{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "shot_chart_url = 'http://stats.nba.com/stats/shotchartdetail?CFID=33&CFPAR'\\\n",
    "                'AMS=2014-15&ContextFilter=&ContextMeasure=FGA&DateFrom=&D'\\\n",
    "                'ateTo=&GameID=&GameSegment=&LastNGames=0&LeagueID=00&Loca'\\\n",
    "                'tion=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&'\\\n",
    "                'PaceAdjust=N&PerMode=PerGame&Period=0&PlayerID=201935&Plu'\\\n",
    "                'sMinus=N&Position=&Rank=N&RookieYear=&Season=2014-15&Seas'\\\n",
    "                'onSegment=&SeasonType=Regular+Season&TeamID=0&VsConferenc'\\\n",
    "                'e=&VsDivision=&mode=Advanced&showDetails=0&showShots=1&sh'\\\n",
    "                'owZones=0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the webpage containing the data\n",
    "response = requests.get(shot_chart_url)\n",
    "# Grab the headers to be used as column headers for our DataFrame\n",
    "headers = response.json()['resultSets'][0]['headers']\n",
    "# Grab the shot chart data\n",
    "shots = response.json()['resultSets'][0]['rowSet']"
   ]
  },
  {
   "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>GRID_TYPE</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_EVENT_ID</th>\n",
       "      <th>PLAYER_ID</th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>MINUTES_REMAINING</th>\n",
       "      <th>SECONDS_REMAINING</th>\n",
       "      <th>EVENT_TYPE</th>\n",
       "      <th>ACTION_TYPE</th>\n",
       "      <th>SHOT_TYPE</th>\n",
       "      <th>SHOT_ZONE_BASIC</th>\n",
       "      <th>SHOT_ZONE_AREA</th>\n",
       "      <th>SHOT_ZONE_RANGE</th>\n",
       "      <th>SHOT_DISTANCE</th>\n",
       "      <th>LOC_X</th>\n",
       "      <th>LOC_Y</th>\n",
       "      <th>SHOT_ATTEMPTED_FLAG</th>\n",
       "      <th>SHOT_MADE_FLAG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shot Chart Detail</td>\n",
       "      <td>0021400003</td>\n",
       "      <td>18</td>\n",
       "      <td>201935</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>58</td>\n",
       "      <td>Missed Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>3PT Field Goal</td>\n",
       "      <td>Right Corner 3</td>\n",
       "      <td>Right Side(R)</td>\n",
       "      <td>24+ ft.</td>\n",
       "      <td>22</td>\n",
       "      <td>226</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shot Chart Detail</td>\n",
       "      <td>0021400003</td>\n",
       "      <td>39</td>\n",
       "      <td>201935</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>Missed Shot</td>\n",
       "      <td>Layup Shot</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Restricted Area</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>Less Than 8 ft.</td>\n",
       "      <td>2</td>\n",
       "      <td>-15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shot Chart Detail</td>\n",
       "      <td>0021400003</td>\n",
       "      <td>41</td>\n",
       "      <td>201935</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>Made Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>3PT Field Goal</td>\n",
       "      <td>Above the Break 3</td>\n",
       "      <td>Left Side Center(LC)</td>\n",
       "      <td>24+ ft.</td>\n",
       "      <td>25</td>\n",
       "      <td>-232</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shot Chart Detail</td>\n",
       "      <td>0021400003</td>\n",
       "      <td>95</td>\n",
       "      <td>201935</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>Missed Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>Left Side Center(LC)</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>19</td>\n",
       "      <td>-146</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shot Chart Detail</td>\n",
       "      <td>0021400003</td>\n",
       "      <td>120</td>\n",
       "      <td>201935</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Made Shot</td>\n",
       "      <td>Driving Layup Shot</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Restricted Area</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>Less Than 8 ft.</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           GRID_TYPE     GAME_ID  GAME_EVENT_ID  PLAYER_ID   PLAYER_NAME  \\\n",
       "0  Shot Chart Detail  0021400003             18     201935  James Harden   \n",
       "1  Shot Chart Detail  0021400003             39     201935  James Harden   \n",
       "2  Shot Chart Detail  0021400003             41     201935  James Harden   \n",
       "3  Shot Chart Detail  0021400003             95     201935  James Harden   \n",
       "4  Shot Chart Detail  0021400003            120     201935  James Harden   \n",
       "\n",
       "      TEAM_ID        TEAM_NAME  PERIOD  MINUTES_REMAINING  SECONDS_REMAINING  \\\n",
       "0  1610612745  Houston Rockets       1                  9                 58   \n",
       "1  1610612745  Houston Rockets       1                  8                 25   \n",
       "2  1610612745  Houston Rockets       1                  8                 21   \n",
       "3  1610612745  Houston Rockets       1                  4                 32   \n",
       "4  1610612745  Houston Rockets       1                  2                 38   \n",
       "\n",
       "    EVENT_TYPE         ACTION_TYPE       SHOT_TYPE    SHOT_ZONE_BASIC  \\\n",
       "0  Missed Shot           Jump Shot  3PT Field Goal     Right Corner 3   \n",
       "1  Missed Shot          Layup Shot  2PT Field Goal    Restricted Area   \n",
       "2    Made Shot           Jump Shot  3PT Field Goal  Above the Break 3   \n",
       "3  Missed Shot           Jump Shot  2PT Field Goal          Mid-Range   \n",
       "4    Made Shot  Driving Layup Shot  2PT Field Goal    Restricted Area   \n",
       "\n",
       "         SHOT_ZONE_AREA  SHOT_ZONE_RANGE  SHOT_DISTANCE  LOC_X  LOC_Y  \\\n",
       "0         Right Side(R)          24+ ft.             22    226     39   \n",
       "1             Center(C)  Less Than 8 ft.              2    -15     15   \n",
       "2  Left Side Center(LC)          24+ ft.             25   -232    110   \n",
       "3  Left Side Center(LC)        16-24 ft.             19   -146    135   \n",
       "4             Center(C)  Less Than 8 ft.              2     -8     22   \n",
       "\n",
       "   SHOT_ATTEMPTED_FLAG  SHOT_MADE_FLAG  \n",
       "0                    1               0  \n",
       "1                    1               0  \n",
       "2                    1               1  \n",
       "3                    1               0  \n",
       "4                    1               1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shot_df = pd.DataFrame(shots, columns=headers)\n",
    "# View the head of the DataFrame and all its columns\n",
    "from IPython.display import display\n",
    "with pd.option_context('display.max_columns', None):\n",
    "    display(shot_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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AgF7CG+y+vMWpl7c4ddeSZ+VwODp1zLXrSnXMlSuT2SKT2aKjzaN9AToAXKgIoAGglyDY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SHPsLAwzZl9pTKuSFdR8S6VtnaoqaZUcSkzZLd13xEsImmiju79h0ZOO71+9Mi0bFX+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e165f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes()\n",
    "plt.figure(figsize=(12,11))\n",
    "plt.scatter(shot_df.LOC_X, shot_df.LOC_Y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}