College and Rookie Trends.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>School</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>...</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "      <th>Year</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>NBATRB</th>\n",
       "      <th>NBAAST</th>\n",
       "      <th>NBASTL</th>\n",
       "      <th>NBABLK</th>\n",
       "      <th>NBAPTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mike Higgins</td>\n",
       "      <td>Navy</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-5.93</td>\n",
       "      <td>1990</td>\n",
       "      <td>SF</td>\n",
       "      <td>22</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ashraf Amaya</td>\n",
       "      <td>Southern Illinois</td>\n",
       "      <td>128.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.567</td>\n",
       "      <td>5.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1996</td>\n",
       "      <td>PF</td>\n",
       "      <td>24</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>James Cotton</td>\n",
       "      <td>Maryland-Eastern Shore</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-7.38</td>\n",
       "      <td>1998</td>\n",
       "      <td>SG</td>\n",
       "      <td>22</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eddie Robinson</td>\n",
       "      <td>Lamar</td>\n",
       "      <td>114.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.365</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>...</td>\n",
       "      <td>5.2</td>\n",
       "      <td>-7.20</td>\n",
       "      <td>2000</td>\n",
       "      <td>SF</td>\n",
       "      <td>23</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gilbert Arenas</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>70.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.466</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>15.8</td>\n",
       "      <td>11.13</td>\n",
       "      <td>2002</td>\n",
       "      <td>PG</td>\n",
       "      <td>20</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name                  School      G     GS    MP   FG   FGA  \\\n",
       "0    Mike Higgins                    Navy   60.0    2.0   9.0  1.1   2.3   \n",
       "1    Ashraf Amaya       Southern Illinois  128.0  120.0  32.2  5.3   9.4   \n",
       "2    James Cotton  Maryland-Eastern Shore   19.0    3.0  13.9  0.8   1.8   \n",
       "3  Eddie Robinson                   Lamar  114.0   75.0  32.6  1.8   4.9   \n",
       "4  Gilbert Arenas                 Arizona   70.0   33.0  30.5  5.6  12.1   \n",
       "\n",
       "     FG%   2P  2PA  ...   PTS    SOS  Year  Pos  Age  NBATRB  NBAAST  NBASTL  \\\n",
       "0  0.464  0.9  1.7  ...   3.2  -5.93  1990   SF   22     0.4     0.3     0.2   \n",
       "1  0.567  5.2  9.0  ...  14.6   0.74  1996   PF   24     5.6     0.6     0.4   \n",
       "2  0.441  0.8  1.7  ...   2.9  -7.38  1998   SG   22     0.7     0.0     0.1   \n",
       "3  0.365  1.5  3.8  ...   5.2  -7.20  2000   SF   23     2.7     0.5     0.7   \n",
       "4  0.466  4.1  7.9  ...  15.8  11.13  2002   PG   20     2.8     3.7     1.5   \n",
       "\n",
       "   NBABLK  NBAPTS  \n",
       "0     0.2     1.3  \n",
       "1     0.2     6.3  \n",
       "2     0.1     2.7  \n",
       "3     0.4     7.0  \n",
       "4     0.2    10.9  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('CollegeRookieStatLog2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>School</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>...</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",
       "      <th>SOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bryce Aiken</td>\n",
       "      <td>Harvard</td>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "      <td>29.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.406</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>16.8</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nickeil Alexander-Walker</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>29.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.464</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Al-Wajid Aminu</td>\n",
       "      <td>North Florida</td>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.542</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>...</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>-1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Desmond Bane</td>\n",
       "      <td>TCU</td>\n",
       "      <td>109</td>\n",
       "      <td>82</td>\n",
       "      <td>28.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.516</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>10.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RJ Barrett</td>\n",
       "      <td>Duke</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>35.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.454</td>\n",
       "      <td>6.5</td>\n",
       "      <td>12.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name         School    G  GS    MP   FG   FGA    FG%  \\\n",
       "0               Bryce Aiken        Harvard   58  34  29.4  5.0  12.4  0.406   \n",
       "1  Nickeil Alexander-Walker  Virginia Tech   67  67  29.9  4.7  10.1  0.464   \n",
       "2            Al-Wajid Aminu  North Florida   34  26  26.0  3.6   6.7  0.542   \n",
       "3              Desmond Bane            TCU  109  82  28.7  4.2   8.2  0.516   \n",
       "4                RJ Barrett           Duke   38  38  35.3  8.4  18.5  0.454   \n",
       "\n",
       "    2P   2PA  ...  ORB  DRB  TRB  AST  STL  BLK  TOV   PF   PTS    SOS  \n",
       "0  2.9   6.1  ...  0.4  2.1  2.4  2.8  0.8  0.0  2.8  1.9  16.8  -1.09  \n",
       "1  3.0   5.6  ...  0.6  3.4  4.0  2.7  1.4  0.5  2.2  1.9  13.5   7.25  \n",
       "2  3.4   5.9  ...  2.6  3.8  6.5  0.7  0.4  1.1  2.1  3.6   9.1  -1.65  \n",
       "3  2.8   4.8  ...  0.8  3.4  4.2  1.9  0.8  0.3  1.3  2.2  11.5  10.08  \n",
       "4  6.5  12.2  ...  1.6  6.0  7.6  4.3  0.9  0.4  3.2  1.8  22.6  11.97  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfend = pd.read_csv('2019DraftClass.csv')\n",
    "dfend.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## College Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>School</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>...</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",
       "      <th>SOS</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Kris Wilkes</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>66.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.436</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>6.82</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>Emmitt Williams</td>\n",
       "      <td>LSU</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.614</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.15</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>Grant Williams</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>104.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>28.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.516</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>15.7</td>\n",
       "      <td>10.15</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Zion Williamson</td>\n",
       "      <td>Duke</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.680</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>11.97</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>Kenny Wooten</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>70.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.634</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.49</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name     School      G     GS    MP   FG   FGA    FG%   2P  \\\n",
       "978      Kris Wilkes       UCLA   66.0   65.0  30.8  5.5  12.6  0.436  3.6   \n",
       "979  Emmitt Williams        LSU   34.0    8.0  19.7  2.6   4.3  0.614  2.6   \n",
       "980   Grant Williams  Tennessee  104.0  101.0  28.9  5.3  10.2  0.516  5.0   \n",
       "981  Zion Williamson       Duke   33.0   33.0  30.0  9.0  13.2  0.680  8.2   \n",
       "982     Kenny Wooten     Oregon   70.0   38.0  21.8  2.6   4.1  0.634  2.6   \n",
       "\n",
       "      2PA  ...  DRB  TRB  AST  STL  BLK  TOV   PF   PTS    SOS  Year  \n",
       "978   7.1  ...  3.6  4.8  1.7  0.7  0.5  1.6  2.3  15.5   6.82  2020  \n",
       "979   4.1  ...  2.8  5.4  0.4  0.3  0.6  1.1  1.9   7.0   9.15  2020  \n",
       "980   9.2  ...  4.0  6.5  2.1  0.9  1.5  2.1  3.2  15.7  10.15  2020  \n",
       "981  11.0  ...  5.4  8.9  2.1  2.1  1.8  2.4  2.1  22.6  11.97  2020  \n",
       "982   4.1  ...  3.3  4.7  0.5  0.5  2.4  1.1  2.6   6.4   5.49  2020  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfend['Year'] = 2020\n",
    "df = df.drop(['Pos','Age','NBATRB','NBAAST','NBASTL','NBABLK','NBAPTS'], axis=1)\n",
    "df1 = df.append(dfend, ignore_index = True)\n",
    "df1 = df1[df1['Year']>2000]\n",
    "df1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'School', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%', '2P', '2PA',\n",
       "       '2P%', '3P', '3PA', '3P%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB',\n",
       "       'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'SOS', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "plt.title('Year vs Points Scored')\n",
    "seaborn.boxplot(df1['Year'], df1['PTS'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Points')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "plt.title('Year vs Assists')\n",
    "seaborn.boxplot(df1['Year'], df1['AST'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Assists')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rebounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "plt.title('Year vs Rebounds')\n",
    "seaborn.boxplot(df1['Year'], df1['TRB'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Rebounds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 Point to 2 Point Attempt Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['ratio'] = df1['3PA']/df1['2PA']\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "plt.title('Year vs 3pt:2pt ratio')\n",
    "plt.ylim(-.2, 2) \n",
    "seaborn.boxplot(df1['Year'], df1['ratio'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('3pt:2pt ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NBA Rookie Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Player</th>\n",
       "      <th>Year</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>FTA</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>Darrell Allums</td>\n",
       "      <td>1981</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>DAL</td>\n",
       "      <td>22</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mel Bennett</td>\n",
       "      <td>1981</td>\n",
       "      <td>PF</td>\n",
       "      <td>26</td>\n",
       "      <td>UTA</td>\n",
       "      <td>28</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norman Black</td>\n",
       "      <td>1981</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>DET</td>\n",
       "      <td>3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dave Britton</td>\n",
       "      <td>1981</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>WSB</td>\n",
       "      <td>2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael Brooks</td>\n",
       "      <td>1981</td>\n",
       "      <td>SF</td>\n",
       "      <td>22</td>\n",
       "      <td>SDC</td>\n",
       "      <td>82</td>\n",
       "      <td>30.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player  Year Pos  Age   Tm   G    MP   FG   FGA   3P  ...  FTA  \\\n",
       "0  Darrell Allums  1981  PF   22  DAL  22  12.5  1.0   3.0  0.0  ...  1.0   \n",
       "1     Mel Bennett  1981  PF   26  UTA  28  11.2  0.9   2.1  0.0  ...  2.9   \n",
       "2    Norman Black  1981  SG   23  DET   3   9.3  1.0   3.3  0.0  ...  2.7   \n",
       "3    Dave Britton  1981  PG   22  WSB   2   4.5  1.0   1.5  0.0  ...  0.0   \n",
       "4  Michael Brooks  1981  SF   22  SDC  82  30.2  6.0  12.4  0.0  ...  3.9   \n",
       "\n",
       "   ORB  DRB  TRB  AST  STL  BLK  TOV   PF   PTS  \n",
       "0  0.9  2.1  3.0  1.1  0.2  0.4  1.0  2.3   2.7  \n",
       "1  1.2  2.1  3.3  0.5  0.1  0.4  1.1  2.0   3.8  \n",
       "2  0.0  0.7  0.7  0.7  0.3  0.0  0.3  0.7   2.7  \n",
       "3  0.0  1.0  1.0  1.5  0.5  0.0  1.0  1.0   2.0  \n",
       "4  2.6  2.8  5.4  2.5  1.2  0.4  2.0  2.9  14.7  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('NBARookieData.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Player</th>\n",
       "      <th>Year</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</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",
       "      <th>3P%</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P%</th>\n",
       "      <th>FT%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Darrell Allums</td>\n",
       "      <td>1981</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>DAL</td>\n",
       "      <td>22</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mel Bennett</td>\n",
       "      <td>1981</td>\n",
       "      <td>PF</td>\n",
       "      <td>26</td>\n",
       "      <td>UTA</td>\n",
       "      <td>28</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.655172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norman Black</td>\n",
       "      <td>1981</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>DET</td>\n",
       "      <td>3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.259259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dave Britton</td>\n",
       "      <td>1981</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>WSB</td>\n",
       "      <td>2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael Brooks</td>\n",
       "      <td>1981</td>\n",
       "      <td>SF</td>\n",
       "      <td>22</td>\n",
       "      <td>SDC</td>\n",
       "      <td>82</td>\n",
       "      <td>30.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>0.717949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player  Year Pos  Age   Tm   G    MP   FG   FGA   3P  ...  AST  \\\n",
       "0  Darrell Allums  1981  PF   22  DAL  22  12.5  1.0   3.0  0.0  ...  1.1   \n",
       "1     Mel Bennett  1981  PF   26  UTA  28  11.2  0.9   2.1  0.0  ...  0.5   \n",
       "2    Norman Black  1981  SG   23  DET   3   9.3  1.0   3.3  0.0  ...  0.7   \n",
       "3    Dave Britton  1981  PG   22  WSB   2   4.5  1.0   1.5  0.0  ...  1.5   \n",
       "4  Michael Brooks  1981  SF   22  SDC  82  30.2  6.0  12.4  0.0  ...  2.5   \n",
       "\n",
       "   STL  BLK  TOV   PF   PTS  3P%       FG%       2P%       FT%  \n",
       "0  0.2  0.4  1.0  2.3   2.7  0.0  0.333333  0.333333  0.600000  \n",
       "1  0.1  0.4  1.1  2.0   3.8  0.0  0.428571  0.428571  0.655172  \n",
       "2  0.3  0.0  0.3  0.7   2.7  0.0  0.303030  0.303030  0.259259  \n",
       "3  0.5  0.0  1.0  1.0   2.0  0.0  0.666667  0.666667  0.000000  \n",
       "4  1.2  0.4  2.0  2.9  14.7  0.0  0.483871  0.487805  0.717949  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[df2['3PA'] > 0, '3P%'] = df2['3P']/df2['3PA']\n",
    "df2.loc[df2['3PA'] <= 0, '3P%'] = 0\n",
    "df2.loc[df2['FGA'] > 0, 'FG%'] = df2['FG']/df2['FGA']\n",
    "df2.loc[df2['FGA'] <= 0, 'FG%'] = 0\n",
    "df2.loc[df2['2PA'] > 0, '2P%'] = df2['2P']/df2['2PA']\n",
    "df2.loc[df2['2PA'] <= 0, '2P%'] = 0\n",
    "df2.loc[df2['FTA'] > 0, 'FT%'] = df2['FT']/df2['FTA']\n",
    "df2.loc[df2['FTA'] <= 0, 'FT%'] = 0\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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0eF1icwNGswBbwSAJaIbp6elMTk4mSSYnJ7N3794RRzTebO/huvzyy9ddBgAAdp4mJSOA9mrS4HWJzXW0eTSLJAo0i0ES0AydTicTE73Tn4mJCXPjbTPbe7geffTRdZebynkrAABsnyYlI4D2atLgdYnNdbR5NIskCjRHmwdJtJEb5Kxnamoq+/btSykl+/fvNzfeNrO9h2vv3r0ppSRJSim56aabRhzRYJy3AgDA9mlSMgJoryYNXpfYXEdbR7NIokCztHmQRBu5Qc5GOp1O9uzZo3pwSGzv4el0Ok/dsLjkkktasc2dtwIAwPZqUjICaK8mDV6X2FxHW0ezSKJAs7R1kEQbuUHOIKampnL48GHVg0Niew/P1NRU9u/f34iLjEE5bwUAmkQHIDbSxn2kSckIoN2aMnhdYnMdbR3NIokCzdLWQRJt5Ab58LXxog7GWVMuMgblvBUAaBIdgNhIW/eRtl0nAM3UlMHrEpvraOtoFkkUaJa2DpJoIzfIh6+tF3UwrppykTEo563DZ0AKAKxNByA20uZ9pG3XCQyf6wTaRGJzA20czSKJAs3S1kESbeQG+XC1+aIOaAbnrcNnQAoArE0HIDZiH2GcuU6gTSQ2N9DG0SySKNA8bRwk0UZukA+XizrgYjlvHS4DUgDg/HQAYiP2EcaV6wTaRmJzTEmiQLO0cZBEG7lBPlwu6oCt4Lx1eAxIAYDz0wGIjdhHGFeuE2ibUmsddQxb4uqrr64PPvjgqMMAYMS63W4OHTqUO++8U2Jzm/3ET/xE3vve9+bJJ5/Mrl27cvPNN+f2228fdVitdOTIkZw6dWrddc6cOZMk2b1797rrXXnllbn11lu3LDZgfLzqVa/K448//tTy05/+9Lz73e8eYUSDc3wHYLt1u9285jWvyWc/+9l83ud9Xn7hF37BMYdz2EcYV22+TmC8lFI+Umu9eqP1VGwCMFZUxw5Pp9PJ8gCpWqtqq222sLCQhYWFUYcBtFibqwzM+QPAdtMBiI3YRxhXbb5OYGeaHHUAAAA73SAVlgcPHkyS3HPPPdsdDjCmOp1O5ubmkrRrLurVc/50Oh03EgHYFp1OJ6dPn27NMZLhs48wjtp6ncDOpWIb0KExAAAgAElEQVQTALggMzMzmZjonUpMTEyoogFouLZWGZjzB4Bh0QGIjdhHGEdtvU5g55LYBAAuyIkTJ7K4uJgkWVxczPz8/IgjAmAjnU4ne/bsadUobMcbAADYXm28TmDnktgEAC6IORgA2qeNVQaON8PV7XZz4MCBnD17dtShAAAwJG28TmDnktikMVxAA7RLp9M5pxWtUX2sxfGdjdhH2IjjzXDNzMzk5MmTWv4CtIjzKeBi+RyhTSQ2aQwX0ADtYg4GBuH4zkbsI2zE8WZ4ut1u5ubmUmvN7OysG1sALeF8CrhYPkdoE4lNGsEFNEA7mYOB9Ti+sxH7CINyvBmOmZmZLC0tJUmWlpbc2AJoAedTwMXyOULbSGzSCC6gAdrJHAysx/GdjdhHGJTjzXCcOHEii4uLSZLFxcXMz8+POCIANuJ8CrhYPkdoG4nNMdW2ntguoAFg/Di+sxH7CDTL9PR0JicnkySTk5PZu3fviCMCYCPOp4CL5XOEtpHYHFNt64ntAppBtS1pD7CTOb6zEfsINEun08nERO82wcTEhNa/AC3gfAq4WD5HaBuJzQ20MYnS7XYzOzubWmuOHTvWithdQDOotiXtAXayNh/f23gO2EZt3kdgHE1NTWXfvn0ppWT//v1a/wK0gPMp4GL5HKFtJDY30MYkyszMzDml422I3QU0gzCRNUC7tPn43sZzwDZq8z4C46rT6WTPnj1uaAG0hPMpaJ62DZT1OULbSGyuo61JlPn5+dRakyS11hw/fnzEEQ3GBTQbMZE1QPu08fje1nPAtmrjPgLjbGpqKocPH3ZDCxqibTfH266t29v5FDRLGwfK+hyhTSQ219HWJMrll1++7nJTuYBmIyayBmifNh7f23oO2FZt3EcAYFjaeHO8zdq6vZ1PQXO0daCszxHaZHLUATTZWkmU22+/fcRRbezRRx9ddxnaanp6OseOHcvi4qKJrHeoI0eO5NSpU+uuc+bMmSTJ7t27113vyiuvzK233rplsQHjo63ngADAeFl9c7zT6bjhvI1sb2iebrebQ4cO5c4772zN3+NaA2VdTzIumvI3qWJzHdPT05mc7OV+25RE2bt3b0opSZJSSm666aYRRwRbw0TWDGJhYSELCwujDoMGa2t7qbZq4/Zu6zkgjLM2fpYAXCxdJIbL9obmaWMVtY5zjLOm/E2q2FxHp9PJ3NxcknYlUTqdTmZnZ/PEE0/kkksuaU3csJHliayPHj1qIusdapAKy4MHDyZJ7rnnnu0Oh5ZaeRJm1OT2a+P2bus5IAyiKSNsN6uNnyUAF0sXieGyvaFZ2lpFreMc46pJf5MqNtexnEQppbQqiTI1NZX9+/e3Lm4YhImsgYvR1rku2qqt27ut54BtpRJvuJoywnYz2vpZAnCxdJEYLtt7uJwDspG2VlHrOMe4atLfpMTmBtqaRGlr3LARE1kDF6NJJ2E7QZu3t3Op4Wljoq2t2pogbPNnCcDFcHN8uGzv4XIOyEba2tLVQFnGVZP+JiU2N9DWJEpb4waA7dSkk7CdoM3b27nUcLQ10dZWbU0QtvmzBOBiuDk+XLb38DgHZBBtrqI2UJZx1KS/SYlNAGDHaNJJ2E5ge7ORtiba2qqtCUKfJYwzrRjZiJvjw2V7D4dzQAbR5ipqA2UZR51O55zP7lH+TUpsAgA7RpsvjNrI9mYjbU20tVVbE4Q+SxhnWjEOT1uTyG6OD5ftPRzOARmEKmrGWVvPS5pCYhMA2DFcGA3X1NRUrr/++iTJDTfcYHvzOdqaaGurtiYIfXYzrrRiHC5JZGgO54AMShU146qN5yUzMzMppSRJSikjjV1iE2AIjMKB5nBhBM3R1kRbW7U5Qeizm3GkFePwSCJDszgHZFCqqBlHbT0vOXHiRJ588skkyZNPPjnSavvJkb0zwA6ychTO7bffPupwYEdbvjBi+3W73dx3331Jkve///255ZZbXJByjuVE29GjR1uXaGurTqeT06dPt+4Gos9uxtFarRhdK2yPtZLItvXOc+TIkZw6dWrddc6cOZMk2b1797rrXXnllbn11lu3LLadxjkgsJO19bxkeno6x44dy+Li4sir7VVsAmyzto7CAbhYKlEYxM0335ynPe1peeUrXznqUHYEo96hObRiHB7z+TGohYWFLCwsjDqMHUE3BmCnaut5SZOq7VVsAmyzmZmZc8r02zIKBwbR7XZz6NCh3HnnnW6S8zlUojCIe++9NwsLCzl69Kj9A9hROp1O5ubmkoz+5tC4a1KFAaMzSIXlwYMHkyT33HPPdoez4+nGAOxU09PTOXr0aGqtKaW05rykSdX2KjYBtlmT+o/DVmvjZOcMj0oUNqKrAbCTtXne27ZpUoUBALCz3Xzzzam1Jklqra3qXtSUanuJzTHV7XZz4MABN4egAa655ppzlq+99toRRQJbS0KCjbiJyEa0KwZ2uqbcHBp3ksgAQFPce++9KaUkSUopOXr06IgjGlxTpjaR2BxTKmgA2G4SEmzETUQ20ta5RQC2SlNuDu0EksgAQBOcOHHinIpN18GbJ7E5hlTQQLM88MAD5yzff//9I4oEtpaEBINwE5H1aFcMbBVdi9iIJDIA0ASugy/e5KgDYOutVUFz++23jzgq2Lmmp6dz7NixLC4uOlgxVuzbDGL5JiLD0e12c+jQodx5552tuHHb6XQyNzeXRLti4OKs7Frk+hcYF0eOHMmpU6c2XO/MmTNJkt27d6+73pVXXplbb711S2ID4MK4Dr54F12xWUq5ZCsCYeuooIFmMccc48q+Dc3TtukItCsGtoKuRcBOt7CwkIWFhVGHAcAApqam8uIXvzhJ8pKXvMR18AXYVMVmKeV7k3y81vrL/eW3J3lNKeVUkq+vtf7BNsTIJrW1gqZtFQYwqOWbtkePHnXTlrFi34ZmWX1jv9PptOLvstPp5PTp0wZHABdM16Lhe+ihh3Lw4MG89a1vzQte8IJRhwNja9DqyoMHDyZJ7rnnnu0MB4At8vDDD5/zlc3ZbCva703yXUlSSrk+yTcn+fYk35jkcJKv3dLouCBtLWXWOohx5qYt48q+Dc3R1hv7TWxXPEjbNy3foDnW6lrUhs+/Nnvzm9+cxx9/PHfffXfe9ra3jTocAIDWeOihh/Lxj388SfLII4/k4YcfNlBskzab2PziJB/r//vrkvzbWusvlVI+muQDG31zPxl6MMlXJdmd5LW11neseP0dSV6z6tv+fa31JZuMcyDjesOijRU0ba0wgEE18aYtbAX7NjSHG/vDpd0bNEdbuxa11UMPPZTTp08nSU6fPu1mHADAJrz5zW8+Z9lAsc3bbGLzU0kuS/Lfk7w8yVv6zz+R5AsG+P5nJjmZ5Bf6j7UcT/LqFcuf3WSMW6qtNyzaVkHT1goDhk/LYoDmGNdBYm11zTXX5Pjx408tX3vttSOMpt0G2Re1fIPmaGvXorZyMw4A4MItDxA733KTNeXe/GYTm3NJ3lZK+d0kVyV5b//5L0/yRxt9c6313iT3Jk9VZ67lM7XWP91kXBdknG9YtK2CRoUBg9KyGKBd2jpIjOFpyoUR0F5t7FrUZm2+GQcAMGpXXHHFOedPV1xxxQij2Zym3JvfbGLzHyW5K8nzknxTrfVs//mvTPKLWxTTdaWUR5M8luT9Se6stT661oqllNcneX2SPO95z9uit2cUtA5iEN1uN7Ozs6m15tixY1oWA4zYOA8Sa6MHHnjgnOX7778/3//93z+iaAbXlAsjoN3a1rWozdp8Mw5gpzKYEJrjjjvuyG233fbU8hve8IYRRjO4Jk0nOLGZlWutn6q13l5r/bu11mMrnv+XtdZDWxDPsSTfkWRvkgNJvjrJiVLK558nnp+utV5da736sssu24K3Z1Q6nU4mJnq7o9ZBnM/MzMw5lb0zMzMjjmhw3W43Bw4cyNmzZzdeGQAuwPT0dHbt2pUk2bVrVysGiq2+MHKcBC7UctciN2u33x133HHOcltuxgHsZCsHEwKjddVVVz01MOyKK65ozVzla00nOCqbqtgspTyZ5LmrKyhLKVNJHq217rqYYGqt71qx+NFSykeSnE7yyiS/cjE/m2bTOohBzM/Pp9aaJKm15vjx462p7GhaNYp58QDGz/Icc08++WR27drVioFi5lkHaJ/lm3GnT59u1c04gJ2qSVVWQM8dd9yRgwcPtmqAWJOmE9xsK9pynuc/P8lnLzKWz1FrPVNKeSTJ39zqn03zaB3ERi6//PJzWh5dfvnlI4xmcG09gTQv3taRSAaGYWpqKtdff32OHz+eG264oRXHmiZdGAEwuDbejAPYqQwmhOa56qqr8u53v3vUYWxKk6YTHCixWUr5vv4/a5LvKaX8jxUv70ryNUl+f4tjSynl2Um+OMmfbPXPHndt7Ju+3DoIzufRRx9dd7mpmngCaV685pFIBnaiJl0YAT0PPfRQDh48mLe+9a0q8TivNt6MA9ipDCYEtsJyl6hk9NMJDlqxufxJV5J8d5InV7z22SQfS/I9G/2QUsozk1zVX5xI8rxSyv+e5Gz/8cYkv5xeIvP5Se5O8miSXx0wTvqa1vYStsLevXtz9OjR1FpTSslNN9006pAG4gSScU4kt3EgDYyrbreb++67L0ny/ve/P7fcckvj/y6bdGEE9Lz5zW/O448/nrvvvjtve9vbRh0OAHCRDCZkEO7vsJEmTSc4MchKtdYvqbV+SZL3J/mK5eX+48tqrftrrf9+gB91dZLf7T+eluSH+v/+4fSSpS9K8mtJ/jDJzyf5gyQvrbV+erP/sZ1sddvLs2fPjjok2BKdTieTk73xGJdccklrbn5OT08/FbcTSMbNyoE0wGit1SGg6ZYvjEopI78wAnrVmstTP5w+fToPP/zwiCMCAC5Wp9PJxEQvDWAwIefj/g6D6HQ62bNnz8g/RwZKbC6rtb6s1vrJC32zWutv1VrLGo/vrLUu9BOkl9daP6/WekX/+T++0Pfbqdp4UwsGMTU1lf3797fu5qcTSMaVgTTQLGt1CGiDplwYAb1qzZXuvvvuEUUCAGwVgwnZiPs7DGp5OsFRf44M2or2KaWUb0myN8nlWZUYrbV+/RbFxUXQ9pJx1ul0cvr06Vbd/GxSmT5spSbOH3vkyJGcOnVq3XXOnDmTJNm9e/eGP+/KK68cqJUwNEFbW0yZZx2aY7la83zLALBVtL0crjbeT2N4mnh/B9azqYrNUspbkrwzvfkvH0vSXfWgAbS9hOZRjcI4amt12MLCQhYWFkYdBmw5HQKAi3XFFVesuwwAW0Xby+FqSpUVzdTW+zvsXJut2PyOJN9Wa/132xEMW6PT6WRubi6Jm1qMn5Unvm0aOaQahXF0zTXX5Pjx408tX3vttSOMpmeQ6sqDBw8mSe65557tDgeGSocA4GLdcccdue22255afsMb3jDCaAAYV6vbXnY6Heeu20yFLOtpa/efNvM3eXE2m9icSPJ72xEIW8dNLbayFWOT2jA68QVgK4zrcTLRYoqNuYBmPVdddVWuuOKKnD59OldccUVe8IIXjDokAMaQtpfD19ZCAeeuw9HpdDI7O5tEodSwtPVvsik21Yo2yU8n+QfbEQhbS9tLNtLGVoxrnfgCo/PAAw+cs3z//fePKBLYem08TiZaTLExbd/YyB133JGnP/3pqjUB2DbaXg7X6kKBs2fPjjqkgTl3HY6pqamnBvU+97nPdT25zdr8N9kUm63Y/KIk315KeXmS/5TkiZUv1lq/d6sC4+Joe7mzjWsrxrVOfI1ogdGZnp7Oe9/73jz55JPZtWuXViW0xrgeJ2Ejul8wiKuuuirvfve7Rx0GAGNM28vhamuFrHPX4el2u091LTpz5kzOnj1rW2+jtv5NNslmKzb/VnqtaD+b5IVJXrTisWdrQwM41/T0dCYne+MxnPjC6HU6nezatStJsmvXLl0CABpO9wsAoAk6nU4mJnq3pbW93H5trZB17jo8MzMzqbUmSWqttvU2a+vfZJNsKrFZa33ZOo/p7QoSIHHiC02zPKdzKcWczgAt4AKacfbQQw/lVa96VR5++OFRhwLABlxLDtf09PQ5g5LbUijg3HV4bOvhUrxz8TZbsQnbptvt5sCBA3pKc15OfKF5zOkMzdLGG/vOAYfHBTTj7K677srjjz+eN73pTaMOBYABuJYcnk6nc041Xlu2uXPX4bGth0vxzsXbMLFZSvn1UsoXrvj3eR/bHy7jzGTQDMKJLzTL8pzOBhpAM7Txxr5zwOFxAc24euihh/Lxj388SfLII4+0anAHwE7lWpKNOHcdHtt6uBTvXLxBKja7SeqKf6/3gAuyejJoI/Y5Hye+ALC2Nt7Ydw44XC6gGVd33XXXOcttGtwBANttZmbmnKRVWwYUOncdHtt6+BTvXJwNE5u11tfWWj+94t/nfWx/uIwrk0EDAFycNt7Ydw44fDfffHOe9rSn5ZWvfOWoQ9kRtFoejuVBHcseeeSREUUCAM3T5vkTJX+Gx3XCcCneuTiTF/JNpZQvSHJVepWcp2qtf7WlUXFeR44cyalTpzZc78yZM0mS3bt3r7velVdemVtvvXVLYrsYax1gb7/99hFHBQDQHm28se8ccPjuvffeLCws5OjRo7b1EKxstWx7b59SylNzhy0vAwA909PTOXbsWBYXF1s3f+Jy8oft5zqBNhmkFe1TSimXlFLekuSTSf5jko8m+WQp5UdKKZdsR4BcmIWFhSwsLIw6jIGZoBgA4OKsvpHfhhv7zgGHS+vf4bK9h+e66647Z/n6668fUSQA0DzmT2Qjzltpm81WbL45ybcl+Z4kH+w/9zVJ7k4vSXpw60JjLYNWVx482PtV3HPPPdsZzpbpdDqZm5tL4gALAHAhrrvuunzgAx94arkNN/adAw7XWq1/jcbePrb38Nx222354Ac/mFprSimN6EoEAE2xPH/i0aNHzZ/Impy30jabTWx+e5LvqrXeu+K5U6WUTyT5mUhscoEcYAEALk4bb+w7BxwurX+Hy/YenqmpqacGd1x//fU+SwDYNt1uN4cOHcqdd97ZquNNp9PJ6dOnWzeQsK3bu23afN7axH1kkOkE2zaV4LKmbO/NJjb/lyRr/UZOJfmiiw+HnaytB1gAgCZo641954DDMz09naNHjz6V/Nb6d3u1eT6rNrrtttvy2GOPNerGD8OzlTcQk+bdRASao63zZ7d1rsq2bu+2afN1Qlv3kTZNI7hSU7b3ZhOb/zHJ9yb5R6ue/ydJfm9LImLHausBFgCgKdp4Y9854PDcfPPNec973pMkqbXmla985YgjGm9aLQ+XzxI20tYbiEBzrJ6HsNPptGYwYRvZ3sPT1uuEpu4jg1yPt20qwaRZ23uzic1/luTeUsrLk3woSU3y0iS7k/ydLY4NAADYBDf2Wc+9996bUspTI7GPHj3aqlHNbaPVMgzPuN5ABJrFPITDZXsPT1uvE+wjw9Wk7T2xmZVrrfcl+dIkv5TkmUm+MMm/TfJltdYPbn14AOfqdrs5cOBAzp49O+pQAABa5cSJE6m1JumNxJ6fnx9xROOv0+lkz549qjUBYAysNQ8h28f2Hp62XifYR4arSdt7oIrNUsrTk7wlyauSXJJkPsk/rLX++TbGBvA5mtLHGwCgbcz5OHyqqIFxtJVzmprPlDa55pprcvz48aeWr7322hFGM/6cuw5PW7d1W+NuqyZt70ErNn8oyXcmOZrkXUluSnJkm2ICWNPqPt6qNgEABtfpdDIx0bsENOcjANtpYWHBvKaMnc985jPrLrO1nLsOT1u3dVvjbqsmbe9B59j8e0luqbW+K0lKKe9Mcn8pZVet9cltiw5ghSb18QYAaBtzPtJWqsOgWcxpyk71wAMPnLN8//33jyiSnaHN567dbjeHDh3KnXfe2Yq427qt2xp3WzVpew+a2PwbST6wvFBr/e1SymKS3Un+eDsCgyZwAd0sa/XxltgEABhcp9PJ6dOnjWZm7KgMA2C7lVLWXWbrtfXctY1TabV1W7c17rZqyvYeNLG5K8lnVz23uInvh7HlAnp4mtTHGwCgjcz5SBupDgOgCW688cZz5th82cteNsJodoY2nruunkqr0+m0opKwjds6aW/cbdWU7T1oYrIkeWcpZWXj8C9I8rZSyuPLT9Rav34rg4NRcwHdLJ1OJ3Nzc0lG38cbAAA20rY2ZAxfW/eRtsYNcDFuueWWcxKbt9xyywijoalMpQXbb2LA9X4+yZkk3RWPd6bXhnblcwDbZrmPdyll5H28AQBgIyvbkMFa2rqPtDVugIs1MTFxzldYba2ptICtNdAncK31tYM8tjtYgE6nkz179qjWBACg0brdbmZnZ1NrzbFjx3L27NlRh0TDrG5V15Z9pK1xA1ysmZmZcxKbBnewlunp6ezatStJsmvXLlNpwTYwRybQKk3p4w07wZEjR3Lq1Kl11zlz5kySZPfu3euud+WVVw7U3huAnWdcjzczMzPnjNbXhozV2tqqrq1xt5W2v9Aca1Xi+fxjtU6nk/e85z1JkieffFJxBmwDNfMAwAVbWFjIwsLCqMMAYMy18XgzPz+fWmuSpNZ6zpxckLS3VV1b424rbX8ZV91uNwcOHGhV1ff09HQmJ3t1QpOTkyrxWNMnP/nJc5Yfe+yxEUUC40vFJgCwpkEqXg4ePJgkueeee7Y7HADG1Lgeby6//PKcPn36nGVYaXp6OseOHcvi4mKrbpC3Ne42WtnSenZ2Np1OR9UmY2Nl0r4tVY+dTidzc3NJeq1oVeKxlrvuuuuc5Te96U352Z/92RFFA+NJxSYAAABssUcffXTdZeh0OufM1daWG+RtjbuNVra0fuKJJ1RtMjbaOlfv1NRUrr/++iTJDTfcYKA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      "text/plain": [
       "<Figure size 2304x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(32,7))\n",
    "plt.title('Year vs Points Scored')\n",
    "seaborn.boxplot(df2['Year'], df2['PTS'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Points')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2304x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(32,7))\n",
    "plt.title('Year vs Assists')\n",
    "seaborn.boxplot(df2['Year'], df2['AST'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Assists')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rebounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2304x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(32,7))\n",
    "plt.title('Year vs Rebounds')\n",
    "seaborn.boxplot(df2['Year'], df2['TRB'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Rebounds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Evolution of the 3 Point Shot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2304x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(32,7))\n",
    "plt.title('Year vs 3 Point Attempts')\n",
    "seaborn.boxplot(df2['Year'], df2['3PA'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('3 Point Attempts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(32,7))\n",
    "plt.title('Year vs 3 Point Percent')\n",
    "seaborn.boxplot(df2['Year'], df2['3P%'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('3 Point Percent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2304x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['ratio'] = df2['3PA']/df2['2PA']\n",
    "fig, ax = plt.subplots(figsize=(32,7))\n",
    "plt.title('Year vs 3pt:2pt ratio')\n",
    "plt.ylim(-.2, 2.4) \n",
    "seaborn.boxplot(df2['Year'], df2['ratio'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('3pt:2pt ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fantasy Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YH9bPBmifNq4f6/2GQbS1IhnYehKbAIyVmZmZnD59WisbABgjJi4NhwFEgPZp42RT7zcMwvI3wKVIbAIwNrrdbubm5lJrzYkTJwx+AsCYMHFpOAwgAjAM3m8YVBsrkoGtJ7EJwNiYmZnJ0tJSkmRpaalVg58qUQBgbSYuDZcBRACGwfsNg2hjRTKw9SZHHQAAbJb5+fksLi4mSRYXF3Pq1KncdtttI45qMCsrUdoSM5vn6NGjOXPmzLr7nDt3Lkmyc+fOdffbtWtXbr311k2LDbZat9vNoUOHcvDgQQMWrGmtiUveK7fO8gAiAGwl7zcAXC0VmwCMjenp6UxO9ubsTE5OZs+ePSOOaDAqURjEwsJCFhYWRh0GbDotRrmctSYuAQAAsD2p2ARgbHQ6nZw4cSJJUkppTUsblSgMUmF5++23J0kOHz681eHA0Kye2NHpdFRt8iA33HBDTp48+cD2jTfeOMJoAAAAGCUVmwBbzNqJwzM1NfVAm86dO3e2ZnBcJQqwXbV5bWQAAABg+CQ2x5RECjSHFnvD0+12H1iH8AMf+EBrXgPb2kIXYKNM7Bi+Nn5OuPvuuy/afutb3zqiSIBlbXwtAQBgPEhsjimJFGgGaycO18zMTGqtSdpV+dPpdDIx0XtLnpiYaE0LXYCNMrFj+Nr4OWF6ejo7duxIkuzYscPzBBqgja8lMK5MNABgWJryniOxOYa63W5OnDiRWmuOHz8+8icZbGda7A1XWyt/pqamsnfv3pRSsm/fvta00AXYKBM7hqutE646nc5FiU3PExittr6WwLgy0QCAYTl27Fje9a535dixYyONQ2JzDM3MzOT+++9Pktx///1ObGCE2ppoa6s2V/50Op3s3r3bYC2wrZjYMVxtnXDleQLN0tbXEhhHJhoAMCzdbjfz8/NJklOnTo30PUdicwydPHnyou3Xv/71I4oEaHOirY3aXPkzNTWVI0eOGKwFth0TO4anzROuPE8YV01p53Ul2vxaAuPGRAMAhuXYsWMXveeMsmpzoMRmKeXLSynPWLH9LaWUN5ZSfq6U8vCtC4+rsZxEudQ2MDxtTrS1kYoOYLO0caC5rUzsGJ42T7jyPGFctbGFZJtfS2APqYcQAAAgAElEQVTcmGgAwLC88Y1vvGj7DW94w2gCyeAVmz+d5HFJUkr53CT/Jcl7kjw3yeEtiYyr9rGPfWzdbWB4JNqGT0UHsBnaONAMl2PCFTRLW1tIei2B5jDRAIBhqbWuuz1MgyY2r0/yzv73X5/kZK31hUn2J/nqrQiMq/fEJz5x3W1guCTahktFB7BRbR1ohssx4Qqapa0tJL2WQHOYaADQTm3sEvW85z3vou3p6ekRRTJ4YrMm2dH/fjrJif7355JMbXZQbMyLXvSii7Zf/OIXjygSIJFoA2ibtg40wyBMuILmaHMLSa8l0AwmGgC0Uxu7RO3fv/+iyTT79+8fWSyDJjbvSfIjpZR/nuQ5Se7qX/+kJB/cgrjYgOuvv/6BKs0nPvGJecpTnjLiiAAA2qPNA81wOSZcQXO0uYWk1xJoDhMNANqlrV2ipqamHqjS3LNnz0jPAwdNbP5gkn+c5BeTvLTWem//+q9P8ntbERgb86IXvSgPf/jDVWtCA7SxtQDAdtbmgWYA2kMLSWAzmGgA0C5t7hK1f//+PPWpTx1ptWYyYGKz1vqHtdYvqLV+Rq31x1bc9OIk37YlkbEh119/fX7rt35LtSY0QBtbCwBsZwaah8sEoOFyvKE5tJAEANh+2twlqimTaSYH2amUMpfkBbXWv1l100SSO5Ps3ezAAMbB6tYCnU5n5C/8bXb06NGcOXNm3X3OnTuXJNm5c+e6++3atSu33nrrpsUGjI/lgebZ2dlWDTR3u90cOnQoBw8ebE3MycUTgG677bZRhzP2HG9olk6nk7Nnz5pEAwCwTUxPT+f48eNZXFzUJeoqDdqK9vlJHrrG9dcked7mhQMwXtrcWqCtFhYWsrCwMOowgJZr41pFbewQ0Na1RdrK8YbmacqsdwAAhqPT6aSUkiQppbRq3KEp1q3YLKV80YrNLyilPHbF9o4kNyU5txWBsbZBqpUSFUvQFGu1FlAdcfUGeb26/fbbkySHDx/e6nCAMbY80NwWbe0QsNYEIO+TW8fxBgAAGK2pqans3LkzZ8+ezc6dO1vx2b1pLlex+Y4kf5CkJpnvby9f3p7kx5L8xFYGyNVRsQTNMD09ncnJ3hwSrQUA2Cpt7RDQ5rVF2sjxBgDYnqyzDs3R7XYfKEz7wAc+4P/yKlxujc3PTVKSvCfJP0nykRW3fTrJB2ut929RbKxh0OpKFUvQDJ1OJ3Nzc0mSiYkJrQUAGmAc1+tta4cAa4sMl+MNALA9WWcdmmNmZia11iQ66VytdSs2a61naq33JnlIrfX3+9vLl7+U1ARY39TUVPbu3ZtSSvbt26e1AEBLtK37xfT09EVrdLQlYdXpdDIx0ftIYgLQ1nO8AQA2po2Vj91uNydOnLDOOjSETjobd7mKzSRJrfVCKWVnkmcluS6rEqK11p/ZgtiALdTtdnPo0KEcPHiwVcm2Nsbd6XRy9uxZg4cADTGO6/XefPPNufPOO5MktdbccsstI45oMMsTgGZnZ00AGgLHGwBgY9pY+TgzM/NAEuX+++9vVewwjnTS2bjLrbGZJCmlfFOS9yb5lSQ/nOSOFZfbtyw6YMusPBFrk7bGDQBb6a677rqoYnN2dnbEEQ2u0+lk9+7dJgANieMNAHB1ut1u5ubmWlf5eOrUqQfaXtZac/LkyRFHBNubTjobN1BiM8lPJPmZJI+stT6+1vo5Ky5P2ML4gC3Q1hOxtsYtGQvAVpufn79osKJNrWympqZy5MgR1YND4ngDMAxtbNcJlzMzM5MLFy4kSS5cuNCacZ7rrrtu3W1guCxdtnGDJjYfm+Q/W1MTxsPMzEyWlpaS/N0CxW3QxrjbmowFoF2mp6czOdlbZaJtrWwMfALA+DHBl3E0Pz9/UWKzLZMJ77vvvnW3geHTSWdjBk1sHk/yjK0MBBieti5Q3Ma425iMBaB92tzKxsAnAIwXE3wZVzfccMNF2zfeeOOIIrkye/bsuWjZiuc///kjjgjQSWdjBk1svi7Jy0spLymlfF0p5atXXrYyQGDztbWqo40nkG1MxgLQPm1tZdPtdnPixInUWnP8+HEDnwAwBkzwhWbpdDoXjQO2aRIkwFoGTWz+YpLPSfJjSf5nkt9acfnNrQkN2Cptrupom7YmkRk+rRiBjWpjK5uZmZmLJgAZ+ASA9jPBl3F19913X7T91re+dUSRXJmpqans27cvpZTcdNNNrZkE2VbGd2DrTQ6430O2NApgqJarOmZnZ1tV1bHWCeQdd9wxomgG0+l0Mjc3l0QSmfWtbMV42223jTocoIWWW9m0yalTp1JrTZLUWnPy5MlGvAYePXo0Z86cWXefc+fOJUl27ty57n67du3KrbfeummxATRBt9vNoUOHcvDgwdZ8nmR4pqenc/z48SwuLprgy1hp83O70+nk7NmzxqWGwPgObL2BKjZrrRfWu2x1kMDma2NVx/T0dHbs2JEk2bFjRytOINvaGpDhsgYNsF1dd91162432cLCQhYWFkYdBsBIWB+Z9egSxbhq83Pben7D0ebxHZWmtMklKzZLKf8qyStrrZ/sf39Jtdaf2fTIgC3VxqqO5erHCxcuZMeOHa05gTQrjstZaw0as/qAK9XG6pn77rtv3e1RGaTC8vbbb0+SHD58eKvDgaFq42sJw7V60LbT6XiucJGmdonSkYGNaupzm+Zo8/iOSlPaZL1WtHckmUnyyf73l1KTSGxCg4zryXpbTyDbmERmuNZag8ZJJHCl2vhB9MYbb8zJkycf2H7Ws541wmiApJ2vJQxXmwdtGZ62TvDVjYHLaetzm+Fo6/iOSUu0zSUTm7XWz1nre2A8tPVk3Qkk46jN63Q0zWZO7EiaNbkD1uODKLAZvJYMXxsrZNs6aMtwNXGCr44MbIYmPrdpjraO75i0RNusV7EJtNQ4n6w7gWQcLbdZTtq3TkcbtXViB6ynrR9E77777ou23/rWt+aOO9ZrFgNspba+liTtTBAm7ayQbeugLQBstbaO75i0RNsMnNgspexL8qIkX5Be+9k/TvKyWuvcFsUGANtCW9ssN9E4T+yA9bT1g+j09HRe97rXPbB+tsFxGK22vpYk7UwQtrVCtq2Dtm3V1qQ9XI7nNuOoreM7Ji3RNhOD7FRK+fYkdyZ5f5J/k+QlST6Q5M5SyrdtVXAAq3W73Rw4cCDnz58fdSiwqTqdTnbv3m1gCLgq09PTmZzszVls0wfRTqeTHTt2JEl27NjhNZCx0sbz1ra+lqxOELblmK9VIdsGy4O2pZRWDdq21cqkPYwTz23GVRvHdzqdzkXnJG2Kne1poMRmkhcnub3W+i211l/oX/5Fkh/q33ZZpZTvLaW8s5Ty0f7l90opt6y4vZRSXlJKOVdKWSilvLGU8oVX/isB48yJL+Nquc2ygSHganQ6nUxM9E7t21Q9Y3CccdbG89a2vpa0NUG4VoVsW7Rx0LaN2pq0h8vx3GacGd+BrTdoK9onJpld4/o7k7x0wPt4X3qtbP93egnVb03yW6WUL6u1vjO9JOmBJN+W5N1JfizJ60spn1dr/dsBHwMYY21t1QQAW62tLY+S3uD42bNnDY4PibZvw9HW89a2vpa0tYVum9u+LQ/asrXavO4tm+Po0aM5c+bMuvucO3cuSbJz587L3t+uXbsGWr5jq3luQ7PMzMyklJIkKaX4n6TxBk1s/mWSPUnuXXX98/u3XVat9bdXXXWwlHJrkn9SSnlXku9P8tJa62uTpJTyrUnuS/LNSX5hwDhpqM08EWvKSRjD58QXAC6trQlCg+PD1cZ1CNuozeetbXwtmZ6ezuzsbGqtKaW0JkForUoup61Je4ZrYWFh1CFcMc9taJb5+flcuHAhSXLhwgX/kzTeoInNn0ryM6WUL0lyd5Ka5FnpVVd+/5U+aCllR5IXJPmM/v09Ocljk8wt71NrXSilvDnJDblEYrOU8sIkL0ySJzzhCVcaBg3TxhMxhsuJLwBcmgQhl9PWKsI2avN5axtfS26++ebceeedSZJaa2655ZbL/EQztLVCluFpc1Uvm2OQif233357kuTw4cNbHc6m8dyGZvE/SdsMlNistf58KeXD6bWK/eb+1X+SpLNcYTmIUspTk/xekocl+ViSr621vquUckN/lw+t+pEPJXncOnG9Mskrk+TpT396HTQOhm9cT8QYLm+yAABXr81VhG3T5vPWNrYrvuuuu1JKeaBic3Z2tjXP7TZWyDI8qnoZV57b0Cz+J2mbiUF3rLX+z1rrM2utn9m/PPNKkpp9707yJUmemeRokleVUnavfJhV+5c1rgO2qU6nk4mJ3suWN1kAgCuzVhUhW6PN560r2xW3xfz8fGrtDR3UWlv13F6ukG1LEpnhWq7qLaWo6mWseG5Ds/ifpG0um9gspXxtKeVVpZRXl1K+YyMPVmv9dK313lrrPbXWFyd5R5IfSPLB/i6PXfUj1+XBVZzANuVNFgDg6k1PT2dyste0p21VhG3T1vPW1e2Kz58/P+qQBjI9PZ0dO3YkSXbs2OG5zVjpdDrZvXt3qyZIwCA8t6FZ/E/SJusmNksp+5O8Nr31NL84yS+WUv79Jj/+Q5P8WXrJza9Y8dgPS/Ll6a3BCZDEmywAwNVqcxVhG7XxvHWtdsVt0Ol0LqrYbNMxh8tR1cu48tyGZvE/SZtcbo3N/zvJS2utP5I8kOj8j0kOXukDlVJemmQ2yV8meWR6a3U+N8kttdZaSnlFkoOllD9N8p4kP5reOpyvvtLHAsbX8pssG3P06NGcOXNm3X3OnTuXJNm5c+e6++3atWugdXQBgNFariKcnZ1tVRVhW7XxvHWtdsVtWasSAADYHi7XinZXkv+yYvuXkzyslLK6ZewgHpvkV9NbZ/NUkmck+cpa6+v6t788yU8l+bkk9yT57CR7a61/exWPBcAGLSwsZGFhYdRhADCge++9N1/zNV+T9773vaMOhQa7+eabc8011+SWW24ZdSg0UFvbFc/MzFxUjdyWStOk1/73wIEDrWn7CwDA9tWUc9fLVWxek17VZJKk1nqhlPKpJA+/0geqtX7bZW6vSV7SvwCwhQapsLz99tuTJIcPH97qcMaeCllgGF72spflE5/4RH7yJ38yv/iLvzjqcGio3/iN38gnPvGJvPa1r80dd9wx6nBomE6nk7m5uSTtalfc5krTmZmZnD59OjMzM62JGQCA7akp566XS2wmyXeWUj62YnsyybeVUj6yfEWt9Wc2PTIA2EZUxwIbce+99+bs2bNJkrNnz+a9731vnvKUp4w4Kpqm2+1mfn4+SXLq1Kns379fO1ou0tZ2xdPT0zl+/HgWFxdbVWna7XYzNzeXWmtOnDiRTqfTmmMOAKyt2+3m0KFDOXjwoPf1bWpcCxyadO56ucTmuSSrj9pHknz7iu2aRGITAC5BhSyw1V72spddtK1qk7UcO3YsS0tLSZKlpaUcO3ZM1SYP0ul0cvbs2dZUaybtrTSdmZm56H9y1DPfAaBp2pgkbEpFG83WxgKHJp27rpvYrLU+fliBALTRZs7ASZo1CweA9liu1rzUNiTJG97whou25+fnJTZ5kKmpqRw5cmTUYVyRtlaatrmFLgAMQ9uShE2qaGN0xrXAoUnnrhMjeVSAbWRhYaGVs3AAaI8nPvGJ625DkpRS1t2GNut0Otm9e3drqjWTXgvdycnefPM2tdAFgGFYnSQ8f/78qEO6rLUq2mBcNOncdZA1NgG4hHGdgQNAu7zoRS/K93zP9zyw/eIXv3iE0dBUz33uc3Py5MkHtp/3vOeNMBrYXG2sNG1rC10AGIYmtb0cVJMq2mCzNencVcUmAAC03PXXX5/HPe5xSZLHP/7xecpTnjLiiGii/fv3Z2Ki9xFwYmIi+/fvH3FEsL0tt9AtpbSqhS4ADMNaScKma1JFG2y2Jp27SmwCAMAYWE5mSmpyKVNTU5menk6S7NmzRxKFNXW73Rw4cKAV7d7Gwc0335xrrrkmt9xyy6hDAYBGaWOSsNPpXDSRUDcGxk1Tln+Q2AQAgJbrdrv5/d///STJ2972NgkJLmn//v156lOfqlqTS5qZmcnp06etCTUkd911VxYWFjI7OzvqUACgUdqYJJyamsqzn/3sJMlznvMcEwkZO8vLP4z6uT1QYrOU8uullJtKKWWrAwIAAK7MWuvPwFqa8kGUZup2uzlx4kRqrTlx4oRJElus2+1mbm7O8QaANTSp7SXQLJMD7nchyW8m+Ugp5ZeS/HKt9b1bFxYAADCotdafue2220YcFdA2MzMzD7yW3H///ZmZmfFasoXWmpTieNMGR48ezZkzZ9bd59y5c0mSnTt3rrvfrl27cuutt25abMB46XQ6OXv2bCuqNZPepKU3v/nNSZI3velN2b9/v4QsbIGBKjZrrd+YZGeSlyf5qiTvKaXMl1K+uZTy0K0MEAAAWF8b158BmufUqVOptSZJaq05efLkiCMab2tNSoFxsbCwkIWFhVGHAbRc27qNtLmTjnXWaZNBKzZTa/2rJD+b5GdLKV+cZH+SY0n+Uynl1Ul+ptb6nq0JExh3g8z4TMz6BIC1dDqdzM3NJWnP+jNA81x33XU5e/bsRdtsnenp6Rw/fjyLi4smpdAqg3zWvv3225Mkhw8f3upwABqjzZ10Vq6z3paY2b4GqthcqZTy95PsS7I3vRa1v5PkHyT5o1LKD2xueAAXM+sTAB5samoqz372s5Mkz3nOc1ozoxlolvvuu2/dbTZXp9PJxERvWMaklK2nEgWArdbWTjrW/aZtBqrYLKXsSPJPk3xHkpuSnE7y00lmaq0f7e/zz5P8fJL/uDWhwvBZN2J4Bj02Zn0CANAG3W43hw4dysGDB1sz2WDPnj2ZnZ1NrTWllDz/+c8fdUhjbWpqKnv37s3s7Gz27dvXmudJW6lEAWCrtbWTjnW/aZtBKzbPJfmlJO9P8sxa69NqrUeXk5p9x5N8bLMDhKZTQQgAjFq3282b3/zmJMmb3vQmM2yhAVYmUdqi0+lcVGXQlsG4Nut0Otm9e7djvcVUogAwDMuTlkoprZq0ZN1v2mbQNTZ/OMmv1Vo/cakd+mtwfs6mRAUNYd0IgPW1sRoFxpEZttAsq5MonU6nFe+TU1NT2bdvX2ZnZ3PTTTe1Iua2m5qaypEjR0YdxtibmZnJhQsXkiQXLlzwPgnAlul0Ojl79myrJi1Z95tBNWUccKDEZq31l1Zul1IeluSZSe6ttb5vKwIDAJpPSy9ohrVm2PqfhNFp82SDNg7GsXkGWY4lad+SLPPz8xclNr1PArBV2jhpqdPp5MSJE0na1UKX4WvKOOBArWhLKcdKKd/d//4hSd6WZD7JvaWUfVsYHwDQUFp6QXNMT09f1D7SDFsYrTa381oejFOtyXratiTLDTfccNH2jTfeOKJIAKB5pqamHpis9Nmf/dnOA1lTk8YBB21F+5VJfr7//VcneVSSxyf59iQ/nuTE5ocGADRZm6tRYNx0Op3Mzc0lMcOWdhnX6jDtvGirQf9/LMkCwDA0pe3luOt2uw+cb587dy7nz593vHmQJo0DDprYvDbJh/rf35TktbXWc6WUmSQv2pLIAIBG0/oSmmNqaip79+7N7Oxs9u3b50MoY6dNlWGJyQabbZAEeNuS3wzX3XfffdH2W9/61txxxx0jigaAQTWl7eW4m5mZSa01SVJrdbxZU5PGAQdNbH4wyReUUj6QZF+S5U8Bj0iyuBWBAQDNphoFmsW6eLTRuFaHmWwwfG1LfjNczlsB2md128tOp+Ocaos0KWFFc01PT2d2dja11pRSRno+NWhi85eT/FqS9/e3T/a//qMk797kmACAFlCNAs2yvC4e0AwmG2yeQRLgbUt+M1zOWwHap0ltL8ddmycAaVc8PDfffHPuvPPOJL3K3ltuuWVksUwMslOt9SVJvjvJq5I8q9b6qRU3v3wL4gIAGm65GqWUohoFAFZZnmzg/RFGz3krQPusVUXI1uh0OpmY6KWK2jYBaGW7YrbWXXfdlVJKkqSUktnZ2ZHFMmjFZmqtv7bGdb+0ueEAAE0wyFpWSfK+970vO3bsyL333vtApcRarGcFAMAoqaIGaJcmVhFu5rrfSXPGStq6jIJ2xcM1Pz9/0VqsbVhjM6WUHUm+LMkTkvy9lbfVWl+9yXEBAC3wqU99Kg996EPzkIc8ZNShAADAJWnZDsOzmcmfYSZ+xN2cRFvS3jbibV33u40TgLQrHq4mTTYYKLFZSvkHSX4nyfX9q2p6bWwvJLk/icQmAIyRQT/IWM8KAACAK9XW5I+4h6eJVYTjvO53GycArdWuWGJz6zRpssGgFZuvSPLOJM9I8v4kT0vyqCT/KckPb01oAAAAAAC0SVuTP+JunjZWETI8Taog3A6aNNlg0MTmP07y3FrrR0spS0kmaq3/bynlh5L8dJIv3rIIAQAAAACAbaWNVYQMT5MqCNtukJbWSfK+970vO3bsyL333vvAhIm1bHVb60ETmxNJPt7//iNJdiZ5d5K/TPK5WxAXAAAAAFzWuK4vBwBcWpMqCLeLT33qU3noQx+ahzzkISONY9DE5ukkX5TkvUl+P8kPlVI+neSFSS6fxgUAAK7aZg7YJs0atO12uzl06FAOHjzogygAW6aN68sBwGYY5wlA2hVvjkH/pk1paz1oYvNQkkf0v/+xJHcleUuS80m+cQviAgAArkBbB2yPHTuWd73rXTl27FjuuOOOUYcDQAuN8/pyADAMbf08qV3x9jRQYrPW+roV39+b5B+UUq5L8pFa69JWBQcAAIzvgG232838/HyS5NSpU9m/f7+qTQAAgE00rp8n2b4mBtmplPLKUspnrLyu1npfkmtKKa/cksgAAICxduzYsSwt9eZJLi0t5dixYyOOCAAAAGiygRKbSfYnefga11+T5Ds2LxwAAGC7eMMb3nDR9nL1JgAAAMBa1m1FW0r5P5KU/uWRpZRPrrh5R5KvTHLf1oUHAACMq1LKutsAAAAAK11ujc2/TlL7l/dcYp8f39SIAACAbeG5z31uTp48+cD28573vBFGA+Pv6NGjOXPmzLr7nDt3Lkmyc+fOdffbtWvXQOs1AQAAbKbLJTa/Ir1qzbkk35Dkr1bc9ukkZ2utf7FFsQHA/8/evYfJcdV3/n9/dRnfJEuWNLq0EhmQTEjIhQQHFoOJwTGEBGyy5MrlB6yJN2aXBBIDAbyEBNkEMAQvCV4cNj8ghvWGdUC2F2KwwWBjMDG5yGCLyxAkcNuSZiSNRhekkXT2j1MzHo8lTWuqu6t65v16nnnG0119+qNydZ+q+tY5JUmawS6++GI+//nPc+TIEebMmcPFF19cdaSeZtGqe9q5rqFe63v//v1VR5AkSZKkYzpuYTOldBtARJwF/HtK6UhXUkmSJEma8ZYuXcqzn/1sbr31Vs4//3yWLFlSdaQZz6JV99RxXbdSPL3ssssAuOqqqzodR5IkSZJO2FQjNgFIKQ1ExMkR8bPAcmDOpOdv7EQ4SZIkSTPbxRdfzNatWx2t2QYWrbrHdS1JkiRJ1WipsBkRzwKuB/qP8nQC5rYzlCRJkqTZYenSpbznPe+pOoYkSZIkqUa81YaOZc7UiwDwfvJ9Nh8D9AHzJ/z0dSSZJEmSJEmSJEmSdBT79++v5S0g1FktjdgEHgtclFLa0skwkiRJkiRJkiRJmt28/YOOpdXC5leAs4Djj/uVJEmSJEmS06dpSm4jkiRJJ67VwuZfAe+JiJXAvcDoxCdTShvbHUySJEmSJGkmc+o0TcVtRJIk6ZFaLWz+Q/H7byc8loAofs9tZyhJkiRJkqRe5vRpmorbiCRJ0olrtbB5VkdTSJIkSZIkSZIkSdJxtFTYTCl5b01JkiRJkiRJkiRJlWl1xCYRMRd4MrAG6Jv4XErp423OJUmSJEmSJEmSJEnjWipsRsTjgZuAdcVDCZgDHAZGAQubkiRJkiRJkiRJkjpmTovLvQ/YCJwB7AN+EvgPwL8Az+9MNEmSJEmSJEmSJEnKWi1sPhX485TSbuAIMCel9DXgDcBfttJARLwpIv4pInZHxPaIuCkifnrSMhERb4uIZkTsj4jbI+KJJ/IPkiRJkiRJkiRJkjTztFrYnAPsLf57EGgU//0D4KwW2zgP+ABwDvBs4BBwa0QsmbDMG4A/Bl4D/CKwDfhcRCxs8T0kSZIkSZIkSZIkzUAt3WMT+Abws8D3gLuBN0TEQeASYKCVBlJKz534d0S8DBgGng7cFBEBvBb4i5TSDcUyLycXN18MfLDFrJIkSZIkSZIkSZJmmFZHbF7Jw0XQtwJrgTuAXwP+cJrvvbB4/53F348FVgKfHVsgpbQf+BJ5lKckSZIkSZIkSZKkWaqlEZsppc9M+O/vAo+PiOXAYErpyDTf+2rgX4GvFH+vLH5vnbTcVmD10RqIiEvIo0ZZs2bNNGNIkiRJkiRJkiRJqrvjjtiMiJ+NiKMuk1LaNt2iZkS8F3gG8KKU0uHJTU9e/CiPjWW4NqV0dkrp7P7+/ulEkSRJkiRJkiRJktQDppqK9l+AZWN/RMT/jYhVZd4wIv4S+F3g2Sml70146qHi98pJL1nOo0dxSpIkSZIkSZIkSZpFppqKNib9/UzglOm+WURcDfwOcF5KadOkp/+dXNy8APinYvmTgXOB15/I+1xzzTUMDAxMN+YjjLVz2WWXlW5r7dq1XHrppaXbkSRJknpBK/vlzWYTgEajcdzl3JeWJEmSJEkt3WOzHSLir4GXAS8EdkbE2MjMPSmlPSmlFBHvA94SEZuAbwOXA3uAj5/Iew0MDPDd++/nzEVLSufuO5xnwR1tlhs0unl4R+kskiRJ0kyzf//+qiNIkiRJkqQeMVVhM/Ho+1se9X6XLXh18fu2SY//GfC24r/fRR4R+tfAGcDdwHNSSiMn+mZnLlrC5ec+d3pJO2D9HbdUHUGSJEnqqiHypvEAACAASURBVFZGWI7NjHLVVVd1Oo4kSZIkSepxrUxFe11EHCj+Phn4m4jYN3GhlNKFU71RSmnytLZHWyaRi5xvm2pZSZIkSZIkSZIkSbPHVIXNj0z6+7pOBZEkSZIkSZIkSZKkYzluYTOl9MpuBZEkSZqJrrnmGgYGBkq3M9bG2LSdZaxdu7alKUIlSZIkSWq3Vo+Tm80mAI1G47jLeYwrzS5TjdiUJElSCQMDA3xz00ZOXVqunYPFXc7/ffvGUu3sGyqXQ5IkSZKkbti/f3/VESTVkIVNSZKkDjt1KTzh+XOqjgHAppuPtLScI00lSZLUC9q13wruu0rd0upnY+yzeNVVV3UyjqQeY2FTkiRJjzIwMMC9mzYyf1m5dg4VvzcNlhtpOjpYLockSZJmpoGBAe7f9F2WLD2zdFsp9QGwdftoqXZ2DG0unUWSJB2dhU1JkiQd1fxl0H9RPUaabt/Q2khTSZIkzT5Llp7Jcy+6vOoY427ZsL7qCJIkzVj1OFMlSZIkSZIkSZIkScfhiM0a8Z4AkiRJkiRJkiRJ0tFZ2KyRgYEBvnv//Zy5aFHptvoOHwZgtNks1c7m4eHSWSRJkiRJkiRJkqSyLGzWzJmLFnH5M8+tOsa49V+6o+oIkiRJUtu0a5YUZ0iRJEmSJKn7LGyqtDqeHAJPEEmSNNs4rb9aMTAwwMZN9xFLy82SklKeIeXe7Q+Ua2fIGVIkSZqNPJ8mSdL0WNhUaXkK3W+yZtGCUu30HR4F4GBzc+lMW4b3lG5DkiT1llywuheWzW9Da4cA2Di4qVwzg6NtyKJ2i6WLmHfROVXHAODQhruqjiBJkiowMDDApk3fpX/JmeUaSn0ADG0rv9+5fUf5c3KSJHWahU21xZpFC3jzuT9bdYxxV96xseoIkk5QHa9W9UpVqQctm8/cX19edYpxhz+5reoIkiRJqqn+JWfyouf/t6pjjLvh5rdXHUGSpClZ2JSkGaaOBUKYukg4MDDApvs3suSMcu+TjuTf2x4qd4HDjp3lckiSJEmSJEmS2svCpiTNMGMFwv7FJRsqCoRDD5YfAb19V2vLLTkDnn/+nNLv1w4333ak6giapFeL9pIkSZIkSZLaw8KmJM1A/YvhRefPrTrGuBtuO1x1BM0AAwMD3LdpIwuXlGvnUMq/f7CtfNF+ZEfpJiRJkiRJkiS1yMKm1GPqOGLJ0UqSumXhEjj7eVF1jHH3fCZVHUGSJEmSJEmaNSxsatbq1QLhwMAA37lvI6sXlTuxP+9wPhm/74F7S7XzwLAn9SVJkiRJkiRJUudZ2NSslQuE97Jm0fxS7fQdPgTAgQc2lWpny/Boy8uuXhS85hknlXq/dnn/nQeqjiBJkqQaateFhOBsI5IkSZKkzMKmZrU1i+bzxqcvrzoGAO/88raqI0iSJEltMzAwwMZN9xNLzyjdVkpHALh3+0Pl2hnaWTqLJM1kvTq7lSRJmj0sbErSMdTxgA48qJMkSb0jlp7B/AufU3WMcaM3frbqCJJUawMDA9y36bssXLamVDuH6APgB4MHS7UzMril1OslSdLMY2FTko5hYGCAb92/kRWLy93PdM6RfB/SXQ+Wu58pwNZd3tNUkiRJktQ5C5et4akXvbnqGADcveHKqiNIkqSasbApqeN6+f5KKxYHL3lWfb4qP/aFQ1VHkCRJkiRJkiSpEvU5Wy9pxhoYGODb929k5aJyIx8B5hzOIxZ3N8uNfnxo2JGPkiRJkiRJkiT1Egubkrpi5aLgVb/UV3WMcR/6Yrn7fEiSJEmSJEmSpO6ysClJkiRJkiRJkiQdQy/fbm2msbApSdI0uUMjSZIkSZIkzXwDAwN8975vsWZRf+m2+g7nW7YdfGBHqXa2DG8vnaUXWdiUJGmaBgYGuP/+jZxxRvm2jhzJvx96aGOpdnbuLJ9FkiRJkiRJ0iOtWdTP5ef8TtUxxq2/6/qqI1TCwqYkSSWccQb88gVVp3jYrZ+rOoEkSdLM5qwdktR72vXd7fe2JFXPwqYkSZIkSapEL55oHhgYYOOmbzFn6YrS73UkzQHgG9t3lWtnaOuUy1iQlTSbDQwM8O37v8uqxWtKtTP3SB8AIw8eLNXOg7u2lHq92q+O+yRgPykdjYVNSZIkSZJ6XK+ejHu4SFjuXkVHUr5P0Te2l7tP0ZGh1u5TNGfpCk658KWl3qud9t943ZTL5HX9beYuXVn6/cYKst/cvrtUO4eHHiqdRZJatWrxGi45//KqYwBw7W3rq46gSfL9E7/DmoWrS7XTdyiXXA7+YF/pTFtGHijdhjQTWdiUJEmSJKnH5aLVJmLpslLtpJR/37t9sHSmNNRaG3OW9nPSC36z9Pu1w4GbPlF1hI6au3Qlp174qqpjjNt344eqjiBJ0rg1C1fz5qe8tuoY46782vuqjiDVkoVNSZIkSZJmgFi6jL4XXFR1jHEHb9pQdQRJUpv16gwB0kxVx8+kn0d1moVNSZIkSZIkSdKUBgYG+Nb932XFkjNLtTMn5XtV7to6WjrT1h2bS7ch9ao8he63WXP6qlLt9B2aC8DBH46UamfL7gdLvV5qhYVNSZJmmXZdzQde0Se1Qx2vsAU/k5IkSTq6FUvO5KXPrce9KgGuu8X7VWp2W3P6Kt7y1P9cdQwArrj7g1VH0CxgYVOSpFlmYGCA++7fyKIl5ds6XNyH64GtG0u1M7yjfBapV+X74n0Tlp1SsqWDAGwc/F75UIP7y7ehtqpjAdzityRJkqTp8PhGZVjYlCRpFlq0BM59blQdY9wdt6SqI0jVWnYK8174hKpTjDv0qU1VR9AkuQB+P7F0cal2UjoCwL3by00RlYZ2lXq9JLWqjic+wZOfkiSVkafQ/RZrTl9Zqp2+Q3MAOPjD4VLtbNn9UKnX19lMnLnNwqYkSZIk9YBYuph5F55XdQwADt14e9URJM0S+cKO7zB36epS7RxJ+RTYN7fvK53p8NADpduQpFbU8eIOL+xQu6w5fSVvOeflVccA4Iq7PlJ1hI7JReRNrFm0rHRbfYfz74MPDJZqZ8twuddb2JQkSZIkqVDHE4jgSUTNbnOXrmbhha+pOsa4kRvfX3UESbPEwMAA37nvu6xetKZUO/MO9wGw74GDpdp5YHhLqddLqsaaRcu4/BkvrDrGuPV3fqrU6y1sSpIkSZJUyKPDNhFLy92MOqU8zfq927eVzpSGvBm1JEmz1epFa/jDZ7yl6hgAXH3nFVVHkCQLm5IkSZIkTRRLlzD/Bb9adYxxozd9uuoIkk5QHUd/O/JbkiTNBBY2JUmSJEmSpDYaGBjgG5u+w0nLyk0feZA8feR3Bg+UaufAoNNHSpKkmcHCpiRJkiRJktRmJy1bw5kXvaHqGABs3vCuqiNIkiS1xZyqA0iSJEmSJEmSJEnSVByxKUmSJEmSJEld1Gw22T28jxtufnvVUcZtH9rMgUOnVh1DkqTjsrApSZIkSZIkSZJKu+aaaxgYGGhLW2PtXHbZZaXbWrt2LZdeemnpdiRVz8KmJEmSpBPWrhMW7TxZAZ6wkCRJvaHRaHDSvFFe9Pz/VnWUcTfc/HaWLp9fdQz1uIGBAb5733dZs2BN6bb6RvsAOLjlYKl2tuzZUjqLpPqwsClJkiTphA0MDLBx032wbEHJlkYB2DjYhpMNg3vKtyFJkiSplDUL1vCms99UdYxx77jnHVVHkNRGFjYlSZIkTc+yBcy76MlVpxh3aMPXq44gSZK6rNlsMrx7H7dsWF91lHE7hjZzeNR7VUqS1Alzqg4gSZIkSZIkSZIkSVPp6ojNiHgmcBnwZKABvDKl9OEJzwfwp8AlwBnA3cB/SSl9s5s5JUmSJEmSJNVfo9Fg7vxRnnvR5VVHGXfLhvWs6PdelZIkdUK3p6JdAHwD+GjxM9kbgD8GXgF8C3gr8LmI+ImU0ki3QurENJtN9u7aw5V3bKw6yrjNu/ZwGs2qY0iSJEmSJEmSJKlNulrYTCl9Gvg0QER8eOJzxWjN1wJ/kVK6oXjs5cA24MXAB7uZVZIkqR2azSb7dsOmm49UHQWAfUPQHPXiH0mSJEmSel2z2WTv7j1ccXc9yiebdz/IaU3HqKmzuj1i83geC6wEPjv2QEppf0R8CTgHC5u11Wg0OMgobz73Z6uOMu7KOzbS12hUHUOSJEmSJEmSJEltUqfC5sri99ZJj28FVh/tBRFxCfl+nKxZs6ZzyaQaaTab7BlOvP/OA1VHAeCHw4kF4cgfSTqWRqPBgfmDPOH5c6qOAuSRo41+L/6RJEmSJKnXNRoNDh4Z4S1P/c9VRwHgirs/SF9jYdUxNMPVqbA5Jk36O47yWF4wpWuBawHOPvvs8WWazSb7dg2z/o5bOhbyRG3etYNTOVx1DEmSJEmSJEmSJJ2AZrPJ3uHdrL/r+qqjjNs8vI3T4kdVx+i6OhU2Hyp+rwR+MOHx5Tx6FKc0azUaDfalIV7zjJOqjgLA++88wKlO+ytJkiRJkiRJkjqsToXNfycXNy8A/gkgIk4GzgVefyINNRoNRpnL5ec+t+0hp2v9Hbcwv7Gi6hiSTkCz2WRkOPGxLxyqOsq4rbsS+5JT/2p2yp9JuOczR53IoRIjQ9A8NDM/k81mk9HdsH3DkaqjADA6CM2DM3NdS5Ik1UWz2WRk917u3nBl1VEAGBncTPPgaVXHkCQpT/ubTubyc36n6ijj1t91PX2NJVXH6LquFjYjYgGwrvhzDrAmIp4E7EgpbYmI9wFviYhNwLeBy4E9wMe7mbMqeQrdXaz/0h1VRxm3edcuTq06RIfkoeOjvPPL26qOAsCW4VFOm6H3qmw2m4zsSnzoiwerjjLuwV2JPczM9S1JkiRJktQJ+YLTfVx3y/qqo4zbumMz+w7PzDOYzWaTPcN7ufrOK6qOAsAPhzezILzYQFK1uj1i82zgCxP+/rPi5yPAK4B3AacAfw2cAdwNPCelNNLdmJKUr8LZFUO85Fn1Gdz+sS8cYvEqp/7V7NRoNDg8b5CznxdVRxl3z2cSjeUz8zPZaDTY3TdI/0Vzqo4C5JGjjWUzc11LkiTVRaPR4HDfQZ560ZurjgLA3RuupLGsr+oYkmaBZrPJ3pE9XPm191UdZdzmkR9yWnNB1TGk2unq2fqU0u3AMc9GppQS8LbiZ9bJU+jC5c88t+oo49Z/6Q7mz9D7JzYaDQ6k3bzx6curjgLAO7+8jZNm8LrezRCv+qX6HIx86IsHOX2Grm9JkiRJmqzZbHJ49wj7bvxQ1VHGHR56kObonqpjSDoBjUaDXXNHeelzL686yrjrblnP4hXzq47REY1Gg33pIH/4jLdUHQWAq++8glMb9Tm/p97VbDbZu3uEK+76SNVRANi8+yFOa+6tOoZaVJ9hSJKktmg2m+wehhtuO1x1lHHbd8EB7w0qSZIkSZIqMDal67W31WMK3Qd3bWYkOaVrnTQaDQ4e3sebn/LaqqOMu/Jr76OvMTOnWZbKsLApSZKkGaHZbMLuUQ5/sh73zwZgcJTmQS/skCRVr9FosHP+bk698FVVRxm378YP0eg/veoYkiSpyxqNBgePDPOWc15edRQArrjrI/Q1FlUdoyOazSZ7h3ez/s5PVR1l3ObhQU6Lg9N+vYVNSZphGo0GJ8UgLzp/btVRxt1w22GWem9QSZKkjmk2m6Tduzl404aqo4xLQ4M0R6d/wkKSpHZpNBqMxEEuOb8eU+hee9t6Fq5ySldJmg4Lm5IkSZoRGo0Gg327mfvr9bh/NsDhT26jscwLOzQ75ULbMKM3frbqKOPS0E6ao0eqjqEe12w2ObJ7hP03Xld1lHFHhrbSHN1XdQxJkiTVTKPR4GDq4/JnvLDqKOPW3/kp+hrLpv16C5uSpFpoNpsMD8PNt9XjZOPQTjh05PjTR45lvvVzXQrVgp074cgUuSVJ0szTaDQYmt9H3wsuqjrKuIM3baDRP/0TFpIkSZI0mYVNSZJmmbGC7B23pKqjjBveAXHYgqwkzSS50DaH+Rc+p+oo40Zv/CyN/pVVx9AEefTjbg7c9ImqowBwZGgbzdEfHXeZRqPBjvm7OOXCl3Yp1dT233gdjf7FVcfQBM1mkwO797J5w7uqjgLAgcEtNA+eVnUMSZKk0ixsSpJqodFoMG/OIM8/f07VUYA8cnT5yuNPH9loNJgzZ5BfvqBLoVpw6+dg5RS5JUmSJEmSJKkXWdiUJGmWaTQapLmDnPvcqDrKuDtuSTRWWJCVekmz2YTdIxza8PWqozxscITmQUd/S70kj348mZNe8JtVRwHgwE2foNG/pOoYmgEajQZ7+w5w5kVvqDoKAJs3vIvGspOqjtExO4Y2c8uG9aXbGRl+CICFi8qN7t8xtJkV/etK55EkSY9mYVOSJEmSaq7ZbJJ2D3PoxturjgJAGtpFc7Q+U5pLkmavtWvXtq2tPbsPArCif36pdlb0r2trLqmXNJtN9o7s5R33vKPqKOM2j2zmtKbTcUszhYVNSZIkqUJ55OM+Dn1qU9VRHja4b8qRj41Gg8G+Q8y76MldCjW1Qxu+TmOZo78lSZpNLr300ra1ddlllwFw1VVXta1NSZLUXhY2JUmSJM0a4yMfN9xVdRQA0tAwzdGppwZvNBoMzQ/mXXhe50O14NCNt9PoX1V1DEmSJNVMo9Hg4KGDvOnsN1UdZdw77nkHfY2+qmNIahMLm5IkSVKF8sjHHzHvhU+oOsq4Q5/a5MhHzVpjxe/Rmz5ddZRxaWiI5uihqmNIktps+47N3HDz20u1sWt3vi/o4tPL3Rd0LM/S5d4bVJJUbxY2JUmSJM0aeeRjYt5F51QdBYBDG+6i0W8RWZKk2aZd9+DcNZLvC7p0ebn7guY2vDeoJKn+LGzWzObhYdZ/6Y7S7Ty0Zw8AKxcsKJ1nXcMTLZIkSZJmh1z8nsf8F/xq1VHGjd70aRr9y6uOIVWi2WxyePceRm58f9VRxh0e+iHN0XLnW6R23RvU+4JKkmYbC5s10s4rog4ODAAwv2RRcl2j4ZVakiRJkiRJkiRJqpyFzRpp15Va0P2rtbYM7+HKOzaWamPrnv0ArFhwSlvyrHOgqSRJkiRJPa3RaLBz/j4WXviaqqOMG7nx/TT6T606hiRJ0qw0Ywubm4d3sP6OW0q389CeEQBWLlhYOs+6xorSeeqoXSM6x0aZ9jXOLN3WukZ7R8CqvIeGEx/64sHS7QztSQAsXRCl85w+g4vf23fBDbcdLtXGrjyjNYvbMMPS9l2wdFX5dqRetW8INt18pFQbPxrOv09eVD4L/eXakCRJkiRJkqowIwub7Z3SNZ/Zn1+yKLmusWLGFtq8J4Cm0s5tf3tRAD+9Ua7N02dw8btd/65dxbpeuqp8e0tXzdz1LU2lXdv+wO78mXxsf8n2+v08SpIkSZp9Hty1hWtvW1+qjaE9WwFYuqDcueIHd21h4ap1pdqQet2W3Q9xxV0fKdXG1r07AFhx2pLSWdZR8kpydc2MLGz28pSuUiseGE68/84DpdrYvjePfOw/rdzIxweGE2etPv4yfia7y4sNpHrxM9llg6Mc/uS28u0MH8q/F5XcXR4chWXl40iSJHXLyOAW7t5wZak29g3n4s+pi8oVf0YGt8Ayiz8qr10Xd24byLORLVzVV6qdhavWecGpZrX2zQI5CEDfj5UrSq5jkZ/JHjIjC5tSq7YMj/LOL5c7+bltbz7xufy0ch+nLcOjUxYIoX1f+oeK0Xinri7X3lmrHfkjSaqHdvZHA8O5n1y7rGSby+wnJUlS72jbbCPDufjz48vKFX9YZvFH7eEFp5rJtux+kCvu/mCpNrbuHQJgxWlLS2dZx9S39fMzqTIsbGrWave9QU/qUoHQL/3u2ror8bEvHCrVxs7ivqBnlLwv6Fiexd6rUpKOyhkCJEmSyvGcgyT1lvad486Df/p+bOqi5PGsY6EXpKjjLGxq1nJnXVNpVyc8VBS/F7fhXpWLvVel2mR4B9xxSyrdzt6R/Pu0cvu9DO+A1eVmqZIkadZLQ4McvGlDuTaGhwGIReXvMZSGBqF/Zs7HfWRoK/tvvK58O8M7AZiz6IzSeehfXDqPJLVi647NXHdLuXtV7hx5CIAzFq5sS57FK5yyWOVtGXmAK7/2vlJtbN23HYAVp/a3Jc86zjruMp7j1mxkYVOSjsEdA81UbZ2uc08u3K9eUa7N1Sss2tfR6CBs33CkVBuH8vlx5pU8Pz46iPeqlKTjaNv0kbvzF/fadhQk+5fNyP69rftSu/O0b2vLFiX7F8/IdS2pftp2EfiePF3x4hXzS7e1eIVTFqu89o18zDO/9f34qaXbWsdZbts1s2V4O+vvur50O1v37gJgxWnl9gG3DG9n3eolpfP0GgubkiTNMr08XefIDrjnM+VGmu7bnX+fenp78rC8fDt11L77K3mvypYM7ufQpzaVa2P4QP696KS25LGQrHZIQzsZvfGz5dsZzlMExKJyUwSkoZ3QX35kSB15UV739PK+lCSVZX+jmcptW1Np5zmJgwN51o6+kkXJdauXtJRry/Ag6+/8VKn3Ati6N18EueK0clevbxkeZN3q6Z90sLApSaqNHTvh5tvKjQ7bXUyNenrJqVF37ITlM/O8Z89qW6FtJBfafnx5G9pbPnOLbR7UdU/7i8iPK9/YTC8kqyvaO6ptb26zbFGyf6XbtiRJkqQT1qsXt7W3IJsLm30lipIA61aXm9nFwqYkqRba1cmO7M0n9pevLNfe8pWe1K8bC22aqXp62x7cw6ENXy/XxvC+/HtR+amaGNzT0kjTNDTMoQ13lXqrNJwLbbHotHLtDA1D/+pSbdRVrx74A6ShHYze9OlybQznKQJiUfkpAtLQDuifoVMESJKk43pgeAtX33lFqTa2790KQP9pK0pnOWu19zOVekkvH5cdi4VNSVIt9OqJ/Z074dbPlW9npBhpurDkSNOdO2GlI00ldUH7R5quKd9YCyNN23cfwiJ32aJk/+qWM6WhXRy68fZSb5eG9wAQixaUa2doF/SvKtVGXbVvG8md+9p2FCT7l3vBlWa1w0MPMHLj+0u1cWR4OwBzFvW3JQ/9Z5VuR5Km0q7+/9BAvqfpqav7SrVz1mrvZyqpehY2JUmapnbuzO8tRpquLDnSdKUjTSV1Sa9ekNKrudtfkC1ZlOxfNWP7m17dRnrZkaHtHLjpE+XaGN4FwJxFi0tnob/cvY7UXu37/juU2+tvwwwB/WfN2O9ASfXSq/slW/Zs4R33vKN0O1v35ZGmK04tN9J0y54trMORptJMYWFTkqRpmolTOUiS6qlXT2pJU2lf0Wpnbq9sUbJ/iQWrmunl778Dg1vYvOFdpdo4OLwNgL5F5UZ/HxjcAsscZSqp89p7P7880rRvTbmRputwpKk0k1jYlCRJkiRJlejlolUvOjz0EPtu/FDpdo4MDwEwZ9HS0nnoL38f2jpq35Tt+aT+2mUnlWtomaNMJXWHF4FL6jQLm5IkSZIkSTNcO4taA7vz/SrXli1K9p8+Y4ttFu0lSZI6w8KmJEmSJEnSDOcIGkmSJM0Ec6oOIEmSJEmSJEmSJElTsbApSZIkSZIkSZIkqfYsbEqSJEmSJEmSJEmqPQubkiRJkiRJkiRJkmrPwqYkSZIkSZIkSZKk2rOwKUmSJEmSJEmSJKn2LGxKkiRJkiRJkiRJqj0Lm5IkSZIkSZIkSZJqb17VASRJkma7a665hoGBgeMuM/b8ZZdddtzl1q5dy6WXXtq2bFIZbtuSJEmSJKmdLGyqKzypJUm9pZXvbfC7u5tOOeWUqiNIHeG2LUmSJEmSWmVhU7XhSS1J6j1+d7eHRV/NVG7bkiRJkiSpnWZ1YdNRhN3jupGk3uL3tiRJM4/HwJIkSZJ63awubLbCkSiSJEmSpNnCY2BJkiRJdTarC5teXSpJkiRJmi08BpbUDo7+lurFz6Sk2WZWFzYlSZI0fR5Ad0871zW4viVJUmc5+lu9aCYf3/iZnN1m8rat2cnCpiRJkjrGA+jucV1LkqRu8IS2ZrM67nP7meyemXzBaR23belYLGxKUgm9ukPjlVrd5frWTOW22D2ua0mSJKmz3OdWO9SxQOi2rZnGwqZ0HBYj1A513KFpRa/m7lWub0mSJEmSpPry3K5UDxY2pZIsRsxuvbpD06u5e5XrW5Kko2vlQkLwYsLZzgtOJUmSZh/3AXUstSxsRsSrgdcDq4BvAq9NKd1RbSrNRn7ZdY8ntSRJknQsXkyoqbiNSJqKJ8glaeZxH3B2ql1hMyJ+G7gaeDVwZ/H7MxHxUymlLZWGk1Q5OytJUlme1JLqw8+PWuF2IqlbPOcgSfXhPqCOpXaFTeCPgA+nlP6m+Ps1EfErwKXAm6qLVQ+OatNM5XYoqR3aWbAC+8nZzJNako7H/kat8EKa7nFdqxW9+P+1V/sbP5Oaiue4NZP14ndgr30ma1XYjIg+4MnAVZOe+ixwzlGWvwS4BGDNmjUdz9dLPBknSdLR2UfKA15J3WB/o1bUbTvptZNaJ6Ju61pql17dtns1t7rL7UQzVa9u23XJXavCJrAMmAtsnfT4VuCXJy+cUroWuBbg7LPPTh1PVwN1OSCQJKmO7CclSd1gf6NWzOTtpC4ntcbM5HWt2a1Xt+1eza3ucRvRTNaL23evZa5bYXPM5CJlHOUxScfQi8PdpVa4bUuSJEmd4/5xd3l8I0mSdOLqVtgcBA4DKyc9vpxHj+KUVELdrrCV2sVtW5IkSdJM4fGNJEnSI9WqsJlSOhgRXwcuAD4x4akLgBuqSSX1Hq/S1Ezlti1J6gZH0EiSusH+QZIk6cTVqrBZeC/wdxHxNeDLwO8DDeB/VJpKkiRJkgqOoJEkSZIkqfsipfrdujIiXg28AVgFfAN4XUrpS8d7zdlnn53uueeebsSTJEmSpNo5kZGma9euPe5yjjSVJEmSJHVLRHw9pXR2K8vWccQmKaUPAB+oKz+9IQAAG5BJREFUOockSZIkzSSONJUkSZIk9bJaFjYlSZIkSSfGEZaSJEmSpJluTtUBJEmSJEmSJEmSJGkqFjYlSZIkSZIkSZIk1Z6FTUmSJEmSJEmSJEm1Z2FTkiRJkiRJkiRJUu1Z2JQkSZIkSZIkSZJUexY2JUmSJEmSJEmSJNWehU1JkiRJkiRJkiRJtWdhU5IkSZIkSZIkSVLtWdiUJEmSJEmSJEmSVHsWNiVJkiRJkiRJkiTVnoVNSZIkSZIkSZIkSbVnYVOSJEmSJEmSJElS7VnYlCRJkiRJkiRJklR7FjYlSZIkSZIkSZIk1Z6FTUmSJEmSJEmSJEm1FymlqjO0RURsBzZ3qPllwGCH2u6kXszdi5nB3N3Wi7l7MTOYu9vM3T29mBnM3W29mLsXM4O5u83c3dOLmcHc3Wbu7unFzGDubuvF3L2YGczdbebunl7MDObuNnM/0pkppf5WFpwxhc1Oioh7UkpnV53jRPVi7l7MDObutl7M3YuZwdzdZu7u6cXMYO5u68XcvZgZzN1t5u6eXswM5u42c3dPL2YGc3dbL+buxcxg7m4zd/f0YmYwd7eZe/qcilaSJEmSJEmSJElS7VnYlCRJkiRJkiRJklR7FjZbc23VAaapF3P3YmYwd7f1Yu5ezAzm7jZzd08vZgZzd1sv5u7FzGDubjN39/RiZjB3t5m7e3oxM5i723oxdy9mBnN3m7m7pxczg7m7zdzT5D02JUmSJEmSJEmSJNWeIzYlSZIkSZIkSZIk1Z6FTUmSJEmSJEmSJEm1Z2FTkiRJkiRJkiRJUu3NisJmRDwzIm6MiAciIkXEKyY9vyIiPhwRzYjYFxH/GBFnTVpmZUT8XUQ8FBF7I+LfIuIlk5Z5S0R8uXi+1M1Lu5E5Ih4TEf8zIr4XEfuL3++IiFNqnntO8R5bIuJHEfFgRFwXEavrnHvSsicXz6eIOLvuuSPi+0X7E3/+os6Zi+WeGxFfKdrZFRG3TSdzt3JHxHlHWc9jP79Z19zFMo+PiE9FxGBEjETEVyPiV6aTucu5fyEiPldsH0MRcW1ELKg499qI+GREbI+I3RHx9xGxYtIyZxT/tuHi5+8iYnHNM7etj+xW7qhvPzlV7rr2k1NuJxOWrVM/2cr2Xbd+sqV1HfXrJ6fatuvaT7ayjdSxn2wld9v6yYh4U0T8U/Fe2yPipoj46UnLRES8rci9PyJuj4gnTlpmyj4wIn4mIr5YtPFARLw1IqLOuSN/7304IjZGxGhE3D6dvBXkPi8iNkTua/YV+f9TzTP/VER8ISK2Ru4nvxcRV0ZEX51zT1r2rMjfJXumk7mbuSPvTx3te3ta34HdXN9FO6+NiE0RcaDYzk+4f+/iun7bMdZ1iojldc1dLDO2TzISua/cEBGPP9HMFeT+rYj418jff5sj4vXTydzm3FMed0XEmqL9vcX6/u9R/XdgK7mvjoh7In93f386ebuZOSJ+LiL+V0T8oGjjWxHx+oiY1vn4Lubuj4hbijYOFPn/OiIW1Tn3pGWXxcP7ycvqnjuO/r39+3XOXCz30sjfgT+K/F3y0RPN3M3cEfGKY6zrFBG/WNfcxTK/GBG3RsTOKI7dI+IpJ5q5gtznR8Rdkfv3ByPinRExbzq5J5sVhU1gAfAN4A+B/ROfiIgAPgWcBbwQ+HlgM3BrRJw2YdGPAj8JXAT8TPH330XEMycscxLwD8D7eiTzE4C5wKXAE4HXAP8fcHXNcwN8Hvgt4CeAFwGPAz7ZA7nHXAX8sETeKnL/ObBqws/6OmeOiBcC1wN/V7TzNOBvp5m5W7nv4pHreBXwDmAP8Jka5wa4GTgZOL9o505gQ0SsrWvuiGgAtwLfA54K/Ar5u/DD08xcOnfx+7NAkNfl04E+4KZ45MHPx4FfAJ5X5P4F8rZe58zt7CO7lbt2/eQJrO9a9ZMnkHtMLfrJE8xdi36y1cx16ydbzF27fvIEtpFa9ZOt5O5AP3ke8AHgHODZwKEi05IJy7wB+GPy9+0vAtuAz0XEwgnLHLcPjIjTgc8BW4s2/gB4PfBHdc5N7m9+BPwV8H+nmbWK3OcA9wK/Afw0cA1wbUS8uMaZDwIfAZ5D7idfC1zM9L+3u5UbgMjFh+uBL00zbyW5i+cmfn9/vgdyvwd4NfBG8nHFrzK99d6tzFfx6H7yi8DtKaVtdc0dEY8FNgB3kPusXwZOAT49jczdzP28Yplryd9/rwZeFxH/teLcxz3uioi55H5mIXAu8Lvk7/D31Dl3YQ75+3taxZMKMj8Z2A68jLwP9afAW4E/qXnuI+RjxxcAjwdeQd5f/Jua557o/wf+dZp5q8r9ezzy+/sjdc4cEX8AvJvc9/w08Czyd/l0dCv3/+bR/eR1wL8D99Q1d+QLS/8RaBbv9TTgQeCWSe3ULffPkvvyz5L7998BLgSmdRH4o6SUZtUP+cTHKyb8/XggAT834bE5xf+sV0163SsntbUZuOwo7/EbedX2TuYJz78aGOrB3BcWbZ9c99zkIss3yQdGCTi77usb+P7x1n/dMpNPDG0Bfq/dmSvYtr8FXFvn3MCyop1nTXh+HnAY+I0a574EGATmTnj+Z4q211WRm3yC7QhwxoRlFhWP/XLx99h3x9MnLPOM4rGfqGPmSe/R1j6yW7knLFNpP1kid6X95Inkpkb9ZKu5qVE/2UpmathPlti2K+0nW1zftesnW8zd6X5yQbEOXlD8HeQD9LdMWOYUYAT4z8XfU/aB5ItRdgOnTFjmcuABIOqae9J7/BW5ENHOz2XHc09Y5u+BG3os83uBr/TCugb+knzC9hXAnrpvI8BjaGN/3sXcPwGMAj/ZK5mP8j4/XrzPi+ucm3x8cJhH9jfPKpZZVuPcHwc+Oem9XgP8gIr6m0mvP+pxF7lQewT48QmPvZR8cc3pdc09aZnLgO+3Y7vuVuYJy74L+HoP5v4D4MFeyE2+8O82csGmLd8jnc5d5Cx9XNCtzMBiYC9wQbszd2MbmbDcqcAu4M11zg2cXWwjj53w2GNp0/5VB3NfCfzLpMdeQL4wd2HZ3LNlxObxnFT8/tHYAymlI8AB8k7LmDuB34qIpZGneLsI6Cdfxdxtncx8OrCzzXnHdCR3cTXBS4C7U0o/OtoydckdET9Gvor5JUy6ur7OuQuXRZ6C7F8jDzWf1jQlXcr8ZPJB3IGI+OfIU5F+NiJ+vgOZ25n7ESLiPPLJyWs7EZr25R4C7gdeFhELiitALyF3eF+uce6TgNGU0uEJrxn7XE5sp5u5TyLvmEz8LvsR+cBzbJmnkU9i3zVhmS+TdyrPqWnmbutk7qr7yRPOXZN+sqXcNewnT2R916WfbCVzHfvJ6Wzb51F9P9lK7jr2k63k7nQ/uZBccB37Tn0ssJJ8Re9Y7v3k0VFj/VsrfeDTgDuK1465BWiQCyx1zd1p3czdrr6yK5kjYh15NNYX25C5o7kj4teA55NPMLdbp9f3P0TEtshTlv1GD+S+iDxi/VciT1f8/Yj4SExjStcuZp7sYvIJ2xvakLmTue8hF5FfFRFzi9EgLwf+KaU0WOPcJ/HIfhRyP/ljwJkV5W7F04D7U0o/mPDYLeR/z5PLBC50KncndTNzO48nu5K7mMXjP1JtP9mS4njmjeSZlo60I+wEnV7fV0eezvWfIuL3Y5pTFk/SqczPIV8suyIi7os87e8nI+JxbcgM3ftM/hZwGvmCsXboVO5vkUd/XxwRJ0XESeQRvlvIF4XXNfex+smTaUN/Y2ETNpFH8FwZEUsioi8i3kjeEVk1YbnfIh/8D5JPCnwM+N2UUtlh7dPRkcwRsYZ81dMHeiF35DmZ95JPFq0hH+DVNndxEutjwHu6tN20c33/d/L0JM8iX0H+OjqznbQr81hH+nby1SG/Rp7S8IvFDlldc092CfBvKaXpTIfQtdwpX3JzAXnqid3FMm8DnpdSerCuucnTXy2LiD8p2jiDh6dDmNhON3N/lXwA/e6IOC3yVIFXkXcYx5ZZCWwv1jsw/v9gW/FcHTN3W0dy16SfbDl3zfrJKXPXtJ9sdX3XqZ9sJXMd+8npfJfUoZ+cMndN+8lW1nen+8mryVOEfaX4e6wP2zppua0TnmulD1x5jDYmvkcZncrdaV3JHRHPJ09X144LDjqaOfL9fn4EfId8Qdyb25C5Y7kjYhV5GsCXpZRG2pS147nJ3zWXkffNf5U8iuZ/R8RLa577ceTi1O+QR8e+jHybgmNNo1+HzOOKjP8J+GhK6UDJvB3NnVL6Prmf/DNyHzlMniGgXfuunVrftwAvjIjnRL6Q9vHkafugun6yFUfrJwfJo3Wq6ier1pXMEfEL5O+Ta6bbxiQdzR35/qD7yLNejACvnGbOyTqSu9if/V/Aa1JKD5QNeRSdXN9vBX6bPBX39eSpoduxX9KpzI8j15UuJ9/u4deB+cAXIuLUaad9WLe+Ry4Bbm7jMVlHchf7feeRt5F9xc9vk0fMtuOC8E6t71uAp0bEyyJiXkSsJm/r0IZ+ctYXNlNKo+R7T60ln/jbRz4p9Rlypz5mPXkaqV8mD/99N/DRiPi5rgamM5kjYgV5Y/sceWqbXsj9bvL8zM8pXn9dRESNc7+ZfBXie9udscO5SSm9N6X0hZTSxpTSh8hTMV4cEUtrmnnsu+2KlNL/SSl9ndxZ7SIfkLZVhz6TS8hXxHVqFErbchefuw8UbZwLPAX4P8ANRadVy9wppW+SrwR+bdHGQ+R59bdOaqdruVNK24HfJE8PNEI+qF8M/POkTIlHi2M8XpfMXdOJ3HXpJ08wd236yRZz166fbHV916mfbDFz7frJaXwma9FPtpK7jv1kK7k72U9GxHvJoz5flB45IhQe3ZdN7t9a6QOP1saxXtuyLuTuiG7ljoink6dm/IOU0temn7hrmX+bfM+8F5MLbm+cduCxN+ls7uuAa1JKXy2b81Fv0sHcKaXBlNJ7UkpfTSndk1J6K/BB8r2bapub3FeeRC4kfymldAe5j3wK+V5Tdcw80fPIszN8aJpRH/kmHcwdESuB/0m+b+Ivkk/ejgB/X7aI3OH1/Tfki9s2kO/d+1VyQQKq7Sdbcazlq+wnK9GtzBHxE+R7m74vpVR6FHWXcr+O3E++kFzIauXelsfV4dz/HfhyO9bvZJ1e3ymlt6eU7kwp/WtK6T3kCz1eP+3AdDzzHHIh8w9SSv9Y7Pe9BFhOnmp02rr4mXwieQT7dO8dO7m9juWOiFOAvyUXHv8D8HTgX4ANRUF/2jqZO6X0WfLFbX9FHrn5bR6+f3bp84XzyjYwExQnc54UEYuAvpTS9oi4m+KmsRGxljxP/pNSSv9WvOzfIuLc4vFX9XLmYify88A3yDvtHduxaGfulKckGQS+HRH3k+9j8AzyDefrmPt88sms0Unnlb8aEf87pfSSmuY+mruL3+vIJ8zqlnnsSpv7JrR7KCK+Qx611HYdWNcvJ0+b8bFO5G1z7meTd1yWpJR2Fcu8OiIuIF/Rt76muUkpfRz4eFG02kvuoP+IfOK27abKXSzzWWBtRCwDDqWUdkXE2MlkyCeWl0dEjH1fFyfN+3n01VR1ydx17cxdp37yRHLXqZ9sMXft+skWcx9NZf1ki5lr10+2mHuiWvSTLeauXT/ZYu6O9JMR8ZfkEVDPSil9b8JTDxW/V5K/r8Ys5+H+rZU+8CEefSXx2NSR0+4nu5C7I7qVOyKeQT5R8daUUqmRKN3KnB6ehvG+yLMGfCgi3p1SOlTT3M8Gfiki/nTsLYE5EXEIeHVKaVoXelS0bd9NyZE/Xcj9IPm78dsT2vgOcIjcV97NCeryur4EuCvli1RK6ULu/wLsTSmNF7sjj+j9AXnKuzvrmLt4/I0R8eaire3k/VnI91+flpK5W/EQ+aT4RMvIszZU1U9WoluZI+IJwBeA61NKfzLNuBPb60rulNJDRZubImIIuCMi1qdHTmNcp9znAz8eES8fe8ux9iPinSmlt0wjdlXb9t3A6RGxIqU0nW2u05mPdjw5HBFNShxPdnldX1K09Y/TfP24LuR+Mfmi1aenovgYES8mTx376+SL3+qYm5TSe4v3WVXkfQzwDtpwvnDWj9icKKU0XBz0n0Ue3bOheGpsCPXkSvJhKl6HZTNHns7mdvJ9f353ugdxJ6oD63rsuZOOs0xpJXO/Evg54EnFz68Wj7+ENlwdfDwdWN9PKn53Yvo0oHTmr5OnsPmJsScjX+m5ljw1W8e0cV2/Cvj7lNJwZ5I+UsncY8tMvn/BETr8Hdmu9Z1S2ppS2kO+cv9H5FF5HXOc3BOXGSxOMj+bvPNwY/HUV8g39n7ahMWfRr4vwF10SMnMlSmbu4b9ZEu5j6LqfnLiMsfKXcd+spXcR1NlPzlxmWNlrmM/2UruierST05c5li569hPTlxmyvXdrn4yIq4mH5g/O6W0adLT/04+iL5gwvInky94GOvfWukDvwKcW7x2zAVAk2meaO5S7rbrVu6IeCZ5RPCfpZRKjeaocF3PIV/4PbfGuX+Gh/vIJ5Gn8tpf/Pcnapz7aJ5EiT6yS7m/DMyLfMHkmMeRt5MT7iu7ua4jTyv/a7RhFEqXcp/K0Y/bYJr9ZDfXd0rpcErpgZTSQfKtCb6SUtpWUe5WfAX4ycj3th9zAXnf8Os1zt1W3cocET9FPp78RErpdWUyF+1Vta5LHU92KfdzeOTx5NgAgvPIoznrmvtonkTe59411YKTdSnzl4vfE48nF5CLV9M6nuzmui5e+zLgb1NKpe7F2qXcp5IvMJ2Y9UjxWFX9ZMtS1kx52tzfJRdL/3k6uSc3PON/yDsjY19q+8gHAE8C1hTP/yZ5eqbHkW8Q/33ghgmvn0++Mu9L5GlH1pLnzT8CvGDCcmuKdi8jb1hj77mgjpmBBnkI8O3kKUpWTviZW9d1Td6Z/C/kzupM8pWrXyZ/6E6ua+6jvOdjiu3k7Dpv28X6fl3R7mPJ90Z5ANhQ18zFcu8j3y/sueSO9v3kadYadc5dLPuMYtt4+nS3jS5vI8vIo8JuIH8uH0+eAnMU+IW65i6W+6/kqVUeT/5e2UeeSqOS9V0s80ry524t8FLyaK/3TFrmM8C95Ckonlb89001z9y2PrJbualhP9li7tr1k61uJ5OWfwwV95MnsL5r00+ewGeyVv3kiWwj1KifbHEbqV0/eQLbSdv6SeCvyfcYfTaP/D5dMGGZNxbL/EfyPUmvJxckF05Y5rh9ILCIfCB+fdHGfyza/OM65y6W+ani/+P15NG1TyLPPlHb3OSThXuLbXri+/TXOPPLyJ+RJ5A/I2Pf29fXeV0f5X1fAeyZTuYur++Xk0+c/SS5v7mMPG3n62qeew65yPNF8tT+P1/891eBOXXMPGG5y8l9+qnT3T66vK6fTT5O+1PgLHK/84/AFuC0GudeBlxabNtPIt+jbD/wlIrX93GPu8gXcNxLnpHm58m3bnkAeH+dcxfLrCsee2/x+rFl+uqYGXgieXTT9ZPeZ2Wd1zX5/rYvL17/GPKFEveRi/a1zX2U9z2vWG5ZnXOTZ3b5veL1a8kF2WHg6rpmLpb5FHlWq6eT92E/QT7mOOG+p9vbCPnY5zDFsdN0f7q4jTyBXOi+htznPBH4u2I7+bG65i6WeT354rwnAv+NvA/4wjLrfbztdjRS9x8e/iKb/PPh4vk/IFeKD5KvKng7kzpF8s7VDeQOaS/wb8DLJy3z4WO8z3l1zEw+EDraeyTgMXVd18UH5Avkky8HyCdqr5nOB7nb28ik5R9D+RO23Vjfv0A+eNtF3knfBLyNaR4kdWtdkwtb7yKf2NpNLkxM6+Rht7cR4CPAfdPNWtH6Ppt8/8GhYn3fDfxaD+T+KA9/l/wbeZrRqtf3XxTb7UFyUe2PgJi0zBLyVBO7i5/rgMU1z/zhY7zPeXXNTX37yaly17WfnHI7mbT8Y6hHPznV+q5jP9nKZ7KO/WRL2wj16ydbWd917Cdbyd22fvIYeRPwtgnLBPnz8yD54P2LwE9PamfKPpB88Pyloo0HySfLj/l9U6Pc3z/ae9U5N8fu379f48y/S75KfATYA3yTfJ/nU+q8ro/yvq+gXGGzW+v75eQT4nuL5+8BXlr33MUyq8gnakeAbeTpz1fUPHOQ9/0+MN11XFHu3yF/LveQp3S9CfipOucmFza/UmTeC9wKPLUG6/vDx2jnvAnLrAFuJl+wNES+wO2kHsh9+zGWeUwdMxevP+p71Xldk4vdX+Hh45tvA+8Ezqhz7qO873nF89MtbHZrff8K+X6JI+TvknuBPwTm1TVzscxC8n2cd5CnGL0JWFvndT1huS8Cn55O1grX9wXkqdl3Fev7C8A5PZD78zz8XfJV4Hll1/vYTxRvIEmSJEmSJEmSJEm15T02JUmSJEmSJEmSJNWehU1JkiRJkiRJkiRJtWdhU5IkSZIkSZIkSVLtWdiUJEmSJEmSJEmSVHsWNiVJkiRJkiRJkiTVnoVNSZIkSZIkSZIkSbVnYVOSJEmSJEmSJElS7VnYlCRJkiR1XERcFxH/GhF9kx4/PyJGI+KcqrJJkiRJknqDhU1JkiRJUjf8V2Ap8KdjD0TE6cDfAu9OKd3ViTedXEiVJEmSJPUuC5uSJEmSpI5LKe0CXgm8ISKeUjz8l8BO4G0AEfHTEfGZiBiJiG0R8bGIWDHWRkQ8NSI+FxGDETEcEXdMaIuImBcRKSJ+PyI2RMRe4M+79o+UJEmSJHWUhU1JkiRJUleklG4FrgE+GhG/AbwEeFlK6WBErAa+CPwL8IvABcBi4JMREUUTC4GPAOcC/wG4F/hMRJwx6a3+DNgA/AzwPzr7r5IkSZIkdUuklKrOIEmSJEmaJSLiFHLx8izgT1JK7y4evxJ4ckrpuROWXQZsLx7/56O0FcA24DUppesjYh4wCrwvpfS6zv9rJEmSJEnd5IhNSZIkSVLXpJT2A1cBB4D3THjqycCzImLP2A/w/eK5tQARsSIiro2Ib0fEMDBCvm/nmklvc08n/w2SJEmSpGrMqzqAJEmSJGnWOQQcSSkdmfDYHOAm4I1HWf6h4vd15OlpXwtsJhdHbwf6Ji2/t51hJUmSJEn1YGFTkiRJklQH/wxcBHw/pXToGMs8A7gkpfRpgIhYBazsUj5JkiRJUsWcilaSJEmSVAfvB5YB/ysinhIRj4uICyLiQ8V9OQG+DbwsIn4yIp4CXE8etSlJkiRJmgUsbEqSJEmSKpdS+iHwdGAucAvwTeCvgH3AaLHYK8hT0f4L8HHgg8APup1VkiRJklSNSClVnUGSJEn/r307pgEAAGAQ5t/1HOzmaG0QAAAAAIDLsQkAAAAAAADkCZsAAAAAAABAnrAJAAAAAAAA5AmbAAAAAAAAQJ6wCQAAAAAAAOQJmwAAAAAAAECesAkAAAAAAADkCZsAAAAAAABA3gCFnbP/dzSNQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2304x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['Value'] = 1*df2['PTS'] + 1.2*df2['TRB'] + 1.5*df2['AST'] + 3*df2['STL'] + 3*df2['BLK'] - 1*df2['TOV']\n",
    "fig, ax = plt.subplots(figsize=(32,7))\n",
    "plt.title('Year vs Fantasy Points')\n",
    "seaborn.boxplot(df2['Year'], df2['Value'], ax=ax)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Fantasy Points')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Player</th>\n",
       "      <th>Year</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ratio</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>David Robinson*</td>\n",
       "      <td>1990</td>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>SAS</td>\n",
       "      <td>82</td>\n",
       "      <td>36.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>0.531646</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>Shaquille O'Neal*</td>\n",
       "      <td>1993</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>ORL</td>\n",
       "      <td>81</td>\n",
       "      <td>37.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559006</td>\n",
       "      <td>0.559006</td>\n",
       "      <td>0.595506</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Michael Jordan*</td>\n",
       "      <td>1985</td>\n",
       "      <td>SG</td>\n",
       "      <td>21</td>\n",
       "      <td>CHI</td>\n",
       "      <td>82</td>\n",
       "      <td>38.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.526042</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>50.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Hakeem Olajuwon*</td>\n",
       "      <td>1985</td>\n",
       "      <td>C</td>\n",
       "      <td>22</td>\n",
       "      <td>HOU</td>\n",
       "      <td>82</td>\n",
       "      <td>35.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.542484</td>\n",
       "      <td>0.542484</td>\n",
       "      <td>0.611940</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>Tim Duncan</td>\n",
       "      <td>1998</td>\n",
       "      <td>PF</td>\n",
       "      <td>21</td>\n",
       "      <td>SAS</td>\n",
       "      <td>82</td>\n",
       "      <td>39.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>15.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.547771</td>\n",
       "      <td>0.551282</td>\n",
       "      <td>0.661017</td>\n",
       "      <td>0.006410</td>\n",
       "      <td>45.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Terry Cummings</td>\n",
       "      <td>1983</td>\n",
       "      <td>PF</td>\n",
       "      <td>21</td>\n",
       "      <td>SDC</td>\n",
       "      <td>70</td>\n",
       "      <td>36.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.524064</td>\n",
       "      <td>0.524064</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>Blake Griffin</td>\n",
       "      <td>2011</td>\n",
       "      <td>PF</td>\n",
       "      <td>21</td>\n",
       "      <td>LAC</td>\n",
       "      <td>82</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.505952</td>\n",
       "      <td>0.509091</td>\n",
       "      <td>0.635294</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Ralph Sampson*</td>\n",
       "      <td>1984</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>HOU</td>\n",
       "      <td>82</td>\n",
       "      <td>32.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.520958</td>\n",
       "      <td>0.524096</td>\n",
       "      <td>0.660377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>Alonzo Mourning*</td>\n",
       "      <td>1993</td>\n",
       "      <td>C</td>\n",
       "      <td>22</td>\n",
       "      <td>CHH</td>\n",
       "      <td>78</td>\n",
       "      <td>33.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.510490</td>\n",
       "      <td>0.510490</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>Allen Iverson*</td>\n",
       "      <td>1997</td>\n",
       "      <td>PG</td>\n",
       "      <td>21</td>\n",
       "      <td>PHI</td>\n",
       "      <td>76</td>\n",
       "      <td>40.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>19.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.414141</td>\n",
       "      <td>0.449275</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>42.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>Ben Simmons</td>\n",
       "      <td>2018</td>\n",
       "      <td>PG</td>\n",
       "      <td>21</td>\n",
       "      <td>PHI</td>\n",
       "      <td>81</td>\n",
       "      <td>33.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.544715</td>\n",
       "      <td>0.549180</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.008197</td>\n",
       "      <td>42.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Ron Harper</td>\n",
       "      <td>1987</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>CLE</td>\n",
       "      <td>82</td>\n",
       "      <td>37.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.456853</td>\n",
       "      <td>0.470270</td>\n",
       "      <td>0.681159</td>\n",
       "      <td>0.059459</td>\n",
       "      <td>42.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>Dikembe Mutombo*</td>\n",
       "      <td>1992</td>\n",
       "      <td>C</td>\n",
       "      <td>25</td>\n",
       "      <td>DEN</td>\n",
       "      <td>71</td>\n",
       "      <td>38.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>16.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.491803</td>\n",
       "      <td>0.491803</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Chris Webber</td>\n",
       "      <td>1994</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>GSW</td>\n",
       "      <td>76</td>\n",
       "      <td>32.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.551471</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.531915</td>\n",
       "      <td>0.014815</td>\n",
       "      <td>41.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Clark Kellogg</td>\n",
       "      <td>1983</td>\n",
       "      <td>PF</td>\n",
       "      <td>21</td>\n",
       "      <td>IND</td>\n",
       "      <td>81</td>\n",
       "      <td>34.1</td>\n",
       "      <td>8.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.479769</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>40.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>Larry Johnson</td>\n",
       "      <td>1992</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>CHH</td>\n",
       "      <td>82</td>\n",
       "      <td>37.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.496689</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.019868</td>\n",
       "      <td>40.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2705</th>\n",
       "      <td>Luka Doncic</td>\n",
       "      <td>2019</td>\n",
       "      <td>SG</td>\n",
       "      <td>19</td>\n",
       "      <td>DAL</td>\n",
       "      <td>72</td>\n",
       "      <td>32.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0.323944</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>0.505376</td>\n",
       "      <td>0.716418</td>\n",
       "      <td>0.763441</td>\n",
       "      <td>40.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>Grant Hill*</td>\n",
       "      <td>1995</td>\n",
       "      <td>SF</td>\n",
       "      <td>22</td>\n",
       "      <td>DET</td>\n",
       "      <td>70</td>\n",
       "      <td>38.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>19.9</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.480263</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>0.726027</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>40.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>Patrick Ewing*</td>\n",
       "      <td>1986</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>NYK</td>\n",
       "      <td>50</td>\n",
       "      <td>35.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>16.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.472393</td>\n",
       "      <td>0.475309</td>\n",
       "      <td>0.737705</td>\n",
       "      <td>0.006173</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>Mark Jackson</td>\n",
       "      <td>1988</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>NYK</td>\n",
       "      <td>82</td>\n",
       "      <td>39.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.427419</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>39.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>2004</td>\n",
       "      <td>SG</td>\n",
       "      <td>19</td>\n",
       "      <td>CLE</td>\n",
       "      <td>79</td>\n",
       "      <td>39.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>18.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.417989</td>\n",
       "      <td>0.440994</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.167702</td>\n",
       "      <td>39.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>Elton Brand</td>\n",
       "      <td>2000</td>\n",
       "      <td>PF</td>\n",
       "      <td>20</td>\n",
       "      <td>CHI</td>\n",
       "      <td>81</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.484472</td>\n",
       "      <td>0.484472</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>Lionel Simmons</td>\n",
       "      <td>1991</td>\n",
       "      <td>SF</td>\n",
       "      <td>22</td>\n",
       "      <td>SAC</td>\n",
       "      <td>79</td>\n",
       "      <td>37.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418182</td>\n",
       "      <td>0.423313</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>0.006135</td>\n",
       "      <td>39.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2483</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>2017</td>\n",
       "      <td>C</td>\n",
       "      <td>22</td>\n",
       "      <td>PHI</td>\n",
       "      <td>31</td>\n",
       "      <td>25.4</td>\n",
       "      <td>6.5</td>\n",
       "      <td>13.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>20.2</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.471014</td>\n",
       "      <td>0.495327</td>\n",
       "      <td>0.784810</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>39.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>Damon Stoudamire</td>\n",
       "      <td>1996</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>TOR</td>\n",
       "      <td>70</td>\n",
       "      <td>40.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.395833</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.442478</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.424779</td>\n",
       "      <td>39.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>Karl-Anthony Towns</td>\n",
       "      <td>2016</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>MIN</td>\n",
       "      <td>82</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.539007</td>\n",
       "      <td>0.561538</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.084615</td>\n",
       "      <td>38.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Mychal Thompson</td>\n",
       "      <td>1981</td>\n",
       "      <td>C</td>\n",
       "      <td>26</td>\n",
       "      <td>POR</td>\n",
       "      <td>79</td>\n",
       "      <td>35.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.493151</td>\n",
       "      <td>0.493151</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>2006</td>\n",
       "      <td>PG</td>\n",
       "      <td>20</td>\n",
       "      <td>NOK</td>\n",
       "      <td>78</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.429752</td>\n",
       "      <td>0.464646</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>38.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>Derrick Coleman</td>\n",
       "      <td>1991</td>\n",
       "      <td>PF</td>\n",
       "      <td>23</td>\n",
       "      <td>NJN</td>\n",
       "      <td>74</td>\n",
       "      <td>35.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>18.4</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.463087</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.034722</td>\n",
       "      <td>38.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>Tyreke Evans</td>\n",
       "      <td>2010</td>\n",
       "      <td>SG</td>\n",
       "      <td>20</td>\n",
       "      <td>SAC</td>\n",
       "      <td>72</td>\n",
       "      <td>37.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>16.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.456790</td>\n",
       "      <td>0.485915</td>\n",
       "      <td>0.738462</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>37.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2641</th>\n",
       "      <td>Jacob Pullen</td>\n",
       "      <td>2018</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>PHI</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>Casey Shaw</td>\n",
       "      <td>1999</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>PHI</td>\n",
       "      <td>9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Ron Crevier</td>\n",
       "      <td>1986</td>\n",
       "      <td>C</td>\n",
       "      <td>27</td>\n",
       "      <td>TOT</td>\n",
       "      <td>3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>Nathan Jawai</td>\n",
       "      <td>2009</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>TOR</td>\n",
       "      <td>6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>Pavel Podkolzin</td>\n",
       "      <td>2005</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>DAL</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>Yinka Dare</td>\n",
       "      <td>1995</td>\n",
       "      <td>C</td>\n",
       "      <td>22</td>\n",
       "      <td>NJN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Terrance Roberson</td>\n",
       "      <td>2001</td>\n",
       "      <td>SF</td>\n",
       "      <td>24</td>\n",
       "      <td>CHH</td>\n",
       "      <td>3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2716</th>\n",
       "      <td>Donte Grantham</td>\n",
       "      <td>2019</td>\n",
       "      <td>SF</td>\n",
       "      <td>23</td>\n",
       "      <td>OKC</td>\n",
       "      <td>3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2080</th>\n",
       "      <td>Hassan Whiteside</td>\n",
       "      <td>2011</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>SAC</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>Jonathan Kerner</td>\n",
       "      <td>1999</td>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>ORL</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>Slavko Vranes</td>\n",
       "      <td>2004</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>POR</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>Deng Gai</td>\n",
       "      <td>2006</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>PHI</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>Alex Scales</td>\n",
       "      <td>2006</td>\n",
       "      <td>SG</td>\n",
       "      <td>27</td>\n",
       "      <td>SAS</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>JamesOn Curry</td>\n",
       "      <td>2010</td>\n",
       "      <td>PG</td>\n",
       "      <td>24</td>\n",
       "      <td>LAC</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Carl Bailey</td>\n",
       "      <td>1982</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>POR</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>Gani Lawal</td>\n",
       "      <td>2011</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>PHO</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Cedric Hunter</td>\n",
       "      <td>1992</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>CHH</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>David Wood</td>\n",
       "      <td>1989</td>\n",
       "      <td>PF</td>\n",
       "      <td>24</td>\n",
       "      <td>CHI</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>Barry Sumpter</td>\n",
       "      <td>1989</td>\n",
       "      <td>PF</td>\n",
       "      <td>23</td>\n",
       "      <td>LAC</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <td>Royce White</td>\n",
       "      <td>2014</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>SAC</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2615</th>\n",
       "      <td>Tyler Lydon</td>\n",
       "      <td>2018</td>\n",
       "      <td>PF</td>\n",
       "      <td>21</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2621</th>\n",
       "      <td>Trey McKinney-Jones</td>\n",
       "      <td>2018</td>\n",
       "      <td>SG</td>\n",
       "      <td>27</td>\n",
       "      <td>IND</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>Andy Panko</td>\n",
       "      <td>2001</td>\n",
       "      <td>SF</td>\n",
       "      <td>23</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>Ibo Kutluay</td>\n",
       "      <td>2005</td>\n",
       "      <td>SG</td>\n",
       "      <td>30</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>Mile Ilic</td>\n",
       "      <td>2007</td>\n",
       "      <td>C</td>\n",
       "      <td>22</td>\n",
       "      <td>NJN</td>\n",
       "      <td>5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>Chris McCray</td>\n",
       "      <td>2007</td>\n",
       "      <td>SG</td>\n",
       "      <td>22</td>\n",
       "      <td>MIL</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Mike Champion</td>\n",
       "      <td>1989</td>\n",
       "      <td>SF</td>\n",
       "      <td>24</td>\n",
       "      <td>SEA</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Earl Jones</td>\n",
       "      <td>1985</td>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>LAL</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>Michael McDonald</td>\n",
       "      <td>1998</td>\n",
       "      <td>C</td>\n",
       "      <td>28</td>\n",
       "      <td>CHH</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>LaMark Baker</td>\n",
       "      <td>1999</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>TOR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2773 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Player  Year Pos  Age   Tm   G    MP    FG   FGA   3P  ...  \\\n",
       "655       David Robinson*  1990   C   24  SAS  82  36.6   8.4  15.9  0.0  ...   \n",
       "848     Shaquille O'Neal*  1993   C   20  ORL  81  37.9   9.0  16.1  0.0  ...   \n",
       "298       Michael Jordan*  1985  SG   21  CHI  82  38.3  10.2  19.8  0.1  ...   \n",
       "302      Hakeem Olajuwon*  1985   C   22  HOU  82  35.5   8.3  15.3  0.0  ...   \n",
       "1161           Tim Duncan  1998  PF   21  SAS  82  39.1   8.6  15.7  0.0  ...   \n",
       "160        Terry Cummings  1983  PF   21  SDC  70  36.2   9.8  18.7  0.0  ...   \n",
       "2041        Blake Griffin  2011  PF   21  LAC  82  38.0   8.5  16.8  0.1  ...   \n",
       "254        Ralph Sampson*  1984   C   23  HOU  82  32.8   8.7  16.7  0.0  ...   \n",
       "845      Alonzo Mourning*  1993   C   22  CHH  78  33.9   7.3  14.3  0.0  ...   \n",
       "1105       Allen Iverson*  1997  PG   21  PHI  76  40.1   8.2  19.8  2.0  ...   \n",
       "2648          Ben Simmons  2018  PG   21  PHI  81  33.7   6.7  12.3  0.0  ...   \n",
       "409            Ron Harper  1987  SG   23  CLE  82  37.4   9.0  19.7  0.2  ...   \n",
       "784      Dikembe Mutombo*  1992   C   25  DEN  71  38.3   6.0  12.2  0.0  ...   \n",
       "937          Chris Webber  1994   C   20  GSW  76  32.1   7.5  13.6  0.0  ...   \n",
       "177         Clark Kellogg  1983  PF   21  IND  81  34.1   8.4  17.5  0.0  ...   \n",
       "774         Larry Johnson  1992  PF   22  CHH  82  37.2   7.5  15.3  0.1  ...   \n",
       "2705          Luka Doncic  2019  SG   19  DAL  72  32.2   7.0  16.5  2.3  ...   \n",
       "958           Grant Hill*  1995  SF   22  DET  70  38.3   7.3  15.2  0.1  ...   \n",
       "344        Patrick Ewing*  1986   C   23  NYK  50  35.4   7.7  16.3  0.0  ...   \n",
       "477          Mark Jackson  1988  PG   22  NYK  82  39.6   5.3  12.4  0.4  ...   \n",
       "1570         LeBron James  2004  SG   19  CLE  79  39.5   7.9  18.9  0.8  ...   \n",
       "1284          Elton Brand  2000  PF   20  CHI  81  37.0   7.8  16.1  0.0  ...   \n",
       "730        Lionel Simmons  1991  SF   22  SAC  79  37.7   6.9  16.5  0.0  ...   \n",
       "2483          Joel Embiid  2017   C   22  PHI  31  25.4   6.5  13.8  1.2  ...   \n",
       "1062     Damon Stoudamire  1996  PG   22  TOR  70  40.9   6.9  16.1  1.9  ...   \n",
       "2454   Karl-Anthony Towns  2016   C   20  MIN  82  32.0   7.6  14.1  0.4  ...   \n",
       "77        Mychal Thompson  1981   C   26  POR  79  35.3   7.2  14.6  0.0  ...   \n",
       "1724           Chris Paul  2006  PG   20  NOK  78  36.0   5.2  12.1  0.6  ...   \n",
       "687       Derrick Coleman  1991  PF   23  NJN  74  35.2   6.9  14.9  0.2  ...   \n",
       "1977         Tyreke Evans  2010  SG   20  SAC  72  37.2   7.4  16.2  0.5  ...   \n",
       "...                   ...   ...  ..  ...  ...  ..   ...   ...   ...  ...  ...   \n",
       "2641         Jacob Pullen  2018  PG   28  PHI   3   2.0   0.3   0.7  0.0  ...   \n",
       "1257           Casey Shaw  1999   C   23  PHI   9   1.6   0.1   0.9  0.0  ...   \n",
       "341           Ron Crevier  1986   C   27  TOT   3   1.3   0.0   1.0  0.0  ...   \n",
       "1924         Nathan Jawai  2009  PF   22  TOR   6   3.2   0.2   0.7  0.0  ...   \n",
       "1644      Pavel Podkolzin  2005   C   20  DAL   5   2.0   0.0   0.0  0.0  ...   \n",
       "951            Yinka Dare  1995   C   22  NJN   1   3.0   0.0   1.0  0.0  ...   \n",
       "1386    Terrance Roberson  2001  SF   24  CHH   3   4.3   0.0   0.7  0.0  ...   \n",
       "2716       Donte Grantham  2019  SF   23  OKC   3   0.7   0.0   0.7  0.0  ...   \n",
       "2080     Hassan Whiteside  2011   C   21  SAC   1   2.0   0.0   0.0  0.0  ...   \n",
       "1235      Jonathan Kerner  1999   C   24  ORL   1   5.0   0.0   1.0  0.0  ...   \n",
       "1595        Slavko Vranes  2004   C   21  POR   1   3.0   0.0   1.0  0.0  ...   \n",
       "1691             Deng Gai  2006   C   23  PHI   2   2.5   0.0   0.0  0.0  ...   \n",
       "1733          Alex Scales  2006  SG   27  SAS   1   0.0   0.0   0.0  0.0  ...   \n",
       "1971        JamesOn Curry  2010  PG   24  LAC   1   0.0   0.0   0.0  0.0  ...   \n",
       "90            Carl Bailey  1982   C   23  POR   1   7.0   1.0   1.0  0.0  ...   \n",
       "2053           Gani Lawal  2011  PF   22  PHO   1   2.0   0.0   0.0  0.0  ...   \n",
       "772         Cedric Hunter  1992  PG   27  CHH   1   1.0   0.0   0.0  0.0  ...   \n",
       "590            David Wood  1989  PF   24  CHI   2   1.0   0.0   0.0  0.0  ...   \n",
       "584         Barry Sumpter  1989  PF   23  LAC   1   1.0   0.0   1.0  0.0  ...   \n",
       "2307          Royce White  2014  PF   22  SAC   3   3.0   0.0   0.3  0.0  ...   \n",
       "2615          Tyler Lydon  2018  PF   21  DEN   1   2.0   0.0   0.0  0.0  ...   \n",
       "2621  Trey McKinney-Jones  2018  SG   27  IND   1   1.0   0.0   0.0  0.0  ...   \n",
       "1378           Andy Panko  2001  SF   23  ATL   1   1.0   0.0   0.0  0.0  ...   \n",
       "1638          Ibo Kutluay  2005  SG   30  SEA   5   2.4   0.0   0.2  0.0  ...   \n",
       "1789            Mile Ilic  2007   C   22  NJN   5   1.2   0.0   0.6  0.0  ...   \n",
       "1799         Chris McCray  2007  SG   22  MIL   5   2.4   0.0   0.6  0.0  ...   \n",
       "522         Mike Champion  1989  SF   24  SEA   2   2.0   0.0   1.5  0.0  ...   \n",
       "296            Earl Jones  1985   C   24  LAL   2   3.5   0.0   0.5  0.0  ...   \n",
       "1180     Michael McDonald  1998   C   28  CHH   1   4.0   0.0   0.0  0.0  ...   \n",
       "1210         LaMark Baker  1999  PG   29  TOR   1   2.0   0.0   1.0  0.0  ...   \n",
       "\n",
       "      BLK  TOV   PF   PTS       3P%       FG%       2P%       FT%     ratio  \\\n",
       "655   3.9  3.1  3.2  24.3  0.000000  0.528302  0.531646  0.735294  0.000000   \n",
       "848   3.5  3.8  4.0  23.4  0.000000  0.559006  0.559006  0.595506  0.000000   \n",
       "298   0.8  3.5  3.5  28.2  0.166667  0.515152  0.526042  0.846154  0.031250   \n",
       "302   2.7  2.9  4.2  20.6  0.000000  0.542484  0.542484  0.611940  0.000000   \n",
       "1161  2.5  3.4  3.1  21.1  0.000000  0.547771  0.551282  0.661017  0.006410   \n",
       "160   0.9  2.9  4.2  23.7  0.000000  0.524064  0.524064  0.711864  0.000000   \n",
       "2041  0.5  2.7  3.1  22.5  0.333333  0.505952  0.509091  0.635294  0.018182   \n",
       "254   2.4  3.6  4.1  21.0  0.000000  0.520958  0.524096  0.660377  0.000000   \n",
       "845   3.5  3.0  3.7  21.0  0.000000  0.510490  0.510490  0.777778  0.000000   \n",
       "1105  0.3  4.4  3.1  23.5  0.333333  0.414141  0.449275  0.694444  0.434783   \n",
       "2648  0.9  3.4  2.6  15.8  0.000000  0.544715  0.549180  0.571429  0.008197   \n",
       "409   1.0  4.2  3.0  22.9  0.181818  0.456853  0.470270  0.681159  0.059459   \n",
       "784   3.0  3.5  3.8  16.6  0.000000  0.491803  0.491803  0.642857  0.000000   \n",
       "937   2.2  2.7  3.3  17.5  0.000000  0.551471  0.555556  0.531915  0.014815   \n",
       "177   0.5  2.7  3.7  20.1  0.000000  0.480000  0.479769  0.744186  0.011561   \n",
       "774   0.6  2.0  2.7  19.2  0.333333  0.490196  0.496689  0.820000  0.019868   \n",
       "2705  0.3  3.4  1.9  21.2  0.323944  0.424242  0.505376  0.716418  0.763441   \n",
       "958   0.9  2.9  2.9  19.9  0.250000  0.480263  0.486486  0.726027  0.027027   \n",
       "344   2.1  3.4  3.8  20.0  0.000000  0.472393  0.475309  0.737705  0.006173   \n",
       "477   0.1  3.1  3.0  13.6  0.266667  0.427419  0.462963  0.781250  0.138889   \n",
       "1570  0.7  3.5  1.9  20.9  0.296296  0.417989  0.440994  0.758621  0.167702   \n",
       "1284  1.6  2.8  3.2  20.1  0.000000  0.484472  0.484472  0.681818  0.000000   \n",
       "730   1.1  2.9  3.2  18.0  0.000000  0.418182  0.423313  0.745455  0.006135   \n",
       "2483  2.5  3.8  3.6  20.2  0.375000  0.471014  0.495327  0.784810  0.299065   \n",
       "1062  0.3  3.8  2.4  19.0  0.395833  0.428571  0.442478  0.809524  0.424779   \n",
       "2454  1.7  2.2  3.0  18.3  0.363636  0.539007  0.561538  0.794118  0.084615   \n",
       "77    2.2  3.1  3.3  17.0  0.000000  0.493151  0.493151  0.634146  0.000000   \n",
       "1724  0.1  2.3  2.8  16.1  0.260870  0.429752  0.464646  0.850000  0.232323   \n",
       "687   1.3  2.9  2.9  18.4  0.400000  0.463087  0.472222  0.733333  0.034722   \n",
       "1977  0.4  3.0  2.8  20.1  0.250000  0.456790  0.485915  0.738462  0.140845   \n",
       "...   ...  ...  ...   ...       ...       ...       ...       ...       ...   \n",
       "2641  0.0  0.3  0.0   0.7  0.000000  0.428571  1.000000  0.000000  1.000000   \n",
       "1257  0.0  0.2  0.2   0.2  0.000000  0.111111  0.111111  0.000000  0.000000   \n",
       "341   0.0  0.0  0.7   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1924  0.0  0.3  0.5   0.3  0.000000  0.285714  0.285714  0.000000  0.000000   \n",
       "1644  0.0  0.4  0.8   0.2  0.000000  0.000000  0.000000  0.500000       NaN   \n",
       "951   0.0  1.0  2.0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1386  0.0  0.7  1.0   0.0  0.000000  0.000000  0.000000  0.000000  1.000000   \n",
       "2716  0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       inf   \n",
       "2080  0.0  0.0  2.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "1235  0.0  0.0  2.0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1595  0.0  0.0  1.0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1691  0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "1733  0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "1971  0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "90    0.0  2.0  2.0   2.0  0.000000  1.000000  1.000000  0.000000  0.000000   \n",
       "2053  0.0  0.0  1.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "772   0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "590   0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "584   0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2307  0.0  0.0  0.7   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2615  0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "2621  0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "1378  0.0  0.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "1638  0.0  0.4  0.2   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1789  0.0  0.6  0.2   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1799  0.0  0.4  0.2   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "522   0.0  0.5  1.0   0.0  0.000000  0.000000  0.000000  0.000000  0.500000   \n",
       "296   0.0  0.5  0.0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1180  0.0  2.0  1.0   0.0  0.000000  0.000000  0.000000  0.000000       NaN   \n",
       "1210  0.0  1.0  0.0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "      Value  \n",
       "655   55.40  \n",
       "848   51.73  \n",
       "298   50.95  \n",
       "302   45.78  \n",
       "1161  45.63  \n",
       "160   45.37  \n",
       "2041  43.92  \n",
       "254   43.62  \n",
       "845   43.26  \n",
       "1105  42.47  \n",
       "2648  42.22  \n",
       "409   42.16  \n",
       "784   41.96  \n",
       "937   41.32  \n",
       "177   40.92  \n",
       "774   40.60  \n",
       "2705  40.36  \n",
       "958   40.28  \n",
       "344   40.00  \n",
       "477   39.96  \n",
       "1570  39.75  \n",
       "1284  39.35  \n",
       "730   39.16  \n",
       "2483  39.11  \n",
       "1062  39.05  \n",
       "2454  38.90  \n",
       "77    38.74  \n",
       "1724  38.52  \n",
       "687   38.06  \n",
       "1977  37.86  \n",
       "...     ...  \n",
       "2641   0.40  \n",
       "1257   0.36  \n",
       "341    0.36  \n",
       "1924   0.36  \n",
       "1644   0.28  \n",
       "951    0.20  \n",
       "1386   0.11  \n",
       "2716   0.00  \n",
       "2080   0.00  \n",
       "1235   0.00  \n",
       "1595   0.00  \n",
       "1691   0.00  \n",
       "1733   0.00  \n",
       "1971   0.00  \n",
       "90     0.00  \n",
       "2053   0.00  \n",
       "772    0.00  \n",
       "590    0.00  \n",
       "584    0.00  \n",
       "2307   0.00  \n",
       "2615   0.00  \n",
       "2621   0.00  \n",
       "1378   0.00  \n",
       "1638  -0.16  \n",
       "1789  -0.36  \n",
       "1799  -0.40  \n",
       "522   -0.50  \n",
       "296   -0.50  \n",
       "1180  -0.80  \n",
       "1210  -1.00  \n",
       "\n",
       "[2773 rows x 30 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values('Value', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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