2        101165\n",
       "3        203112\n",
       "4        200801\n",
       "5        203919\n",
       "6        203500\n",
       "7        202399\n",
       "8        201167\n",
       "9        200772\n",
       "10       201336\n",
       "11       201582\n",
       "12       203006\n",
       "13       202374\n",
       "14       203128\n",
       "15       202332\n",
       "16       200746\n",
       "17      1626146\n",
       "18          724\n",
       "19         2042\n",
       "20       201570\n",
       "21         2349\n",
       "22       202730\n",
       "23         2124\n",
       "24          951\n",
       "25         2754\n",
       "26       200984\n",
       "27       201165\n",
       "28         1747\n",
       "29          680\n",
       "         ...   \n",
       "1468       2106\n",
       "1469     203489\n",
       "1470    1626174\n",
       "1471       2254\n",
       "1472       2417\n",
       "1473       1897\n",
       "1474     101120\n",
       "1475     101152\n",
       "1476     201148\n",
       "1477     202874\n",
       "1478     203203\n",
       "1479    1626153\n",
       "1480       2748\n",
       "1481     201153\n",
       "1482        953\n",
       "1483     203100\n",
       "1484       2397\n",
       "1485       2428\n",
       "1486     201146\n",
       "1487     203923\n",
       "1488    1626202\n",
       "1489     201156\n",
       "1490     201970\n",
       "1491     201152\n",
       "1492     201180\n",
       "1493     203469\n",
       "1494     202545\n",
       "1495     203092\n",
       "1496       1917\n",
       "1497       2583\n",
       "Name: PERSON_ID, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_with_shots.PERSON_ID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用官网提供的API抓取数据\n",
    "## http://stats.nba.com/stats/playerprofile\n",
    "LeagueID is required; PlayerID is required; Season is required; SeasonType is required; The GraphStartSeason property is required.; The GraphEndSeason property is required.; The GraphStat property is required.\n",
    "## http://stats.nba.com/stats/shotchartdetail\n",
    "SeasonType is required; TeamID is required; PlayerID is required; The GameID property is required.; The Outcome property is required.; The Location property is required.; Month is required; The SeasonSegment property is required.; The DateFrom property is required.; The DateTo property is required.; OpponentTeamID is required; The VsConference property is required.; The VsDivision property is required.; The Position property is required.; The RookieYear property is required.; The GameSegment property is required.; Period is required; LastNGames is required; The ContextMeasure property is required.\n",
    "## shots的api格式\n",
    "http://stats.nba.com/stats/shotchartdetail?Period=0&VsConference=&LeagueID=00&LastNGames=0&TeamID=0&Position=&Location=&Outcome=&ContextMeasure=FGA&DateFrom=&StartPeriod=&DateTo=&OpponentTeamID=0&ContextFilter=&RangeType=&Season=2014-15&AheadBehind=&PlayerID=201935&EndRange=&VsDivision=&PointDiff=&RookieYear=&GameSegment=&Month=0&ClutchTime=&StartRange=&EndPeriod=&SeasonType=Regular+Season&SeasonSegment=&GameID=\n",
    "\n",
    "￥￥￥￥￥"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nba_py import player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd = player.PlayerProfile('203507')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://stats.nba.com/stats/playerprofilev2/?PlayerID=203507&LeagueID=00&PerMode=PerGame\n",
    "    \n",
    "用chrome打开这网站，用json的插件解析\n",
    "![image](http://7xkeje.com1.z0.glb.clouddn.com/Screen%20Shot%202016-03-26%20at%2011.04.06%20PM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "G - Games Played\n",
    "\n",
    "Min - Minutes Played\n",
    "\n",
    "MPG - Minutes per Game\n",
    "\n",
    "Pts - Total Points\n",
    "\n",
    "PPG - Points per Game\n",
    "\n",
    "FGM - Field Goals Made\n",
    "\n",
    "FGA - Field Goals Attempted\n",
    "\n",
    "FGP - Field Goal Percentage\n",
    "\n",
    "FTM - Free Throws Made\n",
    "\n",
    "FTA - Free Throws Attempted\n",
    "\n",
    "FTP - Free Throw Percentage\n",
    "\n",
    "3PM - Three Pointers Made*\n",
    "\n",
    "3PA - Three Pointers Attempted*\n",
    "\n",
    "3PP\t- Three Point Percentage*\n",
    "\n",
    "RPG - Rebounds per Game\n",
    "\n",
    "AST - Total Assists\n",
    "\n",
    "APG - Assists per Game\n",
    "\n",
    "STL - Steals**\n",
    "\n",
    "BLK - Blocks**\n",
    "\n",
    "TO - Turnovers**\n",
    "\n",
    "EFF - Efficiency***\n",
    "\n",
    "AV - Approximate Value (see below)\n",
    "\n",
    "PF - Total Personal Fouls\n",
    "\n",
    "VI - Versatility Index (ppg*rpg*apg)^.333 (see below)\n",
    "\n",
    "PPFGA - Points per FGA   Points/(FGA+(FTA*.44)) (see below)\n",
    "\n",
    "PPR - Assist/Turnover Rating   (((ASTS*.66)-TO)*100)/Minutes (see below)\n",
    "\n",
    "RbRate - Rebound Rate   (Reb*Team Minutes)/(Player Minutes*(Team Reb + Opp Reb)) (see below)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Header上简写字母的具体说明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "playerprofile_url = 'http://stats.nba.com/stats/playerprofilev2/?PlayerID=203507&LeagueID=00&PerMode=PerGame'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response = requests.get(playerprofile_url,headers={'User-Agent':'test'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "headers = response.json()['resultSets'][0]['headers']\n",
    "\n",
    "profile = response.json()['resultSets'][0]['rowSet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>PLAYER_ID</th>\n",
       "      <th>SEASON_ID</th>\n",
       "      <th>LEAGUE_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>PLAYER_AGE</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>FG3M</th>\n",
       "      <th>FG3A</th>\n",
       "      <th>FG3_PCT</th>\n",
       "      <th>FTM</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT_PCT</th>\n",
       "      <th>OREB</th>\n",
       "      <th>DREB</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>203507</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>00</td>\n",
       "      <td>1610612749</td>\n",
       "      <td>MIL</td>\n",
       "      <td>19</td>\n",
       "      <td>77</td>\n",
       "      <td>23</td>\n",
       "      <td>24.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.347</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.683</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>203507</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>00</td>\n",
       "      <td>1610612749</td>\n",
       "      <td>MIL</td>\n",
       "      <td>20</td>\n",
       "      <td>81</td>\n",
       "      <td>71</td>\n",
       "      <td>31.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.159</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.741</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>203507</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>00</td>\n",
       "      <td>1610612749</td>\n",
       "      <td>MIL</td>\n",
       "      <td>21</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>35.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.202</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.726</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>16.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PLAYER_ID SEASON_ID LEAGUE_ID     TEAM_ID TEAM_ABBREVIATION  PLAYER_AGE  \\\n",
       "0     203507   2013-14        00  1610612749               MIL          19   \n",
       "1     203507   2014-15        00  1610612749               MIL          20   \n",
       "2     203507   2015-16        00  1610612749               MIL          21   \n",
       "\n",
       "   GP  GS   MIN  FGM   FGA  FG_PCT  FG3M  FG3A  FG3_PCT  FTM  FTA  FT_PCT  \\\n",
       "0  77  23  24.6  2.2   5.4   0.414   0.5   1.5    0.347  1.8  2.6   0.683   \n",
       "1  81  71  31.4  4.7   9.6   0.491   0.1   0.5    0.159  3.2  4.3   0.741   \n",
       "2  71  70  35.4  6.4  12.6   0.506   0.2   1.2    0.202  3.7  5.1   0.726   \n",
       "\n",
       "   OREB  DREB  REB  AST  STL  BLK  TOV   PF   PTS  \n",
       "0   1.0   3.4  4.4  1.9  0.8  0.8  1.6  2.2   6.8  \n",
       "1   1.2   5.5  6.7  2.6  0.9  1.0  2.1  3.1  12.7  \n",
       "2   1.4   6.2  7.6  4.1  1.2  1.4  2.5  3.3  16.7  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile_df = pd.DataFrame(shots, columns=headers)\n",
    "\n",
    "from IPython.display import display\n",
    "with pd.option_context('display.max_columns', None):\n",
    "    display(profile_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### player_id 是203507的选手打球三个赛季"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>PF</td>\n",
       "      <td>25</td>\n",
       "      <td>SAC</td>\n",
       "      <td>49</td>\n",
       "      <td>24</td>\n",
       "      <td>678</td>\n",
       "      <td>87</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.745</td>\n",
       "      <td>51</td>\n",
       "      <td>94</td>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>87</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jordan Adams</td>\n",
       "      <td>SG</td>\n",
       "      <td>21</td>\n",
       "      <td>MEM</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>22</td>\n",
       "      <td>OKC</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1730</td>\n",
       "      <td>221</td>\n",
       "      <td>362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.593</td>\n",
       "      <td>181</td>\n",
       "      <td>270</td>\n",
       "      <td>451</td>\n",
       "      <td>50</td>\n",
       "      <td>34</td>\n",
       "      <td>78</td>\n",
       "      <td>69</td>\n",
       "      <td>191</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Rk        Player Pos Age   Tm   G  GS    MP   FG  FGA ...     FT%  ORB  DRB  \\\n",
       "0  1    Quincy Acy  PF  25  SAC  49  24   678   87  149 ...   0.745   51   94   \n",
       "1  2  Jordan Adams  SG  21  MEM   2   0    15    2    6 ...     0.6    0    2   \n",
       "2  3  Steven Adams   C  22  OKC  70  70  1730  221  362 ...   0.593  181  270   \n",
       "\n",
       "   TRB AST STL BLK TOV   PF  PTS  \n",
       "0  145  20  21  18  20   87  223  \n",
       "1    2   3   3   0   2    2    7  \n",
       "2  451  50  34  78  69  191  541  \n",
       "\n",
       "[3 rows x 30 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "nba = pd.read_csv(\"nba.csv\")\n",
    "nba.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "point_guards = nba[nba['Pos'] == 'PG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 592 entries, 0 to 591\n",
      "Data columns (total 30 columns):\n",
      "Rk        592 non-null object\n",
      "Player    592 non-null object\n",
      "Pos       592 non-null object\n",
      "Age       592 non-null object\n",
      "Tm        592 non-null object\n",
      "G         592 non-null object\n",
      "GS        592 non-null object\n",
      "MP        592 non-null object\n",
      "FG        592 non-null object\n",
      "FGA       592 non-null object\n",
      "FG%       589 non-null object\n",
      "3P        592 non-null object\n",
      "3PA       592 non-null object\n",
      "3P%       532 non-null object\n",
      "2P        592 non-null object\n",
      "2PA       592 non-null object\n",
      "2P%       583 non-null object\n",
      "eFG%      589 non-null object\n",
      "FT        592 non-null object\n",
      "FTA       592 non-null object\n",
      "FT%       568 non-null object\n",
      "ORB       592 non-null object\n",
      "DRB       592 non-null object\n",
      "TRB       592 non-null object\n",
      "AST       592 non-null object\n",
      "STL       592 non-null object\n",
      "BLK       592 non-null object\n",
      "TOV       592 non-null object\n",
      "PF        592 non-null object\n",
      "PTS       592 non-null object\n",
      "dtypes: object(30)\n",
      "memory usage: 143.4+ KB\n"
     ]
    }
   ],
   "source": [
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone20091652/anaconda/lib/python2.7/site-packages/ipykernel/__main__.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",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "point_guards['PTS'] = point_guards['PTS'].astype(float)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone20091652/anaconda/lib/python2.7/site-packages/ipykernel/__main__.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",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "point_guards['G'] = point_guards['G'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone20091652/anaconda/lib/python2.7/site-packages/ipykernel/__main__.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",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "point_guards['PPG'] = point_guards['PTS'] / point_guards['G']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "51      9.564516\n",
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       "93     10.295082\n",
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       "95     10.818182\n",
       "100    10.600000\n",
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       "117     4.235294\n",
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       "149    15.160000\n",
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       "155     9.253521\n",
       "161     3.100000\n",
       "         ...    \n",
       "467    17.016949\n",
       "469     1.709677\n",
       "470    10.333333\n",
       "474    13.114286\n",
       "479    11.267606\n",
       "484     9.527778\n",
       "489     6.882353\n",
       "490     9.254902\n",
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       "493     8.851852\n",
       "494    14.675000\n",
       "501     1.533333\n",
       "516     9.021277\n",
       "519    15.014286\n",
       "523    22.152778\n",
       "541     4.659091\n",
       "542     5.875000\n",
       "543     4.388889\n",
       "548     7.125000\n",
       "557    21.070423\n",
       "558    20.138889\n",
       "560     4.240000\n",
       "561     4.833333\n",
       "566    23.611111\n",
       "572    13.983871\n",
       "574     1.600000\n",
       "577     8.885714\n",
       "584     2.000000\n",
       "585     8.375000\n",
       "587     3.916667\n",
       "Name: PPG, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_guards['PPG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>PTS</th>\n",
       "      <th>G</th>\n",
       "      <th>PPG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>391</td>\n",
       "      <td>53</td>\n",
       "      <td>7.377358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>142</td>\n",
       "      <td>34</td>\n",
       "      <td>4.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>249</td>\n",
       "      <td>19</td>\n",
       "      <td>13.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>663</td>\n",
       "      <td>66</td>\n",
       "      <td>10.045455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PTS   G        PPG\n",
       "25    6   5   1.200000\n",
       "29  391  53   7.377358\n",
       "30  142  34   4.176471\n",
       "31  249  19  13.105263\n",
       "36  663  66  10.045455"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_guards[['PTS', 'G', 'PPG']].head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "point_guards = point_guards[point_guards['TOV'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>PPG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23</td>\n",
       "      <td>Keith Appling</td>\n",
       "      <td>PG</td>\n",
       "      <td>23</td>\n",
       "      <td>ORL</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27</td>\n",
       "      <td>D.J. Augustin</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>TOT</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>987</td>\n",
       "      <td>124</td>\n",
       "      <td>291</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>159</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>76</td>\n",
       "      <td>391</td>\n",
       "      <td>7.377358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>27</td>\n",
       "      <td>D.J. Augustin</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>OKC</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>519</td>\n",
       "      <td>46</td>\n",
       "      <td>121</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>142</td>\n",
       "      <td>4.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>27</td>\n",
       "      <td>D.J. Augustin</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>DEN</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>468</td>\n",
       "      <td>78</td>\n",
       "      <td>170</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>93</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>249</td>\n",
       "      <td>13.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>32</td>\n",
       "      <td>J.J. Barea</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>DAL</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>1441</td>\n",
       "      <td>258</td>\n",
       "      <td>591</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>103</td>\n",
       "      <td>126</td>\n",
       "      <td>259</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>663</td>\n",
       "      <td>10.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>46</td>\n",
       "      <td>Patrick Beverley</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>HOU</td>\n",
       "      <td>62</td>\n",
       "      <td>54</td>\n",
       "      <td>1760</td>\n",
       "      <td>216</td>\n",
       "      <td>508</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>151</td>\n",
       "      <td>211</td>\n",
       "      <td>187</td>\n",
       "      <td>82</td>\n",
       "      <td>23</td>\n",
       "      <td>83</td>\n",
       "      <td>203</td>\n",
       "      <td>593</td>\n",
       "      <td>9.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>51</td>\n",
       "      <td>Steve Blake</td>\n",
       "      <td>PG</td>\n",
       "      <td>35</td>\n",
       "      <td>DET</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>818</td>\n",
       "      <td>76</td>\n",
       "      <td>198</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>62</td>\n",
       "      <td>70</td>\n",
       "      <td>159</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>73</td>\n",
       "      <td>64</td>\n",
       "      <td>205</td>\n",
       "      <td>4.183673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>52</td>\n",
       "      <td>Eric Bledsoe</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>PHO</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>1059</td>\n",
       "      <td>223</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>107</td>\n",
       "      <td>125</td>\n",
       "      <td>190</td>\n",
       "      <td>63</td>\n",
       "      <td>19</td>\n",
       "      <td>110</td>\n",
       "      <td>75</td>\n",
       "      <td>632</td>\n",
       "      <td>20.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>62</td>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>CHI</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>944</td>\n",
       "      <td>159</td>\n",
       "      <td>415</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>94</td>\n",
       "      <td>152</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>116</td>\n",
       "      <td>420</td>\n",
       "      <td>7.118644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>64</td>\n",
       "      <td>Lorenzo Brown</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>PHO</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>69</td>\n",
       "      <td>Trey Burke</td>\n",
       "      <td>PG</td>\n",
       "      <td>23</td>\n",
       "      <td>UTA</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1356</td>\n",
       "      <td>261</td>\n",
       "      <td>629</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>94</td>\n",
       "      <td>112</td>\n",
       "      <td>146</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>676</td>\n",
       "      <td>10.730159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>75</td>\n",
       "      <td>Jose Calderon</td>\n",
       "      <td>PG</td>\n",
       "      <td>34</td>\n",
       "      <td>NYK</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>1963</td>\n",
       "      <td>202</td>\n",
       "      <td>440</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>198</td>\n",
       "      <td>218</td>\n",
       "      <td>285</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>126</td>\n",
       "      <td>527</td>\n",
       "      <td>7.637681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>77</td>\n",
       "      <td>Isaiah Canaan</td>\n",
       "      <td>PG</td>\n",
       "      <td>24</td>\n",
       "      <td>PHI</td>\n",
       "      <td>71</td>\n",
       "      <td>33</td>\n",
       "      <td>1800</td>\n",
       "      <td>238</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>141</td>\n",
       "      <td>161</td>\n",
       "      <td>129</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "      <td>88</td>\n",
       "      <td>123</td>\n",
       "      <td>771</td>\n",
       "      <td>10.859155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>81</td>\n",
       "      <td>Michael Carter-Williams</td>\n",
       "      <td>PG</td>\n",
       "      <td>24</td>\n",
       "      <td>MIL</td>\n",
       "      <td>54</td>\n",
       "      <td>37</td>\n",
       "      <td>1649</td>\n",
       "      <td>251</td>\n",
       "      <td>555</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>228</td>\n",
       "      <td>276</td>\n",
       "      <td>281</td>\n",
       "      <td>80</td>\n",
       "      <td>41</td>\n",
       "      <td>153</td>\n",
       "      <td>162</td>\n",
       "      <td>623</td>\n",
       "      <td>11.537037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>84</td>\n",
       "      <td>Mario Chalmers</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>TOT</td>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>1373</td>\n",
       "      <td>182</td>\n",
       "      <td>444</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>133</td>\n",
       "      <td>157</td>\n",
       "      <td>230</td>\n",
       "      <td>88</td>\n",
       "      <td>13</td>\n",
       "      <td>111</td>\n",
       "      <td>181</td>\n",
       "      <td>628</td>\n",
       "      <td>10.295082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>84</td>\n",
       "      <td>Mario Chalmers</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>MIA</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>84</td>\n",
       "      <td>Mario Chalmers</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>MEM</td>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>1253</td>\n",
       "      <td>172</td>\n",
       "      <td>412</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>120</td>\n",
       "      <td>143</td>\n",
       "      <td>211</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>102</td>\n",
       "      <td>165</td>\n",
       "      <td>595</td>\n",
       "      <td>10.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>89</td>\n",
       "      <td>Norris Cole</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>NOP</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>1198</td>\n",
       "      <td>196</td>\n",
       "      <td>484</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>141</td>\n",
       "      <td>151</td>\n",
       "      <td>167</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>102</td>\n",
       "      <td>477</td>\n",
       "      <td>10.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>90</td>\n",
       "      <td>Darren Collison</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>SAC</td>\n",
       "      <td>67</td>\n",
       "      <td>11</td>\n",
       "      <td>1982</td>\n",
       "      <td>331</td>\n",
       "      <td>690</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>122</td>\n",
       "      <td>148</td>\n",
       "      <td>273</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>127</td>\n",
       "      <td>909</td>\n",
       "      <td>13.567164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>92</td>\n",
       "      <td>Mike Conley</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>MEM</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>1760</td>\n",
       "      <td>294</td>\n",
       "      <td>696</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>138</td>\n",
       "      <td>164</td>\n",
       "      <td>342</td>\n",
       "      <td>67</td>\n",
       "      <td>16</td>\n",
       "      <td>84</td>\n",
       "      <td>100</td>\n",
       "      <td>857</td>\n",
       "      <td>15.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>95</td>\n",
       "      <td>Bryce Cotton</td>\n",
       "      <td>PG</td>\n",
       "      <td>23</td>\n",
       "      <td>PHO</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>103</td>\n",
       "      <td>Seth Curry</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>SAC</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>391</td>\n",
       "      <td>48</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>144</td>\n",
       "      <td>4.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>104</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>GSW</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>2341</td>\n",
       "      <td>703</td>\n",
       "      <td>1387</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>312</td>\n",
       "      <td>373</td>\n",
       "      <td>452</td>\n",
       "      <td>145</td>\n",
       "      <td>14</td>\n",
       "      <td>221</td>\n",
       "      <td>137</td>\n",
       "      <td>2082</td>\n",
       "      <td>30.173913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>117</td>\n",
       "      <td>Spencer Dinwiddie</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>DET</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "      <td>4.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>118</td>\n",
       "      <td>Toney Douglas</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>NOP</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>919</td>\n",
       "      <td>124</td>\n",
       "      <td>314</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>113</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>108</td>\n",
       "      <td>358</td>\n",
       "      <td>7.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>130</td>\n",
       "      <td>Tyler Ennis</td>\n",
       "      <td>PG</td>\n",
       "      <td>21</td>\n",
       "      <td>MIL</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>390</td>\n",
       "      <td>54</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>44</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>42</td>\n",
       "      <td>133</td>\n",
       "      <td>3.594595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>132</td>\n",
       "      <td>Tyreke Evans</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>NOP</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>765</td>\n",
       "      <td>136</td>\n",
       "      <td>314</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>109</td>\n",
       "      <td>129</td>\n",
       "      <td>164</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>73</td>\n",
       "      <td>66</td>\n",
       "      <td>379</td>\n",
       "      <td>15.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>135</td>\n",
       "      <td>Jordan Farmar</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>MEM</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>10.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>138</td>\n",
       "      <td>Raymond Felton</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>DAL</td>\n",
       "      <td>71</td>\n",
       "      <td>28</td>\n",
       "      <td>1929</td>\n",
       "      <td>241</td>\n",
       "      <td>588</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>193</td>\n",
       "      <td>224</td>\n",
       "      <td>245</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>118</td>\n",
       "      <td>657</td>\n",
       "      <td>9.253521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>141</td>\n",
       "      <td>Tim Frazier</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>TOT</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>402</td>\n",
       "      <td>45</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>44</td>\n",
       "      <td>59</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>124</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>375</td>\n",
       "      <td>Derrick Rose</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>CHI</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>1890</td>\n",
       "      <td>414</td>\n",
       "      <td>948</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>167</td>\n",
       "      <td>206</td>\n",
       "      <td>284</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>158</td>\n",
       "      <td>80</td>\n",
       "      <td>1004</td>\n",
       "      <td>17.016949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>377</td>\n",
       "      <td>Terry Rozier</td>\n",
       "      <td>PG</td>\n",
       "      <td>21</td>\n",
       "      <td>BOS</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>21</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>1.709677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>378</td>\n",
       "      <td>Ricky Rubio</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>MIN</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>2038</td>\n",
       "      <td>192</td>\n",
       "      <td>513</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>250</td>\n",
       "      <td>286</td>\n",
       "      <td>584</td>\n",
       "      <td>136</td>\n",
       "      <td>8</td>\n",
       "      <td>163</td>\n",
       "      <td>174</td>\n",
       "      <td>682</td>\n",
       "      <td>10.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>381</td>\n",
       "      <td>D'Angelo Russell</td>\n",
       "      <td>PG</td>\n",
       "      <td>19</td>\n",
       "      <td>LAL</td>\n",
       "      <td>70</td>\n",
       "      <td>38</td>\n",
       "      <td>1922</td>\n",
       "      <td>345</td>\n",
       "      <td>821</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>198</td>\n",
       "      <td>237</td>\n",
       "      <td>239</td>\n",
       "      <td>81</td>\n",
       "      <td>12</td>\n",
       "      <td>165</td>\n",
       "      <td>119</td>\n",
       "      <td>918</td>\n",
       "      <td>13.114286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>384</td>\n",
       "      <td>Dennis Schroder</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>ATL</td>\n",
       "      <td>71</td>\n",
       "      <td>6</td>\n",
       "      <td>1451</td>\n",
       "      <td>298</td>\n",
       "      <td>686</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>164</td>\n",
       "      <td>186</td>\n",
       "      <td>320</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>158</td>\n",
       "      <td>120</td>\n",
       "      <td>800</td>\n",
       "      <td>11.267606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>389</td>\n",
       "      <td>Ramon Sessions</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>WAS</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>1420</td>\n",
       "      <td>234</td>\n",
       "      <td>503</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>153</td>\n",
       "      <td>174</td>\n",
       "      <td>180</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>88</td>\n",
       "      <td>686</td>\n",
       "      <td>9.527778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>394</td>\n",
       "      <td>Donald Sloan</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>BRK</td>\n",
       "      <td>51</td>\n",
       "      <td>33</td>\n",
       "      <td>1117</td>\n",
       "      <td>126</td>\n",
       "      <td>288</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>141</td>\n",
       "      <td>156</td>\n",
       "      <td>222</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>351</td>\n",
       "      <td>6.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>395</td>\n",
       "      <td>Marcus Smart</td>\n",
       "      <td>PG</td>\n",
       "      <td>21</td>\n",
       "      <td>BOS</td>\n",
       "      <td>51</td>\n",
       "      <td>10</td>\n",
       "      <td>1379</td>\n",
       "      <td>156</td>\n",
       "      <td>455</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>152</td>\n",
       "      <td>213</td>\n",
       "      <td>142</td>\n",
       "      <td>81</td>\n",
       "      <td>13</td>\n",
       "      <td>63</td>\n",
       "      <td>158</td>\n",
       "      <td>472</td>\n",
       "      <td>9.254902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>397</td>\n",
       "      <td>Ish Smith</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>TOT</td>\n",
       "      <td>67</td>\n",
       "      <td>43</td>\n",
       "      <td>1881</td>\n",
       "      <td>338</td>\n",
       "      <td>839</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>215</td>\n",
       "      <td>251</td>\n",
       "      <td>433</td>\n",
       "      <td>78</td>\n",
       "      <td>15</td>\n",
       "      <td>145</td>\n",
       "      <td>112</td>\n",
       "      <td>826</td>\n",
       "      <td>12.328358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>397</td>\n",
       "      <td>Ish Smith</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>NOP</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>617</td>\n",
       "      <td>98</td>\n",
       "      <td>228</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "      <td>154</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>38</td>\n",
       "      <td>239</td>\n",
       "      <td>8.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>397</td>\n",
       "      <td>Ish Smith</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>PHI</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>1264</td>\n",
       "      <td>240</td>\n",
       "      <td>611</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>137</td>\n",
       "      <td>159</td>\n",
       "      <td>279</td>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>587</td>\n",
       "      <td>14.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>401</td>\n",
       "      <td>Russ Smith</td>\n",
       "      <td>PG</td>\n",
       "      <td>24</td>\n",
       "      <td>MEM</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>1.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>410</td>\n",
       "      <td>Rodney Stuckey</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>IND</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1070</td>\n",
       "      <td>153</td>\n",
       "      <td>372</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>117</td>\n",
       "      <td>133</td>\n",
       "      <td>115</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>424</td>\n",
       "      <td>9.021277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>413</td>\n",
       "      <td>Jeff Teague</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>ATL</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>1984</td>\n",
       "      <td>366</td>\n",
       "      <td>851</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>159</td>\n",
       "      <td>189</td>\n",
       "      <td>411</td>\n",
       "      <td>88</td>\n",
       "      <td>20</td>\n",
       "      <td>194</td>\n",
       "      <td>148</td>\n",
       "      <td>1051</td>\n",
       "      <td>15.014286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>417</td>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>BOS</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>2341</td>\n",
       "      <td>520</td>\n",
       "      <td>1212</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>176</td>\n",
       "      <td>215</td>\n",
       "      <td>464</td>\n",
       "      <td>83</td>\n",
       "      <td>6</td>\n",
       "      <td>187</td>\n",
       "      <td>149</td>\n",
       "      <td>1595</td>\n",
       "      <td>22.152778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>430</td>\n",
       "      <td>Beno Udrih</td>\n",
       "      <td>PG</td>\n",
       "      <td>33</td>\n",
       "      <td>TOT</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>707</td>\n",
       "      <td>86</td>\n",
       "      <td>198</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>72</td>\n",
       "      <td>116</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>51</td>\n",
       "      <td>205</td>\n",
       "      <td>4.659091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>430</td>\n",
       "      <td>Beno Udrih</td>\n",
       "      <td>PG</td>\n",
       "      <td>33</td>\n",
       "      <td>MEM</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>20</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>47</td>\n",
       "      <td>5.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>430</td>\n",
       "      <td>Beno Udrih</td>\n",
       "      <td>PG</td>\n",
       "      <td>33</td>\n",
       "      <td>MIA</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>587</td>\n",
       "      <td>66</td>\n",
       "      <td>152</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>57</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>158</td>\n",
       "      <td>4.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>433</td>\n",
       "      <td>Greivis Vasquez</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>MIL</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>39</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>114</td>\n",
       "      <td>7.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>441</td>\n",
       "      <td>Kemba Walker</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>CHO</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>2555</td>\n",
       "      <td>508</td>\n",
       "      <td>1181</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>263</td>\n",
       "      <td>315</td>\n",
       "      <td>371</td>\n",
       "      <td>114</td>\n",
       "      <td>32</td>\n",
       "      <td>151</td>\n",
       "      <td>102</td>\n",
       "      <td>1496</td>\n",
       "      <td>21.070423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>442</td>\n",
       "      <td>John Wall</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>WAS</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>2603</td>\n",
       "      <td>541</td>\n",
       "      <td>1269</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>311</td>\n",
       "      <td>352</td>\n",
       "      <td>729</td>\n",
       "      <td>139</td>\n",
       "      <td>54</td>\n",
       "      <td>295</td>\n",
       "      <td>147</td>\n",
       "      <td>1450</td>\n",
       "      <td>20.138889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>444</td>\n",
       "      <td>C.J. Watson</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>ORL</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>505</td>\n",
       "      <td>33</td>\n",
       "      <td>107</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>71</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "      <td>106</td>\n",
       "      <td>4.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>445</td>\n",
       "      <td>Briante Weber</td>\n",
       "      <td>PG</td>\n",
       "      <td>23</td>\n",
       "      <td>MEM</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>166</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>4.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>448</td>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>OKC</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>2485</td>\n",
       "      <td>596</td>\n",
       "      <td>1312</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>427</td>\n",
       "      <td>557</td>\n",
       "      <td>752</td>\n",
       "      <td>150</td>\n",
       "      <td>20</td>\n",
       "      <td>305</td>\n",
       "      <td>183</td>\n",
       "      <td>1700</td>\n",
       "      <td>23.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>454</td>\n",
       "      <td>Deron Williams</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>DAL</td>\n",
       "      <td>62</td>\n",
       "      <td>60</td>\n",
       "      <td>2004</td>\n",
       "      <td>302</td>\n",
       "      <td>735</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>161</td>\n",
       "      <td>181</td>\n",
       "      <td>362</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>143</td>\n",
       "      <td>146</td>\n",
       "      <td>867</td>\n",
       "      <td>13.983871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>456</td>\n",
       "      <td>Elliot Williams</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>MEM</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>459</td>\n",
       "      <td>Mo Williams</td>\n",
       "      <td>PG</td>\n",
       "      <td>33</td>\n",
       "      <td>CLE</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>691</td>\n",
       "      <td>122</td>\n",
       "      <td>278</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>93</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>54</td>\n",
       "      <td>57</td>\n",
       "      <td>311</td>\n",
       "      <td>8.885714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>465</td>\n",
       "      <td>Delon Wright</td>\n",
       "      <td>PG</td>\n",
       "      <td>23</td>\n",
       "      <td>TOR</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>14</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>466</td>\n",
       "      <td>Tony Wroten</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>PHI</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>144</td>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>67</td>\n",
       "      <td>8.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>468</td>\n",
       "      <td>Joseph Young</td>\n",
       "      <td>PG</td>\n",
       "      <td>23</td>\n",
       "      <td>IND</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>340</td>\n",
       "      <td>56</td>\n",
       "      <td>146</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>141</td>\n",
       "      <td>3.916667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk                   Player Pos Age   Tm   G  GS    MP   FG   FGA  \\\n",
       "25    23            Keith Appling  PG  23  ORL   5   0    27    2     8   \n",
       "29    27            D.J. Augustin  PG  28  TOT  53   0   987  124   291   \n",
       "30    27            D.J. Augustin  PG  28  OKC  34   0   519   46   121   \n",
       "31    27            D.J. Augustin  PG  28  DEN  19   0   468   78   170   \n",
       "36    32               J.J. Barea  PG  31  DAL  66  10  1441  258   591   \n",
       "51    46         Patrick Beverley  PG  27  HOU  62  54  1760  216   508   \n",
       "56    51              Steve Blake  PG  35  DET  49   0   818   76   198   \n",
       "57    52             Eric Bledsoe  PG  26  PHO  31  31  1059  223   492   \n",
       "68    62             Aaron Brooks  PG  31  CHI  59   0   944  159   415   \n",
       "70    64            Lorenzo Brown  PG  25  PHO   8   0    61    8    25   \n",
       "77    69               Trey Burke  PG  23  UTA  63   0  1356  261   629   \n",
       "83    75            Jose Calderon  PG  34  NYK  69  69  1963  202   440   \n",
       "85    77            Isaiah Canaan  PG  24  PHI  71  33  1800  238   656   \n",
       "90    81  Michael Carter-Williams  PG  24  MIL  54  37  1649  251   555   \n",
       "93    84           Mario Chalmers  PG  29  TOT  61   7  1373  182   444   \n",
       "94    84           Mario Chalmers  PG  29  MIA   6   0   120   10    32   \n",
       "95    84           Mario Chalmers  PG  29  MEM  55   7  1253  172   412   \n",
       "100   89              Norris Cole  PG  27  NOP  45  23  1198  196   484   \n",
       "101   90          Darren Collison  PG  28  SAC  67  11  1982  331   690   \n",
       "103   92              Mike Conley  PG  28  MEM  56  56  1760  294   696   \n",
       "106   95             Bryce Cotton  PG  23  PHO   3   0    33    2     8   \n",
       "117  103               Seth Curry  PG  25  SAC  34   1   391   48   110   \n",
       "118  104            Stephen Curry  PG  27  GSW  69  69  2341  703  1387   \n",
       "131  117        Spencer Dinwiddie  PG  22  DET   9   0   111   14    35   \n",
       "132  118            Toney Douglas  PG  29  NOP  50   8   919  124   314   \n",
       "147  130              Tyler Ennis  PG  21  MIL  37   5   390   54   125   \n",
       "149  132             Tyreke Evans  PG  26  NOP  25  25   765  136   314   \n",
       "152  135            Jordan Farmar  PG  29  MEM   3   1    77   10    24   \n",
       "155  138           Raymond Felton  PG  31  DAL  71  28  1929  241   588   \n",
       "161  141              Tim Frazier  PG  25  TOT  40   1   402   45   105   \n",
       "..   ...                      ...  ..  ..  ...  ..  ..   ...  ...   ...   \n",
       "467  375             Derrick Rose  PG  27  CHI  59  59  1890  414   948   \n",
       "469  377             Terry Rozier  PG  21  BOS  31   0   209   21    79   \n",
       "470  378              Ricky Rubio  PG  25  MIN  66  66  2038  192   513   \n",
       "474  381         D'Angelo Russell  PG  19  LAL  70  38  1922  345   821   \n",
       "479  384          Dennis Schroder  PG  22  ATL  71   6  1451  298   686   \n",
       "484  389           Ramon Sessions  PG  29  WAS  72   0  1420  234   503   \n",
       "489  394             Donald Sloan  PG  28  BRK  51  33  1117  126   288   \n",
       "490  395             Marcus Smart  PG  21  BOS  51  10  1379  156   455   \n",
       "492  397                Ish Smith  PG  27  TOT  67  43  1881  338   839   \n",
       "493  397                Ish Smith  PG  27  NOP  27   3   617   98   228   \n",
       "494  397                Ish Smith  PG  27  PHI  40  40  1264  240   611   \n",
       "501  401               Russ Smith  PG  24  MEM  15   0    66    7    22   \n",
       "516  410           Rodney Stuckey  PG  29  IND  47   0  1070  153   372   \n",
       "519  413              Jeff Teague  PG  27  ATL  70  69  1984  366   851   \n",
       "523  417            Isaiah Thomas  PG  26  BOS  72  69  2341  520  1212   \n",
       "541  430               Beno Udrih  PG  33  TOT  44   5   707   86   198   \n",
       "542  430               Beno Udrih  PG  33  MEM   8   0   120   20    46   \n",
       "543  430               Beno Udrih  PG  33  MIA  36   5   587   66   152   \n",
       "548  433          Greivis Vasquez  PG  29  MIL  16   0   361   39   112   \n",
       "557  441             Kemba Walker  PG  25  CHO  71  71  2555  508  1181   \n",
       "558  442                John Wall  PG  25  WAS  72  72  2603  541  1269   \n",
       "560  444              C.J. Watson  PG  31  ORL  25   2   505   33   107   \n",
       "561  445            Briante Weber  PG  23  MEM   6   4   166   13    38   \n",
       "566  448        Russell Westbrook  PG  27  OKC  72  72  2485  596  1312   \n",
       "572  454           Deron Williams  PG  31  DAL  62  60  2004  302   735   \n",
       "574  456          Elliot Williams  PG  26  MEM   5   0    45    2    10   \n",
       "577  459              Mo Williams  PG  33  CLE  35  14   691  122   278   \n",
       "584  465             Delon Wright  PG  23  TOR  21   0   116   14    39   \n",
       "585  466              Tony Wroten  PG  22  PHI   8   3   144   22    65   \n",
       "587  468             Joseph Young  PG  23  IND  36   0   340   56   146   \n",
       "\n",
       "       ...      ORB  DRB  TRB  AST  STL BLK  TOV   PF   PTS        PPG  \n",
       "25     ...        0    1    1    1    1   0    3    5     6   1.200000  \n",
       "29     ...        7   75   82  159   31   3   65   76   391   7.377358  \n",
       "30     ...        5   38   43   66   13   2   30   41   142   4.176471  \n",
       "31     ...        2   37   39   93   18   1   35   35   249  13.105263  \n",
       "36     ...       23  103  126  259   20   2   97  100   663  10.045455  \n",
       "51     ...       60  151  211  187   82  23   83  203   593   9.564516  \n",
       "56     ...        8   62   70  159   19   4   73   64   205   4.183673  \n",
       "57     ...       18  107  125  190   63  19  110   75   632  20.387097  \n",
       "68     ...       18   76   94  152   25   9   68  116   420   7.118644  \n",
       "70     ...        0    7    7   11    3   1    9    4    20   2.500000  \n",
       "77     ...       18   94  112  146   35   6   80   86   676  10.730159  \n",
       "83     ...       20  198  218  285   60   7   84  126   527   7.637681  \n",
       "85     ...       20  141  161  129   53  10   88  123   771  10.859155  \n",
       "90     ...       48  228  276  281   80  41  153  162   623  11.537037  \n",
       "93     ...       24  133  157  230   88  13  111  181   628  10.295082  \n",
       "94     ...        1   13   14   19    8   1    9   16    33   5.500000  \n",
       "95     ...       23  120  143  211   80  12  102  165   595  10.818182  \n",
       "100    ...       10  141  151  167   37   5   75  102   477  10.600000  \n",
       "101    ...       26  122  148  273   70   3  115  127   909  13.567164  \n",
       "103    ...       26  138  164  342   67  16   84  100   857  15.303571  \n",
       "106    ...        0    0    0    3    3   0    5    2     4   1.333333  \n",
       "117    ...        6   28   34   26   13   3   17   24   144   4.235294  \n",
       "118    ...       61  312  373  452  145  14  221  137  2082  30.173913  \n",
       "131    ...        0    9    9   13    2   0    5   16    40   4.444444  \n",
       "132    ...       17   83  100  113   58   5   45  108   358   7.160000  \n",
       "147    ...        7   37   44   50   13   1   24   42   133   3.594595  \n",
       "149    ...       20  109  129  164   32   8   73   66   379  15.160000  \n",
       "152    ...        0    6    6   11    6   0    5    6    31  10.333333  \n",
       "155    ...       31  193  224  245   60  12  100  118   657   9.253521  \n",
       "161    ...       15   44   59   66   15   1   43   44   124   3.100000  \n",
       "..     ...      ...  ...  ...  ...  ...  ..  ...  ...   ...        ...  \n",
       "467    ...       39  167  206  284   39  13  158   80  1004  17.016949  \n",
       "469    ...       18   24   42   33    3   1   14   15    53   1.709677  \n",
       "470    ...       36  250  286  584  136   8  163  174   682  10.333333  \n",
       "474    ...       39  198  237  239   81  12  165  119   918  13.114286  \n",
       "479    ...       22  164  186  320   64   8  158  120   800  11.267606  \n",
       "484    ...       21  153  174  180   40   5   92   88   686   9.527778  \n",
       "489    ...       15  141  156  222   30   3   71   64   351   6.882353  \n",
       "490    ...       61  152  213  142   81  13   63  158   472   9.254902  \n",
       "492    ...       36  215  251  433   78  15  145  112   826  12.328358  \n",
       "493    ...       14   78   92  154   24   6   45   38   239   8.851852  \n",
       "494    ...       22  137  159  279   54   9  100   74   587  14.675000  \n",
       "501    ...        1    8    9   11    5   1    7    5    23   1.533333  \n",
       "516    ...       16  117  133  115   35   7   61   58   424   9.021277  \n",
       "519    ...       30  159  189  411   88  20  194  148  1051  15.014286  \n",
       "523    ...       39  176  215  464   83   6  187  149  1595  22.152778  \n",
       "541    ...        8   64   72  116   14   1   53   51   205   4.659091  \n",
       "542    ...        2    7    9   26    3   1   11   11    47   5.875000  \n",
       "543    ...        6   57   63   90   11   0   42   40   158   4.388889  \n",
       "548    ...        4   33   37   71    7   0   30   33   114   7.125000  \n",
       "557    ...       52  263  315  371  114  32  151  102  1496  21.070423  \n",
       "558    ...       41  311  352  729  139  54  295  147  1450  20.138889  \n",
       "560    ...        9   41   50   71   15   4   19   38   106   4.240000  \n",
       "561    ...        8   16   24   20    9   3    9   11    29   4.833333  \n",
       "566    ...      130  427  557  752  150  20  305  183  1700  23.611111  \n",
       "572    ...       20  161  181  362   59  14  143  146   867  13.983871  \n",
       "574    ...        1    3    4    4    0   0    1    2     8   1.600000  \n",
       "577    ...        5   61   66   93   11   5   54   57   311   8.885714  \n",
       "584    ...        3   18   21   13    4   0    7    5    42   2.000000  \n",
       "585    ...        2   19   21   20    3   0   29   11    67   8.375000  \n",
       "587    ...        6   39   45   55   13   0   28   28   141   3.916667  \n",
       "\n",
       "[124 rows x 31 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_guards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "point_guards['AST'] = point_guards['AST'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "point_guards['TOV'] = point_guards['TOV'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "point_guards['ATR'] = point_guards['AST'] / point_guards['TOV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25     0.333333\n",
       "29     2.446154\n",
       "30     2.200000\n",
       "31     2.657143\n",
       "36     2.670103\n",
       "51     2.253012\n",
       "56     2.178082\n",
       "57     1.727273\n",
       "68     2.235294\n",
       "70     1.222222\n",
       "77     1.825000\n",
       "83     3.392857\n",
       "85     1.465909\n",
       "90     1.836601\n",
       "93     2.072072\n",
       "94     2.111111\n",
       "95     2.068627\n",
       "100    2.226667\n",
       "101    2.373913\n",
       "103    4.071429\n",
       "106    0.600000\n",
       "117    1.529412\n",
       "118    2.045249\n",
       "131    2.600000\n",
       "132    2.511111\n",
       "147    2.083333\n",
       "149    2.246575\n",
       "152    2.200000\n",
       "155    2.450000\n",
       "161    1.534884\n",
       "         ...   \n",
       "467    1.797468\n",
       "469    2.357143\n",
       "470    3.582822\n",
       "474    1.448485\n",
       "479    2.025316\n",
       "484    1.956522\n",
       "489    3.126761\n",
       "490    2.253968\n",
       "492    2.986207\n",
       "493    3.422222\n",
       "494    2.790000\n",
       "501    1.571429\n",
       "516    1.885246\n",
       "519    2.118557\n",
       "523    2.481283\n",
       "541    2.188679\n",
       "542    2.363636\n",
       "543    2.142857\n",
       "548    2.366667\n",
       "557    2.456954\n",
       "558    2.471186\n",
       "560    3.736842\n",
       "561    2.222222\n",
       "566    2.465574\n",
       "572    2.531469\n",
       "574    4.000000\n",
       "577    1.722222\n",
       "584    1.857143\n",
       "585    0.689655\n",
       "587    1.964286\n",
       "Name: ATR, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_guards['ATR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11414f5d0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JmZ0NnAS8HbF7FHCSuz8VM+aGM2vmVAZtny65MCtW6xg7biIzb7mSlcsX0bFy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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136a4750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(point_guards['PPG'], point_guards['ATR'], c='y')\n",
    "plt.title(\"Point Guards\")\n",
    "plt.xlabel('Points Per Game', fontsize=13)\n",
    "plt.ylabel('Assist Turnover Ratio', fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_clusters = 5\n",
    "# Use numpy's random function to generate a list, length: num_clusters, of indices\n",
    "random_initial_points = np.random.choice(point_guards.index, size=num_clusters)\n",
    "# Use the random indices to create the centroids\n",
    "centroids = point_guards.ix[random_initial_points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116b78ed0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wcYKup5XAH4mZIIA/Ax8B7ozYNwyYYmaHAHPc/eqYx2wYQQHfrezHbEZ1oYwY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\nISMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a86f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(point_guards['PPG'], point_guards['ATR'], c='yellow')\n",
    "plt.scatter(centroids['PPG'], centroids['ATR'], c='red')\n",
    "plt.title(\"Centroids\")\n",
    "plt.xlabel('Points Per Game', fontsize=13)\n",
    "plt.ylabel('Assist Turnover Ratio', fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def centroids_to_dict(centroids):\n",
    "    dictionary = dict()\n",
    "    # iterating counter we use to generate a cluster_id\n",
    "    counter = 0\n",
    "\n",
    "    # iterate a pandas data frame row-wise using .iterrows()\n",
    "    for index, row in centroids.iterrows():\n",
    "        coordinates = [row['PPG'], row['ATR']]\n",
    "        dictionary[counter] = coordinates\n",
    "        counter += 1\n",
    "\n",
    "    return dictionary\n",
    "\n",
    "centroids_dict = centroids_to_dict(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2360679775\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def calculate_distance(centroid, player_values):\n",
    "    root_distance = 0\n",
    "    \n",
    "    for x in range(0, len(centroid)):\n",
    "        difference = centroid[x] - player_values[x]\n",
    "        squared_difference = difference**2\n",
    "        root_distance += squared_difference\n",
    "\n",
    "    euclid_distance = math.sqrt(root_distance)\n",
    "    return euclid_distance\n",
    "\n",
    "q = [5, 2]\n",
    "p = [3,1]\n",
    "\n",
    "# Sqrt(5) = ~2.24\n",
    "print(calculate_distance(q, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def assign_to_cluster(row):\n",
    "    lowest_distance = -1\n",
    "    closest_cluster = -1\n",
    "    \n",
    "    for cluster_id, centroid in centroids_dict.items():\n",
    "        df_row = [row['PPG'], row['ATR']]\n",
    "        euclidean_distance = calculate_distance(centroid, df_row)\n",
    "        \n",
    "        if lowest_distance == -1:\n",
    "            lowest_distance = euclidean_distance\n",
    "            closest_cluster = cluster_id \n",
    "        elif euclidean_distance < lowest_distance:\n",
    "            lowest_distance = euclidean_distance\n",
    "            closest_cluster = cluster_id\n",
    "    return closest_cluster\n",
    "\n",
    "point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+EMeZ2Yfd/UUz2wmYAqwFbnJ3FfTrJZWo3hq366i7RXlxzlOLGUndqdSaB838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\nIhJJCUJERCIpQYiISCQlCBERifT/Afl1z2fcdW64AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a99090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_clusters(df, num_clusters):\n",
    "    colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n",
    "\n",
    "    for n in range(num_clusters):\n",
    "        clustered_df = df[df['cluster'] == n]\n",
    "        plt.scatter(clustered_df['PPG'], clustered_df['ATR'], c=colors[n-1])\n",
    "        plt.xlabel('Points Per Game', fontsize=13)\n",
    "        plt.ylabel('Assist Turnover Ratio', fontsize=13)\n",
    "\n",
    "visualize_clusters(point_guards, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def recalculate_centroids(df):\n",
    "    new_centroids_dict = dict()\n",
    "    # 0..1...2...3...4\n",
    "    for cluster_id in range(0, num_clusters):\n",
    "        # Finish the logic\n",
    "        return new_centroids_dict\n",
    "\n",
    "centroids_dict = recalculate_centroids(point_guards)\n",
    "def recalculate_centroids(df):\n",
    "    new_centroids_dict = dict()\n",
    "    \n",
    "    for cluster_id in range(0, num_clusters):\n",
    "        values_in_cluster = df[df['cluster'] == cluster_id]\n",
    "        # Calculate new centroid using mean of values in the cluster\n",
    "        new_centroid = [np.average(values_in_cluster['PPG']), np.average(values_in_cluster['ATR'])]\n",
    "        new_centroids_dict[cluster_id] = new_centroid\n",
    "    return new_centroids_dict\n",
    "\n",
    "centroids_dict = recalculate_centroids(point_guards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z5VrzoJk/0iji/GV/BzjEzPoCxwMHAcuBF4Dvx7mImU0GTgLeizg8GjjJ3Z+P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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b3be90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)\n",
    "visualize_clusters(point_guards, num_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M7BAzK9yTGjObamZLzeyh8J+9yzlvT5BpaWHyJVd1+0BxU6qtwwyVrtZzinOe\n7rpWd6rUNFrN/JHeIs5f9reAQ81sIHACcDCwCngO+G6ci5jZdOBk4N2Iw+OBk9392VgR90BzFi7i\n/fSIsr+Bd9bqmDZxArNvvR1f+y7vv/s2u44aTqalpeyWSZy6UEmsHVWpgegZU07ksnAMYlhjg2b+\nSI8VJ0EMd/cXzOwLQLO7LzWzvkD/Mq7zEjABuDXi2HhgppmNBBa7+6wyztsjZNpSbNm8sewy2VF7\nRkw94csdksaWLVtIH3xE2M3StX0l4tSFSmLtqO0ZiC4lN/MnN1j93Utv1WC19EhxEoSb2TTgi8C9\nZjYAmAH8Je5F3P1uM9uzyOEFBF1X64FFZnaMu98b99w9QVOqjaH7jWflk4/Rp/8A+mVWMfv8aZ2+\nL6rfvzBpvPXwLxmxT7LGBqql0tNoVX1Vero4CeJM4MfAJuD7wCeBfyLobuoOV7v7egAzWwwcBPSq\nBJHrntll+K5B98y3p8XqBoqanlqYNPrt1Fh2y6Se1LI6qwarpaeLM4vpT8DheS89Auzfxet1+Ppq\nZo3AUjP7MLAROAq4oYvnrltd7Z6J6ve/asHdHZKGDd6ZHVYsSdTYQHfqzplK5SabWlRfFammon/R\nZnaLu3/dzO6gyP7U7v7PZV4vG557IjDI3eeZ2UyCpLMJeNDd7y/znL1WVGLZJmmc/M+JXrC2vbpz\nplK5yUaD1dLTlfrK89fw30u740Lu/ipwaPh4Qd7r84H53XENSeZgcSV150ylcpONylRIT1c0Qbj7\nj8J/X5z/upmNAN5z93cqHJtIp7pzppLqM4l01GmnqZl9DLjS3Y80s1MIxgg2mtlX3f2+SgcoUkp3\nzlSq1LRYkXoVZ1TtSuAhM0sB/w58DVhLsJJaCUJ6jFpWlxVJojgbBn3E3f8dOAAYDNwRDiQXW9cg\nIiI9QJwEscHMRgNfBR519/fN7CBgdWVDExGRWorTxTQHeAHYAfhHM/sE8H/AuZUMTEREaqvTFoS7\nXwV8FNjH3R8GXgaOcvfrKh2ciIjUTtyln3u6+0NmNpRgp7m1Zva8u2+sYGySp952bBOR+hdnP4jL\ngJvCp/OAfQnqMRVuQyoVNPvW2zvsFzH71ttrHZKI9HBxWhDHA58ys12AY4B9CAaoX61kYNKRr32X\nXT+0dZWvr43aWkNEpPvEmcU02N3fBD4HLHf35cAHFBTek8p6/923O+xi9v67b9c4IhHp6eK0IJ41\ns6uAI4ChAT4SAAAN80lEQVS7zWxX4HLg9xWNTDrYd9Rwlj75KA0DdqR10wb2Hz2i1iGJSA8XpwVx\nCsECuaeAHwB7A8OB31UuLCk089QTOXjkrozZZSAHj9yVGaeocqiIVFac/SBWEJTXIFwgdwbwWeBA\n4JKKRifteluVVhGpvTjF+hoIVlFPIZi9dDtwHPCbyoYmIiK1VGrDoBEE242eBjQD1xHMYDrb3Zur\nE56IiNRKqTGIV4FxwAR3P9DdrwW06a6ISC9RqovpdoJ1D4PM7AbgXopsPRqHmR0CzHL3zxS8fixw\nAcHU2ZvcfV5XryEiIt2naAvC3U8GPkSwX/QsghZFE0GroixmNh24Huhf8HoDwX4TnwWOBE4Ly3mI\niEiNlZzm6u4t7v5jd98fOIGgVfGgmT0b3vTjegmYEPH6vsAyd1/v7h8AjwOHl3FeERGpkDjrIABw\n99+5+ynASIJtR08q4713Ez1+0QjkLwl+B9gl7nlFRKRy4lZzbefu64G54T/baz1BksjZGWjphvOK\niMh2KjtBbKfC+k0vAHuZWROwgaB7aXaVYxIRkQjVThBZADObCAxy93lmdg7wAEHymOfuK6sck4iI\nRKhagnD3V4FDw8cL8l5fDCyuVhwiIhJP7EFqERHpXZQgREQkkhKEiIhEUoIQEZFIShAiIhJJCUJE\nRCIpQYiISCQlCBERiaQEISIikZQgREQkUrVrMUkvlslkmD3/ata2tjC4oYnpk84m3ZSudVgiUoRa\nEFI1s+dfzbIhq8iM3MyyIauYfdvVtQ5JREpQgpCqWdvaQioVVHxPpVKsbdXWHyJJpgQhVTO4oYls\nNgtANptlcENTjSMSkVI0BlFjmZYW5ixcRKYtRVOqjWkTJ5Bu6pk3zumTzmb2bR3HIEQkuZQgamzO\nwkW8sfv+pFIpNmSzzFm4iIvPOKXWYVVEuinNpVMuqnUYIhKTuphqLNOW6tAvn2kr3JVVRKQ2qtKC\nMLMUcA1wILAJmOzuL+cdnwpMBprDl05392XViK3WmlJtbMhmSaVSZLNZ0n2ytQ5JRASoXhfTl4H+\n7n6omR0CXBm+ljMeONndn61SPIkxbeKE9jGIdJ8sU0/4cudvEhGpgmoliMOA+wHc/Q9m9vGC4+OB\nmWY2Eljs7rOqFFfNpZuaeuyYg4jUt2qNQTQCb+c9bzWz/GsvAM4APgMcZmbHVCkuEREpoloJYj2w\nc/513b0t7/nV7r7O3VuBxcBBVYpLRESKqFaCeAI4BsDMPgksyR0ws0ZgqZntGA5mHwU8XaW4RESk\niGqNQdwNfM7Mngifn2pmE4FB7j7PzGYCjxDMcHrQ3e+vUlwiIlJEVRKEu2eBMwtefjHv+HxgfjVi\nERGReLRQTkREIilBiIhIJNViqqHeVKhPROqPWhA1lCvUt3HM/ry5xwHMWbio1iGJiLRTgqghFeoT\nkSRTgqihplRbhw10VKhPRJJECaKGpk2cwKgVSxj4yhJGrViiQn0ikigapK4hFeoTkSRTC0JERCIp\nQYiISCQlCBERiaQEISIikZQgREQkkhKEiIhEUoIQEZFIShAiIhKpKgvlwq1ErwEOJNg1brK7v5x3\n/FjgAuAD4CZ3n1eNuEREpLhqtSC+DPR390OBmcCVuQNm1hA+/yxwJHCamQ2tUlwiIlJEtRLEYcD9\nAO7+B+Djecf2BZa5+3p3/wB4HDi8SnGJiEgR1UoQjcDbec9bzaxPkWPvALtUKS4RESmiWsX61gM7\n5z3v4+5tecca847tDLSUOFdfgFWrVnVrgCIiPVnePbNv3PdUK0E8AXwR+IWZfRJYknfsBWAvM2sC\nNhB0L80uca6RACeddFKFQhUR6dFGAn+L84PVShB3A58zsyfC56ea2URgkLvPM7NzgAeAFDDP3VeW\nONcfgU8DK4EtlQxaRKQH6UuQHP4Y9w2p3I5mIiIi+bRQTkREIilBiIhIJCUIERGJpAQhIiKRqjWL\nqduZ2evAi+HT37v7+bWMJ19ntaeSxMyeZutCxeXu/s1axpPPzA4BZrn7Z8zsQ8DNQBuw1N2/U9Pg\n8hTE+VHgf9n6t3mtu99Ru+jay9ncCIwB+gGXAM+TsM+zSJwrSN7n2Qe4HjCCz+8MYDPJ+zyj4uxH\nGZ9nXSaI8GbxtLt/qdaxFNFeeyq8eVwZvpYoZtYfwN2PqnUshcxsOnAy8G740pXAee7+WzO71sy+\n5O731C7CQESc44H/dPerahfVNiYBa9z9a+F6oz8DfyJ5n2d+nOkwxotJ3ud5LJB198PM7AjgUoIp\n+kn7PKPi/BVlfJ712sU0HhhtZg+Z2f+a2T61DqhAqdpTSXIgMMjMfm1mvwmTWVK8BEzIez7e3X8b\nPr6PoLhjEmwTJ/AFM3vUzOaZ2aAaxZXvdoJqyRDMhW8FPpbAzzM/zj4E1Z3HA19M0ucZ3vhPC5/u\nCWRI4OdZEOcYgjjL+jwTnyDM7BtmtsTM/pL7N8EiuUvDb74/Am6rbZTbKFV7Kkk2ALPd/e+BM4H5\nSYnT3e8muJHlpPIeJ6ZeV0ScfwCmu/sRwMvARbWIK5+7b3D398xsZ+AO4HwS+HlGxPl94Eng35L0\neQK4e5uZ3Qz8GPgZCfw8oUOcVwPzCf4+Y3+eibgZlOLuN7r7/u5+QO7fwFPAL8PjTxCW30iQUrWn\nkuRFgj8a3H0ZsJbkfZY5+Z9fZ/W6ammRuz8bPr4b+Ggtg8kxs92Bh4Bb3H0hCf08I+JM5OcJ4O6n\nAPsA84CBeYcS83nCNnE+UM7nmfgEUcQPgKkAZnYgwUBWkjwBHAMQUXsqSb4B/CeAme1G8IddqsxJ\nLT1jZrky8P8I/LbUD9fQr80s16V4NPB0LYMBMLPhwK+B77n7LeHLzybt8ywSZxI/z0lmNiN8uomg\n5M9TYT8/JOfzLIyzDbjLzA4OX+v086zLQWpgFnCbmX2BoJ/ylNqGs41tak/VMpgSbgBuMrPfEvzx\nfCOhLR2AfwOuN7MdCAo8/qLG8RRzJvBfZvY+sIqtfcC1NBNoAi4wswuBLHA2QZxJ+jyj4pwGzEnY\n53kXwf83jxLcQ88C/grMS9jnWRjn2QRfpufG/TxVi0lERCLVaxeTiIhUmBKEiIhEUoIQEZFIShAi\nIhJJCUJERCIpQUivZGYDzWxYreMQSbJ6XQchPZSZtRGUAGkjmAefBX5PUB7guU7eexjwU3cfF+NS\njxEsuLy3jNj2BJaztTBflqDMxsPAWe7+ZtxzdXKdTwPTgUOAHQnmq98JXOzuG7vjGiJxqAUhSZMF\nDnb3RnffBRgMLAXuC8uoF+Xuj8dMDgBDtiO+4XnxjQLep5sWRpnZ8QQVN+8lKLDWCHyRoLBi0mqO\nSQ+nFoQkTYq8wmfuvsXMbgLOAdLAOjM7m2D1ahPwR4Jv7y+GpQ5+4e5DzezrwAnAauBLQDNwkbvP\nN7O7gD2AO8zsXOBWglr+hxPU0foN8B13f79EjLn4NpnZbcDC3GtmdibBCuA0QUvl2+7+VhjftQSt\nkEOA4939sbz3NQA/Ac5x9xvzrudmNhGYbmZ9wgJsRxOUwt6HoMb/A8DXwngeDp+fAHwofHxFeO0x\nwGLgJHfPhmW1fwx8HngP+G93v6zI7y29jFoQkmjhDexsYIm7rzOz04DvAscBw4HfEbQu+odvyS8N\n8PcEZdfTwH8BPzGzfu5+PPAa8BV3nxuerxUYRlC87GPASSXCak8QZjYSOJ2gwBxm9lXg3DC+UQQV\nM3+e994Ph89HA48XnPdTBElvm5aCu7e4+/lhctiRoMvpR+4+DPg74GBgYt5bvh7+/mOAI4Drwucf\nAT4H/EP4c7eFv/uewJHASWFyFVELQhLpd+FYBAQ7df0B+Kfw+SRgTm48wsx+SFBP5ojwZ/O96u4/\nC3/up8AcgiTweng8d6PfRFAn/yTgfncfXyK2FLDCzHKP3wUeJWgxQFAA8Sp3/2t43fOBt81sr/D4\nFmCBu38Qce7dgLX5LZewdfKF8Gl/gm/6TwAHuftyM2skSDZrCBJSznx3XxWe47nw98o9fx7YMyyO\n9w/AEHffBLxmZlcQJLxbkF5PCUKS6FPu/kKRY8OAV3JPwm6SFQQ3yb8V/OzqvMe5G3JUq3kWQcvj\nu8CNYfHCb7n7SxE/mwVGlRgs3gP4DzP7Qfg8RZAU9iT4pt5SJDlA0A22q5k1uHtr+PtNyh00s9UE\npeOzZvYlM5saxvNngsHs/N9tXd7jLXQsP90W/uweYXx/C8d3suHra4vEJ72MupgkiUoNRr9GcLMF\n2vf/3gN4azuutx9wm7sfSJBomgn65bsS30rgX9191/CfNMGOgo+Gx0tVx3yCoEUyqcTPYGafAi4E\njnL3se7+ZYKZTvniVOFcSZA4h7l72t13Jfhsjyj9Nukt1IKQenMLcImZPUCw4dF5BDfDh4BPlHGe\nzQQzhAC+BYwzsxMIvnlvJJiZFKXkTKowvu+FrZDlwHeAfycYCyjJ3d83s9OBG8Jxhvnu/raZ/R0w\nA9iJYNvI3QhaI5vNrC9B19jhBOMxsbn762Gcs81sJkEr5A6CLjiNQ4haEJI4Jb/5uvt84CrgHoJ+\n908Dn425PiD/3LcQ7C9xHkGSeYfght5MMFB8ThfjuxW4nmBf4nUEN+9j3P3tUu/Le//dBPsZHwH8\n1czeIZh1tAHY392XEMxKuoNgI6qVBLOVbgL2LRJjqecTCQb7XwGcYL+A78SJVXo+7QchIiKR1IIQ\nEZFIShAiIhJJCUJERCIpQYiISCQlCBERiaQEISIikZQgREQkkhKEiIhEUoIQEZFI/x+owefF2xzK\nsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170d7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "centroids_dict = recalculate_centroids(point_guards)\n",
    "point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)\n",
    "visualize_clusters(point_guards, num_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-114-262ef8954985>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mkmeans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_clusters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint_guards\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PPG'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ATR'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mpoint_guards\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cluster'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/stone20091652/anaconda/lib/python2.7/site-packages/sklearn/cluster/k_means_.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    810\u001b[0m         \"\"\"\n\u001b[1;32m    811\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 812\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_fit_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    813\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    814\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster_centers_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minertia_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter_\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/stone20091652/anaconda/lib/python2.7/site-packages/sklearn/cluster/k_means_.pyc\u001b[0m in \u001b[0;36m_check_fit_data\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    784\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_fit_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m         \u001b[0;34m\"\"\"Verify that the number of samples given is larger than k\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    787\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m             raise ValueError(\"n_samples=%d should be >= n_clusters=%d\" % (\n",
      "\u001b[0;32m/Users/stone20091652/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    396\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    397\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/stone20091652/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     52\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     53\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 54\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=num_clusters)\n",
    "kmeans.fit(point_guards[['PPG', 'ATR']])\n",
    "point_guards['cluster'] = kmeans.labels_\n",
    "\n",
    "visualize_clusters(point_guards, num_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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