empirical bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Translating Empirical Bayes examples from variance explained from R to Python**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://varianceexplained.org/r/empirical_bayes_baseball/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AllstarFull.csv\t\t Fielding.csv\t   PitchingPost.csv\r\n",
      "Appearances.csv\t\t FieldingOF.csv    Salaries.csv\r\n",
      "AwardsManagers.csv\t FieldingPost.csv  Schools.csv\r\n",
      "AwardsPlayers.csv\t HallOfFame.csv    SchoolsPlayers.csv\r\n",
      "AwardsShareManagers.csv  Managers.csv\t   SeriesPost.csv\r\n",
      "AwardsSharePlayers.csv\t ManagersHalf.csv  Teams.csv\r\n",
      "Batting.csv\t\t Master.csv\t   TeamsFranchises.csv\r\n",
      "BattingPost.csv\t\t Pitching.csv\t   TeamsHalf.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls lahmanbaseballdatabase/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import pymc3 as pm\n",
    "import theano.tensor as tt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = Path('lahmanbaseballdatabase/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "batting_df = pd.read_csv(data_folder / 'Batting.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
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       "      <th>GIDP</th>\n",
       "      <th>G_old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>2004</td>\n",
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       "      <td>43.0</td>\n",
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       "      <td>aardsda01</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>aardsda01</td>\n",
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       "      <td>1</td>\n",
       "      <td>SEA</td>\n",
       "      <td>AL</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 24 columns</p>\n",
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      "text/plain": [
       "    playerID  yearID  stint teamID lgID   G  G_batting   AB    R    H  ...  \\\n",
       "0  aardsda01    2004      1    SFN   NL  11       11.0  0.0  0.0  0.0  ...   \n",
       "1  aardsda01    2006      1    CHN   NL  45       43.0  2.0  0.0  0.0  ...   \n",
       "2  aardsda01    2007      1    CHA   AL  25        2.0  0.0  0.0  0.0  ...   \n",
       "3  aardsda01    2008      1    BOS   AL  47        5.0  1.0  0.0  0.0  ...   \n",
       "4  aardsda01    2009      1    SEA   AL  73        3.0  0.0  0.0  0.0  ...   \n",
       "\n",
       "    SB   CS   BB   SO  IBB  HBP   SH   SF  GIDP  G_old  \n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   11.0  \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0   0.0   45.0  \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0    2.0  \n",
       "3  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0   0.0    5.0  \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0    NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batting_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pitching_df = pd.read_csv(data_folder / 'Pitching.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>aardsda01</td>\n",
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       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
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       "      <td>aardsda01</td>\n",
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       "      <td>NaN</td>\n",
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       "    playerID  yearID  stint teamID lgID  W  L   G  GS  CG  ...  IBB   WP  HBP  \\\n",
       "0  aardsda01    2004      1    SFN   NL  1  0  11   0   0  ...  0.0  0.0  2.0   \n",
       "1  aardsda01    2006      1    CHN   NL  3  0  45   0   0  ...  0.0  1.0  1.0   \n",
       "2  aardsda01    2007      1    CHA   AL  2  1  25   0   0  ...  3.0  2.0  1.0   \n",
       "3  aardsda01    2008      1    BOS   AL  4  2  47   0   0  ...  2.0  3.0  5.0   \n",
       "4  aardsda01    2009      1    SEA   AL  3  6  73   0   0  ...  3.0  2.0  0.0   \n",
       "\n",
       "    BK    BFP    GF   R  SH  SF  GIDP  \n",
       "0  0.0   61.0   5.0   8 NaN NaN   NaN  \n",
       "1  0.0  225.0   9.0  25 NaN NaN   NaN  \n",
       "2  0.0  151.0   7.0  24 NaN NaN   NaN  \n",
       "3  0.0  228.0   7.0  32 NaN NaN   NaN  \n",
       "4  0.0  296.0  53.0  23 NaN NaN   NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pitching_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**H** : Career Hits <br>\n",
    "**AB** : At Bats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R code below used to create a new dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``` r \n",
    "career <- Batting %>%\n",
    "  filter(AB > 0) %>%\n",
    "  anti_join(Pitching, by = \"playerID\") %>%\n",
    "  group_by(playerID) %>%\n",
    "  summarize(H = sum(H), AB = sum(AB)) %>%\n",
    "  mutate(average = H / AB)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anti-join example in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``` python\n",
    "# Identify what values are in TableB and not in TableA\n",
    "key_diff = set(TableB.Key).difference(TableA.Key)\n",
    "where_diff = TableB.Key.isin(key_diff)\n",
    "\n",
    "# Slice TableB accordingly and append to TableA\n",
    "TableA.append(TableB[where_diff], ignore_index=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>playerID</th>\n",
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       "      <td>aardsda01</td>\n",
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       "      <td>CHN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1954</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>122</td>\n",
       "      <td>122.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1955</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>153</td>\n",
       "      <td>153.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1956</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>153</td>\n",
       "      <td>153.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>151</td>\n",
       "      <td>151.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>153</td>\n",
       "      <td>153.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>154</td>\n",
       "      <td>154.0</td>\n",
       "      <td>629.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>153</td>\n",
       "      <td>153.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1961</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>155</td>\n",
       "      <td>155.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1962</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>156</td>\n",
       "      <td>156.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1963</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>161</td>\n",
       "      <td>161.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1964</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>145</td>\n",
       "      <td>145.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1965</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>150</td>\n",
       "      <td>150.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1966</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>158</td>\n",
       "      <td>158.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>155</td>\n",
       "      <td>155.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1968</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>160</td>\n",
       "      <td>160.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>147</td>\n",
       "      <td>147.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>150</td>\n",
       "      <td>150.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1971</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>139</td>\n",
       "      <td>139.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1972</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>129</td>\n",
       "      <td>129.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1973</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>120</td>\n",
       "      <td>120.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1974</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>112</td>\n",
       "      <td>112.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1975</td>\n",
       "      <td>1</td>\n",
       "      <td>ML4</td>\n",
       "      <td>AL</td>\n",
       "      <td>137</td>\n",
       "      <td>137.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>ML4</td>\n",
       "      <td>AL</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>1962</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>141</td>\n",
       "      <td>141.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>1963</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>72</td>\n",
       "      <td>72.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>1965</td>\n",
       "      <td>1</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>1968</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>98</td>\n",
       "      <td>98.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>49</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96561</th>\n",
       "      <td>zuberjo01</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>PHI</td>\n",
       "      <td>NL</td>\n",
       "      <td>38</td>\n",
       "      <td>38.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96562</th>\n",
       "      <td>zuletju01</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96563</th>\n",
       "      <td>zuletju01</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>49</td>\n",
       "      <td>49.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96569</th>\n",
       "      <td>zupcibo01</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96570</th>\n",
       "      <td>zupcibo01</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>124</td>\n",
       "      <td>124.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96571</th>\n",
       "      <td>zupcibo01</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>141</td>\n",
       "      <td>141.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96572</th>\n",
       "      <td>zupcibo01</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96573</th>\n",
       "      <td>zupcibo01</td>\n",
       "      <td>1994</td>\n",
       "      <td>2</td>\n",
       "      <td>CHA</td>\n",
       "      <td>AL</td>\n",
       "      <td>32</td>\n",
       "      <td>32.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96574</th>\n",
       "      <td>zupofr01</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96575</th>\n",
       "      <td>zupofr01</td>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96576</th>\n",
       "      <td>zupofr01</td>\n",
       "      <td>1961</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96577</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1982</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96578</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1983</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96579</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1984</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96580</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>81</td>\n",
       "      <td>81.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96581</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1986</td>\n",
       "      <td>1</td>\n",
       "      <td>NYA</td>\n",
       "      <td>AL</td>\n",
       "      <td>21</td>\n",
       "      <td>21.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96582</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>NYA</td>\n",
       "      <td>AL</td>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96583</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>AL</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96584</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>AL</td>\n",
       "      <td>24</td>\n",
       "      <td>24.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96588</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>1954</td>\n",
       "      <td>1</td>\n",
       "      <td>CIN</td>\n",
       "      <td>NL</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96589</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>1954</td>\n",
       "      <td>2</td>\n",
       "      <td>DET</td>\n",
       "      <td>AL</td>\n",
       "      <td>35</td>\n",
       "      <td>35.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96590</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>1955</td>\n",
       "      <td>1</td>\n",
       "      <td>DET</td>\n",
       "      <td>AL</td>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96591</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>1955</td>\n",
       "      <td>2</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>28</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96592</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>1956</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>62</td>\n",
       "      <td>62.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96593</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>56</td>\n",
       "      <td>56.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96594</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>45</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96596</th>\n",
       "      <td>zwilldu01</td>\n",
       "      <td>1910</td>\n",
       "      <td>1</td>\n",
       "      <td>CHA</td>\n",
       "      <td>AL</td>\n",
       "      <td>27</td>\n",
       "      <td>27.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96597</th>\n",
       "      <td>zwilldu01</td>\n",
       "      <td>1914</td>\n",
       "      <td>1</td>\n",
       "      <td>CHF</td>\n",
       "      <td>FL</td>\n",
       "      <td>154</td>\n",
       "      <td>154.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96598</th>\n",
       "      <td>zwilldu01</td>\n",
       "      <td>1915</td>\n",
       "      <td>1</td>\n",
       "      <td>CHF</td>\n",
       "      <td>FL</td>\n",
       "      <td>150</td>\n",
       "      <td>150.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96599</th>\n",
       "      <td>zwilldu01</td>\n",
       "      <td>1916</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>35</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83361 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  yearID  stint teamID lgID    G  G_batting     AB      R  \\\n",
       "1      aardsda01    2006      1    CHN   NL   45       43.0    2.0    0.0   \n",
       "3      aardsda01    2008      1    BOS   AL   47        5.0    1.0    0.0   \n",
       "7      aaronha01    1954      1    ML1   NL  122      122.0  468.0   58.0   \n",
       "8      aaronha01    1955      1    ML1   NL  153      153.0  602.0  105.0   \n",
       "9      aaronha01    1956      1    ML1   NL  153      153.0  609.0  106.0   \n",
       "10     aaronha01    1957      1    ML1   NL  151      151.0  615.0  118.0   \n",
       "11     aaronha01    1958      1    ML1   NL  153      153.0  601.0  109.0   \n",
       "12     aaronha01    1959      1    ML1   NL  154      154.0  629.0  116.0   \n",
       "13     aaronha01    1960      1    ML1   NL  153      153.0  590.0  102.0   \n",
       "14     aaronha01    1961      1    ML1   NL  155      155.0  603.0  115.0   \n",
       "15     aaronha01    1962      1    ML1   NL  156      156.0  592.0  127.0   \n",
       "16     aaronha01    1963      1    ML1   NL  161      161.0  631.0  121.0   \n",
       "17     aaronha01    1964      1    ML1   NL  145      145.0  570.0  103.0   \n",
       "18     aaronha01    1965      1    ML1   NL  150      150.0  570.0  109.0   \n",
       "19     aaronha01    1966      1    ATL   NL  158      158.0  603.0  117.0   \n",
       "20     aaronha01    1967      1    ATL   NL  155      155.0  600.0  113.0   \n",
       "21     aaronha01    1968      1    ATL   NL  160      160.0  606.0   84.0   \n",
       "22     aaronha01    1969      1    ATL   NL  147      147.0  547.0  100.0   \n",
       "23     aaronha01    1970      1    ATL   NL  150      150.0  516.0  103.0   \n",
       "24     aaronha01    1971      1    ATL   NL  139      139.0  495.0   95.0   \n",
       "25     aaronha01    1972      1    ATL   NL  129      129.0  449.0   75.0   \n",
       "26     aaronha01    1973      1    ATL   NL  120      120.0  392.0   84.0   \n",
       "27     aaronha01    1974      1    ATL   NL  112      112.0  340.0   47.0   \n",
       "28     aaronha01    1975      1    ML4   AL  137      137.0  465.0   45.0   \n",
       "29     aaronha01    1976      1    ML4   AL   85       85.0  271.0   22.0   \n",
       "30     aaronto01    1962      1    ML1   NL  141      141.0  334.0   54.0   \n",
       "31     aaronto01    1963      1    ML1   NL   72       72.0  135.0    6.0   \n",
       "32     aaronto01    1965      1    ML1   NL    8        8.0   16.0    1.0   \n",
       "33     aaronto01    1968      1    ATL   NL   98       98.0  283.0   21.0   \n",
       "34     aaronto01    1969      1    ATL   NL   49       49.0   60.0   13.0   \n",
       "...          ...     ...    ...    ...  ...  ...        ...    ...    ...   \n",
       "96561  zuberjo01    1998      1    PHI   NL   38       38.0   45.0    6.0   \n",
       "96562  zuletju01    2000      1    CHN   NL   30       30.0   68.0   13.0   \n",
       "96563  zuletju01    2001      1    CHN   NL   49       49.0  106.0   11.0   \n",
       "96569  zupcibo01    1991      1    BOS   AL   18       18.0   25.0    3.0   \n",
       "96570  zupcibo01    1992      1    BOS   AL  124      124.0  392.0   46.0   \n",
       "96571  zupcibo01    1993      1    BOS   AL  141      141.0  286.0   40.0   \n",
       "96572  zupcibo01    1994      1    BOS   AL    4        4.0    4.0    0.0   \n",
       "96573  zupcibo01    1994      2    CHA   AL   32       32.0   88.0   10.0   \n",
       "96574   zupofr01    1957      1    BAL   AL   10       10.0   12.0    2.0   \n",
       "96575   zupofr01    1958      1    BAL   AL    1        1.0    2.0    0.0   \n",
       "96576   zupofr01    1961      1    BAL   AL    5        5.0    4.0    1.0   \n",
       "96577  zuvelpa01    1982      1    ATL   NL    2        2.0    1.0    0.0   \n",
       "96578  zuvelpa01    1983      1    ATL   NL    3        3.0    5.0    0.0   \n",
       "96579  zuvelpa01    1984      1    ATL   NL   11       11.0   25.0    2.0   \n",
       "96580  zuvelpa01    1985      1    ATL   NL   81       81.0  190.0   16.0   \n",
       "96581  zuvelpa01    1986      1    NYA   AL   21       21.0   48.0    2.0   \n",
       "96582  zuvelpa01    1987      1    NYA   AL   14       14.0   34.0    2.0   \n",
       "96583  zuvelpa01    1988      1    CLE   AL   51       51.0  130.0    9.0   \n",
       "96584  zuvelpa01    1989      1    CLE   AL   24       24.0   58.0   10.0   \n",
       "96588  zuverge01    1954      1    CIN   NL    2        2.0    2.0    1.0   \n",
       "96589  zuverge01    1954      2    DET   AL   35       35.0   64.0    1.0   \n",
       "96590  zuverge01    1955      1    DET   AL   14       14.0    4.0    0.0   \n",
       "96591  zuverge01    1955      2    BAL   AL   28       28.0   23.0    1.0   \n",
       "96592  zuverge01    1956      1    BAL   AL   62       62.0   17.0    0.0   \n",
       "96593  zuverge01    1957      1    BAL   AL   56       56.0   23.0    1.0   \n",
       "96594  zuverge01    1958      1    BAL   AL   45       45.0    9.0    0.0   \n",
       "96596  zwilldu01    1910      1    CHA   AL   27       27.0   87.0    7.0   \n",
       "96597  zwilldu01    1914      1    CHF   FL  154      154.0  592.0   91.0   \n",
       "96598  zwilldu01    1915      1    CHF   FL  150      150.0  548.0   65.0   \n",
       "96599  zwilldu01    1916      1    CHN   NL   35       35.0   53.0    4.0   \n",
       "\n",
       "           H  ...    SB    CS    BB    SO   IBB  HBP    SH    SF  GIDP  G_old  \n",
       "1        0.0  ...   0.0   0.0   0.0   0.0   0.0  0.0   1.0   0.0   0.0   45.0  \n",
       "3        0.0  ...   0.0   0.0   0.0   1.0   0.0  0.0   0.0   0.0   0.0    5.0  \n",
       "7      131.0  ...   2.0   2.0  28.0  39.0   NaN  3.0   6.0   4.0  13.0  122.0  \n",
       "8      189.0  ...   3.0   1.0  49.0  61.0   5.0  3.0   7.0   4.0  20.0  153.0  \n",
       "9      200.0  ...   2.0   4.0  37.0  54.0   6.0  2.0   5.0   7.0  21.0  153.0  \n",
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       "96580   48.0  ...   2.0   0.0  16.0  14.0   1.0  0.0   4.0   0.0   3.0   81.0  \n",
       "96581    4.0  ...   0.0   0.0   5.0   4.0   0.0  0.0   4.0   0.0   1.0   21.0  \n",
       "96582    6.0  ...   0.0   0.0   0.0   4.0   0.0  0.0   2.0   0.0   1.0   14.0  \n",
       "96583   30.0  ...   0.0   0.0   8.0  13.0   0.0  0.0   8.0   0.0   3.0   51.0  \n",
       "96584   16.0  ...   0.0   0.0   1.0  11.0   0.0  1.0   0.0   0.0   0.0   24.0  \n",
       "96588    1.0  ...   0.0   0.0   0.0   1.0   NaN  0.0   0.0   0.0   0.0    2.0  \n",
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       "96592    2.0  ...   0.0   0.0   1.0   7.0   0.0  0.0   4.0   0.0   0.0   62.0  \n",
       "96593    3.0  ...   0.0   0.0   3.0   8.0   0.0  0.0   2.0   0.0   0.0   56.0  \n",
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       "96596   16.0  ...   1.0   NaN  11.0   NaN   NaN  1.0   1.0   NaN   NaN   27.0  \n",
       "96597  185.0  ...  21.0   NaN  46.0  68.0   NaN  1.0  10.0   NaN   NaN  154.0  \n",
       "96598  157.0  ...  24.0   NaN  67.0  65.0   NaN  2.0  18.0   NaN   NaN  150.0  \n",
       "96599    6.0  ...   0.0   NaN   4.0   6.0   NaN  0.0   2.0   NaN   NaN   35.0  \n",
       "\n",
       "[83361 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batting_df.loc[batting_df['AB'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_diff = set(pitching_df['playerID']).difference(batting_df['playerID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "where_diff = pitching_df.isin(key_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "career = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    }
   ],
   "source": [
    "career['H'] = batting_df.loc[batting_df['AB'] > 0].append(pitching_df[where_diff], ignore_index= True).groupby('playerID').sum()['H']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "career['AB'] = batting_df.loc[batting_df['AB'] > 0].append(pitching_df[where_diff], ignore_index= True).groupby('playerID').sum()['AB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "career.loc[:, 'average'] = career['H'] / career['AB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>zinteal01</th>\n",
       "      <td>13.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipfebu01</th>\n",
       "      <td>78.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0.220339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ziskri01</th>\n",
       "      <td>1477.0</td>\n",
       "      <td>5144.0</td>\n",
       "      <td>0.287131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zitoba01</th>\n",
       "      <td>30.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zitzmbi01</th>\n",
       "      <td>268.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>0.266932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zmiched01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zobribe01</th>\n",
       "      <td>693.0</td>\n",
       "      <td>2668.0</td>\n",
       "      <td>0.259745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zoccope01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zoldasa01</th>\n",
       "      <td>50.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.174825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zoskyed01</th>\n",
       "      <td>8.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuberbi01</th>\n",
       "      <td>31.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.135371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuberjo01</th>\n",
       "      <td>34.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuletju01</th>\n",
       "      <td>43.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.247126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zupcibo01</th>\n",
       "      <td>199.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>0.250314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zupofr01</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuvelpa01</th>\n",
       "      <td>109.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>0.221996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuverge01</th>\n",
       "      <td>21.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.147887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zwilldu01</th>\n",
       "      <td>364.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0.284375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16187 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                H       AB   average\n",
       "playerID                            \n",
       "aardsda01     0.0      3.0  0.000000\n",
       "aaronha01  3771.0  12364.0  0.304998\n",
       "aaronto01   216.0    944.0  0.228814\n",
       "aasedo01      0.0      5.0  0.000000\n",
       "abadan01      2.0     21.0  0.095238\n",
       "abadfe01      1.0      8.0  0.125000\n",
       "abadijo01    11.0     49.0  0.224490\n",
       "abbated01   772.0   3044.0  0.253614\n",
       "abbeybe01    38.0    225.0  0.168889\n",
       "abbeych01   492.0   1751.0  0.280982\n",
       "abbotda01     1.0      7.0  0.142857\n",
       "abbotfr01   107.0    513.0  0.208577\n",
       "abbotje01   157.0    596.0  0.263423\n",
       "abbotji01     2.0     21.0  0.095238\n",
       "abbotku01   523.0   2044.0  0.255871\n",
       "abbotky01     3.0     31.0  0.096774\n",
       "abbotod01    13.0     70.0  0.185714\n",
       "abbotpa01     5.0     20.0  0.250000\n",
       "aberal01     14.0    100.0  0.140000\n",
       "abercda01     0.0      4.0  0.000000\n",
       "abercre01    86.0    386.0  0.222798\n",
       "abernbi01     0.0      1.0  0.000000\n",
       "abernbr01   212.0    868.0  0.244240\n",
       "abernte01     1.0      5.0  0.200000\n",
       "abernte02    25.0    181.0  0.138122\n",
       "abernwo01     0.0      8.0  0.000000\n",
       "aberscl01    45.0    179.0  0.251397\n",
       "ablesha01     0.0     26.0  0.000000\n",
       "abnersh01   191.0    840.0  0.227381\n",
       "abramca01   433.0   1611.0  0.268777\n",
       "...           ...      ...       ...\n",
       "zimmech01  1224.0   4546.0  0.269248\n",
       "zimmedo01   773.0   3283.0  0.235455\n",
       "zimmeed01    80.0    431.0  0.185615\n",
       "zimmehe01  1566.0   5304.0  0.295249\n",
       "zimmeje01   203.0    994.0  0.204225\n",
       "zimmejo02    26.0    137.0  0.189781\n",
       "zimmero01    27.0     98.0  0.275510\n",
       "zimmery01  1110.0   3861.0  0.287490\n",
       "zinkwa01      0.0      1.0  0.000000\n",
       "zinnfr01      0.0      7.0  0.000000\n",
       "zinngu01    297.0   1103.0  0.269266\n",
       "zinnji01     34.0    120.0  0.283333\n",
       "zinteal01    13.0     78.0  0.166667\n",
       "zipfebu01    78.0    354.0  0.220339\n",
       "ziskri01   1477.0   5144.0  0.287131\n",
       "zitoba01     30.0    310.0  0.096774\n",
       "zitzmbi01   268.0   1004.0  0.266932\n",
       "zmiched01     1.0     17.0  0.058824\n",
       "zobribe01   693.0   2668.0  0.259745\n",
       "zoccope01     4.0     37.0  0.108108\n",
       "zoldasa01    50.0    286.0  0.174825\n",
       "zoskyed01     8.0     50.0  0.160000\n",
       "zuberbi01    31.0    229.0  0.135371\n",
       "zuberjo01    34.0    136.0  0.250000\n",
       "zuletju01    43.0    174.0  0.247126\n",
       "zupcibo01   199.0    795.0  0.250314\n",
       "zupofr01      3.0     18.0  0.166667\n",
       "zuvelpa01   109.0    491.0  0.221996\n",
       "zuverge01    21.0    142.0  0.147887\n",
       "zwilldu01   364.0   1280.0  0.284375\n",
       "\n",
       "[16187 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "career"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``` r \n",
    "# use names along with the player IDs\n",
    "career <- Master %>%\n",
    "  tbl_df() %>%\n",
    "  select(playerID, nameFirst, nameLast) %>%\n",
    "  unite(name, nameFirst, nameLast, sep = \" \") %>%\n",
    "  inner_join(career, by = \"playerID\") %>%\n",
    "  select(-playerID)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df = pd.read_csv(data_folder / 'Master.csv', encoding = 'ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lahmanID', 'playerID', 'managerID', 'hofID', 'birthYear', 'birthMonth',\n",
       "       'birthDay', 'birthCountry', 'birthState', 'birthCity', 'deathYear',\n",
       "       'deathMonth', 'deathDay', 'deathCountry', 'deathState', 'deathCity',\n",
       "       'nameFirst', 'nameLast', 'nameNote', 'nameGiven', 'nameNick', 'weight',\n",
       "       'height', 'bats', 'throws', 'debut', 'finalGame', 'college',\n",
       "       'lahman40ID', 'lahman45ID', 'retroID', 'holtzID', 'bbrefID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df['name'] = master_df['nameFirst'] + ' ' + master_df['nameLast']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "career = pd.merge(career, master_df[['playerID', 'name']], on='playerID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>H</th>\n",
       "      <th>AB</th>\n",
       "      <th>average</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>3771.0</td>\n",
       "      <td>12364.0</td>\n",
       "      <td>0.304998</td>\n",
       "      <td>Hank Aaron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID       H       AB   average        name\n",
       "1  aaronha01  3771.0  12364.0  0.304998  Hank Aaron"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "career.loc[career['name'] == 'Hank Aaron']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The player stats above matches with the post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c0d997c50>"
      ]
     },
     "execution_count": 21,
     "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": [
    "career.average.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X ~ Beta(α, β)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a Beta distribution to model the career average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``` r\n",
    "# just like the graph, we have to filter for the players we actually\n",
    "# have a decent estimate of\n",
    "career_filtered <- career %>%\n",
    "    filter(AB >= 500)\n",
    "\n",
    "m <- MASS::fitdistr(career_filtered$average, dbeta,\n",
    "                    start = list(shape1 = 1, shape2 = 10))\n",
    "\n",
    "alpha0 <- m$estimate[1]\n",
    "beta0 <- m$estimate[2]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "career_filtered = career.loc[career['AB'] >= 500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c0da5def0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLSOHQl3RI0qSkfZL2pLazJO2S9GT6uSK1S9KtkqYkPSrpLUWf38zMGteqPf2NEXF+zd3XtwIPRsRa4ME0DtWbqK9Nj1Hg9hY9v5mZNaBdh3c2A3el4buAy2va746q3cCZ826kbmZmbaSIKLYC6Sngeao3Qv+7iBiX9N8RcWbNPM9HxApJXwVuiYhvp/YHgY9ExJ556xyl+p8ApVJpw8TERKE+dtvMzAzLly/vdjdaYlBqaUUdk4dfaFFviiktg2Mvd7sXrbFQLcOrXtv5zhTUzdfKxo0b99YceTlBoRujJ2+LiCOSXgfskvSDReZVnbaT3nUiYhwYBxgZGYlyudyCbnZPpVKh32uYMyi1tKKOa7Y+0JrOFDQ2PMu2yVa8lLtvoVoOXVXufGcK6tXXSuHDOxFxJP18BvgycAFwbO6wTfr5TJp9GlhTs/hq4EjRPpiZWWMKhb6kMyT9wtwwcDHwGLAD2JJm2wLcn4Z3AFenq3guBF6IiKNF+mBmZo0r+j9hCfiypLl1/UNE/LOkh4F7JV0L/BB4b5p/J3AZMAW8BLy/4PObmdkSFAr9iDgI/Fqd9meBi+q0B3Bdkec0M7Pm+RO5ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRgbjzguWtaEmbmYyNjzbMzdBMesk7+mbmWXEoW9mlhGHvplZRpoOfUlrJH1T0uOS9kv6s9T+cUmHJe1Lj8tqlrlB0pSkJyRd0ooCzMyscUVO5M4CYxHxSLpP7l5Ju9K0T0XE39TOLGkdcAWwHvgl4BuSzouIVwr0wczMlqDpPf2IOBoRj6ThHwOPA6sWWWQzMBERxyPiKar3yb2g2ec3M7Ola8kxfUlDwJuB/0hN10t6VNJ2SStS2yrg6ZrFpln8TcLMzFpM1XuVF1iBtBz4N+DmiPiSpBLwIyCAm4CVEfEBSbcBD0XE59NydwA7I+K+OuscBUYBSqXShomJiUJ97LaZmRmWL1/e7W60RC/WMnn4hSUvU1oGx15uQ2e6IIdahle9tvOdKaibr5WNGzfujYiRetMKfThL0s8C9wFfiIgvAUTEsZrpnwO+mkangTU1i68GjtRbb0SMA+MAIyMjUS6Xi3Sz6yqVCv1ew5xerKWZD1mNDc+ybXIwPpuYQy2Hrip3vjMF9eJrBYpdvSPgDuDxiPhkTfvKmtneAzyWhncAV0g6XdK5wFrgO80+v5mZLV2R3YO3Ae8DJiXtS21/CVwp6Xyqh3cOAR8EiIj9ku4Fvk/1yp/rfOWOmVlnNR36EfFtQHUm7VxkmZuBm5t9TjMzK8afyDUzy4hD38wsIw59M7OMOPTNzDLi0Dczy8hgfKLDuq6Zu1eZNaqbf1+HbnlX1567Hbynb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEV+9M2DafZXD2PBsU19lbGa9wXv6ZmYZceibmWXEoW9mlhGHvplZRnwitw3mn0z1yU8z6xUdD31Jm4BPA6cBfx8Rt3S6D2ZmjWr2iriiO3vt+s6fjh7ekXQacBtwKbCO6v1013WyD2ZmOev0nv4FwFREHASQNAFspnqz9JbzNz+amZ1IEdG5J5N+H9gUEX+Sxt8HvDUirp833ygwmkbfCDzRsU62x9nAj7rdiRYZlFoGpQ5wLb2qm7X8ckScU29Cp/f0VaftpHediBgHxtvfnc6QtCciRrrdj1YYlFoGpQ5wLb2qV2vp9CWb08CamvHVwJEO98HMLFudDv2HgbWSzpX0auAKYEeH+2Bmlq2OHt6JiFlJ1wNfp3rJ5vaI2N/JPnTJwByqYnBqGZQ6wLX0qp6spaMncs3MrLv8NQxmZhlx6JuZZcShX4CkTZKekDQlaWud6e+Q9Iik2fQZhdppWyQ9mR5bOtfr+grW8oqkfenR9RPzDdTyF5K+L+lRSQ9K+uWaaf22XRarpWe2SwN1fEjSZOrrt2s/qS/phrTcE5Iu6WzPT9ZsLZKGJL1cs00+2/neAxHhRxMPqieiDwBvAF4NfA9YN2+eIeBXgbuB369pPws4mH6uSMMr+rGWNG2m29tjibVsBH4+DX8Y+GIfb5e6tfTSdmmwjtfUDL8b+Oc0vC7NfzpwblrPaX1ayxDwWLe3h/f0m/fTr5SIiP8B5r5S4qci4lBEPAr877xlLwF2RcRzEfE8sAvY1IlOL6BILb2mkVq+GREvpdHdVD8vAv25XRaqpZc0UseLNaNn8P8f2twMTETE8Yh4CphK6+uWIrX0BId+81YBT9eMT6e2di/bDkX783OS9kjaLeny1nZtyZZay7XA15pctt2K1AK9s10aqkPSdZIOAH8F/OlSlu2gIrUAnCvpu5L+TdLb29vV+vx9+s1r6Csl2rBsOxTtz+sj4oikNwD/KmkyIg60qG9L1XAtkv4YGAF+c6nLdkiRWqB3tkujX79yG3CbpD8CPgZsaXTZDipSy1Gq2+RZSRuAr0haP+8/g7bznn7zinylRK99HUWh/kTEkfTzIFAB3tzKzi1RQ7VIeifwUeDdEXF8Kct2UJFaemm7LPX3OgHM/WfSl9ukxk9rSYeonk3De6meGzivTf1cWLdPKvTrg+p/SQepnlyaO6GzfoF57+TkE7lPUT1ZuCINn9WntawATk/DZwNPMu/EVq/VQjX8DgBr57X33XZZpJae2S4N1rG2Zvh3gD1peD0nnsg9SHdP5Bap5Zy5vlM9EXy4G39fXfnFDcoDuAz4z/Si+2hq+wTVPS6AX6e6Z/AT4Flgf82yH6B6UmoKeH+/1gL8BjCZ/vgngWv7oJZvAMeAfemxo4+3S91aem27NFDHp4H9qYZv1gYp1f9iDlD9ivVL+2Cb1K0F+L3U/j3gEeB3utF/fw2DmVlGfEzfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMvJ/D5NxV6/rHc0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "career_filtered.average.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06646525679758308"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(career_filtered.average.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as model:\n",
    "    alpha = pm.Normal('alpha', mu = 50, sigma= 100)\n",
    "    beta = pm.Normal('beta', mu = 100, sigma= 200)\n",
    "    obs = pm.Beta('obs', alpha = alpha, beta = beta, observed = career_filtered.average.values)\n",
    "    \n",
    "    trace = pm.sample(10000, tune=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/distributions/continuous.py:88: UserWarning: The variable specified for alpha has negative support for Beta, likely making it unsuitable for this parameter.\n",
      "  warnings.warn(msg)\n",
      "/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/distributions/continuous.py:88: UserWarning: The variable specified for beta has negative support for Beta, likely making it unsuitable for this parameter.\n",
      "  warnings.warn(msg)\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta, alpha]\n",
      "Sampling 4 chains:   0%|          | 33/60000 [00:00<03:16, 305.15draws/s]/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "Sampling 4 chains:   0%|          | 45/60000 [00:00<04:26, 225.23draws/s]\n",
      "Bad initial energy, check any log probabilities that are inf or -inf, nan or very small:\n",
      "obs   -inf\n"
     ]
    },
    {
     "ename": "ParallelSamplingError",
     "evalue": "Bad initial energy",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRemoteTraceback\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/parallel_sampling.py\", line 160, in _start_loop\n    point, stats = self._compute_point()\n  File \"/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/parallel_sampling.py\", line 191, in _compute_point\n    point, stats = self._step_method.step(self._point)\n  File \"/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/step_methods/arraystep.py\", line 247, in step\n    apoint, stats = self.astep(array)\n  File \"/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/step_methods/hmc/base_hmc.py\", line 144, in astep\n    raise SamplingError(\"Bad initial energy\")\npymc3.exceptions.SamplingError: Bad initial energy\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mSamplingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;31mSamplingError\u001b[0m: Bad initial energy",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mParallelSamplingError\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-4ecf0007a78e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBeta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'obs'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcareer_filtered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtune\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, **kwargs)\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0m_print_step_hierarchy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m                 \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_mp_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msample_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPickleError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m                 \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Could not pickle model, sampling singlethreaded.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36m_mp_sample\u001b[0;34m(draws, tune, step, chains, cores, chain, random_seed, start, progressbar, trace, model, **kwargs)\u001b[0m\n\u001b[1;32m    967\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 969\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mdraw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    970\u001b[0m                     \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraces\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchain\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mchain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m                     if (trace.supports_sampler_stats\n",
      "\u001b[0;32m~/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/parallel_sampling.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_active\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m             \u001b[0mdraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mProcessAdapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_active\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m             \u001b[0mproc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_last\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_progress\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tempo/lib/python3.6/site-packages/pymc3/parallel_sampling.py\u001b[0m in \u001b[0;36mrecv_draw\u001b[0;34m(processes, timeout)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m                 \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Chain %s failed.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mproc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mold_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"writing_done\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m             \u001b[0mproc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_readable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mParallelSamplingError\u001b[0m: Bad initial energy"
     ]
    }
   ],
   "source": [
    "with pm.Model() as model:\n",
    "    alpha = pm.Normal('alpha', mu = 0, sigma= 2)\n",
    "    beta = pm.Normal('beta', mu = 100, sigma= 200)\n",
    "    obs = pm.Beta('obs', alpha = alpha, beta = beta, observed = career_filtered.average.values)\n",
    "    \n",
    "    trace = pm.sample(10000, tune=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f401fa4a160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f401fbc6630>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f401f30bf28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4028d40128>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_mean = trace.get_values('alpha', chains=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_mean = trace.get_values('beta', chains =1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:00<00:00, 911.98it/s]\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    post_pred = pm.sample_posterior_predictive(trace, samples= 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "sns.distplot(post_pred['obs'].mean(axis=1), label='Posterior predictive means', ax=ax)\n",
    "ax.axvline(career_filtered.average.values.mean(), ls='--', color='r', label='True mean')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using Scipy to estimate the beta distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import beta\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha0, beta0, _, _ = beta.fit(career_filtered.average, floc=0, fscale= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.934263480835025"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.02871581265897"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/tempo/lib/python3.6/site-packages/pandas/plotting/_core.py:1344: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)\n"
     ]
    },
    {
     "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": [
    "fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
    "x = np.linspace(beta.ppf(0.001, alpha0, beta0),\n",
    "               beta.ppf(0.999, alpha0, beta0))\n",
    "ax.plot(x, beta.pdf(x, alpha0, beta0), 'r-', label='beta pdf')\n",
    "career_filtered.average.plot.hist(bins=50, normed=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "career['eb_estimate'] = (career['H'] + alpha_mean) / (career['AB'] + alpha_mean + beta_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "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>H</th>\n",
       "      <th>AB</th>\n",
       "      <th>average</th>\n",
       "      <th>name</th>\n",
       "      <th>eb_estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>David Aardsma</td>\n",
       "      <td>0.245717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>3771.0</td>\n",
       "      <td>12364.0</td>\n",
       "      <td>0.304998</td>\n",
       "      <td>Hank Aaron</td>\n",
       "      <td>0.304454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>216.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>Tommie Aaron</td>\n",
       "      <td>0.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Don Aase</td>\n",
       "      <td>0.241995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>Andy Abad</td>\n",
       "      <td>0.229351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>Fernando Abad</td>\n",
       "      <td>0.244024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abadijo01</td>\n",
       "      <td>11.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.224490</td>\n",
       "      <td>John Abadie</td>\n",
       "      <td>0.243996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abbated01</td>\n",
       "      <td>772.0</td>\n",
       "      <td>3044.0</td>\n",
       "      <td>0.253614</td>\n",
       "      <td>Ed Abbaticchio</td>\n",
       "      <td>0.253530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abbeybe01</td>\n",
       "      <td>38.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.168889</td>\n",
       "      <td>Bert Abbey</td>\n",
       "      <td>0.198708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abbeych01</td>\n",
       "      <td>492.0</td>\n",
       "      <td>1751.0</td>\n",
       "      <td>0.280982</td>\n",
       "      <td>Charlie Abbey</td>\n",
       "      <td>0.278989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>abbotda01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>Dan Abbott</td>\n",
       "      <td>0.245845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>abbotfr01</td>\n",
       "      <td>107.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>0.208577</td>\n",
       "      <td>Fred Abbott</td>\n",
       "      <td>0.217101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>157.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>0.263423</td>\n",
       "      <td>Jeff Abbott</td>\n",
       "      <td>0.261331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>abbotji01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>Jim Abbott</td>\n",
       "      <td>0.229351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>abbotku01</td>\n",
       "      <td>523.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>0.255871</td>\n",
       "      <td>Kurt Abbott</td>\n",
       "      <td>0.255616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>abbotky01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>Kyle Abbott</td>\n",
       "      <td>0.221166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>abbotod01</td>\n",
       "      <td>13.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.185714</td>\n",
       "      <td>Ody Abbott</td>\n",
       "      <td>0.228142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>abbotpa01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>Paul Abbott</td>\n",
       "      <td>0.251312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>aberal01</td>\n",
       "      <td>14.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>Al Aber</td>\n",
       "      <td>0.202401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>abercda01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Francis Abercrombie</td>\n",
       "      <td>0.243842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>abercre01</td>\n",
       "      <td>86.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.222798</td>\n",
       "      <td>Reggie Abercrombie</td>\n",
       "      <td>0.229910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abernbi01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Bill Abernathie</td>\n",
       "      <td>0.249555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>abernbr01</td>\n",
       "      <td>212.0</td>\n",
       "      <td>868.0</td>\n",
       "      <td>0.244240</td>\n",
       "      <td>Brent Abernathy</td>\n",
       "      <td>0.245169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>abernte01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>Tal Abernathy</td>\n",
       "      <td>0.249568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>abernte02</td>\n",
       "      <td>25.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.138122</td>\n",
       "      <td>Ted Abernathy</td>\n",
       "      <td>0.184889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>abernwo01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Woody Abernathy</td>\n",
       "      <td>0.236619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>aberscl01</td>\n",
       "      <td>45.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.251397</td>\n",
       "      <td>Cliff Aberson</td>\n",
       "      <td>0.251447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ablesha01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Harry Ables</td>\n",
       "      <td>0.208790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>abnersh01</td>\n",
       "      <td>191.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>0.227381</td>\n",
       "      <td>Shawn Abner</td>\n",
       "      <td>0.230552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>abramca01</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>0.268777</td>\n",
       "      <td>Cal Abrams</td>\n",
       "      <td>0.267516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16157</th>\n",
       "      <td>zimmech01</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>4546.0</td>\n",
       "      <td>0.269248</td>\n",
       "      <td>Chief Zimmer</td>\n",
       "      <td>0.268766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16158</th>\n",
       "      <td>zimmedo01</td>\n",
       "      <td>773.0</td>\n",
       "      <td>3283.0</td>\n",
       "      <td>0.235455</td>\n",
       "      <td>Don Zimmer</td>\n",
       "      <td>0.236054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16159</th>\n",
       "      <td>zimmeed01</td>\n",
       "      <td>80.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>0.185615</td>\n",
       "      <td>Eddie Zimmerman</td>\n",
       "      <td>0.200619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16160</th>\n",
       "      <td>zimmehe01</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>5304.0</td>\n",
       "      <td>0.295249</td>\n",
       "      <td>Heinie Zimmerman</td>\n",
       "      <td>0.294226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16161</th>\n",
       "      <td>zimmeje01</td>\n",
       "      <td>203.0</td>\n",
       "      <td>994.0</td>\n",
       "      <td>0.204225</td>\n",
       "      <td>Jerry Zimmerman</td>\n",
       "      <td>0.209585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16162</th>\n",
       "      <td>zimmejo02</td>\n",
       "      <td>26.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.189781</td>\n",
       "      <td>Jordan Zimmermann</td>\n",
       "      <td>0.219486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16163</th>\n",
       "      <td>zimmero01</td>\n",
       "      <td>27.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.275510</td>\n",
       "      <td>Roy Zimmerman</td>\n",
       "      <td>0.261966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16164</th>\n",
       "      <td>zimmery01</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>3861.0</td>\n",
       "      <td>0.287490</td>\n",
       "      <td>Ryan Zimmerman</td>\n",
       "      <td>0.286344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16165</th>\n",
       "      <td>zinkwa01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Walter Zink</td>\n",
       "      <td>0.249555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16166</th>\n",
       "      <td>zinnfr01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Frank Zinn</td>\n",
       "      <td>0.238384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16167</th>\n",
       "      <td>zinngu01</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>0.269266</td>\n",
       "      <td>Guy Zinn</td>\n",
       "      <td>0.267433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16168</th>\n",
       "      <td>zinnji01</td>\n",
       "      <td>34.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>Jimmy Zinn</td>\n",
       "      <td>0.266973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16169</th>\n",
       "      <td>zinteal01</td>\n",
       "      <td>13.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>Alan Zinter</td>\n",
       "      <td>0.219241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16170</th>\n",
       "      <td>zipfebu01</td>\n",
       "      <td>78.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0.220339</td>\n",
       "      <td>Bud Zipfel</td>\n",
       "      <td>0.228574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16171</th>\n",
       "      <td>ziskri01</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>5144.0</td>\n",
       "      <td>0.287131</td>\n",
       "      <td>Richie Zisk</td>\n",
       "      <td>0.286272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16172</th>\n",
       "      <td>zitoba01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>Barry Zito</td>\n",
       "      <td>0.141757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16173</th>\n",
       "      <td>zitzmbi01</td>\n",
       "      <td>268.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>0.266932</td>\n",
       "      <td>Billy Zitzmann</td>\n",
       "      <td>0.265201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16174</th>\n",
       "      <td>zmiched01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>Ed Zmich</td>\n",
       "      <td>0.228777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16175</th>\n",
       "      <td>zobribe01</td>\n",
       "      <td>693.0</td>\n",
       "      <td>2668.0</td>\n",
       "      <td>0.259745</td>\n",
       "      <td>Ben Zobrist</td>\n",
       "      <td>0.259371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16176</th>\n",
       "      <td>zoccope01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>Pete Zoccolillo</td>\n",
       "      <td>0.219173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16177</th>\n",
       "      <td>zoldasa01</td>\n",
       "      <td>50.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.174825</td>\n",
       "      <td>Sam Zoldak</td>\n",
       "      <td>0.198415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16178</th>\n",
       "      <td>zoskyed01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>Eddie Zosky</td>\n",
       "      <td>0.225673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16179</th>\n",
       "      <td>zuberbi01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.135371</td>\n",
       "      <td>Bill Zuber</td>\n",
       "      <td>0.176815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16180</th>\n",
       "      <td>zuberjo01</td>\n",
       "      <td>34.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>Jon Zuber</td>\n",
       "      <td>0.250734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16181</th>\n",
       "      <td>zuletju01</td>\n",
       "      <td>43.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.247126</td>\n",
       "      <td>Julio Zuleta</td>\n",
       "      <td>0.248980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16182</th>\n",
       "      <td>zupcibo01</td>\n",
       "      <td>199.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>0.250314</td>\n",
       "      <td>Bob Zupcic</td>\n",
       "      <td>0.250480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16183</th>\n",
       "      <td>zupofr01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>Frank Zupo</td>\n",
       "      <td>0.240989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16184</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>109.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>0.221996</td>\n",
       "      <td>Paul Zuvella</td>\n",
       "      <td>0.228065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16185</th>\n",
       "      <td>zuverge01</td>\n",
       "      <td>21.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.147887</td>\n",
       "      <td>George Zuverink</td>\n",
       "      <td>0.196823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16186</th>\n",
       "      <td>zwilldu01</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0.284375</td>\n",
       "      <td>Dutch Zwilling</td>\n",
       "      <td>0.281408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16187 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID       H       AB   average                 name  eb_estimate\n",
       "0      aardsda01     0.0      3.0  0.000000        David Aardsma     0.245717\n",
       "1      aaronha01  3771.0  12364.0  0.304998           Hank Aaron     0.304454\n",
       "2      aaronto01   216.0    944.0  0.228814         Tommie Aaron     0.231507\n",
       "3       aasedo01     0.0      5.0  0.000000             Don Aase     0.241995\n",
       "4       abadan01     2.0     21.0  0.095238            Andy Abad     0.229351\n",
       "5       abadfe01     1.0      8.0  0.125000        Fernando Abad     0.244024\n",
       "6      abadijo01    11.0     49.0  0.224490          John Abadie     0.243996\n",
       "7      abbated01   772.0   3044.0  0.253614       Ed Abbaticchio     0.253530\n",
       "8      abbeybe01    38.0    225.0  0.168889           Bert Abbey     0.198708\n",
       "9      abbeych01   492.0   1751.0  0.280982        Charlie Abbey     0.278989\n",
       "10     abbotda01     1.0      7.0  0.142857           Dan Abbott     0.245845\n",
       "11     abbotfr01   107.0    513.0  0.208577          Fred Abbott     0.217101\n",
       "12     abbotje01   157.0    596.0  0.263423          Jeff Abbott     0.261331\n",
       "13     abbotji01     2.0     21.0  0.095238           Jim Abbott     0.229351\n",
       "14     abbotku01   523.0   2044.0  0.255871          Kurt Abbott     0.255616\n",
       "15     abbotky01     3.0     31.0  0.096774          Kyle Abbott     0.221166\n",
       "16     abbotod01    13.0     70.0  0.185714           Ody Abbott     0.228142\n",
       "17     abbotpa01     5.0     20.0  0.250000          Paul Abbott     0.251312\n",
       "18      aberal01    14.0    100.0  0.140000              Al Aber     0.202401\n",
       "19     abercda01     0.0      4.0  0.000000  Francis Abercrombie     0.243842\n",
       "20     abercre01    86.0    386.0  0.222798   Reggie Abercrombie     0.229910\n",
       "21     abernbi01     0.0      1.0  0.000000      Bill Abernathie     0.249555\n",
       "22     abernbr01   212.0    868.0  0.244240      Brent Abernathy     0.245169\n",
       "23     abernte01     1.0      5.0  0.200000        Tal Abernathy     0.249568\n",
       "24     abernte02    25.0    181.0  0.138122        Ted Abernathy     0.184889\n",
       "25     abernwo01     0.0      8.0  0.000000      Woody Abernathy     0.236619\n",
       "26     aberscl01    45.0    179.0  0.251397        Cliff Aberson     0.251447\n",
       "27     ablesha01     0.0     26.0  0.000000          Harry Ables     0.208790\n",
       "28     abnersh01   191.0    840.0  0.227381          Shawn Abner     0.230552\n",
       "29     abramca01   433.0   1611.0  0.268777           Cal Abrams     0.267516\n",
       "...          ...     ...      ...       ...                  ...          ...\n",
       "16157  zimmech01  1224.0   4546.0  0.269248         Chief Zimmer     0.268766\n",
       "16158  zimmedo01   773.0   3283.0  0.235455           Don Zimmer     0.236054\n",
       "16159  zimmeed01    80.0    431.0  0.185615      Eddie Zimmerman     0.200619\n",
       "16160  zimmehe01  1566.0   5304.0  0.295249     Heinie Zimmerman     0.294226\n",
       "16161  zimmeje01   203.0    994.0  0.204225      Jerry Zimmerman     0.209585\n",
       "16162  zimmejo02    26.0    137.0  0.189781    Jordan Zimmermann     0.219486\n",
       "16163  zimmero01    27.0     98.0  0.275510        Roy Zimmerman     0.261966\n",
       "16164  zimmery01  1110.0   3861.0  0.287490       Ryan Zimmerman     0.286344\n",
       "16165   zinkwa01     0.0      1.0  0.000000          Walter Zink     0.249555\n",
       "16166   zinnfr01     0.0      7.0  0.000000           Frank Zinn     0.238384\n",
       "16167   zinngu01   297.0   1103.0  0.269266             Guy Zinn     0.267433\n",
       "16168   zinnji01    34.0    120.0  0.283333           Jimmy Zinn     0.266973\n",
       "16169  zinteal01    13.0     78.0  0.166667          Alan Zinter     0.219241\n",
       "16170  zipfebu01    78.0    354.0  0.220339           Bud Zipfel     0.228574\n",
       "16171   ziskri01  1477.0   5144.0  0.287131          Richie Zisk     0.286272\n",
       "16172   zitoba01    30.0    310.0  0.096774           Barry Zito     0.141757\n",
       "16173  zitzmbi01   268.0   1004.0  0.266932       Billy Zitzmann     0.265201\n",
       "16174  zmiched01     1.0     17.0  0.058824             Ed Zmich     0.228777\n",
       "16175  zobribe01   693.0   2668.0  0.259745          Ben Zobrist     0.259371\n",
       "16176  zoccope01     4.0     37.0  0.108108      Pete Zoccolillo     0.219173\n",
       "16177  zoldasa01    50.0    286.0  0.174825           Sam Zoldak     0.198415\n",
       "16178  zoskyed01     8.0     50.0  0.160000          Eddie Zosky     0.225673\n",
       "16179  zuberbi01    31.0    229.0  0.135371           Bill Zuber     0.176815\n",
       "16180  zuberjo01    34.0    136.0  0.250000            Jon Zuber     0.250734\n",
       "16181  zuletju01    43.0    174.0  0.247126         Julio Zuleta     0.248980\n",
       "16182  zupcibo01   199.0    795.0  0.250314           Bob Zupcic     0.250480\n",
       "16183   zupofr01     3.0     18.0  0.166667           Frank Zupo     0.240989\n",
       "16184  zuvelpa01   109.0    491.0  0.221996         Paul Zuvella     0.228065\n",
       "16185  zuverge01    21.0    142.0  0.147887      George Zuverink     0.196823\n",
       "16186  zwilldu01   364.0   1280.0  0.284375       Dutch Zwilling     0.281408\n",
       "\n",
       "[16187 rows x 6 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "career"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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