example_notebook.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:9b2e2fb4ed440392735a6ecdf2f25830120294b0e16ede087e05036feeda0cc6"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import basketballCrawler as bc\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Loading in the player dictionary from JSON is straightforward:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "players = bc.loadPlayerDictionary(\"players.json\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Player positions, height, and weight, are already stored in the player objects. We could make a scatter plot by position:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "point_guards =  [players[name] for name in players if \"Point Guard\" in players[name].positions]\n",
      "centers = [players[name] for name in players if \"Center\" in players[name].positions]\n",
      "\n",
      "pg_weights = [int(p.weight) for p in point_guards]\n",
      "c_weights = [int(p.weight) for p in centers]\n",
      "\n",
      "# getting the heights requires parsing of strings, e.g. \"6-3\" -> 75 (inches)\n",
      "def parse_height_value(height_string):\n",
      "    ft,inches = [int(val) for val in height_string.split('-')]\n",
      "    return ft*12 + inches\n",
      "\n",
      "pg_heights = [parse_height_value(p.height) for p in point_guards]\n",
      "c_heights = [parse_height_value(p.height) for p in centers]  \n",
      "\n",
      "plt.scatter(pg_weights,pg_heights,c=\"red\",label=\"PG\")\n",
      "plt.scatter(c_weights,c_heights,c=\"blue\",label=\"C\")\n",
      "plt.xlabel(\"Weight\")\n",
      "plt.ylabel(\"Height\")\n",
      "plt.legend(loc=4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.legend.Legend at 0x7fa7f9c34c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KH3PL177m+zY3+2Vm/uGmJl940EG+ffv29P7zPvEJ/0rGE8Sj4Afvv3/B9Rre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\nATL1LewwoQArAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa7f7e390d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's a pretty interesting outlier. Let's see who it is:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print [p.name for p in centers if int(p.weight) > 350]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'Sim Bhullar']\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also delve into game-by-game stats for individual players."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pau_games = bc.gameLogs(players, u'Pau Gasol')\n",
      "pau_games.reset_index().head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>+/-</th>\n",
        "      <th>3P</th>\n",
        "      <th>3P%</th>\n",
        "      <th>3PA</th>\n",
        "      <th>AST</th>\n",
        "      <th>Age</th>\n",
        "      <th>BLK</th>\n",
        "      <th>DFS</th>\n",
        "      <th>DRB</th>\n",
        "      <th>...</th>\n",
        "      <th>ORB</th>\n",
        "      <th>Opp</th>\n",
        "      <th>PF</th>\n",
        "      <th>PTS</th>\n",
        "      <th>Rk</th>\n",
        "      <th>STL</th>\n",
        "      <th>TOV</th>\n",
        "      <th>TRB</th>\n",
        "      <th>Tm</th>\n",
        "      <th>WinLoss</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2015-10-27</td>\n",
        "      <td> -8</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 35-113</td>\n",
        "      <td> 6</td>\n",
        "      <td> 18.5</td>\n",
        "      <td> 2</td>\n",
        "      <td>...</td>\n",
        "      <td> 0</td>\n",
        "      <td> CLE</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> CHI</td>\n",
        "      <td>  W (+2)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2015-10-28</td>\n",
        "      <td> 23</td>\n",
        "      <td> 0</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 35-114</td>\n",
        "      <td> 1</td>\n",
        "      <td> 32.3</td>\n",
        "      <td> 8</td>\n",
        "      <td>...</td>\n",
        "      <td> 1</td>\n",
        "      <td> BRK</td>\n",
        "      <td> 2</td>\n",
        "      <td> 16</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>  9</td>\n",
        "      <td> CHI</td>\n",
        "      <td> W (+15)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2015-10-30</td>\n",
        "      <td>-16</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 35-116</td>\n",
        "      <td> 3</td>\n",
        "      <td> 45.5</td>\n",
        "      <td> 9</td>\n",
        "      <td>...</td>\n",
        "      <td> 3</td>\n",
        "      <td> DET</td>\n",
        "      <td> 4</td>\n",
        "      <td> 16</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 12</td>\n",
        "      <td> CHI</td>\n",
        "      <td>  L (-4)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2015-11-01</td>\n",
        "      <td>  8</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 35-118</td>\n",
        "      <td> 1</td>\n",
        "      <td> 31.0</td>\n",
        "      <td> 5</td>\n",
        "      <td>...</td>\n",
        "      <td> 3</td>\n",
        "      <td> ORL</td>\n",
        "      <td> 1</td>\n",
        "      <td> 16</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  8</td>\n",
        "      <td> CHI</td>\n",
        "      <td>  W (+5)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2015-11-03</td>\n",
        "      <td> -6</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 35-120</td>\n",
        "      <td> 0</td>\n",
        "      <td> 27.5</td>\n",
        "      <td> 7</td>\n",
        "      <td>...</td>\n",
        "      <td> 1</td>\n",
        "      <td> CHO</td>\n",
        "      <td> 0</td>\n",
        "      <td> 13</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  8</td>\n",
        "      <td> CHI</td>\n",
        "      <td> L (-25)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 31 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "        Date  +/-  3P  3P%  3PA  AST     Age  BLK   DFS  DRB   ...     ORB  \\\n",
        "0 2015-10-27   -8   0    0    1    0  35-113    6  18.5    2   ...       0   \n",
        "1 2015-10-28   23   0  NaN    0    3  35-114    1  32.3    8   ...       1   \n",
        "2 2015-10-30  -16   0    0    1    3  35-116    3  45.5    9   ...       3   \n",
        "3 2015-11-01    8   0    0    2    1  35-118    1  31.0    5   ...       3   \n",
        "4 2015-11-03   -6   0    0    1    3  35-120    0  27.5    7   ...       1   \n",
        "\n",
        "   Opp  PF  PTS  Rk  STL  TOV TRB   Tm  WinLoss  \n",
        "0  CLE   1    2   1    1    0   2  CHI   W (+2)  \n",
        "1  BRK   2   16   2    0    3   9  CHI  W (+15)  \n",
        "2  DET   4   16   3    2    3  12  CHI   L (-4)  \n",
        "3  ORL   1   16   4    1    1   8  CHI   W (+5)  \n",
        "4  CHO   0   13   5    0    0   8  CHI  L (-25)  \n",
        "\n",
        "[5 rows x 31 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we could observe some individual stats for a player, **using the same API calls as before this PR**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "recent_games = pau_games.loc[pau_games.index > '2016-01-01']\n",
      "recent_games.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>+/-</th>\n",
        "      <th>3P</th>\n",
        "      <th>3P%</th>\n",
        "      <th>3PA</th>\n",
        "      <th>AST</th>\n",
        "      <th>Age</th>\n",
        "      <th>BLK</th>\n",
        "      <th>DFS</th>\n",
        "      <th>DRB</th>\n",
        "      <th>FG</th>\n",
        "      <th>...</th>\n",
        "      <th>ORB</th>\n",
        "      <th>Opp</th>\n",
        "      <th>PF</th>\n",
        "      <th>PTS</th>\n",
        "      <th>Rk</th>\n",
        "      <th>STL</th>\n",
        "      <th>TOV</th>\n",
        "      <th>TRB</th>\n",
        "      <th>Tm</th>\n",
        "      <th>WinLoss</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2016-01-03</th>\n",
        "      <td> 13</td>\n",
        "      <td> 0</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 6</td>\n",
        "      <td> 35-181</td>\n",
        "      <td> 4</td>\n",
        "      <td> 52.8</td>\n",
        "      <td> 12</td>\n",
        "      <td>  7</td>\n",
        "      <td>...</td>\n",
        "      <td> 1</td>\n",
        "      <td> TOR</td>\n",
        "      <td> 0</td>\n",
        "      <td> 19</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 13</td>\n",
        "      <td> CHI</td>\n",
        "      <td>  W (+2)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-01-05</th>\n",
        "      <td> 11</td>\n",
        "      <td> 2</td>\n",
        "      <td>  1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 35-183</td>\n",
        "      <td> 3</td>\n",
        "      <td> 55.8</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td>...</td>\n",
        "      <td> 1</td>\n",
        "      <td> MIL</td>\n",
        "      <td> 3</td>\n",
        "      <td> 26</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 11</td>\n",
        "      <td> CHI</td>\n",
        "      <td> W (+11)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-01-07</th>\n",
        "      <td> 14</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 35-185</td>\n",
        "      <td> 1</td>\n",
        "      <td> 47.5</td>\n",
        "      <td> 13</td>\n",
        "      <td>  8</td>\n",
        "      <td>...</td>\n",
        "      <td> 5</td>\n",
        "      <td> BOS</td>\n",
        "      <td> 0</td>\n",
        "      <td> 17</td>\n",
        "      <td> 34</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 18</td>\n",
        "      <td> CHI</td>\n",
        "      <td>  W (+9)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-01-09</th>\n",
        "      <td> -5</td>\n",
        "      <td> 0</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> 35-187</td>\n",
        "      <td> 1</td>\n",
        "      <td> 26.3</td>\n",
        "      <td>  6</td>\n",
        "      <td>  4</td>\n",
        "      <td>...</td>\n",
        "      <td> 1</td>\n",
        "      <td> ATL</td>\n",
        "      <td> 0</td>\n",
        "      <td> 10</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td>  7</td>\n",
        "      <td> CHI</td>\n",
        "      <td> L (-15)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-01-11</th>\n",
        "      <td>-10</td>\n",
        "      <td> 0</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 35-189</td>\n",
        "      <td> 1</td>\n",
        "      <td> 31.5</td>\n",
        "      <td>  9</td>\n",
        "      <td>  6</td>\n",
        "      <td>...</td>\n",
        "      <td> 1</td>\n",
        "      <td> WAS</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15</td>\n",
        "      <td> 36</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 10</td>\n",
        "      <td> CHI</td>\n",
        "      <td> L (-14)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 30 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "            +/-  3P  3P%  3PA  AST     Age  BLK   DFS  DRB  FG   ...     ORB  \\\n",
        "Date                                                             ...           \n",
        "2016-01-03   13   0  NaN    0    6  35-181    4  52.8   12   7   ...       1   \n",
        "2016-01-05   11   2    1    2    5  35-183    3  55.8   10  10   ...       1   \n",
        "2016-01-07   14   0    0    1    4  35-185    1  47.5   13   8   ...       5   \n",
        "2016-01-09   -5   0  NaN    0    5  35-187    1  26.3    6   4   ...       1   \n",
        "2016-01-11  -10   0  NaN    0    1  35-189    1  31.5    9   6   ...       1   \n",
        "\n",
        "            Opp  PF  PTS  Rk  STL TOV  TRB   Tm  WinLoss  \n",
        "Date                                                      \n",
        "2016-01-03  TOR   0   19  32    0   2   13  CHI   W (+2)  \n",
        "2016-01-05  MIL   3   26  33    1   4   11  CHI  W (+11)  \n",
        "2016-01-07  BOS   0   17  34    0   3   18  CHI   W (+9)  \n",
        "2016-01-09  ATL   0   10  35    0   4    7  CHI  L (-15)  \n",
        "2016-01-11  WAS   0   15  36    0   2   10  CHI  L (-14)  \n",
        "\n",
        "[5 rows x 30 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(recent_games.TRB,'-o')\n",
      "plt.plot(recent_games.PF,'-o')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fa7f93dc310>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KpKdPwerVySgomILMzBVIS/NyMoODus3qUmERd0JRkQxHvP9+bW3SijNnpP91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\nGBPz/wncjADkSE/VAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa7f93dc590>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We could use this to compare the recent performance (by +/-) of two players."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rw = bc.gameLogs(players, u'Russell Westbrook')\n",
      "sc = bc.gameLogs(players, u'Stephen Curry')\n",
      "\n",
      "recent_rw = rw.loc[rw.index >= '2016-01-01']\n",
      "recent_sc = sc.loc[rw.index >= '2016-01-01']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(recent_rw[\"+/-\"],'-o')\n",
      "plt.plot(recent_sc[\"+/-\"],'-o')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fa7f7ad5c50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yz8dewIk7CD3AHLLbTuxEXR3Qs6f1e3FxLDv25El5YzqqVOkMe60Fly0D/vY3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\nBqm+m4EDgcuXgRMnRHe3QopDdt8+1pj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       "text": [
        "<matplotlib.figure.Figure at 0x7fa7f7ad5a90>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}