5_result_data_luther.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Project Luther\n",
    "\n",
    "Kenny Leung - kenleung11@gmail.com\n",
    "\n",
    "Part 5/8 - Combining and cleaning data obtained\n",
    "\n",
    "This notebook documents the process of combining and cleaning the data scraped previously and obtaining a final dataset to be used in the regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in all the csv files and create dataframes with pandas\n",
    "college_df = pd.read_csv('college_data.csv',index_col=0)\n",
    "draft_df = pd.read_csv('draft_data.csv',index_col=0)\n",
    "player_df = pd.read_csv('player_raw.csv',index_col=0)\n",
    "rookie_df = pd.read_csv('rookie_raw.csv', index_col=0)\n",
    "rookie_advanced_df = pd.read_csv('rookie_advanced_raw.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# filter out draft data before 2003 since college data is only available from 2003, remove other columns\n",
    "draft_df = draft_df[draft_df['Draft_Yr'] > 2002]\n",
    "draft_df = draft_df.loc[:,['Player','College','BPM']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# merge dataframes on Player\n",
    "result_df = player_df.merge(college_df, on='Player', how='left')\n",
    "result_df = result_df.merge(draft_df, on='Player', how='left')\n",
    "#result_df = result_df.merge(rookie_df, on='Player', how='left')\n",
    "result = result_df.merge(rookie_advanced_df, on='Player', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 55)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drafted players who did not play college\n",
    "no_college = result[np.isnan(result['Yrs'])]\n",
    "no_college.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 55)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drafted players who did not play in the NBA\n",
    "no_nba = result[np.isnan(result['G'])]\n",
    "no_nba.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = result[np.isfinite(result['Yrs'])]\n",
    "result = result[np.isfinite(result['G'])]\n",
    "result = result[np.isfinite(result['BPM_x'])]\n",
    "result = result[np.isfinite(result['BPM_y'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(544, 55)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rookie Year BPM mean:  -3.4297794117647062\n",
      "Rookie Year BPM std:  3.7876453162209676\n",
      "Rookie Year BPM max:  Demetrius Jackson 6.3\n",
      "Rookie Year BPM min:  Ben Bentil -26.9\n"
     ]
    }
   ],
   "source": [
    "print('Rookie Year BPM mean: ',result['BPM_y'].mean())\n",
    "print('Rookie Year BPM std: ',result['BPM_y'].std())\n",
    "print('Rookie Year BPM max: ',result.loc[result['BPM_y'].idxmax(axis=1)]['Player'],result['BPM_y'].max())\n",
    "print('Rookie Year BPM min: ',result.loc[result['BPM_y'].idxmin(axis=1)]['Player'],result['BPM_y'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Career Average BPM mean:  -2.5231098430813095\n",
      "Career Average BPM std:  4.34622753012674\n",
      "Career Average BPM max:  LeBron James 9.1\n",
      "Career Average BPM min:  Mile Ilic -53.6\n"
     ]
    }
   ],
   "source": [
    "print('Career Average BPM mean: ',draft_df['BPM'].mean())\n",
    "print('Career Average BPM std: ',draft_df['BPM'].std())\n",
    "print('Career Average BPM max: ',draft_df.loc[draft_df['BPM'].idxmax(axis=1)]['Player'],draft_df['BPM'].max())\n",
    "print('Career Average BPM min: ',draft_df.loc[draft_df['BPM'].idxmin(axis=1)]['Player'],draft_df['BPM'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-20, 10)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Yp5deeonvv/8+130/ffo0u3btol27dkyaNElJ12q1SiAZERFBw4YNad++PQsWLCAkJITO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MAAAg\nAElEQVRbVvDZlTzjnkaeaFnDN+NahNhToZQdfHkUbl+GT3aHNTeiiyGghHbt2rVITEzEvffei+ee\new5PP/00fvrTn6KwsNDTM7a+vh47duxAfX19UOOIiILhcGtY+14rjrSJx2N/pzAW98+JG2DU0AzO\nHiz66HkhdnT+V2FJ5koVjUzfiRdL+krtcTho9y8BHIpfT9pPdvcehUs0igTU5SA5ORkbN27Eo48+\niieeeAJmsxnLli3DfffdB5Opt01OeXk5Hn74YTz44IPIzs4OeBwRjT+bK4Nro3V3Qe/GU03TsP7j\nNvztnLjZ9NrJZvzX4sSge832NXffG4i1eTeXOQ1R2Hf5HSHfjyjSphntWG624P0eb3eFJztT8X9R\ndUHdx51XAMO+v0Fy9/6RKDfUQa6ugDq1/5JCopEo4LZdubm5eOyxxwZ8fPXq1Vi9enXQ4/qTlZWF\n0lI2eCYi0cP7LfjLyW4hdkmaEZuuSoZBDj2ZjeruRPGnLwuxg4tuRFdcSsj3JBoO34tvEhLav9hS\n8EhKPRLkILoDmaPhnrcEhrKPPCHDJ7vgYEJLo0gIPW2IiIbfU8esePSQeCrhlHgFL1w9ATGG8N7K\nij99BeYe76pxjzkWZUtuCeueRMPhupgOZCkOz7VNU/C8Nfg/xJxLVwrXvUfhugZ4NtHIw4SWiEa8\nt2q78ZNPO4RYqlnGKytTkWoOrT3XBTHWVszd94YQK19yK+zRY6+5PI09Bgm426eW9qnOtKBLYN3z\nlkDrc6CC1NkB5RA/KaXRgwktEY1opzpd+O7fWtH393OMQcJfrp6A/ISAq6YGdMlHL8Do8tbk2mKT\nceCSG8O+L9FwuTuuBUqfn5Bjzmh8bA9yg6TRBFfJVULIwJ60NIowoSWiEaux242njtnQ06c7lyIB\nT1+VguK08DeWJrSdw6wvdgqxfZffDpfJHPa9iYZLlsGJ62PahVgoLbycl4llB4YvPgZsnQM8m2hk\nYUJLRCOSxaFi41EbbD6HJDx6WRK+OlmfhHPxh1ugqN5suSMpE0fnf1WXexMNp+/5lB28bktGtTO4\nP/rU6bOhpmV5rnkULo0mTGiJaMSxuzU8ecyGFru4U/tf5sfj72fE6vI1JjSewowj4i/rz678JlTF\nqMv9iYbTVeZOTDf0eK5dkPDr9iB7KEsSXJeJhyUZecgCjRLhF6AREenIrWp4utKGOp9TwL45PQY/\nm6/fRq1rtv0PpD51h9b4CTA6ujGrfIduX4NouEgS8K9JDfhuc54n9oItBf+ceB5Fpp6BB/pwXnYN\nTNv+7LlWKg/wKFwaFbhCS0QjhqZpeLG6GxXtYrugq7Oj8OhlSWEdnNBXZt1RpDbVCLHqGUt6swKi\nUerrsa0oNHr7NGuQ8O9BrtJqmZP8Tg4zvfGsLvMjiiQmtEQ0Yuys60Fpo0OITYpVsHl5CoxhHJwg\n0DRc9v5mIdSRlImWtDx97k90kSgS8Iukc0JsW1cyvgjyOFznsuuFa8OenZDPVIc9P6JIYkJLRCPC\n3vN2vHNGPNI2JUrGPxTFIs6o31vV3M/fRNaZI0LsZMFlXJ2lMeHGmHYsMIlHSz/cnjXAs/vnWvpV\nqBMne64lTYXpxY26zI8oUpjQEtFF90Wzw+9I21iDhO/PjEWCSb+3qZTGGlz23iYh1pKWi46U4H7h\nE41UkgQ86LNKu6s7EXt7gthMaTDA/nffF0MHP4NyZL8eUySKCCa0RHRRHW1z4tmqLuHgBKMMfLco\nFhnR4Z0C1pficmDltt/A4HZ6Yi6DCcdnLtPtaxCNBFdHW3BplHhM9K/asoI6Pcy9YCncM+YKMdOL\nfwRUdYARRBcXE1oiumhOdrjwdKUN7j6/aGUAfz8jFvnx+jZhufT9zX4bwSpnLUNPTIKuX4foYpMk\n4KHks0LsI3s83u8JokuIJMF++z1CSKmtgmHvu3pMkUh3TGiJ6KI4bXXhiQornD4LPndMi8GcFH17\nweac/Bzz920TYg1ZBWjMKtD16xCNFJebrfiK2SLEgl2lVacWwbl4uRAzvfInwGEfYATRxcOEloiG\nXUOXGxuP2mAXW83i1vxoLEoP/0jbvqJt7fjKW48KMUtiBo7PvFLXr0M00jzos0q73xGL7d2JQd3D\ncev3oCneT0vklvMw7n5Vl/kR6YkJLRENq5pOF35/xOp3pO3qHDMunxil7xfTNKzYvgGxNu8596ok\nY9eNP4bbqPPXIhphFkZ1YXVMuxD7t7YsqEGs0mrpWXBefZMQM721BbB26DFFIt0woSWiYXPa6sLX\ndjbD4hR/o34lOwpXTzLr/vVml21H/olSIfb50tvRMHnmACOIxpZfJJ0VTsQ74ozGU52pQd3DceNa\naDFxnmupywbTtmd0myORHpjQEtGwqLa4cN3bzai1inUGl2easDpH/2Q2uek0Lv/rn4TYuexC7Lv8\ndt2/FtFINcvUg6/HtgmxX7Rlo8YZRGlPXAIcN3xTCBn/ug3S+Xo9pkikCya0RBRxFe1OXPd2E87Y\nxGT2kjQjbs6P1u1I2wtklxMrt/03DC7vqWMOUzR23/hjaLJ+rcCIRoNfJdcjXvL+7Nk0BetacoMq\nPXBefRPU1AzPteR2wfTSk3pOkygs+vbFISLycbDFgZveaUGLXWxnsCDViDumxkAOIJndXGkb8jl9\nLf3gz0hrPCXE/vbVdbAkB3euPdFYMNngxCMpZ3BfS64n9mFPPDZ1puK7Cc2B3cQUBcct34V54yOe\nkHHfB3CeOAJ12iy9p0wUNK7QElHE7G9y4IadzX7JbEmaCWunx0CR9T9udnJ1GRaUvibEjs9chsrZ\nywcYQTT2fSuuBct92ng90JaN2iBKD1xLvgJ37gwhFvXCHxBULzCiCGFCS0QR8UmDHWveaUaHQ/xl\n953CWNw+LTqgldlgmbs6cPWb/yvELInp+ODadb3d5onGKUkCHk89jbg+pQdWTcG6lpzA81FZhuMO\nn8MWqg5D2f+RjjMlCg0TWiLS3a66Hty6uwWdPt0M7p0Vh/9ZkhiRZNbbosu7AUaVZOy+4UdwmOMG\nGUg0PuQYHHgkRdzI9beeBGyyBt71wF20AK55S4RY1F82Ai6XLnMkChUTWiLS1cajVtz+1xZ0+fSZ\n/fG8ePz7ogTdN4BdMKt8J6ZUfSbE9l/2dziXMzsiX49oNPp2XDOu8ik9+H+t2ThtDTwhtd/2A2iS\nN32Qz5+B8YM3dZsjUSiY0BKRLlyqhp/sbce/ftbht3v6F8UJeKA4csls/vG9uGL3RiHWkFWAfZff\nEZGvRzRaSRLw+AT/0oP7Pm6HFmDtgZadB9eV1wkx4+t/BrqD27xJpCcmtEQUNotDxe3vtuDJCvEX\nmiwBv1mSiB/Ni4/Y155ZvhOrXvkPGNxOT8xhisaur/0EqsJGLkS+co0O/HuyWHrwwVk7Nh4LPCF1\n3PwtaFHe/tFyZztM25/XbY5EwQr43b6+vh4bNmxAWVkZAGDp0qW4//77kZycrMu4vXv3YtOmTaio\nqIAsy5g9ezZ+8IMfYM6cOcF+T0Skg0BbZbX2qHiywopzXWIngziDhE1XpWDlZP0PTQAAaBou+fgF\nLPlwi99DbNFF5LX3vN0vVqTVY76ciC/URE/s5591oCjJiGVZQx8LrSVNgHPVbTC9/mdPzPjOS3Be\ntRpaaqY+EycKQkArtO3t7Vi3bh0OHz6MtWvX4s4778SePXuwfv16OJ3OsMeVlZXh/vvvh9VqxT33\n3IPvfve7OHPmDH7wgx/gyJEj4X+XRBQRNZ0uPHqo0y+ZnRSrYOf1aRFLZiXVjSvf+YNfMqtKMt67\n7j5UzlkRka9LNFbIEvAj40lEw1t6oAK46/0WVFsCq6d1rLoNamKK51py2GHe8P9YekAXRUArtFu3\nbkVjYyO2bt2K/Px8AMDs2bOxfv16bN++HWvWrAlr3P/+7/8iIyMDTz/9NMzm3l+A1113HW677Tb8\n4Q9/wO9+97uwv1Ei0o+mafiwwYE3arrh9im7K041YutXJiAzJjIncikuB655438wreJjIe4ymLBz\nzb+iZsaSAUYSUV+Zsh0/NVXhl44CaOitb293aLjj3RbsWp2GRNMQa17mGDhu/jbMT/+PJ6ScPgnz\n736Jnn/6NWBgyQ8Nn4D+te3evRvFxcWepBQASkpKkJubi127dg2Y0AYyzmKxoKqqCt/4xjc8ySwA\nTJgwAQsWLMBnn33W362J6CLpcWl4/mQXDrT4fzpzY64Zf7wyGTGGyJTnm3psuP7lf0P26UPinMxx\neOvrD2FCcy1mle+IyNcmGosuU9rwLcNpbHJ5TxGr7HDhe69W4KX0k1CG2sepaXBPngql7qQnZDi8\nD+b/vB+uy1bCteLGCM2cSDTkbx2LxYL6+noUFhb6PVZQUICKioqwxsXGxuKll17CHXf470Zub2+H\novDcdaKRot7mxv8c7Ow3mf2nOXHYvDwlYslsjLUVN235V79ktjM+Fa+u/W80TJ4Zka9LNNbdbjiL\nFUqTENvVnYgH27KHHixJcC67HqpP3ayh6jCUg1yQouEz5G+epqbef+Tp6el+j6WmpsJqtcJqtYY8\nTlEU5OTkIC0tTXhOVVUVDh48iLlz5wb2nRBRRH163o7HDnWiuUeslzUrwLcKYvDQJRE6MAFAUks9\nbv3z/2/vzuOjKu/Fj3/OObNmspOEkIQECPsqYFGhioJIbVGxelVsLXr1trW2XG2v1rbW7XZx6b1u\ntW70J62KS9XrAi4sIuDWWFFAdggkEAhJyDKZyezn/P4YEjJMlsk+Cd/36zWvmTznOWee4fDMfOfM\n83yf/yKzYn9EefWgoby2+E9UZxa0sqcQoj2KAj83FzNGifwsf8Q5mBdc6a3s1YzJjH/upeiJKRHF\n5i8/xvTxqu5sqhCtajegdbvDg7ut1uhZj41lHo+n2/YDaGho4O677wZg8eLF7TVRCNGDvCGDF/a4\neWmfh0BkLEuuQ+MXk5OYMij29eA7Kuvwbi77+3+RXHc0ovxI7lhe+8GDuJIzW9lTCBErq6Jzt3Un\nQzR/RPnPqvL5zOto/wD2BALzvhuRygvA+tcH0LZv6s6mCtGimEdst5UQvTu3eb1efvGLX7Bnzx6u\nvfZapk2bFmsT2bNnT8x1Re+T8xPfTj4/Rys0yrwKbx01UxuI7qunJYc4P8OHXt/A0Xq4v6L72zTy\n0NdcsuZxrMHItEM7h07hlbk/JlDfAPUNTeVD650nH2LAcA7g1zZQ9NY5er++Z45rAf7i+BdXhc7A\nR3i4nx+Vy8uHs9xWxFg1+tfYL52R7w3po2cz8+vVaEb4268SCsJDv+HZBb+iIj2P72aHoo7RW+Qz\nKH6NGjWqy8doN6C12+0A+HzReewayxyO6G9vndmvvr6en//852zevJmLLrqIG2+8sb3mReiOfxDR\nM/bs2SPnJ46dfH6CusGXJdWsOuTlpIuyWFS4ojCB0zN77qqszV3HWR8uY8Lm6J8rt0+Zx7oLf0a6\nGj2+Prksucfa1Jec9U6SkwbmaxsoBso5OmuwlSdcpfx71YnJ3LVYWOw/g/eydzPGEvmZnmxE/h1M\nSmanBhO+er+pzO73cO2ax3h18f8watSInn0BrZDPoIGv3YA2Ozs80LuqqipqW1VVFUlJSU3Ba1f2\nq66uZsmSJezevZtLL72U22+/vceWyRRCtO5AfZAfbajhnxX+qG3ZdpXFYxwM6aGUXIoeYuKmdzlj\nw9+xeaNzWf5r5hV8NvsH4UF/QogecUViDbsCNu6vO7E4SaVuZsHRUbyfvZsR5uj3huYqhozC6qln\n5K5PmsqSnJV855V7YMJjYE/osbaLU1e7AW1SUhI5OTns2rUratuuXbsYN25cl/dzu91NweyiRYu4\n5ZZbOvIahBDdwDAMXtrn4bbPaqkPRK/pfna2hYsK7FjazePTOUMObuOc95+ImvgFYKCwcd4P2fIN\nSQEkRG+4I/UIdbrGk/UnJnYfCVlYUD6K94fsZqip9UWVAA4On4rNU09es6wkWUf3EXz8bry3/AFk\nWWrRzWLKrzNnzhyKioo4cOBAU1lRURElJSXMmzevy/s98MAD7N69m6uuukqCWSH6QE0AfrCumhs3\n1kQFs4lmhR+Oc3DZiIQeCWYTXNWc/9afuOy521oMZqsH5fHG1b+XYFaIXqQo8ED6Ia5NjPyVtTRk\nZUH5KMqD7QSkisLecWezf+SMiGLT1iKsf3sYjOgvzUJ0hVJbW9vu/6qamhoWLVqEpmlcffXV+P1+\nnnvuOfLy8li6dCkWi4WysrKmNFu5ubkx77d//36uvPJKkpKSuOWWW1rMO3vhhRd2/ysXvUrGL8Wv\nlSUefrbxGNUtTPwan2ZiUWECSe2tGNQJaijI5H+9zYyNL2DxR2c88VvsFJ19NVtOvwhdM8d0zIG6\nqMJAGZ85kA2Uc3TW4MjMRCEDflRVwEvuQRHlY80e3s3ew96qtpe53TXhPL77/C/JKt8bUe67/D8I\nXPS97ml0DOQzaOCL6Zp/WloaTz31FA899BBPP/00NpuN2bNns2TJEiyW8MSQL7/8knvvvZc777yz\nKaCNZb9Nm8LpPOrr67n33ntbfH4JaIXofnV+ndv/WceLexuAyGDWpsGCAjuzBlt6ZCx77oHNnLPq\nSQZVlba4fdeEc/lkzr/jThrU4nYhRO/QFHgyowSvofJGQ1pT+c6AnYvLR3Knto1UJdjq/kGLjRVX\n3MXlf4tMvWd99RkAAguuljHxolvEdIVWiK6Sb8c9Y9mutq+OnOzaMeHMIusPe7npo1oOuaNT6EzN\nMPPk2Wl8erTtiR+dkVm+l+kfv8LIXR+3uL0qcxgb5t/I4fyJnTq+XKEVfWWgnKOTr9A28hsK368Y\nzjue1IjyoUoDD1h3kKG0/H6xbWr4glRaVSmX/f1WbN7I1F+Bb5yL74bbwNazE8XkM2jgk1HZQgwA\nsQZynkN2fpswi6U7owNhkwK3npbEzycnYVYVPj3q71CA2PjBFcUwGHrgK6Z++ir5B75qsUrAZGH/\nqDM4nD+JtGMHSTt2MObnFUL0PIti8PfM/VxRUcgH3hOB+0EjgVt8E3jAsp0hanSazkY1Gfm8c/lv\nufSlO1CCJyaUmT//EPVIKd7//B3ati9ibk/wvIs690LEgCUBrRCniM9DKfx7WSEHQ9HB7PAEnWfn\nDua0jO7LLavoIUbu2Mi0z14j82hxq/WO5I5j35izCFgllY8Q8cymGryUtY+rTgpqyw0bN/sm8oB1\nOwVqyyuAAhzOn4j35/dhe/weFPeJRSi0Q8Uk3P0jAjMvQM8d1pMvQQxgEtAKMcDVGxpPBoaxKpQV\ntU0BfjoxkSuTK5jYTcGsye9l3JbVTP3n/0UtVxvRruRMdo8/B2fakFbrCCHiS4Jq8ErWPq6tHM6K\nZsMPqrHwc98E/mjdwWi19aFQoQnTabjnKWyP3IF2cF9TueKux7zmdYLTzyY04XQZVys6TAJaIQaw\nj0NpPOofQTXRwerwJI3Hv5nGzGwre/Z0fd1aW0Mdk/+1gklfrMDuaX0J0LKhEzmWNYxjmQXyoSVE\nP2RTDZ7LKubHVcN42Z3eVO7EzK2+8fzOspNJWuvr8xqZQ/D89s9Y//oA5n+uaypXDAPzvzagHjtK\nYOZ8MMeW3UQIkIBWiAGp0rDwZKCADaGMqG2qAj8Zn8ivpyWRYOp6Oq6E+mpyS7fyzdXPYA62PIbO\nQKF49JlsOutyjuaOHbCTt4Q4VZgVeCbjAG6vnxWh7KbyBkz8yj+O31p2c4ZW2/oBrHZ8N96JXjAa\nyz+eQTFOLLKt7d+FUltNYM4lGEkpPfkyxAAiAa0QA4jfUHgtOITlwTy8ROd0Hmv28Of5+Zye2bXh\nBYoeIuPofnJLt5JWXdZqPV1RKc8bS+mwqXgS08io2E9GC4snCCH6H1WBJeb9JCghXgnmNpX70LjT\nP5Yl5mKGtXUARSHwnUXo+YXY/nIvSsOJDAhqTSWWFc8TmL0APaegx16DGDgkoBWiEzqSLqsxVVZP\nKwql8nhgGIcNe9Q2DZ2rTId5NKcSLXNkp5/D4nWRc3AbOQe3YfU1tFovYLJwOH8Shwom47f1zusX\nQnTdp0dbz1TQEkWBG0ylOAjxbDC/qVxH4eFAIeeXePh2vg21jeFFoUkzaLj7Kex/+E/U2hMrkyk+\nL+bVrxE8/RxC46fLECXRJglohejnqrwh7vSN4VM9vcXtoxQXv7Dso1BtwKpYaT0FeisMg9TqMnJL\ntpJRUYzaxpKVXquDQ8NP4/DQCYRM3ZcxQQgRvxQFrjaXkagEeTwwHL3ZQi1rynzU+HQWjUzApLYe\nkBqDc/F/ZxHmj95HK9l94tiGgfnz9aiH9hM863yM5LRWjyFObRLQCtFPlblDvFrcwKdH/YSM6GA2\niQDXm0v5llaB1okLGxavi9wDm8kt3YrD3cZYOKA2LYey/IlUZhdiqNFDHYQQA9/FpqNkKH7+4B+F\nr9mQpy+qAtT6XVw/tp1fa8wWAucuQN9ahGnTRxHrF2pHSlHf/DvBKWeGsyAIcRIJaIXoZw65gjy8\n1cXfd7vx69HbFQwWaEe51nyQ5DaWpGyNeqgY85r/47qPVmEOtP7zY1AzczR3DGX5E3EnRU8+E0Kc\nemZqNTxo3c6dvrHUciJLwT5niEe3ulhQYCc/sY3QQ1EITT4DIz0L8/qVKM3eg5RQEPOmj9CKd6Dn\nF6KPntSTL0X0MxLQCtFPHHIFeWiri+daCWQBxqv1/NS8n1Ft5IFskR5C+9dGzKtfw7Sz5dW8GrkS\n0ynLn8TRnDGEzDKsQAgRaZzq4hHrVn7tH0dZszH95R6d896q5Nnz0jlnSMtL7DbS84bjv/j7mD5Z\njXakNGKbWnuMhN//jMB5F+H7tx+CI6lHXofoXySgFSKO6YbBxiN+ntvj5s0DHgKtBLKDFS+LTQeZ\nq1XRxjC1aPW1mNevZML7r2FxVrfeDkWlMruQsvyJ1KXlyOQMIUSbclQfj1i/5k7/GLbrJ1YVO+bT\nWfheJf+dVsaS5Io230qMpFQCF1xOqHgH5qIPUXyRq5CZ172Ntukj/N/7GcEZ5/Xq+5Jp3dsx15Vl\nenuHBLRCxKEjDSGW72nguT1uDtSHWq2XZlW4IM/G4rLPMCutT9Y6mVqyB/Pq1zF9thYl4G+1ntfq\n4HD+RI4MHY/fKtkKhBCxS1GCPGDZwf3+kWzUBzWV6yj8piaPL3wO/pJRQqLayjd1AEVBLxyPL3c4\npn+tx7R3W8Rmta4G21/uJbjxPXyLb8HIlJUHT1US0AoRJ+r8OmsPeXml2MOqQ170NuLT/ESN/5qS\nhD9kYFIVzIfbD2YVPcS+r74mbc275Bza3mbdsqET2XL6AuwNdTLJSwjRaVZF5w7LbpYHc/l7cChG\ns6lerzekseOIjRezihlpbiddmM1O8JvfQi+cgOnT1ajOmojNpq1FaL++Fv/CawnM+y5Y2h7SIAYe\nCWiF6EMHXUHeLfXy7kEvH5X7Wh1S0GhEksbNk5NYNDIBs6rElA9XC/oZcnA7Qw9sxuZtfTnKoMnC\nronnsWX6Ao4NHgEgK3oJIbpMVeD75jJGqW7+FBxFrX4i9NgRsHPO4bE8nXmABQl17R5LHzIU/yU/\nQK2vxbxiOUow0LRN8fuwvvIUlndeJDD7O+GVxjKy2ziaGEgkoBWiF/lDBp9X+ll32Mf7B71srQ60\nu49Vg4sL7Fwz2sE3sy1tJihvzuJ1kVeyhZzSrzEHWx9W4EzJ4tPRszk467t4E5JbrSeEEF1xhlbL\nhsydLKooZFvgxGQxp6FxVUUh1yRW8ce0MlK11odZAaCZ8F96HYEz5mD72/+i7dwcsVlxObGsfBHz\nOy8TmnoWgfMvBbO8tw10EtAK0YMMw2BnbYB1ZT4+POzlo3I/7mBsY13Hp5lYPNrBFYUJpFnVmJ/T\nUX+Mofu/ZPDh3ahG65d8Dw6bwpbTL+LAyBmUV1UxWIJZIUQPG2H288GQXfzsWD6vuCPzZz/nymCN\nJ5nHBpXyrQRnu8cycgrw3P4wpo3vYn3pCRR35C9QiqFj2vQxpk0fM25QNsqFVxD45nywy3yAgUgC\nWiG6Wb1fZ1ddkF21AXbXBanzxz5Za2SyiW/n27hkmJ1pGWaUWGftHl/NK794E4OqSlutFlI1dkye\nx+ZvXEJNZn6r9YQQoqc4VJ2/ZhxgutXNr6vzCDUbV3skZOHyipF8z3GM+9IPkdbe1VpFIXjOtwmd\ndhbmd17CvOGdqMAWwHasHJ5/FMurzxCYNZ/A3IUYucO6+ZWJviQBrRBd5A8Z7HMG2X08iD3c0M5A\n2GZUBc7IsnDhUBsX5tsYlWJuf6fm+4eCFO78iNM/eYUkZ2XrbTTbKCuYRFn+JL4687IOPYcQQnQ3\nRYGbkis50+rmR1UF7Gw2BAHgBfcg1nqTeGTQQb4Tw9haIzkN/1U34r/0OkyfrcW85nW00n3Rz+v1\nYFn7Bpa1bxAaO4XQmNMI5Rei548Mj7dVY/81TMQXCWiF6CDdMDjoCrKrNsiuuiD7nUFiHEUAwGC7\nyuwcK+fl2JiXZyXD1vEsAsqxo5g/XMHitStwuGtardeQkMLBYadRnjcWXetYsCyEED1turWBj3N2\n8sfaITxUNzjiam15yMKVFYWcbavnjtTDzLLFsGCM1UZw9ncInvNt1D1fY17zf5j+tR4lFH2lV9u5\nOWL8rWFLQM8vJDQ0HODq+SPR84ZLxoR+QgLaXhLLbPTmrh0z8Mf49Kd/k1JXkA8P+1hX5mP9ER/V\nvtivwlpUKEw2MSbVxJgUM9kJKoqi4AsZrCjxRtVv9XXqIbQtRZjXvYW2+Z8ohk5r63TVpWZTOnwq\nVYOHgyJXHIQQ8cuqGNyddphLEmr4UdUwtp90tXajN4n55WOYY3Py27TDfMPa0P5BFQV99CR8oyfh\nrz2G87VlDN78MWpd6wvIKN4GtN1b0XZvbSozFBVjyFD2peTjThpEgyOVBkcamem5E2AAABtUSURB\nVOV78VsTwjeLPeJ99qzBEgD3BQlohWhBnV9n4xFfOIg97GWfs51xXM0oQJ5DCwewqSaGJ5kwdWj5\nrpOOV3sM08Z3MX/4NmrV0VbrGUDV4BGUDp+KM02Siwsh+pepVg8bc3Zyf202/1OXHXG1FuADbzIf\nHEnmW/Y6bq/yc1pGbEtvG6mDKD/nYpIWL8H0xUbMa/4vImhti2LoKIdLGHW4pPXjoxCw2I4HtwmY\nU5PQU9Ix0jLQ0zLBMGR1xV4gAe0poqeW6Yv1uIMqKmDUqJiP21L+U9Phlr/1dseyghWeEEUVfooq\n/Hx21M8XVX5CHRhGkK14mabWMU2rZarqJFkPQjXhWzPbpl4Y2wENA23Hl5g+eAvTpo0t/lzWyG+x\nU5E9kkPDJuNxpMXeaCGEiDNWxeDOtCNcklDLnTW5rPVGZ195z5PCe29XMsPq4gpHDZc5asjUgm0e\nd1BFBaZDOwEIzLyA4PjpqJWHUaorUasrUaorUQLtLO7QCgUDi9+Dxe8BjsExaD6QzHjzb+HgNjWj\nKcg10jLAaos6liyT23kS0IpTTo1PZ09dgK+rg/yzwkdRhZ/9bSwv2xKbBqNTzIxONTEmxcTsHZ92\n+Qu4oocYXLYLy9dbMBV9iFp+sM36ofyRbJjwLXaPn83o7eu79uRCCBFHplg9vJm9l4+9Dn5Xm8NG\nb1JUnSJfIkW+RH5Zncdcu5MrHdUsSKjD0dZSuscZqYMIpQ5qVmAQmjwDtXQvauk+tOP3auXhLr8W\nJeBHqTiMWhF5LCMhET0tAyNjCHp2Hros29slEtDGqaBu4AoYNAQNGoJ602N30CBkGNg0BaumRNzb\nTQrpVhWrdur+tGEYBnV+gypviEqvTqVH56A7xJ7jKbT21AWp9MY+/rWRSYEZWRbOy7Fybo6NLdV+\ntGYRbGeD2cS6CvKLN1FQ/AV5BzZj9bU9rtgwWwieMYfAnIvRR4xj2+4YxpIJIUQ/Ncvm5t3sPXzo\nSeR3tTl85kuMqhNCYZUnhVWeFBxKiFk2F9+wujnD6ma61U1KDAEuioKRkU0oI5vQtG/StOSNx416\ncB+fbN6P3V2Lw11DgquGzKP7sPgasPjcmDt7ZbfBhdbggrIDsBkMVcNc9GE4+8LY0wiNnNDiVVzR\nMglo+4hhGDgDBuUNIap9OtU+nRqfTrU3/Pjnn9aid+An7+aSLQqZNpVMm0aGTSXTrpJVM4QsLUCm\nFgzf1CCZWoA0NUQXhnd2mWEYBHTw6wa+kIEvBD7dwBtKoQENr6HiQcNjaKypseA2VFy6RoOh0qCr\nuA0V94pK3EGdWp9BpTeEv+PxaovGpJg4N8fKeblWZmVbSTKfGPS/rab9Fb5aogV85JZuJb94E/nF\nm0g/1vZV2Eb6kHwCcy4mMGs+OKKvVAghxEB2rt3FbNtu1niS+VPdYD72tfw+6Da0puAWwsMBxpq9\nTNIz+YYTCkx+8k0+8k1+kmMJdO0O9NGT2WkURhQ3Hxan6CEsPg8WvxuLz8M4UwNKTRVqbRVKTRVK\noPWVGptT9BDa7i1ou7fAW89haCb0EWPDwe2YKYRGTQBbQkzHOhVJQNsLQno4cC1zR95cHcn11AFO\nv4HTHzppIlPLP2XY9ADDDSfD9XpyDTc5upusyrVk4iUx6CEh6CUh6MEW8GDze7AGPGh+L+h6eKC7\nswYMA8MwMAwIGeEgVTdAJ5ziyqNacCpWdmy/kzrVTq1mo0axUYGNWtVGnWanXrNTb7JRZU6i3JJC\ngzY+urGtpSL0xvZm0RaTApMHmZmRZeGMLAtnZFnJcXQ8nVZzaigY/kbvqsbhqia57ihnr34aUxvL\n0DZnaCaCp59N8LyLCY09TSYVCCFOaYoC8xKczEtwUhKw8A93Gi+506Ny2DZnoLAjYGcHQ3nlpDkN\naWqQfJOfPM1PuhYk9fM60q0q6VaVNKtKuk1t+juoG61O7jVUDZ89EZ89fPU41DzLgWGAux61pgql\npvJEkFtXjaK3HVAroSDanq/R9nwNbz+PoWnoQwvRh40hNGw0+rDR6ENHgElSMkIHAtqysjIeeeQR\nNm3aBMCsWbO4+eabSUtrexJKrPt19vjxpiEQYmelh21VPrbVBPi6xs+OmiANJw3RNFCwNT0+adtJ\ngYvRbKanyQhh1/3YQ34SdH/TY7vux64HsId8TY8TQ15Sgw2kBN2kBhtIO37f9HfATaLeuZ9KOqUq\n9qr1mo1ySwrlltTjtxSOHr9vXlZpTiakdizwtKjhFblGppiYmmFhRpaFbdUBLMeHatT5DVYdik6n\n1Rqz30NS3VESXDVNwWuCqwZ7gxMl6uy2zWNP5uDwqZQUTqdkxOl4HeGrDMjQAiHEKebTo219PvmY\nRT0ztVL2qQl8EMpkXXAQVcSeMqtGN1HjN7GZ41c9v3a1Wd+qgsOs4jApZPnGkawESSYQvleCJBMk\nSQlg9amkq0HStSBJio6SmIyemAxDR9AUCoRCKM4a1KpylKOHUMsPobraXkBCCYXQDuxGO7CbxhDW\n0EzoeSPQh40mNPx4kJs3AsyxZYAYSJTa2tp2P3Fra2tZvHgxgUCAK6+8klAoxPPPP8+QIUNYtmwZ\nZnPL3w5i3a+zx+8JTbP2Q0GUBjd4XCgNbhSPG/y+8E8HAT9KwIffF6DBHyTo96P4/ZiDPhxBDxaj\nYxOMROeFUKg0J0cEvBW2VLyONHzJ6QST01FT08nKTGVEipXCVAt5yWY0TQNNA1UDRYnMiWsYmP0e\nbB4n9oY67A3h+/DfTmwNddiPb3M4q0huY4Wu9uiKSnnuOEpHTKO0cDoV2YWdyhvbUlaIjnDWO0lO\nip5NLOKDnJ/4J+coNh3J0dp2QBtNN+CgYWe7nsQOPZEdehIlhj3iolBvM6OTpoYYpAXJ1fwMNfnJ\nP35rfDxEC6ApgMsJ6ZloO79C2/lV1CSyWBmqipGagZE6CCMhESMhkeA3ZmOkpjeVt7VYRE9lRepp\nMV2hXb58ORUVFSxfvpzhw4cDMHHiRH7605+ycuVKFi5c2KX9Onv8DtND4KpHqa8N35y1qLXHUJrd\n1LL9KB43iq/9K3T24zfRdzQMsgN1ZAfqwF3aqWMYisKNioqhauiKiqqHMIU6N0Y2Fs6ULEqHhwPY\nQwVT8NsG/iIaQgjR01QFChQPBaqHC6kAwmNqd+qJbPGYqLakUGFYKTesVBpWAvT8ojMBVCp0lQrd\nzI5WhkZoGE3Bbl5KFkOnnsnQb2qMClUz+vA2Bh/YinX3ZtSjh2J6TkXXUaoroLqiqcxctC6ijuFI\nQk8dFA5w0wZhJKaA1Y5htaGW7AGTGcNsDt+bwvcnP0br2rC87hZTQLt69WqmTZvWFGwCzJgxg4KC\nAlatWtVqwBnrfp09ftSLWftmOD2Gqw7FWRu+bwxe6+vAXY9i9My41f7KQMFvsuAz2/BqVhpMNqod\ng6jXbLg1GyaHA7dmw2myU6/aqNNseEw20DRURUV11YKiomGgqQpW1cCmGNiO39tVnaSQD7X+GBmj\nxuAINGAPhsfjllY6sQW8WP0N4Rx+Pjf2BicJrho0ve2cgt1JMQw0IxT+wtPNPPZkGhLTcCem43ak\n8cXMK6gdlCfjYYUQohc4lBDTtTpGGU6SLSeuoJ+RZeVoyExJ0MLRkJlqXaNq2CSqfTrHvM0majeb\nrN1T0UMIhdKQldKQFfZ5mm1RgUmgTSJz6veZbKrnbM8BptYfYGT1PnLLdpLoaXuYQmsUdz2auz6c\nYaGTDEXB+vKTGFYbWG0YVvvx+2Z/W2xtbz/+t17YwryZDmo3oHU6nZSVlTFnzpyobWPGjOGTTz7p\n0n6dPX5LbH9/KOa6PSmgaIRUDRUDFVAJT5g6oY1ucXKgc1JVQ1UJmiwETVaC5hP3IZOVgNlKyGQJ\nbzdbCZht+K0OvLZEMo/uI2i2Ejxer/FxyGSOes7myf/bW242tp8mkqio0Em/KPKniY9bW/rWMJhS\n9AYWXwNWnzucGsXfcDxFSjhNStPjQOxjXXuKrqh4E5LDQWtiGg2Oxvs09JMG69dmDO2jVgohhGik\nKjDEFGCI6cSvccHJrWeQ+X87XXhDBu5AOH1mxq7PcRom6jHhNMw4DRNOTDgNEyGTheqQiWpdo8Ho\nnquYlV6dtThYywRImAAJQB5k+uuYXr+fqa4DTKvfz/T6/eT7jnXLc7ZHMQzwNqB4uz7Hw/W3D7t8\njHYD2srK8PjArKysqG0ZGRm4XC5cLheJiYmd2q+zx+9pIRTKLakcsaRyxJrGEUsqNWYHTs2O02Sn\nTkugwWQj3aaRY4d8O4xKMBjj0Ek+nt5JP36D2McCxbySVAd1dYxlr1IUghYbQYuNhqT0tqvqIfaP\nOjM8Eet4fsAEdw3TzK7wcJK66vBwEm9DODND0y18Rba1WaZBzYwnIQVPQgrehOTwvT35eFky3oQU\nPPZkPI4U6lKHMG7rmp74lxBCCBEHVEUhwaSQYIJMYIJW22rd5uOEPbpCtW6iMmTiYNBCacgSvg+G\n7w8GLVTpnZ8nVGlJ4b1Bp/HeoNOayjL8Tk5zlTDUd4whvhqG+GvJ8dWQ469hiK+GbH8dJropv2Uc\naTegdbvDV9Gs1ugBxI1lHo8nKuCMdb/OHr8l3RHhN5dy/Da2g/u19GP5N2LcN9Z6HTbm8pirdqQN\nsQ4IbyksbfPqbwfaezoABRFlXU/kBQrhL8ExZf2bEFt74+H8CiFEX+nIYLIee7+kY+2AFj6v2njP\nbX5sMzD4+G1iB5+z83KBca1u7fvfNXtGzCOilTbG/HXHts4eQwghhBBCnNraDWjt9vCsPJ8v+ifz\nxjKHI/pKW6z7dfb4QgghhBBCQAwBbXZ2NgBVVdFZ8auqqkhKSmoKSjuzX2ePL4QQQgghBMQQ0CYl\nJZGTk8OuXbuitu3atYtx41oepxHrfp09vhBCCCGEEBDjGNo5c+ZQVFTEgQMHmsqKioooKSlh3rx5\nXd6vs8cXQgghhBAipqVva2pqWLRoEZqmcfXVV+P3+3nuuefIy8tj6dKlWCwWysrK2LJlC5MnTyY3\nNzfm/TpSTwghhBBCiJPFFNAClJSU8NBDD/Hll19is9mYOXMmS5YsIS0tDYAVK1Zw7733cuedd7Jg\nwYKY9+toPSGEEEIIIZqLOaCNNy6Xi6eeeop169ZRXV1NZmYm8+fP5z/+4z8wmyOTFL/66qu8/PLL\nlJeXk5eXx3XXXccFF1zQRy0/9Rw6dIhFixbx8MMPM3369IhtH3/8MbfcckuL+7344osUFhb2RhNP\neW2dI5A+FC+kv8SfsrIyHnnkETZt2gTArFmzuPnmm+ViTJy49tpr2b59e1T5nDlzuO+++/qgRaLR\nH/7wB0pLS3nyyScjyjvbp9pdWCEeGYbBL3/5SzZt2sSll15KYWEhW7du5W9/+xv79+/nwQcfbKr7\n/PPP8+ijjzJ37lwWLVrEhx9+yB133IGiKDI+txfU19dz2223tZiWDaC4uBhFUfjtb3+LpkUuETh4\n8ODeaOIpr71zJH0ofkh/iS+1tbX85Cc/IRAIcM011xAKhXj++efZu3cvy5Yti7q4InqXYRjs37+f\n2bNnM2fOnIhtjRmWRN948803eeONN5g2bVpEeVf6VL8MaDds2MDnn3/Orbfeyr/9278BcNlll5GV\nlcWyZcvYvHkzU6ZMob6+nmeeeYb58+fz3//93wAsXLiQH//4xzz66KPMmTMn6kNBdJ/9+/fzy1/+\nMmKy38n27dtHdnZ2xDAV0XvaO0fSh+KL9Jf4snz5cioqKli+fDnDhw8HYOLEifz0pz9l5cqVLFy4\nsI9beGo7fPgwHo+H2bNnc+GFPbOsvOiYUCjEs88+yzPPPNPi9q70qZhXCosnX3zxBUDUm/r5558P\nwJYtWwDYuHEjHo+Hyy67rKmOqqpcfvnlHD16tKme6H4rVqzg+9//PnV1dVxyySWt1isuLmbYsGG9\n1zDRJJZzJH0ovkh/iS+rV69m2rRpTR+8ADNmzKCgoIBVq1b1YcsEhPsLIH0mTvh8Pq655hqefvpp\nLrzwQrKysqLqdKVP9cuA9vrrr+f555+PWnChtrYWoOmK0Y4dOwAYO3ZsRL0xY8ZEbBfdb+/evcyb\nN4/ly5czZcqUFusYhsGBAwea/uP6fD6CwY6usC06K5ZzJH0ofkh/iS9Op5OysrKovgHh/rFz584+\naJVo7uSA1uPx9GFrhN/vx+128/vf/56777476te9rvapfjnkICUlhZSUlKjy119/HYDJkycDUFlZ\nSXJyMjabLaJeRkYGAOXl5T3c0lPXTTfd1O74sbKyMrxeb9OEpOLiYkwmE+eeey633norqampvdTa\nU1Ms50j6UPyQ/hJfKisrAVq8ypSRkYHL5cLlcpGYmNjbTRPHFRcX43A4ePjhh1mzZg0NDQ3k5uZy\n4403yqTWPuBwOHjttdcwmVoOPbvap+IqoG1p+dvmEhISSEhIaHHbO++8wwcffMD06dOZOHEiAG63\nO+qDGMBqtQLg9Xq72OJTS0fOTyyTIRq/PX/99dcsXryYnJwcNm/ezIsvvkhxcTHPPvtsi+dPtK67\nz5H0oZ4X6zmT/hJf3G43cKIvNNdY5vF4JKDtQ8XFxbjdblwuF3fddRcul4uXXnqJO+64g2AwyLe/\n/e2+buIpRVVVVLX1gQFd7VNxFdC295/ruuuu48Ybb4wq37BhA7/73e8YNGgQd911V8zPpyhKh9t4\nKuvs+WlNbm4uN9xwA/Pnz6egoACA2bNnk5eXx3333cdbb73FFVdc0aU2n2q6+xy1R/pQ18V6zqS/\nxKe2+oD0j761cOFCdF1vmjwOMG/ePBYtWsSjjz7K/PnzZVJrHOpsn4qrgPbXv/51m9sbx+019/77\n73PPPfeQkJDAI488EpGKw263t5iKqLHM4XB0scWnls6cn7YUFha2mDfz4osv5sEHH+SLL76QD+gO\n6u5zJH2o58V6zqS/xJfGORzSP+JX88msjWw2GxdeeCFLly5l//79jBw5sg9aJlrS1T4VVwFtR1Oc\nvP766zzwwAMkJyfz5z//mdGjR0dsz87Oxul04vf7I5bPbfyJLzMzs+uNPoX0Vgoak8lEcnIyDQ0N\nvfJ8A0l3nyPpQz2vq+dM+kvfaLx40tKQkaqqKpKSkqImLov4kJ6eDiB9Js50tU/1yywHACtXruS+\n++5j0KBBPPXUU1HBLISvbBiGwe7duyPKd+3aBcD48eN7pa2iZU8++SQLFy5sGjfTqK6ujpqaGnJy\ncvqoZaKR9KH4If0lviQlJZGTk9PUF5rbtWsX48aN64NWiUYVFRVceeWVLF26NGpbY95t6TPxpat9\nql8GtMXFxfzxj38kLS2NJ554IiJfWXOzZs3CarXyyiuvNJXpus6rr77KkCFDmiaPib6RnZ3N4cOH\nefPNNyPK//rXvwIwf/78vmiWaEb6UPyQ/hJ/5syZQ1FRUcTCJEVFRZSUlMgqen0sKysLl8vFG2+8\ngcvlaiovLy9n5cqVTJ8+vSlbi4gfXelTcTXkIFZLly7F7/czd+5ctm3bxrZt2yK2jxw5klGjRpGa\nmsoPfvADnnnmGXRd5/TTT2fdunVs3ryZ3//+9zIYvI8tWLCAN954g8cee4yDBw9SWFjI559/zrp1\n67j00kujlsQTvU/6UPyQ/hJ/rrnmGt555x1uuukmrr76avx+P8899xxjx46VlaniwK233sptt93G\nDTfc0PTrxj/+8Q80TeO2227r6+aJFnSlTym1tbVGL7Wz23zrW9+iurq61e033HADP/zhD4FwMvIX\nXniBf/zjH1RXVzN06FCuv/565s6d21vNPeWtWLGCe++9lyeeeILp06dHbKurq+Mvf/kL69evp76+\nntzcXBYuXMhVV13VZnoP0b3aOkfSh+KH9Jf4U1JSwkMPPcSXX36JzWZj5syZLFmyhLS0tL5umgDW\nr1/PsmXL2LNnD1arlWnTpnHTTTfJ6mFx4JJLLmHIkCE8+eSTEeWd7VP9MqAVQgghhBCikXylF0II\nIYQQ/ZoEtEIIIYQQol+TgFYIIYQQQvRrEtAKIYQQQoh+TQJaIYQQQgjRr0lAK4QQQggh+jUJaIUQ\nQgghRL/WL1cKE0KI/uTHP/4xmzZtiihzOBwUFhZyzTXXMHv2bADuueceVq5cGVFP0zRSU1OZMWMG\nP/nJTxg8eDAATz/9NEuXLsVsNvPee++RlJQU9byvvfYa999/P1lZWaxYsaKHXp0QQvQ9CWiFEKIX\njBw5kl/96lcA6LqO0+nk3Xff5bbbbuOxxx5jxowZAKSlpfGnP/2pab9gMEhJSQmPP/44W7Zs4cUX\nX8Rms0VsX79+PQsWLIh6ztWrV/fwqxJCiPggAa0QQvQCh8PBpEmTIspmzZrFli1beP3115sCWrPZ\nHFVv6tSpWCwW7r77bjZs2MAFF1zQtG3KlCmsXbs2KqCtrKzkq6++YvTo0dTW1vbQqxJCiPggY2iF\nEKKPqKpKYmIiiqK0W3fcuHEAHDlyJKL8/PPPp6ioiPr6+ojytWvXUlBQwKhRo7qvwUIIEackoBVC\niF4SDAabbjU1NbzwwguUlJRw+eWXt7tvSUkJAHl5eRHlc+bMQdd11q9fH1G+atUq5s2b132NF0KI\nOCZDDoQQohds3ryZmTNnRpVfddVVTJs2LaIsGAw2PXa73Wzfvp2HH36YnJwcZs2aFVE3NTWV6dOn\nRww7OHLkCNu2beOuu+5i2bJl3f9ihBAizkhAK4QQvWDUqFH85je/afrb6XTyySef8PLLL2MymViy\nZAkAFRUVLQa+EydO5Pbbb4+YENbo/PPP58EHH6S+vp6kpCRWrVrFmDFjKCgo6LkXJIQQcUQCWiGE\n6AUJCQmMHz8+ouzMM8/E4/Hw4osv8r3vfQ+A9PR0/vd//7epjsViISsri+Tk5FaPfd5553H//fc3\nZTtYs2ZNxMQxIYQY6GQMrRBC9KFx48YRCoWaJnuZTCbGjx/fdBs5cmSbwSxASkoKM2bMYO3atZSW\nlrJ7924ZPyuEOKVIQCuEEH1o27ZtaJpGbm5ul47TmO3grbfeYvLkyWRnZ3dTC4UQIv7JkAMhhOgF\nbrebrVu3Nv0dCATYsGEDb7/9NpdeeilpaWldOv65557Lfffdx/Lly7n55pu72lwhhOhXJKAVQohe\nsHfvXq6//vqmv61WK7m5udx4441cc801XT5+UlISZ5xxBp9++ilz587t8vGEEKI/UWpra42+boQQ\nQgghhBCdJWNohRBCCCFEvyYBrRBCCCGE6NckoBVCCCGEEP2aBLRCCCGEEKJfk4BWCCGEEEL0axLQ\nCiGEEEKIfk0CWiGEEEII0a9JQCuEEEIIIfo1CWiFEEIIIUS/9v8B6HWLvC5GpVgAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d6e5b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style.use('fivethirtyeight')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 18)\n",
    "\n",
    "sns.distplot(result['BPM_y'], ax=ax, bins=60)\n",
    "sns.distplot(draft_df['BPM'], ax=ax, bins=120)\n",
    "plt.title('Distribution of rookie year and career average BPMs in the NBA\\n')\n",
    "plt.xlabel('BPM')\n",
    "plt.legend(['Rookie','Career'],fontsize=18)\n",
    "ax.set_xlim(-20, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating dummy variables for the College column\n",
    "\n",
    "There are certain colleges in NCAA basketball that produce a larger majority of good NBA players. Here, I grouped the drafted players by their college teams and looked at the top 10 colleges that contributed to the highest rookie year BPM. However, I only looked at colleges that produced more than 5 drafted NBA players in my timeframe. I then created a new college column, and changed the college value to \"Bad\" if the player did not attend one of the top 10 colleges. I then used this column to get dummy variables using pandas get_dummies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Birthday', 'Height_ft', 'Height_in', 'Weight', 'Yrs', 'GP',\n",
       "       'MPG', 'FGM', 'FGA', 'FG_Perc', '3PM', '3PA', '3P_Perc', 'FTM', 'FTA',\n",
       "       'FT_Perc', 'TOV', 'PF', 'ORB', 'DRB', 'RPG', 'APG', 'SPG', 'BPG', 'PPG',\n",
       "       'College', 'BPM_x', 'Season', 'Age', 'Tm', 'Lg', 'Pos', 'G', 'MP',\n",
       "       'PER', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'TRB%', 'AST%', 'STL%',\n",
       "       'BLK%', 'TOV%', 'USG%', 'OWS', 'DWS', 'WS', 'WS/48', 'OBPM', 'DBPM',\n",
       "       'BPM_y', 'VORP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by box plus/minus (BPM)\n",
    "sorted_BPM = result.sort_values(['BPM_y'], ascending=False)\n",
    "sorted_BPM.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check number of unique colleges\n",
    "sorted_BPM['College'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College\n",
       "Morehead State University                     3.400000\n",
       "Louisiana Tech University                     2.900000\n",
       "0                                             2.500000\n",
       "University of South Carolina                  1.400000\n",
       "University of North Texas                     1.300000\n",
       "Davidson College                              1.000000\n",
       "Western Kentucky University                   0.850000\n",
       "University of Tulsa                           0.600000\n",
       "University of Utah                            0.133333\n",
       "Weber State University                       -0.250000\n",
       "Wake Forest University                       -0.400000\n",
       "Saint Joseph's University                    -0.550000\n",
       "San Diego State University                   -0.550000\n",
       "University of Illinois at Urbana-Champaign   -0.750000\n",
       "Norfolk State University                     -0.900000\n",
       "Bowling Green State University               -0.900000\n",
       "Villanova University                         -0.950000\n",
       "St. John's University                        -1.000000\n",
       "New Mexico State University                  -1.100000\n",
       "Eastern Washington University                -1.100000\n",
       "Name: BPM_y, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average rookie year BPM grouped by College\n",
    "sorted_BPM.groupby(['College']).mean()['BPM_y'].sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a dictionary with college and the count of how many NBA drafts they produced\n",
    "college_count = dict(sorted_BPM.groupby(['College']).count()['BPM_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def CollegeCountAbove5(college):\n",
    "    '''\n",
    "    Function to be used to create new column to filter out colleges that did not produce more than 5\n",
    "    drafted players in the NBA\n",
    "    '''\n",
    "    if college_count[college] > 5:\n",
    "        return 'Y'\n",
    "    else:\n",
    "        return 'N'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a new column to see whether that player went to a college that produced more than 5 NBA drafts\n",
    "result['College_above_5'] = result['College'].apply(CollegeCountAbove5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College\n",
       "University of Illinois at Urbana-Champaign   -0.750000\n",
       "University of Florida                        -1.792308\n",
       "University of Pittsburgh                     -1.933333\n",
       "Stanford University                          -2.142857\n",
       "Georgia Institute of Technology              -2.228571\n",
       "University of Kansas                         -2.447368\n",
       "Indiana University                           -2.533333\n",
       "University of Washington                     -2.541667\n",
       "University of Memphis                        -2.577778\n",
       "University of North Carolina                 -2.583333\n",
       "Oklahoma State University                    -2.583333\n",
       "Louisiana State University                   -2.600000\n",
       "Georgetown University                        -2.600000\n",
       "University of Kentucky                       -2.653571\n",
       "Duke University                              -2.710000\n",
       "University of California, Los Angeles        -2.789474\n",
       "University of Southern California            -2.800000\n",
       "Ohio State University                        -3.025000\n",
       "University of Connecticut                    -3.193750\n",
       "University of Michigan                       -3.533333\n",
       "Name: BPM_y, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top colleges and their average rookie BPMs\n",
    "result[result['College_above_5'] == 'Y'].groupby(['College']).mean()['BPM_y'].sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# find top colleges based on drafted players BPM\n",
    "top_colleges = list(result[result['College_above_5'] == 'Y'].groupby(['College']).mean()['BPM_y'].sort_values(ascending=False).head(10).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['University of Illinois at Urbana-Champaign',\n",
       " 'University of Florida',\n",
       " 'University of Pittsburgh',\n",
       " 'Stanford University',\n",
       " 'Georgia Institute of Technology',\n",
       " 'University of Kansas',\n",
       " 'Indiana University',\n",
       " 'University of Washington',\n",
       " 'University of Memphis',\n",
       " 'University of North Carolina']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_colleges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def TopCollege(x):\n",
    "    '''\n",
    "    Function to categorize all colleges outside of the top 10 colleges as \"bad\"\n",
    "    '''\n",
    "    if x in top_colleges:\n",
    "        return x\n",
    "    else:\n",
    "        return 'Bad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create new column, shows college if it is in top college, bad otherwise\n",
    "result['College_adj'] = result['College'].apply(TopCollege)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create new dataframe of dummy variables, 1 if attended top college, 0 otherwise\n",
    "dummies = pd.get_dummies(result['College_adj'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# glue dummy and result dataframe\n",
    "result = pd.concat([result, dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# renaming column headers to something easier to read\n",
    "new_columns = ['Player',\n",
    " 'Birthday',\n",
    " 'Height_ft',\n",
    " 'Height_in',\n",
    " 'Weight',\n",
    " 'Yrs_Played_College',\n",
    " 'GP_College',\n",
    " 'MPG_College',\n",
    " 'FGM_College',\n",
    " 'FGA_College',\n",
    " 'FG_Perc_College',\n",
    " '3PM_College',\n",
    " '3PA_College',\n",
    " '3P_Perc_College',\n",
    " 'FTM_College',\n",
    " 'FTA_College',\n",
    " 'FT_Perc_College',\n",
    " 'TOV_College',\n",
    " 'PF_College',\n",
    " 'ORB_College',\n",
    " 'DRB_College',\n",
    " 'RPG_College',\n",
    " 'APG_College',\n",
    " 'SPG_College',\n",
    " 'BPG_College',\n",
    " 'PPG_College',\n",
    " 'College',\n",
    " 'Total_BPM_NBA',\n",
    " 'Rookie_Season',\n",
    " 'Rookie_Age',\n",
    " 'Draft_Tm',\n",
    " 'League',\n",
    " 'Position',\n",
    " 'GP_Rookie',\n",
    " 'MP_Total_Rookie',\n",
    " 'PER',\n",
    " 'TS%',\n",
    " '3PAr',\n",
    " 'FTr',\n",
    " 'ORB%',\n",
    " 'DRB%',\n",
    " 'TRB%',\n",
    " 'AST%',\n",
    " 'STL%',\n",
    " 'BLK%',\n",
    " 'TOV%',\n",
    " 'USG%',\n",
    " 'OWS',\n",
    " 'DWS',\n",
    " 'WS_Rookie',\n",
    " 'WS_per_48_Rookie',\n",
    " 'OBPM',\n",
    " 'DBPM',\n",
    " 'BPM_Rookie',\n",
    " 'VORP_Rookie',\n",
    " 'College_above_5',\n",
    " 'College_adj',\n",
    " 'Bad'] + top_colleges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result.columns = new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create new column for total height in inches\n",
    "result['Total_Height_in'] = result['Height_ft'] * 12 + result['Height_in']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Birthday</th>\n",
       "      <th>Height_ft</th>\n",
       "      <th>Height_in</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Yrs_Played_College</th>\n",
       "      <th>GP_College</th>\n",
       "      <th>MPG_College</th>\n",
       "      <th>FGM_College</th>\n",
       "      <th>FGA_College</th>\n",
       "      <th>...</th>\n",
       "      <th>University of Florida</th>\n",
       "      <th>University of Pittsburgh</th>\n",
       "      <th>Stanford University</th>\n",
       "      <th>Georgia Institute of Technology</th>\n",
       "      <th>University of Kansas</th>\n",
       "      <th>Indiana University</th>\n",
       "      <th>University of Washington</th>\n",
       "      <th>University of Memphis</th>\n",
       "      <th>University of North Carolina</th>\n",
       "      <th>Total_Height_in</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>David Lee</td>\n",
       "      <td>1983-04-29</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>245</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>28.980000</td>\n",
       "      <td>4.640000</td>\n",
       "      <td>9.280000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>James White</td>\n",
       "      <td>1982-10-21</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>25.820000</td>\n",
       "      <td>3.780000</td>\n",
       "      <td>7.680000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Paul Davis</td>\n",
       "      <td>1984-07-21</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>270</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.857143</td>\n",
       "      <td>25.142857</td>\n",
       "      <td>4.071429</td>\n",
       "      <td>7.585714</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Dee Brown</td>\n",
       "      <td>1984-08-17</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>32.185714</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Jason Thompson</td>\n",
       "      <td>1986-07-21</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>29.280000</td>\n",
       "      <td>5.540000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Ryan Anderson</td>\n",
       "      <td>1988-05-06</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>240</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31.600000</td>\n",
       "      <td>29.490000</td>\n",
       "      <td>4.530000</td>\n",
       "      <td>9.570000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Terrence Williams</td>\n",
       "      <td>1987-06-28</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>220</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.714286</td>\n",
       "      <td>29.642857</td>\n",
       "      <td>3.685714</td>\n",
       "      <td>9.414286</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>James Johnson</td>\n",
       "      <td>1987-02-20</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>250</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.428571</td>\n",
       "      <td>30.357143</td>\n",
       "      <td>4.614286</td>\n",
       "      <td>10.642857</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Marcus Thornton</td>\n",
       "      <td>1987-06-05</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>205</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.250000</td>\n",
       "      <td>32.137500</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>12.325000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Derrick Williams</td>\n",
       "      <td>1991-05-25</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>240</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>29.220000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Tristan Thompson</td>\n",
       "      <td>1991-03-13</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>238</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.800000</td>\n",
       "      <td>26.360000</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>8.280000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Reggie Jackson</td>\n",
       "      <td>1990-04-16</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>208</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>26.160000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>8.680000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Jordan Williams</td>\n",
       "      <td>1990-10-11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>260</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>29.860000</td>\n",
       "      <td>4.820000</td>\n",
       "      <td>10.720000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>Mike Scott</td>\n",
       "      <td>1988-07-16</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>237</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.769231</td>\n",
       "      <td>27.938462</td>\n",
       "      <td>3.684615</td>\n",
       "      <td>8.030769</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>Tony Mitchell</td>\n",
       "      <td>1992-04-07</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>235</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>29.316667</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>10.050000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>Kyle Anderson</td>\n",
       "      <td>1993-09-20</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>230</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.166667</td>\n",
       "      <td>32.666667</td>\n",
       "      <td>3.850000</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>Anthony Brown</td>\n",
       "      <td>1992-10-10</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>210</td>\n",
       "      <td>8.0</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>3.450000</td>\n",
       "      <td>7.275000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>Marcus Thornton</td>\n",
       "      <td>1993-02-09</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>190</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.250000</td>\n",
       "      <td>32.137500</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>12.325000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player    Birthday  Height_ft  Height_in  Weight  \\\n",
       "133          David Lee  1983-04-29          6          9     245   \n",
       "196        James White  1982-10-21          6          7     200   \n",
       "199         Paul Davis  1984-07-21          6         11     270   \n",
       "210          Dee Brown  1984-08-17          6          0     185   \n",
       "292     Jason Thompson  1986-07-21          6         11     250   \n",
       "301      Ryan Anderson  1988-05-06          6         10     240   \n",
       "346  Terrence Williams  1987-06-28          6          6     220   \n",
       "351      James Johnson  1987-02-20          6          9     250   \n",
       "378    Marcus Thornton  1987-06-05          6          4     205   \n",
       "447   Derrick Williams  1991-05-25          6          8     240   \n",
       "449   Tristan Thompson  1991-03-13          6          9     238   \n",
       "469     Reggie Jackson  1990-04-16          6          3     208   \n",
       "481    Jordan Williams  1990-10-11          6         10     260   \n",
       "547         Mike Scott  1988-07-16          6          8     237   \n",
       "601      Tony Mitchell  1992-04-07          6          8     235   \n",
       "653      Kyle Anderson  1993-09-20          6          9     230   \n",
       "717      Anthony Brown  1992-10-10          6          7     210   \n",
       "728    Marcus Thornton  1993-02-09          6          4     190   \n",
       "\n",
       "     Yrs_Played_College  GP_College  MPG_College  FGM_College  FGA_College  \\\n",
       "133                 5.0   30.000000    28.980000     4.640000     9.280000   \n",
       "196                 5.0   30.400000    25.820000     3.780000     7.680000   \n",
       "199                 7.0   30.857143    25.142857     4.071429     7.585714   \n",
       "210                 7.0   33.000000    32.185714     4.285714    10.100000   \n",
       "292                 5.0   29.800000    29.280000     5.540000    10.740000   \n",
       "301                10.0   31.600000    29.490000     4.530000     9.570000   \n",
       "346                 7.0   33.714286    29.642857     3.685714     9.414286   \n",
       "351                 7.0   31.428571    30.357143     4.614286    10.642857   \n",
       "378                 8.0   32.250000    32.137500     5.450000    12.325000   \n",
       "447                 5.0   31.000000    29.220000     4.300000     8.560000   \n",
       "449                 5.0   32.800000    26.360000     3.820000     8.280000   \n",
       "469                 5.0   31.400000    26.160000     3.900000     8.680000   \n",
       "481                 5.0   32.000000    29.860000     4.820000    10.720000   \n",
       "547                13.0   30.769231    27.938462     3.684615     8.030769   \n",
       "601                 6.0   31.500000    29.316667     4.400000    10.050000   \n",
       "653                 6.0   32.166667    32.666667     3.850000     9.666667   \n",
       "717                 8.0   33.250000    27.300000     3.450000     7.275000   \n",
       "728                 8.0   32.250000    32.137500     5.450000    12.325000   \n",
       "\n",
       "          ...         University of Florida  University of Pittsburgh  \\\n",
       "133       ...                             0                         0   \n",
       "196       ...                             0                         0   \n",
       "199       ...                             0                         0   \n",
       "210       ...                             0                         0   \n",
       "292       ...                             0                         0   \n",
       "301       ...                             0                         0   \n",
       "346       ...                             0                         0   \n",
       "351       ...                             0                         0   \n",
       "378       ...                             0                         0   \n",
       "447       ...                             0                         0   \n",
       "449       ...                             0                         0   \n",
       "469       ...                             0                         0   \n",
       "481       ...                             0                         0   \n",
       "547       ...                             0                         0   \n",
       "601       ...                             0                         0   \n",
       "653       ...                             0                         0   \n",
       "717       ...                             0                         1   \n",
       "728       ...                             0                         0   \n",
       "\n",
       "     Stanford University  Georgia Institute of Technology  \\\n",
       "133                    1                                0   \n",
       "196                    0                                0   \n",
       "199                    0                                0   \n",
       "210                    0                                1   \n",
       "292                    0                                0   \n",
       "301                    0                                0   \n",
       "346                    0                                0   \n",
       "351                    0                                0   \n",
       "378                    0                                0   \n",
       "447                    0                                0   \n",
       "449                    0                                0   \n",
       "469                    0                                0   \n",
       "481                    0                                0   \n",
       "547                    0                                0   \n",
       "601                    0                                0   \n",
       "653                    0                                0   \n",
       "717                    0                                0   \n",
       "728                    0                                0   \n",
       "\n",
       "     University of Kansas  Indiana University  University of Washington  \\\n",
       "133                     0                   0                         0   \n",
       "196                     0                   0                         0   \n",
       "199                     0                   0                         0   \n",
       "210                     0                   0                         0   \n",
       "292                     0                   0                         0   \n",
       "301                     0                   0                         0   \n",
       "346                     0                   0                         0   \n",
       "351                     0                   0                         0   \n",
       "378                     0                   0                         0   \n",
       "447                     0                   0                         0   \n",
       "449                     0                   0                         0   \n",
       "469                     0                   0                         0   \n",
       "481                     0                   0                         0   \n",
       "547                     0                   0                         0   \n",
       "601                     0                   0                         0   \n",
       "653                     0                   0                         0   \n",
       "717                     0                   0                         0   \n",
       "728                     0                   0                         0   \n",
       "\n",
       "     University of Memphis  University of North Carolina  Total_Height_in  \n",
       "133                      0                             0               81  \n",
       "196                      0                             0               79  \n",
       "199                      0                             0               83  \n",
       "210                      0                             0               72  \n",
       "292                      0                             0               83  \n",
       "301                      0                             0               82  \n",
       "346                      0                             0               78  \n",
       "351                      0                             0               81  \n",
       "378                      0                             0               76  \n",
       "447                      0                             0               80  \n",
       "449                      0                             0               81  \n",
       "469                      0                             0               75  \n",
       "481                      0                             0               82  \n",
       "547                      0                             0               80  \n",
       "601                      0                             0               80  \n",
       "653                      0                             0               81  \n",
       "717                      0                             0               79  \n",
       "728                      0                             0               76  \n",
       "\n",
       "[18 rows x 69 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a college player typically plays for a max 4 years in college, the datafram shows a few players who played\n",
    "# more than 4 years. This is because the college stats dataframe was grouped by name and averaged and these players\n",
    "# share the same name\n",
    "result[result['Yrs_Played_College'] > 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define a function max4 that changes the years in college to max 4\n",
    "def max4(yrs):\n",
    "    '''\n",
    "    Function that limits the maximum years played in college to 4\n",
    "    '''\n",
    "    if yrs > 4:\n",
    "        return 4\n",
    "    else:\n",
    "        return yrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# apply max4 function\n",
    "result['Yrs_Played_College'] = result['Yrs_Played_College'].apply(max4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create new column for rookie draft year\n",
    "result['Draft_Yr'] = result['Rookie_Season'].apply(lambda x:x[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a column of draft year and draft team to be used as a key to merge team BPM csv file\n",
    "result['Yr_Tm'] = result[\"Draft_Yr\"].map(str) + result[\"Draft_Tm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read team BPM file\n",
    "BPMs = pd.read_csv('team_bpm.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# merge dataframes\n",
    "result = pd.merge(result,BPMs,on=['Yr_Tm'],how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Birthday', 'Height_ft', 'Height_in', 'Weight',\n",
       "       'Yrs_Played_College', 'GP_College', 'MPG_College', 'FGM_College',\n",
       "       'FGA_College', 'FG_Perc_College', '3PM_College', '3PA_College',\n",
       "       '3P_Perc_College', 'FTM_College', 'FTA_College', 'FT_Perc_College',\n",
       "       'TOV_College', 'PF_College', 'ORB_College', 'DRB_College',\n",
       "       'RPG_College', 'APG_College', 'SPG_College', 'BPG_College',\n",
       "       'PPG_College', 'College', 'Total_BPM_NBA', 'Rookie_Season',\n",
       "       'Rookie_Age', 'Draft_Tm_x', 'League', 'Position', 'GP_Rookie',\n",
       "       'MP_Total_Rookie', 'PER', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'TRB%',\n",
       "       'AST%', 'STL%', 'BLK%', 'TOV%', 'USG%', 'OWS', 'DWS', 'WS_Rookie',\n",
       "       'WS_per_48_Rookie', 'OBPM', 'DBPM', 'BPM_Rookie', 'VORP_Rookie',\n",
       "       'College_above_5', 'College_adj', 'Bad',\n",
       "       'University of Illinois at Urbana-Champaign', 'University of Florida',\n",
       "       'University of Pittsburgh', 'Stanford University',\n",
       "       'Georgia Institute of Technology', 'University of Kansas',\n",
       "       'Indiana University', 'University of Washington',\n",
       "       'University of Memphis', 'University of North Carolina',\n",
       "       'Total_Height_in', 'Draft_Yr_x', 'Yr_Tm', 'Draft_Tm_y', 'Draft_Yr_y',\n",
       "       'BPM_top3', 'BPM_top5', 'BPM_top7', 'BPM_top13', 'top3/top5',\n",
       "       'top3/top7', 'top3/top13', 'top5/top7', 'top5/top13', 'top7/top13'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove columns to create a final dataframe\n",
    "result = result[['Player', 'Birthday', 'Total_Height_in', 'Weight',\n",
    "       'Yrs_Played_College', 'GP_College', 'MPG_College', 'FGM_College',\n",
    "       'FGA_College', 'FG_Perc_College', '3PM_College', '3PA_College',\n",
    "       '3P_Perc_College', 'FTM_College', 'FTA_College', 'FT_Perc_College',\n",
    "       'TOV_College', 'PF_College', 'ORB_College', 'DRB_College',\n",
    "       'RPG_College', 'APG_College', 'SPG_College', 'BPG_College',\n",
    "       'PPG_College', 'College', 'Total_BPM_NBA',\n",
    "       'Rookie_Age', 'Position', 'GP_Rookie',\n",
    "       'MP_Total_Rookie', 'PER', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'TRB%',\n",
    "       'AST%', 'STL%', 'BLK%', 'TOV%', 'USG%', 'OWS', 'DWS', 'WS_Rookie',\n",
    "       'WS_per_48_Rookie', 'OBPM', 'DBPM', 'BPM_Rookie', 'VORP_Rookie',\n",
    "       'College_above_5', 'College_adj', 'Bad',\n",
    "       'Georgia Institute of Technology', 'Indiana University',\n",
    "       'Stanford University', 'University of Florida',\n",
    "       'University of Illinois at Urbana-Champaign', 'University of Kansas',\n",
    "       'University of Memphis', 'University of North Carolina',\n",
    "       'University of Pittsburgh', 'University of Washington',\n",
    "       'Yr_Tm','BPM_top3', 'BPM_top5', 'BPM_top7', 'BPM_top13',\n",
    "       'top3/top5', 'top3/top7', 'top3/top13', 'top5/top13', 'top7/top13']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = result[np.isfinite(result['Weight'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I scraped 13162 college player stats.\n",
      "There have been 798 players drafted into the NBA since 2003.\n",
      "Of those 798 drafted players, 96 did not play in the NBA.\n",
      "Of the 798 drafted players, 226 did not play in the NCAA.\n",
      "Filtering out these players, the resulting data set has 509 players, and 74 features.\n"
     ]
    }
   ],
   "source": [
    "print('I scraped',college_df.shape[0],'college player stats.')\n",
    "print('There have been',player_df.shape[0], 'players drafted into the NBA since 2003.')\n",
    "print('Of those',player_df.shape[0], 'drafted players,',no_nba.shape[0],'did not play in the NBA.')\n",
    "print('Of the',player_df.shape[0], 'drafted players,',no_college.shape[0],'did not play in the NCAA.')\n",
    "print('Filtering out these players, the resulting data set has',result.shape[0],'players, and',result.shape[1],'features.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save result dataframe as csv\n",
    "result.to_csv('result_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>BPM_Rookie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Demetrius Jackson</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player  BPM_Rookie\n",
       "481  Demetrius Jackson         6.3"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result['BPM_Rookie'] == result['BPM_Rookie'].max()][['Player','BPM_Rookie']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>BPM_Rookie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Demetrius Jackson</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>James Augustine</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player  BPM_Rookie\n",
       "481  Demetrius Jackson         6.3\n",
       "58          Chris Paul         6.1\n",
       "104    James Augustine         5.0\n",
       "170      Kawhi Leonard         4.3"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result['BPM_Rookie'] > 4].sort_values(['BPM_Rookie'], ascending=False)[['Player','BPM_Rookie']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(509, 74)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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