10-something-interesting/10.03-nba-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 探索 NBA 数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们首先安装 `Goldsberry` 包，项目源地址：\n",
    "\n",
    "https://github.com/bradleyfay/py-Goldsberry\n",
    "\n",
    "使用 `pip` 安装：\n",
    "\n",
    "    pip install py-goldsberry\n",
    "\n",
    "该包的接口与 `pandas` 兼容，可以与 `pandas` 的 `DataFrame` 一起使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import goldsberry as gb\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当前使用的版本号为："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.8.0.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 球员信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获得 `2015-2016` 赛季运动员的名单："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>DISPLAY_LAST_COMMA_FIRST</th>\n",
       "      <th>FROM_YEAR</th>\n",
       "      <th>GAMES_PLAYED_FLAG</th>\n",
       "      <th>PERSON_ID</th>\n",
       "      <th>PLAYERCODE</th>\n",
       "      <th>ROSTERSTATUS</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_CITY</th>\n",
       "      <th>TEAM_CODE</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>TO_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acy, Quincy</td>\n",
       "      <td>2012</td>\n",
       "      <td>Y</td>\n",
       "      <td>203112</td>\n",
       "      <td>quincy_acy</td>\n",
       "      <td>1</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>kings</td>\n",
       "      <td>1610612758</td>\n",
       "      <td>Kings</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adams, Jordan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Y</td>\n",
       "      <td>203919</td>\n",
       "      <td>jordan_adams</td>\n",
       "      <td>1</td>\n",
       "      <td>MEM</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>grizzlies</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>Grizzlies</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adams, Steven</td>\n",
       "      <td>2013</td>\n",
       "      <td>Y</td>\n",
       "      <td>203500</td>\n",
       "      <td>steven_adams</td>\n",
       "      <td>1</td>\n",
       "      <td>OKC</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>thunder</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>Thunder</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afflalo, Arron</td>\n",
       "      <td>2007</td>\n",
       "      <td>Y</td>\n",
       "      <td>201167</td>\n",
       "      <td>arron_afflalo</td>\n",
       "      <td>1</td>\n",
       "      <td>NYK</td>\n",
       "      <td>New York</td>\n",
       "      <td>knicks</td>\n",
       "      <td>1610612752</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ajinca, Alexis</td>\n",
       "      <td>2008</td>\n",
       "      <td>Y</td>\n",
       "      <td>201582</td>\n",
       "      <td>alexis_ajinca</td>\n",
       "      <td>1</td>\n",
       "      <td>NOP</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>pelicans</td>\n",
       "      <td>1610612740</td>\n",
       "      <td>Pelicans</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DISPLAY_LAST_COMMA_FIRST FROM_YEAR GAMES_PLAYED_FLAG  PERSON_ID  \\\n",
       "0              Acy, Quincy      2012                 Y     203112   \n",
       "1            Adams, Jordan      2014                 Y     203919   \n",
       "2            Adams, Steven      2013                 Y     203500   \n",
       "3           Afflalo, Arron      2007                 Y     201167   \n",
       "4           Ajinca, Alexis      2008                 Y     201582   \n",
       "\n",
       "      PLAYERCODE  ROSTERSTATUS TEAM_ABBREVIATION      TEAM_CITY  TEAM_CODE  \\\n",
       "0     quincy_acy             1               SAC     Sacramento      kings   \n",
       "1   jordan_adams             1               MEM        Memphis  grizzlies   \n",
       "2   steven_adams             1               OKC  Oklahoma City    thunder   \n",
       "3  arron_afflalo             1               NYK       New York     knicks   \n",
       "4  alexis_ajinca             1               NOP    New Orleans   pelicans   \n",
       "\n",
       "      TEAM_ID  TEAM_NAME TO_YEAR  \n",
       "0  1610612758      Kings    2015  \n",
       "1  1610612763  Grizzlies    2015  \n",
       "2  1610612760    Thunder    2015  \n",
       "3  1610612752     Knicks    2015  \n",
       "4  1610612740   Pelicans    2015  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = gb.PlayerList().players()\n",
    "players = pd.DataFrame(players)\n",
    "\n",
    "players.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "球员总数为："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "464\n"
     ]
    }
   ],
   "source": [
    "print len(players)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过查询特定的 `TEAM_ABBREVIATION`，我们可以查看某个球队本赛季的球员，比如 `2014-2015` 赛季的总冠军金州勇士 `GSW`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISPLAY_LAST_COMMA_FIRST</th>\n",
       "      <th>FROM_YEAR</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_CITY</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>PERSON_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Barbosa, Leandro</td>\n",
       "      <td>2003</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>2571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Barnes, Harrison</td>\n",
       "      <td>2012</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>203084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Bogut, Andrew</td>\n",
       "      <td>2005</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>101106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Clark, Ian</td>\n",
       "      <td>2013</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>203546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Curry, Stephen</td>\n",
       "      <td>2009</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>201939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Ezeli, Festus</td>\n",
       "      <td>2012</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>203105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Green, Draymond</td>\n",
       "      <td>2012</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>203110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Iguodala, Andre</td>\n",
       "      <td>2004</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>2738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Livingston, Shaun</td>\n",
       "      <td>2004</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>2733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Looney, Kevon</td>\n",
       "      <td>2015</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>1626172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>McAdoo, James Michael</td>\n",
       "      <td>2014</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>203949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Rush, Brandon</td>\n",
       "      <td>2008</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>201575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Speights, Marreese</td>\n",
       "      <td>2008</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>201578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Thompson, Jason</td>\n",
       "      <td>2008</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>201574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Thompson, Klay</td>\n",
       "      <td>2011</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>202691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DISPLAY_LAST_COMMA_FIRST FROM_YEAR TEAM_ABBREVIATION     TEAM_CITY  \\\n",
       "30          Barbosa, Leandro      2003               GSW  Golden State   \n",
       "33          Barnes, Harrison      2012               GSW  Golden State   \n",
       "52             Bogut, Andrew      2005               GSW  Golden State   \n",
       "86                Clark, Ian      2013               GSW  Golden State   \n",
       "103           Curry, Stephen      2009               GSW  Golden State   \n",
       "135            Ezeli, Festus      2012               GSW  Golden State   \n",
       "164          Green, Draymond      2012               GSW  Golden State   \n",
       "209          Iguodala, Andre      2004               GSW  Golden State   \n",
       "262        Livingston, Shaun      2004               GSW  Golden State   \n",
       "263            Looney, Kevon      2015               GSW  Golden State   \n",
       "279    McAdoo, James Michael      2014               GSW  Golden State   \n",
       "377            Rush, Brandon      2008               GSW  Golden State   \n",
       "398       Speights, Marreese      2008               GSW  Golden State   \n",
       "414          Thompson, Jason      2008               GSW  Golden State   \n",
       "415           Thompson, Klay      2011               GSW  Golden State   \n",
       "\n",
       "    TEAM_NAME  PERSON_ID  \n",
       "30   Warriors       2571  \n",
       "33   Warriors     203084  \n",
       "52   Warriors     101106  \n",
       "86   Warriors     203546  \n",
       "103  Warriors     201939  \n",
       "135  Warriors     203105  \n",
       "164  Warriors     203110  \n",
       "209  Warriors       2738  \n",
       "262  Warriors       2733  \n",
       "263  Warriors    1626172  \n",
       "279  Warriors     203949  \n",
       "377  Warriors     201575  \n",
       "398  Warriors     201578  \n",
       "414  Warriors     201574  \n",
       "415  Warriors     202691  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsw_players = players.ix[players[\"TEAM_ABBREVIATION\"] == \"GSW\"]\n",
    "\n",
    "gsw_players[[\"DISPLAY_LAST_COMMA_FIRST\", \"FROM_YEAR\", \"TEAM_ABBREVIATION\", \"TEAM_CITY\", \"TEAM_NAME\", \"PERSON_ID\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 球员比赛数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过 `DISPLAY_LAST_COMMA_FIRST`，我们来查询宣布本赛季之后退役的科比布莱恩特（`Kobe, Bryant`）的信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>DISPLAY_LAST_COMMA_FIRST</th>\n",
       "      <th>FROM_YEAR</th>\n",
       "      <th>GAMES_PLAYED_FLAG</th>\n",
       "      <th>PERSON_ID</th>\n",
       "      <th>PLAYERCODE</th>\n",
       "      <th>ROSTERSTATUS</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_CITY</th>\n",
       "      <th>TEAM_CODE</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>TO_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Bryant, Kobe</td>\n",
       "      <td>1996</td>\n",
       "      <td>Y</td>\n",
       "      <td>977</td>\n",
       "      <td>kobe_bryant</td>\n",
       "      <td>1</td>\n",
       "      <td>LAL</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>lakers</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Lakers</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DISPLAY_LAST_COMMA_FIRST FROM_YEAR GAMES_PLAYED_FLAG  PERSON_ID  \\\n",
       "64             Bryant, Kobe      1996                 Y        977   \n",
       "\n",
       "     PLAYERCODE  ROSTERSTATUS TEAM_ABBREVIATION    TEAM_CITY TEAM_CODE  \\\n",
       "64  kobe_bryant             1               LAL  Los Angeles    lakers   \n",
       "\n",
       "       TEAM_ID TEAM_NAME TO_YEAR  \n",
       "64  1610612747    Lakers    2015  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobe = players.ix[players[\"DISPLAY_LAST_COMMA_FIRST\"].str.contains(\"Kobe\")]\n",
    "\n",
    "kobe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了方便，我们将 `Kobe` 的 `ID` 放到变量中去："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "kobe_id = 977"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来看本赛季 `Kobe` 的比赛记录："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>DREB</th>\n",
       "      <th>FG3A</th>\n",
       "      <th>FG3M</th>\n",
       "      <th>FG3_PCT</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>...</th>\n",
       "      <th>PF</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>REB</th>\n",
       "      <th>SEASON_ID</th>\n",
       "      <th>STL</th>\n",
       "      <th>TOV</th>\n",
       "      <th>VIDEO_AVAILABLE</th>\n",
       "      <th>WL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.429</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0.313</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>-19</td>\n",
       "      <td>17</td>\n",
       "      <td>977</td>\n",
       "      <td>6</td>\n",
       "      <td>22015</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0.286</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>0.240</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-6</td>\n",
       "      <td>19</td>\n",
       "      <td>977</td>\n",
       "      <td>5</td>\n",
       "      <td>22015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0.286</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>0.321</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>25</td>\n",
       "      <td>977</td>\n",
       "      <td>2</td>\n",
       "      <td>22015</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0.364</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>0.417</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>977</td>\n",
       "      <td>12</td>\n",
       "      <td>22015</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0.636</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.476</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>977</td>\n",
       "      <td>5</td>\n",
       "      <td>22015</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AST  BLK  DREB  FG3A  FG3M  FG3_PCT  FGA  FGM  FG_PCT  FTA ...  PF  \\\n",
       "0    3    0     6     7     3    0.429   16    5   0.313    4 ...   2   \n",
       "1    0    0     4    14     4    0.286   25    6   0.240    4 ...   0   \n",
       "2    4    1     1    14     4    0.286   28    9   0.321    3 ...   4   \n",
       "3    2    0     9    11     4    0.364   24   10   0.417    4 ...   0   \n",
       "4    5    0     3    11     7    0.636   21   10   0.476   12 ...   3   \n",
       "\n",
       "   PLUS_MINUS PTS Player_ID REB  SEASON_ID  STL  TOV  VIDEO_AVAILABLE  WL  \n",
       "0         -19  17       977   6      22015    1    3                1   L  \n",
       "1          -6  19       977   5      22015    0    0                1   L  \n",
       "2          -2  25       977   2      22015    0    2                1   L  \n",
       "3          16  27       977  12      22015    2    1                1   W  \n",
       "4           6  38       977   5      22015    2    2                1   W  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobe_logs = gb.player.game_logs(kobe_id)\n",
    "\n",
    "kobe_logs = pd.DataFrame(kobe_logs.logs())\n",
    "\n",
    "# 最近五场比赛\n",
    "kobe_logs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "截至到全明星赛前，本赛季 `Kobe` 一共参加了 44 场比赛，其场均数据为："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0021500795\n",
       "1     0021500776\n",
       "2     0021500767\n",
       "3     0021500747\n",
       "4     0021500734\n",
       "5     0021500720\n",
       "6     0021500697\n",
       "7     0021500662\n",
       "8     0021500653\n",
       "9     0021500638\n",
       "10    0021500614\n",
       "11    0021500608\n",
       "12    0021500592\n",
       "13    0021500576\n",
       "14    0021500549\n",
       "15    0021500539\n",
       "16    0021500476\n",
       "17    0021500458\n",
       "18    0021500455\n",
       "19    0021500440\n",
       "20    0021500435\n",
       "21    0021500422\n",
       "22    0021500385\n",
       "23    0021500370\n",
       "24    0021500349\n",
       "25    0021500342\n",
       "26    0021500325\n",
       "27    0021500308\n",
       "28    0021500301\n",
       "29    0021500286\n",
       "30    0021500269\n",
       "31    0021500263\n",
       "32    0021500253\n",
       "33    0021500244\n",
       "34    0021500214\n",
       "35    0021500201\n",
       "36    0021500188\n",
       "37    0021500151\n",
       "38    0021500135\n",
       "39    0021500095\n",
       "40    0021500077\n",
       "41    0021500059\n",
       "42    0021500045\n",
       "43    0021500031\n",
       "44    0021500017\n",
       "Name: Game_ID, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobe_logs.Game_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "得分：16.9\n",
      "篮板：4.2\n",
      "助攻：3.4\n",
      "盖帽：0.2\n",
      "时间：29.3\n",
      "抢断：1.0\n",
      "失误：2.2\n",
      "犯规：1.9\n",
      "投篮：34.9%\n",
      "三分：28.0%\n",
      "罚篮：80.3%\n",
      "后篮板：3.5\n",
      "前篮板：0.7\n",
      "正负值：-7.9\n"
     ]
    }
   ],
   "source": [
    "def show_avg_info(avg):\n",
    "    print \"得分：{:.1f}\".format(avg.ix[\"PTS\"])\n",
    "    print \"篮板：{:.1f}\".format(avg.ix[\"REB\"])\n",
    "    print \"助攻：{:.1f}\".format(avg.ix[\"AST\"])\n",
    "    print \"盖帽：{:.1f}\".format(avg.ix[\"BLK\"])\n",
    "    print \"时间：{:.1f}\".format(avg.ix[\"MIN\"])\n",
    "    print \"抢断：{:.1f}\".format(avg.ix[\"STL\"])\n",
    "    print \"失误：{:.1f}\".format(avg.ix[\"TOV\"])\n",
    "    print \"犯规：{:.1f}\".format(avg.ix[\"PF\"])\n",
    "    print \"投篮：{:.1f}%\".format(avg.ix[\"FGM\"] * 100 / avg.ix[\"FGA\"])\n",
    "    print \"三分：{:.1f}%\".format(avg.ix[\"FG3M\"] * 100 / avg.ix[\"FG3A\"])\n",
    "    print \"罚篮：{:.1f}%\".format(avg.ix[\"FTM\"] * 100 / avg.ix[\"FTA\"])\n",
    "    print \"后篮板：{:.1f}\".format(avg.ix[\"DREB\"])\n",
    "    print \"前篮板：{:.1f}\".format(avg.ix[\"OREB\"])\n",
    "    print \"正负值：{:.1f}\".format(avg.ix[\"PLUS_MINUS\"])\n",
    "    \n",
    "show_avg_info(kobe_logs.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再看一下史提芬库里的场均数据（不要问我为什么跪着看球）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "得分：29.8\n",
      "篮板：5.3\n",
      "助攻：6.6\n",
      "盖帽：0.2\n",
      "时间：33.9\n",
      "抢断：2.1\n",
      "失误：3.3\n",
      "犯规：2.0\n",
      "投篮：50.8%\n",
      "三分：45.4%\n",
      "罚篮：91.2%\n",
      "后篮板：4.5\n",
      "前篮板：0.9\n",
      "正负值：15.5\n"
     ]
    }
   ],
   "source": [
    "curry_id = 201939\n",
    "curry_logs = gb.player.game_logs(curry_id)\n",
    "curry_logs = pd.DataFrame(curry_logs.logs())\n",
    "\n",
    "show_avg_info(curry_logs.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然我们也可以对比一下职业生涯的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kobe_career = gb.player.career_stats(kobe_id)\n",
    "curry_career = gb.player.career_stats(curry_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "职业生涯最高："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kobe\n",
      "     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME\n",
      "0  JAN 22 2006  PTS          81      Toronto      Raptors\n",
      "1  JAN 24 2010  REB          16      Toronto      Raptors\n",
      "5  JAN 15 2015  AST          17    Cleveland    Cavaliers\n",
      "Curry\n",
      "     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME\n",
      "0  FEB 27 2013  PTS          54     New York       Knicks\n",
      "1  DEC 28 2015  REB          14   Sacramento        Kings\n",
      "5  DEC 27 2013  AST          16      Phoenix         Suns\n"
     ]
    }
   ],
   "source": [
    "def show_career_high(career):\n",
    "    career_high = pd.DataFrame(career.career_high()).ix[[0,1,5]]\n",
    "    print career_high[[\"GAME_DATE\", \"STAT\", \"STAT_VALUE\", \"VS_TEAM_CITY\", \"VS_TEAM_NAME\"]]\n",
    "\n",
    "print \"Kobe\"\n",
    "show_career_high(kobe_career)\n",
    "\n",
    "print \"Curry\"\n",
    "show_career_high(curry_career)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本赛季最高："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kobe\n",
      "     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY  VS_TEAM_NAME\n",
      "0  FEB 02 2016  PTS          38    Minnesota  Timberwolves\n",
      "1  FEB 04 2016  REB          12  New Orleans      Pelicans\n",
      "5  NOV 15 2015  AST           9      Detroit       Pistons\n",
      "Curry\n",
      "     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME\n",
      "0  OCT 31 2015  PTS          53  New Orleans     Pelicans\n",
      "1  DEC 28 2015  REB          14   Sacramento        Kings\n",
      "5  JAN 25 2016  STL           5  San Antonio        Spurs\n"
     ]
    }
   ],
   "source": [
    "def show_season_high(career):\n",
    "    career_high = pd.DataFrame(career.season_high()).ix[[0,1,5]]\n",
    "    print career_high[[\"GAME_DATE\", \"STAT\", \"STAT_VALUE\", \"VS_TEAM_CITY\", \"VS_TEAM_NAME\"]]\n",
    "\n",
    "print \"Kobe\"\n",
    "show_season_high(kobe_career)\n",
    "\n",
    "print \"Curry\"\n",
    "show_season_high(curry_career)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 比赛信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>DREB</th>\n",
       "      <th>FG3A</th>\n",
       "      <th>FG3M</th>\n",
       "      <th>FG3_PCT</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>...</th>\n",
       "      <th>PTS</th>\n",
       "      <th>REB</th>\n",
       "      <th>SEASON_ID</th>\n",
       "      <th>STL</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>TOV</th>\n",
       "      <th>VIDEO_AVAILABLE</th>\n",
       "      <th>WL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>0.276</td>\n",
       "      <td>124</td>\n",
       "      <td>56</td>\n",
       "      <td>0.452</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>64</td>\n",
       "      <td>22015</td>\n",
       "      <td>7</td>\n",
       "      <td>DET</td>\n",
       "      <td>1610612765</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>0.391</td>\n",
       "      <td>87</td>\n",
       "      <td>53</td>\n",
       "      <td>0.609</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>142</td>\n",
       "      <td>46</td>\n",
       "      <td>22015</td>\n",
       "      <td>9</td>\n",
       "      <td>SAC</td>\n",
       "      <td>1610612758</td>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>0.429</td>\n",
       "      <td>86</td>\n",
       "      <td>52</td>\n",
       "      <td>0.605</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>38</td>\n",
       "      <td>22015</td>\n",
       "      <td>10</td>\n",
       "      <td>SAS</td>\n",
       "      <td>1610612759</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>0.457</td>\n",
       "      <td>95</td>\n",
       "      <td>52</td>\n",
       "      <td>0.547</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>46</td>\n",
       "      <td>22015</td>\n",
       "      <td>10</td>\n",
       "      <td>GSW</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>0.258</td>\n",
       "      <td>104</td>\n",
       "      <td>52</td>\n",
       "      <td>0.500</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>46</td>\n",
       "      <td>22015</td>\n",
       "      <td>10</td>\n",
       "      <td>SAC</td>\n",
       "      <td>1610612758</td>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AST  BLK  DREB  FG3A  FG3M  FG3_PCT  FGA  FGM  FG_PCT  FTA ...  PTS  REB  \\\n",
       "0   28    4    45    29     8    0.276  124   56   0.452   46 ...  147   64   \n",
       "1   30    2    36    23     9    0.391   87   53   0.609   34 ...  142   46   \n",
       "2   34    2    30    21     9    0.429   86   52   0.605   13 ...  123   38   \n",
       "3   29    6    36    35    16    0.457   95   52   0.547   15 ...  131   46   \n",
       "4   34    8    38    31     8    0.258  104   52   0.500   16 ...  122   46   \n",
       "\n",
       "  SEASON_ID STL TEAM_ABBREVIATION     TEAM_ID              TEAM_NAME  TOV  \\\n",
       "0     22015   7               DET  1610612765        Detroit Pistons   11   \n",
       "1     22015   9               SAC  1610612758       Sacramento Kings   15   \n",
       "2     22015  10               SAS  1610612759      San Antonio Spurs   13   \n",
       "3     22015  10               GSW  1610612744  Golden State Warriors   15   \n",
       "4     22015  10               SAC  1610612758       Sacramento Kings   20   \n",
       "\n",
       "   VIDEO_AVAILABLE  WL  \n",
       "0                1   W  \n",
       "1                1   W  \n",
       "2                1   W  \n",
       "3                1   W  \n",
       "4                1   L  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_ids = gb.GameIDs()\n",
    "game_ids = pd.DataFrame(game_ids.game_list())\n",
    "\n",
    "game_ids.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获得运动员的头像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAAC5CAMAAAALbLOrAAAC91BMVEUAAAD9uzT/wzlMTlBaYGNc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\n5c8G4TdahtfBbxc1YQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(\"http://stats.nba.com/media/players/230x185/\"+str(kobe_id)+\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAAC5CAMAAAALbLOrAAADAFBMVEUAAADrsIz4wJs7ccLRmHz7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\n75pOOIevo2HHnQXPLa1zp81/D1j2g3LkhLH2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"http://stats.nba.com/media/players/230x185/\"+str(curry_id)+\".png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "修改了 `goldsberry\\player\\_Player.py` 代码中的错误，使之能够查询退役球员的信息，修改后的代码在本文件夹下，放到安装目录之后下面的代码均可以运行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from goldsberry.player import _Player as pl_old"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1997 年的球员列表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "players_1997 = pl_old.PlayerList(1997)\n",
    "\n",
    "players_1997 = pd.DataFrame(players_1997)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乔丹的球员 ID："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "893"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jordan_id = players_1997[\"PERSON_ID\"].ix[players_1997[\"DISPLAY_LAST_COMMA_FIRST\"].str.contains(\"Jordan, Michael\")]\n",
    "jordan_id = jordan_id[jordan_id.index[0]]\n",
    "jordan_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乔丹在 1997-1998 赛季常规赛表现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "得分：28.7\n",
      "篮板：5.8\n",
      "助攻：3.5\n",
      "盖帽：0.5\n",
      "时间：38.9\n",
      "抢断：1.7\n",
      "失误：2.3\n",
      "犯规：1.8\n",
      "投篮：46.5%\n",
      "三分：23.8%\n",
      "罚篮：78.4%\n",
      "后篮板：4.2\n",
      "前篮板：1.6\n",
      "正负值：7.3\n"
     ]
    }
   ],
   "source": [
    "jordan_logs_1997 = pl_old.game_logs(jordan_id, season=\"1997\")\n",
    "jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs())\n",
    "\n",
    "show_avg_info(jordan_logs_1997.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乔丹在 1997-1998 赛季季后赛表现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "得分：32.4\n",
      "篮板：5.1\n",
      "助攻：3.5\n",
      "盖帽：0.6\n",
      "时间：41.0\n",
      "抢断：1.5\n",
      "失误：2.1\n",
      "犯规：2.2\n",
      "投篮：46.2%\n",
      "三分：30.2%\n",
      "罚篮：81.2%\n",
      "后篮板：3.5\n",
      "前篮板：1.6\n",
      "正负值：7.5\n"
     ]
    }
   ],
   "source": [
    "jordan_logs_1997 = pl_old.game_logs(jordan_id, season=\"1997\", seasontype=2)\n",
    "jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs())\n",
    "\n",
    "show_avg_info(jordan_logs_1997.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "头像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAAC5CAMAAAALbLOrAAAABGdBTUEAALGOfPtRkwAAAwBQTFRF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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"http://stats.nba.com/media/players/230x185/\"+str(jordan_id)+\".png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}