NBA_TEAM_FEATURES.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rossmcdonald/Library/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.py:878: 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": [
    "from IPython.core.display import HTML\n",
    "HTML('<style>.container {width:95% !important; ) </style>')\n",
    "\n",
    "import pandas   as pd\n",
    "import sqlite3  as lite\n",
    "import numpy    as np\n",
    "import datetime as dt\n",
    "from NBA_df_scripts import (getNBAteamdata, getNBAplayerdata, getsportsbookdata, merge_team_line_data, team_feature_generation)\n",
    "con = lite.connect(r'/Users/rossmcdonald/projects/NBA Daily Fantasy Data/nba_df.db')\n",
    "cur = con.cursor()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team data build assembles the final Team data frame to be incorporated into Model building. I should Write that data  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def team_data_build(db_con):\n",
    "    \n",
    "    # I need to update all of these functions to delete and update the most recent season of data.\n",
    "    \n",
    "    # read team from DB\n",
    "    teambox = pd.read_sql('SELECT * FROM team_box_scores;',db_con) \n",
    "    teambox['GAME_DATE'] = teambox['GAME_DATE'].apply(pd.to_datetime)\n",
    "    \n",
    "    # read line data from DB\n",
    "    nbalines = pd.read_sql('SELECT * FROM line_results;',db_con)   \n",
    "    nbalines['date'] = nbalines['date'].apply(pd.to_datetime) \n",
    " \n",
    "    teamdf = team_feature_generation(merge_team_line_data(line_data = nbalines,team_data = teambox))\n",
    "    \n",
    "    return teamdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NBA_df_scripts.py:217: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:219: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:221: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:223: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:225: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:227: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:229: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:231: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:233: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:235: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:237: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:239: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:262: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n",
      "NBA_df_scripts.py:264: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(min_periods=1,window=82,center=False).mean(how=down)\n",
      "  lambda x: pd.rolling_mean(x,window=82,min_periods=1,how=\"down\").shift(1))\n"
     ]
    }
   ],
   "source": [
    "teamdf = team_data_build(db_con = con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prep data set to merge in Travel distance data\n",
    "teamdf = teamdf.set_index(['season','game_num','Team_ID','Location'], drop = False)\n",
    "teamdf['LAST_OPP_ID'] = teamdf.sort_index(ascending=True).groupby(level = [0,2])['Team_ID_OPP'].shift(1)\n",
    "teamdf['GAME_LOC_ID'] = np.where(teamdf['Location'] == 'Home', teamdf['Team_ID'], teamdf['Team_ID_OPP'])\n",
    "teamdf['LAST_GAME_LOC_ID'] = np.where(teamdf['LAST_GAME_Location'] == 'Home', teamdf['Team_ID'], teamdf['LAST_OPP_ID']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read travel distance data from DB\n",
    "Travel_Distance = pd.read_sql('SELECT * FROM Travel_Distance;',con)\n",
    "\n",
    "# Merge in travel distance\n",
    "finaldf =  teamdf.merge(Travel_Distance[['travel_distance_km','team_id_home','team_id_away']],\n",
    "            how = 'left', left_on= ['GAME_LOC_ID','LAST_GAME_LOC_ID'], right_on = ['team_id_home','team_id_away'])\n",
    "\n",
    "del finaldf['team_id_home']; del finaldf['team_id_away']; #del finaldf['GAME_LOC_ID']; del finaldf['LAST_GAME_LOC_ID']\n",
    "\n",
    "# Merge in Opponent Travel distance\n",
    "merge_fields = finaldf[[\"Team_ID\",\"Game_ID\",\"travel_distance_km\"]]\n",
    "\n",
    "teamfinal = finaldf.merge(merge_fields, how = 'left', on = ['Game_ID'], suffixes=(\"\", \"_OPP_P\")).query(\n",
    "    \"Team_ID != Team_ID_OPP\").set_index(['season','game_num','Team_ID','Location'],drop = False)\n",
    "\n",
    "del teamfinal['Team_ID_OPP_P']\n",
    "\n",
    "teamfinal = teamfinal.rename(columns = {'travel_distance_km_OPP_P':'travel_distance_km_OPP'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Useful variables to build the model off of.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final step is to make a final DF in our DB, and write code to run an efficient update on that table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Cursor at 0x1172b98f0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute('DROP TABLE team_feature_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "teamfinal.to_sql('team_feature_data',con=con,schema='dbo',if_exists = 'append',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "finaldf = pd.read_sql('SELECT * FROM team_feature_data',con=con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AST', 'AST_OPP', 'AVG_AST', 'AVG_AST_OPP', 'AVG_BLK', 'AVG_BLK_OPP', 'AVG_DREB', 'AVG_DREB_OPP', 'AVG_FGA', 'AVG_FGA_OPP', 'AVG_FGM', 'AVG_FGM_OPP', 'AVG_FTA', 'AVG_FTA_OPP', 'AVG_FTM', 'AVG_FTM_OPP', 'AVG_OREB', 'AVG_OREB_OPP', 'AVG_PACE', 'AVG_PACE_OPP', 'AVG_POSS', 'AVG_POSS_OPP', 'AVG_PTS', 'AVG_PTS_OPP', 'AVG_REB', 'AVG_REB_OPP', 'AVG_STL', 'AVG_STL_OPP', 'AVG_TOV', 'AVG_TOV_OPP', 'BLK', 'BLK_OPP', 'DAYS_OFF', 'DAYS_OFF_OPP', 'DREB', 'DREB_OPP', 'FG3A', 'FG3M', 'FG3_PCT', 'FGA', 'FGA_OPP', 'FGM', 'FGM_OPP', 'FG_PCT', 'FTA', 'FTA_OPP', 'FTM', 'FTM_OPP', 'FT_PCT', 'GAME_DATE', 'GAME_LOC_ID', 'GameOutome', 'Game_ID', 'L', 'LAST_GAME', 'LAST_GAME_LOC_ID', 'LAST_GAME_Location', 'LAST_OPP_ID', 'Line_Pt_Prediction', 'Location', 'MATCHUP', 'MIN', 'OREB', 'OREB_OPP', 'OT', 'OverUnderLine', 'OverUnderOutcome', 'PACE', 'PF', 'POSS', 'PTS', 'PTS_OPP', 'Points_Opponent', 'Points_Team', 'REB', 'REB_OPP', 'STL', 'STL_OPP', 'Spread', 'SpreadOutcome', 'TEAM_NAME', 'TOV', 'TOV_OPP', 'TRAVEL_FLAG', 'TRAVEL_FLAG_OPP', 'Team_ID', 'Team_ID_OPP', 'W', 'WL', 'W_PCT', 'date', 'date_as_int', 'game_num', 'index_x', 'index_y', 'opponent', 'season', 'season_type', 'team', 'team_OPP', 'travel_distance_km']\n"
     ]
    }
   ],
   "source": [
    "print sorted(finaldf.columns) ,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outcome_vars = ['REB','AST','STL','BLK','TOV','PTS']\n",
    "\n",
    "input_vars = [  'season'            ,'team_OPP'     ,'TEAM_NAME'           , # team variables to use\n",
    "'AVG_PTS_OPP'  ,'AVG_REB_OPP'       ,'AVG_BLK_OPP'  ,'AVG_AST_OPP'         , # AVG Stats Opponent\n",
    "'AVG_STL_OPP'  ,'AVG_TOV_OPP'       ,'AVG_FGM_OPP'  ,'AVG_FGA_OPP'         ,\n",
    "'AVG_FTM_OPP'  ,'AVG_FTA_OPP'       ,'AVG_OREB_OPP' ,'AVG_DREB_OPP'        ,\n",
    "'AVG_PACE_OPP' ,'AVG_POSS_OPP'      ,'AVG_PTS'      ,'AVG_REB'             , #AVG Stats Team\n",
    "'AVG_BLK'      ,'AVG_AST'           ,'AVG_STL'      ,'AVG_TOV'             ,\n",
    "'AVG_FGM'      ,'AVG_FGA'           ,'AVG_FTA'      ,'AVG_FTM'             ,\n",
    "'AVG_OREB'     ,'AVG_DREB'          ,'AVG_PACE'     ,'AVG_POSS'            , \n",
    "'DAYS_OFF'     ,'TRAVEL_FLAG'       ,'Location'     ,'travel_distance_km'  ,     # General Game Information \n",
    "'SpreadOutcome','Line_Pt_Prediction','Spread'       ,'travel_distance_km_OPP' ] # Vegas information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>travel_distance_km</th>\n",
       "      <th>GAME_LOC_ID</th>\n",
       "      <th>LAST_GAME_LOC_ID</th>\n",
       "      <th>LAST_OPP_ID</th>\n",
       "      <th>team_OPP</th>\n",
       "      <th>Team_ID_OPP</th>\n",
       "      <th>Team_ID</th>\n",
       "      <th>date_as_int</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>Location</th>\n",
       "      <th>POSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581.117361</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Los Angeles      Lakers</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020417</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Away</td>\n",
       "      <td>98.234476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Golden State      Warriors</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020416</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Home</td>\n",
       "      <td>101.526186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>581.117361</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Dallas      Mavericks</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020414</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Home</td>\n",
       "      <td>90.386826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552.529654</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Los Angeles      Clippers</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020412</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Away</td>\n",
       "      <td>90.310897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>109.872535</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Golden State      Warriors</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020410</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Away</td>\n",
       "      <td>103.054595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Minnesota      Timberwolves</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020409</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Home</td>\n",
       "      <td>95.463297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>858.252280</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>New York      Knicks</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020407</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Home</td>\n",
       "      <td>97.030342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2401.527260</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Utah      Jazz</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020405</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Away</td>\n",
       "      <td>96.971660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1002.375834</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Detroit      Pistons</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020403</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Away</td>\n",
       "      <td>94.702121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>542.829684</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>Memphis      Grizzlies</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>20020402</td>\n",
       "      <td>Kings</td>\n",
       "      <td>Away</td>\n",
       "      <td>98.595180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   travel_distance_km   GAME_LOC_ID  LAST_GAME_LOC_ID   LAST_OPP_ID  \\\n",
       "0          581.117361  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "1            0.000000  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "2          581.117361  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "3          552.529654  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "4          109.872535  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "5            0.000000  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "6          858.252280  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "7         2401.527260  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "8         1002.375834  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "9          542.829684  1.610613e+09      1.610613e+09  1.610613e+09   \n",
       "\n",
       "                      team_OPP   Team_ID_OPP       Team_ID  date_as_int  \\\n",
       "0      Los Angeles      Lakers  1.610613e+09  1.610613e+09     20020417   \n",
       "1   Golden State      Warriors  1.610613e+09  1.610613e+09     20020416   \n",
       "2        Dallas      Mavericks  1.610613e+09  1.610613e+09     20020414   \n",
       "3    Los Angeles      Clippers  1.610613e+09  1.610613e+09     20020412   \n",
       "4   Golden State      Warriors  1.610613e+09  1.610613e+09     20020410   \n",
       "5  Minnesota      Timberwolves  1.610613e+09  1.610613e+09     20020409   \n",
       "6         New York      Knicks  1.610613e+09  1.610613e+09     20020407   \n",
       "7               Utah      Jazz  1.610613e+09  1.610613e+09     20020405   \n",
       "8         Detroit      Pistons  1.610613e+09  1.610613e+09     20020403   \n",
       "9       Memphis      Grizzlies  1.610613e+09  1.610613e+09     20020402   \n",
       "\n",
       "  TEAM_NAME Location        POSS  \n",
       "0     Kings     Away   98.234476  \n",
       "1     Kings     Home  101.526186  \n",
       "2     Kings     Home   90.386826  \n",
       "3     Kings     Away   90.310897  \n",
       "4     Kings     Away  103.054595  \n",
       "5     Kings     Home   95.463297  \n",
       "6     Kings     Home   97.030342  \n",
       "7     Kings     Away   96.971660  \n",
       "8     Kings     Away   94.702121  \n",
       "9     Kings     Away   98.595180  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf[['travel_distance_km','GAME_LOC_ID','LAST_GAME_LOC_ID','LAST_OPP_ID','team_OPP','Team_ID_OPP','Team_ID',\n",
    "         'date_as_int','TEAM_NAME','Location','POSS']][finaldf['Team_ID']==1610612758].head(10)"
   ]
  },
  {
   "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>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>index_x</th>\n",
       "      <th>Team_ID</th>\n",
       "      <th>Game_ID</th>\n",
       "      <th>GAME_DATE</th>\n",
       "      <th>MATCHUP</th>\n",
       "      <th>WL</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>...</th>\n",
       "      <th>POSS</th>\n",
       "      <th>PACE</th>\n",
       "      <th>AVG_PACE</th>\n",
       "      <th>AVG_POSS</th>\n",
       "      <th>AVG_PACE_OPP</th>\n",
       "      <th>AVG_POSS_OPP</th>\n",
       "      <th>LAST_OPP_ID</th>\n",
       "      <th>GAME_LOC_ID</th>\n",
       "      <th>LAST_GAME_LOC_ID</th>\n",
       "      <th>travel_distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38954</th>\n",
       "      <td>38954</td>\n",
       "      <td>38954</td>\n",
       "      <td>0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0021600089</td>\n",
       "      <td>2016-11-06 00:00:00</td>\n",
       "      <td>SAC @ TOR</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>inf</td>\n",
       "      <td>92.282630</td>\n",
       "      <td>93.698223</td>\n",
       "      <td>93.676980</td>\n",
       "      <td>93.676980</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>694.134950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39025</th>\n",
       "      <td>39025</td>\n",
       "      <td>39025</td>\n",
       "      <td>66</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0021600089</td>\n",
       "      <td>2016-11-06 00:00:00</td>\n",
       "      <td>TOR vs. SAC</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>inf</td>\n",
       "      <td>93.676980</td>\n",
       "      <td>93.676980</td>\n",
       "      <td>92.282630</td>\n",
       "      <td>93.698223</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39555</th>\n",
       "      <td>39555</td>\n",
       "      <td>39555</td>\n",
       "      <td>47</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0021600356</td>\n",
       "      <td>2016-12-11 00:00:00</td>\n",
       "      <td>DET vs. PHI</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>38.655000</td>\n",
       "      <td>154.620000</td>\n",
       "      <td>93.458957</td>\n",
       "      <td>93.458957</td>\n",
       "      <td>96.294362</td>\n",
       "      <td>98.026230</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>874.501506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39609</th>\n",
       "      <td>39609</td>\n",
       "      <td>39609</td>\n",
       "      <td>101</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0021600356</td>\n",
       "      <td>2016-12-11 00:00:00</td>\n",
       "      <td>PHI @ DET</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>38.655000</td>\n",
       "      <td>154.620000</td>\n",
       "      <td>96.294362</td>\n",
       "      <td>98.026230</td>\n",
       "      <td>93.458957</td>\n",
       "      <td>93.458957</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>811.470289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39610</th>\n",
       "      <td>39610</td>\n",
       "      <td>39610</td>\n",
       "      <td>101</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0021600356</td>\n",
       "      <td>2016-12-11 00:00:00</td>\n",
       "      <td>PHI @ DET</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>38.655000</td>\n",
       "      <td>154.620000</td>\n",
       "      <td>96.294362</td>\n",
       "      <td>98.026230</td>\n",
       "      <td>93.458957</td>\n",
       "      <td>93.458957</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1510.906445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39716</th>\n",
       "      <td>39716</td>\n",
       "      <td>39716</td>\n",
       "      <td>67</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0021600561</td>\n",
       "      <td>2017-01-08 00:00:00</td>\n",
       "      <td>LAC vs. MIA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>37.059783</td>\n",
       "      <td>148.239130</td>\n",
       "      <td>95.566721</td>\n",
       "      <td>96.103532</td>\n",
       "      <td>93.792366</td>\n",
       "      <td>94.593214</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>581.117361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39996</th>\n",
       "      <td>39996</td>\n",
       "      <td>39996</td>\n",
       "      <td>337</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0021600561</td>\n",
       "      <td>2017-01-08 00:00:00</td>\n",
       "      <td>MIA @ LAC</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>37.059783</td>\n",
       "      <td>148.239130</td>\n",
       "      <td>93.792366</td>\n",
       "      <td>94.593214</td>\n",
       "      <td>95.566721</td>\n",
       "      <td>96.103532</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       level_0  index  index_x       Team_ID     Game_ID            GAME_DATE  \\\n",
       "38954    38954  38954        0  1.610613e+09  0021600089  2016-11-06 00:00:00   \n",
       "39025    39025  39025       66  1.610613e+09  0021600089  2016-11-06 00:00:00   \n",
       "39555    39555  39555       47  1.610613e+09  0021600356  2016-12-11 00:00:00   \n",
       "39609    39609  39609      101  1.610613e+09  0021600356  2016-12-11 00:00:00   \n",
       "39610    39610  39610      101  1.610613e+09  0021600356  2016-12-11 00:00:00   \n",
       "39716    39716  39716       67  1.610613e+09  0021600561  2017-01-08 00:00:00   \n",
       "39996    39996  39996      337  1.610613e+09  0021600561  2017-01-08 00:00:00   \n",
       "\n",
       "           MATCHUP    WL     W     L         ...               POSS  \\\n",
       "38954    SAC @ TOR  None  None  None         ...           7.416667   \n",
       "39025  TOR vs. SAC  None  None  None         ...           7.416667   \n",
       "39555  DET vs. PHI  None  None  None         ...          38.655000   \n",
       "39609    PHI @ DET  None  None  None         ...          38.655000   \n",
       "39610    PHI @ DET  None  None  None         ...          38.655000   \n",
       "39716  LAC vs. MIA  None  None  None         ...          37.059783   \n",
       "39996    MIA @ LAC  None  None  None         ...          37.059783   \n",
       "\n",
       "             PACE   AVG_PACE   AVG_POSS  AVG_PACE_OPP  AVG_POSS_OPP  \\\n",
       "38954         inf  92.282630  93.698223     93.676980     93.676980   \n",
       "39025         inf  93.676980  93.676980     92.282630     93.698223   \n",
       "39555  154.620000  93.458957  93.458957     96.294362     98.026230   \n",
       "39609  154.620000  96.294362  98.026230     93.458957     93.458957   \n",
       "39610  154.620000  96.294362  98.026230     93.458957     93.458957   \n",
       "39716  148.239130  95.566721  96.103532     93.792366     94.593214   \n",
       "39996  148.239130  93.792366  94.593214     95.566721     96.103532   \n",
       "\n",
       "        LAST_OPP_ID   GAME_LOC_ID  LAST_GAME_LOC_ID  travel_distance_km  \n",
       "38954  1.610613e+09  1.610613e+09      1.610613e+09          694.134950  \n",
       "39025  1.610613e+09  1.610613e+09      1.610613e+09            0.000000  \n",
       "39555  1.610613e+09  1.610613e+09      1.610613e+09          874.501506  \n",
       "39609  1.610613e+09  1.610613e+09      1.610613e+09          811.470289  \n",
       "39610  1.610613e+09  1.610613e+09      1.610613e+09         1510.906445  \n",
       "39716  1.610613e+09  1.610613e+09      1.610613e+09          581.117361  \n",
       "39996  1.610613e+09  1.610613e+09      1.610613e+09            0.000000  \n",
       "\n",
       "[7 rows x 103 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf[finaldf['POSS'] < 50] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}