Doing things in Python that you would normally do in Excel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "* Primary objective is to perform several different operations in Python that you might normally do in excel.\n",
    "\n",
    "\n",
    "**Operations Covered:**\n",
    "\n",
    "1) Explore features of the data set\n",
    "\n",
    "2) Sort columns\n",
    "\n",
    "3) Filter columns\n",
    "\n",
    "4) Creating new features\n",
    "\n",
    "5) Calculating summary statistics\n",
    "\n",
    "6) Pivot tables\n",
    "\n",
    "7) Data Visualization\n",
    "\n",
    "**Data comes from the `pybaseball` library:** https://pypi.org/project/pybaseball/\n",
    "\n",
    "The original blog post can be found here: http://optimumsportsperformance.com/blog/doing-things-in-python-that-you-would-normally-do-in-excel/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Libraries & Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pybaseball import pitching_stats\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "pitchers = pitching_stats(2012, 2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDfg</th>\n",
       "      <th>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "      <th>G</th>\n",
       "      <th>...</th>\n",
       "      <th>LA</th>\n",
       "      <th>Barrels</th>\n",
       "      <th>Barrel%</th>\n",
       "      <th>maxEV</th>\n",
       "      <th>HardHit</th>\n",
       "      <th>HardHit%</th>\n",
       "      <th>Events</th>\n",
       "      <th>CStr%</th>\n",
       "      <th>CSW%</th>\n",
       "      <th>xERA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2036</td>\n",
       "      <td>2015</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
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       "      <td>8.9</td>\n",
       "      <td>15</td>\n",
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       "      <td>113.3</td>\n",
       "      <td>132</td>\n",
       "      <td>0.244</td>\n",
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       "      <td>0.164</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2036</td>\n",
       "      <td>2014</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.77</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2429</td>\n",
       "      <td>2014</td>\n",
       "      <td>Corey Kluber</td>\n",
       "      <td>CLE</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.44</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.323</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2036</td>\n",
       "      <td>2013</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.83</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4153</td>\n",
       "      <td>2015</td>\n",
       "      <td>Jake Arrieta</td>\n",
       "      <td>CHC</td>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.022</td>\n",
       "      <td>111.5</td>\n",
       "      <td>143</td>\n",
       "      <td>0.247</td>\n",
       "      <td>580</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.287</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8700</td>\n",
       "      <td>2012</td>\n",
       "      <td>Justin Verlander</td>\n",
       "      <td>DET</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.64</td>\n",
       "      <td>33</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11713</td>\n",
       "      <td>2013</td>\n",
       "      <td>Matt Harvey</td>\n",
       "      <td>NYM</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6.9</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3184</td>\n",
       "      <td>2015</td>\n",
       "      <td>David Price</td>\n",
       "      <td>- - -</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.45</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>12.7</td>\n",
       "      <td>27</td>\n",
       "      <td>0.044</td>\n",
       "      <td>114.7</td>\n",
       "      <td>187</td>\n",
       "      <td>0.305</td>\n",
       "      <td>613</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.294</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>4772</td>\n",
       "      <td>2012</td>\n",
       "      <td>Felix Hernandez</td>\n",
       "      <td>SEA</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.06</td>\n",
       "      <td>33</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.177</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3137</td>\n",
       "      <td>2015</td>\n",
       "      <td>Max Scherzer</td>\n",
       "      <td>WSN</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.79</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>17.1</td>\n",
       "      <td>30</td>\n",
       "      <td>0.051</td>\n",
       "      <td>114.7</td>\n",
       "      <td>173</td>\n",
       "      <td>0.296</td>\n",
       "      <td>584</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.310</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IDfg  Season              Name   Team  Age   W   L  WAR   ERA   G  ...  \\\n",
       "4    2036    2015   Clayton Kershaw    LAD   27  16   7  8.6  2.13  33  ...   \n",
       "2    2036    2014   Clayton Kershaw    LAD   26  21   3  7.9  1.77  27  ...   \n",
       "14   2429    2014      Corey Kluber    CLE   28  18   9  7.2  2.44  34  ...   \n",
       "3    2036    2013   Clayton Kershaw    LAD   25  16   9  7.2  1.83  33  ...   \n",
       "1    4153    2015      Jake Arrieta    CHC   29  22   6  7.0  1.77  33  ...   \n",
       "27   8700    2012  Justin Verlander    DET   29  17   8  6.9  2.64  33  ...   \n",
       "10  11713    2013       Matt Harvey    NYM   24   9   5  6.9  2.27  26  ...   \n",
       "15   3184    2015       David Price  - - -   29  18   5  6.7  2.45  32  ...   \n",
       "77   4772    2012   Felix Hernandez    SEA   26  13   9  6.6  3.06  33  ...   \n",
       "44   3137    2015      Max Scherzer    WSN   30  14  12  6.5  2.79  33  ...   \n",
       "\n",
       "      LA  Barrels  Barrel%  maxEV  HardHit  HardHit%  Events  CStr%   CSW%  \\\n",
       "4    8.9       15    0.028  113.3      132     0.244     542  0.164  0.323   \n",
       "2    NaN        0      NaN    NaN        0       NaN       0  0.161  0.303   \n",
       "14   NaN        0      NaN    NaN        0       NaN       0  0.203  0.323   \n",
       "3    NaN        0      NaN    NaN        0       NaN       0  0.178  0.293   \n",
       "1    4.4       13    0.022  111.5      143     0.247     580  0.177  0.287   \n",
       "27   NaN        0      NaN    NaN        0       NaN       0  0.167  0.285   \n",
       "10   NaN        0      NaN    NaN        0       NaN       0  0.160  0.286   \n",
       "15  12.7       27    0.044  114.7      187     0.305     613  0.175  0.294   \n",
       "77   NaN        0      NaN    NaN        0       NaN       0  0.177  0.285   \n",
       "44  17.1       30    0.051  114.7      173     0.296     584  0.157  0.310   \n",
       "\n",
       "    xERA  \n",
       "4    NaN  \n",
       "2    NaN  \n",
       "14   NaN  \n",
       "3    NaN  \n",
       "1    NaN  \n",
       "27   NaN  \n",
       "10   NaN  \n",
       "15   NaN  \n",
       "77   NaN  \n",
       "44   NaN  \n",
       "\n",
       "[10 rows x 334 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pitchers.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['IDfg', 'Season', 'Name', 'Team', 'Age', 'W', 'L', 'WAR', 'ERA', 'G',\n",
       "       ...\n",
       "       'LA', 'Barrels', 'Barrel%', 'maxEV', 'HardHit', 'HardHit%', 'Events',\n",
       "       'CStr%', 'CSW%', 'xERA'],\n",
       "      dtype='object', length=334)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pitchers.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice that `pandas` doesn't show us all 334 column names. We can see them with the following code.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['IDfg',\n",
       " 'Season',\n",
       " 'Name',\n",
       " 'Team',\n",
       " 'Age',\n",
       " 'W',\n",
       " 'L',\n",
       " 'WAR',\n",
       " 'ERA',\n",
       " 'G',\n",
       " 'GS',\n",
       " 'CG',\n",
       " 'ShO',\n",
       " 'SV',\n",
       " 'BS',\n",
       " 'IP',\n",
       " 'TBF',\n",
       " 'H',\n",
       " 'R',\n",
       " 'ER',\n",
       " 'HR',\n",
       " 'BB',\n",
       " 'IBB',\n",
       " 'HBP',\n",
       " 'WP',\n",
       " 'BK',\n",
       " 'SO',\n",
       " 'GB',\n",
       " 'FB',\n",
       " 'LD',\n",
       " 'IFFB',\n",
       " 'Balls',\n",
       " 'Strikes',\n",
       " 'Pitches',\n",
       " 'RS',\n",
       " 'IFH',\n",
       " 'BU',\n",
       " 'BUH',\n",
       " 'K/9',\n",
       " 'BB/9',\n",
       " 'K/BB',\n",
       " 'H/9',\n",
       " 'HR/9',\n",
       " 'AVG',\n",
       " 'WHIP',\n",
       " 'BABIP',\n",
       " 'LOB%',\n",
       " 'FIP',\n",
       " 'GB/FB',\n",
       " 'LD%',\n",
       " 'GB%',\n",
       " 'FB%',\n",
       " 'IFFB%',\n",
       " 'HR/FB',\n",
       " 'IFH%',\n",
       " 'BUH%',\n",
       " 'Starting',\n",
       " 'Start-IP',\n",
       " 'Relieving',\n",
       " 'Relief-IP',\n",
       " 'RAR',\n",
       " 'Dollars',\n",
       " 'tERA',\n",
       " 'xFIP',\n",
       " 'WPA',\n",
       " '-WPA',\n",
       " '+WPA',\n",
       " 'RE24',\n",
       " 'REW',\n",
       " 'pLI',\n",
       " 'inLI',\n",
       " 'gmLI',\n",
       " 'exLI',\n",
       " 'Pulls',\n",
       " 'WPA/LI',\n",
       " 'Clutch',\n",
       " 'FB% 2',\n",
       " 'FBv',\n",
       " 'SL%',\n",
       " 'SLv',\n",
       " 'CT%',\n",
       " 'CTv',\n",
       " 'CB%',\n",
       " 'CBv',\n",
       " 'CH%',\n",
       " 'CHv',\n",
       " 'SF%',\n",
       " 'SFv',\n",
       " 'KN%',\n",
       " 'KNv',\n",
       " 'XX%',\n",
       " 'PO%',\n",
       " 'wFB',\n",
       " 'wSL',\n",
       " 'wCT',\n",
       " 'wCB',\n",
       " 'wCH',\n",
       " 'wSF',\n",
       " 'wKN',\n",
       " 'wFB/C',\n",
       " 'wSL/C',\n",
       " 'wCT/C',\n",
       " 'wCB/C',\n",
       " 'wCH/C',\n",
       " 'wSF/C',\n",
       " 'wKN/C',\n",
       " 'O-Swing%',\n",
       " 'Z-Swing%',\n",
       " 'Swing%',\n",
       " 'O-Contact%',\n",
       " 'Z-Contact%',\n",
       " 'Contact%',\n",
       " 'Zone%',\n",
       " 'F-Strike%',\n",
       " 'SwStr%',\n",
       " 'HLD',\n",
       " 'SD',\n",
       " 'MD',\n",
       " 'ERA-',\n",
       " 'FIP-',\n",
       " 'xFIP-',\n",
       " 'K%',\n",
       " 'BB%',\n",
       " 'SIERA',\n",
       " 'RS/9',\n",
       " 'E-F',\n",
       " 'FA% (sc)',\n",
       " 'FT% (sc)',\n",
       " 'FC% (sc)',\n",
       " 'FS% (sc)',\n",
       " 'FO% (sc)',\n",
       " 'SI% (sc)',\n",
       " 'SL% (sc)',\n",
       " 'CU% (sc)',\n",
       " 'KC% (sc)',\n",
       " 'EP% (sc)',\n",
       " 'CH% (sc)',\n",
       " 'SC% (sc)',\n",
       " 'KN% (sc)',\n",
       " 'UN% (sc)',\n",
       " 'vFA (sc)',\n",
       " 'vFT (sc)',\n",
       " 'vFC (sc)',\n",
       " 'vFS (sc)',\n",
       " 'vFO (sc)',\n",
       " 'vSI (sc)',\n",
       " 'vSL (sc)',\n",
       " 'vCU (sc)',\n",
       " 'vKC (sc)',\n",
       " 'vEP (sc)',\n",
       " 'vCH (sc)',\n",
       " 'vSC (sc)',\n",
       " 'vKN (sc)',\n",
       " 'FA-X (sc)',\n",
       " 'FT-X (sc)',\n",
       " 'FC-X (sc)',\n",
       " 'FS-X (sc)',\n",
       " 'FO-X (sc)',\n",
       " 'SI-X (sc)',\n",
       " 'SL-X (sc)',\n",
       " 'CU-X (sc)',\n",
       " 'KC-X (sc)',\n",
       " 'EP-X (sc)',\n",
       " 'CH-X (sc)',\n",
       " 'SC-X (sc)',\n",
       " 'KN-X (sc)',\n",
       " 'FA-Z (sc)',\n",
       " 'FT-Z (sc)',\n",
       " 'FC-Z (sc)',\n",
       " 'FS-Z (sc)',\n",
       " 'FO-Z (sc)',\n",
       " 'SI-Z (sc)',\n",
       " 'SL-Z (sc)',\n",
       " 'CU-Z (sc)',\n",
       " 'KC-Z (sc)',\n",
       " 'EP-Z (sc)',\n",
       " 'CH-Z (sc)',\n",
       " 'SC-Z (sc)',\n",
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       " 'wFA (sc)',\n",
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       " 'wFO (sc)',\n",
       " 'wSI (sc)',\n",
       " 'wSL (sc)',\n",
       " 'wCU (sc)',\n",
       " 'wKC (sc)',\n",
       " 'wEP (sc)',\n",
       " 'wCH (sc)',\n",
       " 'wSC (sc)',\n",
       " 'wKN (sc)',\n",
       " 'wFA/C (sc)',\n",
       " 'wFT/C (sc)',\n",
       " 'wFC/C (sc)',\n",
       " 'wFS/C (sc)',\n",
       " 'wFO/C (sc)',\n",
       " 'wSI/C (sc)',\n",
       " 'wSL/C (sc)',\n",
       " 'wCU/C (sc)',\n",
       " 'wKC/C (sc)',\n",
       " 'wEP/C (sc)',\n",
       " 'wCH/C (sc)',\n",
       " 'wSC/C (sc)',\n",
       " 'wKN/C (sc)',\n",
       " 'O-Swing% (sc)',\n",
       " 'Z-Swing% (sc)',\n",
       " 'Swing% (sc)',\n",
       " 'O-Contact% (sc)',\n",
       " 'Z-Contact% (sc)',\n",
       " 'Contact% (sc)',\n",
       " 'Zone% (sc)',\n",
       " 'Pace',\n",
       " 'RA9-WAR',\n",
       " 'BIP-Wins',\n",
       " 'LOB-Wins',\n",
       " 'FDP-Wins',\n",
       " 'Age Rng',\n",
       " 'K-BB%',\n",
       " 'Pull%',\n",
       " 'Cent%',\n",
       " 'Oppo%',\n",
       " 'Soft%',\n",
       " 'Med%',\n",
       " 'Hard%',\n",
       " 'kwERA',\n",
       " 'TTO%',\n",
       " 'CH% (pi)',\n",
       " 'CS% (pi)',\n",
       " 'CU% (pi)',\n",
       " 'FA% (pi)',\n",
       " 'FC% (pi)',\n",
       " 'FS% (pi)',\n",
       " 'KN% (pi)',\n",
       " 'SB% (pi)',\n",
       " 'SI% (pi)',\n",
       " 'SL% (pi)',\n",
       " 'XX% (pi)',\n",
       " 'vCH (pi)',\n",
       " 'vCS (pi)',\n",
       " 'vCU (pi)',\n",
       " 'vFA (pi)',\n",
       " 'vFC (pi)',\n",
       " 'vFS (pi)',\n",
       " 'vKN (pi)',\n",
       " 'vSB (pi)',\n",
       " 'vSI (pi)',\n",
       " 'vSL (pi)',\n",
       " 'vXX (pi)',\n",
       " 'CH-X (pi)',\n",
       " 'CS-X (pi)',\n",
       " 'CU-X (pi)',\n",
       " 'FA-X (pi)',\n",
       " 'FC-X (pi)',\n",
       " 'FS-X (pi)',\n",
       " 'KN-X (pi)',\n",
       " 'SB-X (pi)',\n",
       " 'SI-X (pi)',\n",
       " 'SL-X (pi)',\n",
       " 'XX-X (pi)',\n",
       " 'CH-Z (pi)',\n",
       " 'CS-Z (pi)',\n",
       " 'CU-Z (pi)',\n",
       " 'FA-Z (pi)',\n",
       " 'FC-Z (pi)',\n",
       " 'FS-Z (pi)',\n",
       " 'KN-Z (pi)',\n",
       " 'SB-Z (pi)',\n",
       " 'SI-Z (pi)',\n",
       " 'SL-Z (pi)',\n",
       " 'XX-Z (pi)',\n",
       " 'wCH (pi)',\n",
       " 'wCS (pi)',\n",
       " 'wCU (pi)',\n",
       " 'wFA (pi)',\n",
       " 'wFC (pi)',\n",
       " 'wFS (pi)',\n",
       " 'wKN (pi)',\n",
       " 'wSB (pi)',\n",
       " 'wSI (pi)',\n",
       " 'wSL (pi)',\n",
       " 'wXX (pi)',\n",
       " 'wCH/C (pi)',\n",
       " 'wCS/C (pi)',\n",
       " 'wCU/C (pi)',\n",
       " 'wFA/C (pi)',\n",
       " 'wFC/C (pi)',\n",
       " 'wFS/C (pi)',\n",
       " 'wKN/C (pi)',\n",
       " 'wSB/C (pi)',\n",
       " 'wSI/C (pi)',\n",
       " 'wSL/C (pi)',\n",
       " 'wXX/C (pi)',\n",
       " 'O-Swing% (pi)',\n",
       " 'Z-Swing% (pi)',\n",
       " 'Swing% (pi)',\n",
       " 'O-Contact% (pi)',\n",
       " 'Z-Contact% (pi)',\n",
       " 'Contact% (pi)',\n",
       " 'Zone% (pi)',\n",
       " 'Pace (pi)',\n",
       " 'FRM',\n",
       " 'K/9+',\n",
       " 'BB/9+',\n",
       " 'K/BB+',\n",
       " 'H/9+',\n",
       " 'HR/9+',\n",
       " 'AVG+',\n",
       " 'WHIP+',\n",
       " 'BABIP+',\n",
       " 'LOB%+',\n",
       " 'K%+',\n",
       " 'BB%+',\n",
       " 'LD%+',\n",
       " 'GB%+',\n",
       " 'FB%+',\n",
       " 'HR/FB%+',\n",
       " 'Pull%+',\n",
       " 'Cent%+',\n",
       " 'Oppo%+',\n",
       " 'Soft%+',\n",
       " 'Med%+',\n",
       " 'Hard%+',\n",
       " 'EV',\n",
       " 'LA',\n",
       " 'Barrels',\n",
       " 'Barrel%',\n",
       " 'maxEV',\n",
       " 'HardHit',\n",
       " 'HardHit%',\n",
       " 'Events',\n",
       " 'CStr%',\n",
       " 'CSW%',\n",
       " 'xERA']"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pitchers.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Explore the Features of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Look at the type of data frame\n",
    "\n",
    "type(pitchers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 408 entries, 4 to 407\n",
      "Columns: 334 entries, IDfg to xERA\n",
      "dtypes: float64(266), int64(64), object(4)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "## General info about the columns in the data frame\n",
    "\n",
    "pitchers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(408, 334)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Number of rows and columns\n",
    "\n",
    "pitchers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IDfg        False\n",
       "Season      False\n",
       "Name        False\n",
       "Team        False\n",
       "Age         False\n",
       "            ...  \n",
       "HardHit%     True\n",
       "Events      False\n",
       "CStr%       False\n",
       "CSW%        False\n",
       "xERA         True\n",
       "Length: 334, dtype: bool"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find if there are NAs in any columns\n",
    "\n",
    "pitchers.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IDfg          0\n",
       "Season        0\n",
       "Name          0\n",
       "Team          0\n",
       "Age           0\n",
       "           ... \n",
       "HardHit%    257\n",
       "Events        0\n",
       "CStr%         0\n",
       "CSW%          0\n",
       "xERA        408\n",
       "Length: 334, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many NAs in each column?\n",
    "\n",
    "len(pitchers) - pitchers.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Sort Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>IDfg</th>\n",
       "      <th>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "      <th>G</th>\n",
       "      <th>...</th>\n",
       "      <th>LA</th>\n",
       "      <th>Barrels</th>\n",
       "      <th>Barrel%</th>\n",
       "      <th>maxEV</th>\n",
       "      <th>HardHit</th>\n",
       "      <th>HardHit%</th>\n",
       "      <th>Events</th>\n",
       "      <th>CStr%</th>\n",
       "      <th>CSW%</th>\n",
       "      <th>xERA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>512</td>\n",
       "      <td>2014</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.59</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.270</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>512</td>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.307</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>512</td>\n",
       "      <td>2015</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.18</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7</td>\n",
       "      <td>20</td>\n",
       "      <td>0.04</td>\n",
       "      <td>113.0</td>\n",
       "      <td>194</td>\n",
       "      <td>0.391</td>\n",
       "      <td>496</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>512</td>\n",
       "      <td>2012</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.51</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.283</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>11132</td>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Griffin</td>\n",
       "      <td>OAK</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</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>26</th>\n",
       "      <td>1943</td>\n",
       "      <td>2013</td>\n",
       "      <td>Zack Greinke</td>\n",
       "      <td>LAD</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.63</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.264</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1943</td>\n",
       "      <td>2015</td>\n",
       "      <td>Zack Greinke</td>\n",
       "      <td>LAD</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.66</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>9.8</td>\n",
       "      <td>24</td>\n",
       "      <td>0.04</td>\n",
       "      <td>113.9</td>\n",
       "      <td>165</td>\n",
       "      <td>0.276</td>\n",
       "      <td>598</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1943</td>\n",
       "      <td>2012</td>\n",
       "      <td>Zack Greinke</td>\n",
       "      <td>- - -</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.48</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.284</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1943</td>\n",
       "      <td>2014</td>\n",
       "      <td>Zack Greinke</td>\n",
       "      <td>LAD</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.71</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.288</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>10310</td>\n",
       "      <td>2014</td>\n",
       "      <td>Zack Wheeler</td>\n",
       "      <td>NYM</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.54</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.269</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IDfg  Season          Name   Team  Age   W   L  WAR   ERA   G  ...   LA  \\\n",
       "352    512    2014  A.J. Burnett    PHI   37   8  18  1.7  4.59  34  ...  NaN   \n",
       "109    512    2013  A.J. Burnett    PIT   36  10  11  4.0  3.30  30  ...  NaN   \n",
       "95     512    2015  A.J. Burnett    PIT   38   9   7  2.6  3.18  26  ...  5.7   \n",
       "168    512    2012  A.J. Burnett    PIT   35  16  10  3.5  3.51  31  ...  NaN   \n",
       "248  11132    2013  A.J. Griffin    OAK   25  14  10  1.5  3.82  32  ...  NaN   \n",
       "..     ...     ...           ...    ...  ...  ..  ..  ...   ...  ..  ...  ...   \n",
       "26    1943    2013  Zack Greinke    LAD   29  15   4  3.4  2.63  28  ...  NaN   \n",
       "0     1943    2015  Zack Greinke    LAD   31  19   3  5.3  1.66  32  ...  9.8   \n",
       "158   1943    2012  Zack Greinke  - - -   28  15   5  4.8  3.48  34  ...  NaN   \n",
       "33    1943    2014  Zack Greinke    LAD   30  17   8  4.5  2.71  32  ...  NaN   \n",
       "179  10310    2014  Zack Wheeler    NYM   24  11  11  2.4  3.54  32  ...  NaN   \n",
       "\n",
       "     Barrels  Barrel%  maxEV  HardHit  HardHit%  Events  CStr%   CSW%  xERA  \n",
       "352        0      NaN    NaN        0       NaN       0  0.185  0.270   NaN  \n",
       "109        0      NaN    NaN        0       NaN       0  0.200  0.307   NaN  \n",
       "95        20     0.04  113.0      194     0.391     496  0.193  0.282   NaN  \n",
       "168        0      NaN    NaN        0       NaN       0  0.187  0.283   NaN  \n",
       "248        0      NaN    NaN        0       NaN       0  0.196  0.282   NaN  \n",
       "..       ...      ...    ...      ...       ...     ...    ...    ...   ...  \n",
       "26         0      NaN    NaN        0       NaN       0  0.159  0.264   NaN  \n",
       "0         24     0.04  113.9      165     0.276     598  0.161  0.281   NaN  \n",
       "158        0      NaN    NaN        0       NaN       0  0.197  0.284   NaN  \n",
       "33         0      NaN    NaN        0       NaN       0  0.172  0.288   NaN  \n",
       "179        0      NaN    NaN        0       NaN       0  0.170  0.269   NaN  \n",
       "\n",
       "[408 rows x 334 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sort the data by pitcher name\n",
    "\n",
    "pitchers.sort_values(by='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDfg</th>\n",
       "      <th>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
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       "      <th>Barrels</th>\n",
       "      <th>Barrel%</th>\n",
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       "      <th>HardHit</th>\n",
       "      <th>HardHit%</th>\n",
       "      <th>Events</th>\n",
       "      <th>CStr%</th>\n",
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       "      <th>xERA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>5551</td>\n",
       "      <td>2012</td>\n",
       "      <td>Matt Harrison</td>\n",
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       "      <th>21</th>\n",
       "      <td>3184</td>\n",
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       "      <td>David Price</td>\n",
       "      <td>TBR</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
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       "      <td>8678</td>\n",
       "      <td>2012</td>\n",
       "      <td>Paul Maholm</td>\n",
       "      <td>- - -</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.273</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>3551</td>\n",
       "      <td>2012</td>\n",
       "      <td>Clayton Richard</td>\n",
       "      <td>SDP</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.99</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.246</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2717</td>\n",
       "      <td>2012</td>\n",
       "      <td>Rick Porcello</td>\n",
       "      <td>DET</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.59</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.250</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7410</td>\n",
       "      <td>2016</td>\n",
       "      <td>J.A. Happ</td>\n",
       "      <td>TOR</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.18</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>12.2</td>\n",
       "      <td>37</td>\n",
       "      <td>0.065</td>\n",
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       "      <td>0.372</td>\n",
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       "      <td>0.162</td>\n",
       "      <td>0.258</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>6345</td>\n",
       "      <td>2016</td>\n",
       "      <td>Chris Archer</td>\n",
       "      <td>TBR</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.02</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>42</td>\n",
       "      <td>0.077</td>\n",
       "      <td>112.5</td>\n",
       "      <td>220</td>\n",
       "      <td>0.402</td>\n",
       "      <td>547</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.296</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1507</td>\n",
       "      <td>2016</td>\n",
       "      <td>John Lackey</td>\n",
       "      <td>CHC</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.35</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>14.4</td>\n",
       "      <td>27</td>\n",
       "      <td>0.053</td>\n",
       "      <td>111.9</td>\n",
       "      <td>191</td>\n",
       "      <td>0.377</td>\n",
       "      <td>506</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.280</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>18498</td>\n",
       "      <td>2016</td>\n",
       "      <td>Kenta Maeda</td>\n",
       "      <td>LAD</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>12.7</td>\n",
       "      <td>25</td>\n",
       "      <td>0.052</td>\n",
       "      <td>111.2</td>\n",
       "      <td>121</td>\n",
       "      <td>0.253</td>\n",
       "      <td>479</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.301</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>7059</td>\n",
       "      <td>2016</td>\n",
       "      <td>James Shields</td>\n",
       "      <td>- - -</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.85</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>12.8</td>\n",
       "      <td>46</td>\n",
       "      <td>0.077</td>\n",
       "      <td>115.8</td>\n",
       "      <td>198</td>\n",
       "      <td>0.332</td>\n",
       "      <td>597</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.245</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IDfg  Season             Name   Team  Age   W   L  WAR   ERA   G  ...  \\\n",
       "108   5551    2012    Matt Harrison    TEX   26  18  11  3.5  3.29  32  ...   \n",
       "21    3184    2012      David Price    TBR   26  20   5  4.3  2.56  31  ...   \n",
       "205   8678    2012      Paul Maholm  - - -   30  13  11  1.7  3.67  32  ...   \n",
       "273   3551    2012  Clayton Richard    SDP   28  14  14  0.4  3.99  33  ...   \n",
       "353   2717    2012    Rick Porcello    DET   23  10  12  3.0  4.59  31  ...   \n",
       "..     ...     ...              ...    ...  ...  ..  ..  ...   ...  ..  ...   \n",
       "96    7410    2016        J.A. Happ    TOR   33  20   4  2.8  3.18  32  ...   \n",
       "279   6345    2016     Chris Archer    TBR   27   9  19  2.9  4.02  33  ...   \n",
       "123   1507    2016      John Lackey    CHC   37  11   8  2.9  3.35  29  ...   \n",
       "161  18498    2016      Kenta Maeda    LAD   28  16  11  2.9  3.48  32  ...   \n",
       "407   7059    2016    James Shields  - - -   34   6  19 -1.0  5.85  33  ...   \n",
       "\n",
       "       LA  Barrels  Barrel%  maxEV  HardHit  HardHit%  Events  CStr%   CSW%  \\\n",
       "108   NaN        0      NaN    NaN        0       NaN       0  0.191  0.270   \n",
       "21    NaN        0      NaN    NaN        0       NaN       0  0.209  0.294   \n",
       "205   NaN        0      NaN    NaN        0       NaN       0  0.198  0.273   \n",
       "273   NaN        0      NaN    NaN        0       NaN       0  0.183  0.246   \n",
       "353   NaN        0      NaN    NaN        0       NaN       0  0.174  0.250   \n",
       "..    ...      ...      ...    ...      ...       ...     ...    ...    ...   \n",
       "96   12.2       37    0.065  113.6      211     0.372     567  0.162  0.258   \n",
       "279  10.3       42    0.077  112.5      220     0.402     547  0.174  0.296   \n",
       "123  14.4       27    0.053  111.9      191     0.377     506  0.166  0.280   \n",
       "161  12.7       25    0.052  111.2      121     0.253     479  0.185  0.301   \n",
       "407  12.8       46    0.077  115.8      198     0.332     597  0.153  0.245   \n",
       "\n",
       "     xERA  \n",
       "108   NaN  \n",
       "21    NaN  \n",
       "205   NaN  \n",
       "273   NaN  \n",
       "353   NaN  \n",
       "..    ...  \n",
       "96    NaN  \n",
       "279   NaN  \n",
       "123   NaN  \n",
       "161   NaN  \n",
       "407   NaN  \n",
       "\n",
       "[408 rows x 334 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sort the data by season\n",
    "\n",
    "pitchers.sort_values(by = 'Season')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>IDfg</th>\n",
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       "      <th>0</th>\n",
       "      <td>1943</td>\n",
       "      <td>2015</td>\n",
       "      <td>Zack Greinke</td>\n",
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       "      <td>Jake Arrieta</td>\n",
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       "      <td>143</td>\n",
       "      <td>0.247</td>\n",
       "      <td>580</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2015</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.13</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>8.9</td>\n",
       "      <td>15</td>\n",
       "      <td>0.028</td>\n",
       "      <td>113.3</td>\n",
       "      <td>132</td>\n",
       "      <td>0.244</td>\n",
       "      <td>542</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.323</td>\n",
       "      <td>NaN</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>403</th>\n",
       "      <td>3374</td>\n",
       "      <td>2012</td>\n",
       "      <td>Ubaldo Jimenez</td>\n",
       "      <td>CLE</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5.40</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>3990</td>\n",
       "      <td>2013</td>\n",
       "      <td>Edinson Volquez</td>\n",
       "      <td>- - -</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.71</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.267</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>6943</td>\n",
       "      <td>2012</td>\n",
       "      <td>Luke Hochevar</td>\n",
       "      <td>KCR</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.73</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.267</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>3403</td>\n",
       "      <td>2012</td>\n",
       "      <td>Ricky Romero</td>\n",
       "      <td>TOR</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.77</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.235</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>7059</td>\n",
       "      <td>2016</td>\n",
       "      <td>James Shields</td>\n",
       "      <td>- - -</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.85</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>12.8</td>\n",
       "      <td>46</td>\n",
       "      <td>0.077</td>\n",
       "      <td>115.8</td>\n",
       "      <td>198</td>\n",
       "      <td>0.332</td>\n",
       "      <td>597</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.245</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IDfg  Season             Name   Team  Age   W   L  WAR   ERA   G  ...  \\\n",
       "0    1943    2015     Zack Greinke    LAD   31  19   3  5.3  1.66  32  ...   \n",
       "2    2036    2014  Clayton Kershaw    LAD   26  21   3  7.9  1.77  27  ...   \n",
       "1    4153    2015     Jake Arrieta    CHC   29  22   6  7.0  1.77  33  ...   \n",
       "3    2036    2013  Clayton Kershaw    LAD   25  16   9  7.2  1.83  33  ...   \n",
       "4    2036    2015  Clayton Kershaw    LAD   27  16   7  8.6  2.13  33  ...   \n",
       "..    ...     ...              ...    ...  ...  ..  ..  ...   ...  ..  ...   \n",
       "403  3374    2012   Ubaldo Jimenez    CLE   28   9  17  0.9  5.40  31  ...   \n",
       "404  3990    2013  Edinson Volquez  - - -   29   9  12  0.8  5.71  33  ...   \n",
       "405  6943    2012    Luke Hochevar    KCR   28   8  16  1.4  5.73  32  ...   \n",
       "406  3403    2012     Ricky Romero    TOR   27   9  14  0.4  5.77  32  ...   \n",
       "407  7059    2016    James Shields  - - -   34   6  19 -1.0  5.85  33  ...   \n",
       "\n",
       "       LA  Barrels  Barrel%  maxEV  HardHit  HardHit%  Events  CStr%   CSW%  \\\n",
       "0     9.8       24    0.040  113.9      165     0.276     598  0.161  0.281   \n",
       "2     NaN        0      NaN    NaN        0       NaN       0  0.161  0.303   \n",
       "1     4.4       13    0.022  111.5      143     0.247     580  0.177  0.287   \n",
       "3     NaN        0      NaN    NaN        0       NaN       0  0.178  0.293   \n",
       "4     8.9       15    0.028  113.3      132     0.244     542  0.164  0.323   \n",
       "..    ...      ...      ...    ...      ...       ...     ...    ...    ...   \n",
       "403   NaN        0      NaN    NaN        0       NaN       0  0.177  0.250   \n",
       "404   NaN        0      NaN    NaN        0       NaN       0  0.180  0.267   \n",
       "405   NaN        0      NaN    NaN        0       NaN       0  0.180  0.267   \n",
       "406   NaN        0      NaN    NaN        0       NaN       0  0.151  0.235   \n",
       "407  12.8       46    0.077  115.8      198     0.332     597  0.153  0.245   \n",
       "\n",
       "     xERA  \n",
       "0     NaN  \n",
       "2     NaN  \n",
       "1     NaN  \n",
       "3     NaN  \n",
       "4     NaN  \n",
       "..    ...  \n",
       "403   NaN  \n",
       "404   NaN  \n",
       "405   NaN  \n",
       "406   NaN  \n",
       "407   NaN  \n",
       "\n",
       "[408 rows x 334 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sort the data by ERA from lowest to highest\n",
    "\n",
    "pitchers.sort_values(by = 'ERA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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",
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       "        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>IDfg</th>\n",
       "      <th>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "      <th>G</th>\n",
       "      <th>...</th>\n",
       "      <th>LA</th>\n",
       "      <th>Barrels</th>\n",
       "      <th>Barrel%</th>\n",
       "      <th>maxEV</th>\n",
       "      <th>HardHit</th>\n",
       "      <th>HardHit%</th>\n",
       "      <th>Events</th>\n",
       "      <th>CStr%</th>\n",
       "      <th>CSW%</th>\n",
       "      <th>xERA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>7059</td>\n",
       "      <td>2016</td>\n",
       "      <td>James Shields</td>\n",
       "      <td>- - -</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.85</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>12.8</td>\n",
       "      <td>46</td>\n",
       "      <td>0.077</td>\n",
       "      <td>115.8</td>\n",
       "      <td>198</td>\n",
       "      <td>0.332</td>\n",
       "      <td>597</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.245</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>3403</td>\n",
       "      <td>2012</td>\n",
       "      <td>Ricky Romero</td>\n",
       "      <td>TOR</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.77</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.235</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>6943</td>\n",
       "      <td>2012</td>\n",
       "      <td>Luke Hochevar</td>\n",
       "      <td>KCR</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.73</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.267</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>3990</td>\n",
       "      <td>2013</td>\n",
       "      <td>Edinson Volquez</td>\n",
       "      <td>- - -</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.71</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.267</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>3374</td>\n",
       "      <td>2012</td>\n",
       "      <td>Ubaldo Jimenez</td>\n",
       "      <td>CLE</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5.40</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.250</td>\n",
       "      <td>NaN</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>5</th>\n",
       "      <td>12049</td>\n",
       "      <td>2016</td>\n",
       "      <td>Kyle Hendricks</td>\n",
       "      <td>CHC</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.13</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>10.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.031</td>\n",
       "      <td>114.3</td>\n",
       "      <td>136</td>\n",
       "      <td>0.260</td>\n",
       "      <td>523</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.321</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2036</td>\n",
       "      <td>2013</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.83</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.293</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4153</td>\n",
       "      <td>2015</td>\n",
       "      <td>Jake Arrieta</td>\n",
       "      <td>CHC</td>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.022</td>\n",
       "      <td>111.5</td>\n",
       "      <td>143</td>\n",
       "      <td>0.247</td>\n",
       "      <td>580</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.287</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2036</td>\n",
       "      <td>2014</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.77</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1943</td>\n",
       "      <td>2015</td>\n",
       "      <td>Zack Greinke</td>\n",
       "      <td>LAD</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.66</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>9.8</td>\n",
       "      <td>24</td>\n",
       "      <td>0.040</td>\n",
       "      <td>113.9</td>\n",
       "      <td>165</td>\n",
       "      <td>0.276</td>\n",
       "      <td>598</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IDfg  Season             Name   Team  Age   W   L  WAR   ERA   G  ...  \\\n",
       "407   7059    2016    James Shields  - - -   34   6  19 -1.0  5.85  33  ...   \n",
       "406   3403    2012     Ricky Romero    TOR   27   9  14  0.4  5.77  32  ...   \n",
       "405   6943    2012    Luke Hochevar    KCR   28   8  16  1.4  5.73  32  ...   \n",
       "404   3990    2013  Edinson Volquez  - - -   29   9  12  0.8  5.71  33  ...   \n",
       "403   3374    2012   Ubaldo Jimenez    CLE   28   9  17  0.9  5.40  31  ...   \n",
       "..     ...     ...              ...    ...  ...  ..  ..  ...   ...  ..  ...   \n",
       "5    12049    2016   Kyle Hendricks    CHC   26  16   8  4.2  2.13  31  ...   \n",
       "3     2036    2013  Clayton Kershaw    LAD   25  16   9  7.2  1.83  33  ...   \n",
       "1     4153    2015     Jake Arrieta    CHC   29  22   6  7.0  1.77  33  ...   \n",
       "2     2036    2014  Clayton Kershaw    LAD   26  21   3  7.9  1.77  27  ...   \n",
       "0     1943    2015     Zack Greinke    LAD   31  19   3  5.3  1.66  32  ...   \n",
       "\n",
       "       LA  Barrels  Barrel%  maxEV  HardHit  HardHit%  Events  CStr%   CSW%  \\\n",
       "407  12.8       46    0.077  115.8      198     0.332     597  0.153  0.245   \n",
       "406   NaN        0      NaN    NaN        0       NaN       0  0.151  0.235   \n",
       "405   NaN        0      NaN    NaN        0       NaN       0  0.180  0.267   \n",
       "404   NaN        0      NaN    NaN        0       NaN       0  0.180  0.267   \n",
       "403   NaN        0      NaN    NaN        0       NaN       0  0.177  0.250   \n",
       "..    ...      ...      ...    ...      ...       ...     ...    ...    ...   \n",
       "5    10.1       16    0.031  114.3      136     0.260     523  0.221  0.321   \n",
       "3     NaN        0      NaN    NaN        0       NaN       0  0.178  0.293   \n",
       "1     4.4       13    0.022  111.5      143     0.247     580  0.177  0.287   \n",
       "2     NaN        0      NaN    NaN        0       NaN       0  0.161  0.303   \n",
       "0     9.8       24    0.040  113.9      165     0.276     598  0.161  0.281   \n",
       "\n",
       "     xERA  \n",
       "407   NaN  \n",
       "406   NaN  \n",
       "405   NaN  \n",
       "404   NaN  \n",
       "403   NaN  \n",
       "..    ...  \n",
       "5     NaN  \n",
       "3     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "0     NaN  \n",
       "\n",
       "[408 rows x 334 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sort the data by ERA from highest to lowest\n",
    "\n",
    "pitchers.sort_values(by = 'ERA', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Team</th>\n",
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       "      <th>W</th>\n",
       "      <th>L</th>\n",
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       "      <th>ERA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Justin Verlander</td>\n",
       "      <td>DET</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Felix Hernandez</td>\n",
       "      <td>SEA</td>\n",
       "      <td>26</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Gio Gonzalez</td>\n",
       "      <td>WSN</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Cliff Lee</td>\n",
       "      <td>PHI</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Team  Age   G   W  L  WAR   ERA\n",
       "27  Justin Verlander  DET   29  33  17  8  6.9  2.64\n",
       "77   Felix Hernandez  SEA   26  33  13  9  6.6  3.06\n",
       "19   Clayton Kershaw  LAD   24  33  14  9  6.1  2.53\n",
       "54      Gio Gonzalez  WSN   26  32  21  8  5.0  2.89\n",
       "93         Cliff Lee  PHI   33  30   6  9  5.0  3.16"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Filter to only see the 2012 season\n",
    "# Keep only columns: Name, Team, Age, G, W, L, WAR, and ERA\n",
    "\n",
    "season2012 = pitchers.loc[pitchers['Season']== 2012, ['Name', 'Team', 'Age', 'G', 'W', 'L', 'WAR', 'ERA']]\n",
    "season2012.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
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       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>Clayton Kershaw</td>\n",
       "      <td>LAD</td>\n",
       "      <td>27</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Season             Name Team  Age   G   W  L  WAR   ERA\n",
       "19    2012  Clayton Kershaw  LAD   24  33  14  9  6.1  2.53\n",
       "3     2013  Clayton Kershaw  LAD   25  33  16  9  7.2  1.83\n",
       "2     2014  Clayton Kershaw  LAD   26  27  21  3  7.9  1.77\n",
       "4     2015  Clayton Kershaw  LAD   27  33  16  7  8.6  2.13"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Filter Clayton Kershaw's seasons\n",
    "# Keep only columns: Season, Name, Team, Age, G, W, L, WAR, and ERA\n",
    "# arrange the data set from earliest season to latest\n",
    "\n",
    "kershaw = pitchers.loc[pitchers['Name']=='Clayton Kershaw', ['Season', 'Name', 'Team', 'Age', 'G', 'W', 'L', 'WAR', 'ERA']].sort_values('Season', ascending = True)\n",
    "kershaw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2012</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Burnett</td>\n",
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       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2014</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.59</td>\n",
       "      <td>213.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2015</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.18</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Griffin</td>\n",
       "      <td>OAK</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2012</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>LAD</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.61</td>\n",
       "      <td>179.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2014</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>ATL</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.57</td>\n",
       "      <td>204.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2015</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.86</td>\n",
       "      <td>172.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2016</td>\n",
       "      <td>Aaron Sanchez</td>\n",
       "      <td>TOR</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>192.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2012</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.94</td>\n",
       "      <td>198.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2013</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.94</td>\n",
       "      <td>241.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.38</td>\n",
       "      <td>227.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2016</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.62</td>\n",
       "      <td>198.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2014</td>\n",
       "      <td>Alex Cobb</td>\n",
       "      <td>TBR</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.87</td>\n",
       "      <td>166.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014</td>\n",
       "      <td>Alex Wood</td>\n",
       "      <td>ATL</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.78</td>\n",
       "      <td>156.1</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2015</td>\n",
       "      <td>Alex Wood</td>\n",
       "      <td>- - -</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.84</td>\n",
       "      <td>189.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2014</td>\n",
       "      <td>Alfredo Simon</td>\n",
       "      <td>CIN</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.44</td>\n",
       "      <td>196.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>2015</td>\n",
       "      <td>Alfredo Simon</td>\n",
       "      <td>DET</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.05</td>\n",
       "      <td>187.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2013</td>\n",
       "      <td>Andrew Cashner</td>\n",
       "      <td>SDP</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.09</td>\n",
       "      <td>165.2</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2015</td>\n",
       "      <td>Andrew Cashner</td>\n",
       "      <td>SDP</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.34</td>\n",
       "      <td>184.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2013</td>\n",
       "      <td>Andy Pettitte</td>\n",
       "      <td>NYY</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.74</td>\n",
       "      <td>185.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2012</td>\n",
       "      <td>Anibal Sanchez</td>\n",
       "      <td>- - -</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.86</td>\n",
       "      <td>195.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>Anibal Sanchez</td>\n",
       "      <td>DET</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.57</td>\n",
       "      <td>182.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2015</td>\n",
       "      <td>Anthony DeSclafani</td>\n",
       "      <td>CIN</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.05</td>\n",
       "      <td>184.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2012</td>\n",
       "      <td>Barry Zito</td>\n",
       "      <td>SFG</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>184.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>OAK</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>190.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2014</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>NYM</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.09</td>\n",
       "      <td>202.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2015</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>NYM</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.16</td>\n",
       "      <td>192.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2016</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>NYM</td>\n",
       "      <td>43</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.43</td>\n",
       "      <td>190.1</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2016</td>\n",
       "      <td>Brandon Finnegan</td>\n",
       "      <td>CIN</td>\n",
       "      <td>23</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.98</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season                Name   Team  Age   G   W   L  WAR   ERA  Start-IP  \\\n",
       "168    2012        A.J. Burnett    PIT   35  31  16  10  3.5  3.51     202.1   \n",
       "109    2013        A.J. Burnett    PIT   36  30  10  11  4.0  3.30     191.0   \n",
       "352    2014        A.J. Burnett    PHI   37  34   8  18  1.7  4.59     213.2   \n",
       "95     2015        A.J. Burnett    PIT   38  26   9   7  2.6  3.18     164.0   \n",
       "248    2013        A.J. Griffin    OAK   25  32  14  10  1.5  3.82     200.0   \n",
       "190    2012        Aaron Harang    LAD   34  31  10  10  2.2  3.61     179.2   \n",
       "184    2014        Aaron Harang    ATL   36  33  12  12  2.8  3.57     204.1   \n",
       "381    2015        Aaron Harang    PHI   37  29   6  15  1.2  4.86     172.1   \n",
       "64     2016       Aaron Sanchez    TOR   23  30  15   2  3.5  3.00     192.0   \n",
       "263    2012     Adam Wainwright    STL   30  32  14  13  3.9  3.94     198.2   \n",
       "59     2013     Adam Wainwright    STL   31  34  19   9  6.2  2.94     241.2   \n",
       "11     2014     Adam Wainwright    STL   32  32  20   9  4.9  2.38     227.0   \n",
       "357    2016     Adam Wainwright    STL   34  33  13   9  2.8  4.62     198.2   \n",
       "52     2014           Alex Cobb    TBR   26  27  10   9  2.5  2.87     166.1   \n",
       "40     2014           Alex Wood    ATL   23  35  11  11  2.9  2.78     156.1   \n",
       "252    2015           Alex Wood  - - -   24  32  12  12  2.7  3.84     189.2   \n",
       "149    2014       Alfredo Simon    CIN   33  32  15  10  1.3  3.44     196.1   \n",
       "392    2015       Alfredo Simon    DET   34  31  13  12  1.4  5.05     187.0   \n",
       "81     2013      Andrew Cashner    SDP   26  31  10   9  2.7  3.09     165.2   \n",
       "323    2015      Andrew Cashner    SDP   28  31   6  16  1.8  4.34     184.2   \n",
       "224    2013       Andy Pettitte    NYY   41  30  11  11  2.7  3.74     185.1   \n",
       "255    2012      Anibal Sanchez  - - -   28  31   9  13  3.5  3.86     195.2   \n",
       "22     2013      Anibal Sanchez    DET   29  29  14   8  5.9  2.57     182.0   \n",
       "284    2015  Anthony DeSclafani    CIN   25  31   9  13  3.1  4.05     184.2   \n",
       "302    2012          Barry Zito    SFG   34  32  15   8  1.0  4.15     184.1   \n",
       "29     2013       Bartolo Colon    OAK   40  30  18   6  4.0  2.65     190.1   \n",
       "290    2014       Bartolo Colon    NYM   41  31  15  13  2.8  4.09     202.1   \n",
       "304    2015       Bartolo Colon    NYM   42  33  14  13  2.5  4.16     192.2   \n",
       "147    2016       Bartolo Colon    NYM   43  34  15   8  2.4  3.43     190.1   \n",
       "271    2016    Brandon Finnegan    CIN   23  31  10  11  0.7  3.98     172.0   \n",
       "\n",
       "     Relief-IP  \n",
       "168        NaN  \n",
       "109        NaN  \n",
       "352        NaN  \n",
       "95         NaN  \n",
       "248        NaN  \n",
       "190        NaN  \n",
       "184        NaN  \n",
       "381        NaN  \n",
       "64         NaN  \n",
       "263        NaN  \n",
       "59         NaN  \n",
       "11         NaN  \n",
       "357        NaN  \n",
       "52         NaN  \n",
       "40        15.1  \n",
       "252        NaN  \n",
       "149        NaN  \n",
       "392        NaN  \n",
       "81         9.1  \n",
       "323        NaN  \n",
       "224        NaN  \n",
       "255        NaN  \n",
       "22         NaN  \n",
       "284        NaN  \n",
       "302        NaN  \n",
       "29         NaN  \n",
       "290        NaN  \n",
       "304        2.0  \n",
       "147        1.1  \n",
       "271        NaN  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a smaller data set\n",
    "# Keep only columns: Season, Name, Team, Age, G, W, L, WAR, ERA, Start-IP, and Relief-IP\n",
    "# arrange the data set from earliest season to latest for each pitcher\n",
    "\n",
    "pitchers_small = pitchers[['Season', 'Name', 'Team', 'Age', 'G', 'W', 'L', 'WAR', 'ERA', 'Start-IP','Relief-IP']].sort_values(['Name', 'Season'], ascending = True)\n",
    "pitchers_small.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Creating New Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "      <th>Start-IP</th>\n",
       "      <th>Relief-IP</th>\n",
       "      <th>season_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2012</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.51</td>\n",
       "      <td>202.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2014</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.59</td>\n",
       "      <td>213.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2015</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.18</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Griffin</td>\n",
       "      <td>OAK</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2012</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>LAD</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.61</td>\n",
       "      <td>179.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2014</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>ATL</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.57</td>\n",
       "      <td>204.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2015</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.86</td>\n",
       "      <td>172.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2016</td>\n",
       "      <td>Aaron Sanchez</td>\n",
       "      <td>TOR</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>192.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2012</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.94</td>\n",
       "      <td>198.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2013</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.94</td>\n",
       "      <td>241.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.38</td>\n",
       "      <td>227.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2016</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.62</td>\n",
       "      <td>198.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2014</td>\n",
       "      <td>Alex Cobb</td>\n",
       "      <td>TBR</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.87</td>\n",
       "      <td>166.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014</td>\n",
       "      <td>Alex Wood</td>\n",
       "      <td>ATL</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.78</td>\n",
       "      <td>156.1</td>\n",
       "      <td>15.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2015</td>\n",
       "      <td>Alex Wood</td>\n",
       "      <td>- - -</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.84</td>\n",
       "      <td>189.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2014</td>\n",
       "      <td>Alfredo Simon</td>\n",
       "      <td>CIN</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.44</td>\n",
       "      <td>196.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>2015</td>\n",
       "      <td>Alfredo Simon</td>\n",
       "      <td>DET</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.05</td>\n",
       "      <td>187.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2013</td>\n",
       "      <td>Andrew Cashner</td>\n",
       "      <td>SDP</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.09</td>\n",
       "      <td>165.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2015</td>\n",
       "      <td>Andrew Cashner</td>\n",
       "      <td>SDP</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.34</td>\n",
       "      <td>184.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2013</td>\n",
       "      <td>Andy Pettitte</td>\n",
       "      <td>NYY</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.74</td>\n",
       "      <td>185.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2012</td>\n",
       "      <td>Anibal Sanchez</td>\n",
       "      <td>- - -</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.86</td>\n",
       "      <td>195.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>Anibal Sanchez</td>\n",
       "      <td>DET</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.57</td>\n",
       "      <td>182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2015</td>\n",
       "      <td>Anthony DeSclafani</td>\n",
       "      <td>CIN</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.05</td>\n",
       "      <td>184.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2012</td>\n",
       "      <td>Barry Zito</td>\n",
       "      <td>SFG</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>184.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>OAK</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>190.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2014</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>NYM</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.09</td>\n",
       "      <td>202.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2015</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>NYM</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.16</td>\n",
       "      <td>192.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2016</td>\n",
       "      <td>Bartolo Colon</td>\n",
       "      <td>NYM</td>\n",
       "      <td>43</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.43</td>\n",
       "      <td>190.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2016</td>\n",
       "      <td>Brandon Finnegan</td>\n",
       "      <td>CIN</td>\n",
       "      <td>23</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.98</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season                Name   Team  Age   G   W   L  WAR   ERA  Start-IP  \\\n",
       "168    2012        A.J. Burnett    PIT   35  31  16  10  3.5  3.51     202.1   \n",
       "109    2013        A.J. Burnett    PIT   36  30  10  11  4.0  3.30     191.0   \n",
       "352    2014        A.J. Burnett    PHI   37  34   8  18  1.7  4.59     213.2   \n",
       "95     2015        A.J. Burnett    PIT   38  26   9   7  2.6  3.18     164.0   \n",
       "248    2013        A.J. Griffin    OAK   25  32  14  10  1.5  3.82     200.0   \n",
       "190    2012        Aaron Harang    LAD   34  31  10  10  2.2  3.61     179.2   \n",
       "184    2014        Aaron Harang    ATL   36  33  12  12  2.8  3.57     204.1   \n",
       "381    2015        Aaron Harang    PHI   37  29   6  15  1.2  4.86     172.1   \n",
       "64     2016       Aaron Sanchez    TOR   23  30  15   2  3.5  3.00     192.0   \n",
       "263    2012     Adam Wainwright    STL   30  32  14  13  3.9  3.94     198.2   \n",
       "59     2013     Adam Wainwright    STL   31  34  19   9  6.2  2.94     241.2   \n",
       "11     2014     Adam Wainwright    STL   32  32  20   9  4.9  2.38     227.0   \n",
       "357    2016     Adam Wainwright    STL   34  33  13   9  2.8  4.62     198.2   \n",
       "52     2014           Alex Cobb    TBR   26  27  10   9  2.5  2.87     166.1   \n",
       "40     2014           Alex Wood    ATL   23  35  11  11  2.9  2.78     156.1   \n",
       "252    2015           Alex Wood  - - -   24  32  12  12  2.7  3.84     189.2   \n",
       "149    2014       Alfredo Simon    CIN   33  32  15  10  1.3  3.44     196.1   \n",
       "392    2015       Alfredo Simon    DET   34  31  13  12  1.4  5.05     187.0   \n",
       "81     2013      Andrew Cashner    SDP   26  31  10   9  2.7  3.09     165.2   \n",
       "323    2015      Andrew Cashner    SDP   28  31   6  16  1.8  4.34     184.2   \n",
       "224    2013       Andy Pettitte    NYY   41  30  11  11  2.7  3.74     185.1   \n",
       "255    2012      Anibal Sanchez  - - -   28  31   9  13  3.5  3.86     195.2   \n",
       "22     2013      Anibal Sanchez    DET   29  29  14   8  5.9  2.57     182.0   \n",
       "284    2015  Anthony DeSclafani    CIN   25  31   9  13  3.1  4.05     184.2   \n",
       "302    2012          Barry Zito    SFG   34  32  15   8  1.0  4.15     184.1   \n",
       "29     2013       Bartolo Colon    OAK   40  30  18   6  4.0  2.65     190.1   \n",
       "290    2014       Bartolo Colon    NYM   41  31  15  13  2.8  4.09     202.1   \n",
       "304    2015       Bartolo Colon    NYM   42  33  14  13  2.5  4.16     192.2   \n",
       "147    2016       Bartolo Colon    NYM   43  34  15   8  2.4  3.43     190.1   \n",
       "271    2016    Brandon Finnegan    CIN   23  31  10  11  0.7  3.98     172.0   \n",
       "\n",
       "     Relief-IP  season_id  \n",
       "168        NaN          1  \n",
       "109        NaN          2  \n",
       "352        NaN          3  \n",
       "95         NaN          4  \n",
       "248        NaN          1  \n",
       "190        NaN          1  \n",
       "184        NaN          2  \n",
       "381        NaN          3  \n",
       "64         NaN          1  \n",
       "263        NaN          1  \n",
       "59         NaN          2  \n",
       "11         NaN          3  \n",
       "357        NaN          4  \n",
       "52         NaN          1  \n",
       "40        15.1          1  \n",
       "252        NaN          2  \n",
       "149        NaN          1  \n",
       "392        NaN          2  \n",
       "81         9.1          1  \n",
       "323        NaN          2  \n",
       "224        NaN          1  \n",
       "255        NaN          1  \n",
       "22         NaN          2  \n",
       "284        NaN          1  \n",
       "302        NaN          1  \n",
       "29         NaN          1  \n",
       "290        NaN          2  \n",
       "304        2.0          3  \n",
       "147        1.1          4  \n",
       "271        NaN          1  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Add a sequence counter for each season for each pitcher\n",
    "\n",
    "pitchers_small['season_id'] = pitchers_small.groupby(['Name']).cumcount() + 1\n",
    "pitchers_small.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "      <th>Start-IP</th>\n",
       "      <th>Relief-IP</th>\n",
       "      <th>season_id</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2012</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.51</td>\n",
       "      <td>202.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>31 to 35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>36 to 40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2014</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.59</td>\n",
       "      <td>213.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>36 to 40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2015</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.18</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>36 to 40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Griffin</td>\n",
       "      <td>OAK</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;= 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2012</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>LAD</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.61</td>\n",
       "      <td>179.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>31 to 35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2014</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>ATL</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.57</td>\n",
       "      <td>204.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>36 to 40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2015</td>\n",
       "      <td>Aaron Harang</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.86</td>\n",
       "      <td>172.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>36 to 40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2016</td>\n",
       "      <td>Aaron Sanchez</td>\n",
       "      <td>TOR</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>192.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;= 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2012</td>\n",
       "      <td>Adam Wainwright</td>\n",
       "      <td>STL</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.94</td>\n",
       "      <td>198.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>25 to 30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season             Name Team  Age   G   W   L  WAR   ERA  Start-IP  \\\n",
       "168    2012     A.J. Burnett  PIT   35  31  16  10  3.5  3.51     202.1   \n",
       "109    2013     A.J. Burnett  PIT   36  30  10  11  4.0  3.30     191.0   \n",
       "352    2014     A.J. Burnett  PHI   37  34   8  18  1.7  4.59     213.2   \n",
       "95     2015     A.J. Burnett  PIT   38  26   9   7  2.6  3.18     164.0   \n",
       "248    2013     A.J. Griffin  OAK   25  32  14  10  1.5  3.82     200.0   \n",
       "190    2012     Aaron Harang  LAD   34  31  10  10  2.2  3.61     179.2   \n",
       "184    2014     Aaron Harang  ATL   36  33  12  12  2.8  3.57     204.1   \n",
       "381    2015     Aaron Harang  PHI   37  29   6  15  1.2  4.86     172.1   \n",
       "64     2016    Aaron Sanchez  TOR   23  30  15   2  3.5  3.00     192.0   \n",
       "263    2012  Adam Wainwright  STL   30  32  14  13  3.9  3.94     198.2   \n",
       "\n",
       "     Relief-IP  season_id age_group  \n",
       "168        NaN          1  31 to 35  \n",
       "109        NaN          2  36 to 40  \n",
       "352        NaN          3  36 to 40  \n",
       "95         NaN          4  36 to 40  \n",
       "248        NaN          1     <= 25  \n",
       "190        NaN          1  31 to 35  \n",
       "184        NaN          2  36 to 40  \n",
       "381        NaN          3  36 to 40  \n",
       "64         NaN          1     <= 25  \n",
       "263        NaN          1  25 to 30  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a new column called 'age_group'\n",
    "\n",
    "# create conditions for the age_group bins\n",
    "\n",
    "bins = [\n",
    "    (pitchers_small['Age'] <= 25),\n",
    "    (pitchers_small['Age'] > 25) & (pitchers_small['Age'] <= 30),\n",
    "    (pitchers_small['Age'] > 30) & (pitchers_small['Age'] <= 35),\n",
    "    (pitchers_small['Age'] > 35) & (pitchers_small['Age'] <= 40),\n",
    "    (pitchers_small['Age'] > 40)\n",
    "]\n",
    "\n",
    "# create the age_group names to be assigned to each bin\n",
    "\n",
    "age_group = ['<= 25', '25 to 30', '31 to 35', '36 to 40', '> 40']\n",
    "\n",
    "# add the age_group bins into the data\n",
    "\n",
    "pitchers_small['age_group'] = np.select(bins, age_group)\n",
    "\n",
    "pitchers_small.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Season</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "      <th>Start-IP</th>\n",
       "      <th>Relief-IP</th>\n",
       "      <th>season_id</th>\n",
       "      <th>age_group</th>\n",
       "      <th>pitcher_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2012</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.51</td>\n",
       "      <td>202.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>starter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>36 to 40</td>\n",
       "      <td>starter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2014</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PHI</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.59</td>\n",
       "      <td>213.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>36 to 40</td>\n",
       "      <td>starter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2015</td>\n",
       "      <td>A.J. Burnett</td>\n",
       "      <td>PIT</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.18</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>36 to 40</td>\n",
       "      <td>reliever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2013</td>\n",
       "      <td>A.J. Griffin</td>\n",
       "      <td>OAK</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;= 25</td>\n",
       "      <td>starter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season          Name Team  Age   G   W   L  WAR   ERA  Start-IP  \\\n",
       "168    2012  A.J. Burnett  PIT   35  31  16  10  3.5  3.51     202.1   \n",
       "109    2013  A.J. Burnett  PIT   36  30  10  11  4.0  3.30     191.0   \n",
       "352    2014  A.J. Burnett  PHI   37  34   8  18  1.7  4.59     213.2   \n",
       "95     2015  A.J. Burnett  PIT   38  26   9   7  2.6  3.18     164.0   \n",
       "248    2013  A.J. Griffin  OAK   25  32  14  10  1.5  3.82     200.0   \n",
       "\n",
       "     Relief-IP  season_id age_group pitcher_type  \n",
       "168        NaN          1  31 to 35      starter  \n",
       "109        NaN          2  36 to 40      starter  \n",
       "352        NaN          3  36 to 40      starter  \n",
       "95         NaN          4  36 to 40     reliever  \n",
       "248        NaN          1     <= 25      starter  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a pitcher_type column which makes a distinction between starters and relievers\n",
    "\n",
    "pitchers_small['pitcher_type'] = np.where(pitchers_small['Start-IP'] >= pitchers_small['Start-IP'].median(), 'starter', 'reliever') \n",
    "pitchers_small.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Calculating Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Season</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>WAR</th>\n",
       "      <th>ERA</th>\n",
       "      <th>Start-IP</th>\n",
       "      <th>Relief-IP</th>\n",
       "      <th>season_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>408.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.919118</td>\n",
       "      <td>28.767157</td>\n",
       "      <td>31.487745</td>\n",
       "      <td>12.360294</td>\n",
       "      <td>10.122549</td>\n",
       "      <td>2.798529</td>\n",
       "      <td>3.705441</td>\n",
       "      <td>192.015441</td>\n",
       "      <td>4.934426</td>\n",
       "      <td>1.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.402289</td>\n",
       "      <td>4.044252</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>3.323669</td>\n",
       "      <td>2.856895</td>\n",
       "      <td>1.552646</td>\n",
       "      <td>0.742643</td>\n",
       "      <td>18.953453</td>\n",
       "      <td>5.774077</td>\n",
       "      <td>1.117856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>138.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>177.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>3.665000</td>\n",
       "      <td>190.600000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>204.100000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>247.400000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Season         Age           G           W           L  \\\n",
       "count   408.000000  408.000000  408.000000  408.000000  408.000000   \n",
       "mean   2013.919118   28.767157   31.487745   12.360294   10.122549   \n",
       "std       1.402289    4.044252    1.840000    3.323669    2.856895   \n",
       "min    2012.000000   20.000000   25.000000    6.000000    2.000000   \n",
       "25%    2013.000000   26.000000   30.000000   10.000000    8.000000   \n",
       "50%    2014.000000   28.000000   32.000000   12.000000   10.000000   \n",
       "75%    2015.000000   31.000000   33.000000   14.000000   12.000000   \n",
       "max    2016.000000   43.000000   42.000000   22.000000   19.000000   \n",
       "\n",
       "              WAR         ERA    Start-IP  Relief-IP   season_id  \n",
       "count  408.000000  408.000000  408.000000  61.000000  408.000000  \n",
       "mean     2.798529    3.705441  192.015441   4.934426    1.941176  \n",
       "std      1.552646    0.742643   18.953453   5.774077    1.117856  \n",
       "min     -1.000000    1.660000  138.200000   0.000000    1.000000  \n",
       "25%      1.700000    3.210000  177.100000   1.100000    1.000000  \n",
       "50%      2.650000    3.665000  190.600000   2.200000    2.000000  \n",
       "75%      3.500000    4.180000  204.100000   7.000000    3.000000  \n",
       "max      8.600000    5.850000  247.400000  30.200000    5.000000  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get summary stats for each column\n",
    "\n",
    "pitchers_small.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.705441176470588"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get the mean ERA\n",
    "\n",
    "pitchers_small['ERA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7426433970651792"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get the standard deviation of ERA\n",
    "\n",
    "pitchers_small['ERA'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    88\n",
       "2012    88\n",
       "2013    81\n",
       "2015    78\n",
       "2016    73\n",
       "Name: Season, dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## How many pitcher observations per season?\n",
    "\n",
    "pitchers_small['Season'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6) Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>11</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "W       6   7   8   9   10  11  12  13  14  15  16  17  18  19  20  21  22\n",
       "Season                                                                    \n",
       "2012     1   2   5   8   7   8  14  11   6   5  11   3   2   1   3   1   0\n",
       "2013     0   1   6   7  11  11   8   8  15   6   3   1   1   2   0   1   0\n",
       "2014     1   3  11   4   9   9   5  12  10  11   4   2   4   0   2   1   0\n",
       "2015     5   3   2   9   3  18   6  11   8   2   1   2   3   3   1   0   1\n",
       "2016     2   3   4  10   8   8   6   6   4   5   8   2   3   1   2   0   1"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Pivot Table of counts of wins across seasons\n",
    "\n",
    "pd.crosstab(pitchers_small['Season'], pitchers_small['W'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>28.352273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>28.617284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>29.227273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>28.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>28.876712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age\n",
       "Season           \n",
       "2012    28.352273\n",
       "2013    28.617284\n",
       "2014    29.227273\n",
       "2015    28.769231\n",
       "2016    28.876712"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Pivot Table of the Average Age by season\n",
    "\n",
    "pitchers_small.pivot_table(values = ['Age'], index = ['Season'], aggfunc = np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>28.4</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>28.6</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>29.2</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>28.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>28.9</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age     \n",
       "        mean  std\n",
       "Season           \n",
       "2012    28.4  3.7\n",
       "2013    28.6  4.3\n",
       "2014    29.2  4.2\n",
       "2015    28.8  4.0\n",
       "2016    28.9  4.1"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Pivot Table of Average and Standard Deviation of Wins by Season\n",
    "# Round the results to 1 significant digit\n",
    "\n",
    "round(pitchers_small.pivot_table(values = ['Age'], index = ['Season'], aggfunc = (np.mean, np.std)), ndigits = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           L\n",
       "Season      \n",
       "2012    10.5\n",
       "2013    10.0\n",
       "2014    10.5\n",
       "2015    10.0\n",
       "2016    10.0"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Alternate approach to creating a pivot table using group_by()\n",
    "# Get the median number of losses by season\n",
    "\n",
    "pitchers_small[['Season', 'L']].groupby(['Season']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27    49\n",
       "25    41\n",
       "28    41\n",
       "29    38\n",
       "26    36\n",
       "31    30\n",
       "24    28\n",
       "30    27\n",
       "33    21\n",
       "23    19\n",
       "32    18\n",
       "34    16\n",
       "35    12\n",
       "36     8\n",
       "22     5\n",
       "37     5\n",
       "38     4\n",
       "41     3\n",
       "39     2\n",
       "40     2\n",
       "43     1\n",
       "42     1\n",
       "20     1\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## How many observations of each Age are there?\n",
    "\n",
    "pitchers_small['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25 to 30    191\n",
       "31 to 35     97\n",
       "<= 25        94\n",
       "36 to 40     21\n",
       "> 40          5\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many players in each age group?\n",
    "\n",
    "pitchers_small['age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">WAR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th>25 to 30</th>\n",
       "      <th>31 to 35</th>\n",
       "      <th>36 to 40</th>\n",
       "      <th>&lt;= 25</th>\n",
       "      <th>&gt; 40</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>2.669048</td>\n",
       "      <td>2.440909</td>\n",
       "      <td>3.233333</td>\n",
       "      <td>2.514286</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>3.105405</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>3.247619</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3.152500</td>\n",
       "      <td>1.872727</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.847368</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3.220000</td>\n",
       "      <td>2.637500</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2.978125</td>\n",
       "      <td>2.309524</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>3.094118</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WAR                                   \n",
       "age_group  25 to 30  31 to 35  36 to 40     <= 25 > 40\n",
       "Season                                                \n",
       "2012       2.669048  2.440909  3.233333  2.514286  NaN\n",
       "2013       3.105405  2.600000  2.466667  3.247619  2.7\n",
       "2014       3.152500  1.872727  2.000000  2.847368  2.8\n",
       "2015       3.220000  2.637500  2.220000  3.000000  2.5\n",
       "2016       2.978125  2.309524  2.900000  3.094118  1.6"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculate the average WAR per season across age group\n",
    "\n",
    "pitchers_small.pivot_table(values = ['WAR'], index = ['Season'], columns = ['age_group'], aggfunc = np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reliever    204\n",
       "starter     204\n",
       "Name: pitcher_type, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many pitchers in each group?\n",
    "\n",
    "pitchers_small['pitcher_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7) Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'WAR')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of WAR values\n",
    "\n",
    "plt.hist(x = pitchers_small['WAR'])\n",
    "\n",
    "# Add title and axis names\n",
    "plt.title('Pitcher WAR')\n",
    "plt.xlabel('WAR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='WAR', ylabel='Density'>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j6VhGsRWrYQRBZWUlQ4YM4corr2TkyJH85je/Yfz48YwePforU3u33n/kyJGANQHfzTfffGz/Bx98EIBLL7302OywYI2wnjdv3nH3X7x4MWeeeSazZ89m+PCTn1rflCCM4Nq0AApLwr96CSC9NxyuhuYjEG/fhGdG91FWVsbjjz/OoUOHmDdvHp999hmqyuzZs1myZAlTpkxp97hHHnmE9PR0li9fTlNTE5MnT+a8887jO9/5Di+88AKzZs2iubmZ9957jwceeOC4+wOsXLmSdevWUVxcfNKvxyQII7g2vAJ9xjsdRWBcsdCzL6vXruGRzQms23kIr08Z27cnP5jSnxEF6U5HaESYfv36MXHiRP7rv/6Lt99+mzFjrJJ0fX09ZWVlx00Qb7/9NmvWrDlW5VRXV0dZWRnnn38+P/nJT2hqauKtt95iypQp9OjR47j7x8fHM2HChKAkBzAJwgimxkOwYxlMuN7pSALS7FV+3/BtXp9fw/lj+vPDswYgwNqddVzx8DIuKenDLTOH4nKFyTTlRthrmXpbVbn11lu5/vrAPguqyt///ndmzJjxteemTp3KwoULef7557n00ktPuP/ixYuDOv23aYMwgqdikbWkaARU17i9ynULj7K2uYA/FK/lglH59M1Mok9mEheMyueP/zaaj8r2cdPzq/H57JnQ0oheM2bM4NFHH6W+vh6AnTt3snfv8cfczJgxgwceeAC32w3A5s2bOXLkCGAtCPTYY4/x4YcfMnPmzA73DyZTgjCC54s3oHCc01F0SFX56fsN1LvhphGNxG4v+9o+qYlx/GLmUP534Rfc8/Ym/nvmUAciNbosrXenex51eL5OOO+889i4cSOTJk0CrK6tTz31FLm5ue3uf+2111JZWcnYsWNRVXJycnjllVeOneu73/0uc+bMIT4+vsP9g8m26b5DzUz37TCfF+4eABfcY83gGsYeXdvE0xvc3H56AvHeo9Ya1f/+PNa6qF91qMHN7fPXceeckcwY0Sv0wRoBMdN9Byacpvs2upOdK6BHRtgnhw21Xv62opkfj40nPkas6jCJgaP7290/rUccPzprILe9vJYDR5pDHK1hOMskCCM4Nr0Z9tVLXp/y34sb+PbQOPKSW731U3vBgcrjHjekVyoTijO547UNx93HMKKRSRBGcJS9DQXhnSCe2tCMAlP7xHz1ieQcOLj9hMdeMq4PH5bV8PmOg7bFZ5ycaKkut0tX/j4mQRgn7/Ae6xd4bvg25NY1KfeWNnPViHhE2rQ1pOTCga0nPD4xLoZ/G9ebO1/fYL6IwlBiYiK1tbXm/+Y4VJXa2loSEzteW7s104vJOHkVi6y1H1zh+3a6f2UT43q56JPWzm+ilLyA5mQ6a1AOC9fvZvGmGqYNbb83iuGM3r17U1VVhVlZ8vgSExPp3btzvbHC9xNtRI6yhZB/qtNRHNeeIz6e/aKZu846zq+nlDxrcjf1WetVH4fLJcw5pZC/vLOZqUNyvl4SMRwTFxcXtNHDxpdMFZNxcnw+2LLYKkGEqQdWNzGldyyZicd5u8clQlwSHOl48aDT+mdysMHNkrJ9QY7SMMKPSRDGydm9BhJSrXr8MLT3qI9/bXYza0DciXdMyeuwoRrAJcI3TingvkVfH1xnGNHG1gQhIjNFZJOIlIvI19aUFpEpIrJSRDwicnGr7aeKyFIRWS8ia0TkO3bGaZyELYuh1ylOR3Fcj65p5vSCWDISO6gOSs6GgzsCOufE/plU1h5lbVVdECI0jPBlW4IQkRjgfuB8YDhwmYi0naB8O3A18Eyb7UeBK1V1BDATuFdEetoVq3ESyt+F/PBMEEfcyrNfNHN+/wCa2pJzTjgWorVYl4uZI3rxwAflJxegYYQ5O0sQE4ByVd2iqs3Ac8Cc1juoaqWqrgF8bbZvVtUy//1dwF4gx8ZYja5wN1gjqHuNcjqSdj3/RTPDs2K+OijueFLy4OC2gM89dUgOH5bto7qu4SQiNIzwZmeCKARal9mr/Ns6RUQmAPFARZDiMoJlx2eQWRyWs7d6fcoja5qZWRxgR73kHKjbCQTWjz4pPpbJA7N5cmngScUwIk1YN1KLSD7wJPA9VfW18/x1IlIqIqWm/7MDti6B3BFOR9GuxTs8JMcJgzNjOt4ZID4JYuPhSOC9k84dlsdzn22n0e3tYpSGEd7sTBA7gT6tHvf2bwuIiKQBbwC3qeqn7e2jqg+paomqluTkmBqokNvyPvQa7XQU7XpsbTPT+3VymE9Kr4B6MrUo6NmDflnJLFhb3cnoDCMy2JkglgODRKRYROKBS4H5gRzo3/9l4AlVnWdjjEZXNdXD3g1hOb3G1jov6/Z5mVgQYOmhRVI21AXWk6nFtKG5PGGqmYwoZVuCUFUPcAOwENgIvKCq60XkThGZDSAi40WkCrgEeFBE1vsP/zYwBbhaRFb7b6faFavRBTs+hezBENu5uV1C4ekNzUzpHWtN590ZKdlwoHNf9mP7ZlB14Cgbqw917lqGEQFsnWpDVRcAC9psu73V/eVYVU9tj3sKeMrO2IyTtPXDsGx/aPIq/9rk4X9OT+j8wcm5UPlRpw6JcQlTh+TyxNJK/vCt8KxuM4yuCutGaiOMVX4IeW2HtThv4VYPfdKE/JQuvLWTc6GuqtOHTRuSy+trqjnS5On8NQ0jjJkEYXRe81Gr/SEn/NofntnQzFl9ulgwTkgB9UJj50ZIZybHMyw/jfmf7+radQ0jTJkEYXRe1XLIHBB27Q87DvvYUOulpFcnG6dbiEBqYHMytTVtSI4ZE2FEHZMgjM6r/Ahyw2+B+Be+aGZyYRcap1tLyul0TyaA0YU9qT3SZOZnMqKKSRBG51V+BLnh1f7gU+XFTe6uVy+1SM6Gus6XIFwuYergXJ781JQijOhhEoTROV43VK8OuxLE0p1ekuKEfukn+ZZOzoUDnU8QAGcNyeHNtdUcbnSfXAyGESZMgjA6Z/caSCuA+BSnI/mKFzY1c0ZhF9seWkvOgUMBD/j/ioykeEYWpvHKatNYbUQHkyCMztn+KeSEV+nhcLPy3jYPkwuDMKynR4bVi8nTtVlazx6axxOfVKIa2KR/hhHOTIIwOqfyo7Dr3vrmFjfDs2JISwjCGtEu10mVIkYUpNHg9lK67cDJx2IYDjMJwgicqjXFd5i1P7y4yc3pwaheanFs6u/OExHOHprLYx9vDV48huEQkyCMwB2oBHGF1frTOw/72LTfy9i8YCaIrC6NhWgxZVAOSzbvY8+hxuDFZBgOMAnCCNyOz8Ju9tZXypo5rSCWuJMZ+9BWUk6XezIBJCfEMnlAFk+ZLq9GhDMJwgjcjk8ha5DTURyjqszb7GZyMKuXwD8nU+cHy7V27vBePL1sO00es5iQEblMgjACt31ZWDVQr9/no8EDgzOC/DZOzob63da8TF1UmNGDfllJvGq6vBoRzCQIIzBN9bC/ArIGOh3JMf/a3MzpBTGIBLF6CaylRxPS4PDukzrN+SPz+ccHFfh8psurEZlMgjACs2sVZA2AmDinIwHA61Neq/BwejDGPrQnpWtTf7c2siANFBZv3hukoAwjtEyCMAJTVRpW7Q+f7PLSM1EoTLXpLZyUddIJQkS4YFQ+f19UbgbOGRHJJAgjMDuWhVWCeMlfvWSb5Bw4ePK9kCb1z2Lv4SaWVtQGISjDCC1bE4SIzBSRTSJSLiK3tPP8FBFZKSIeEbm4zXNXiUiZ/3aVnXEaAdi1AnKGOB0FAI0e5d1tHiYV2LhibnLOSY2FaOFyCbNHF/DndzYHISjDCC3bEoSIxAD3A+cDw4HLRKTtHNHbgauBZ9ocmwn8CjgNmAD8SkQy7IrV6EDdTvA0Q0qe05EA8O42DwN6uuiZGOTG6dZScuHQLuDkq4YmD8ymuq6RJZtrTj4uwwghO0sQE4ByVd2iqs3Ac8Cc1juoaqWqrgF8bY6dAbyjqvtV9QDwDjDTxliNE9lZanVvDXZvoS56ebObiXaWHgDikwGBhpOfUynGJVwyrje/W7DR9GgyIoqdCaIQaD3aqMq/LWjHish1IlIqIqU1NebXmW2qSq0eTGGgrkn5tNrD+K4uK9oZKXknPWCuxYTiTFB4eVXX5ngyDCdEdCO1qj6kqiWqWpKTk+N0ONErjHowLdjiZnRODElxISjNJGfDweAkCBHh8tP68oc3N3LILChkRAg7E8ROoE+rx7392+w+1ggmn89aJCg7PBLES5vdTLKz91JrSdlBaahuMSgvlVP79ORPCzcF7ZyGYSc7E8RyYJCIFItIPHApMD/AYxcC54lIhr9x+jz/NiPUasshIRUS052OhOp6a+bWU3NDlCBScoPS1bW1b5f04fU11ZRW7g/qeQ3DDrYlCFX1ADdgfbFvBF5Q1fUicqeIzAYQkfEiUgVcAjwoIuv9x+4HfoOVZJYDd/q3GaG2ayVkD3Y6CgBeq3AzvldMcGduPZEgjKZuKzUxjqtOL+Knz6/mSJMnqOc2jGALKEGIyEsiMktEOpVQVHWBqg5W1QGq+jv/tttVdb7//nJV7a2qyaqapaojWh37qKoO9N8e68x1jSCqKoXM/k5HAcDLZSHovdRaYhq4G6C5PqinHV+UyaC8FG59aa0ZYW2EtUC/8P8P+HegTETuEpHwGDFl2G/nirBooK446GXPEWV4dgj7VYjLP/V3cEsRAFdOKmJN1UGzZoQR1gL6tKnqu6p6OTAWqATeFZFPROR7IhIes7cZwef1QM3GsOji+kqZm4kFMbhCPRYj5eTXhmhPQmwMN04fxJ/f2cwHZgCdEaYC/jkmIllYo56vBVYBf8VKGO/YEpnhvH2brCkn4pMdDUNVeaUsyOtOByo5Gw7Y8ys/P70HN04fxE+eXcWq7Sc/IM8wgi3QNoiXgQ+BJOAbqjpbVZ9X1R8DKXYGaDho1+qwWP9h7T4fXh/0T3dg2E5y8HsytTa0Vxo/OLM/18xdztqqOtuuYxhdEegn7p+qOlxV/6Cq1QAikgCgqiW2RWc4a9dKyChyOgpe3tzMJDsWBgpESm7QBssdz9h+GXxvcjFXPrqM5ab7qxFGAk0Qv21n29JgBmKEoTBooG5ZGGiSXQsDdaRHJjTWgafB1suML8rkh2cN4AdPlPL2+pNbyc4wguWEnzoR6YU1B1IPERkDtPyES8OqbjKildcDe7+ATGcbqJdVe0lLsHFhoI64XF+WImweDzK6d0/+67wh3PrSWnbXNXLl6UW2Xs8wOtLRz7IZWA3TvYE/t9p+GPilTTEZ4WDfZkjJgXhnfwe8XOZmUr4DjdOthShBAAzISeF/LhzOPW9vYseBo9x6/jBcrvCYRdfofk74s0xVH1fVacDVqjqt1W22qr4UohgNJ1R/7njpodmrLNxqdW91VFK2rQ3VbeWlJXL7hcP5sGwfNz2/Gre37Wz4hhEaJ0wQInKF/26RiPys7S0E8RlO2bXK8QbqD3Z46J3qIjvJ4UmHk3PhQGVIL5maGMet5w9j18EGrnuilEa3N6TXNwzouJG6pQN8CpDazs2IVrtWOV6CeLnMzWlOlx4AUvOgLnizugYqPtbFT84ZRJPHx/VPrqDJY5KEEVodVTE96P/3jvZuoQnRCDmfD/aud3QOpqNuZckODxPzHeq91FqPTGios+ZlCrFYl4sfTR1Ao9vLDc+swmtWpDNCKNCBcv8rImkiEici74lITavqJyPa7N8CCWnWZHUOeWebh8GZMaQlhEEDrctlrS4XxLUhOiPW5eKGaQPZXdfIna+tdyQGo3sKtHL3PFU9BFyINRfTQOBmu4IyHFa92vEZXF/Z3MxpTvdeai0lL6QN1W3Fxrj4yfRBvL+phqfNBH9GiASaIFrK+bOAF1XVzAkQzao/d7SBuq5J+Wy3l5JQrDsdqJTskDdUt5WcEMtN0wdx99ubWL3joKOxGN1DoAnidRH5AhgHvCciOUCjfWEZjnK4BPHWVjejskO07nSgUvJg/1anoyC/Zw+uOb2YG55ZSb1ZcMiwWaDTfd8CnA6UqKobOALMsTMwwyGqsHudoz2YXi4Lg7EPbaX0cqQnU3vGF2cyJC+V219Z53QoRpTrTAfzocB3RORK4GKsdaJPSERmisgmESkXkVvaeT5BRJ73P79MRIr82+NE5HERWSsiG0Xk1k7EaZyMw9WgPkjKcuTyNUd9rKsJ4brTgUpMB0+TNS9TGLhiYj8+qajl/U17nQ7FiGKB9mJ6ErgHOAMY77+dcBZXEYkB7gfOB4YDl4nI8Da7fR84oKoDgb8Af/RvvwRIUNVRWNVa17ckD8Nm1WusKb6dmDkVeHOLh7F5MSTEhlH1Elh/j9QCOFjpdCQAJMbF8L3JRfzypbVmbWvDNoF2Mi8BhmvnFtCdAJSr6hYAEXkOq1pqQ6t95gC/9t+fB9wn1pzOCiSLSCzQA2gGDnXi2kZX7V7jaAP1y2VuzikKg7EP7UnJhf2V0OsUpyMBrMn9BuWm8Lf3yrj1gmFOh2NEoUCrmNYBvTp57kKg9UT6Vf5t7e6jqh6gDsjCShZHgGpgO3CPqn5tonwRuU5ESkWktKbGLNsYFLtWQWaxM5eu91Fx0MvoHIen1jielDxrjEgYuXRCX55bvoPKfUecDsWIQoF+ErOBDSKyUETmt9xsjGsC4AUKgGLg5yLytW41qvqQqpaoaklOTo6N4XQju9c61kD9eoWb8b1iiA3X2UtTe4VFT6bWMpLiuWBUL37z+oaOdzaMTgq0LP/rLpx7J9Cn1ePe/m3t7VPlr05KB2qBfwfe8veY2isiH2NVc4XXz7do01gHR/dBar4jl3+13M3sgXGOXDsgKXlQVwXqBQmfRvSZI/K5ed7nlFbup6Qo0+lwjCgSaDfXD7BGUMf57y8HVnZw2HJgkIgUi0g8cCnQttQxH7jKf/9iYJG/nWM7cDaAiCQDE4EvAonVOAm711njH1yh//LbfsjHzsM+RmSFafUSQFwiJKTAofBa8S0+1sW3xhby+wUb6VwzoWGcWKC9mH6A1S7woH9TIfDKiY7xtyncACwENgIvqOp6EblTRGb7d3sEyBKRcuBnQEtX2PuBFBFZj5VoHlPVNQG/KqNrdq91rIH69YpmTsuPJSZcq5dapOXD/gqno/iaMwfmUFPfxAebTVucETyBVjH9J1a7wDIAVS0TkdyODlLVBcCCNttub3W/EatLa9vj6tvbbtisejX0LHLk0q+We/j2kDCuXmqR4m+HKJ7idCRf4XIJ3zy1kD+/s5mzBucgDnVTNqJLoOX5JlVtbnngby8wZdloU/25I1NsbDnopeaoMjScq5dapOZDbZnTUbRrYnEWB442s6Rsn9OhGFEi0E/kByLyS6CHiJwLvAi8Zl9YRsh5mqG2AjL6hfzSb1S4mZAfgysSfvWmFcCB8OrJ1MLlEuacUsjf3gvPBGZEnkATxC1ADbAWuB6r2uj/2RWU4YB9m6369djEkF96foUnvKb2PpHEdPC6oeGA05G0a2L/LHYeaKC08mvDhgyj0wLtxeTDapT+D1W9WFX/2clR1Ua4273WkeqlioNe9jcogzMjoHoJrCk30grCbsBcixiXcMGoXtz/frnToRhR4ISfSrH8WkT2AZuATf7V5G4/0XFGBKr+HHr2DfllI6p6qUVqr7DsydTirMG5rN5xkPK99U6HYkS4jn62/RSYDIxX1UxVzQROAyaLyE9tj84InerPISP0JYjXKjxMiJTqpRYp+bAvfH+hx8e6mD40l4c/DM9SjhE5OkoQ3wUuU9VjrXL+yfeuAK60MzAjhFRhz/qQz8EUcdVLLdILw7YnU4vpw/J4fU01+480d7yzYRxHR5/MOFX9Wp85Va0BIqDTuhGQuiqIiYUeGSG97IIKN+MjrXoJICkbGg9B82GnIzmunknxTCjOMOtXGyelowRxop8f5qdJtNi91loDIsRe3xKB1UsALhekFYZ1NRPAecN78eSn23B7fU6HYkSojhLEKSJyqJ3bYWBUKAI0QqD6c+gZ2vEPlXU+9h5VhkRa9VKL9AKoDe8E0S8rmZzUBBauD6+5o4zIccJPp6rGqGpaO7dUVTVVTNGienXI2x/e3NLMhF4RWL3UIjUfajY5HUWHzh2ex6MfhefAPiP8RejPNyOodq8LeQ+m1ys8lERi9VKLtN5h31ANUNIvk+37j7Kx2izIaHSeSRDdXcNBaKi1+vaHyI7DPnbW+xgWqdVLAMlZ4D5q/f3CWIxLOHtoLo9/Uul0KEYEiuBPqBEUe9ZZK8iFcA2It7a4KekVE/5Te5+IuCC9D+wL/2qmqUNyeWNNNYca3U6HYkQYkyC6u+o1IV8D4vUKN+N6BTrTfBhLK4S94b+OVUZSPKN6p/PSiiqnQzEijEkQ3V316pAmiN1HfGyp8zEyOwreeul9oCb8EwTA9KG5PLF0m1lxzuiUKPiUGiclxGtAvL3Vw9jcGGIjuXqpRc8+sL8cNPzHGQzLT6PZ62N5ZXjOQmuEJ1sThIjMFJFNIlIuIre083yCiDzvf36ZiBS1em60iCwVkfUislZEQj8PdbTzNFmro4WwBPF6hTuyey+1Fp8McUnWSPQwJ2I1Vj+xtNLpUIwIYluCEJEYrLWlzweGA5eJyPA2u30fOKCqA4G/AH/0HxsLPAX8UFVHAFMB08IWbHs3WPMKxcSH5HL7G3xsqPUyOidKEgRYAwz3bnA6ioCcOTCHxZtqzPxMRsDsLEFMAMpVdYt/udLngDlt9pkDPO6/Pw+YLtZiuucBa1T1cwBVrVVVr42xdk8hXgPinW0eRufEEB8TBdVLLdL7WD3BIkBKYizj+mXwYukOp0MxIoSdCaIQaP1OrPJva3cfVfUAdUAWMBhQEVkoIitF5L/bu4CIXCcipSJSWlNTE/QXEPV2rYaeRSG73BsVbsb1iqLSA1hLtO6JjBIEwNlDc3l62XbTWG0EJFwbqWOBM4DL/f9eJCLT2+6kqg+paomqluTk5IQ6xshXvTpkJYhDTUrpbi+n5kZZgkjJhaZDYT9grsWg3BRcAksrap0OxYgAdiaInUCfVo97+7e1u4+/3SEdqMUqbSxR1X2qehRrDeyxNsba/fi8sHcjZIUmQby/w8PwrBiS4qKoegmsAXMZRVATGaUIEWHqkFyeNNOAGwGwM0EsBwaJSLGIxAOXAvPb7DMfuMp//2JgkX+t64XAKBFJ8ieOs4DI+ARGitoKa/2H+JSQXG5BhZux0Va91KJnX2s+qwhxxsBsPizbx776JqdDMcKcbQnC36ZwA9aX/UbgBVVdLyJ3ishs/26PAFkiUg78DLjFf+wB4M9YSWY1sFJV37Ar1m6p+nPIGhCSSzV6lI92ehiXF6UJIqPY+ntGiOSEWCYUm8Zqo2O2znegqguwqodab7u91f1G4JLjHPsUVldXww67VllfbCGwpMpD/3QXaQlRVr3UIq0QDu+2VpiLT3U6moBMG5LLPz7YwvVTBuCKhkGLhi3CtZHasFv1qpA1UC/YEoW9l1qLiYWMvhHVm2lATgoJsS4+rvjaisKGcYxJEN2RqlVnHoIE4fYqi7Z5KInmBAFWd+HqNU5HEbCWxuonlprGauP4TILojg5UQmyi1Uhts0+rvfRKdpHVI8rfapn9rW7DEeSMgdksrahlz6FGp0MxwlSUf2qNdlWvhuxBIbnUgmgcHNee9N5QvydixkMA9IiPYdKALJ77bLvToRhhyiSI7mjnypCsQe31KW9XehjfHRKEK8bqFbY7cnozwZcjqz3e8J+R1gg9kyC6o50rIXOg7ZdZtddLagLkp3STt1lGsfW3jSBFWclkJMez6Iu9TodihKFu8sk1jlGF3Wsgy/4EsWCLm5JoHfvQnqyBVvdhImueo+lDc5lr1qw22mESRHdzYCvE9YAePW29jKry1lYPJflRsLRooJL984EdjKw6/dOKs9iw6xBb9x1xOhQjzJgE0d3sXBmS0sO6fVaddt/UbjQISwSyB8OOUqcj6ZT4WBdnDcnhcVOKMNowCaK72bkiZNVL43vFYC3v0Y1kD4aqZU5H0WnTh+bx0soqjjR5nA7FCCMmQXQ3Vctt7+KqqlaCiJalRTsjq781EWJzvdORdEpOagLD8tN4eVXbCZeN7swkiO7E67FWP7O5BLH5gI9GD/RP74Zvr5h4qwtxhPVmAjhnWB6PfrzVLCZkHNMNP8HdWM0XkJxr+xTfb1S4KemO1UstcobCto+djqLTRhSk4fMpH5ebxYQMi0kQ3cmulSEZQf3GFg8TumP1UovcYVZ3V2+z05F0iohw3vBe/PPDLU6HYoQJkyC6kx3LIdPeNSDKD3g51KQMzOjGb62EVEjJs8abRJjJA7NZU3WQiprIakMx7NGNP8Xd0I5lkDPE1ku84W+cdnXX6qUWOUNh64dOR9Fp8bEuzh6ayz+XmFKEYRJE99F0GOq2275I0Gvl3WTupY70GgXbl4LX7XQknXbu8F68sbbaLElq2JsgRGSmiGwSkXIRuaWd5xNE5Hn/88tEpKjN831FpF5E/svOOLuFlvEPMXG2XaLsgJe6JmVwpvndQY+ekJIbcVOAA6T3iGNi/ywzcM6wL0GISAxwP3A+MBy4TESGt9nt+8ABVR0I/AX4Y5vn/wy8aVeM3cqO5dYgLhu9Vu5mgqle+lLeSNiy2OkouuT8kb146tNtZuBcN2fnT70JQLmqblHVZuA5YE6bfeYAj/vvzwOmi79vpIh8E9gKrLcxxu5j+1JbE4SqMr/CzWkFpnrpmLxR1sBET+QtyJOf3oNh+Wk8s8ysONed2ZkgCoEdrR5X+be1u4+qeoA6IEtEUoBfAHec6AIicp2IlIpIaU1NTdACjzo+n1XFlDPUtkts3O+jwQ0De5rqpWMSUyG9L2z/1OlIuuTC0QU8tGQrTR6v06EYDgnXT/Ovgb+o6gn72qnqQ6paoqolOTk5oYksEtWWQXwyJGXZdolXy9xMLOjGg+OOJ/8UKHvb6Si6pDg7mb5ZSbywfEfHOxtRyc4EsRPo0+pxb/+2dvcRkVggHagFTgP+V0QqgZuAX4rIDTbGGt22L4Xcts0/weNTZX65m0kF3Whq70DlDYfacjgSmSXcb55ayH3vl5tSRDdlZ4JYDgwSkWIRiQcuBea32Wc+cJX//sXAIrWcqapFqloE3Av8XlXvszHW6Fb5ka3jH1bu8RLngr5ppvTwNTFxVpfXsnedjqRLBuamUNgziec/M6WI7si2BOFvU7gBWAhsBF5Q1fUicqeIzPbv9ghWm0M58DPga11hjSDYvhRyR9h2+pc3u5lYEGuql46nsATK3gKNzHWfvzW2kL8vKqeh2ZQiuhtb6wRUdQGwoM2221vdbwQu6eAcv7YluO7i0C5rkFx6b1tO3+xV3tji5o4zEm05f1RIL4TYHtb8TIXjnI6m0wbkpDAgN5m5n2zlR1PtX0vECB/h2khtBEvlx1Z3S5t+3X+ww0NBiovcJPNWOqHe42FD2xrWyHHx2D48+MEWDh6NrAkIjZNjPtXRbstiyLOvemneJjenF5rG6Q4VnGJNt3642ulIuqQwowfjizP523tlTodihJBJENGuconVSGqDg43Kxzs9nNadp/YOVEw8FI6Fja85HUmXfWtMIf9auZPKfUecDsUIEZMgolldldX+0LOfLaefX+7m1NwYUuJN43RA+k6C8ncjbjnSFj2T4pk1qhe/fs1MbtBdmAQRzbZ+aJUebGp/eO6LZs7sbaqXAtajpzXdyeaFTkfSZTNH5lO2p573Nu5xOhQjBEyCiGbl71oN1Db4otZLzVFlZI55C3VKv8mw4ZWInAYcIC7GxZWT+vE/r6zjaLOZyC/amU93tPL5YMv7Vr23DZ7Z2MyUPmbm1k5LL4TkbNiyyOlIumx0754MzEvhnoWbnA7FsJlJENFqz1pr/qWUvKCfusGtvFLmZmofU73UJcVT4fPnwRe5A88uP60fL6/ayYptB5wOxbCRSRDRqvw9yD/VllO/scXNoIwYss3Yh67JLIb4FNi6xOlIuiwtMY6rTy/mpudXmaqmKGY+4dFq80LbEsQT65uZ2td0bT0pA86GVU9GdCliQnEmA7JT+NWrpldTtDIJIhod3Q+710L+6KCfek2Nl91HlLF5JkGclKz+kJAW0W0RAFdOKuKTilpeXd12omYjGpgEEY3K37VG7sYGf36kx9Y2Mb1frGmcDoaB02HlU+CN3OkresTH8OOzB/KrV9fzxe5DTodjBJlJENFo42vWDKJBtq/BxzuVHtM4HSwZ/SCtV0SPrgbol5XMFRP78f25pew/ErnJzvg6kyCijafJmn+p9/ign/rxdc1MKoglLcGUHoJm4Lmw9kVoiuxf35MHZjO+KINr5i6n0R257SrGV5kEEW0qFlm9ZHpkBPW0jR7lqQ1uZvY3pYegSsmFvJGw6mmnIzlpl5T0ISUhlv98eiVub2SufWF8lUkQ0Wbti9D39KCfdt6mZgb2dFGQYt4yQTdwulXqO7jd6UhOikuE66f058DRZn72/Gq8PnU6JOMkmU97NHE3QNk70C+4CcLtVf5vdTMXDjClB1vEJ8OAafDp/wGR/aUaG+PiJ9MHs33/UX78rClJRDpbE4SIzBSRTSJSLiJfW05URBJE5Hn/88tEpMi//VwRWSEia/3/nm1nnFFj81uQPTDo1UuvlrvJ6iEMzjRdW23TZyIcrbUmWIxw8bEufnbuEPYcauLax0vNQLoIZluCEJEY4H7gfGA4cJmIDG+z2/eBA6o6EPgL8Ef/9n3AN1R1FHAV8KRdcUaVlU9A/2lBPaXHp/x9ZROzB8QF9bxGGy4XDJsNnz0UsdOBtxYf6+KmcwbhEvj2g0vZe6jR6ZCMLrCzBDEBKFfVLaraDDwHzGmzzxzgcf/9ecB0ERFVXaWqu/zb1wM9RCTBxlgjX91O2Lki6O0PL212kxovjMg2tZG2y+gHOUNg+SNORxIUsS4XPzizPyPy0/nGfR/x+Y6DTodkdJKdn/pCYEerx1X+be3uo6oeoA7IarPPvwErVbWp7QVE5DoRKRWR0pqamqAFHpFWPQ1FZ0Bs8PJos1f5S2kTFw+JQ8zAuNAYNAOqlkP1aqcjCQoR4ZtjCrn8tH5c9dhnPP3pNlQju52lOwnrn4UiMgKr2un69p5X1YdUtURVS3JyckIbXDjxuqH0YevLJYieXN9MQYqLIabtIXTiEmH4HPj4XmiOnqU9xxdl8j+zhvPPD7dwwzMrOdQYmethdDd2JoidQJ9Wj3v7t7W7j4jEAulArf9xb+Bl4EpVrbAxzsi3/hVIK4DM/kE75cFG5b6VzVw6zLQ9hFzOEMjoD8v+4XQkQVXQswd3zB6J26vMvHcJyyv3Ox2S0QE7E8RyYJCIFItIPHApML/NPvOxGqEBLgYWqaqKSE/gDeAWVf3Yxhgjnyp88lcYMiuop/1LaSPj82PonRrWhczoNeR8a02PCJ4SvD3xsS6+N7mYy8b35fonV3DXmxtp8piR1+HKtk+/v03hBmAhsBF4QVXXi8idIjLbv9sjQJaIlAM/A1q6wt4ADARuF5HV/luuXbFGtLK3raqIPhOCdsr1+7zML3fzb4NN6cExsQkw6jvw6QNweFfH+0eYkqJMfvfNkazYdoBv/P0jNuyK7KlGopVES4NRSUmJlpaWOh1GaKnCg2fC0AuttY6DwKfKRS8fYXx+LNP7mYFxjtv+CVSvhVl/smV2XqepKh+W7+PZZdv53uQi/mPaQOJiTKk1lERkhaq2O7un+Z+IZOv+BZ5G6DspaKd8Yl0zTV6YZhYECg99JkFSJnz8dyJ9lHV7RIQpg3L47TdHsnhzDXPu+5jNew47HZbhZxJEpGqqh4W/hPE/AAnOf+OOQz7+sqKJa0fHm/UewoWI1atpfwWsecHpaGyTlZLAzecNYfLALC75x1Luf78cj5mmw3EmQUSq9+6AvFGQ23Zwetd4fMqN7zXwjQFxFJqG6fASEw9jrrDWjdiy2OlobCMinD00j9/MGcHCdbv51gOfUFET+aPKI5n5JohEFYusrq3jvx+0U963sgmvKueb6bzDU2IajL3S6vq6c4XT0dgqJzWRX5w/lJJ+GXzr/z7hn0sqzMywDjEJItIc2AYvXQeTb4SE1KCc8qMqD0+ud3P9qaZqKayl9oJTL4cld8Puz52OxlYuEc4d3os7Zo/g5dW7uPgfn7B1X/QMHIwUJkFEkoYD8My3YfhFkH9qUE5ZddjHT95r4Edj4slMNG+HsJfRD065FN7/Q9SXJADy0hK57YJhjCpM55v3f2xKEyFmvhEixdH98Pg3rDaHYd8IyikPNytXLzjKrAGxjMg2vZYiRmb/L0sSWz9wOhrbuUQ4f2Q+v/7GCF5dvYuL/u9jNu02PZ1CwSSISLCvDP45DbKHwrjvWT1bTlKTV7lu4VH693Qxs9i0O0ScjH5Qcg189k9Y/SzR2AW2rV7pidx6wTAmFGXy7QeX8se3vqCh2YzCtpNJEOFM1Vrj4ZFzrVLDuKuCkhw8PuXGdxtQhStHmJlaI1ZqLzjth7DtQ1j026hYR6IjLhGmD8vjD98axZqqOs7+02LeXFttZoi1iRlJHa6qP4c3f2GtMnb6jZBRFJTTur3Kj99rYM8RHz8tSSAuxiSHiOf1WKsJ7tsEZ/w0aO1TkWD9rjqe+nQb2SkJ3DZrGGP6Bnc1xe7gRCOpTYIIN3vWw+K7YNvHMPo71hTeruC0Dxx1Kz965yj1zcqN4xKIN8khutR8ARtehcJxMO4a6NEz+NfwNELTYfA0gwBxyZCYCuJcG5bXp3ywuYaXV1UxundPfnrOYEb1TncsnkhjEkS4U7UGQH3yN6heA8Nnw+DzIa5H0C6x54iP7791lOwewvdHxxPrMskhKrkbYcsi2LkKhs6yljHtUqJQqN8DezZYiae2HOqqwNsM8SkQEwfqs67naYCkbOjZF7IHQc5QqzNFEN+/gWj2+Fi0aQ8L1lQzIDeFa8/oz7ShucSY9/oJmQQRrtwN1vQJS++3PnjDvgH9p1ojZ4Pos2oPN7zbwLQ+scwZFGvaHLqDo/uh8kPrB0fhGCiaAgWnQPxxxs74vFC3HWo2we61sHsd+JohoxjSCq1bSo6VHNq+f7weaNhvJZS6XVC3Aw5VWQmjYCwUjrWShis0nSE8Xh9Lt9Ty7sY91DW4+bexvfnmmEIG5wVn3FC0MQki3BzebS1Ov2IuZA2yfunlnxqUBujWmr3K31c28dQGNz8YHc+YPNOVtdtpPmp94dd8AQcqITEdUvIgPhkQcB+x2rnq90KPDEjvDel9rK60ydldf0963XBwG9RWWLeGWsgbAYUlkD8G0gus69tsW+0RPirfx7IttSTFx3LO8DymDM6hpF8GyQmm9x6YBBE+dq2GpffB5oVQfJY1TXd622W6g6N0t4dfLmkkNR6uGRVPZg/TYa3b8/ng6D5oOGi1JQhWaTUhHZKyIDa4JdevaKq3EsX+cutfcUHBqVAwBnqNtq5vI1Vl674jrN5xkA3Vh9hSc4R+WUmM6duTUYU9GZqfyqDcFFITu98aKCZBOMnTDBvnW3PoHNwOQy6wGp4TUmy5XMVBL/d81sTy3V4uGxbHpIIYU6VkhBdVOFJjtWsc2Ar7t1olm/xTraSRN9J6bKNmj4/K2iNU1NSzY/9Rqg40sOPAUVIT4ijKTqI4O4X+Ocn0y0yiX1Yy/bKSorbEYRKEE2o2w6onYfUzVrF98Exr3YYg9UhqTVVZVu3l0bXNLKv2cH5xHDOKY0mMNYnBiAA+Hxyuhv1brIRxoBKSc6xk0esU6DXSavuwOwxVauubqa5rYM+hRvYebqLmcBN7DjVSXddIUnwM/bKS6Z+dzIDclGP/9stKIiE2cqtvHUsQIjIT+CsQAzysqne1eT4BeAIYB9QC31HVSv9ztwLfB7zAjaq68ETXcjxBqMLejbDpTVj/Lzi8x2pwHjjdqtMN+uWU9ft8LKx082qZGwWm94vlrD4mMRgRzue1eky1JIsD2yAt3ypZ5I2E3GG2V0m1paocOOpmd10Duw81sbuugT2Hm9h1sIG9h5rIS0ugf04Kg/JSGJybyoDcZPpnp5CRbGO1XZA4kiBEJAbYDJwLVAHLgctUdUOrff4DGK2qPxSRS4GLVPU7IjIceBaYABQA7wKDVfW44+pDniCaDsPeL6B6NWxfCpUfWfWqvUugz0TrjRyk0oKqUtOglO33saHWS+luL59Ve+kRB2NzYzitIIYBPV2mKsmITl4PHNppJYy6KithxMRB1kDrllEEPftAan7QewAGwuPzsafOShY76xrYXddIdV0DOw80EBvjoigriaKsZIqzk+mTmURhRg8K0nuQm5ZAYpzzJQ+nEsQk4NeqOsP/+FYAVf1Dq30W+vdZKiKxwG4gB7il9b6t9zve9U4qQXia4PPnrNlSvW6ry6mnEdxHrca1pkNwZB8c2QuHdlnPt8gcANmDrWJwakGHvT7q3cK8bYkccQvNPsHtg2af0OCFBo9w2OOirlk40OSipsnFweYvG5cTXEpRipf+qR6GpnvITjArbhndkVqfyfoaf6P7fusH2zEua/2M+BTrFpdorecdm2AlFles/8eby/o3Nd/qhhv8KKlrcLO7rpHdhxrZe7iRfYeb2VffRJPnq5/dxDgXPZPiSe8RR1piLCkJsSQlxJIUF0NiXAzxsS7iY13ExbiIcwkxMUKMCDEuQUQYkpfKGYOyuxTniRKEna0uhcCOVo+rgNOOt4+qekSkDsjyb/+0zbFf6+4jItcB1/kf1ovIpq4EmhxHj6HZrnaXZqs5quQkffVL36t4PF7cVmr93H97MaBrHZUerp2u/ITOxBerHnVhvaF2AZ905mDA3VBPXA/763DtZF5DeAjn19DyKRU5cf/Z5qP1xCd9+RoUaPTQYGNoxyEgrlhxuTo9OMl7tI6YpC8b8tXn87r3blndxUD6He+JiG6WV9WHgIfsvIaIlG476Gs3u0YKESltrKs1r8Fh5jWEBxEpbYiC1+Cp22v7a7Czc/xOoHXrbG//tnb38VcxpWM1VgdyrGEYhmEjOxPEcmCQiBSLSDxwKTC/zT7zgav89y8GFqnVKDIfuFREEkSkGBgEfGZjrIZhGEYbtlUx+dsUbgAWYnVzfVRV14vInUCpqs4HHgGeFJFyYD9WEsG/3wvABsAD/OeJejDZzNYqrBAxryE8mNcQHsxrCFDUDJQzDMMwgstM0GMYhmG0yyQIwzAMo10mQQRARO4WkS9EZI2IvCwiPZ2OKVAiMlNENolIuYjc4nQ8nSUifUTkfRHZICLrReQnTsfUFSISIyKrROR1p2PpKhHpKSLz/J+Fjf7BsBFDRH7qfw+tE5FnRSTR6ZgCISKPisheEVnXalumiLwjImX+f21Za9UkiMC8A4xU1dFY04fc6nA8AfFPd3I/cD4wHLjMP41JJPEAP1fV4cBE4D8j8DUA/ATY6HQQJ+mvwFuqOhQ4hQh6PSJSCNwIlKjqSKyOM5c6G1XA5gIz22y7BXhPVQcB7/kfB51JEAFQ1bdV1eN/+CnWuIxIMAEoV9UtqtoMPAfMcTimTlHValVd6b9/GOtLyZ5FNGwiIr2BWcDDTsfSVSKSDkzB6nmIqjar6kFHg+q8WKCHf8xVEtbEBGFPVZdg9fJsbQ7wuP/+48A37bi2SRCddw3wptNBBKi96U4i6su1NREpAsYAyxwOpbPuBf4biOTJs4qBGuAxf1XZwyKS7HRQgVLVncA9wHagGqhT1bedjeqk5Klqtf/+biDPjouYBOEnIu/66ybb3ua02uc2rCqPp52LtHsSkRTgX8BNqnrI6XgCJSIXAntVdYXTsZykWGAs8ICqjgGOYFO1hh38dfRzsBJdAZAsIlc4G1Vw+AcX2zJeIaLnYgomVT3nRM+LyNXAhcB0jZzBI1ExZYmIxGElh6dV9SWn4+mkycBsEbkASATSROQpVY20L6cqoEpVW0pv84igBAGcA2xV1RoAEXkJOB14ytGoum6PiOSrarWI5AN77biIKUEEwL/w0X8Ds1X1qNPxdEIg052ENbFmuXwE2Kiqf3Y6ns5S1VtVtbeqFmH9/RdFYHJAVXcDO0RkiH/TdKyZDiLFdmCiiCT531PTiaBG9na0nqboKuBVOy5iShCBuQ9IAN7xz8r7qar+0NmQOna86U4cDquzJgPfBdaKyGr/tl+q6gLnQuq2fgw87f+xsQX4nsPxBExVl4nIPGAlVjXxKiJkyg0ReRaYCmSLSBXwK+Au4AUR+T6wDfi2LdeOnNoSwzAMI5RMFZNhGIbRLpMgDMMwjHaZBGEYhmG0yyQIwzAMo10mQRiGYRjtMgnCMLpIRP4iIje1erxQRB5u9fhPIvIzEYkVkRoRuavN8Yv9M+1+LiLLReTU0EVvGB0zCcIwuu5jrNG4iIgLyAZGtHr+dOAT4FysWYAv8Q/Sau1yVT0F+D/gbtsjNoxOMAnCMLruE6BlTYQRwDrgsIhkiEgCMAxrYNZlWFNlb2+1f1tLieCJFI3oZEZSG0YXqeouEfGISF+s0kLLl/wkoA5Yi/Uj7BzgeqAnVrL4pJ3TzQResT9qwwicGUltGCdBRJ4GXsNalOnPWAnidKwEkQWUAhep6uUikgWsBopU1Ssii4F8IB5IAU71T0ttGGHBVDEZxslpaYcYhVXF9ClWCaKl/eEy4BwRqQRWYCWNs1sdfznQH2vRl7+HLGrDCIBJEIZxcj7BmgZ+v6p6VXU/VlXSJKzSwplAX1Ut8s/o+p9YSeMY//Tx/4M12+jQ0IVuGCdmEoRhnJy1WL2XPm2zrQ6YhjW9d1Or514FvuFvxD5GVRuAPwE32xuuYQTOtEEYhmEY7TIlCMMwDKNdJkEYhmEY7TIJwjAMw2iXSRCGYRhGu0yCMAzDMNplEoRhGIbRLpMgDMMwjHb9f8dC0EujbvH8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Density plot of WAR by pitcher type\n",
    "\n",
    "sns.kdeplot(x = pitchers_small['WAR'], shade = True, hue = pitchers_small['pitcher_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='WAR', ylabel='age_group'>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot of WAR by age_group\n",
    "\n",
    "sns.boxplot(x = 'WAR', y = 'age_group', data = pitchers_small, orient = 'h', palette = 'rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WAR')"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of WAR and ERA\n",
    "\n",
    "plt.scatter(x = pitchers_small['ERA'], y = pitchers_small['WAR'])\n",
    "\n",
    "# plot and axes titles\n",
    "plt.title('WAR ~ ERA')\n",
    "plt.xlabel('ERA')\n",
    "plt.ylabel('WAR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1330d6f98>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 434x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of WAR and ERA colored by pitcher_type\n",
    "\n",
    "sns.relplot(x = pitchers_small['ERA'], y = pitchers_small['WAR'], hue = pitchers_small['pitcher_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age_group', ylabel='count'>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot of the pitchers in each age_group\n",
    "\n",
    "sns.countplot(x = 'age_group', data = pitchers_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Clayton Kershaw: ERA by Season')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot of Kersaw's ERA in each season of the data\n",
    "\n",
    "sns.lineplot(x = 'Season', y = 'ERA', data = kershaw)\n",
    "plt.title('Clayton Kershaw: ERA by Season')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pitchers ERA by Season ID')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ukp7B0/N38fu+S4xqU51XetTJtTvi8U61cHdx5p3fDpJqtjDpwUa4mYrc1WVFw6Yv4fx26DcdvOy+TpMQt8VuHx+UUj2BK1rr7Tls9gsQpLWuD6wEZmdzrLFKqW1KqW1Xr0pXlhC3KyYxjQenbWbZ/ku82jOMV3uG5Xkcw5h2NXird11WHbzMmDnbSU7LsHO1JVDUcfjzbQjpDuH9HF2NENmyZ9tja6CXUuoU8D1wl1JqXtYNtNZRWutU681pQMStDqS1nqK1bqK1blKmTJlbbSKEyMXZ6CT6fbWBvefjmDS4MaPaVM/3MR5qGcQH/eqz7uhVRszaQmKq2Q6VllAWCyx5ApxdoefHIFe3ZCsvS3I3a9as9tq1a2269saoUaOqvPnmm9fnYmjTpk2tAQMGVMu8PWbMmMqvv/56uez2f+qppyouXrw4p5b4fDt16pTL3XffXSO37bJbonzu3Ll+27dvz/e02XYLD1rr/2itK2utg4CBwJ9a6yFZt1FKVchysxfGwEohhI3tORdL3y//JioxjW9GN6dHvQq575SN/k2r8OmAhmw9FcND0zcTn5L/ZYnFLWyfCafXQ9e3oVRFR1dTqNlySe78aNOmTcKmTZu8ATIyMoiJiTEdPnzYI/PxrVu3erdt2zYhu/0//fTTC3369Llmq3rS09MJCgpKX7Zs2YnbPcbixYv99uzZ45H7ljcq8FFPSqk3lVK9rDefUErtV0rtBp4Ahhd0PUIUd6sPXWHA5E24mZxZ+HArmgbd+d/b3g0rMWlwI/aej+PBqZuJSUyzQaUlWOxZWPl/UL09NB7q6GoKteyW5E5ISFA9e/asUaNGjbpdunSpmZKScr3p5sEHH6waHh5eJzg4uG7m0tgAlSpVqvfoo49WCg0NDQsPD6+zfv16zzZt2tSqUqVK+AcffPCvZu6OHTsm7Nixwxtg+/btHrVr10728vLKuHr1qnNycrI6fvy4e+vWrZOee+65CuHh4XVq1apVd9CgQdUsFmMpjH79+gVlLtldqVKlek8//XTFsLCwOiEhIWGZrSghISFhkZGRzhaLBT8/v4ZffPFFAEDfvn2DFi1aVGrixIkBd911V3CLFi1CWrVqVfvw4cOutWrVqgvGFNs9evSoUbNmzbpdunSpWb9+/dCsrS+PP/54pdq1a4c1aNAg9OzZs6aVK1d6rVq1yu+///1v5dDQ0LD9+/e75fV1KJBJorTWa4A11u9fy3L/f4D/FEQNQpRE3205w38X76NOBR9mDG9KWR/bLep3d3gFJj/kxPh5Oxg0dRPzRjcn0DvPf3tEJq1h6VOgM6DXxKLTXbH40SpcOWDTbgHKhiXRZ9JtLck9YcKEsh4eHpYTJ07s37x5s0fr1q2vT0n98ccfny9XrlyG2WymVatWtTdv3uzRvHnzZICqVaumHTp06MCoUaOqjBw5Mmjz5s2HkpOTnerVq1f35vkigoKC0p2dnfXRo0dd//rrL68WLVoknj9/3uXPP//09vf3N4eEhCS7u7vr559//sqECRMuAvTp06f6999/7zt48OC4m88lMDDQfODAgYPvvfdemffee6/c/PnzTzdp0iRh1apV3jVr1kytXLly6vr1670fe+yxqB07dnjPnj37zKxZs/z379/vuWfPnv3lypXLyLrQ1ocffljGz88v4/jx4/u3bt3q3rJly7qZjyUnJzu1bNky4fPPPz8/fvz4yp9//nmZDz744GLnzp1je/bsGTdixIh8LWgj11sJUQxprfl4xWH+89Ne2gQHMn9sS5sGh0x3hZZjxrCmnIpKZMDkjVyKS7H5cxR7u7+HY6ug0/+Bf5Cjqyn0sluSe/369d4PPfRQFEDz5s2TQ0JCkjL3mT17dumwsLA6YWFhYUePHnXfvXv39f8M/fv3jwWoV69eUuPGjRP9/f0tFStWNLu6uloiIyP/dUlRREREwurVq702btzo3bZt24RWrVol/v33317r1q3zbt68eQLA77//7lO/fv3QkJCQsA0bNvjs27fvlt0CgwcPjgFo1qxZ0tmzZ90A2rZtm/DXX395//HHHz6jR4++cvDgQY+TJ0+6lCpVKqNUqVIW6zbx5cqV+9eI5Q0bNngPGjQoGqBp06YpWX8GLi4ueuDAgXHWc0g8ffr0Ha3uKdNTC1HMpGdYeGnhXhbuOMeAJlV4u2+4XedlaFMrkDkjmzNi5hYGTNnIN6ObU9nfth9Ii61rl2HZS1ClOTQb6+hq8ieXFgJ7yGlJ7uz2OXTokOsXX3xRbvv27QfLlCmT0a9fv6CUlJTr/yEyl9N2cnK6YTEtJycn0tPT/9UM1KpVq4QNGzZ4Hzp0yKNp06bJNWrUSPv000/LeXt7ZwwfPjwyKSlJPfvss9U2b958IDg4OP2ZZ56pmPX5ssp8bpPJpM1mswLo0qXLtSlTppQ9d+5c6vvvv39+yZIl/vPmzfNv0aLF9bESnp6ellsdLycmk0lnLltuMpnIfL7bJS0PQhQj11LSGTlrKwt3nOPpziG8169egUzo1Kx6aeaNbk5MYhoDJm/iVGSi3Z+zWPjtOWPJ7V5fgJP8Oc5NTktyt2nTJuGbb74pDbB161b3I0eOeALExMQ4e3h4WEqXLp1x9uxZ05o1a3zvpIZ27dolrFq1ys/Pzy/DZDJRrly5jPj4eOedO3d633XXXYlJSUlOAOXLlzfHxcU5/fLLL/lazjs4ODg9JibGdPLkSfewsLC0li1bJkyaNKl8+/btsx2Imally5YJ33//vT/A9u3b3Y8cOZLrQEhvb++M+Pj4fP/yyW+rEMXE5fgU+k/exMbjUXx4f32e7FyrQBezalTVn2/HtCApzUz/yRs5dsVmg8qLpwM/w8El0OFFKBPi6GqKhJyW5H7uueeuJCYmOteoUaPuK6+8UiksLCwRoGXLlsnh4eFJNWvWDO/fv3+NiIiIXN+Ec9KsWbPk2NhYU5MmTa4fJzQ0NNnb2zujQoUK5sDAwIwHH3zwap06dep27NgxpEGDBvlO0g0bNkysXr16CkCHDh2uXblyxaVz5865/od6/vnnr0ZFRZlq1qxZ9z//+U+l4ODgFH9//xwnZHnwwQejJ06cWL5OnTr5GjApS3ILUQwcuXyN4TO2EJeczpdDImgf4rj5UI5cvsbgqZvRWjN3VHPCKma3rE0JlhQNk5pDqQow+g9wdrH7U5akJblLKrPZTFpamvL09NT79+9369q1a8jx48f3ZXaP5FdOS3JLy4MQRdzG41H0+2oD6RbN/HEtHRocAELK+fDDuBa4mpwYNHUTe87FOrSeQmnZfyA5GnpPKpDgIEqGa9euOTVr1iy0du3aYX379q35ySefnL7d4JAbGTApRBG2ZPcFnvthN1UDPJk1ommhGahYo4w3P4xryaCpm3hw6mZmjWxKRLUCnc+n8DqyAvZ8D+2eh/L1HF2NKEb8/f0t+/btK5DJFqXlQYgiSGvNlLXHeeK7nTSs6sfC8a0KTXDIVKW0Jz+Ob0mgjxsPTd/CxuNRji7J8VLijTkdyoQa4UGIIkrCgxBFTIZF8/qS/bz72yHuqV+BOSOb4etZOJu+K/h6MH9cCyr7ezB85hb+OlLCF7Zb9X8Qf8G4usIkE2qJokvCgxBFSEp6Bg/P287sjacZ07Y6nw9shLtL4V4au6yPO9+PbUnNMt6Mmb2NFfsvObokxzi5DrbNgBaPQJWmjq5GiDsi4UGIIiI6MY3BUzex8uBl/u/eMF65J+/LaTtaaS9XvhvTgjoVS/HINztYuueCo0sqWGlJsORx8K8Od/3X0dUIccckPAhRBJyJMpbT3n8hnq8ebMyI1vlfTtvRfD1dmDeqGY2r+vPEdztZuD3bSQGLn9XvQMxJ6PU5uBausSlFjbOzc0RoaGhY7dq1w8LCwuqsXLnSK7/HeOmll8rnZbvslrHOzpkzZ0w9e/asUaVKlfC6devWad++ffCePXuy7Z/KuqhVUSPhQYhCbvfZWO776m9ikozltO8Ov/3ltB3Nx92FWSOb0qpmIM/+uJtvN59xdEn2d24bbPoSIkZA9baOrqbIc3Nzsxw6dOjA4cOHD7z11lvnX3755cp53ddisZCRkcHEiRNt/p/IYrHQq1ev4Hbt2l07e/bsvv379x987733zl+4cKFwDki6QxIehCjE/jh4mYFTNuHhaiyn3cQGy2k7mqeriWnDmnBXaFleXrSXGetPOrok+zGnws+Pgk8F6PKmo6spduLi4px9fX3N1u+dWrZsGZK5xPW8efP8wPh0HxQUFN63b9+gkJCQugMGDAhKTU11Cg0NDevVq1d1gNdff71crVq16taqVavum2++Wfbm57FYLIwbN65yrVq16oaEhIRNnTr1X1NOL1261MdkMumsK3G2bNky+e67707Iy/5JSUnq/vvvDwoJCQmrU6dO2C+//OIDMHHixICuXbvWbNu2ba1q1aqFjx8/Ps9hyZ5kngchCqlvNp/m1cX7qFvRlxnDm1LGp/iMznd3cebrIRE88d1O3lx6gBRzBo90CHZ0Wba3dgJcPQSDfwT34jXT5qt/v1rlWMwxm/bBBPsHJ73V+q0cF9zKfONPTU1VkZGRLr/99tsRMBaL+vXXX4+VLl3acvHiRVPz5s1DBw8eHAtw5swZt+nTp5/s1KnTKeu2/ocOHToAsG7dOs9vv/02YPv27Qe11kRERNTp1KnTtdatWydnPuecOXP89u7d63Hw4MH9Fy9eNDVr1qxO165dE6pVq5aeuc2ePXs8GjRokMQtZLd/1m3ef//9skopjhw5cmDnzp3uPXr0qHX8+PF9AAcOHPDcvXv3AQ8PD0twcHD4c889dzk4ODj9Vs9VUKTlQYhCRmvNh8sP8cqifbQPKcP3Y1sUq+CQydXkxBeDG9G7YUU+WHaYj1ceoahNl5+jS3th/cdQfwCEdHV0NcVGZrfFyZMn9y9atOjoiBEjqlssFiwWi3rqqacqh4SEhHXs2DHkypUrrufOnTMBVKhQIa1Tp063XGNizZo13j169IgtVaqUxdfX13LPPffErF692ifrNuvWrfPp379/tMlkokqVKubmzZsnrF+/Ps/BKS/7b9iw4fqS4o0aNUqpWLFi2t69e90B2rRpEx8QEJDh6empg4ODU44fP+7wPwjS8iBEIZJmtvDSwj38tPM8g5pV4a3e4ZgKYFVMRzE5O/Fx/4a4mZyY+MdRUtMzeKl7aIEu6GUXGWaju8LDH+5+z9HV2EVuLQQFoXPnzokxMTGmixcvmhYuXOgbFRVl2rt370E3NzddqVKlesnJyU5we0tY51e9evWSFy9enK8VNPMq61Lhzs7O+lZLhRe04vtXSYgiJj4lnRGztvDTzvM81zWEd/vWs2lw0FqTlJ5EZHIkZ+PPcjj6MDsu72D9+fWsOLWCxccW883Bb5i2dxpzD8wlLjXOZs+dE2cnxXv31eehFtWYvPYEry/Zj8VSxFsgNkyEi7uhxwTwLPrjVAqrnTt3ulssFsqVK2eOi4tzDgwMTHdzc9O//PKLz4ULF1yz289kMunU1FQF0LFjx4TffvvN79q1a07x8fFOv/32m3/Hjh1vWMGyXbt21xYsWFDabDZz4cIF05YtW7zbtm17Q0vGvffeey0tLU1NmDAhMPO+zZs3eyxbtsw7L/u3bt06Yd68eaUB9uzZ43bx4kXX+vXrp9ji52QP0vIgRCFwKS6F4TO3cOxKAhMeaEC/xpVIzUglMT2RJHMSSelJJJuTb7idZE4yblsfy3o76zZZ/9Xk/U150q5JDA4dzLC6w/B187Xj2YOTk+LN3nVxd3Fi6rqTpJotvNO3Hs5FZB6LG0QehTXvQZ17oW4fR1dT7GSOeQAjEH/11VenTCYTo0ePju7evXtwSEhIWP369ZMyl7S+FeuS2WHh4eFJS5YsOTl48OCoxo0b1wF46KGHrmYd72C9L3bDhg3ederUqauU0m+88ca5qlWrmrNu4+TkxJIlS44/8sgjVT777LPybm5uunLlyqmff/752a5duybcav/Dhw9fDzgvvPDClaFDh1YLCQkJc3Z2ZvLkyac8PDwKbYqWJbmFuENaa9IsaSSlJ/3rzf3mf294s09PItGcSGTiNQ5djiSDFPy8LWToVJLMSVh03lpanZQTniZPPF08b/jXy8Xr+m0Pk4dxO8s2XiYvPFw8/rmdZfvT8aeZvHsyK06vwMvFi8GhgxkaNhQ/dz+7/yw/WXmEiX8eo0/Dikx4oEHR6raxWGBmd2OQ5KNbwKecoyu6TpbkFvmV05Lc0vIgShStNemW9BvfzG9+k8/8/qZP8snpySSaE2+5bYbOyNPzK9QNb+6WDBfORGbghDetgkKoUMoXT1P2b/aZQSDrm72bs5vNxwiE+IfwUYePOBJzhMm7JzN171S+OfgND9Z50K4hQinFM11r4+bizIfLD5NqtvDZwEa4mopIgNg6Fc5ugj5fFargIIStSXgQhVp6Rvq/P7ln92Zv/SR/w5t95vZZtjVrc+5PbHXzJ3kPkwf+7v5UMlX615v7zf9e/yRv8sTDxQgD7s7u19/of951nud+3E1QgBezRjajkp+HvX6Mty0zRByNOcrkPZOZtndagYSIRzsG4+7izFtLD5A2bzuTHmxc6NfwIOY0rHoDgjtDg0GOrkYIu5LwIBzGoi3M2DeDPVf3ZPtmn27J+6XMHiaPf73Z+7r7UsFU4Z9m/Ly82Vvvcze546Rs/4lXa83Xf53g/WWHaF69NFMealJoV8XMVMu/FhPaT+Bo/RtDxOA6RneGv7vtB5mPalMdN5MT/128jzFztjHloSZ4uBbSAKE1/PIEKAU9PzX+FaIYk/AgHMKiLby58U0WHl1IDd8alHIthY+rD+W8yl0PAf96s8/8/hZv9u7O7jg7FdI3liwyl9Oeu+k09zaoyIQH6uNmKvx1Z8oMEcfqH2PynslM3zudbw9+a7cQMaRFNdxdnHlhwW6GzdzCjOFN8XYrhH+2ds6FE2vgno/Ar4qjqxHC7grh/0JR3GVYMnh94+ssPraYMfXG8Hijx4v+df15kJyWwRPf72TlgcuMa1+DF7uFFplVMW8W7B/Mh+0/ZFz9cXYPEfdHVMbN5MRT83fx0PTNzBrRDF+PQtRSE38Rlv8XqrWGiJGOrkaIAlFERiGJ4iLDksFrG15j8bHFPNzg4RITHKISUhk0dROrDl7mjV51+U/3OkU2OGSVGSIW9V5E+8rtmb53Ot0WduPT7Z8SkxJjs+e5t0FFvnywMfvOxzF46iaiE9Nsduw7ojX8+gxkpBorZjrJn1RRMtj9N10p5ayU2qmUWnqLx9yUUvOVUseUUpuVUkH2rkc4jtli5uX1L7Pk+BIebfgojzR8pEQEh1ORifT7agMHL8bz1YMRDGsV5OiSbK6mX00+aP8Bi3ovokPlDszYN8PmIaJb3fJMGdqEY1cSGDRlE1evpdrkuHdk30I4/Bt0fAUCajq6mhIhu2Wv72R56w0bNnjMnz/fZpOZVKpUqd7FixdNYKydUalSpXp///33HY2IfuaZZyq+9tprheYSnoKIyU8CB7N5bBQQo7UOBj4B3i+AeoQDmC1m/rPuP/x28jeebPwk4xuMd3RJBWLnmRju+2oDccnpfDumBXeHl3d0SXZ1Q4io8k+I+GT7J0SnRN/x8TvWLsvM4U05E53EgMkbuRiXnPtO9pIYCb+/AJUioOWjjqujBLHXstfbtm3z/PXXX20+E9rmzZs9Bg4cWHPevHnHb554Kjtmc96vBnMku4YHpVRl4B5gWjab9AZmW79fAHRSJeGjaAmTbknnhbUvsOzUMp6JeIbR9UY7uqQCsfLAZQZN3YS3m4mFD7cioppdpr0vlGr61eSDdh+wuPdiOlTpwMx9M7l74d02CRGtggOZO6oZV66l0n/yRs5G33IhQ/v7/UVIiYfek6AIDNYtDnJa9jrrdocPH3aNiIioHRYWVicsLKzOypUrvQD69u0bNHfuXL/M7Xr16lV93rx5fv/73/8q/vLLL/6hoaFhU6dO9b98+bJz586da4aEhIQ1aNAgdPPmzR5gfPp/4IEHgpo1a1a7cuXK9d5+++1/Ld+daffu3e79+vULnjFjxsmOHTsmAfz000+lGjZsGBoWFlane/fuNeLi4pzAaKl4+OGHK4WFhdWZMWOG/9tvv122Zs2adUNCQsJ69uxZI/OYBw8e9LjVc3fu3Llm3bp16wQHB9fNnB57xowZ/qNHj64M8NZbb5WtXLlyPYADBw64Nm7cOPQOXgbA/gMmPwVeAHyyebwScBZAa21WSsUBAcANM5gppcYCYwGqVq1qr1qFHaRnpPPcX8/x59k/eb7J8wytO9TRJRWIuZtO838/76NeJV+mD29KoLfDF8FziBp+Nfig3QeMrz+eyXsmM3PfTL479B0DQwcyvO5wSrvf3roPTYJK883o5gydsYUBkzfyzZgWVA/0snH1OTj0G+xbAB1ehrJ1Cu55C5ELL79SJfXoUZsuye1Wq1ZSxXffyXbBrZyWvc6qYsWK5nXr1h3x9PTUe/fudRs0aFCNffv2HRw9enTkJ598Uu6hhx6KjYqKct6+fbv3woULT0ZHRztv27bNa86cOWcAhg0bVqVBgwZJq1atOr5kyRKfYcOGVc9cwvvYsWPuGzZsOBwbG+tcp06d8Oeff/6qm5vbv6ZqHjBgQPCUKVNOduvWLQHg4sWLpnfffbfC2rVrj5QqVcryyiuvlH/rrbfKTZgw4SJAQECA+cCBAwcBypYtW//06dN7PTw8dGRk5PVkmt1zf/PNN6fKlSuXkZCQoBo1ahQ2ZMiQmM6dO1/7+OOPywP8/fff3n5+fuaTJ0+6/PHHHz4tW7a8dnO9+WW3lgelVE/gitZ6+50eS2s9RWvdRGvdpEyZMjaoThSEtIw0nlnzDH+e/ZOXmr1UIoKDxaJ5f9khXl28j461y/Ld2BYlNjhkVcOvBu+3e5/FvRfTsUpHZu2bxd0L7+bj7R/fdktEgyp+fDemBSlmC/0nb+To5Tv+e5g3ybGw9GkoWxfaPF0wzynyJS0tTQ0ePDgoJCQk7IEHHqh5/Phxd4B77rkn4dSpU+4XLlwwTZ8+vfQ999wT4+Ly7x6PLVu2+IwaNSoKoFevXtdiY2NN0dHRTgBdu3aN9fDw0BUqVDCXLl06PXPZ75u1bt06fvr06YGZ3RBr1qzxOn78uHuzZs1CQ0NDw77//vuAM2fOXF/bYujQodcHB9WuXTu5b9++1b/88svSLi4u14NJds/9/vvvl6tdu3ZYREREnUuXLrns37/fvWrVquakpCSnmJgYpwsXLrg+8MADUStWrPBZv369d7t27W5oqbkd9mx5aA30Ukr1ANyBUkqpeVrrIVm2OQ9UAc4ppUyALxBlx5pEAUnNSOXp1U+z7vw6/tv8vwwIHeDokuwuzWzhhQW7WbzrAoObV+XNXnWL1roMBSAzRIxrMI4pe6Ywe/9svj/0PQNrD2RY3WEEeATk63hhFUsxf2wLHpy2mQFTNjF3VDPqVrTvIl6s+C8kXoFB34Ep24Ubi72cWgjsJa/LXr/zzjvlypYtm75w4cKTFosFDw+PiMzHBgwYEDV16tTSCxcuLD1z5sxT+a0hayuDs7MzZrP5ll3tU6dOPTNixIhqQ4cOrfbtt9+e1lrTpk2b+F9++eXkrbb38fG5vpjN6tWrj/7+++8+P//8s++ECRMqHD58eH92z7106VKfv/76y2fbtm2HfHx8LM2aNauduRR5kyZNEiZNmhRYs2bNlI4dOyZMmTIlcPv27d5ffvnlufye983s9pdNa/0frXVlrXUQMBD486bgALAEGGb9/n7rNvZZqSsjHQ7/blxaJewqxZzCk38+ybrz6/i/lv9XIoJDfEo6w2duYfGuCzzfrTbv9AmX4JCDGr41eK/teyzqvYi7qt7F7AOz6f5Tdz7e9jFRyfn7/FCrnA/zx7XE3eTEoCmb2HU21j5FAxxfbUwI1epxqNTYfs8jbimnZa+zbhcXF+dcoUKFdGdnZ7788suAjIx/1p4ZP3585OTJk8sBREREpACUKlUqIyEh4fp/2ObNm1+bOXNmABjjLPz9/c2lS5fO20p1Vk5OTixevPjE0aNH3Z966qmKHTp0SNy2bZv3vn373ADi4+Od9uzZ869myYyMDI4fP+567733Xps0adL5hIQE57i4uGwH1cTGxjr7+vpm+Pj4WHbu3Om+e/fu6/13bdq0SZg0aVK5tm3bJrRq1Sppw4YNPq6urpaAgIC8LcaT0/nd6QHySyn1plKql/XmdCBAKXUMeAZ4yW5PvOsb+G4g/DgMEqVxw16Szck8/ufjbLiwgTdbvcn9Ifc7uiS7uxiXTP+vN7LlZDQf92/Aox2DS8QlqLaQGSIW915Mp6qdroeIj7Z9lK8QUT3Qi/njWuLn6cqQaZvZeurOr+z4l9QEYwrqgGDo8B/bH1/kKnPZ6z///LNUlSpVwoODg+u++OKLlSpVqnTDPPZPPfXUle+++y6gdu3aYYcOHXL38PC4/sZfpUoVc82aNVOGDBly/Rese/fu144cOeKROWDy/fffv7Bz507PkJCQsFdeeaXSrFmzbtlakBtPT0+9bNmyY8uWLfObPXu2/+TJk08NHDiwRkhISFiTJk1C9+7d637zPmazWQ0ePLh6SEhIWHh4eNjo0aOvBAYGZvtm369fvziz2axq1KhR9/nnn6/UoEGDxMzHOnXqlHDp0iXXzp07XzOZTFSoUCGtWbNmd9xlASVpSW5LBvz9Gax+Fzz8oddEqN3d9gWWYEnpSTz+5+NsvbSVt9u8Ta+avXLfqYg7dCme4TO2kpBq5ushEbSpFZj7TiJbJ+NOMmXPFH47+Rtuzm4MqD2A4XWH57k741JcCoOnbeJibArThzWhVbANX4/fX4TNk2HE71Ctpe2OW0BkSW7DtWvXnMLCwsJ27dp10BafwIuznJbkLjntqk7O0PYZGLsGvMsarRCLHzUutRJ3LCk9iUf+eIRtl7fxbtt3S0Rw2HAskge+2gjAj+NbSnCwgeq+1flf2//xc++f6Vy1M3MOzOHuhXczYesEIpNzf88q7+vO/LEtqVrak+GztrL60BXbFHZmkxEcmo0pksFBGBYvXuxTu3btumPGjLkiweHOlJyWh6zMafDXe7D+EyhVCfp8CdXb2abAEigxPZGHVz3Mnqt7eK/te9xd/W5Hl2R3i3ae44UFe6ge6MWsEc2oWAiX0y4OTsWdYsqeKfx68ldcnVyNlojw4QR65BzUohPTGDpjM4cvXePzQY3vbHKu9BT4ug2YU+GRjeDmnfs+hZC0PIj8kpaHm5lcodNrMHIFOLvC7HuNJsk0B002U4RdS7vGuJXj2Ht1Lx+0+6DYBwetNZNWH+Pp+btpUq00P45vJcHBjoJ8g3i37bv83PtnugZ1Ze7BuXRf2J0Pt36YY0tEaS9XvhndgvBKvjz67Q6W7L5w+0X89R5EHYV7Py2ywcGGLBaLRQb0lADW1znbQaIlMzxkqtIUxq+HZuNg89cwuS2cu8NWjRIkPi2ecSvHsT9yPxPaT6BrUFdHl2RX5gwL/128jw+XH6Z3w4rMGtm0cK3uWIwF+QbxTpt3WNJnCV2DujLv4LxcQ4SvhwtzRzUnopo/T36/kx+33caVhRd2wd8ToeEQCO50ZydRPOy7evWqrwSI4s1isairV6/6Avuy26Zkdlvcyok1xhiIaxegzTPQ/sUSfQ13buJS4xi7cixHYo7wcfuP6Vi1o6NLsqukNDNPfLeTVQev8HCHmjzftXaxWBWzqDodf5ope6aw9MRSXJxc6F+7PyPDR96yOyM5LYOxc7ex7mgkb/UJ56EW1fL2JBnpMKUjJF6FRzcZA62LMFt0W2zfvr2syWSaBoRT0j98Fm8WYJ/ZbB4dERFxy4FDEh6ySomDZf8xLussXw/6ToZyt7VIW7EWmxLL2JVjORZ7jE87fkq7ysV7vEhkQiqjZm9j77lY3uhVl4daBjm6JGGVGSJ+PfErJidTtiEiJT2Dx77dwaqDV/jvPXUY3bZGNkfM4q8PYfXbMPBbCL3HTmdQcGwRHoTIJOHhVg79Cr88aYSJji9Dqydk4Rur6JRoxqwYw6m4U3x212e0qdTG0SXZ1cnIRIbP3MLl+BQmDmxE17rFe1XMoupM/JnrLREmJxMPhDzAyPCRlPH8Zzr7NLOFp+bv5Le9l3i+W20e7Ric/QGvHISv20Kde+GBmQVwBvYn4UHYkoSH7CRGwtKn4OAvUKU59PkKAmra/3kLsajkKEavGM3Za2eZeNdEWlVs5eiS7GrHmRhGzzZ+16YNa0LjqkW72bokyC1EmDMsPL9gD4t2nuexjsE82zXk3xN6WTJgeleIPgGPbQWv4nEJroQHYUsSHnKiNez9EX57zuj/7PImNB0NJXD2wMjkSEYvH835hPN80ekLmldo7uiS7GrF/ks88f1OypVyZ9aIZgW7YqO4Y2fjzzJl7xR+Of7Lv0JEhkXzyqK9fL/1LKPbVOeVe+rcGCA2fAErXoH7pkH9Bxx3EjYm4UHYkoSHvIg7D0seg+N/Qo2O0HsS+FYq2Boc6ErSFUYtH8XlpMtM6jSJpuWbOroku5qz8RSvL9lPvcp+TB/WRFbFLMKyCxEB7oG8ufQAszac4qEW1XijV11jAGzUcfiqNdRoD4O+L1YfFCQ8CFuS8JBXWsO2GcaKek4u0OMDqD+gWP1xuZXLiZcZtWIUV5Ou8mXnL4koF5H7TkWUxaL5YPlhvv7rOJ3rlOXzQY3xcJWxLsXB2fizTN07lSXHl1wPESPqjmD62igm/3WCByIq89594TjP7Q0Xd8Ojm6FURUeXbVMSHoQtSXjIr+gTsOhhOLvJGEzV89Ni0yd6s0uJlxi5fCTRKdF83flrGpZt6OiS7CbVnMELC/bw864LDGlRldfvleW0i6Oz184ydY8RIpyVM/eH3I8lpiNT10TxXrVtDLz8Mdw7ESKG5X6wIkbCg7AlCQ+3w5IBG7+AP98Gt1Jw72dQp6dja7KxCwkXGLl8JHGpcUzuMpn6Zeo7uiS7iUtOZ9zcbWw6Ec0Ld9fm4fY1ZVXMYu7mEBHu1pr/O7SAVLfa1HxmFa4uxa/FScKDsCUJD3fi8gFYNA4u7YEGg+Du98DDz9FV3bFz184xavkorqVfY0qXKYQHhju6JLu5EJvM8JlbOBmZyIf3N6BPo5IzlkUYIWLanqn8fGwRzlpDTCNC/YYwfUhn3ItZgJDwIGxJ2mXvRLkwGP0HtHsB9vwAX7WC46sdXdUdORt/lhHLR5CQnsC0rtOKdXA4eDGevl/+zcXYFGaPaCbBoQSq4lOFN7zD+OXseXr6hWEuvZfdvMTdc5/hVOwdrIchRDEn4eFOmVzhrldg1Epw8YS5feDX5yAt0dGV5dvp+NMMXz6cFHMK07tNJywgzNEl2c36o5E88PVGFIofH25Jq+DiOW5F5CLhCix7iSrlI3ij13f8et9SIgI6E+m0hl6L7+GNv9/mcuJlR1cpRKEj4cFWKkfA+HXQ4hHYOtVYwvfsFkdXlWcn4k4wYtkIzBYz07tNJ7R0qKNLspufdpxj+MwtVPb3YNGjrQgtX8rRJQlH+e05YzXd3pPAyZnKPpWZ3etDXqw7k/S4Riw4+iPdf+rOu5vflRAhRBYSHmzJxQPu/h8MWwoZZpjRDVa9DuZUR1eWo+Oxxxm5bCQZOoPpXacT4h/i6JLsInM57Wd+2E2z6qX5YXxLKvjKctol1oGfja8OL0KZG3/nH2ramIld/kfqyRdwTW7GD4d/oPtP3Xln0ztcSrzkoIKFKDxkwKS9pMTD8pdh51woFw59vzYW2ypkjsYcZfSK0TgpJ6Z3nU4NvzwsGFQEmTMsvPrzfr7bcoY+DSvywf0NcDVJdi6xkqJhUnPwKQ9j/gTnWy+t/teRq4yds42Kgck0b7ST5aeXopSiX61+jKo3ivJeRWetExkwKWxJ/nrai3sp6P0FDJpv9KtO6QhrJxgtEoXE4ejDjFo+CpMyMaPbjGIbHJLSzIydu53vtpzhkQ41+WRAQwkOJd3ylyE52uiuyCY4ALQPKcOsEc24HO3J5q2dmN55Ab2De7PgyAJ6/NSDtze9LS0RokSSloeCkBgFvz4DBxZD5abQ52sIzGFFvwJwMOogY1aOwd3ZnRndZlC1VFWH1mMvV6+lMmr2Vvadj+PN3uEMaVHN0SUJRzu6Er65H9o+B51ezdMu209HM3zGVnw9XfhuTAucXGOYtncai48uRinFfbXuY3S90YW6JUJaHoQtSXgoKFrDvoXw67PGGIgub0DTMeBU8J+A90fuZ8zKMXi7eDO923Sq+FQp8BoKwomrCQyfuZUr11L4YlBjOoeVc3RJwtFS4uHLluDqZQxwNuV93ZK95+J4aMZm3E3OfDumOTXKeHMh4QLT9k5j0bFFKAp3iJDwIGxJwkNBi79oLLJ1bBVUbwe9vwS/gnvz3nt1L+NWjqOUWymmd5tOJe/iObfB9tPRjJ69DSelmD68KQ2r+Dm6JFEYLH3GWKNm1Eqokv8F3g5ejGfItM0opfhmdHNql/cBuCFEAPSr1a/QhQgJD8KWJDw4gtawfRYsfwWcnI2ZKRsOtvsiW7uu7OLhVQ/j5+bHjG4zqOBdwa7P5yjL9l3iye93UsHXndkjm1EtQJbTFsCp9TDrHmjxKNz97m0f5tiVBB6ctok0s4W5o5oTXsn3+mMXEy4ybe80fjr2EwD3BRstEYXh/5qEB2FLEh4cKfokLH4EzmyA2j2MNTK8y9rlqXZc3sHDqx4m0COQ6d2mF6pPRLY0e8MpXv9lPw2r+DFtaBMCZDltAcZcDl+1AjQ8vBFcPe/ocKejEhk8dTPxKenMHtmMxlX9b3i8MIYICQ/CliQ8OJolAzZ9CX+8BW7e0PMTCOtt06fYdmkbj/zxCOU8yzG923TKetonoDiSxaJ5b9khpqw9QZewckwc2EiW0xb/WP6KsZjdsF+M7kIbOBeTxIPTNhN5LZUZw5vSvEbAv7a5mHCR6fums/DoQsCxIULCg7Alu4UHpZQ7sBZwA0zAAq31/920zXDgQ+C89a4vtNbTcjpusQsPma4cgkVj4eJuqNcfenwAHv6575eLLRe38Nifj1HBqwLTuk6jjGcZGxRbuKSaM3j2h90s3XORh1pU4/VedXF2klUxhdW57TC9MzQearTu2dDl+BQGT93E+dhkpg1tSptat57m/OYQ0Te4L6Prjaaid0Wb1pMTCQ/CluwZHhTgpbVOUEq5AOuBJ7XWm7JsMxxoorV+LK/Hvd3wcCYqidWHr1AtwJPqgV5U8vPA5FzIrvXPSDfmglj7IXiXM+aJCO5024fbeGEjT/z5BJV9KjO161QCPYrf+g1xSemMnbuNzSejeal7KOPa1ZDltMU/zKkwuT2kxsMjG8HdN/d98ikyIZUh0zZzIjKRrx5sTKc62V/VcynxktGdcfQnNLpAQ4SEB2FLBdJtoZTyxAgPD2utN2e5fzgFFB5+2nGOZ37Yff22i7Oiir8n1QI8CQr0onqgF0EBxlclfw/HfnI9vwMWjYfIw9BkFHR50+jSyIe/z//Nk6ufpGqpqkzrOo3S7qXtVKzjnI9NZviMLZyKSmTCAw3o3bB4Xjki7sDqd+Gv92HwjxDS1W5PE5uUxtAZWzh4MZ6JAxvRvV7O3RI3h4g+wX0YU2+MXUOEhAdhS3YND0opZ2A7EAxM0lq/eNPjw4H/AVeBI8DTWuuztzjOWGAsQNWqVSNOnz6d71q01lxNSOVUZBKnIhM5FWV8nYxM4nRUIklpGde3dXFWVCntSfUAL6oFeFE90AgYQQFeVPQroGCRngx/vg0bJ4F/kDG9ddUWedp17bm1PL36aWr41WBKlyn4u99590dhs/9CHCNmbiU5PYPJD0XQqmbxa1URd+jSPpjSHsL7wX1T7P508SnpjJi5lV1nY/m4f97C7KXES0zfa3Rn2DtESHgQtlRQLQ9+wCLgca31viz3BwAJWutUpdQ4YIDW+q6cjmWPMQ9aa65eS+Vk5I2B4mRkIqejkkhO/ydYuDo7UaW0x/WWimqBXlQP8CIo0JOKvh442TpYnPobFo+H2LPQ+gno8DK4uGe7+Zqza3hmzTME+wUztetUfN1s30zraGuPXOXhedvx9XBh5ohm16+1F+K6DDNM6wTx5+HRLeBZMC1vialmRs3eyuaT0bx/X336N83bHC43hAit6R3cmzH1x9h0HhYJD8KWCuxqC6XUa0CS1npCNo87A9Fa6xzf7Qp6wKTWmsvxqUZLRWQiJ63/no5K4lRUIinpluvbupqcqFrak6CbWiuCAr2oUMr99oNF6jVY8V9jbogydeC+yVChwb82++PMHzz313OE+ofydZevi2VwWLD9HC8t3ENwWW9mjWhGed/sg5QowdZ/Yqxo+8AsqNu3QJ86JT2DsXO3s/bIVd7sXZehLYPyvO+lxEvM2DeDBUcW2DxESHgQtmTPAZNlgHStdaxSygNYAbyvtV6aZZsKWuuL1u/7Ai9qrXNsmy9MV1tYLJrL11Kut1Cciky83npxOiqJVPONwaJa6ZvGVwQaQaN8XoPFkRWw5HFIioT2L0Gbp8HZBMDK0yt54a8XCAsM4+vOX+PjWrw+jWut+fzPY3y88ghtggP5akhjfNyzX9BIlGCRR+Gr1lCrCwyYZ/fJ124l1ZzBY9/uZOWBy7zcI5Sx7Wrma/9bhYjR9UZT2afybdck4UHYkj3DQ31gNuCMsXrnD1rrN5VSbwLbtNZLlFL/A3oBZiAaY0DloZyOW5jCQ04sFs2l+JTrrRWno5KMYBGZyOnoJNKyBAt3Fyeqlf4nTGS2WFQP9KKsj9uNwSIpGn57HvYtgIqNoe9kliWe4KW1L1EvsB5fdf4Kb9f8Da4s7MwZFv67eB/fbz3LfY0q8V6/+rIqprg1iwVm9YArB+HRzcaS2w6SnmHhqfm7+HXPRZ7pEsLjdwXn+0qgy4mXr4cIi7bwaKNHGV1v9G3VI+FB2JJMEuUAFovmYmawsAaKU9ZukDNRSaRl3BgsMq8CMUKF0XoRGrUK3z9f5DcXCy8H+NGwbCO+7PwlXi7FayrmxFQzj327g9WHr/JYx2Ce7Roil2KK7G2eAr8/D32+MqZ8dzBzhoUXFu7hpx3neaRDTZ7vVvu2fn8zQ0TrSq1pV/n2JrmS8CBsScJDIZNh0VyITbZeDWK9MsTaenE2Oon0jH9er3Kl/ya57C80SUnhxaRKHIn4H2WrhFA90IsyPm5F/k326rVURs7ayv4Lcbzdpx6DmxfPZcOFjcScNlbMrNYSHlzgkO6KW7FYNP/9eR/fbj7DyNbVebVnHYf835TwIGzJ5OgCxI2cnYzLRKuU9qRtrRsfywwWJyMTWXriZ36/vJRSug7BsTWolDyTin/25S3zEH7I6ICXq4lqATd2hVQP9KJagCdlvAt/sDh+NYHhM7cQeS2NqUOb5DjxjhBoDb88YQSGnp8WmuAA4OSkeKdPOG4mJ2b8fZIUcwZv9w63/ZVZQhQgCQ9FSGaw2BL5O8suT6RVxZZ81vEz3E3umKPGY/5pPB+cn8rjFQ4zv8Lz7I934+DFa6zYfxmz5Z8WC2830/XJsYICPK+Pr6gW4EWgt6vDg8W2U9GMnrMNk5Pi+7EtaCDLaYvc7JwHJ9ZAjwkFusR9XimleK1nGO4uzny15jip6RY+uL++TKMuiizptihifjj8A29teovWlVrzWcfPcHPOsmqkxQKbv4Y/3gAXD7jnYwi/j/QMC+djkq9fbnrKOnjzdFQiZ2OSycgSLHzcTFTLbK243mJh3C7tZf9g8fveizw5fxeV/DyYPaIZVQPubPVDUQLEX4RJzaF8OAxbCk6FdzBt1quGetavwCcDGuJSQNPkS7eFsCVpeShCvjv0He9ufpd2ldvxSYdPcHV2vXEDJydo+QgEd4ZF42DBCDi0FJceEwgKLE1QoBfUvnGX9AwL52KS/5l1MzKRk1FJ7DkXx297L5IlV+DjbvonUAR4WrtFjFYLf0+XOw4WM9af5K1fD9Coih/ThjWltJdr7juJkk1r+PVZyEiFXp8X6uAARgvEE51q4WZy4n+/HyLVbOGLwY1wM8kKsKJokZaHImLugbl8sPUDOlbpyIT2E/4dHG6WYYb1Hxvz+nsGGots1eqSr+dMM1s4F5P0r1k3T0Ulcj4m+YZgUcrddMOkWNUDjXBRPcAL/1xCgMWiefe3g0xbf5Judcvx2cBGuLvIH1ORB/sWwoKR0OUtYwbWImT2hlP835L9tA8pw+SHIuz+Oy8tD8KWJDwUAbP3z2bCtgl0rtqZD9p9gItzPiZHurDLWGTr6kFoPAy6vQNudz6BVKo543qLRdaJsU5GJnI+Npmsv1a+Hi7/Gl+RedvdxZlnf9zNr3suMrxVEK/2DJN+YJE3iZFGd4VfVRi18vqEaUXJ/K1neOmnvbSoHsC0YU3wcrPfOUh4ELYk4aGQm753Op/u+JSu1bryXrv3cHG6jVkV01Ng9Tuw4XPjD22fryCote2LtUo1Z3A2OslYhCxLa8WpyCQuxN0YLNxMTqSaLbzSow6j21Z3+GBNUYQsGAUHfoZxa6FcmKOruW2Ld57n2R9306iKHzNGNKWUnWZOlfAgbOm2woNSyh24V2v9o+1LyllJCg9T9kzh852f0z2oO++2fReT0x1+Kjm90VhkK+Y0tHwU7no1x0W27CEl3QgWmVN6n4lOol1IGbqEyaWYIh8O/w7fDYQO/4EOLzm6mjv2+96LPP7dTsIqlmLOyGb4edp+vI+EB2FLeQ4P1oWrugGDgK7AOq31/Xas7ZZKSnj4atdXfLn7S3rW6Mlbrd+68+CQKTUBVr4K22ZAmVBjqe+KjWxzbCEKQnIsfNkCPErD2DVgKh4Da/84eJmH5+2gRhkv5o1uTqC3W+475YOEB2FLuQ5NVkq1V0pNBk4Bo4AuQHVHBIeSQGvNFzu/4MvdX9K7Zm/ebv227YIDgJs39PwEhiyElDiY1hnWvAcZ6bZ7DiHsaeWrkHDZGARcTIIDQKc65Zg+vAmnohIZOGUTl+NTHF2SENnKMTwopc4B/wPWA2Fa635AstY6qSCKK2m01kzcOZHJeybTr1Y/3mz9Js5OdhqBHdwZHtkIde+DNf8zQsSVHNckE8Lxjq+GHXOg1eNQqbGjq7G5trXKMGtEMy7GJtN/8kbOxyY7uiQhbim3locFQEVgAHCvUsoLKFojLIsIrTWfbP+EaXun8UDIA7zW8jWclJ2vWffwh35Tof8ciD0Dk9vBhi+MyaaEKGxSE4wpqEvXNMY6FFMtagQwd3RzohPT6P/1Rk5HJTq6JCH+Jcd3J631U0B14COgA3AYKKOU6q+UKl7rPjuQ1poPt33IzP0zGVh7IK+2eNX+wSGrsN7G8sXBnWDFKzC7J8ScKrjnFyIv/nzLCLm9vzBmUC3GGlf157sxLUhKM9N/8kaOXUlwdElC3CDXdyhtWK21HosRJAYDvTHGQIg7pLXmvS3vMffAXIbUGcLLzV92zOWK3mVh4LfQ+0u4tBe+ag3bZ0ERu5RXFFNnNsHmydB0DFRr5ehqCkR4JV++H9uSDAsMnLKRQ5fiHV2SENfl6+Ot1jpda/0L8CawxD4llRwWbeGdze/w7aFvGRo2lBeavuDYeQ6UgkYPwsMbjP7kX56Ebx4w1g4QwlHSU+Dnx8C3CnT+P0dXU6Bql/dh/rgWmJycGDhlE3vPxTm6JCGA3AdM1ldKrVBK7VNKva2UqqCUWgj8AewtmBKLJ4u28Namt5h/eD4jwkfwXJPnCs8ESX5V4KGfofsHcGq9cVnc3gWOrkqUVH+9D1FH4d5PbTI7alFTs4w3P4xribebiRUHLjm6HCGA3FsepgLfAv2Aq8Au4DgQrLX+xL6lFV8WbeH1Da+z4MgCxtQbw9ONny48wSGTkxM0Hwfj10NAMCwcBT8Oh6RoR1cmSpILu+Dvz6DhEGNMTglVNcCTXx5rwzNdQhxdihBA7uHBTWs9S2t9WGv9GZCotX5Bay0XIN+mDEsGr/79KouOLWJ8g/E83ujxwhccsgoMhpHLjdkoDy41WiGOLHd0VaIkyEg3uiu8AqHb246uxuH8vVwL998KUaLkFh7clVKNlFKNlVKNgdSbbot8MFvMvPL3Kyw5voRHGj7Cow0fLRp/DJxN0O45GLvaWKHz2/7GH/UUGcAl7Gj9p3B5L9zzsXFZsRCi0Mhxemql1Bqyn9dBa63vskdROSmq01ObLWZeXvcyv5/6nScaPcGY+mMcXdLtMacak0r9/RmUqgx9voTqbR1dlShurhyCyW0h9B54YJajqykWZHpqYUs5znuste5QQHUUa+mWdF5a+xIrTq/g6YinGRk+0tEl3T6TG3R+HUK6G4tsze4JLR6BTq8V+2vvRQGxZMDPj4KrN3T/0NHVCCFuIberLV7I8v0DNz32rr2KKk7SM9J5/q/nWXF6Bc81ea5oB4esqjY3BlM2HQObvjRmpzy/3dFVieJg89dwfptxtY93GUdXI4S4hdzGPAzM8v3N88HebeNaip20jDSe+esZ/jjzBy81e4lhdYc5uiTbcvWCeybAQ4shLRGmdYE/3wFzmqMrE0VV9An44y0IuRvqydp7QhRWuYUHlc33t7otskjNSOXpNU+z5uwaXm7+Mg/WedDRJdlPzY7GxFL1+8PaD2BaJ7h8wNFViaLGYoElT4Czi7Hya1EYTCxECZVbeNDZfH+r2zdQSrkrpbYopXYrpfYrpd64xTZuSqn5SqljSqnNSqmgvJVduKVmpPLk6idZe24tr7Z4lUGhgxxdkv15+EHfr2HANxB/Aaa0NwZVWjIcXZkoKnbMglProOtbUKqio6sRQuQgt/DQQCkVr5S6BtS3fp95u14u+6YCd2mtGwANgbuVUi1u2mYUEKO1DgY+Ad7P/ykULsnmZB7/43E2nN/AG63eoH/t/o4uqWDV6QmPbIJaXWHlazDrHqMpWoicxJ2DFa9B9XbQuJh17wlRDOW2qqaz1rqU1tpHa22yfp952yWXfbXWOnMpOBfr182tFb2B2dbvFwCdVJGY+ODWktKTePyPx9l0cRNvtn6T+2rd5+iSHMO7DAyYB30nG90XX7WBrdNlkS1xa1rD0qdBZ8C9E6W7QogiwK7rPiulnJVSu4ArwEqt9eabNqkEnAXQWpuBOCDAnjXZS1J6Eo/+8ShbL2/lnTbv0Ce4j6NLciyloMFAeGQDVGkGvz4D8/oZXRpCZLXnBzi6wrjct3R1R1cjhMgDu4YHrXWG1rohUBloppQKv53jKKXGKqW2KaW2Xb161aY12kJieiIPr3qYHVd28L82/+Pemvc6uqTCw7cyPLQIekyAMxuN6a33/CCtEMKQcAWWvQiVm0GzsY6uRgiRR3YND5m01rHAav59eed5oAqAUsoE+AJRt9h/ita6ida6SZkyheu674S0BMatHMfuq7t5v9379KjRw9ElFT5KQbMxxrwQZULhpzHww1BIjHR0ZcLRfnvOuMy39xfg5OzoaoQQeWS38KCUKqOU8rN+7wF0AQ7dtNkSIHN01P3Anzqn+bILmfi0eMatHMf+yP182P5D7g6SqS9yFFATRvxuzFB5ZJnRCnHoN0dXJRzlwBI48DO0fxHK1HZ0NUKIfLBny0MFYLVSag+wFWPMw1Kl1JtKqV7WbaYDAUqpY8AzwEt2rMem4lLjGLtiLAeiD/BRh4/oUq2Lo0sqGpycoc3TMHYNeJeH7wfBvPuNNxKZXKrkSIqGX5+F8vWg9ZOOrkYIkU85LoxVGBWGhbFiU2IZu3Isx2KP8UmHT2hfpb1D6ymyzGmw4TPjSoxrF8EzAOoPhEZDoFyYo6sT9rToYdj7A4xZDRXqO7qaEkEWxhK2VCBjHoqTmJQYRq8YzfHY43zW8TMJDnfC5Artnoen9sGDCyCoDWyZAl+1hCkdjVCRHOvoKoWtHV0Fu7+F1k9JcBCiiJKWh3yISo5izMoxnIk/w8SOE2lVqZVD6ijWEqOMT6Q75sKV/WByhzq9jNaIoLbgJHm3SEuJhy9bGuuijF9nrNIqCoS0PAhbynFJbvGPyORIRi8fzfmE83zR6QtaVLh5skxhE14B0OJhaD4eLuyEnfNg7wIjUPhVhYZDoOEg43tR9Kx6HeLPw6gVEhyEKMKk5SEPriZdZdSKUVxKvMSkTpNoWr5pgT5/iZeeDId+hZ1z4cQaQEGNDkZrRGhPcHF3cIEiT06tN6Yrb/EI3P0/R1dT4kjLg7AlCQ+5uJx4mdErRnM56TJfdf6KiHIRBfbc4hZiTsPu72DnNxB3Btx9oV5/I0hUaCBTGxdWaUnwdWvQFmMFVlcvR1dU4kh4ELYk3RY5uJR4iZHLRxKdEs3kLpNpVLaRo0sS/tWgw0vQ7gU4tdbo1tgxB7ZOhXL1jBBRvz94lnZ0pSKrNe8aC6QN+0WCgxDFgLQ8ZONCwgVGLh9JXGocX3f5mgZlGtj9OcVtSo4xxkXs+sYYJ+HsCrV7QKOHoGZHmbnQ0c5vh2mdofFQuPczR1dTYknLg7AlCQ+3cO7aOUYtH8W19GtM6TKF8MDbWpJDOMKlfUaI2P09JEeDT0VoOBgaPQilazi6upLHnAqT20NKHDy6yehmEg4h4UHYklz3dpOz8WcZuXwkCekJTO06VYJDUVM+3BiM9+wh6D8HytWF9R/DxEYw8x7Y9Z2xloIoGOs+gqsH4d5PJTgIUYxIy0MWp+NPM2r5KFIzUpnadSqhpUPt8jyigMWdtw6ynAcxJ8HVB8LvM7o1KjeRQZb2cmkfTGkPde+DflMdXU2JJy0PwpYkPFidjDvJ6OWjSbekM7XrVGqXloV6ih2t4fQGo1tj/yJIT4LA2sYgywYDwbusoyssPjLMMK2TMafDo1tkAGshIOFB2JJ0WwAnYk8wcvlIzNrM9G7TJTgUV0pBUGvo8yU8dwTunWg0pa98FT6uA98NNlb5zEh3dKVF38Yv4OIu6PGhBAchiqESf6nmsZhjjFoxCiflxMxuM6nhJ4PqSgQ3H4gYZnxdPWx0aez+Hg7/Cl5ljZaIRkNkqejbEXkM1vzPmMArrI+jqxFC2EGJ7rY4HH2YMSvGYHIyMb3bdKr7VrfJcUURlZEOR1caQeLIMtAZULmZESLq9gX3Uo6usPCzWGBWD7hywOiu8Cnv6IqElXRbCFsqsS0Ph6IPMWbFGFydXZnRbQbVSlVzdEnC0ZxdILSH8ZVwxWiJ2DkPfnkClr1kfIpuNASqtZJBltnZOg3ObITeX0pwEKIYK5EtD/uj9jN2xVg8XTyZ0XUGVUpVsVF1otjRGs5tM9bV2PcTpF0z5oto+KAxf0Spio6usPCIOW2smFm1BQxZKAGrkJGWB2FLJS487L26l3Erx+Hj6sP0btOp7FPZhtWJYi0tEQ4sMVojTq8H5QQ1OxmtEbW7l+xVIrWGuX3h3FZ4ZKOseloISXgQtlSiui12X93N+JXj8XXzZUa3GVT0lk+NIh9cvYzlwBsOgqjjsOtbY/6IH4eBR2moP8AIEuVL4MRiu76BE6uhxwQJDkKUACWm5WHXlV2MXzWe0u6lmdFtBuW9pD9W2IAlA46vNro1Dv0KlnSo0NAIEfXuBw9/R1dof9cuwaRmUC4chi0FJ7kCvDCSlgdhSyXmf7mbsxs1/Woys9tMCQ7CdpycoVZn6D8bnj0Md78PFjP89hxMqA0LRhnhwmJxdKX2oTUsfcZYw6LX5xIchCghSkzLA4DWGiWDuIS9aQ0XdxtjI/b+YCwK5VvVGGDZcLCxrHhxsW8hLBgJXd6E1k86uhqRA2l5ELZUosKDEAUuPQUOLTWCxIk1gIbq7Y11Ner0BBcPR1d4+xKjjO4KvyowahU4l6ghVEWOhAdhS/K/XQh7cnE3xj7Uux9izxireu6aBz+NBjdf4/5GQ6Bio6J3aeOyF41Wld5LJDgIUcLI/3ghCopfVejwIrR7Hk6tM1ojdn0D26ZD2bpGiKg/ALwCHF1p7g4vg70/Qof/GMueCyFKFOm2EMKRkmONcQM758GFHeDkYswZ0eghqHlX4fxEnxIHk1oYV5KMXQMmV0dXJPJAui2ELRXCv0xClCAeftB0lPF1eT/s/Ab2fA8Hl4BPBWgwyGiRCKjp6Er/seJVSLgEA+dJcBCihLLbdVVKqSpKqdVKqQNKqf1KqX8NxVZKdVBKxSmldlm/XrNXPeaYGK5OmkRGbKy9nkKIO1OuLtz9LjxzCPrPhfL14e9P4fPGMKO7ESzSEh1b44k1sGM2tHwMKkU4thYhhMPYrdtCKVUBqKC13qGU8gG2A3201geybNMBeE5r3TOvx73dbou4JUu48MKLOHl64jdwIKWHD8OlbNl8H0eIAhV/0ZjFcuc8iD4Ort4Qfh80HAJVmhXsIMu0RGPtCicTPPx30b5SpASSbgthS3ZredBaX9Ra77B+fw04CFSy1/PlxrdXL6ov+Rnvu+4ietYsjnfuwsU33iDt3HlHlSRE7kpVgLbPwOPbYcQyY2XPvQthRlf4oims/xSuXS6YWv54C2JPQ+8vJDgIUcIVyIBJpVQQsBYI11rHZ7m/A7AQOAdcwGiF2H+L/ccCYwGqVq0acfr06TuqJ+30aaKmTSd28WKwWPDt2ZOAcWNxq1Hjjo4rRIFIvQb7FxutEWc3gXKGWl2h8UPGv84utn/OM5thRjdoOhrumWD74wu7k5YHYUt2Dw9KKW/gL+AdrfVPNz1WCrBorROUUj2Az7TWtXI6ni2vtki/dInomTOJmf8DOjUVn65dCRw3FvewMJscXwi7izxqhIjd30HCZfAqAw0GGt0aZUNt8xzpKTC5LaQnGytmuvnY5riiQEl4ELZk1/CglHIBlgLLtdYf52H7U0ATrXVkdtvY41JNc3Q00XPmEDPvGywJCXi1a0vguHF4RsiAMFFEZJjh2Cpjga4jy4z1NSo1Ma7UCL8P3H1v/9h/vAnrPoIhP0FwJ9vVLAqUhAdhS/YcMKmA2UC01vqpbLYpD1zWWmulVDNgAVBN51CUPed5yLh2jZhvvyN69mwyoqPxbNKEgHHj8GrTWtbEEEVHwlXYM98IElcPgckDwnob3RrVWudvkOWFXTD1LqM1o8+XditZ2J+EB2FL9gwPbYB1wF4gc0nBl4GqAFrrr5VSjwEPA2YgGXhGa70hp+MWxCRRluRkYn9cQNSMGZgvXcK9bl0Cxo3Fp3NnlKwaKIoKreH8DiNE7FsIqfHgXx0aPQgNBoNvLuOXM9JhakdIuAKPbi4Zy4sXYxIehC3JDJM50GlpxC1ZQuTUqaSfPoNrzZoEjh1DqXvuQZlkfi1RhKQlwcFfjCBxah2gjC6IRkOgdg8wuf17n7Ufwp9vw4BvjEW8RJEm4UHYkoSHPNBmM/HLlxM1eQqpR47gUrkyAaNH4du3L05ut/ijK0RhFn0Sdn1rfMWfM1oU6g8wgkT5esY2Vw4ZgyRD74EHZjm0XGEbEh6ELUl4yAdtsZCw5i8iJ39Nyu49mMqUofSIEfgP6I+Tl5dDahLitlkyjBkjd84zlg3PSIMKDYwrNfb+AFHH4dEt4F3G0ZUKG5DwIGxJwsNt0FqTtHkzkV9PJmnTJpx9ffEf+hClhwzB2fcORrUL4ShJ0bB3gdGtcWmPcd99U6F+f8fWJWxGwoOwJQkPdyh51y4iJ08hYfVqnDw98R88iNLDh2MKDHR0aULcnou7jVaHun0LdvprYVcSHoQtSXiwkZTDh4maPIX4ZctQLi749etHwKiRuFRy2IzcQghxnYQHYUsSHmws7dQpIqdNI+7nJaA1vvfeS8CYMbjVqO7o0oQQJZiEB2FLEh7sJP3iRaJmzCT2xx+Nqa+7dTOmvq5Tx9GlCSFKIAkPwpYkPNiZOSqK6NlziPn2W2Pq6/btCBw3Hs/GjRxdmhCiBJHwIGxJpku0M1NAAGWfeZrgP/+gzFNPkrJnL6cHD+b0Q0NJ+Ptvilp4E0IIISQ8FBDnUqUIHD+e4D9WUe4/L5F25gxnR43mVP8BXFu1Cm2x5H4QIYQQohCQ8FDAnDw9KT1sGDVXrqD8m2+QERvLucce52Tv3sT98gvabHZ0iUIIIUSOJDw4iJOrK/79+1Pz99+o+OGHAFx4/gWOd+9BzPwfsKSlObhCIYQQ4tYkPDiYMpnwvbcn1X/+mcqTvsDZz49L//d/HO/chahZs7AkJTm6RCGEEOIGEh4KCeXkhE+nTgT9MJ+qM6bjWr06V957n2N3dSLyq6/IiI93dIlCCCEEIOGh0FFK4dWqFdVmz6Lad9/i0bAhVz+byLGOd3Hlo48xR0U5ukQhhBAlnMzzUASkHDpE1JQpxP++DOXqit8DDxhTX1eo4OjShBBFhMzzIGxJwkMRknryJFGZU18rhW+vewkcMwbXoCBHlyaEKOQkPAhbkvBQBKVfuEDU9BnELliATk+n1N3dCBg3DvfatR1dmhCikJLwIGxJwkMRZo6MJHr2bGK+/Q5LYiLeHToQOH4cHg0bOro0IUQhI+FB2JKEh2IgIy6O6G++IWb2HDLi4vBs3pzA8ePwbNECpZSjyxNC3CZtsZB69BhJW7eStHUr3h074Nenz20dS8KDsCWTowsQd87Z15cyjzxCwLBhxPzwI9EzZnBmxEjc69cncPw4vDt0QDnJhTVCFHY6I4PUw4dJ2rqVxK1bSd62nYzYWABMFSrg2bSpYwsUwkpaHoohS1oacT8tImraNNLPncOtVi0Cxo2j1N3dUCbJi0IUFtpsJuXgQZK2GC0LSTt2YLHO6eJSuTKeTZsaX82a4lKp0h21JErLg7AlCQ/FmDabif/tNyKnTCHt2HFcqlYlYMxofHv3xsnV1dHlCVHi6PR0kvftI2nrNpK2biV5xw4siYkAuFarhmezptcDg60vxZbwIGxJwkMJoC0Wrv3xB1FfTyZl/35M5coRMGokfvffj5Onp6PLE6LYsqSlkbJnz/UxC0k7d6GTkwFwrVkTz6ZNjLDQpCku5cratRYJD8KWJDyUIFprEv/eQNTkySRt3Yqzvz+lhw3Df/AgnEuVcnR5QhR5lpQUknfvIWnLFqNlYfdudGoqAG4hIf90QzRtgikgoEBrk/AgbEnCQwmVtGMHkZMnk/jXWpy8vfF/8EFKDxuKqXRpR5cmRJFhSUoiedcuEq0tCym796DT00Ep3OqE4mUNCx4REZj8/R1aq4QHYUt2Cw9KqSrAHKAcoIEpWuvPbtpGAZ8BPYAkYLjWekdOx5XwYFspBw4QOWUq15YvR7m54df/AQJGjsSlfHlHlyZEoZORkEjyzh3XBzgm79sHZjM4O+MeFna9VcEzIqLQteZJeBC2ZM/wUAGooLXeoZTyAbYDfbTWB7Js0wN4HCM8NAc+01o3z+m4Eh7sI/XECaKmTiPul19AKfz69CZg9Ghcq1VzdGlCOExGfDxJ27dfH+CYcuAAZGSAyYRHePj1KyE8GjXG2dvL0eXmSMKDsKUC67ZQSv0MfKG1XpnlvsnAGq31d9bbh4EOWuuL2R1HwoN9pZ8//8/U12Yzpbp3J2DsWNxrhzi6NCHszhwTQ/L27dfnWUg9eAi0Rrm44N6gPp5Nm+LVtCkeDRsWucHGEh6ELRVIeFBKBQFrgXCtdXyW+5cC72mt11tv/wG8qLXedtP+Y4GxAFWrVo04ffq03Wsu6cxXr/4z9XVSEt533UXguLF4NGjg6NKEsBlzVNT1VoWkrVtJPXIEAOXmhkfDhtcHOHo0qI+Tu7uDq70zEh6ELdk9PCilvIG/gHe01j/d9FiewkNW0vJQsDJiY42pr+fMNaa+btmCwHHj8GzeXKa+FkVO+pUr/1w2uXUbacePA6A8PPBs1Oj6PAvu9eoVu7lQJDwIW7LrdINKKRdgIfDNzcHB6jxQJcvtytb7RCHh7OdHmUcfJWD4cGLm/0DUzBmcGT4CjwYNCBg3Du+OHSREiEIr/eLFf8LClq2kWVstnby88IhojG+f3ng1bYp73booFxcHVytE0WHPAZMKmA1Ea62fymabe4DH+GfA5EStdbOcjistD45lSU0lbtEioqZOI/38edxq1yZg7BhK3X03ytnZ0eWJEkxrTfr58/9M9bx1K+nnzgHgVKoUnhER17sh3OuElrip2qXlQdiSPcNDG2AdsBewWO9+GagKoLX+2howvgDuxrhUc0ROXRYg4aGw0Onp1qmvp5J2/Diu1aoRMHYMvvfeiypmzb2icNJak3769PU5FpK2bsN80Rhr7ezn98/sjU2b4hYSUuLDrYQHYUsySZS4I9pi4dqqVcbU1wcOYKpQgYCRI/G7vx9OHh6OLk8UI1pr0k6cuN4FkbR1K+arVwFwDgj4Z46Fpk1xCw6WlWRvIuFB2JKEB2ETWmsS1/9N5OSvSd62HefSpSk9fDj+gwbi7OPj6PJEEaQtFlKPHvtnzMK2bWRERQFgKlv2hhUnXatXl7E3uZDwIGxJwoOwuaRt24icPIXEdetw8vHBf8iDlB461OHT84rCTWdkkHr48PU5FpK3bScjNhYAU8UK16d69mzaFJeqVSUs5JOEB2FLEh6E3STv30/U5ClcW7kS5e6Of//+lB45Apdy5RxdmigEtNlMysGD/wxw3L4dy7VrALhUqZJlEammuFau5OBqiz4JD8KWJDwIu0s9fpyoKVOJW7oU5eSEb9++BIwehWvVqo4uTRQgnZ5O8r591ydlSt6xA0tiIgCuQUHXuyA8mzaVtVXsQMKDsCUJD6LApJ07T9T0acQt/MmY+vqeewgYMxr3EJn6ujiypKWRsmfPP2MWdu5CJycD4Bpc85+pnps0waVsWQdXW/xJeBC2JOFBFLj0K1eInjWbmO+/RyclYapYASd3D5w8PFAe7jh5eOLk7o6Tpwcqp/s9PXByd0d5GNsY22X5voRdx+9olpQUknftvh4WknfvRqemAuBWu3aWbogmsvS7A0h4ELYk4UE4jDkmhtj5P5B26hSWlBQsyUno5BQsycnolGQsScnW+5ONT6z5/V11cTGChLv7TcHCHXWLgGLc72EEGU8PlLs1sHjcFFDc3XHy9ES5upboQXuWpCSSdu68PsdCyp496PR0cHLCPTT0n26IiAic/fwcXW6JJ+FB2JJ8NBMOY/L3J3D8uDxtq7VGp6ZeDxKWlBQsSdaQkZz8z/23DB+3CCXRMTfcb0lJgfT0/J2Ak9NNLR9ZQsm/wkoOoSTr/Z5Z9nd3L1QTG2UkJJC8Y8f1eRaS9+8HsxmcnXGvWxf/oQ8ZgSEiQi7PFaKYk/AgigSllPHm6u4OdrrkU6enZx9Kst6fUyix3m+OjLSGkmS09X6dkpLvmpSbmxFQPHMJJVm7dnIIKzfs7+6e42ygGfHxJG3bfr0bIuXAAbBYwMUFj/BwAkaONFacbNQIZ2+vO/nRCyGKGAkPQlgpFxecXVzs9qlZWyxoazeMJTkFnZx0i7CSJZSkZN+aknEtHvOVKzcGlORk4809P0ymf4cSd3csKSnG8tRao1xc8GjQgMDx44yw0LChzB4qRAkn4UGIAqKcnIwWBE9Puxxfa41OT7cGjmxaTfLYxeNUqhQ+XbsYYaFBA5zc3OxSsxCiaJLwIEQxoZQyuiFcXXH29XV0OUKIYkxWjhFCCCFEvkh4EEIIIUS+SHgQQgghRL5IeBBCCCFEvkh4EEIIIUS+SHgQQgghRL5IeBBCCCFEvkh4EEIIIUS+FLlVNZVSV4HTt7l7IBBpw3IcSc6lcCou51JczgPkXDJV01qXsWUxouQqcuHhTiilthWXJWnlXAqn4nIuxeU8QM5FCHuQbgshhBBC5IuEByGEEELkS0kLD1McXYANybkUTsXlXIrLeYCcixA2V6LGPAghhBDizpW0lgchhBBC3CEJD0IIIYTIl2IZHpRSM5RSV5RS+7J5XCmlJiqljiml9iilGhd0jXmRh/PooJSKU0rtsn69VtA15pVSqopSarVS6oBSar9S6slbbFPoX5c8nkeReF2UUu5KqS1Kqd3Wc3njFtu4KaXmW1+TzUqpIAeUmqs8nstwpdTVLK/LaEfUmhdKKWel1E6l1NJbPFYkXhNRvJkcXYCdzAK+AOZk83h3oJb1qznwlfXfwmYWOZ8HwDqtdc+CKeeOmIFntdY7lFI+wHal1Eqt9YEs2xSF1yUv5wFF43VJBe7SWicopVyA9Uqp37XWm7JsMwqI0VoHK6UGAu8DAxxRbC7yci4A87XWjzmgvvx6EjgIlLrFY0XlNRHFWLFsedBarwWic9ikNzBHGzYBfkqpCgVTXd7l4TyKDK31Ra31Duv31zD+MFa6abNC/7rk8TyKBOvPOcF608X6dfMI6t7AbOv3C4BOSilVQCXmWR7PpUhQSlUG7gGmZbNJkXhNRPFWLMNDHlQCzma5fY4i+gYAtLQ21f6ulKrr6GLywtrM2gjYfNNDRep1yeE8oIi8Ltbm8V3AFWCl1jrb10RrbQbigIACLTKP8nAuAP2sXWILlFJVCrbCPPsUeAGwZPN4kXlNRPFVUsNDcbEDY776BsDnwGLHlpM7pZQ3sBB4Smsd7+h6blcu51FkXhetdYbWuiFQGWimlAp3cEm3LQ/n8gsQpLWuD6zkn0/vhYZSqidwRWu93dG1CJGTkhoezgNZP3VUtt5XpGit4zObarXWvwEuSqlAB5eVLWtf9ELgG631T7fYpEi8LrmdR1F7XQC01rHAauDumx66/poopUyALxBVoMXlU3bnorWO0lqnWm9OAyIKuLS8aA30UkqdAr4H7lJKzbtpmyL3mojip6SGhyXAUOvo/hZAnNb6oqOLyi+lVPnMvk6lVDOM17NQ/hGx1jkdOKi1/jibzQr965KX8ygqr4tSqoxSys/6vQfQBTh002ZLgGHW7+8H/tSFcGa5vJzLTeNnemGMVylUtNb/0VpX1loHAQMxft5DbtqsSLwmongrlldbKKW+AzoAgUqpc8D/YQygQmv9NfAb0AM4BiQBIxxTac7ycB73Aw8rpcxAMjCwEP8RaQ08BOy19ksDvAxUhSL1uuTlPIrK61IBmK2UcsYIOD9orZcqpd4Etmmtl2AEpblKqWMYg3cHOq7cHOXlXJ5QSvXCuGImGhjusGrzqYi+JqIYk+mphRBCCJEvJbXbQgghhBC3ScKDEEIIIfJFwoMQQggh8kXCgxBCCCHyRcKDEEIIIfJFwoMQQggh8kXCgxCFiFLqt8zJjm66/3Wl1HMOKEkIIf6lWE4SJURRpbXu4egahBAiN9LyIEoUpZSXUupX64qX+5RSA5RSEUqpv5RS25VSyzOnMVZKjVFKbbVuu1Ap5Wm9/wHrvruVUmut97krpWYqpfYqpXYqpTpa7x+ulPpJKbVMKXVUKfVBLvWdylwHQyn1ilLqiFJqPVDbrj8YIYTIB2l5ECXN3cAFrfU9AEopX+B3oLfW+qpSagDwDjAS+ElrPdW63dvAKIxVMl8Dummtz2fpYngU0FrrekqpUGCFUirE+lhDjKW7U4HDSqnPtdZZlx7/F6VUBMa0ww0x/p/uAGSlRSFEoSDhQZQ0e4GPlFLvA0uBGCAcWGldy8oZyFyMK9waGvwAb2C59f6/gVlKqR+AzFU122AEC7TWh5RSp4HM8PCH1joOQCl1AKgG5BgegLbAIq11knW/Jbd7wkIIYWsSHkSJorU+opRqjLEA19vAn8B+rXXLW2w+C+ijtd6tlBqOsUgZWuvxSqnmwD3AdmsrQU5Ss3yfgfy/E0IUcTLmQZQoSqmKQJLWeh7wIdAcKKOUaml93EUpVde6uQ9wUSnlAjyY5Rg1tdabtdavAVeBKsC6zG2s3RVVgcN3UOpaoI9SykMp5QPcewfHEkIIm5JPQKKkqQd8qJSyAOnAwxhLNE+0jn8wAZ8C+4FXgc0YAWEzRpjAun8tQAF/ALuBQ8BXSqm91uMN11qnWrtC8k1rvUMpNd967CvA1ts6kBBC2IEsyS2EEEKIfJFuCyGEEELki3RbCOEASqnNgNtNdz+ktd7riHqEECI/pNtCCCGEEPki3RZCCCGEyBcJD0IIIYTIFwkPQgghhMgXCQ9CCCGEyJf/B4BbESnEx5UgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Line plot for a few pitchers over the same time period\n",
    "\n",
    "# Select the pitchers of interest\n",
    "pitcher_list = ['Clayton Kershaw', 'A.J. Burnett', 'Bartolo Colon', 'Adam Wainwright']\n",
    "\n",
    "# Create the plot (use season_id instead of the season year)\n",
    "sns.lineplot(x = 'season_id',\n",
    "            y = 'ERA',\n",
    "            data = pitchers_small[pitchers_small['Name'].isin(pitcher_list)],\n",
    "            hue = 'Name')\n",
    "\n",
    "# put the legend on the outside of the plot\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)\n",
    "plt.title(\"Pitchers ERA by Season ID\")"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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