NeuralNetProject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from math import exp\n",
    "#data = pd.read_csv(\"PerMinUnlabeledData.csv\", delimiter = ',')\n",
    "#data = data.drop(['Unnamed: 0','Min'],axis=1)\n",
    "#wins = pd.read_csv(\"WinsForPerMinUnlabeledData.csv\", delimiter = ',')\n",
    "data = pd.read_csv(\"SHOT_DATA.csv\", delimiter = ',')\n",
    "FGM = data['FGM']\n",
    "data = data.drop(['FGM','PTS','player_name'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LAYER_1_SIZE = 10\n",
    "LAYER_2_SIZE = 6\n",
    "LAYER_3_SIZE = 4\n",
    "def correct(x):\n",
    "    for i in range(x.size):\n",
    "        if x[i]<0:\n",
    "            x[i]=0\n",
    "    return x\n",
    "\n",
    "def sigmoid(x):\n",
    "    for i in range(x.size):\n",
    "        x[i] = 1/(1+exp(-x[i]))\n",
    "    return x\n",
    "\n",
    "def sigmoidDerivative(x):\n",
    "    inter = (1/(1+exp(-x)))*(1-(1/(1+exp(-x))))\n",
    "    return inter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "LAYER_1_WEIGHTS = (np.random.rand(LAYER_1_SIZE,x.size)*2)-1\n",
    "LAYER_2_WEIGHTS = (np.random.rand(LAYER_2_SIZE,LAYER_1_SIZE)*2)-1\n",
    "LAYER_3_WEIGHTS = (np.random.rand(LAYER_3_SIZE,LAYER_2_SIZE)*2)-1\n",
    "OUTPUT_WEIGHTS = (np.random.rand(1,LAYER_3_SIZE)*2)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(x):\n",
    "    if(x[0].size!=1):\n",
    "        print('input must be 1d array')\n",
    "        return 0\n",
    "    global LAYER_1\n",
    "    global LAYER_2\n",
    "    global LAYER_3\n",
    "    global OUTPUT\n",
    "    global LAYER_2_SIG\n",
    "    global LAYER_3_SIG\n",
    "    LAYER_1 = x*LAYER_1_WEIGHTS\n",
    "    LAYER_1 = np.sum(LAYER_1,axis=1)\n",
    "    LAYER_2 = LAYER_1*LAYER_2_WEIGHTS\n",
    "    LAYER_2 = np.sum(LAYER_2,axis=1)\n",
    "    LAYER_2_SIG = sigmoid(LAYER_2)\n",
    "    LAYER_3 = LAYER_2_SIG*LAYER_3_WEIGHTS\n",
    "    LAYER_3 = np.sum(LAYER_3,axis=1)\n",
    "    LAYER_3_SIG = sigmoid(LAYER_3)\n",
    "    OUTPUT = LAYER_3_SIG*OUTPUT_WEIGHTS\n",
    "    OUTPUT = np.sum(OUTPUT,axis=1)\n",
    "    return OUTPUT[0]\n",
    "\n",
    "def error(input_array,expected_output):\n",
    "    return expected_output - predict(input_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8290780194939738"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array(data.iloc[4])\n",
    "x = x.astype(float)\n",
    "INPUT = x\n",
    "LAYER_1 = INPUT*LAYER_1_WEIGHTS\n",
    "LAYER_1 = np.sum(LAYER_1,axis=1)\n",
    "LAYER_2 = LAYER_1*LAYER_2_WEIGHTS\n",
    "LAYER_2 = np.sum(LAYER_2,axis=1)\n",
    "LAYER_2_SIG = sigmoid(LAYER_2)\n",
    "LAYER_3 = LAYER_2_SIG*LAYER_3_WEIGHTS\n",
    "LAYER_3 = np.sum(LAYER_3,axis=1)\n",
    "LAYER_3_SIG = sigmoid(LAYER_3)\n",
    "OUTPUT = LAYER_3_SIG*OUTPUT_WEIGHTS\n",
    "OUTPUT = np.sum(OUTPUT,axis=1)\n",
    "INPUT_SIZE = INPUT.size\n",
    "OUTPUT[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OutputWeightDerivative(x):\n",
    "    if(x<LAYER_3_SIZE):\n",
    "        return LAYER_3_SIG[x]\n",
    "    print('OUTPUT_WEIGHT_DERIVATIVE_INDEX_OOB')\n",
    "    return 0\n",
    "\n",
    "def Layer3WeightDerivative(x,y):\n",
    "    if(x<LAYER_2_SIZE):\n",
    "        if(y<LAYER_3_SIZE):\n",
    "            d = OUTPUT_WEIGHTS[0][y]*LAYER_2_SIG[x]*sigmoidDerivative(LAYER_3[y])\n",
    "            return d\n",
    "    print('LAYER_3_WEIGHT_DERIVATIVE_INDEX_OOB')\n",
    "    return 0\n",
    "\n",
    "def Layer2WeightDerivative(x,y):\n",
    "    if(x<LAYER_1_SIZE):\n",
    "        if(y<LAYER_2_SIZE):\n",
    "            s = 0\n",
    "            for i in range(LAYER_3_SIZE):\n",
    "                s = s+(OUTPUT_WEIGHTS[0][i]*LAYER_3_WEIGHTS[i][y]*sigmoidDerivative(LAYER_3[i]))\n",
    "            return s*sigmoidDerivative(LAYER_2[y])*LAYER_1[x]\n",
    "    print('LAYER_2_WEIGHT_DERIVATIVE_INDEX_OOB')\n",
    "    return 0\n",
    "\n",
    "def Layer1WeightDerivative(x,y):\n",
    "    if(x<INPUT_SIZE):\n",
    "        if(y<LAYER_1_SIZE):\n",
    "            s = 0\n",
    "            for i in range(LAYER_3_SIZE):\n",
    "                d = 0\n",
    "                for j in range(LAYER_2_SIZE):\n",
    "                    d = d+(INPUT[x]*sigmoidDerivative(LAYER_2[j])*LAYER_3_WEIGHTS[i][j]*LAYER_2_WEIGHTS[j][y])\n",
    "                s = s + (OUTPUT_WEIGHTS[0][i]*sigmoidDerivative(LAYER_3[i])*d)\n",
    "            return s\n",
    "    print('LAYER_1_WEIGHT_DERIVATIVE_INDEX_OOB')\n",
    "    return 0\n",
    "        \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "deriv1 = np.zeros((LAYER_1_SIZE,INPUT_SIZE))\n",
    "for i in range(INPUT_SIZE):\n",
    "    for j in range(LAYER_1_SIZE):\n",
    "        deriv1[j][i] = Layer1WeightDerivative(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "deriv2 = np.zeros((LAYER_2_SIZE,LAYER_1_SIZE))\n",
    "for i in range(LAYER_1_SIZE):\n",
    "    for j in range(LAYER_2_SIZE):\n",
    "        deriv2[j][i] = Layer2WeightDerivative(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "deriv3 = np.zeros((LAYER_3_SIZE,LAYER_2_SIZE))\n",
    "for i in range(LAYER_2_SIZE):\n",
    "    for j in range(LAYER_3_SIZE):\n",
    "        deriv3[j][i] = Layer3WeightDerivative(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "deriv4 = np.zeros((1,LAYER_3_SIZE))\n",
    "for i in range(LAYER_3_SIZE):\n",
    "    deriv4[0][i] = OutputWeightDerivative(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deriv4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALPHA = 0.01\n",
    "def train(x,EXPECTED_OUTPUT):\n",
    "    e = error(x,EXPECTED_OUTPUT)\n",
    "    deriv1 = np.zeros((LAYER_1_SIZE,INPUT_SIZE))\n",
    "    for i in range(INPUT_SIZE):\n",
    "        for j in range(LAYER_1_SIZE):\n",
    "            deriv1[j][i] = Layer1WeightDerivative(i,j)\n",
    "    deriv2 = np.zeros((LAYER_2_SIZE,LAYER_1_SIZE))\n",
    "    for i in range(LAYER_1_SIZE):\n",
    "        for j in range(LAYER_2_SIZE):\n",
    "            deriv2[j][i] = Layer2WeightDerivative(i,j)\n",
    "    deriv3 = np.zeros((LAYER_3_SIZE,LAYER_2_SIZE))\n",
    "    for i in range(LAYER_2_SIZE):\n",
    "        for j in range(LAYER_3_SIZE):\n",
    "            deriv3[j][i] = Layer3WeightDerivative(i,j)\n",
    "    deriv4 = np.zeros((1,LAYER_3_SIZE))\n",
    "    for i in range(LAYER_3_SIZE):\n",
    "        deriv4[0][i] = OutputWeightDerivative(i)\n",
    "    global LAYER_1_WEIGHTS\n",
    "    LAYER_1_WEIGHTS = LAYER_1_WEIGHTS + (ALPHA*e*deriv1)\n",
    "    global LAYER_2_WEIGHTS\n",
    "    LAYER_2_WEIGHTS = LAYER_2_WEIGHTS + (ALPHA*e*deriv2)\n",
    "    global LAYER_3_WEIGHTS\n",
    "    LAYER_3_WEIGHTS = LAYER_3_WEIGHTS + (ALPHA*e*deriv3) \n",
    "    global OUTPUT_WEIGHTS\n",
    "    OUTPUT_WEIGHTS= OUTPUT_WEIGHTS + (ALPHA*e*deriv4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-9e70cdfb9d92>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mFGM\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
     ]
    }
   ],
   "source": [
    "for i in range(1000):\n",
    "    x = np.array(data.iloc[i])\n",
    "    x = x.astype(float)\n",
    "    train(x,FGM[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 0\n",
    "for i in range(5000):\n",
    "    x = np.array(data.iloc[i])\n",
    "    x = x.astype(float)\n",
    "    s = s + abs(error(x,FGM[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47782473342195153"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s/5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([0,0,0,0,0,0,0,0,0,0,0,0])\n",
    "y = y.astype(float)\n",
    "z = [0.1,0.1,1,1,0.1,1,1,1,1,-1,-0.1,0.5]\n",
    "for i in range(12):\n",
    "    x = np.array([0.5,0.5,0,5,2,24,12,3,3,20,0.5,4])\n",
    "    x = x.astype(float)\n",
    "    p = predict(x)\n",
    "    x[i] = x[i] + z[i]\n",
    "    y[i] = y[i] + (predict(x)-p)\n",
    "    x[i] = x[i] - z[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29833441678879263"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([0,0,0,0,0,0,0,2,0,25,0,0])\n",
    "x = x.astype(float)\n",
    "predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.50541722, 0.72165785, 0.05053444, 0.9058644 , 0.98582897,\n",
       "       0.76385984])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LAYER_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HOME</th>\n",
       "      <th>WIN</th>\n",
       "      <th>FINAL_MARGIN</th>\n",
       "      <th>SHOT_NUMBER</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>MIN_LEFT</th>\n",
       "      <th>SHOT_CLOCK</th>\n",
       "      <th>DRIBBLES</th>\n",
       "      <th>TOUCH_TIME</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>PTS_TYPE</th>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.15</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36.23</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>28.2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>35.78</td>\n",
       "      <td>10.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>34.57</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HOME  WIN  FINAL_MARGIN  SHOT_NUMBER  PERIOD  MIN_LEFT  SHOT_CLOCK  \\\n",
       "0     0    1            24            1       1     37.15        10.8   \n",
       "1     0    1            24            2       1     36.23         3.4   \n",
       "2     0    1            24            3       1     36.00         0.0   \n",
       "3     0    1            24            4       2     35.78        10.3   \n",
       "4     0    1            24            5       2     34.57        10.9   \n",
       "\n",
       "   DRIBBLES  TOUCH_TIME  SHOT_DIST  PTS_TYPE  CLOSE_DEF_DIST  \n",
       "0         2         1.9        7.7         0             1.3  \n",
       "1         0         0.8       28.2         1             6.1  \n",
       "2         3         2.7       10.1         0             0.9  \n",
       "3         2         1.9       17.2         0             3.4  \n",
       "4         2         2.7        3.7         0             1.1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: FGM, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FGM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"SHOT_DATA.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "LAYER_1 = (np.random.rand(LAYER_1_COUNT,x.size)*2)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
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       "        FGM       FGA       FTM       FTA  TPM  TPA       ORB       DRB  \\\n",
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       "4 -0.364706 -0.309388 -0.483077 -0.450000 -1.0 -1.0 -0.709091 -0.614413   \n",
       "\n",
       "        AST       STL        TO       BLK       PTS       PFS  \n",
       "0 -0.867725 -0.455782 -0.476190 -0.555556 -0.247432 -0.365079  \n",
       "1 -1.000000 -0.428571 -0.725000 -1.000000  0.033333 -0.666667  \n",
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       "4 -1.000000 -1.000000 -0.780000 -1.000000 -0.319216 -0.200000  "
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     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['SHOT_RESULT'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>PTS_TYPE</th>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.15</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36.23</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>10.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>35.78</td>\n",
       "      <td>10.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>34.57</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HOME  WIN  FINAL_MARGIN  SHOT_NUMBER  PERIOD  MIN_LEFT  SHOT_CLOCK  \\\n",
       "0     0    1            24            1       1     37.15        10.8   \n",
       "1     0    1            24            2       1     36.23         3.4   \n",
       "2     0    1            24            3       1     36.00         0.0   \n",
       "3     0    1            24            4       2     35.78        10.3   \n",
       "4     0    1            24            5       2     34.57        10.9   \n",
       "\n",
       "   DRIBBLES  TOUCH_TIME  SHOT_DIST  PTS_TYPE  CLOSE_DEF_DIST  \n",
       "0         2         1.9        7.7         2             1.3  \n",
       "1         0         0.8       28.2         3             6.1  \n",
       "2         3         2.7       10.1         2             0.9  \n",
       "3         2         1.9       17.2         2             3.4  \n",
       "4         2         2.7        3.7         2             1.1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1:09'"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['GAME_CLOCK'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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