plottingpowerforwards.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ogdata = pd.read_csv('nbaNew.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>SeasonStart</th>\n",
       "      <th>PlayerName</th>\n",
       "      <th>PlayerSalary</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8035.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF</td>\n",
       "      <td>22.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.10%</td>\n",
       "      <td>160.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8420.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF</td>\n",
       "      <td>23.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>79.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>...</td>\n",
       "      <td>78.00%</td>\n",
       "      <td>210.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8807.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF</td>\n",
       "      <td>24.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2636.0</td>\n",
       "      <td>...</td>\n",
       "      <td>77.30%</td>\n",
       "      <td>245.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>937.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9242.0</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2510.0</td>\n",
       "      <td>...</td>\n",
       "      <td>78.60%</td>\n",
       "      <td>258.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>739.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9688.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>$1,750,000.00</td>\n",
       "      <td>PF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2709.0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.10%</td>\n",
       "      <td>262.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10166.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>$1,750,000.00</td>\n",
       "      <td>PF</td>\n",
       "      <td>27.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>...</td>\n",
       "      <td>73.80%</td>\n",
       "      <td>201.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10617.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>$1,750,000.00</td>\n",
       "      <td>PF</td>\n",
       "      <td>28.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2902.0</td>\n",
       "      <td>...</td>\n",
       "      <td>74.40%</td>\n",
       "      <td>306.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11060.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>$1,885,000.00</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2819.0</td>\n",
       "      <td>...</td>\n",
       "      <td>73.90%</td>\n",
       "      <td>287.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1051.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11529.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>$6,472,600.00</td>\n",
       "      <td>PF</td>\n",
       "      <td>30.0</td>\n",
       "      <td>PHO</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>...</td>\n",
       "      <td>73.50%</td>\n",
       "      <td>275.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11999.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>$6,473,000.00</td>\n",
       "      <td>SF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>PHO</td>\n",
       "      <td>82.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2687.0</td>\n",
       "      <td>...</td>\n",
       "      <td>73.20%</td>\n",
       "      <td>194.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>916.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         #  SeasonStart  PlayerName   PlayerSalary  Pos   Age   Tm     G  \\\n",
       "0   8035.0       1986.0  A.C. Green             NaN  PF  22.0  LAL  82.0   \n",
       "1   8420.0       1987.0  A.C. Green             NaN  PF  23.0  LAL  79.0   \n",
       "2   8807.0       1988.0  A.C. Green             NaN  PF  24.0  LAL  82.0   \n",
       "3   9242.0       1989.0  A.C. Green             NaN  PF  25.0  LAL  82.0   \n",
       "4   9688.0       1990.0  A.C. Green  $1,750,000.00   PF  26.0  LAL  82.0   \n",
       "5  10166.0       1991.0  A.C. Green  $1,750,000.00   PF  27.0  LAL  82.0   \n",
       "6  10617.0       1992.0  A.C. Green  $1,750,000.00   PF  28.0  LAL  82.0   \n",
       "7  11060.0       1993.0  A.C. Green  $1,885,000.00   PF  29.0  LAL  82.0   \n",
       "8  11529.0       1994.0  A.C. Green  $6,472,600.00   PF  30.0  PHO  82.0   \n",
       "9  11999.0       1995.0  A.C. Green  $6,473,000.00   SF  31.0  PHO  82.0   \n",
       "\n",
       "     GS      MP   ...       FT%    ORB    DRB    TRB    AST   STL   BLK  \\\n",
       "0   1.0  1542.0   ...    61.10%  160.0  221.0  381.0   54.0  49.0  49.0   \n",
       "1  72.0  2240.0   ...    78.00%  210.0  405.0  615.0   84.0  70.0  80.0   \n",
       "2  64.0  2636.0   ...    77.30%  245.0  465.0  710.0   93.0  87.0  45.0   \n",
       "3  82.0  2510.0   ...    78.60%  258.0  481.0  739.0  103.0  94.0  55.0   \n",
       "4  82.0  2709.0   ...    75.10%  262.0  450.0  712.0   90.0  66.0  50.0   \n",
       "5  21.0  2164.0   ...    73.80%  201.0  315.0  516.0   71.0  59.0  23.0   \n",
       "6  53.0  2902.0   ...    74.40%  306.0  456.0  762.0  117.0  91.0  36.0   \n",
       "7  55.0  2819.0   ...    73.90%  287.0  424.0  711.0  116.0  88.0  39.0   \n",
       "8  55.0  2825.0   ...    73.50%  275.0  478.0  753.0  137.0  70.0  38.0   \n",
       "9  52.0  2687.0   ...    73.20%  194.0  475.0  669.0  127.0  55.0  31.0   \n",
       "\n",
       "     TOV     PF     PTS  \n",
       "0   99.0  229.0   521.0  \n",
       "1  102.0  171.0   852.0  \n",
       "2  120.0  204.0   937.0  \n",
       "3  119.0  172.0  1088.0  \n",
       "4  116.0  207.0  1061.0  \n",
       "5   99.0  117.0   750.0  \n",
       "6  111.0  141.0  1116.0  \n",
       "7  116.0  149.0  1051.0  \n",
       "8  100.0  142.0  1204.0  \n",
       "9  114.0  146.0   916.0  \n",
       "\n",
       "[10 rows x 54 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ogdata.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2000plus = ogdata[ogdata[\"SeasonStart\"] >= 2000]\n",
    "realplayers = data2000plus[data2000plus[\"MP\"] >= 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>SeasonStart</th>\n",
       "      <th>PlayerName</th>\n",
       "      <th>PlayerSalary</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14648.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>$2,250,000.00</td>\n",
       "      <td>PF</td>\n",
       "      <td>36.0</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>...</td>\n",
       "      <td>69.50%</td>\n",
       "      <td>160.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15148.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF</td>\n",
       "      <td>37.0</td>\n",
       "      <td>MIA</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71.20%</td>\n",
       "      <td>107.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22100.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>A.J. Price</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>WAS</td>\n",
       "      <td>57.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>...</td>\n",
       "      <td>79.00%</td>\n",
       "      <td>20.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19415.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>$1,118,520.00</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HOU</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.60%</td>\n",
       "      <td>33.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>19996.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>HOU</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2919.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82.20%</td>\n",
       "      <td>54.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>20592.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>TOT</td>\n",
       "      <td>59.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1284.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.60%</td>\n",
       "      <td>20.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>22337.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG</td>\n",
       "      <td>29.0</td>\n",
       "      <td>TOT</td>\n",
       "      <td>72.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.40%</td>\n",
       "      <td>43.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>22939.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>$2,000,000.00</td>\n",
       "      <td>PG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>CHI</td>\n",
       "      <td>82.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>...</td>\n",
       "      <td>83.30%</td>\n",
       "      <td>32.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>954.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>23582.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>$2,700,000.00</td>\n",
       "      <td>PG</td>\n",
       "      <td>31.0</td>\n",
       "      <td>CHI</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>...</td>\n",
       "      <td>76.60%</td>\n",
       "      <td>21.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>23698.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>$4,351,320.00</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>ORL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.80%</td>\n",
       "      <td>154.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>719.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          #  SeasonStart    PlayerName   PlayerSalary  Pos   Age   Tm     G  \\\n",
       "16  14648.0       2000.0    A.C. Green  $2,250,000.00   PF  36.0  LAL  82.0   \n",
       "17  15148.0       2001.0    A.C. Green             NaN  PF  37.0  MIA  82.0   \n",
       "28  22100.0       2013.0    A.J. Price             NaN  PG  26.0  WAS  57.0   \n",
       "37  19415.0       2009.0  Aaron Brooks  $1,118,520.00   PG  24.0  HOU  80.0   \n",
       "38  19996.0       2010.0  Aaron Brooks             NaN  PG  25.0  HOU  82.0   \n",
       "39  20592.0       2011.0  Aaron Brooks             NaN  PG  26.0  TOT  59.0   \n",
       "45  22337.0       2014.0  Aaron Brooks             NaN  PG  29.0  TOT  72.0   \n",
       "48  22939.0       2015.0  Aaron Brooks  $2,000,000.00   PG  30.0  CHI  82.0   \n",
       "49  23582.0       2016.0  Aaron Brooks  $2,700,000.00   PG  31.0  CHI  69.0   \n",
       "52  23698.0       2016.0  Aaron Gordon  $4,351,320.00   PF  20.0  ORL  78.0   \n",
       "\n",
       "      GS      MP   ...       FT%    ORB    DRB    TRB    AST   STL   BLK  \\\n",
       "16  82.0  1929.0   ...    69.50%  160.0  326.0  486.0   80.0  53.0  18.0   \n",
       "17   1.0  1411.0   ...    71.20%  107.0  206.0  313.0   39.0  30.0   8.0   \n",
       "28  22.0  1278.0   ...    79.00%   20.0   94.0  114.0  205.0  33.0   3.0   \n",
       "37  35.0  1998.0   ...    86.60%   33.0  124.0  157.0  238.0  46.0   8.0   \n",
       "38  82.0  2919.0   ...    82.20%   54.0  161.0  215.0  434.0  69.0  14.0   \n",
       "39  12.0  1284.0   ...    88.60%   20.0   58.0   78.0  233.0  34.0   3.0   \n",
       "45  12.0  1557.0   ...    87.40%   43.0   97.0  140.0  233.0  52.0  13.0   \n",
       "48  21.0  1885.0   ...    83.30%   32.0  134.0  166.0  261.0  54.0  15.0   \n",
       "49   0.0  1108.0   ...    76.60%   21.0   80.0  101.0  180.0  30.0  10.0   \n",
       "52  37.0  1863.0   ...    66.80%  154.0  353.0  507.0  128.0  59.0  55.0   \n",
       "\n",
       "      TOV     PF     PTS  \n",
       "16   53.0  127.0   413.0  \n",
       "17   45.0  119.0   367.0  \n",
       "28   64.0   73.0   441.0  \n",
       "37  125.0  152.0   894.0  \n",
       "38  232.0  199.0  1604.0  \n",
       "39   99.0  115.0   634.0  \n",
       "45  117.0  146.0   645.0  \n",
       "48  157.0  189.0   954.0  \n",
       "49   82.0  132.0   491.0  \n",
       "52   66.0  153.0   719.0  \n",
       "\n",
       "[10 rows x 54 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realplayers.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['#', 'SeasonStart', 'PlayerName', 'PlayerSalary ', 'Pos', 'Age', 'Tm',\n",
       "       'G', 'GS', 'MP', 'PER', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'TRB%',\n",
       "       'AST%', 'STL%', 'BLK%', 'TOV%', 'USG%', 'blanl', 'OWS', 'DWS', 'WS',\n",
       "       'WS/48', 'blank2', 'OBPM', 'DBPM', 'BPM', 'VORP', 'FG', 'FGA', 'FG%',\n",
       "       '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%',\n",
       "       'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realplayers.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "neededData =  {\n",
    "    \"Name\": realplayers[\"PlayerName\"],\n",
    "    \"Points\": realplayers[\"PTS\"]/(realplayers[\"MP\"]*1000),\n",
    "    \"Assists\": realplayers[\"AST\"]/(realplayers[\"MP\"]*1000),\n",
    "    \"OffensiveRebounds\": realplayers[\"ORB\"]/(realplayers[\"MP\"]*1000),\n",
    "    \"Position\": realplayers[\"Pos\"],\n",
    "    \"Season\": realplayers[\"SeasonStart\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "neededDf = pd.DataFrame(neededData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Name</th>\n",
       "      <th>Points</th>\n",
       "      <th>Assists</th>\n",
       "      <th>OffensiveRebounds</th>\n",
       "      <th>Position</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>PF</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>PF</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>A.J. Price</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>PG</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>PG</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>PG</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>PG</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>PG</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>PG</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>PG</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>PF</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Points   Assists  OffensiveRebounds Position  Season\n",
       "16    A.C. Green  0.000214  0.000041           0.000083       PF  2000.0\n",
       "17    A.C. Green  0.000260  0.000028           0.000076       PF  2001.0\n",
       "28    A.J. Price  0.000345  0.000160           0.000016       PG  2013.0\n",
       "37  Aaron Brooks  0.000447  0.000119           0.000017       PG  2009.0\n",
       "38  Aaron Brooks  0.000550  0.000149           0.000018       PG  2010.0\n",
       "39  Aaron Brooks  0.000494  0.000181           0.000016       PG  2011.0\n",
       "45  Aaron Brooks  0.000414  0.000150           0.000028       PG  2014.0\n",
       "48  Aaron Brooks  0.000506  0.000138           0.000017       PG  2015.0\n",
       "49  Aaron Brooks  0.000443  0.000162           0.000019       PG  2016.0\n",
       "52  Aaron Gordon  0.000386  0.000069           0.000083       PF  2016.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neededDf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#neededDf.to_csv('cleantData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = neededDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "PFdf = neededDf[neededDf[\"Position\"]==\"PF\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Name</th>\n",
       "      <th>Points</th>\n",
       "      <th>Assists</th>\n",
       "      <th>OffensiveRebounds</th>\n",
       "      <th>Position</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>PF</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>PF</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>PF</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Aaron Williams</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>PF</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Aaron Williams</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>PF</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Aaron Williams</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>PF</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Adam Morrison</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>PF</td>\n",
       "      <td>2007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Al Harrington</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>PF</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>Al Harrington</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>PF</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Al Harrington</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>PF</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name    Points   Assists  OffensiveRebounds Position  Season\n",
       "16       A.C. Green  0.000214  0.000041           0.000083       PF  2000.0\n",
       "17       A.C. Green  0.000260  0.000028           0.000076       PF  2001.0\n",
       "52     Aaron Gordon  0.000386  0.000069           0.000083       PF  2016.0\n",
       "98   Aaron Williams  0.000399  0.000038           0.000103       PF  2000.0\n",
       "99   Aaron Williams  0.000359  0.000038           0.000090       PF  2001.0\n",
       "100  Aaron Williams  0.000383  0.000050           0.000074       PF  2002.0\n",
       "140   Adam Morrison  0.000394  0.000070           0.000023       PF  2007.0\n",
       "273   Al Harrington  0.000310  0.000069           0.000063       PF  2001.0\n",
       "274   Al Harrington  0.000439  0.000041           0.000073       PF  2002.0\n",
       "276   Al Harrington  0.000429  0.000054           0.000067       PF  2004.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PFdf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.preprocessing as prepro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(arr):\n",
    "    maxv = max(arr)\n",
    "    minv = min(arr)\n",
    "    diff = maxv-minv\n",
    "    for index,num in enumerate(arr):\n",
    "        arr[index] = (num - minv)/(diff)\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizedp = normalize(np.array(PFdf[\"Points\"]))\n",
    "normalizedr = normalize(np.array(PFdf[\"OffensiveRebounds\"]))\n",
    "normalizeda = normalize(np.array(PFdf[\"Assists\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "PFdf = PFdf.assign(Points = normalizedp)\n",
    "PFdf = PFdf.assign(OffensiveRebounds = normalizedr)\n",
    "PFdf = PFdf.assign(Assists = normalizeda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Name</th>\n",
       "      <th>Points</th>\n",
       "      <th>Assists</th>\n",
       "      <th>OffensiveRebounds</th>\n",
       "      <th>Position</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>0.179569</td>\n",
       "      <td>0.162934</td>\n",
       "      <td>0.563193</td>\n",
       "      <td>PF</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>0.247024</td>\n",
       "      <td>0.096479</td>\n",
       "      <td>0.509155</td>\n",
       "      <td>PF</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>0.431557</td>\n",
       "      <td>0.293777</td>\n",
       "      <td>0.561049</td>\n",
       "      <td>PF</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Aaron Williams</td>\n",
       "      <td>0.450282</td>\n",
       "      <td>0.144044</td>\n",
       "      <td>0.714916</td>\n",
       "      <td>PF</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Aaron Williams</td>\n",
       "      <td>0.391664</td>\n",
       "      <td>0.144673</td>\n",
       "      <td>0.619274</td>\n",
       "      <td>PF</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Aaron Williams</td>\n",
       "      <td>0.427139</td>\n",
       "      <td>0.202972</td>\n",
       "      <td>0.498159</td>\n",
       "      <td>PF</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Adam Morrison</td>\n",
       "      <td>0.443732</td>\n",
       "      <td>0.300364</td>\n",
       "      <td>0.109357</td>\n",
       "      <td>PF</td>\n",
       "      <td>2007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Al Harrington</td>\n",
       "      <td>0.319797</td>\n",
       "      <td>0.293796</td>\n",
       "      <td>0.410861</td>\n",
       "      <td>PF</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>Al Harrington</td>\n",
       "      <td>0.508917</td>\n",
       "      <td>0.161276</td>\n",
       "      <td>0.488505</td>\n",
       "      <td>PF</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Al Harrington</td>\n",
       "      <td>0.495195</td>\n",
       "      <td>0.221520</td>\n",
       "      <td>0.440339</td>\n",
       "      <td>PF</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name    Points   Assists  OffensiveRebounds Position  Season\n",
       "16       A.C. Green  0.179569  0.162934           0.563193       PF  2000.0\n",
       "17       A.C. Green  0.247024  0.096479           0.509155       PF  2001.0\n",
       "52     Aaron Gordon  0.431557  0.293777           0.561049       PF  2016.0\n",
       "98   Aaron Williams  0.450282  0.144044           0.714916       PF  2000.0\n",
       "99   Aaron Williams  0.391664  0.144673           0.619274       PF  2001.0\n",
       "100  Aaron Williams  0.427139  0.202972           0.498159       PF  2002.0\n",
       "140   Adam Morrison  0.443732  0.300364           0.109357       PF  2007.0\n",
       "273   Al Harrington  0.319797  0.293796           0.410861       PF  2001.0\n",
       "274   Al Harrington  0.508917  0.161276           0.488505       PF  2002.0\n",
       "276   Al Harrington  0.495195  0.221520           0.440339       PF  2004.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PFdf.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = [x for x in PFdf[\"Points\"]]\n",
    "Y = [x for x in PFdf[\"OffensiveRebounds\"]]\n",
    "Z = [x for x in PFdf[\"Assists\"]]\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "\n",
    "\n",
    "ax.scatter(X, Y, Z)\n",
    "ax.set_xlabel(\"Points\")\n",
    "ax.set_ylabel(\"Offensive Rebounds\")\n",
    "ax.set_zlabel(\"Assists\")\n",
    "ax.set_title(\"Stats of PF's after 2000 that played\\n real minutes (values normalized between 0 and 1)\")\n",
    "\n",
    "for X,Y,Z,name in zip(X,Y,Z,PFdf[\"Name\"]):\n",
    "    ax.text(X,Y,Z,name)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}