Slug_Datavis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Python 3.8.3\n",
    "\n",
    "# DS libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# CSV readers\n",
    "import csv\n",
    "\n",
    "# Datavis: use matplotlib\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data visualization of batting statistics in the world of Sabermetrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the players and their batting stats (regular season only)\n",
    "batstats = pd.read_csv(\"https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/Batting.csv\",sep=',')\n",
    "slugppl = pd.read_csv(\"https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/People.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthYear</th>\n",
       "      <th>birthMonth</th>\n",
       "      <th>birthDay</th>\n",
       "      <th>deathYear</th>\n",
       "      <th>deathMonth</th>\n",
       "      <th>deathDay</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20247.000000</td>\n",
       "      <td>20079.000000</td>\n",
       "      <td>19938.000000</td>\n",
       "      <td>9996.000000</td>\n",
       "      <td>9995.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>19543.000000</td>\n",
       "      <td>19623.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1935.209117</td>\n",
       "      <td>6.631555</td>\n",
       "      <td>15.618116</td>\n",
       "      <td>1967.026911</td>\n",
       "      <td>6.483742</td>\n",
       "      <td>15.520512</td>\n",
       "      <td>187.959423</td>\n",
       "      <td>72.357794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.013329</td>\n",
       "      <td>3.463773</td>\n",
       "      <td>8.762962</td>\n",
       "      <td>33.331124</td>\n",
       "      <td>3.536257</td>\n",
       "      <td>8.790616</td>\n",
       "      <td>22.430914</td>\n",
       "      <td>2.617253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1820.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1897.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1944.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1943.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1974.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          birthYear    birthMonth      birthDay    deathYear   deathMonth  \\\n",
       "count  20247.000000  20079.000000  19938.000000  9996.000000  9995.000000   \n",
       "mean    1935.209117      6.631555     15.618116  1967.026911     6.483742   \n",
       "std       43.013329      3.463773      8.762962    33.331124     3.536257   \n",
       "min     1820.000000      1.000000      1.000000  1872.000000     1.000000   \n",
       "25%     1897.000000      4.000000      8.000000  1944.000000     3.000000   \n",
       "50%     1943.000000      7.000000     16.000000  1969.000000     6.000000   \n",
       "75%     1974.000000     10.000000     23.000000  1994.000000    10.000000   \n",
       "max     2001.000000     12.000000     31.000000  2022.000000    12.000000   \n",
       "\n",
       "          deathDay        weight        height  \n",
       "count  9994.000000  19543.000000  19623.000000  \n",
       "mean     15.520512    187.959423     72.357794  \n",
       "std       8.790616     22.430914      2.617253  \n",
       "min       1.000000     65.000000     43.000000  \n",
       "25%       8.000000    172.000000     71.000000  \n",
       "50%      15.000000    185.000000     72.000000  \n",
       "75%      23.000000    200.000000     74.000000  \n",
       "max      31.000000    320.000000     83.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugppl.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>108033.000000</td>\n",
       "      <td>106421.000000</td>\n",
       "      <td>85248.000000</td>\n",
       "      <td>108789.000000</td>\n",
       "      <td>106689.000000</td>\n",
       "      <td>72139.000000</td>\n",
       "      <td>105973.000000</td>\n",
       "      <td>102721.000000</td>\n",
       "      <td>72686.000000</td>\n",
       "      <td>83348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1967.221631</td>\n",
       "      <td>1.078914</td>\n",
       "      <td>50.740488</td>\n",
       "      <td>139.241320</td>\n",
       "      <td>18.483496</td>\n",
       "      <td>36.388605</td>\n",
       "      <td>6.202024</td>\n",
       "      <td>1.247075</td>\n",
       "      <td>2.850150</td>\n",
       "      <td>16.790388</td>\n",
       "      <td>2.918775</td>\n",
       "      <td>1.177353</td>\n",
       "      <td>12.842907</td>\n",
       "      <td>20.559814</td>\n",
       "      <td>1.055227</td>\n",
       "      <td>1.057958</td>\n",
       "      <td>2.197087</td>\n",
       "      <td>1.022893</td>\n",
       "      <td>2.893495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.747437</td>\n",
       "      <td>0.286916</td>\n",
       "      <td>46.889589</td>\n",
       "      <td>183.602089</td>\n",
       "      <td>28.043230</td>\n",
       "      <td>52.188884</td>\n",
       "      <td>9.627314</td>\n",
       "      <td>2.595462</td>\n",
       "      <td>6.368678</td>\n",
       "      <td>26.225046</td>\n",
       "      <td>7.608039</td>\n",
       "      <td>2.682099</td>\n",
       "      <td>20.595082</td>\n",
       "      <td>28.514873</td>\n",
       "      <td>2.709589</td>\n",
       "      <td>2.291269</td>\n",
       "      <td>4.154759</td>\n",
       "      <td>1.930262</td>\n",
       "      <td>4.672691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1871.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1937.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1976.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              yearID          stint              G             AB  \\\n",
       "count  108789.000000  108789.000000  108789.000000  108789.000000   \n",
       "mean     1967.221631       1.078914      50.740488     139.241320   \n",
       "std        39.747437       0.286916      46.889589     183.602089   \n",
       "min      1871.000000       1.000000       1.000000       0.000000   \n",
       "25%      1937.000000       1.000000      12.000000       4.000000   \n",
       "50%      1976.000000       1.000000      34.000000      46.000000   \n",
       "75%      2001.000000       1.000000      79.000000     224.000000   \n",
       "max      2020.000000       5.000000     165.000000     716.000000   \n",
       "\n",
       "                   R              H             2B             3B  \\\n",
       "count  108789.000000  108789.000000  108789.000000  108789.000000   \n",
       "mean       18.483496      36.388605       6.202024       1.247075   \n",
       "std        28.043230      52.188884       9.627314       2.595462   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         4.000000       8.000000       1.000000       0.000000   \n",
       "75%        27.000000      56.000000       9.000000       1.000000   \n",
       "max       198.000000     262.000000      67.000000      36.000000   \n",
       "\n",
       "                  HR            RBI             SB            CS  \\\n",
       "count  108789.000000  108033.000000  106421.000000  85248.000000   \n",
       "mean        2.850150      16.790388       2.918775      1.177353   \n",
       "std         6.368678      26.225046       7.608039      2.682099   \n",
       "min         0.000000       0.000000       0.000000      0.000000   \n",
       "25%         0.000000       0.000000       0.000000      0.000000   \n",
       "50%         0.000000       3.000000       0.000000      0.000000   \n",
       "75%         2.000000      24.000000       2.000000      1.000000   \n",
       "max        73.000000     191.000000     138.000000     42.000000   \n",
       "\n",
       "                  BB             SO           IBB            HBP  \\\n",
       "count  108789.000000  106689.000000  72139.000000  105973.000000   \n",
       "mean       12.842907      20.559814      1.055227       1.057958   \n",
       "std        20.595082      28.514873      2.709589       2.291269   \n",
       "min         0.000000       0.000000      0.000000       0.000000   \n",
       "25%         0.000000       1.000000      0.000000       0.000000   \n",
       "50%         2.000000       9.000000      0.000000       0.000000   \n",
       "75%        18.000000      29.000000      1.000000       1.000000   \n",
       "max       232.000000     223.000000    120.000000      51.000000   \n",
       "\n",
       "                  SH            SF          GIDP  \n",
       "count  102721.000000  72686.000000  83348.000000  \n",
       "mean        2.197087      1.022893      2.893495  \n",
       "std         4.154759      1.930262      4.672691  \n",
       "min         0.000000      0.000000      0.000000  \n",
       "25%         0.000000      0.000000      0.000000  \n",
       "50%         0.000000      0.000000      0.000000  \n",
       "75%         3.000000      1.000000      4.000000  \n",
       "max        67.000000     19.000000     36.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batstats.describe()\n",
    "# batstats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(batstats.yearID, batstats.HR)\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Home Runs Hit\")\n",
    "plt.title(\"Home Runs Hit in MLB, 1871-2020\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
       "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH',\n",
       "       'SF', 'GIDP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batstats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "batstats_arr = batstats.to_numpy()\n",
    "# batstats_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pencehu_stats = pd.DataFrame(columns=['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
    "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH', 'SF', 'GIDP'])\n",
    "counter = 0\n",
    "\n",
    "for a in batstats_arr:\n",
    "    if a[0] == 'pencehu01':\n",
    "        pencehu_stats.loc[counter] = a\n",
    "        counter += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>...</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>108</td>\n",
       "      <td>456</td>\n",
       "      <td>57</td>\n",
       "      <td>147</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>157</td>\n",
       "      <td>595</td>\n",
       "      <td>78</td>\n",
       "      <td>160</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>159</td>\n",
       "      <td>585</td>\n",
       "      <td>76</td>\n",
       "      <td>165</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>156</td>\n",
       "      <td>614</td>\n",
       "      <td>93</td>\n",
       "      <td>173</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>41</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>100</td>\n",
       "      <td>399</td>\n",
       "      <td>49</td>\n",
       "      <td>123</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>PHI</td>\n",
       "      <td>NL</td>\n",
       "      <td>54</td>\n",
       "      <td>207</td>\n",
       "      <td>35</td>\n",
       "      <td>67</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>PHI</td>\n",
       "      <td>NL</td>\n",
       "      <td>101</td>\n",
       "      <td>398</td>\n",
       "      <td>59</td>\n",
       "      <td>108</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>59</td>\n",
       "      <td>219</td>\n",
       "      <td>28</td>\n",
       "      <td>48</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>162</td>\n",
       "      <td>629</td>\n",
       "      <td>91</td>\n",
       "      <td>178</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>52</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>162</td>\n",
       "      <td>650</td>\n",
       "      <td>106</td>\n",
       "      <td>180</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>52</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>52</td>\n",
       "      <td>207</td>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>106</td>\n",
       "      <td>395</td>\n",
       "      <td>58</td>\n",
       "      <td>114</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>134</td>\n",
       "      <td>493</td>\n",
       "      <td>55</td>\n",
       "      <td>128</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>97</td>\n",
       "      <td>235</td>\n",
       "      <td>19</td>\n",
       "      <td>53</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>TEX</td>\n",
       "      <td>AL</td>\n",
       "      <td>83</td>\n",
       "      <td>286</td>\n",
       "      <td>53</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>17</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID yearID stint teamID lgID    G   AB    R    H  2B  ...   RBI  \\\n",
       "0   pencehu01   2007     1    HOU   NL  108  456   57  147  30  ...  69.0   \n",
       "1   pencehu01   2008     1    HOU   NL  157  595   78  160  34  ...  83.0   \n",
       "2   pencehu01   2009     1    HOU   NL  159  585   76  165  26  ...  72.0   \n",
       "3   pencehu01   2010     1    HOU   NL  156  614   93  173  29  ...  91.0   \n",
       "4   pencehu01   2011     1    HOU   NL  100  399   49  123  26  ...  62.0   \n",
       "5   pencehu01   2011     2    PHI   NL   54  207   35   67  12  ...  35.0   \n",
       "6   pencehu01   2012     1    PHI   NL  101  398   59  108  15  ...  59.0   \n",
       "7   pencehu01   2012     2    SFN   NL   59  219   28   48  11  ...  45.0   \n",
       "8   pencehu01   2013     1    SFN   NL  162  629   91  178  35  ...  99.0   \n",
       "9   pencehu01   2014     1    SFN   NL  162  650  106  180  29  ...  74.0   \n",
       "10  pencehu01   2015     1    SFN   NL   52  207   30   57  13  ...  40.0   \n",
       "11  pencehu01   2016     1    SFN   NL  106  395   58  114  23  ...  57.0   \n",
       "12  pencehu01   2017     1    SFN   NL  134  493   55  128  13  ...  67.0   \n",
       "13  pencehu01   2018     1    SFN   NL   97  235   19   53  11  ...  24.0   \n",
       "14  pencehu01   2019     1    TEX   AL   83  286   53   85  17  ...  59.0   \n",
       "15  pencehu01   2020     1    SFN   NL   17   52    4    5   0  ...   6.0   \n",
       "\n",
       "      SB    CS  BB     SO  IBB  HBP   SH   SF  GIDP  \n",
       "0   11.0   5.0  26   95.0  0.0  1.0  0.0  1.0  10.0  \n",
       "1   11.0  10.0  40  124.0  2.0  4.0  0.0  3.0  14.0  \n",
       "2   14.0  11.0  58  109.0  1.0  1.0  0.0  3.0  25.0  \n",
       "3   18.0   9.0  41  105.0  2.0  0.0  0.0  3.0  11.0  \n",
       "4    7.0   1.0  30   86.0  1.0  1.0  0.0  2.0   7.0  \n",
       "5    1.0   1.0  26   38.0  2.0  0.0  0.0  3.0   8.0  \n",
       "6    4.0   2.0  37   85.0  1.0  3.0  0.0  2.0  13.0  \n",
       "7    1.0   0.0  19   60.0  1.0  4.0  1.0  5.0   1.0  \n",
       "8   22.0   3.0  52  115.0  3.0  3.0  0.0  3.0  17.0  \n",
       "9   13.0   6.0  52  130.0  3.0  3.0  0.0  3.0  13.0  \n",
       "10   4.0   1.0  16   48.0  0.0  0.0  0.0  0.0   8.0  \n",
       "11   1.0   1.0  43   95.0  1.0  1.0  0.0  3.0  10.0  \n",
       "12   2.0   3.0  40  102.0  1.0  2.0  0.0  4.0   8.0  \n",
       "13   5.0   1.0  11   59.0  1.0  0.0  0.0  2.0   1.0  \n",
       "14   6.0   1.0  26   69.0  0.0  2.0  0.0  2.0   2.0  \n",
       "15   0.0   0.0   3   15.0  0.0  1.0  0.0  0.0   3.0  \n",
       "\n",
       "[16 rows x 22 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hunter Pence statistics, 2007-2020 (14 seasons)\n",
    "pencehu_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>58.875000</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>3.437500</td>\n",
       "      <td>83.437500</td>\n",
       "      <td>1.187500</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>2.437500</td>\n",
       "      <td>9.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.410039</td>\n",
       "      <td>6.65332</td>\n",
       "      <td>3.669128</td>\n",
       "      <td>32.565767</td>\n",
       "      <td>0.981071</td>\n",
       "      <td>1.408309</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>1.314978</td>\n",
       "      <td>6.324226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43.750000</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.500000</td>\n",
       "      <td>5.50000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.500000</td>\n",
       "      <td>11.50000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             RBI        SB         CS          SO        IBB        HBP  \\\n",
       "count  16.000000  16.00000  16.000000   16.000000  16.000000  16.000000   \n",
       "mean   58.875000   7.50000   3.437500   83.437500   1.187500   1.625000   \n",
       "std    24.410039   6.65332   3.669128   32.565767   0.981071   1.408309   \n",
       "min     6.000000   0.00000   0.000000   15.000000   0.000000   0.000000   \n",
       "25%    43.750000   1.75000   1.000000   59.750000   0.750000   0.750000   \n",
       "50%    60.500000   5.50000   1.500000   90.500000   1.000000   1.000000   \n",
       "75%    72.500000  11.50000   5.250000  106.000000   2.000000   3.000000   \n",
       "max    99.000000  22.00000  11.000000  130.000000   3.000000   4.000000   \n",
       "\n",
       "            SH         SF       GIDP  \n",
       "count  16.0000  16.000000  16.000000  \n",
       "mean    0.0625   2.437500   9.437500  \n",
       "std     0.2500   1.314978   6.324226  \n",
       "min     0.0000   0.000000   1.000000  \n",
       "25%     0.0000   2.000000   6.000000  \n",
       "50%     0.0000   3.000000   9.000000  \n",
       "75%     0.0000   3.000000  13.000000  \n",
       "max     1.0000   5.000000  25.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pencehu_stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(pencehu_stats.yearID, pencehu_stats.HR)\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Home Runs\")\n",
    "plt.title(\"Hunter Pence Career Home Runs by Season\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pencehu_stats.AB, pencehu_stats.H/pencehu_stats.AB)\n",
    "plt.xlabel(\"At Bats\")\n",
    "plt.ylabel(\"Hits\")\n",
    "plt.title(\"Hunter Pence Batting Average to At-Bats by Season\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(pencehu_stats.yearID, (pencehu_stats.H/pencehu_stats.AB))\n",
    "plt.title(\"Hunter Pence Batting Average\")\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Batting Average\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(pencehu_stats.yearID, (pencehu_stats.H/pencehu_stats.AB))\n",
    "plt.title(\"Hunter Pence Batting Average\")\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Batting Average\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111.9375"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pencehu_stats.H.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "HR: 15.25 \n",
      "Doubles: 20.25 \n",
      "Triples: 3.4375 \n",
      "RBIs: 58.875 \n",
      "Hits: 111.9375 \n",
      "Singles: 73.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\\nHR:\", pencehu_stats.HR.mean(), \"\\nDoubles:\", pencehu_stats['2B'].mean(), \"\\nTriples:\", pencehu_stats['3B'].mean(),\n",
    "\"\\nRBIs:\", pencehu_stats['RBI'].mean(), \"\\nHits:\", pencehu_stats['H'].mean(), \"\\nSingles:\", \n",
    "(pencehu_stats['H'].mean()- (pencehu_stats['2B'].mean() + pencehu_stats['3B'].mean() + pencehu_stats['HR'].mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penceh_arr = pencehu_stats.to_numpy()\n",
    "len(penceh_arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
       "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH',\n",
       "       'SF', 'GIDP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pencehu_stats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['pencehu01', 2011, 1, 'HOU', 'NL', 100, 399, 49, 123, 26, 3, 11,\n",
       "        62.0, 7.0, 1.0, 30, 86.0, 1.0, 1.0, 0.0, 2.0, 7.0],\n",
       "       ['pencehu01', 2011, 2, 'PHI', 'NL', 54, 207, 35, 67, 12, 2, 11,\n",
       "        35.0, 1.0, 1.0, 26, 38.0, 2.0, 0.0, 0.0, 3.0, 8.0],\n",
       "       ['pencehu01', 2012, 1, 'PHI', 'NL', 101, 398, 59, 108, 15, 2, 17,\n",
       "        59.0, 4.0, 2.0, 37, 85.0, 1.0, 3.0, 0.0, 2.0, 13.0],\n",
       "       ['pencehu01', 2012, 2, 'SFN', 'NL', 59, 219, 28, 48, 11, 2, 7,\n",
       "        45.0, 1.0, 0.0, 19, 60.0, 1.0, 4.0, 1.0, 5.0, 1.0]], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pencehu_stats[4:8]\n",
    "penceh_arr[4:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['pencehu01', 2007, 1, 'HOU', 'NL', 108, 456, 57, 147, 30, 9, 17,\n",
       "        69.0, 11.0, 5.0, 26, 95.0, 0.0, 1.0, 0.0, 1.0, 10.0], dtype=object),\n",
       " array(['pencehu01', 2008, 1, 'HOU', 'NL', 157, 595, 78, 160, 34, 4, 25,\n",
       "        83.0, 11.0, 10.0, 40, 124.0, 2.0, 4.0, 0.0, 3.0, 14.0],\n",
       "       dtype=object),\n",
       " array(['pencehu01', 2009, 1, 'HOU', 'NL', 159, 585, 76, 165, 26, 5, 25,\n",
       "        72.0, 14.0, 11.0, 58, 109.0, 1.0, 1.0, 0.0, 3.0, 25.0],\n",
       "       dtype=object),\n",
       " array(['pencehu01', 2010, 1, 'HOU', 'NL', 156, 614, 93, 173, 29, 3, 25,\n",
       "        91.0, 18.0, 9.0, 41, 105.0, 2.0, 0.0, 0.0, 3.0, 11.0], dtype=object),\n",
       " array(['pencehu01', 2011, 1, 'TWO', 'NL', 154, 606, 84, 190, 38, 5, 22,\n",
       "        97.0, 8.0, 2.0, 56, 124.0, 3.0, 1.0, 0.0, 5.0, 15.0], dtype=object),\n",
       " array(['pencehu01', 2012, 1, 'TWO', 'NL', 160, 617, 87, 156, 26, 4, 24,\n",
       "        104.0, 5.0, 2.0, 56, 145.0, 2.0, 7.0, 1.0, 7.0, 14.0], dtype=object),\n",
       " array(['pencehu01', 2013, 1, 'SFN', 'NL', 162, 629, 91, 178, 35, 5, 27,\n",
       "        99.0, 22.0, 3.0, 52, 115.0, 3.0, 3.0, 0.0, 3.0, 17.0], dtype=object),\n",
       " array(['pencehu01', 2014, 1, 'SFN', 'NL', 162, 650, 106, 180, 29, 10, 20,\n",
       "        74.0, 13.0, 6.0, 52, 130.0, 3.0, 3.0, 0.0, 3.0, 13.0], dtype=object),\n",
       " array(['pencehu01', 2015, 1, 'SFN', 'NL', 52, 207, 30, 57, 13, 1, 9, 40.0,\n",
       "        4.0, 1.0, 16, 48.0, 0.0, 0.0, 0.0, 0.0, 8.0], dtype=object),\n",
       " array(['pencehu01', 2016, 1, 'SFN', 'NL', 106, 395, 58, 114, 23, 1, 13,\n",
       "        57.0, 1.0, 1.0, 43, 95.0, 1.0, 1.0, 0.0, 3.0, 10.0], dtype=object),\n",
       " array(['pencehu01', 2017, 1, 'SFN', 'NL', 134, 493, 55, 128, 13, 5, 13,\n",
       "        67.0, 2.0, 3.0, 40, 102.0, 1.0, 2.0, 0.0, 4.0, 8.0], dtype=object),\n",
       " array(['pencehu01', 2018, 1, 'SFN', 'NL', 97, 235, 19, 53, 11, 1, 4, 24.0,\n",
       "        5.0, 1.0, 11, 59.0, 1.0, 0.0, 0.0, 2.0, 1.0], dtype=object),\n",
       " array(['pencehu01', 2019, 1, 'TEX', 'AL', 83, 286, 53, 85, 17, 1, 18,\n",
       "        59.0, 6.0, 1.0, 26, 69.0, 0.0, 2.0, 0.0, 2.0, 2.0], dtype=object),\n",
       " array(['pencehu01', 2020, 1, 'SFN', 'NL', 17, 52, 4, 5, 0, 1, 2, 6.0, 0.0,\n",
       "        0.0, 3, 15.0, 0.0, 1.0, 0.0, 0.0, 3.0], dtype=object)]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpencehu_stats = [None] * 14\n",
    "# newpencehu_stats\n",
    "newpencehu_stats[0] = penceh_arr[0] # 2007\n",
    "newpencehu_stats[1] = penceh_arr[1] # 2008\n",
    "newpencehu_stats[2] = penceh_arr[2] # 2009\n",
    "newpencehu_stats[3] = penceh_arr[3] # 2010\n",
    "\n",
    "newpencehu_stats[4] = np.array(['pencehu01', 2011, 1, 'TWO', 'NL', 154, 606, 84, 190, 38, 5, 22,\n",
    "        97.0, 8.0, 2.0, 56, 124.0, 3.0, 1.0, 0.0, 5.0, 15.0], dtype=object) # 2011\n",
    "newpencehu_stats[5] = np.array(['pencehu01', 2012, 1, 'TWO', 'NL', 160, 617, 87, 156, 26, 4, 24,\n",
    "        104.0, 5.0, 2.0, 56, 145.0, 2.0, 7.0, 1.0, 7.0, 14.0], dtype=object) # 2012\n",
    "\n",
    "newpencehu_stats[6] = penceh_arr[8] # 2013\n",
    "newpencehu_stats[7] = penceh_arr[9] # 2014\n",
    "newpencehu_stats[8] = penceh_arr[10] # 2015\n",
    "newpencehu_stats[9] = penceh_arr[11] # 2016\n",
    "newpencehu_stats[10] = penceh_arr[12] # 2017\n",
    "newpencehu_stats[11] = penceh_arr[13] # 2018\n",
    "newpencehu_stats[12] = penceh_arr[14] # 2019\n",
    "newpencehu_stats[13] = penceh_arr[15] # 2020\n",
    "\n",
    "newpencehu_stats\n",
    "# print(newpencehu_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>...</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>108</td>\n",
       "      <td>456</td>\n",
       "      <td>57</td>\n",
       "      <td>147</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>157</td>\n",
       "      <td>595</td>\n",
       "      <td>78</td>\n",
       "      <td>160</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>159</td>\n",
       "      <td>585</td>\n",
       "      <td>76</td>\n",
       "      <td>165</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NL</td>\n",
       "      <td>156</td>\n",
       "      <td>614</td>\n",
       "      <td>93</td>\n",
       "      <td>173</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>41</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>TWO</td>\n",
       "      <td>NL</td>\n",
       "      <td>154</td>\n",
       "      <td>606</td>\n",
       "      <td>84</td>\n",
       "      <td>190</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>TWO</td>\n",
       "      <td>NL</td>\n",
       "      <td>160</td>\n",
       "      <td>617</td>\n",
       "      <td>87</td>\n",
       "      <td>156</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>162</td>\n",
       "      <td>629</td>\n",
       "      <td>91</td>\n",
       "      <td>178</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>52</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>162</td>\n",
       "      <td>650</td>\n",
       "      <td>106</td>\n",
       "      <td>180</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>52</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>52</td>\n",
       "      <td>207</td>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>106</td>\n",
       "      <td>395</td>\n",
       "      <td>58</td>\n",
       "      <td>114</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>134</td>\n",
       "      <td>493</td>\n",
       "      <td>55</td>\n",
       "      <td>128</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>97</td>\n",
       "      <td>235</td>\n",
       "      <td>19</td>\n",
       "      <td>53</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>TEX</td>\n",
       "      <td>AL</td>\n",
       "      <td>83</td>\n",
       "      <td>286</td>\n",
       "      <td>53</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pencehu01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>17</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID yearID stint teamID lgID    G   AB    R    H  2B  ...    RBI  \\\n",
       "0   pencehu01   2007     1    HOU   NL  108  456   57  147  30  ...   69.0   \n",
       "1   pencehu01   2008     1    HOU   NL  157  595   78  160  34  ...   83.0   \n",
       "2   pencehu01   2009     1    HOU   NL  159  585   76  165  26  ...   72.0   \n",
       "3   pencehu01   2010     1    HOU   NL  156  614   93  173  29  ...   91.0   \n",
       "4   pencehu01   2011     1    TWO   NL  154  606   84  190  38  ...   97.0   \n",
       "5   pencehu01   2012     1    TWO   NL  160  617   87  156  26  ...  104.0   \n",
       "6   pencehu01   2013     1    SFN   NL  162  629   91  178  35  ...   99.0   \n",
       "7   pencehu01   2014     1    SFN   NL  162  650  106  180  29  ...   74.0   \n",
       "8   pencehu01   2015     1    SFN   NL   52  207   30   57  13  ...   40.0   \n",
       "9   pencehu01   2016     1    SFN   NL  106  395   58  114  23  ...   57.0   \n",
       "10  pencehu01   2017     1    SFN   NL  134  493   55  128  13  ...   67.0   \n",
       "11  pencehu01   2018     1    SFN   NL   97  235   19   53  11  ...   24.0   \n",
       "12  pencehu01   2019     1    TEX   AL   83  286   53   85  17  ...   59.0   \n",
       "13  pencehu01   2020     1    SFN   NL   17   52    4    5   0  ...    6.0   \n",
       "\n",
       "      SB    CS  BB     SO  IBB  HBP   SH   SF  GIDP  \n",
       "0   11.0   5.0  26   95.0  0.0  1.0  0.0  1.0  10.0  \n",
       "1   11.0  10.0  40  124.0  2.0  4.0  0.0  3.0  14.0  \n",
       "2   14.0  11.0  58  109.0  1.0  1.0  0.0  3.0  25.0  \n",
       "3   18.0   9.0  41  105.0  2.0  0.0  0.0  3.0  11.0  \n",
       "4    8.0   2.0  56  124.0  3.0  1.0  0.0  5.0  15.0  \n",
       "5    5.0   2.0  56  145.0  2.0  7.0  1.0  7.0  14.0  \n",
       "6   22.0   3.0  52  115.0  3.0  3.0  0.0  3.0  17.0  \n",
       "7   13.0   6.0  52  130.0  3.0  3.0  0.0  3.0  13.0  \n",
       "8    4.0   1.0  16   48.0  0.0  0.0  0.0  0.0   8.0  \n",
       "9    1.0   1.0  43   95.0  1.0  1.0  0.0  3.0  10.0  \n",
       "10   2.0   3.0  40  102.0  1.0  2.0  0.0  4.0   8.0  \n",
       "11   5.0   1.0  11   59.0  1.0  0.0  0.0  2.0   1.0  \n",
       "12   6.0   1.0  26   69.0  0.0  2.0  0.0  2.0   2.0  \n",
       "13   0.0   0.0   3   15.0  0.0  1.0  0.0  0.0   3.0  \n",
       "\n",
       "[14 rows x 22 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HP data cleaned as dataframe\n",
    "newhpdf = pd.DataFrame(columns=['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
    "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH', 'SF', 'GIDP'])\n",
    "counter = 0\n",
    "\n",
    "for a in newpencehu_stats:\n",
    "    newhpdf.loc[counter] = newpencehu_stats[counter]\n",
    "    counter += 1\n",
    "\n",
    "newhpdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kris Bryant Statistics (CHI)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>...</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bryankr01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>151</td>\n",
       "      <td>559</td>\n",
       "      <td>87</td>\n",
       "      <td>154</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>77</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bryankr01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>155</td>\n",
       "      <td>603</td>\n",
       "      <td>121</td>\n",
       "      <td>176</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>102.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>75</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bryankr01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>151</td>\n",
       "      <td>549</td>\n",
       "      <td>111</td>\n",
       "      <td>162</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>95</td>\n",
       "      <td>128.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bryankr01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>102</td>\n",
       "      <td>389</td>\n",
       "      <td>59</td>\n",
       "      <td>106</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>48</td>\n",
       "      <td>107.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bryankr01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>147</td>\n",
       "      <td>543</td>\n",
       "      <td>108</td>\n",
       "      <td>153</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bryankr01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>34</td>\n",
       "      <td>131</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID yearID stint teamID lgID    G   AB    R    H  2B  ...    RBI  \\\n",
       "0  bryankr01   2015     1    CHN   NL  151  559   87  154  31  ...   99.0   \n",
       "1  bryankr01   2016     1    CHN   NL  155  603  121  176  35  ...  102.0   \n",
       "2  bryankr01   2017     1    CHN   NL  151  549  111  162  38  ...   73.0   \n",
       "3  bryankr01   2018     1    CHN   NL  102  389   59  106  28  ...   52.0   \n",
       "4  bryankr01   2019     1    CHN   NL  147  543  108  153  35  ...   77.0   \n",
       "5  bryankr01   2020     1    CHN   NL   34  131   20   27   5  ...   11.0   \n",
       "\n",
       "     SB   CS  BB     SO  IBB   HBP   SH   SF  GIDP  \n",
       "0  13.0  4.0  77  199.0  0.0   9.0  0.0  5.0   7.0  \n",
       "1   8.0  5.0  75  154.0  5.0  18.0  0.0  3.0   3.0  \n",
       "2   7.0  5.0  95  128.0  5.0  15.0  0.0  6.0   8.0  \n",
       "3   2.0  4.0  48  107.0  6.0  17.0  0.0  3.0   5.0  \n",
       "4   4.0  0.0  74  145.0  1.0  15.0  0.0  2.0  10.0  \n",
       "5   0.0  0.0  12   40.0  0.0   4.0  0.0  0.0   1.0  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kb_stats = pd.DataFrame(columns=['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
    "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH', 'SF', 'GIDP'])\n",
    "counter = 0\n",
    "\n",
    "for a in batstats_arr:\n",
    "    if a[0] == 'bryankr01':\n",
    "        kb_stats.loc[counter] = a\n",
    "        counter += 1\n",
    "\n",
    "kb_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>128.833333</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.828982</td>\n",
       "      <td>4.676181</td>\n",
       "      <td>2.366432</td>\n",
       "      <td>53.266938</td>\n",
       "      <td>2.786874</td>\n",
       "      <td>5.403702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.136976</td>\n",
       "      <td>3.326660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.250000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>112.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>136.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>93.500000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>151.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RBI         SB        CS          SO       IBB        HBP   SH  \\\n",
       "count    6.000000   6.000000  6.000000    6.000000  6.000000   6.000000  6.0   \n",
       "mean    69.000000   5.666667  3.000000  128.833333  2.833333  13.000000  0.0   \n",
       "std     33.828982   4.676181  2.366432   53.266938  2.786874   5.403702  0.0   \n",
       "min     11.000000   0.000000  0.000000   40.000000  0.000000   4.000000  0.0   \n",
       "25%     57.250000   2.500000  1.000000  112.250000  0.250000  10.500000  0.0   \n",
       "50%     75.000000   5.500000  4.000000  136.500000  3.000000  15.000000  0.0   \n",
       "75%     93.500000   7.750000  4.750000  151.750000  5.000000  16.500000  0.0   \n",
       "max    102.000000  13.000000  5.000000  199.000000  6.000000  18.000000  0.0   \n",
       "\n",
       "             SF       GIDP  \n",
       "count  6.000000   6.000000  \n",
       "mean   3.166667   5.666667  \n",
       "std    2.136976   3.326660  \n",
       "min    0.000000   1.000000  \n",
       "25%    2.250000   3.500000  \n",
       "50%    3.000000   6.000000  \n",
       "75%    4.500000   7.750000  \n",
       "max    6.000000  10.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kb_stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(kb_stats.yearID, (kb_stats.H/kb_stats.AB))\n",
    "plt.title(\"Kris Bryant Batting Average\")\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Batting Average\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    199.0\n",
       "1    154.0\n",
       "2    128.0\n",
       "3    107.0\n",
       "4    145.0\n",
       "5     40.0\n",
       "Name: SO, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kb_stats.SO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare Kris Bryant's Home Run stats to Strikeouts\n",
    "\n",
    "plt.plot(kb_stats.yearID, kb_stats.HR)\n",
    "plt.plot(kb_stats.yearID, kb_stats.SO)\n",
    "plt.title(\"Kris Bryant Home Runs vs. Strikeouts\")\n",
    "plt.legend([\"HR\", \"K\"])\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Quantity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
       "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH',\n",
       "       'SF', 'GIDP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kb_stats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kris Bryant On Base via extra base hits (XBH)\n",
    "\n",
    "plt.plot(kb_stats.yearID, kb_stats.HR)\n",
    "plt.plot(kb_stats.yearID, kb_stats['3B'])\n",
    "plt.plot(kb_stats.yearID, kb_stats['2B'])\n",
    "plt.plot(kb_stats.yearID, kb_stats['H'])\n",
    "plt.title(\"Kris Bryant Extra Base Hits\")\n",
    "plt.legend([\"HR\", \"3B\", \"2B\", \"H\"])\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Quantity\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kris Bryant not getting on base statistics via hit\n",
    "\n",
    "plt.plot(kb_stats.yearID, kb_stats['SO'])\n",
    "plt.plot(kb_stats.yearID, kb_stats['GIDP'])\n",
    "plt.plot(kb_stats.yearID, kb_stats['IBB'])\n",
    "plt.plot(kb_stats.yearID, kb_stats['BB'])\n",
    "plt.plot(kb_stats.yearID, kb_stats['HBP'])\n",
    "plt.plot(kb_stats.yearID, kb_stats['SH'])\n",
    "plt.title(\"Kris Bryant Not On Base via Hit\")\n",
    "plt.legend([\"SO\", \"GIDP\", \"IBB\", \"BB\", \"HBP\", \"SH\"])\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Quantity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that each of these visualizations took a dip in the 2020 season. This was expected because of the COVID-19 pandemic, where MLB scheduled a 60-game regular season for all 30 teams that year. This is significantly different from seasons such as 1994-95, where there was an MLB strike, or the 1981 strike which saw a split-season format, and much more different from the 162-game regular season. \n",
    "\n",
    "Thus, Bryant's numbers that year were expected to drop because of this."
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