01_data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mod2 Project - Linear Regression Model\n",
    "\n",
    "First Notebook - Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using statistics, salaries, and awards information in Major League Baseball from 2000-2019\n",
    "\n",
    "Here we are interested in creating a model that can predict a player who has been in the league at least 3 years but less than 6 years. The arbitration process is based on both sides submitting an offer they believe is fair. The arbitration panel chooses the more reasonable offer. This model should be able to give a reasonable offer that can be tailored to fit the party running it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "import helper as helper\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.max_columns', 300)\n",
    "pd.set_option('display.max_rows', 80)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data collection was done from SeanLahman.com using statistics accumulated until the end of the 2019 season.\n",
    "\n",
    "I used the timeframe of 2000-2019. It was the most recent and allowed for a good portion of the information to be from the major shift in team strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these are all of the datasets I will be using to create one dataframe \n",
    "\n",
    "df_people = pd.read_csv('baseballdatabank-master/core/People.csv')\n",
    "df_sal = pd.read_csv('baseballdatabank-master/core/Salaries.csv')\n",
    "df_bat = pd.read_csv('baseballdatabank-master/core/Batting.csv')\n",
    "df_field = pd.read_csv('baseballdatabank-master/core/Fielding.csv')\n",
    "df_award = pd.read_csv('baseballdatabank-master/core/AwardsPlayers.csv')\n",
    "df_allstar = pd.read_csv('baseballdatabank-master/core/AllstarFull.csv')\n",
    "df_appear = pd.read_csv('baseballdatabank-master/core/Appearances.csv')\n",
    "df_pitch = pd.read_csv('baseballdatabank-master/core/Pitching.csv')"
   ]
  },
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       "      <td>25</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>22</td>\n",
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       "      <td>1871</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1871</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>allisdo01</td>\n",
       "      <td>27</td>\n",
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       "      <td>1871</td>\n",
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       "      <td>ansonca01</td>\n",
       "      <td>25</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "   yearID teamID lgID   playerID  G_all    GS  G_batting  G_defense  G_p  G_c  \\\n",
       "0    1871    TRO  NaN  abercda01      1   1.0          1        1.0    0    0   \n",
       "1    1871    RC1  NaN   addybo01     25  25.0         25       25.0    0    0   \n",
       "2    1871    CL1  NaN  allisar01     29  29.0         29       29.0    0    0   \n",
       "3    1871    WS3  NaN  allisdo01     27  27.0         27       27.0    0   27   \n",
       "4    1871    RC1  NaN  ansonca01     25  25.0         25       25.0    0    5   \n",
       "\n",
       "   G_1b  G_2b  G_3b  G_ss  G_lf  G_cf  G_rf  G_of  G_dh  G_ph  G_pr  \n",
       "0     0     0     0     1     0     0     0     0   0.0   0.0   0.0  \n",
       "1     0    22     0     3     0     0     0     0   0.0   0.0   0.0  \n",
       "2     0     2     0     0     0    29     0    29   0.0   0.0   0.0  \n",
       "3     0     0     0     0     0     0     0     0   0.0   0.0   0.0  \n",
       "4     1     2    20     0     1     0     0     1   0.0   0.0   0.0  "
      ]
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   "source": [
    "df_appear.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>aardsda01</td>\n",
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       "      <td>USA</td>\n",
       "      <td>CO</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>David</td>\n",
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       "      <td>David Allan</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>aardd001</td>\n",
       "      <td>aardsda01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AL</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hank</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Henry Louis</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1954-04-13</td>\n",
       "      <td>1976-10-03</td>\n",
       "      <td>aaroh101</td>\n",
       "      <td>aaronha01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AL</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Tommie</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Tommie Lee</td>\n",
       "      <td>190.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1962-04-10</td>\n",
       "      <td>1971-09-26</td>\n",
       "      <td>aarot101</td>\n",
       "      <td>aaronto01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>Orange</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Don</td>\n",
       "      <td>Aase</td>\n",
       "      <td>Donald William</td>\n",
       "      <td>190.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1977-07-26</td>\n",
       "      <td>1990-10-03</td>\n",
       "      <td>aased001</td>\n",
       "      <td>aasedo01</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>FL</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "      <td>Fausto Andres</td>\n",
       "      <td>184.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2001-09-10</td>\n",
       "      <td>2006-04-13</td>\n",
       "      <td>abada001</td>\n",
       "      <td>abadan01</td>\n",
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      ],
      "text/plain": [
       "    playerID  birthYear  birthMonth  birthDay birthCountry birthState  \\\n",
       "0  aardsda01     1981.0        12.0      27.0          USA         CO   \n",
       "1  aaronha01     1934.0         2.0       5.0          USA         AL   \n",
       "2  aaronto01     1939.0         8.0       5.0          USA         AL   \n",
       "3   aasedo01     1954.0         9.0       8.0          USA         CA   \n",
       "4   abadan01     1972.0         8.0      25.0          USA         FL   \n",
       "\n",
       "    birthCity  deathYear  deathMonth  deathDay deathCountry deathState  \\\n",
       "0      Denver        NaN         NaN       NaN          NaN        NaN   \n",
       "1      Mobile        NaN         NaN       NaN          NaN        NaN   \n",
       "2      Mobile     1984.0         8.0      16.0          USA         GA   \n",
       "3      Orange        NaN         NaN       NaN          NaN        NaN   \n",
       "4  Palm Beach        NaN         NaN       NaN          NaN        NaN   \n",
       "\n",
       "  deathCity nameFirst nameLast       nameGiven  weight  height bats throws  \\\n",
       "0       NaN     David  Aardsma     David Allan   215.0    75.0    R      R   \n",
       "1       NaN      Hank    Aaron     Henry Louis   180.0    72.0    R      R   \n",
       "2   Atlanta    Tommie    Aaron      Tommie Lee   190.0    75.0    R      R   \n",
       "3       NaN       Don     Aase  Donald William   190.0    75.0    R      R   \n",
       "4       NaN      Andy     Abad   Fausto Andres   184.0    73.0    L      L   \n",
       "\n",
       "        debut   finalGame   retroID    bbrefID  \n",
       "0  2004-04-06  2015-08-23  aardd001  aardsda01  \n",
       "1  1954-04-13  1976-10-03  aaroh101  aaronha01  \n",
       "2  1962-04-10  1971-09-26  aarot101  aaronto01  \n",
       "3  1977-07-26  1990-10-03  aased001   aasedo01  \n",
       "4  2001-09-10  2006-04-13  abada001   abadan01  "
      ]
     },
     "execution_count": 4,
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    }
   ],
   "source": [
    "df_people.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bechtge01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>PH1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>brainas01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>WS3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>792</td>\n",
       "      <td>361</td>\n",
       "      <td>132</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>0</td>\n",
       "      <td>292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fergubo01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>NY2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fishech01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>RC1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>639</td>\n",
       "      <td>295</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1</td>\n",
       "      <td>257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fleetfr01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>NY2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  yearID  stint teamID lgID   W   L   G  GS  CG  SHO  SV  IPouts  \\\n",
       "0  bechtge01    1871      1    PH1  NaN   1   2   3   3   2    0   0      78   \n",
       "1  brainas01    1871      1    WS3  NaN  12  15  30  30  30    0   0     792   \n",
       "2  fergubo01    1871      1    NY2  NaN   0   0   1   0   0    0   0       3   \n",
       "3  fishech01    1871      1    RC1  NaN   4  16  24  24  22    1   0     639   \n",
       "4  fleetfr01    1871      1    NY2  NaN   0   1   1   1   1    0   0      27   \n",
       "\n",
       "     H   ER  HR  BB  SO  BAOpp    ERA  IBB  WP  HBP  BK     BFP  GF    R  SH  \\\n",
       "0   43   23   0  11   1    NaN   7.96  NaN   7  NaN   0   146.0   0   42 NaN   \n",
       "1  361  132   4  37  13    NaN   4.50  NaN   7  NaN   0  1291.0   0  292 NaN   \n",
       "2    8    3   0   0   0    NaN  27.00  NaN   2  NaN   0    14.0   0    9 NaN   \n",
       "3  295  103   3  31  15    NaN   4.35  NaN  20  NaN   0  1080.0   1  257 NaN   \n",
       "4   20   10   0   3   0    NaN  10.00  NaN   0  NaN   0    57.0   0   21 NaN   \n",
       "\n",
       "   SF  GIDP  \n",
       "0 NaN   NaN  \n",
       "1 NaN   NaN  \n",
       "2 NaN   NaN  \n",
       "3 NaN   NaN  \n",
       "4 NaN   NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pitch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# will limit the need to change every bit of code for easier analysis\n",
    "\n",
    "yr = 2000                # integer format for trimming dataframes based on 'yearID'\n",
    "yr_str = '2000-04-01'     # string format for trimming dataframe based on 'debut'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at the dataframes available, we can see that the dimensions are very different.\n",
    "\n",
    "The awards and all star dataframes will likely be turned into binary variables to indicate the presence of an award. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19937, 24)\n",
      "(26428, 5)\n",
      "(107429, 22)\n",
      "(47628, 30)\n",
      "(143046, 18)\n",
      "(6236, 6)\n",
      "(5375, 8)\n"
     ]
    }
   ],
   "source": [
    "# try to understand what info we are working with\n",
    "\n",
    "# included are the info received from each df and the way it may be used\n",
    "\n",
    "print(df_people.shape)   # the list with all players\n",
    "print(df_sal.shape)      # salary for each player per year\n",
    "print(df_bat.shape)      # batting statistics for each player per year\n",
    "print(df_pitch.shape)    # pitching statistics for each player per year\n",
    "print(df_field.shape)    # fielding statistics for each player\n",
    "print(df_award.shape)    # award info, used as binary (will have 1 if player won any type of award, 0 if they didn't)\n",
    "print(df_allstar.shape)  # all-star info, used as binary (will have 1 if player was an allstar, 0 if not)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In order to make the dataframes more time efficient, limiting their scope and series was needed.\n",
    "\n",
    "Entries that fell before out time frame and columns that held irrelevant data were removed. Each of the dataframes must be scaled back to the time frame needed\n",
    "\n",
    "This is done mostly for information processing speed. Trim the 'people' dataframe so that it only includes players that debuted after 2000.\n",
    "\n",
    "This main df will be the one that all other df's will be joined to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>playerID</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>birthMonth</th>\n",
       "      <th>birthDay</th>\n",
       "      <th>birthCountry</th>\n",
       "      <th>birthState</th>\n",
       "      <th>birthCity</th>\n",
       "      <th>deathYear</th>\n",
       "      <th>deathMonth</th>\n",
       "      <th>deathDay</th>\n",
       "      <th>deathCountry</th>\n",
       "      <th>deathState</th>\n",
       "      <th>deathCity</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>nameGiven</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bats</th>\n",
       "      <th>throws</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>retroID</th>\n",
       "      <th>bbrefID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>abernbr01</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brent</td>\n",
       "      <td>Abernathy</td>\n",
       "      <td>Michael Brent</td>\n",
       "      <td>185.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2001-06-25</td>\n",
       "      <td>2005-09-29</td>\n",
       "      <td>aberb001</td>\n",
       "      <td>abernbr01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>FL</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "      <td>Fausto Andres</td>\n",
       "      <td>184.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2001-09-10</td>\n",
       "      <td>2006-04-13</td>\n",
       "      <td>abada001</td>\n",
       "      <td>abadan01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO</td>\n",
       "      <td>Denver</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>David Allan</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>aardd001</td>\n",
       "      <td>aardsda01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abercre01</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reggie</td>\n",
       "      <td>Abercrombie</td>\n",
       "      <td>Reginald Damascus</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2006-04-04</td>\n",
       "      <td>2008-09-28</td>\n",
       "      <td>aberr001</td>\n",
       "      <td>abercre01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>D.R.</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "      <td>Fernando Antonio</td>\n",
       "      <td>235.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>abadf001</td>\n",
       "      <td>abadfe01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID  birthYear  birthMonth  birthDay birthCountry birthState  \\\n",
       "23  abernbr01     1977.0         9.0      23.0          USA         GA   \n",
       "4    abadan01     1972.0         8.0      25.0          USA         FL   \n",
       "0   aardsda01     1981.0        12.0      27.0          USA         CO   \n",
       "21  abercre01     1980.0         7.0      15.0          USA         GA   \n",
       "5    abadfe01     1985.0        12.0      17.0         D.R.  La Romana   \n",
       "\n",
       "     birthCity  deathYear  deathMonth  deathDay deathCountry deathState  \\\n",
       "23     Atlanta        NaN         NaN       NaN          NaN        NaN   \n",
       "4   Palm Beach        NaN         NaN       NaN          NaN        NaN   \n",
       "0       Denver        NaN         NaN       NaN          NaN        NaN   \n",
       "21    Columbus        NaN         NaN       NaN          NaN        NaN   \n",
       "5    La Romana        NaN         NaN       NaN          NaN        NaN   \n",
       "\n",
       "   deathCity nameFirst     nameLast          nameGiven  weight  height bats  \\\n",
       "23       NaN     Brent    Abernathy      Michael Brent   185.0    73.0    R   \n",
       "4        NaN      Andy         Abad      Fausto Andres   184.0    73.0    L   \n",
       "0        NaN     David      Aardsma        David Allan   215.0    75.0    R   \n",
       "21       NaN    Reggie  Abercrombie  Reginald Damascus   215.0    75.0    R   \n",
       "5        NaN  Fernando         Abad   Fernando Antonio   235.0    74.0    L   \n",
       "\n",
       "   throws       debut   finalGame   retroID    bbrefID  \n",
       "23      R  2001-06-25  2005-09-29  aberb001  abernbr01  \n",
       "4       L  2001-09-10  2006-04-13  abada001   abadan01  \n",
       "0       R  2004-04-06  2015-08-23  aardd001  aardsda01  \n",
       "21      R  2006-04-04  2008-09-28  aberr001  abercre01  \n",
       "5       L  2010-07-28  2019-09-28  abadf001   abadfe01  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people = df_people[df_people.debut > yr_str] # only players that have debuted after the year cutoff are present\n",
    "df_people.head().sort_values(by = 'debut')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4528, 24)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Checking dataframe to see if debut minimum has changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2000-04-03'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people['debut'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Trim the complementary dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df_sal = df_sal[df_sal.yearID >= yr]\n",
    "df_bat = df_bat[df_bat.yearID >= yr]\n",
    "df_pitch = df_pitch[df_pitch.yearID >= yr]\n",
    "df_field = df_field[df_field.yearID >= yr]\n",
    "df_award = df_award[df_award.yearID >= yr]\n",
    "df_allstar = df_allstar[df_allstar.yearID >= yr]\n",
    "df_appear = df_appear[df_appear.yearID >= yr]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>playerID</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>birthMonth</th>\n",
       "      <th>birthDay</th>\n",
       "      <th>birthCountry</th>\n",
       "      <th>birthState</th>\n",
       "      <th>birthCity</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bats</th>\n",
       "      <th>throws</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO</td>\n",
       "      <td>Denver</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>FL</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "      <td>184.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2001-09-10</td>\n",
       "      <td>2006-04-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>D.R.</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "      <td>235.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2019-09-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abercre01</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Reggie</td>\n",
       "      <td>Abercrombie</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2006-04-04</td>\n",
       "      <td>2008-09-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>abernbr01</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Brent</td>\n",
       "      <td>Abernathy</td>\n",
       "      <td>185.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2001-06-25</td>\n",
       "      <td>2005-09-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID  birthYear  birthMonth  birthDay birthCountry birthState  \\\n",
       "0   aardsda01     1981.0        12.0      27.0          USA         CO   \n",
       "4    abadan01     1972.0         8.0      25.0          USA         FL   \n",
       "5    abadfe01     1985.0        12.0      17.0         D.R.  La Romana   \n",
       "21  abercre01     1980.0         7.0      15.0          USA         GA   \n",
       "23  abernbr01     1977.0         9.0      23.0          USA         GA   \n",
       "\n",
       "     birthCity nameFirst     nameLast  weight  height bats throws       debut  \\\n",
       "0       Denver     David      Aardsma   215.0    75.0    R      R  2004-04-06   \n",
       "4   Palm Beach      Andy         Abad   184.0    73.0    L      L  2001-09-10   \n",
       "5    La Romana  Fernando         Abad   235.0    74.0    L      L  2010-07-28   \n",
       "21    Columbus    Reggie  Abercrombie   215.0    75.0    R      R  2006-04-04   \n",
       "23     Atlanta     Brent    Abernathy   185.0    73.0    R      R  2001-06-25   \n",
       "\n",
       "     finalGame  \n",
       "0   2015-08-23  \n",
       "4   2006-04-13  \n",
       "5   2019-09-28  \n",
       "21  2008-09-28  \n",
       "23  2005-09-29  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the columns I don't need. Which includes the death statistics\n",
    "\n",
    "df_people.drop(['deathYear', 'deathMonth','deathDay','deathCountry','deathState','retroID', # drop columns I won't need\n",
    "                'deathCity', 'nameGiven','bbrefID'], axis=1, inplace = True)\n",
    "\n",
    "df_people.head()\n",
    "\n",
    "# now the master dataframe is much more manageable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I want to have the salary of any player that played more than three years. This will be our target variable. \n",
    "\n",
    "The fourth year salary would, ideally, be the pay term that was arbitrated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4528, 15)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>yearID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12263</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>anderga01</td>\n",
       "      <td>3250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12264</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>belchti01</td>\n",
       "      <td>4600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12265</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>botteke01</td>\n",
       "      <td>4000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12266</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>clemeed02</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12267</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>colanmi01</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       yearID teamID lgID   playerID   salary\n",
       "12263    2000    ANA   AL  anderga01  3250000\n",
       "12264    2000    ANA   AL  belchti01  4600000\n",
       "12265    2000    ANA   AL  botteke01  4000000\n",
       "12266    2000    ANA   AL  clemeed02   215000\n",
       "12267    2000    ANA   AL  colanmi01   200000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the salary df\n",
    "\n",
    "df_sal.head()\n",
    "\n",
    "# each entry is a year that a player was paid\n",
    "# will need to be grouped in order tmake it easier to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>419000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1087500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotku01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3354</th>\n",
       "      <td>zobribe01</td>\n",
       "      <td>438100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>zuletju01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>zumayjo01</td>\n",
       "      <td>735000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>zychto01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3359 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID   salary\n",
       "0     aardsda01   419000\n",
       "1      abadan01        0\n",
       "2      abadfe01  1087500\n",
       "3     abbotje01        0\n",
       "4     abbotku01        0\n",
       "...         ...      ...\n",
       "3354  zobribe01   438100\n",
       "3355  zuletju01        0\n",
       "3356  zumayjo01   735000\n",
       "3357  zuninmi01        0\n",
       "3358   zychto01        0\n",
       "\n",
       "[3359 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by player, leads to a player having a list of years that they played and was paid in chrono order\n",
    "# grab the fourth entry for our target variable\n",
    "\n",
    "# we will use our fourth_year() function created in the beginning\n",
    "\n",
    "df_sal_grouped = df_sal.groupby(['playerID','yearID']).agg({'salary':'sum'})\n",
    "df_sal_grouped = df_sal_grouped.groupby('playerID').agg({'salary':helper.fourth_year}).reset_index()\n",
    "\n",
    "df_sal_grouped\n",
    "\n",
    "# output is a player and the salary of their fourth year if they played a fourth year\n",
    "# if they haven't played a fourth year, return a 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining the amount of seasons a player was in the league will help with data cleaning. \n",
    "\n",
    "Arbitration takes place after the third year. So players need to complete at least three years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3359, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>seasons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotku01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  seasons\n",
       "0  aardsda01        7\n",
       "1   abadan01        1\n",
       "2   abadfe01        5\n",
       "3  abbotje01        2\n",
       "4  abbotku01        2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the groupby function twice over, we are able to count the number of seasons a player played\n",
    "# the count is based on how many different years a player was paid\n",
    "\n",
    "df_sal_grouped_seas = df_sal.groupby(['playerID'\n",
    "                                     ])['yearID'\n",
    "                                       ].unique().reset_index() # this groups the player and an array of their years\n",
    "                                    # have to use unique in order to account for trades                            \n",
    "\n",
    "# the below code counts that array and returns the length as the number of seasons\n",
    "\n",
    "df_sal_grouped_seas['seasons'] = df_sal_grouped_seas['yearID'].apply(lambda x: len(x))\n",
    "df_sal_grouped_seas.drop(['yearID'], axis=1, inplace = True)\n",
    "\n",
    "\n",
    "print(df_sal_grouped_seas.shape)\n",
    "df_sal_grouped_seas.head()\n",
    "\n",
    "# the output is a playerID and the amount of seasons played"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3359, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "      <th>seasons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>419000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1087500</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotku01</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID   salary  seasons\n",
       "0  aardsda01   419000        7\n",
       "1   abadan01        0        1\n",
       "2   abadfe01  1087500        5\n",
       "3  abbotje01        0        2\n",
       "4  abbotku01        0        2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the salary with the number of seasons\n",
    "\n",
    "df_sal_grouped = pd.merge(df_sal_grouped, df_sal_grouped_seas, on='playerID', how = 'left')\n",
    "print(df_sal_grouped.shape)\n",
    "df_sal_grouped.head()\n",
    "\n",
    "# output is the playerID, fourth year salary, and how many seasons they will play in their career"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and feature engineering batting & pitching parameters.\n",
    "\n",
    "Discrete variables have to be engineered - including any percentage. This can be done based on the available features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "      <td>28163.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2009.748997</td>\n",
       "      <td>1.090154</td>\n",
       "      <td>49.376948</td>\n",
       "      <td>118.024003</td>\n",
       "      <td>15.779604</td>\n",
       "      <td>30.633775</td>\n",
       "      <td>6.117921</td>\n",
       "      <td>0.631928</td>\n",
       "      <td>3.695594</td>\n",
       "      <td>15.031282</td>\n",
       "      <td>1.960196</td>\n",
       "      <td>0.779427</td>\n",
       "      <td>11.121898</td>\n",
       "      <td>24.808401</td>\n",
       "      <td>0.824522</td>\n",
       "      <td>1.215993</td>\n",
       "      <td>1.007066</td>\n",
       "      <td>0.930725</td>\n",
       "      <td>2.660157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.808928</td>\n",
       "      <td>0.303970</td>\n",
       "      <td>45.527897</td>\n",
       "      <td>179.331556</td>\n",
       "      <td>26.655075</td>\n",
       "      <td>49.820020</td>\n",
       "      <td>10.370286</td>\n",
       "      <td>1.537826</td>\n",
       "      <td>7.583878</td>\n",
       "      <td>26.204407</td>\n",
       "      <td>5.572375</td>\n",
       "      <td>1.897522</td>\n",
       "      <td>19.843270</td>\n",
       "      <td>36.978374</td>\n",
       "      <td>2.533903</td>\n",
       "      <td>2.653344</td>\n",
       "      <td>2.181388</td>\n",
       "      <td>1.840314</td>\n",
       "      <td>4.672562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             yearID         stint             G            AB             R  \\\n",
       "count  28163.000000  28163.000000  28163.000000  28163.000000  28163.000000   \n",
       "mean    2009.748997      1.090154     49.376948    118.024003     15.779604   \n",
       "std        5.808928      0.303970     45.527897    179.331556     26.655075   \n",
       "min     2000.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "25%     2005.000000      1.000000     13.000000      0.000000      0.000000   \n",
       "50%     2010.000000      1.000000     33.000000     16.000000      1.000000   \n",
       "75%     2015.000000      1.000000     72.000000    173.000000     21.000000   \n",
       "max     2019.000000      5.000000    163.000000    716.000000    152.000000   \n",
       "\n",
       "                  H            2B            3B            HR           RBI  \\\n",
       "count  28163.000000  28163.000000  28163.000000  28163.000000  28163.000000   \n",
       "mean      30.633775      6.117921      0.631928      3.695594     15.031282   \n",
       "std       49.820020     10.370286      1.537826      7.583878     26.204407   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        2.000000      0.000000      0.000000      0.000000      1.000000   \n",
       "75%       42.000000      8.000000      0.000000      3.000000     19.000000   \n",
       "max      262.000000     59.000000     23.000000     73.000000    160.000000   \n",
       "\n",
       "                 SB            CS            BB            SO           IBB  \\\n",
       "count  28163.000000  28163.000000  28163.000000  28163.000000  28163.000000   \n",
       "mean       1.960196      0.779427     11.121898     24.808401      0.824522   \n",
       "std        5.572375      1.897522     19.843270     36.978374      2.533903   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      1.000000      5.000000      0.000000   \n",
       "75%        1.000000      1.000000     14.000000     36.000000      0.000000   \n",
       "max       78.000000     24.000000    232.000000    223.000000    120.000000   \n",
       "\n",
       "                HBP            SH            SF          GIDP  \n",
       "count  28163.000000  28163.000000  28163.000000  28163.000000  \n",
       "mean       1.215993      1.007066      0.930725      2.660157  \n",
       "std        2.653344      2.181388      1.840314      4.672562  \n",
       "min        0.000000      0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      0.000000      0.000000  \n",
       "50%        0.000000      0.000000      0.000000      0.000000  \n",
       "75%        1.000000      1.000000      1.000000      3.000000  \n",
       "max       30.000000     24.000000     16.000000     32.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting a feel for the batting stats df\n",
    "\n",
    "df_bat.describe()\n",
    "\n",
    "# the earliest year is the limitation set in the beginning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The features below needed to be created separately due to limitations of hardware. They can be run in the same cell if the machine is capable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#OBP - On base percentage -(H + BB + HBP) divided by at bats plus walks plus hit by pitch \n",
    "# plus sacrifice flies (AB + BB + HBP + SF).\n",
    "df_bat['obp_num'] = (df_bat['H'] + df_bat['BB'] + df_bat['HBP'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bat['obp_de'] = (df_bat['AB'] + df_bat['BB'] + df_bat['HBP'] + df_bat['SF'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bat['OBP'] = df_bat['obp_num']/df_bat['obp_de']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BA - batting average - hits divided by at bats\n",
    "df_bat['BA'] = df_bat['H'] / df_bat['AB']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TB - total bases - one for each single, two for each double, three for each triple, and four for each home run\n",
    "df_bat['TB'] = df_bat['H'] + 2*df_bat['2B'] + 3*df_bat['3B'] + 4 * df_bat['HR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# slugging average is TB divided by AB\n",
    "df_bat['SLG'] = df_bat['TB'] / df_bat['AB']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OPS on base percentage plus slugging\n",
    "df_bat['OPS'] = df_bat['OBP'] + df_bat['SLG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "(5625, 23)\n"
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       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.252730</td>\n",
       "      <td>0.219391</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0.349965</td>\n",
       "      <td>0.602694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID          G          AB          R          H   2B   3B   HR  \\\n",
       "0  aardsda01  27.000000    0.666667   0.000000   0.000000  0.0  0.0  0.0   \n",
       "1   abadan01   5.000000    7.000000   0.333333   0.666667  0.0  0.0  0.0   \n",
       "2   abadfe01  29.333333    2.666667   0.000000   0.333333  0.0  0.0  0.0   \n",
       "3  abbotje01  54.000000  128.500000  18.000000  35.000000  9.0  0.5  1.5   \n",
       "4  abbotku01  42.500000   83.000000  11.000000  18.000000  3.5  0.5  3.0   \n",
       "\n",
       "    RBI   SB        CS         BB         SO  IBB  HBP        SH   SF  \\\n",
       "0   0.0  0.0  0.000000   0.000000   0.000000  0.0  0.0  0.333333  0.0   \n",
       "1   0.0  0.0  0.333333   1.333333   1.666667  0.0  0.0  0.000000  0.0   \n",
       "2   0.0  0.0  0.000000   0.000000   1.333333  0.0  0.0  0.000000  0.0   \n",
       "3  17.0  1.0  0.500000  12.000000  22.500000  0.5  1.5  1.000000  0.5   \n",
       "4   6.0  1.0  0.500000   7.000000  27.000000  1.0  0.5  0.000000  0.5   \n",
       "\n",
       "       GIDP       OBP        BA         TB       SLG       OPS  \n",
       "0  0.000000  0.000000  0.000000   0.000000  0.000000  0.000000  \n",
       "1  0.333333  0.203509  0.039216   0.666667  0.039216  0.242724  \n",
       "2  0.333333  0.047619  0.047619   0.333333  0.047619  0.095238  \n",
       "3  1.500000  0.334592  0.268162  60.500000  0.444241  0.778833  \n",
       "4  1.000000  0.252730  0.219391  38.500000  0.349965  0.602694  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregate the stats for the first three years for each player\n",
    "\n",
    "df_bat_grouped = df_bat.groupby(['playerID','yearID']).agg({'G':'sum', 'AB':'sum', 'R':'sum', 'H':'sum',\n",
    "       '2B':'sum', '3B':'sum', 'HR':'sum', 'RBI':'sum', 'SB':'sum', 'CS':'sum', 'BB':'sum', 'SO':'sum', 'IBB':'sum', \n",
    "        'HBP':'sum', 'SH':'sum','SF':'sum', 'GIDP':'sum', 'obp_num':'sum', 'obp_de':'sum', 'OBP':'sum', \n",
    "        'BA':'sum', 'TB':'sum', 'SLG':'sum', 'OPS':'sum'}).reset_index()\n",
    "df_bat_grouped = df_bat_grouped.groupby('playerID').agg({'G':helper.rook_mean, 'AB':helper.rook_mean, 'R':helper.rook_mean, 'H':helper.rook_mean,\n",
    "       '2B':helper.rook_mean, '3B':helper.rook_mean, 'HR':helper.rook_mean, 'RBI':helper.rook_mean, 'SB':helper.rook_mean, 'CS':helper.rook_mean, 'BB':helper.rook_mean,\n",
    "        'SO':helper.rook_mean, 'IBB':helper.rook_mean,'HBP':helper.rook_mean, 'SH':helper.rook_mean,'SF':helper.rook_mean, 'GIDP':helper.rook_mean,\n",
    "        'OBP':helper.rook_mean,'BA':helper.rook_mean, 'TB':helper.rook_mean, 'SLG':helper.rook_mean, 'OPS':helper.rook_mean}).reset_index()\n",
    "\n",
    "print(df_bat_grouped.shape)\n",
    "df_bat_grouped.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>CG</th>\n",
       "      <th>SHO</th>\n",
       "      <th>SV</th>\n",
       "      <th>IPouts</th>\n",
       "      <th>H</th>\n",
       "      <th>ER</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>BAOpp</th>\n",
       "      <th>ERA</th>\n",
       "      <th>IBB</th>\n",
       "      <th>WP</th>\n",
       "      <th>HBP</th>\n",
       "      <th>BK</th>\n",
       "      <th>BFP</th>\n",
       "      <th>GF</th>\n",
       "      <th>R</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14716.000000</td>\n",
       "      <td>14705.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "      <td>14724.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2010.041089</td>\n",
       "      <td>1.090329</td>\n",
       "      <td>3.299511</td>\n",
       "      <td>3.299511</td>\n",
       "      <td>25.901453</td>\n",
       "      <td>6.599837</td>\n",
       "      <td>0.188807</td>\n",
       "      <td>0.077696</td>\n",
       "      <td>1.663135</td>\n",
       "      <td>176.734719</td>\n",
       "      <td>58.594064</td>\n",
       "      <td>27.840804</td>\n",
       "      <td>7.068663</td>\n",
       "      <td>21.273159</td>\n",
       "      <td>47.451711</td>\n",
       "      <td>0.271303</td>\n",
       "      <td>5.493332</td>\n",
       "      <td>1.577085</td>\n",
       "      <td>2.198587</td>\n",
       "      <td>2.325863</td>\n",
       "      <td>0.206669</td>\n",
       "      <td>253.088427</td>\n",
       "      <td>6.411030</td>\n",
       "      <td>30.182084</td>\n",
       "      <td>1.926243</td>\n",
       "      <td>1.780223</td>\n",
       "      <td>5.088359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.846610</td>\n",
       "      <td>0.302120</td>\n",
       "      <td>4.182932</td>\n",
       "      <td>3.651730</td>\n",
       "      <td>20.940461</td>\n",
       "      <td>10.654880</td>\n",
       "      <td>0.698973</td>\n",
       "      <td>0.344479</td>\n",
       "      <td>6.400494</td>\n",
       "      <td>176.995801</td>\n",
       "      <td>58.643615</td>\n",
       "      <td>27.121762</td>\n",
       "      <td>7.451205</td>\n",
       "      <td>19.753747</td>\n",
       "      <td>49.404910</td>\n",
       "      <td>0.081194</td>\n",
       "      <td>6.135741</td>\n",
       "      <td>1.939055</td>\n",
       "      <td>2.623326</td>\n",
       "      <td>2.783751</td>\n",
       "      <td>0.531179</td>\n",
       "      <td>248.437331</td>\n",
       "      <td>10.738848</td>\n",
       "      <td>29.365759</td>\n",
       "      <td>2.582891</td>\n",
       "      <td>2.058058</td>\n",
       "      <td>5.811151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.264000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.299000</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1071.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             yearID         stint             W             L             G  \\\n",
       "count  14724.000000  14724.000000  14724.000000  14724.000000  14724.000000   \n",
       "mean    2010.041089      1.090329      3.299511      3.299511     25.901453   \n",
       "std        5.846610      0.302120      4.182932      3.651730     20.940461   \n",
       "min     2000.000000      1.000000      0.000000      0.000000      1.000000   \n",
       "25%     2005.000000      1.000000      0.000000      0.000000      8.000000   \n",
       "50%     2010.000000      1.000000      2.000000      2.000000     21.000000   \n",
       "75%     2015.000000      1.000000      5.000000      5.000000     35.000000   \n",
       "max     2019.000000      5.000000     24.000000     21.000000     94.000000   \n",
       "\n",
       "                 GS            CG           SHO            SV        IPouts  \\\n",
       "count  14724.000000  14724.000000  14724.000000  14724.000000  14724.000000   \n",
       "mean       6.599837      0.188807      0.077696      1.663135    176.734719   \n",
       "std       10.654880      0.698973      0.344479      6.400494    176.995801   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000     42.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000    120.000000   \n",
       "75%       10.000000      0.000000      0.000000      0.000000    228.000000   \n",
       "max       36.000000     11.000000      6.000000     62.000000    798.000000   \n",
       "\n",
       "                  H            ER            HR            BB            SO  \\\n",
       "count  14724.000000  14724.000000  14724.000000  14724.000000  14724.000000   \n",
       "mean      58.594064     27.840804      7.068663     21.273159     47.451711   \n",
       "std       58.643615     27.121762      7.451205     19.753747     49.404910   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       15.000000      8.000000      2.000000      6.000000     10.000000   \n",
       "50%       39.000000     19.000000      4.000000     15.000000     31.000000   \n",
       "75%       76.000000     37.000000     10.000000     30.000000     67.000000   \n",
       "max      271.000000    145.000000     48.000000    125.000000    372.000000   \n",
       "\n",
       "              BAOpp           ERA           IBB            WP           HBP  \\\n",
       "count  14716.000000  14705.000000  14724.000000  14724.000000  14724.000000   \n",
       "mean       0.271303      5.493332      1.577085      2.198587      2.325863   \n",
       "std        0.081194      6.135741      1.939055      2.623326      2.783751   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.232000      3.330000      0.000000      0.000000      0.000000   \n",
       "50%        0.264000      4.440000      1.000000      1.000000      1.000000   \n",
       "75%        0.299000      5.950000      2.000000      3.000000      3.000000   \n",
       "max        1.000000    189.000000     17.000000     25.000000     21.000000   \n",
       "\n",
       "                 BK           BFP            GF             R            SH  \\\n",
       "count  14724.000000  14724.000000  14724.000000  14724.000000  14724.000000   \n",
       "mean       0.206669    253.088427      6.411030     30.182084      1.926243   \n",
       "std        0.531179    248.437331     10.738848     29.365759      2.582891   \n",
       "min        0.000000      1.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000     63.000000      0.000000      9.000000      0.000000   \n",
       "50%        0.000000    175.000000      2.000000     20.000000      1.000000   \n",
       "75%        0.000000    324.000000      8.000000     40.000000      3.000000   \n",
       "max        6.000000   1071.000000     79.000000    152.000000     21.000000   \n",
       "\n",
       "                 SF          GIDP  \n",
       "count  14724.000000  14724.000000  \n",
       "mean       1.780223      5.088359  \n",
       "std        2.058058      5.811151  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      1.000000  \n",
       "50%        1.000000      3.000000  \n",
       "75%        3.000000      7.000000  \n",
       "max       14.000000     40.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting a feel for the pitching stats df\n",
    "\n",
    "df_pitch.describe()\n",
    "\n",
    "# the earliest year is the limitation set in the beginning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pitch.rename(columns={\"W\": \"W_p\",\"L\": \"L_p\",\"G\": \"G_p\",\"GS\": \"GS_p\",\"SHO\": \"SHO_p\",\"SV\": \"SV_p\",\"H\": \"H_p\"\n",
    "                  ,\"ER\": \"ER_p\",\"HR\": \"HR_p\",\"IBB\": \"IBB_p\",\"SO\": \"SO_p\",\"R\": \"R_p\",\"GIDP\": \"GIDP_p\",\"SH\": \"SH_p\",\n",
    "                  \"SF\": \"SF_p\",\"HBP\": \"HBP_p\",\"BB\": \"BB_p\"}, errors=\"raise\", inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineer for Pitching Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pitch['WHIP'] = (df_pitch['BB_p'] + df_pitch['H_p']) / ( (df_pitch['IPouts'])/3)\n",
    "\n",
    "df_pitch['IP'] = df_pitch['IPouts'] /3\n",
    "\n",
    "df_pitch['ERA'] = (9 * df_pitch['ER_p']) / df_pitch['IP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'W_p', 'L_p', 'G_p',\n",
       "       'GS_p', 'CG', 'SHO_p', 'SV_p', 'IPouts', 'H_p', 'ER_p', 'HR_p', 'BB_p',\n",
       "       'SO_p', 'BAOpp', 'ERA', 'IBB_p', 'WP', 'HBP_p', 'BK', 'BFP', 'GF',\n",
       "       'R_p', 'SH_p', 'SF_p', 'GIDP_p', 'WHIP', 'IP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pitch.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3308, 27)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>playerID</th>\n",
       "      <th>G_p</th>\n",
       "      <th>W_p</th>\n",
       "      <th>L_p</th>\n",
       "      <th>GS_p</th>\n",
       "      <th>CG</th>\n",
       "      <th>SHO_p</th>\n",
       "      <th>SV_p</th>\n",
       "      <th>IPouts</th>\n",
       "      <th>ER_p</th>\n",
       "      <th>HR_p</th>\n",
       "      <th>SO_p</th>\n",
       "      <th>IBB_p</th>\n",
       "      <th>BAOpp</th>\n",
       "      <th>ERA</th>\n",
       "      <th>WP</th>\n",
       "      <th>HBP_p</th>\n",
       "      <th>BK</th>\n",
       "      <th>BFP</th>\n",
       "      <th>GF</th>\n",
       "      <th>R_p</th>\n",
       "      <th>SH_p</th>\n",
       "      <th>SF_p</th>\n",
       "      <th>GIDP_p</th>\n",
       "      <th>WHIP</th>\n",
       "      <th>IP</th>\n",
       "      <th>BB_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310333</td>\n",
       "      <td>5.742511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145.666667</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.948782</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>18.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.666667</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>5.083699</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.666667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.511177</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abbotpa01</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>19.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>368.333333</td>\n",
       "      <td>65.333333</td>\n",
       "      <td>16.333333</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.277333</td>\n",
       "      <td>6.812341</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>537.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>1.688307</td>\n",
       "      <td>122.777778</td>\n",
       "      <td>62.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abreubr01</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138000</td>\n",
       "      <td>1.038462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abreuju01</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.231000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        G_p       W_p       L_p       GS_p        CG  SHO_p  SV_p  \\\n",
       "0  aardsda01  27.000000  2.000000  0.333333   0.000000  0.000000    0.0   0.0   \n",
       "1   abadfe01  29.333333  0.333333  3.666667   2.000000  0.000000    0.0   0.0   \n",
       "2  abbotpa01  23.333333  9.000000  4.666667  19.666667  0.333333    0.0   0.0   \n",
       "3  abreubr01   7.000000  0.000000  0.000000   0.000000  0.000000    0.0   0.0   \n",
       "4  abreuju01   7.000000  0.000000  0.000000   0.000000  0.000000    0.0   0.0   \n",
       "\n",
       "       IPouts       ER_p       HR_p       SO_p     IBB_p     BAOpp       ERA  \\\n",
       "0   96.000000  18.333333   4.666667  30.000000  1.000000  0.310333  5.742511   \n",
       "1   84.666667  16.000000   4.666667  21.666667  0.333333  0.279000  5.083699   \n",
       "2  368.333333  65.333333  16.333333  80.000000  3.000000  0.277333  6.812341   \n",
       "3   26.000000   1.000000   0.000000  13.000000  0.000000  0.138000  1.038462   \n",
       "4   20.000000   2.000000   1.000000  12.000000  0.000000  0.231000  2.700000   \n",
       "\n",
       "         WP     HBP_p   BK         BFP   GF   R_p      SH_p      SF_p  \\\n",
       "0  1.000000  1.333333  0.0  145.666667  7.0  19.0  1.000000  1.666667   \n",
       "1  1.333333  1.333333  0.0  127.666667  5.0  17.0  1.000000  1.333333   \n",
       "2  5.333333  4.333333  0.0  537.666667  1.0  68.0  1.666667  3.333333   \n",
       "3  0.000000  0.000000  0.0   32.000000  2.0   1.0  0.000000  0.000000   \n",
       "4  0.000000  5.000000  0.0   34.000000  2.0   2.0  0.000000  0.000000   \n",
       "\n",
       "      GIDP_p      WHIP          IP       BB_p  \n",
       "0   1.333333  1.948782   32.000000  18.333333  \n",
       "1   1.333333  1.511177   28.222222  11.000000  \n",
       "2  10.333333  1.688307  122.777778  62.333333  \n",
       "3   1.000000  0.807692    8.666667   3.000000  \n",
       "4   0.000000  1.350000    6.666667   3.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregate the stats for the first three years for each player\n",
    "\n",
    "df_pitch_grouped = df_pitch.groupby(['playerID','yearID']).agg({'W_p':'sum', 'L_p':'sum', 'G_p':'sum', 'GS_p':'sum',\n",
    "       'CG':'sum', 'SHO_p':'sum', 'SV_p':'sum', 'IPouts':'sum', 'ER_p':'sum', 'HR_p':'sum', 'SO_p':'sum', 'BAOpp':'sum', \n",
    "        'HBP_p':'sum', 'SH_p':'sum','SF_p':'sum', 'GIDP_p':'sum', 'ERA':'sum', 'WP':'sum', 'BK':'sum',\n",
    "        'BFP':'sum','GF':'sum','R_p':'sum','SH_p':'sum','WHIP':'sum','IP':'sum','IBB_p':'sum','BB_p':'sum'}).reset_index()\n",
    "df_pitch_grouped = df_pitch_grouped.groupby('playerID').agg({'G_p':helper.rook_mean, 'W_p':helper.rook_mean, 'L_p':helper.rook_mean, 'GS_p':helper.rook_mean,\n",
    "       'CG':helper.rook_mean, 'SHO_p':helper.rook_mean, 'SV_p':helper.rook_mean, 'IPouts':helper.rook_mean, 'ER_p':helper.rook_mean, 'HR_p':helper.rook_mean,\n",
    "        'SO_p':helper.rook_mean, 'IBB_p':helper.rook_mean,'BAOpp':helper.rook_mean, 'ERA':helper.rook_mean,'WP':helper.rook_mean, 'HBP_p':helper.rook_mean,\n",
    "        'BK':helper.rook_mean,'BFP':helper.rook_mean, 'GF':helper.rook_mean, 'R_p':helper.rook_mean, 'SH_p':helper.rook_mean,\n",
    "        'SF_p':helper.rook_mean,'GIDP_p':helper.rook_mean,'WHIP':helper.rook_mean,'IP':helper.rook_mean,'BB_p':helper.rook_mean}).reset_index()\n",
    "\n",
    "print(df_pitch_grouped.shape)\n",
    "df_pitch_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data transformation\n",
    "\n",
    "Turning the presence of an award into a binary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>playerID</th>\n",
       "      <th>award_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abreujo02</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alfonan01</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>altuvjo01</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  award_winner\n",
       "0  abreubo01           1.0\n",
       "1  abreujo02           2.0\n",
       "2  alfonan01           2.0\n",
       "3  alomaro01           2.0\n",
       "4  altuvjo01           2.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a feature that returns a binary if the player received an award\n",
    "\n",
    "df_award['award_winner'] = 1\n",
    "\n",
    "# aggregate the data, anything more than 0 means the player won an award in the first three years\n",
    "\n",
    "df_award_grouped = df_award.groupby(['playerID','yearID']).agg({'award_winner':'sum'}).reset_index()\n",
    "df_award_grouped = df_award_grouped.groupby('playerID').agg({'award_winner':helper.rook_mean}).reset_index()\n",
    "df_award_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5607, 2)\n"
     ]
    },
    {
     "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>playerID</th>\n",
       "      <th>prim_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotku01</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID prim_pos\n",
       "0  aardsda01        P\n",
       "1   abadan01       1B\n",
       "2   abadfe01        P\n",
       "3  abbotje01       OF\n",
       "4  abbotku01       2B"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_field_grouped = df_field.groupby(['playerID'\n",
    "                                     ])['POS'\n",
    "                                       ].unique().reset_index() # this groups the player and an array of their years\n",
    "                                    # have to use unique in order to account for trades                            \n",
    "\n",
    "df_field_grouped['prim_pos'] = df_field_grouped['POS'].apply(lambda x: x[0])\n",
    "\n",
    "df_field_grouped.drop(['POS'], axis=1, inplace = True)\n",
    "print(df_field_grouped.shape)\n",
    "df_field_grouped.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge the dataframes onto the master dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4528, 16)\n",
      "(4528, 38)\n",
      "(4528, 40)\n",
      "(4528, 41)\n",
      "(4528, 67)\n"
     ]
    }
   ],
   "source": [
    "df_people = pd.merge(df_people, df_award_grouped, on='playerID',how = 'left')\n",
    "print(df_people.shape)\n",
    "df_people = pd.merge(df_people, df_bat_grouped, on = 'playerID',how = 'left')\n",
    "print(df_people.shape)\n",
    "df_people = pd.merge(df_people, df_sal_grouped, on='playerID', how = 'left')\n",
    "print(df_people.shape)\n",
    "df_people = pd.merge(df_people, df_field_grouped, on='playerID',how = 'left')\n",
    "print(df_people.shape)\n",
    "df_people = pd.merge(df_people, df_pitch_grouped, on='playerID',how = 'left')\n",
    "print(df_people.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning\n",
    "\n",
    "Using the seasons and at-bats columns, I can make sure that only players that received playing time and made it to a fourth year were included in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>playerID</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>birthMonth</th>\n",
       "      <th>birthDay</th>\n",
       "      <th>birthCountry</th>\n",
       "      <th>birthState</th>\n",
       "      <th>birthCity</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bats</th>\n",
       "      <th>throws</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>award_winner</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "      <th>OBP</th>\n",
       "      <th>BA</th>\n",
       "      <th>TB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OPS</th>\n",
       "      <th>salary</th>\n",
       "      <th>seasons</th>\n",
       "      <th>prim_pos</th>\n",
       "      <th>G_p</th>\n",
       "      <th>W_p</th>\n",
       "      <th>L_p</th>\n",
       "      <th>GS_p</th>\n",
       "      <th>CG</th>\n",
       "      <th>SHO_p</th>\n",
       "      <th>SV_p</th>\n",
       "      <th>IPouts</th>\n",
       "      <th>ER_p</th>\n",
       "      <th>HR_p</th>\n",
       "      <th>SO_p</th>\n",
       "      <th>IBB_p</th>\n",
       "      <th>BAOpp</th>\n",
       "      <th>ERA</th>\n",
       "      <th>WP</th>\n",
       "      <th>HBP_p</th>\n",
       "      <th>BK</th>\n",
       "      <th>BFP</th>\n",
       "      <th>GF</th>\n",
       "      <th>R_p</th>\n",
       "      <th>SH_p</th>\n",
       "      <th>SF_p</th>\n",
       "      <th>GIDP_p</th>\n",
       "      <th>WHIP</th>\n",
       "      <th>IP</th>\n",
       "      <th>BB_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO</td>\n",
       "      <td>Denver</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>P</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310333</td>\n",
       "      <td>5.742511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145.666667</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.948782</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>18.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>FL</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "      <td>184.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2001-09-10</td>\n",
       "      <td>2006-04-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.203509</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>0.242724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>D.R.</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "      <td>235.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>1087500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>P</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.666667</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>5.083699</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.666667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.511177</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abercre01</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Reggie</td>\n",
       "      <td>Abercrombie</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2006-04-04</td>\n",
       "      <td>2008-09-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>128.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>28.666667</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.282414</td>\n",
       "      <td>0.239408</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0.475262</td>\n",
       "      <td>0.757676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abernbr01</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Brent</td>\n",
       "      <td>Abernathy</td>\n",
       "      <td>185.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2001-06-25</td>\n",
       "      <td>2005-09-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.333333</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>65.333333</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>24.333333</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.241254</td>\n",
       "      <td>0.195237</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>0.298054</td>\n",
       "      <td>0.539308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  birthYear  birthMonth  birthDay birthCountry birthState  \\\n",
       "0  aardsda01     1981.0        12.0      27.0          USA         CO   \n",
       "1   abadan01     1972.0         8.0      25.0          USA         FL   \n",
       "2   abadfe01     1985.0        12.0      17.0         D.R.  La Romana   \n",
       "3  abercre01     1980.0         7.0      15.0          USA         GA   \n",
       "4  abernbr01     1977.0         9.0      23.0          USA         GA   \n",
       "\n",
       "    birthCity nameFirst     nameLast  weight  height bats throws       debut  \\\n",
       "0      Denver     David      Aardsma   215.0    75.0    R      R  2004-04-06   \n",
       "1  Palm Beach      Andy         Abad   184.0    73.0    L      L  2001-09-10   \n",
       "2   La Romana  Fernando         Abad   235.0    74.0    L      L  2010-07-28   \n",
       "3    Columbus    Reggie  Abercrombie   215.0    75.0    R      R  2006-04-04   \n",
       "4     Atlanta     Brent    Abernathy   185.0    73.0    R      R  2001-06-25   \n",
       "\n",
       "    finalGame  award_winner          G          AB          R          H  \\\n",
       "0  2015-08-23           NaN  27.000000    0.666667   0.000000   0.000000   \n",
       "1  2006-04-13           NaN   5.000000    7.000000   0.333333   0.666667   \n",
       "2  2019-09-28           NaN  29.333333    2.666667   0.000000   0.333333   \n",
       "3  2008-09-28           NaN  60.000000  128.666667  21.666667  28.666667   \n",
       "4  2005-09-29           NaN  69.333333  267.000000  30.666667  65.333333   \n",
       "\n",
       "          2B        3B        HR        RBI        SB        CS         BB  \\\n",
       "0   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "1   0.000000  0.000000  0.000000   0.000000  0.000000  0.333333   1.333333   \n",
       "2   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "3   6.666667  0.666667  3.000000  11.333333  6.000000  2.666667   7.000000   \n",
       "4  11.666667  1.666667  2.333333  24.333333  6.333333  2.333333  17.666667   \n",
       "\n",
       "          SO       IBB       HBP        SH        SF      GIDP       OBP  \\\n",
       "0   0.000000  0.000000  0.000000  0.333333  0.000000  0.000000  0.000000   \n",
       "1   1.666667  0.000000  0.000000  0.000000  0.000000  0.333333  0.203509   \n",
       "2   1.333333  0.000000  0.000000  0.000000  0.000000  0.333333  0.047619   \n",
       "3  41.000000  0.666667  2.333333  1.666667  0.666667  1.000000  0.282414   \n",
       "4  28.000000  0.333333  2.000000  4.333333  1.000000  4.333333  0.241254   \n",
       "\n",
       "         BA          TB       SLG       OPS     salary  seasons prim_pos  \\\n",
       "0  0.000000    0.000000  0.000000  0.000000   419000.0      7.0        P   \n",
       "1  0.039216    0.666667  0.039216  0.242724        0.0      1.0       1B   \n",
       "2  0.047619    0.333333  0.047619  0.095238  1087500.0      5.0        P   \n",
       "3  0.239408   56.000000  0.475262  0.757676        0.0      1.0       OF   \n",
       "4  0.195237  103.000000  0.298054  0.539308        0.0      2.0       2B   \n",
       "\n",
       "         G_p       W_p       L_p  GS_p   CG  SHO_p  SV_p     IPouts  \\\n",
       "0  27.000000  2.000000  0.333333   0.0  0.0    0.0   0.0  96.000000   \n",
       "1        NaN       NaN       NaN   NaN  NaN    NaN   NaN        NaN   \n",
       "2  29.333333  0.333333  3.666667   2.0  0.0    0.0   0.0  84.666667   \n",
       "3        NaN       NaN       NaN   NaN  NaN    NaN   NaN        NaN   \n",
       "4        NaN       NaN       NaN   NaN  NaN    NaN   NaN        NaN   \n",
       "\n",
       "        ER_p      HR_p       SO_p     IBB_p     BAOpp       ERA        WP  \\\n",
       "0  18.333333  4.666667  30.000000  1.000000  0.310333  5.742511  1.000000   \n",
       "1        NaN       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "2  16.000000  4.666667  21.666667  0.333333  0.279000  5.083699  1.333333   \n",
       "3        NaN       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "4        NaN       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "      HBP_p   BK         BFP   GF   R_p  SH_p      SF_p    GIDP_p      WHIP  \\\n",
       "0  1.333333  0.0  145.666667  7.0  19.0   1.0  1.666667  1.333333  1.948782   \n",
       "1       NaN  NaN         NaN  NaN   NaN   NaN       NaN       NaN       NaN   \n",
       "2  1.333333  0.0  127.666667  5.0  17.0   1.0  1.333333  1.333333  1.511177   \n",
       "3       NaN  NaN         NaN  NaN   NaN   NaN       NaN       NaN       NaN   \n",
       "4       NaN  NaN         NaN  NaN   NaN   NaN       NaN       NaN       NaN   \n",
       "\n",
       "          IP       BB_p  \n",
       "0  32.000000  18.333333  \n",
       "1        NaN        NaN  \n",
       "2  28.222222  11.000000  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(994, 67)\n"
     ]
    }
   ],
   "source": [
    "# strip the dataframe of any players that played less than 4 years or less than 3 at bata in their career\n",
    "\n",
    "df_people = df_people[(df_people['seasons'] > 3)] # at bats are mean over three years\n",
    "                                                                          # an Ab count of < 3 means he had less than\n",
    "                                                                          # one AB per year\n",
    "df_people.rename(columns = {'3B':'TPL','2B':'DBL',},inplace = True)\n",
    "\n",
    "print(df_people.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "playerID          0\n",
       "birthYear         0\n",
       "birthMonth        0\n",
       "birthDay          0\n",
       "birthCountry      0\n",
       "birthState       23\n",
       "birthCity         0\n",
       "nameFirst         0\n",
       "nameLast          0\n",
       "weight            0\n",
       "height            0\n",
       "bats              0\n",
       "throws            0\n",
       "debut             0\n",
       "finalGame         0\n",
       "award_winner    793\n",
       "G                 0\n",
       "AB                0\n",
       "R                 0\n",
       "H                 0\n",
       "DBL               0\n",
       "TPL               0\n",
       "HR                0\n",
       "RBI               0\n",
       "SB                0\n",
       "CS                0\n",
       "BB                0\n",
       "SO                0\n",
       "IBB               0\n",
       "HBP               0\n",
       "SH                0\n",
       "SF                0\n",
       "GIDP              0\n",
       "OBP               0\n",
       "BA                0\n",
       "TB                0\n",
       "SLG               0\n",
       "OPS               0\n",
       "salary            0\n",
       "seasons           0\n",
       "prim_pos          0\n",
       "G_p             420\n",
       "W_p             420\n",
       "L_p             420\n",
       "GS_p            420\n",
       "CG              420\n",
       "SHO_p           420\n",
       "SV_p            420\n",
       "IPouts          420\n",
       "ER_p            420\n",
       "HR_p            420\n",
       "SO_p            420\n",
       "IBB_p           420\n",
       "BAOpp           420\n",
       "ERA             420\n",
       "WP              420\n",
       "HBP_p           420\n",
       "BK              420\n",
       "BFP             420\n",
       "GF              420\n",
       "R_p             420\n",
       "SH_p            420\n",
       "SF_p            420\n",
       "GIDP_p          420\n",
       "WHIP            420\n",
       "IP              420\n",
       "BB_p            420\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing the NaNs\n",
    "\n",
    "The state and city of a player and the lack of an award can be replaced with 0's without affecting the overall model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(994, 67)\n"
     ]
    }
   ],
   "source": [
    "df_people= df_people.replace(np.nan,0)\n",
    "print(df_people.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b714902b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PjRZtvyJSnfzVxCwi13UZTyTp6SzNsO5QMMDe/QPMZ5a2dqMNIS4nT/P7bGn2TnR+9ZFjTM6mFp4PBwPcfE3Pslc5ikhtqtmQnk1mSKYydLRESvY9UrluiyWPBy+/myKVydLe3EB7SwP9AwmmZuZpbKjZ/yYRWUPNdnfk55Fuj5UupEvFcRx29LWRdeHlM5OVLkdEKqh2Qzo3sqOjCkMaYNuGVgIOHDk1gav+DZG6VcMh7bWkqzWkGxtC9HW3MD41z8jkXKXLEZEKqdmQHq/ykAbv4hbwWtMiUp9qNqTHprxls6o5pHu7mmmKhDh2NkF6mVEkIlL7ajakF1rSJRzdUWoBx2H7xlZS6Sz9A/6e6FxESqNmQ3oskaQhFPDlvB2XYvvGfJeHRnmI1KMaDuk52mORqp+XubW5gZ6OKOdGZ5icnq90OSJSZjUZ0ulMlsmZFJ1V3B+92I7cCcTDp8YrXImIlFtNhvR4FV/IspzNPTHCwQBHTk6QzWrMtEg9qc2QTuRGdlTxScPFwqEAW3tjTM+l2X98tNLliEgZ1WRIV/Ml4SvJd3looVqR+lKbIV0Dw+8u1tXWSHtLA88eGmJq0cx4IlLbajSkc/N2tNZOSDuOw85N7aQzLk/sP1fpckSkTGo0pGuvJQ1el0cw4PDo82cBVwsCiNSB6r7SYwWjiSSOA20tDZUupahiTQ1s7mnh2NkEX33sGF1t5xc00IIAIrWpNlvSk3O0t0QIBmrvx8ufQLQnxpesjygitafmUiyTdRlLzNNZQ/3Ri/XFW4hGghw7M6lJl0TqQM2F9Hhijqzr0hlrrHQpJREIOGzf0MZ8OsvJgalKlyMiJVZzIT00PgtQsy1pON/lceS05pkWqXU1F9LD+ZCu0ZY0eJMudXdEOTsyw9SMxkyL1LLaDenW2g1pgB0b1ZoWqQc1F9L10N0BsGV9jFDQ4ehpLVQrUstqLqTrpSXtTbrUyvRcmrMjM5UuR0RKpOZCemhsllDQIdYUrnQpJbfQ5aGFakVqVs2F9PD4LB2xCIE6uE463t5Ia3MD/YNTJOczlS5HREqgpkI6nckyPpWs6ZEdizmOw46+NrJZl6Nn1JoWqUU1FdJjiSSuW/snDRfbvqEVx4HD/VpaS6QW1VRIj056U5TW+knDxaKREH3xFkYmk7x8RiuKi9Sa2grp3BSltbIAbaHM5nYAHnr6VIUrEZFiq62QnsxP9l8/LWmA3nVNtDU38OTBASan5ytdjogUUW2FdJ22pB3HYdfWDtIZlx/tO1PpckSkiAoKaWPMHmPMD0tcyxUbm8yFdJ21pMGbdKmxIcgPnz1NJqspTEVqxZohbYz5IPAPgO+Tb3RyjkhDkObGmlxwZlUN4SC3vnI9Y4kkzxwarnQ5IlIkhaTZUeBe4POF7LCjo4lQKHhFRV2usal5utqidHe3XvB4rHmUdGbp/BaRhgCZjHvJz13JtvnnYi1LP/Ou5HuGgg6/+GbDD549zUPPnOLu27bhXOEFPfF47Iq2L5dqqFM1Fk811FnMGtcMaWvt/caYrYXucGysMvNIJFMZEjPzbN/YxtBQYuFxx4HEdJJ0emkXQCoSIpPJkrrE565k21QkRLQxTGJqrqjfMxQK0BiA3Tu6ePbwMI8/e4qrN7Uvu59CxOOxC95Hv6qGOlVj8VRDnZdb40rBXjMnDvOz3/Wsa6pwJZV1157NADz44/4KVyIixVAzIT045oX0hq7mCldSWTv72tm+sZXnjgxzZni60uWIyBWquZDurfOQBrjrtbnW9JNqTYtUu4JC2lp73Fp7c6mLuRKDub7w3q6WCldSeTfsjNPT2cQTL55b6AYSkepUMy3pgVxLen1nffdJg7ei+Ntet5VM1uUbjx+vdDkicgVqJqSHxmdpb2mgMVJ/Y6SX89pdPWzoaubxF84xUKERNyJy5WoipFPpLCOTc3R3qBWdFwg4vO22q8i6Ll9/9HilyxGRy1QTIT08MYvrQndHtNKlVJTjXHi76RVx+uLN7D1wjtNDU5UuT0QuQ030DeRHdnS3129Ih4IB9u4fYD5z4YUuu7Z2cGpomi89dJjfe9fuK74KUUTKqyZa0gshXect6VQmSzp94W19ZxMb480cOD7Gs4c1p4dItampkO5Rn/QSjuOw59oeggGHLz90mFRaC9aKVJOaCOmBcW/0QryOuztW094S4U039TE8MccDe3WBi0g1qYmQHhybJdYUpqkOpygt1FtfdxXtLQ184/Hj9A/4e4IaETmv6kM6nckyMjFX9/3Ra2lqDPHen91FJuty3zcPrDjTnoj4S9WH9OjkHJmsS3e7+qPXct22ddyxewOnh6b56qMvV7ocESlA1Yf0+ZOGakkX4p1v3EG8vZEH9/bznEZ7iPhe1Yd0fs6OuEK6II0NIX7z7dcRDgX4+2/s10UuIj5X9SF9ctALmY2aorRgW9bHeN/P7SI5n+Hj9z9PYma+0iWJyAqqPqRPDCQIBZ26n+z/Ur12Vw/33LqVofE5/uKfnmNmLnXBJeVw4dciUhlVPWYtnclyemiKvngLoWDVf96U3dtffxWJmXl+9NwZ/vgzT3HXzZtpCHuLCMeaR0lMJwkHA9x8TQ/u8mvjikiJVXWynRmeJp1x2bLe/6sH+1HAcfjluww7+9oWLnRJTM97l5RnXNLpLKmMhuqJVFJVh/SJ3EUZW3oU0pcr4Di87vpetm9sZWRijgd/3M/ktPqoRfyiqkO6/5x30nCzQnpNF09juvgWcBxufeV6XrV9HYmZFA/s7eeMRn2I+EJV90mfGEgQcBz64jppuJqVpjEFiDaEcPAmYtq9s4umxhA/PjDA1x4+yk2v6ObabZ3lL1hEFlRtSGezLv2DCTZ0NS2c7JKV5acxXfJ48MLHrt7UTltzAw/vO8uTBwcZn0pyyzXrdWJWpEKq9jfv3OgM86ms+qNLoKeziV+8cyedrREOnZzgo194hrFEstJlidSlqg3p/EnDzRrZURKxpgbu2rOZ7RtbOXpmkj/5x6c4enqi0mWJ1J3qDelzGtlRaqFggNt3b+A/3LmDyel5PvrFZ3h435lKlyVSV6o2pPsHEjjApu6WSpdS0xzH4a49m/m9d+0mEg7y2Qde4rMPHCSVzuhqRJEyqMqQTqYyHD0zyYZ4M9FI1Z77rAr5kSGjU0nuvmULna0RHt53lg99ai/febJfQS1SYlUZ0geOj5JKZ9m9o6vSpdSF/MiQpkiIu/ds5upNbYxOJrn/Ry/zk5cGK12eSE2rypDOz4N8vUK67ILBADdfu57bXtWL67r83Vde5IvfO0QypQVuRUqh6voKsq7LvqMjtDaF2dbbWuly6ta2Da10d0TZu3+A7z99in1Hh3nPzxiu27Zu4TWalEnkylVdSB87O8nk9Dy3XddLIKAO0Urqao9y957NPHlwgBePjfKX/7SPvngz1+/soi/eotnzRIqg6kJ63xGvq2P3TnV1+IIDN1wdZ8v6GE8dHOTU0DSnhqaJt0eZmEry6p1xutq1ao7I5aq6kH7u8DChYIBrt2pOCT/pbG3kZ/ZsZmB0hhdeHuXM8DRffugIX37oCK3NDWzubqGno4n2WAPtLRE6Yt6tvSWy7Aid1UaNqHUu9aSqQnpwfJZTQ9Nct20dkQbN1+FHPZ1N9HQ2kcpkaQgGeP7oCP0DU7x4bJQXj40uu01jQ3AhuDtbI2zpiTGTTNMWixC8qEtLixBIvamqkP72EycA2HNNd4UrkbVEIyFef10vP7V7IwAzcymGJ+YYn0oyPpXkhZdHScykmEmmmJlLMz6V5NzoDACPvXAOgFDQYX1nE71dzWzsaibWFK7YzyNSKVUT0gOjMzz6/Fl61zWx55qeSpcjl6ipMczmxjCbe2LeHNbBwJJZ+TLZLFMzKSZmUgyMzHB6eHqhj/spoCUaZmO8mWhDkF2bO2hqVGhL7auakP7KIy+TdV1+/vXbCAaqcnh33VmpX3mlx4OBAG0tEXrWNbN9QyupdJap2RRnhqc5OzzN2ZEZbP84tn+cgOOwbUMrr712PRs6o2xdH6M5GlY3iNScqgjp/oEETx4cZMv6GDeaeKXLkQIUstBAIVqiYa7e1M7Vm9rJZl0SsylODU5xcnCKo6cnOLJoZr625gau2drB1t5WNne3sKk7RlPj+UNcJyOlGvk+pCen57nvmwcA+IXbt+FosoiqUehCA4UKBBx6Opvoamvk2qs6SaYyTM6mOXVukqGJOUYm5nhi/wBP7B9Y2KarrZFN3S1s7mlhNpmhLdZASzR8wXGkk5HiZ74O6Ynpef73l57lzPA0d97Yx7VXadidnBcJB9nW0Uy8NQJAMOiwc2Mbx84kODk4xcnBBP2DUzx7eJhnc1MJAIRDAW8kSSzCurZGujuiZLJZAo660cR/1gxpY0wA+ARwPZAEfs1ae6SURaUzWfbuH+Drjx1jeGKON93Ux7vv3KlWtKzKcRw2dDXTu+78mpeu6zIxPc/JwSl+tO8MI+NzjCWSDI3NMjg2u/C6bz1+gk3dLazvbCLeEaUz1khzNERTJITjODiOd1zOp7LMpzML/6bSWZLzGebT3nOpdIZk7t9QKEC0IURnexQ3kyUaCdHcGKIlGqY5GqYld2tsCOrYlhUV0pJ+O9Borb3FGHMz8BfA24pdyPDELM/YIU4MJDhwYoyJqXmCAYefu2UL96qbQwqwVj/47h1dpHLdL+lMlrFEkpFJL7TnkhmOn0tw9MxkucsmGHAWhfb5EG9uDNMQChAMBggFHYKBAMGAQyaTJZN1SWfdha8zGZd01vs6m3s8nXG957K5r/Ovzbqkc1+7rvdBFgwGvPfGdcku6vYJBJyFFeXz/174mEPA8T4gAwHngtd5H2ze90qls96/uS6w/GOpjIvrurmV652FRZEX7zMUdAgFA4SCAaKNIXDdhfvhYIBQyHt/wrnHQiHv8XxkeP865792vbv57xXKvb+hUIBQIEAo5O0rmN//ou+/+HWu65IF3KxL1nVpbAjREYsU/fgoJKRvAx4EsNbuNcbcVPQqgM98+yUOnhgDIBoJ8tOv2cSbb9rEurbGK953eIVFVMPBAAG8/7NLee5Kts3/54dCS2sq5fe81OfyB2M5v+flbJuvM/9cZpmAXrztwnahAL2REL1dzYSDAW65todUOsvwxBxDY7OMTyeZnk0zm0zjui6nhqbB8T4IgkHH+2UOOkQjYcIhZ+G5/C9yMBAgHHKYTWYAh4mpJMlUxrvNey3w2aS3/7n5DHPzGUYm5jgzXPrZBB288A0EvFAkF6qw+OSql2ZeiHtBHgg4ZLNeiLuuF0yF9uM7jvdhlP+gyYdvcziI47CwTzj//XAgk3WZT2XOf/jkbn71kV/bQzxe3NWiHHeNd9kY8w/A/dbaB3L3+4Ft1tp0USsREZElCjlTMgks/mgIKKBFRMqjkJB+DPhZgFyf9AslrUhERBYU0if9FeDNxpjH8Tqq3lvakkREJG/NPmkREakcjd4XEfExhbSIiI8ppEVEfMzXc3csttbl6caYtwB/BKSBT1tr76vEJe0F1Plu4HeADPA88JvW2qwx5lkgP6XbMWttyU7QFlDj7wLvB4ZyD/0n4PBq25SzRmPMeuDLi16+G/h9a+0ny/k+Lqp1D/BRa+0dFz3ui2OygDorfkwWUGPFj8nVaizlMVk1Ic0ql6cbY8LAXwGvAaaBx4wx3wBuXWmbCtUZBT4CXGetnTHGfAm4xxjzXYCLD8xK1JjzauCXrbVP5x8wxty7xjZlq9Faew64I1fXLcCfAvcZYxpzz99RwrouYIz5IPAevONu8eN+OiZXq9Mvx+SKNeb44ZhcscZSHpPV1N1xweXpwOLL03cBR6y1Y9baeeBR4PVrbFOJOpPArdbamdz9EDCH1xJoMsZ81xjz77kDrlI1AtwIfMgY86gx5kMFblPuGjHGOMDfAL9hrc1Q/vcR4Chw7zKP++mYXK1OvxyTq9UI/jgmYfUaS3JMVlNIt3L+TwaAjDEmtMJzCaBtjW1KZcXvaa3NWmsHAIwxvw20AN8DZoCPAT8D/DrwhRLXudb78uVcHW8EbjPG3FPANuWuEeAtwH5rrc3dL/f7iLX2fiC1zFN+OiZXrNNHx+Rq7yX445hcq0s9qaUAAAPqSURBVEYowTFZTd0dq12efvFzMWB8jW1KZdXvmeuT/HPgauAXrLWuMeYQXqvLBQ4ZY0aAXuBkuWvMtQT+j7V2Inf/W8ANa/1c5axxkV8C/nrR/XK/j6vx0zG5Kp8ck6vV55djshBFPyarqSW92uXpB4GdxphOY0wDcDvwxBrbVKJOgL8HGoG3L/oT8314/WkYYzbgtRDOVqjGVuBFY0xL7pfjjcDTa2xT7hrzbgQeX3S/3O/javx0TK7FD8fkavxyTBai6MdkNbWkl1yeboz5j0CLtfZTubO/38H74Pm0tfa0MaYSl7SvWCfwE7wz1I8A/26MAe9T9/8BnzXGPIo3C+f7StwiWOu9/DDwA7z+yoestd/OtbbK+V6uVWMcSORaKHnlfh+X8OkxuWKd+OeYXLFGHx2Ta9VYkmNSl4WLiPhYNXV3iIjUHYW0iIiPKaRFRHxMIS0i4mPVNLpDRMTXzApzjyx6/i7g93N3HbyrJl9prT240j41ukNqmjHmh8AfW2t/WOFSpMYtntfDWrvm5d/GmP8GdFhrP7za69SSFhEpjvy8Hp8HMMZcB3wcr8U8gjdGOn/VZB9eoL9mrZ0qpKXq5A7wLwDNQBb4L8Am4PeAKBDB+4V4fNE2IeD/Aq8EevCm5Hx37usHgWFgFu/Kuz+x1n4vd3XbIeCnrLVnyvPTSbWy1t5vjNm66KH78I7DA8aY9wMfBP4g99zvAn9lrU2utV+dOJRq9H7gm9bam/Dma74db/Kae6y11+PNQ/Ghi7a5FZi31t4C7ADayV1SDBjgl6y1bwY+jdfCAW/WuiMKaLlMu4BP5Lrc3gdsgIW5Uu7hwvmnV6SWtFSj7wP/Zoy5AfgW3p+UnwTeYrzrmu/Am8B+gbX2YWPMiDHmt4BXADvxLosGGLTWHs99/c/AnxljmoFfAT5b2h9FapjFmwO73xjzOryJlcD7a+4la+1sITtRS1qqjrX2MeAavHkx3gU8ADwJXAU8zPl+wAXGmLfidZHMAJ/JvS7/moVfFmvtNPBt4B3AncDXSvijSG37DeBzxphHgP+F18UG3l9uLxe6E7WkpeoYY/4cOG2t/WtjzA+AE8BLwJ/hBe/ngeBFm70J+Gdr7WeMMduAN+C1yJfzaeCfgG9Ya+dK8TNIbcr9RXZz7uunya3WctFr/gX4l0L3qZa0VKO/Ad5hjHkOb7a8e4Dn8IJ6P946eFsu2uY+4N3GmBfwfkEew2t5L5Frqbt4LW6RitI4aZFFciM6Xgl8zlp7Q6XrEVFLWuRCv4PX1/2fK12ICKglLSLia2pJi4j4mEJaRMTHFNIiIj6mkBYR8TGFtIiIj/1/4dIcZKAn3zUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_people['salary'])\n",
    "\n",
    "# not normally distributed, try a log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#\n",
    "not normally distributed, let us try a log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b705d9dc0>"
      ]
     },
     "execution_count": 40,
     "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": [
    "sns.distplot(np.log(df_people['salary']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b59828070>"
      ]
     },
     "execution_count": 41,
     "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": [
    "sns.distplot(np.sqrt(df_people['salary']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \n",
    "Slightly better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we can look at the different categorical options the players can have\n",
    "\n",
    "\n",
    "Throwing and batting can be advantageous in certain matchups. Let's see if that holds for salary, origin, and physical stature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>playerID</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>birthMonth</th>\n",
       "      <th>birthDay</th>\n",
       "      <th>birthCountry</th>\n",
       "      <th>birthState</th>\n",
       "      <th>birthCity</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bats</th>\n",
       "      <th>throws</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>award_winner</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>DBL</th>\n",
       "      <th>TPL</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "      <th>OBP</th>\n",
       "      <th>BA</th>\n",
       "      <th>TB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OPS</th>\n",
       "      <th>salary</th>\n",
       "      <th>seasons</th>\n",
       "      <th>prim_pos</th>\n",
       "      <th>G_p</th>\n",
       "      <th>W_p</th>\n",
       "      <th>L_p</th>\n",
       "      <th>GS_p</th>\n",
       "      <th>CG</th>\n",
       "      <th>SHO_p</th>\n",
       "      <th>SV_p</th>\n",
       "      <th>IPouts</th>\n",
       "      <th>ER_p</th>\n",
       "      <th>HR_p</th>\n",
       "      <th>SO_p</th>\n",
       "      <th>IBB_p</th>\n",
       "      <th>BAOpp</th>\n",
       "      <th>ERA</th>\n",
       "      <th>WP</th>\n",
       "      <th>HBP_p</th>\n",
       "      <th>BK</th>\n",
       "      <th>BFP</th>\n",
       "      <th>GF</th>\n",
       "      <th>R_p</th>\n",
       "      <th>SH_p</th>\n",
       "      <th>SF_p</th>\n",
       "      <th>GIDP_p</th>\n",
       "      <th>WHIP</th>\n",
       "      <th>IP</th>\n",
       "      <th>BB_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO</td>\n",
       "      <td>Denver</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>P</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310333</td>\n",
       "      <td>5.742511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>145.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.948782</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>18.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>D.R.</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "      <td>235.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>1087500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>P</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.666667</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>5.083699</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>127.666667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.511177</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abreuto01</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>D.R.</td>\n",
       "      <td>Puerto Plata</td>\n",
       "      <td>Puerto Plata</td>\n",
       "      <td>Tony</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>B</td>\n",
       "      <td>R</td>\n",
       "      <td>2007-05-22</td>\n",
       "      <td>2014-07-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.666667</td>\n",
       "      <td>122.333333</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.335772</td>\n",
       "      <td>0.251415</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>0.379815</td>\n",
       "      <td>0.715586</td>\n",
       "      <td>745000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2B</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>accarje01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Accardo</td>\n",
       "      <td>195.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2005-05-04</td>\n",
       "      <td>2012-09-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1080000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>P</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.336667</td>\n",
       "      <td>5.652214</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>27.333333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.716016</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>17.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ackledu01</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NC</td>\n",
       "      <td>Winston-Salem</td>\n",
       "      <td>Dustin</td>\n",
       "      <td>Ackley</td>\n",
       "      <td>205.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>L</td>\n",
       "      <td>R</td>\n",
       "      <td>2011-06-17</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.666667</td>\n",
       "      <td>441.333333</td>\n",
       "      <td>54.333333</td>\n",
       "      <td>108.333333</td>\n",
       "      <td>18.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.320469</td>\n",
       "      <td>0.250526</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>0.431767</td>\n",
       "      <td>0.752236</td>\n",
       "      <td>2600000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1B</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID  birthYear  birthMonth  birthDay birthCountry    birthState  \\\n",
       "0   aardsda01     1981.0        12.0      27.0          USA            CO   \n",
       "2    abadfe01     1985.0        12.0      17.0         D.R.     La Romana   \n",
       "8   abreuto01     1984.0        11.0      13.0         D.R.  Puerto Plata   \n",
       "10  accarje01     1981.0        12.0      18.0          USA            AZ   \n",
       "14  ackledu01     1988.0         2.0      26.0          USA            NC   \n",
       "\n",
       "        birthCity nameFirst nameLast  weight  height bats throws       debut  \\\n",
       "0          Denver     David  Aardsma   215.0    75.0    R      R  2004-04-06   \n",
       "2       La Romana  Fernando     Abad   235.0    74.0    L      L  2010-07-28   \n",
       "8    Puerto Plata      Tony    Abreu   200.0    70.0    B      R  2007-05-22   \n",
       "10        Phoenix    Jeremy  Accardo   195.0    72.0    R      R  2005-05-04   \n",
       "14  Winston-Salem    Dustin   Ackley   205.0    73.0    L      R  2011-06-17   \n",
       "\n",
       "     finalGame  award_winner           G          AB          R           H  \\\n",
       "0   2015-08-23           0.0   27.000000    0.666667   0.000000    0.000000   \n",
       "2   2019-09-28           0.0   29.333333    2.666667   0.000000    0.333333   \n",
       "8   2014-07-28           0.0   48.666667  122.333333  11.666667   30.666667   \n",
       "10  2012-09-27           0.0   52.333333    2.333333   0.000000    0.333333   \n",
       "14  2016-05-29           0.0  118.666667  441.333333  54.333333  108.333333   \n",
       "\n",
       "          DBL       TPL        HR        RBI        SB        CS         BB  \\\n",
       "0    0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "2    0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "8    8.333333  0.666667  1.000000  10.333333  0.666667  0.666667   4.666667   \n",
       "10   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "14  18.666667  3.666667  7.333333  39.000000  7.000000  2.000000  45.333333   \n",
       "\n",
       "           SO       IBB       HBP        SH        SF      GIDP       OBP  \\\n",
       "0    0.000000  0.000000  0.000000  0.333333  0.000000  0.000000  0.000000   \n",
       "2    1.333333  0.000000  0.000000  0.000000  0.000000  0.333333  0.047619   \n",
       "8   23.333333  0.333333  1.000000  0.000000  2.000000  4.333333  0.335772   \n",
       "10   0.333333  0.000000  0.000000  0.000000  0.000000  0.000000  0.166667   \n",
       "14  91.666667  3.000000  0.333333  1.666667  1.666667  4.000000  0.320469   \n",
       "\n",
       "          BA          TB       SLG       OPS     salary  seasons prim_pos  \\\n",
       "0   0.000000    0.000000  0.000000  0.000000   419000.0      7.0        P   \n",
       "2   0.047619    0.333333  0.047619  0.095238  1087500.0      5.0        P   \n",
       "8   0.251415   53.333333  0.379815  0.715586   745000.0      4.0       2B   \n",
       "10  0.166667    0.333333  0.166667  0.333333  1080000.0      5.0        P   \n",
       "14  0.250526  186.000000  0.431767  0.752236  2600000.0      5.0       1B   \n",
       "\n",
       "          G_p       W_p       L_p  GS_p   CG  SHO_p  SV_p      IPouts  \\\n",
       "0   27.000000  2.000000  0.333333   0.0  0.0    0.0   0.0   96.000000   \n",
       "2   29.333333  0.333333  3.666667   2.0  0.0    0.0   0.0   84.666667   \n",
       "8    0.000000  0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   \n",
       "10  52.333333  2.333333  4.333333   0.0  0.0    0.0  11.0  166.000000   \n",
       "14   0.000000  0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   \n",
       "\n",
       "         ER_p      HR_p       SO_p     IBB_p     BAOpp       ERA        WP  \\\n",
       "0   18.333333  4.666667  30.000000  1.000000  0.310333  5.742511  1.000000   \n",
       "2   16.000000  4.666667  21.666667  0.333333  0.279000  5.083699  1.333333   \n",
       "8    0.000000  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "10  23.333333  4.333333  42.333333  2.666667  0.336667  5.652214  1.666667   \n",
       "14   0.000000  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "       HBP_p        BK         BFP         GF        R_p      SH_p      SF_p  \\\n",
       "0   1.333333  0.000000  145.666667   7.000000  19.000000  1.000000  1.666667   \n",
       "2   1.333333  0.000000  127.666667   5.000000  17.000000  1.000000  1.333333   \n",
       "8   0.000000  0.000000    0.000000   0.000000   0.000000  0.000000  0.000000   \n",
       "10  1.333333  0.666667  232.000000  27.333333  24.666667  0.666667  2.000000   \n",
       "14  0.000000  0.000000    0.000000   0.000000   0.000000  0.000000  0.000000   \n",
       "\n",
       "      GIDP_p      WHIP         IP       BB_p  \n",
       "0   1.333333  1.948782  32.000000  18.333333  \n",
       "2   1.333333  1.511177  28.222222  11.000000  \n",
       "8   0.000000  0.000000   0.000000   0.000000  \n",
       "10  3.000000  1.716016  55.333333  17.666667  \n",
       "14  0.000000  0.000000   0.000000   0.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(994, 67)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_people[['playerID','throws']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P     495\n",
       "OF    166\n",
       "2B    102\n",
       "C      77\n",
       "1B     73\n",
       "3B     45\n",
       "SS     36\n",
       "Name: prim_pos, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people['prim_pos'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_people.drop(axis=1, columns=['birthMonth','birthState','birthCity','birthDay', 'birthCountry'],\n",
    "                           inplace=True, errors='raise')\n",
    "\n",
    "df_people = pd.get_dummies(df_people, columns=['throws'], drop_first = True)\n",
    "df_people = pd.get_dummies(df_people, columns=['bats'], drop_first = True)\n",
    "df_people = pd.get_dummies(df_people, columns=['prim_pos'], drop_first = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(994, 68)\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>award_winner</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>DBL</th>\n",
       "      <th>TPL</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "      <th>OBP</th>\n",
       "      <th>BA</th>\n",
       "      <th>TB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OPS</th>\n",
       "      <th>salary</th>\n",
       "      <th>seasons</th>\n",
       "      <th>G_p</th>\n",
       "      <th>W_p</th>\n",
       "      <th>L_p</th>\n",
       "      <th>GS_p</th>\n",
       "      <th>CG</th>\n",
       "      <th>SHO_p</th>\n",
       "      <th>SV_p</th>\n",
       "      <th>IPouts</th>\n",
       "      <th>ER_p</th>\n",
       "      <th>HR_p</th>\n",
       "      <th>SO_p</th>\n",
       "      <th>IBB_p</th>\n",
       "      <th>BAOpp</th>\n",
       "      <th>ERA</th>\n",
       "      <th>WP</th>\n",
       "      <th>HBP_p</th>\n",
       "      <th>BK</th>\n",
       "      <th>BFP</th>\n",
       "      <th>GF</th>\n",
       "      <th>R_p</th>\n",
       "      <th>SH_p</th>\n",
       "      <th>SF_p</th>\n",
       "      <th>GIDP_p</th>\n",
       "      <th>WHIP</th>\n",
       "      <th>IP</th>\n",
       "      <th>BB_p</th>\n",
       "      <th>throws_R</th>\n",
       "      <th>bats_L</th>\n",
       "      <th>bats_R</th>\n",
       "      <th>prim_pos_2B</th>\n",
       "      <th>prim_pos_3B</th>\n",
       "      <th>prim_pos_C</th>\n",
       "      <th>prim_pos_OF</th>\n",
       "      <th>prim_pos_P</th>\n",
       "      <th>prim_pos_SS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.000000</td>\n",
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       "      <td>4.666667</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.310333</td>\n",
       "      <td>5.742511</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>145.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.948782</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "      <td>235.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.666667</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>5.083699</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>127.666667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.511177</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abreuto01</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Tony</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2007-05-22</td>\n",
       "      <td>2014-07-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.666667</td>\n",
       "      <td>122.333333</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.335772</td>\n",
       "      <td>0.251415</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>0.379815</td>\n",
       "      <td>0.715586</td>\n",
       "      <td>745000.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>accarje01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Accardo</td>\n",
       "      <td>195.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2005-05-04</td>\n",
       "      <td>2012-09-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1080000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.336667</td>\n",
       "      <td>5.652214</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>27.333333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.716016</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ackledu01</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Dustin</td>\n",
       "      <td>Ackley</td>\n",
       "      <td>205.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2011-06-17</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.666667</td>\n",
       "      <td>441.333333</td>\n",
       "      <td>54.333333</td>\n",
       "      <td>108.333333</td>\n",
       "      <td>18.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.320469</td>\n",
       "      <td>0.250526</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>0.431767</td>\n",
       "      <td>0.752236</td>\n",
       "      <td>2600000.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID  birthYear nameFirst nameLast  weight  height       debut  \\\n",
       "0   aardsda01     1981.0     David  Aardsma   215.0    75.0  2004-04-06   \n",
       "2    abadfe01     1985.0  Fernando     Abad   235.0    74.0  2010-07-28   \n",
       "8   abreuto01     1984.0      Tony    Abreu   200.0    70.0  2007-05-22   \n",
       "10  accarje01     1981.0    Jeremy  Accardo   195.0    72.0  2005-05-04   \n",
       "14  ackledu01     1988.0    Dustin   Ackley   205.0    73.0  2011-06-17   \n",
       "\n",
       "     finalGame  award_winner           G          AB          R           H  \\\n",
       "0   2015-08-23           0.0   27.000000    0.666667   0.000000    0.000000   \n",
       "2   2019-09-28           0.0   29.333333    2.666667   0.000000    0.333333   \n",
       "8   2014-07-28           0.0   48.666667  122.333333  11.666667   30.666667   \n",
       "10  2012-09-27           0.0   52.333333    2.333333   0.000000    0.333333   \n",
       "14  2016-05-29           0.0  118.666667  441.333333  54.333333  108.333333   \n",
       "\n",
       "          DBL       TPL        HR        RBI        SB        CS         BB  \\\n",
       "0    0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "2    0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "8    8.333333  0.666667  1.000000  10.333333  0.666667  0.666667   4.666667   \n",
       "10   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "14  18.666667  3.666667  7.333333  39.000000  7.000000  2.000000  45.333333   \n",
       "\n",
       "           SO       IBB       HBP        SH        SF      GIDP       OBP  \\\n",
       "0    0.000000  0.000000  0.000000  0.333333  0.000000  0.000000  0.000000   \n",
       "2    1.333333  0.000000  0.000000  0.000000  0.000000  0.333333  0.047619   \n",
       "8   23.333333  0.333333  1.000000  0.000000  2.000000  4.333333  0.335772   \n",
       "10   0.333333  0.000000  0.000000  0.000000  0.000000  0.000000  0.166667   \n",
       "14  91.666667  3.000000  0.333333  1.666667  1.666667  4.000000  0.320469   \n",
       "\n",
       "          BA          TB       SLG       OPS     salary  seasons        G_p  \\\n",
       "0   0.000000    0.000000  0.000000  0.000000   419000.0      7.0  27.000000   \n",
       "2   0.047619    0.333333  0.047619  0.095238  1087500.0      5.0  29.333333   \n",
       "8   0.251415   53.333333  0.379815  0.715586   745000.0      4.0   0.000000   \n",
       "10  0.166667    0.333333  0.166667  0.333333  1080000.0      5.0  52.333333   \n",
       "14  0.250526  186.000000  0.431767  0.752236  2600000.0      5.0   0.000000   \n",
       "\n",
       "         W_p       L_p  GS_p   CG  SHO_p  SV_p      IPouts       ER_p  \\\n",
       "0   2.000000  0.333333   0.0  0.0    0.0   0.0   96.000000  18.333333   \n",
       "2   0.333333  3.666667   2.0  0.0    0.0   0.0   84.666667  16.000000   \n",
       "8   0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   0.000000   \n",
       "10  2.333333  4.333333   0.0  0.0    0.0  11.0  166.000000  23.333333   \n",
       "14  0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   0.000000   \n",
       "\n",
       "        HR_p       SO_p     IBB_p     BAOpp       ERA        WP     HBP_p  \\\n",
       "0   4.666667  30.000000  1.000000  0.310333  5.742511  1.000000  1.333333   \n",
       "2   4.666667  21.666667  0.333333  0.279000  5.083699  1.333333  1.333333   \n",
       "8   0.000000   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "10  4.333333  42.333333  2.666667  0.336667  5.652214  1.666667  1.333333   \n",
       "14  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "          BK         BFP         GF        R_p      SH_p      SF_p    GIDP_p  \\\n",
       "0   0.000000  145.666667   7.000000  19.000000  1.000000  1.666667  1.333333   \n",
       "2   0.000000  127.666667   5.000000  17.000000  1.000000  1.333333  1.333333   \n",
       "8   0.000000    0.000000   0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "10  0.666667  232.000000  27.333333  24.666667  0.666667  2.000000  3.000000   \n",
       "14  0.000000    0.000000   0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "        WHIP         IP       BB_p  throws_R  bats_L  bats_R  prim_pos_2B  \\\n",
       "0   1.948782  32.000000  18.333333         1       0       1            0   \n",
       "2   1.511177  28.222222  11.000000         0       1       0            0   \n",
       "8   0.000000   0.000000   0.000000         1       0       0            1   \n",
       "10  1.716016  55.333333  17.666667         1       0       1            0   \n",
       "14  0.000000   0.000000   0.000000         1       1       0            0   \n",
       "\n",
       "    prim_pos_3B  prim_pos_C  prim_pos_OF  prim_pos_P  prim_pos_SS  \n",
       "0             0           0            0           1            0  \n",
       "2             0           0            0           1            0  \n",
       "8             0           0            0           0            0  \n",
       "10            0           0            0           1            0  \n",
       "14            0           0            0           0            0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_people.shape)\n",
    "df_people.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>playerID</th>\n",
       "      <th>prim_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotku01</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID prim_pos\n",
       "0  aardsda01        P\n",
       "1   abadan01       1B\n",
       "2   abadfe01        P\n",
       "3  abbotje01       OF\n",
       "4  abbotku01       2B"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_field_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(994, 70)\n"
     ]
    }
   ],
   "source": [
    "df_people = pd.merge(df_people, df1, on='playerID',how = 'left')\n",
    "df_people = pd.merge(df_people,df_field_grouped, on = 'playerID', how = 'left')\n",
    "print(df_people.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_people['award_winner'] = np.where(df_people['award_winner'] > 0,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(994, 70)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>playerID</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>award_winner</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>DBL</th>\n",
       "      <th>TPL</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "      <th>OBP</th>\n",
       "      <th>BA</th>\n",
       "      <th>TB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OPS</th>\n",
       "      <th>salary</th>\n",
       "      <th>seasons</th>\n",
       "      <th>G_p</th>\n",
       "      <th>W_p</th>\n",
       "      <th>L_p</th>\n",
       "      <th>GS_p</th>\n",
       "      <th>CG</th>\n",
       "      <th>SHO_p</th>\n",
       "      <th>SV_p</th>\n",
       "      <th>IPouts</th>\n",
       "      <th>ER_p</th>\n",
       "      <th>HR_p</th>\n",
       "      <th>SO_p</th>\n",
       "      <th>IBB_p</th>\n",
       "      <th>BAOpp</th>\n",
       "      <th>ERA</th>\n",
       "      <th>WP</th>\n",
       "      <th>HBP_p</th>\n",
       "      <th>BK</th>\n",
       "      <th>BFP</th>\n",
       "      <th>GF</th>\n",
       "      <th>R_p</th>\n",
       "      <th>SH_p</th>\n",
       "      <th>SF_p</th>\n",
       "      <th>GIDP_p</th>\n",
       "      <th>WHIP</th>\n",
       "      <th>IP</th>\n",
       "      <th>BB_p</th>\n",
       "      <th>throws_R</th>\n",
       "      <th>bats_L</th>\n",
       "      <th>bats_R</th>\n",
       "      <th>prim_pos_2B</th>\n",
       "      <th>prim_pos_3B</th>\n",
       "      <th>prim_pos_C</th>\n",
       "      <th>prim_pos_OF</th>\n",
       "      <th>prim_pos_P</th>\n",
       "      <th>prim_pos_SS</th>\n",
       "      <th>throws</th>\n",
       "      <th>prim_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310333</td>\n",
       "      <td>5.742511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>145.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.948782</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "      <td>235.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>1087500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.666667</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>5.083699</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>127.666667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.511177</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>L</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abreuto01</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Tony</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2007-05-22</td>\n",
       "      <td>2014-07-28</td>\n",
       "      <td>0</td>\n",
       "      <td>48.666667</td>\n",
       "      <td>122.333333</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.335772</td>\n",
       "      <td>0.251415</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>0.379815</td>\n",
       "      <td>0.715586</td>\n",
       "      <td>745000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>accarje01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Accardo</td>\n",
       "      <td>195.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2005-05-04</td>\n",
       "      <td>2012-09-27</td>\n",
       "      <td>0</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1080000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.336667</td>\n",
       "      <td>5.652214</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>27.333333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.716016</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ackledu01</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Dustin</td>\n",
       "      <td>Ackley</td>\n",
       "      <td>205.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2011-06-17</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>0</td>\n",
       "      <td>118.666667</td>\n",
       "      <td>441.333333</td>\n",
       "      <td>54.333333</td>\n",
       "      <td>108.333333</td>\n",
       "      <td>18.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.320469</td>\n",
       "      <td>0.250526</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>0.431767</td>\n",
       "      <td>0.752236</td>\n",
       "      <td>2600000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>1B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  birthYear nameFirst nameLast  weight  height       debut  \\\n",
       "0  aardsda01     1981.0     David  Aardsma   215.0    75.0  2004-04-06   \n",
       "1   abadfe01     1985.0  Fernando     Abad   235.0    74.0  2010-07-28   \n",
       "2  abreuto01     1984.0      Tony    Abreu   200.0    70.0  2007-05-22   \n",
       "3  accarje01     1981.0    Jeremy  Accardo   195.0    72.0  2005-05-04   \n",
       "4  ackledu01     1988.0    Dustin   Ackley   205.0    73.0  2011-06-17   \n",
       "\n",
       "    finalGame  award_winner           G          AB          R           H  \\\n",
       "0  2015-08-23             0   27.000000    0.666667   0.000000    0.000000   \n",
       "1  2019-09-28             0   29.333333    2.666667   0.000000    0.333333   \n",
       "2  2014-07-28             0   48.666667  122.333333  11.666667   30.666667   \n",
       "3  2012-09-27             0   52.333333    2.333333   0.000000    0.333333   \n",
       "4  2016-05-29             0  118.666667  441.333333  54.333333  108.333333   \n",
       "\n",
       "         DBL       TPL        HR        RBI        SB        CS         BB  \\\n",
       "0   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "1   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "2   8.333333  0.666667  1.000000  10.333333  0.666667  0.666667   4.666667   \n",
       "3   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   0.000000   \n",
       "4  18.666667  3.666667  7.333333  39.000000  7.000000  2.000000  45.333333   \n",
       "\n",
       "          SO       IBB       HBP        SH        SF      GIDP       OBP  \\\n",
       "0   0.000000  0.000000  0.000000  0.333333  0.000000  0.000000  0.000000   \n",
       "1   1.333333  0.000000  0.000000  0.000000  0.000000  0.333333  0.047619   \n",
       "2  23.333333  0.333333  1.000000  0.000000  2.000000  4.333333  0.335772   \n",
       "3   0.333333  0.000000  0.000000  0.000000  0.000000  0.000000  0.166667   \n",
       "4  91.666667  3.000000  0.333333  1.666667  1.666667  4.000000  0.320469   \n",
       "\n",
       "         BA          TB       SLG       OPS     salary  seasons        G_p  \\\n",
       "0  0.000000    0.000000  0.000000  0.000000   419000.0      7.0  27.000000   \n",
       "1  0.047619    0.333333  0.047619  0.095238  1087500.0      5.0  29.333333   \n",
       "2  0.251415   53.333333  0.379815  0.715586   745000.0      4.0   0.000000   \n",
       "3  0.166667    0.333333  0.166667  0.333333  1080000.0      5.0  52.333333   \n",
       "4  0.250526  186.000000  0.431767  0.752236  2600000.0      5.0   0.000000   \n",
       "\n",
       "        W_p       L_p  GS_p   CG  SHO_p  SV_p      IPouts       ER_p  \\\n",
       "0  2.000000  0.333333   0.0  0.0    0.0   0.0   96.000000  18.333333   \n",
       "1  0.333333  3.666667   2.0  0.0    0.0   0.0   84.666667  16.000000   \n",
       "2  0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   0.000000   \n",
       "3  2.333333  4.333333   0.0  0.0    0.0  11.0  166.000000  23.333333   \n",
       "4  0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   0.000000   \n",
       "\n",
       "       HR_p       SO_p     IBB_p     BAOpp       ERA        WP     HBP_p  \\\n",
       "0  4.666667  30.000000  1.000000  0.310333  5.742511  1.000000  1.333333   \n",
       "1  4.666667  21.666667  0.333333  0.279000  5.083699  1.333333  1.333333   \n",
       "2  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "3  4.333333  42.333333  2.666667  0.336667  5.652214  1.666667  1.333333   \n",
       "4  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "         BK         BFP         GF        R_p      SH_p      SF_p    GIDP_p  \\\n",
       "0  0.000000  145.666667   7.000000  19.000000  1.000000  1.666667  1.333333   \n",
       "1  0.000000  127.666667   5.000000  17.000000  1.000000  1.333333  1.333333   \n",
       "2  0.000000    0.000000   0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "3  0.666667  232.000000  27.333333  24.666667  0.666667  2.000000  3.000000   \n",
       "4  0.000000    0.000000   0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "       WHIP         IP       BB_p  throws_R  bats_L  bats_R  prim_pos_2B  \\\n",
       "0  1.948782  32.000000  18.333333         1       0       1            0   \n",
       "1  1.511177  28.222222  11.000000         0       1       0            0   \n",
       "2  0.000000   0.000000   0.000000         1       0       0            1   \n",
       "3  1.716016  55.333333  17.666667         1       0       1            0   \n",
       "4  0.000000   0.000000   0.000000         1       1       0            0   \n",
       "\n",
       "   prim_pos_3B  prim_pos_C  prim_pos_OF  prim_pos_P  prim_pos_SS throws  \\\n",
       "0            0           0            0           1            0      R   \n",
       "1            0           0            0           1            0      L   \n",
       "2            0           0            0           0            0      R   \n",
       "3            0           0            0           1            0      R   \n",
       "4            0           0            0           0            0      R   \n",
       "\n",
       "  prim_pos  \n",
       "0        P  \n",
       "1        P  \n",
       "2       2B  \n",
       "3        P  \n",
       "4       1B  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_people.shape)\n",
    "df_people.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a CSV to work with going forward.\n",
    "\n",
    "After all of the cleaning and merging, the csv created by the code below will be more smaller and more efficient to work with. \n",
    "\n",
    "After this step, we will be ready for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_people.to_csv('rookie.csv')"
   ]
  }
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