Slugging.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slugging Percentages\n",
    "## Author: Matthew Chin (2021)\n",
    "\n",
    "**Data**\n",
    "https://github.com/chadwickbureau/baseballdatabank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.6.3.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotly standard imports\n",
    "import plotly.graph_objs as go\n",
    "import chart_studio.plotly as py\n",
    "\n",
    "# Cufflinks wrapper on plotly\n",
    "import cufflinks\n",
    "\n",
    "# Data science imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Options for pandas\n",
    "pd.options.display.max_columns = 999\n",
    "\n",
    "# Display all cell outputs\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "\n",
    "# from __future__ import print_function, division\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from plotly.offline import iplot\n",
    "cufflinks.go_offline()\n",
    "\n",
    "# Set global theme\n",
    "cufflinks.set_config_file(world_readable=True, theme='pearl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slugging Percentage\n",
    "\n",
    "A batter's slugging percentage involves not only how frequently but how far they get on base when they hit. When they go to bat and they get on base safely with a hit, it increases their chances at getting home. In that case, one's slugging percentage will go up if they hit a single, but goes even farther up if they get on safely with an extra base hit (XBH) or with a home run and touch all four bases. \n",
    "\n",
    "Extra base hits and home runs matter for the significance of one's slugging percentage because the power and speed of a batter are considered to know how many bases they touch per at-bat.  \n",
    "\n",
    "Walks or base on balls (BB) are not considered because they are not hits. \n",
    "\n",
    "The formula for slugging is as follows \n",
    "(Reference: https://www.mlb.com/glossary/standard-stats/slugging-percentage#:~:text=Definition,hits%20are%20not%20valued%20equally.):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let $1B$ be a single, $2B$ be a double, $3B$ be a triple, and $HR$ be a home run. \n",
    "\n",
    "The formula is:\n",
    "\n",
    "$SLG = \\frac{1B + (2\\times2B) + (3\\times3B )+ (4\\times HR)}{AB}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get slugging percentages from Batting data\n",
    "slugpct = pd.read_csv(\"https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/Batting.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get batters who are sluggers from People data\n",
    "slugppl = pd.read_csv(\"https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/People.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>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>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>108784</th>\n",
       "      <td>zimmebr02</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
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       "    <tr>\n",
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       "      <td>zimmejo02</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>DET</td>\n",
       "      <td>AL</td>\n",
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       "    <tr>\n",
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       "      <td>zimmeky01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
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       "    <tr>\n",
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       "      <td>zuberty01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
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       "    <tr>\n",
       "      <th>108788</th>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>TBA</td>\n",
       "      <td>AL</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
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       "      <td>11</td>\n",
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       "      <td>6</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID   G  AB  R   H  2B  3B  HR   RBI  \\\n",
       "108784  zimmebr02    2020      1    BAL   AL   2   0  0   0   0   0   0   0.0   \n",
       "108785  zimmejo02    2020      1    DET   AL   3   0  0   0   0   0   0   0.0   \n",
       "108786  zimmeky01    2020      1    KCA   AL  16   0  0   0   0   0   0   0.0   \n",
       "108787  zuberty01    2020      1    KCA   AL  23   0  0   0   0   0   0   0.0   \n",
       "108788  zuninmi01    2020      1    TBA   AL  28  75  8  11   4   0   4  10.0   \n",
       "\n",
       "         SB   CS  BB    SO  IBB  HBP   SH   SF  GIDP  \n",
       "108784  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108785  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
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       "108787  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108788  0.0  0.0   6  37.0  0.0  3.0  0.0  0.0   0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample of slugpct beginning or end values\n",
    "# Later cells are the most recent statistics\n",
    "\n",
    "# slugpct.head()\n",
    "slugpct.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>zimmebr02</td>\n",
       "      <td>2020</td>\n",
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       "      <td>zimmejo02</td>\n",
       "      <td>2020</td>\n",
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       "      <td>DET</td>\n",
       "      <td>AL</td>\n",
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       "      <th>108786</th>\n",
       "      <td>zimmeky01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108787</th>\n",
       "      <td>zuberty01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108788</th>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>TBA</td>\n",
       "      <td>AL</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108789 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID   G   AB   R   H  2B  3B  HR  \\\n",
       "0       abercda01    1871      1    TRO  NaN   1    4   0   0   0   0   0   \n",
       "1        addybo01    1871      1    RC1  NaN  25  118  30  32   6   0   0   \n",
       "2       allisar01    1871      1    CL1  NaN  29  137  28  40   4   5   0   \n",
       "3       allisdo01    1871      1    WS3  NaN  27  133  28  44  10   2   2   \n",
       "4       ansonca01    1871      1    RC1  NaN  25  120  29  39  11   3   0   \n",
       "...           ...     ...    ...    ...  ...  ..  ...  ..  ..  ..  ..  ..   \n",
       "108784  zimmebr02    2020      1    BAL   AL   2    0   0   0   0   0   0   \n",
       "108785  zimmejo02    2020      1    DET   AL   3    0   0   0   0   0   0   \n",
       "108786  zimmeky01    2020      1    KCA   AL  16    0   0   0   0   0   0   \n",
       "108787  zuberty01    2020      1    KCA   AL  23    0   0   0   0   0   0   \n",
       "108788  zuninmi01    2020      1    TBA   AL  28   75   8  11   4   0   4   \n",
       "\n",
       "         RBI   SB   CS  BB    SO  IBB  HBP   SH   SF  GIDP  \n",
       "0        0.0  0.0  0.0   0   0.0  NaN  NaN  NaN  NaN   0.0  \n",
       "1       13.0  8.0  1.0   4   0.0  NaN  NaN  NaN  NaN   0.0  \n",
       "2       19.0  3.0  1.0   2   5.0  NaN  NaN  NaN  NaN   1.0  \n",
       "3       27.0  1.0  1.0   0   2.0  NaN  NaN  NaN  NaN   0.0  \n",
       "4       16.0  6.0  2.0   2   1.0  NaN  NaN  NaN  NaN   0.0  \n",
       "...      ...  ...  ...  ..   ...  ...  ...  ...  ...   ...  \n",
       "108784   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108785   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108786   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108787   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108788  10.0  0.0  0.0   6  37.0  0.0  3.0  0.0  0.0   0.0  \n",
       "\n",
       "[108789 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show dataframe values\n",
    "slugpct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['abercda01', 1871, 1, ..., nan, nan, 0.0],\n",
       "       ['addybo01', 1871, 1, ..., nan, nan, 0.0],\n",
       "       ['allisar01', 1871, 1, ..., nan, nan, 1.0],\n",
       "       ...,\n",
       "       ['zimmeky01', 2020, 1, ..., 0.0, 0.0, 0.0],\n",
       "       ['zuberty01', 2020, 1, ..., 0.0, 0.0, 0.0],\n",
       "       ['zuninmi01', 2020, 1, ..., 0.0, 0.0, 0.0]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print array values\n",
    "slugarr = slugpct.to_numpy()\n",
    "slugarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep these columns names\n",
    "slugpct.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "fcsv = \"slugpctAL_2019.csv\"\n",
    "fcsv2 = \"slugpctNL_2019.csv\"\n",
    "mean_ba = \"mean_ba.csv\"\n",
    "# len(slugpct)\n",
    "# len(slugarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Abbreviations</h1>\n",
    "\n",
    "G       Games played<br>\n",
    "AB      At-bats<br>\n",
    "R       Runs<br>\n",
    "H       Hits<br>\n",
    "B       Singles (1 base)<br>\n",
    "2B      Doubles (2 bases)<br>\n",
    "3B      Triples (3 bases)<br>\n",
    "HR      Home Runs (4 bases)<br>\n",
    "RBI     Runs Batted In<br>\n",
    "SB      Stolen Bases<br>\n",
    "CS      Caught Stealing<br>\n",
    "BB      Base on Balls/Walks<br>\n",
    "SO      Struck Out<br>\n",
    "IBB     Intentional Base on Balls/Intentional Walks<br>\n",
    "HBP     Hit By Pitch<br>\n",
    "SH      Sacrifice Bunt<br>\n",
    "SF      Sacrifice Fly<br>\n",
    "GIDP    Grounded Into Double Play<br>\n",
    "BA      Batting Average<br>\n",
    "SLG     Slugging Percentage<br>\n",
    "OBP     On-Base Percentage<br>\n",
    "OPS     On-base percentage Plus Slugging<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take 2019 data\n",
    "# Get data from American League (AL) in players with at least 50 ABs & 25 hits\n",
    "# Write to new CSV file called 'slugpct.csv'\n",
    "\n",
    "# # old columns:\n",
    "# '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",
    "\n",
    "newrows = ['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', \n",
    "            'H', 'B', '2B', '3B', 'HR', 'RBI', 'SB', 'CS', \n",
    "            'BB', 'SO', 'IBB', 'HBP', 'SH', 'SF', 'GIDP', 'BA', 'SLG', 'OBP', 'OPS'\n",
    "            ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AL 2019 data\n",
    "# AL slugging pct in 2019 by players with at least 50 ABs & 25 hits\n",
    "\n",
    "with open(fcsv, 'w') as csvfile:\n",
    "    csvwriter = csv.writer(csvfile)\n",
    "    csvwriter.writerow(newrows)\n",
    "    for a in slugarr:\n",
    "        if a[1] == 2019 and a[4] == 'AL' and a[6] >= 50 and a[8] >= 25:\n",
    "            slg = ((a[8]-(a[9]+a[10]+a[11])) + (2*a[9]) + (3*a[10]) + (4*a[11]))/a[6]\n",
    "            bavg = a[8]/a[6]\n",
    "            obp = (a[8] + a[15] + a[18]) / (a[6] + a[15] + a[18] + a[20])\n",
    "            ops = obp + slg\n",
    "            csvwriter.writerow([a[0], a[1], a[2], a[3], a[4], a[5], a[6], a[7], a[8],\n",
    "                (a[8]-(a[9]+a[10]+a[11])), a[9], a[10], a[11], a[12], a[13], a[14], a[15], \n",
    "                a[16], a[17], a[18], a[19], a[20], a[21], \n",
    "                bavg, slg, obp, ops])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AL 2019 data in Pandas dataframe\n",
    "\n",
    "al_sluggers = pd.read_csv(fcsv)\n",
    "al_sluggers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NL 2019 data\n",
    "# NL slugging pct in 2019 by players with at least 50 ABs & 25 hits\n",
    "\n",
    "\n",
    "with open(fcsv2, 'w') as csvfile:\n",
    "    csvwriter = csv.writer(csvfile)\n",
    "    csvwriter.writerow(newrows)\n",
    "    for a in slugarr:   \n",
    "        if a[1] == 2019 and a[4] == 'NL' and a[6] >= 50 and a[8] >= 25:\n",
    "            slg = ((a[8]-(a[9]+a[10]+a[11])) + (2*a[9]) + (3*a[10]) + (4*a[11]))/a[6]\n",
    "            bavg = a[8]/a[6]\n",
    "            obp = (a[8] + a[15] + a[18]) / (a[6] + a[15] + a[18] + a[20])\n",
    "            ops = obp + slg\n",
    "            csvwriter.writerow([a[0], a[1], a[2], a[3], a[4], a[5], a[6], a[7], a[8],\n",
    "                (a[8]-(a[9]+a[10]+a[11])), a[9], a[10], a[11], a[12], a[13], a[14], a[15], \n",
    "                a[16], a[17], a[18], a[19], a[20], a[21], \n",
    "                bavg, slg, obp, ops])"
   ]
  },
  {
   "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>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>B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "      <th>BA</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acunaro01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>156</td>\n",
       "      <td>626</td>\n",
       "      <td>127</td>\n",
       "      <td>175</td>\n",
       "      <td>110</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>101.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76</td>\n",
       "      <td>188.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.279553</td>\n",
       "      <td>0.517572</td>\n",
       "      <td>0.365169</td>\n",
       "      <td>0.882740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adamsma01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>WAS</td>\n",
       "      <td>NL</td>\n",
       "      <td>111</td>\n",
       "      <td>310</td>\n",
       "      <td>42</td>\n",
       "      <td>70</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.464516</td>\n",
       "      <td>0.276276</td>\n",
       "      <td>0.740792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aguilje01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>MIL</td>\n",
       "      <td>NL</td>\n",
       "      <td>94</td>\n",
       "      <td>222</td>\n",
       "      <td>26</td>\n",
       "      <td>50</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.225225</td>\n",
       "      <td>0.373874</td>\n",
       "      <td>0.320463</td>\n",
       "      <td>0.694337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ahmedni01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>158</td>\n",
       "      <td>556</td>\n",
       "      <td>79</td>\n",
       "      <td>141</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.253597</td>\n",
       "      <td>0.437050</td>\n",
       "      <td>0.315705</td>\n",
       "      <td>0.752755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>albieoz01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>160</td>\n",
       "      <td>640</td>\n",
       "      <td>102</td>\n",
       "      <td>189</td>\n",
       "      <td>114</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>86.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54</td>\n",
       "      <td>112.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.295312</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.351852</td>\n",
       "      <td>0.851852</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>wongko01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>SLN</td>\n",
       "      <td>NL</td>\n",
       "      <td>148</td>\n",
       "      <td>478</td>\n",
       "      <td>61</td>\n",
       "      <td>136</td>\n",
       "      <td>96</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>59.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47</td>\n",
       "      <td>83.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.284519</td>\n",
       "      <td>0.422594</td>\n",
       "      <td>0.360958</td>\n",
       "      <td>0.783552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>yastrmi01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>107</td>\n",
       "      <td>371</td>\n",
       "      <td>64</td>\n",
       "      <td>101</td>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.272237</td>\n",
       "      <td>0.517520</td>\n",
       "      <td>0.334146</td>\n",
       "      <td>0.851667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>yelicch01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>MIL</td>\n",
       "      <td>NL</td>\n",
       "      <td>130</td>\n",
       "      <td>489</td>\n",
       "      <td>100</td>\n",
       "      <td>161</td>\n",
       "      <td>85</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>97.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80</td>\n",
       "      <td>118.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.329243</td>\n",
       "      <td>0.670757</td>\n",
       "      <td>0.429310</td>\n",
       "      <td>1.100067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>zimmery01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>WAS</td>\n",
       "      <td>NL</td>\n",
       "      <td>52</td>\n",
       "      <td>171</td>\n",
       "      <td>20</td>\n",
       "      <td>44</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.257310</td>\n",
       "      <td>0.415205</td>\n",
       "      <td>0.321053</td>\n",
       "      <td>0.736257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>zobribe01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>47</td>\n",
       "      <td>150</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.313333</td>\n",
       "      <td>0.357955</td>\n",
       "      <td>0.671288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID  yearID  stint teamID lgID    G   AB    R    H    B  2B  3B  \\\n",
       "0    acunaro01    2019      1    ATL   NL  156  626  127  175  110  22   2   \n",
       "1    adamsma01    2019      1    WAS   NL  111  310   42   70   36  14   0   \n",
       "2    aguilje01    2019      1    MIL   NL   94  222   26   50   33   9   0   \n",
       "3    ahmedni01    2019      1    ARI   NL  158  556   79  141   83  33   6   \n",
       "4    albieoz01    2019      1    ATL   NL  160  640  102  189  114  43   8   \n",
       "..         ...     ...    ...    ...  ...  ...  ...  ...  ...  ...  ..  ..   \n",
       "206   wongko01    2019      1    SLN   NL  148  478   61  136   96  25   4   \n",
       "207  yastrmi01    2019      1    SFN   NL  107  371   64  101   55  22   3   \n",
       "208  yelicch01    2019      1    MIL   NL  130  489  100  161   85  29   3   \n",
       "209  zimmery01    2019      1    WAS   NL   52  171   20   44   29   9   0   \n",
       "210  zobribe01    2019      1    CHN   NL   47  150   24   39   33   5   0   \n",
       "\n",
       "     HR    RBI    SB   CS  BB     SO   IBB   HBP   SH    SF  GIDP        BA  \\\n",
       "0    41  101.0  37.0  9.0  76  188.0   4.0   9.0  0.0   1.0   8.0  0.279553   \n",
       "1    20   56.0   0.0  0.0  20  115.0   1.0   2.0  0.0   1.0   7.0  0.225806   \n",
       "2     8   34.0   0.0  0.0  31   59.0   0.0   2.0  0.0   4.0  11.0  0.225225   \n",
       "3    19   82.0   8.0  2.0  52  113.0   2.0   4.0  1.0  12.0  15.0  0.253597   \n",
       "4    24   86.0  15.0  4.0  54  112.0   6.0   4.0  0.0   4.0   2.0  0.295312   \n",
       "..   ..    ...   ...  ...  ..    ...   ...   ...  ...   ...   ...       ...   \n",
       "206  11   59.0  24.0  4.0  47   83.0   5.0  13.0  6.0   5.0   2.0  0.284519   \n",
       "207  21   55.0   2.0  4.0  32  107.0   1.0   4.0  1.0   3.0   4.0  0.272237   \n",
       "208  44   97.0  30.0  2.0  80  118.0  16.0   8.0  0.0   3.0   8.0  0.329243   \n",
       "209   6   27.0   0.0  0.0  17   39.0   0.0   0.0  0.0   2.0   4.0  0.257310   \n",
       "210   1   17.0   0.0  0.0  23   24.0   0.0   1.0  0.0   2.0   6.0  0.260000   \n",
       "\n",
       "          SLG       OBP       OPS  \n",
       "0    0.517572  0.365169  0.882740  \n",
       "1    0.464516  0.276276  0.740792  \n",
       "2    0.373874  0.320463  0.694337  \n",
       "3    0.437050  0.315705  0.752755  \n",
       "4    0.500000  0.351852  0.851852  \n",
       "..        ...       ...       ...  \n",
       "206  0.422594  0.360958  0.783552  \n",
       "207  0.517520  0.334146  0.851667  \n",
       "208  0.670757  0.429310  1.100067  \n",
       "209  0.415205  0.321053  0.736257  \n",
       "210  0.313333  0.357955  0.671288  \n",
       "\n",
       "[211 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NL 2019 data in Pandas dataframe\n",
    "\n",
    "nl_sluggers = pd.read_csv(fcsv2)\n",
    "nl_sluggers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fcb341f0910>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test Scatterplot 1\n",
    "%matplotlib inline\n",
    "\n",
    "plt.scatter(slugpct.H, slugpct.AB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'At Bats')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Hits')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AL Batters Batting Average, 2019')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb786d8baf0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bIT9TmtyChJndBpwKHGxm24Cr3f2LwPmMbWoCeAvwSTPbCwwDf+buz+RVNhGZ2LJWzEsWzmXJ1zcyNDJ2BFKhw9puPkNezN3L79Wi5s+f72vXrm12MUSkSeoxEW7eJ+9hV0yzU3dXJ2t6J+YATDNb5+7zs+yrGdciUrFmJNCLK0M9Euol9Uu00zDVPCl3k4hUpFkJ9ErVK0fTRBimmicFCRGpSKtkO61XQr240VBG8sztyUbNTSJSkWZlOy1t4uqaXojtS6j0DmDRvG7WPvYMt9z3+GhKDgdWrutn/pEHTfrOa91JiEhFmtE8E9fE9cKLeyl0jJ0NV+28htVbdlI6hKfdcizlRUFCRCrSjFnEcU1cQyPOAftNrctEtYm6FkQ9qLlJRCpSyWS1ekmqrJ8dHGLD1WfUfPykSXXqvFaQEJEqJE1Wy2tobCWVeLEM/QODdJgx7F529vSShXPHDKeF9suxlBcFCRGpi3LzFmoJIFkr8dIyDIeThcvNoWjG3VG7UJAQkbooNzS2lolvWSvxuDKUliXp89o9x1JeFCREpC7SOn/TAki1C/pUUoasr8t4Gt0kInWRNjS2UaOHynU0qyO6cgoSIlIXaUNjGzW3Iq4MpWXpW9/PgmX3clTv3SxYdm/D04m0GzU3iUhdlOs3qPfooaRRTOec2M3qLTtjRzeVlqPapICTiVKFi0hDREc3zegsYBZkYK1mJFHpKKaozkJH4qS6BcvujR1KO5HTgsepJFW4mptEpCEWzetmTe/p3Li4h5f2jrBrz1DVWWSzjGKKo5nVlVOQEJGGqkcW2WpHMSkteOUUJESkobJezad1MFc7iqkZeafanYKEiDRUlqv5cgsbZRnFFGfRvG5uOPv4uiQFnCxyG91kZl8C3gU85e7HhduuAf4E2BnudoW7fyd8bSlwETAMfNTdV+VVNhFpniwpNspNvouOpKokRxNoZnWl8hwC+2XgH4Cvlmy/0d3/LrrBzN4AnA8cCxwG/IeZvd7d43umRKTpqs3FlCXFRiUdzAa8ZsY05VrKSW5Bwt1/aGZzMu7+HuBr7v4S8IiZ/RI4CfhxTsUTkRqUS+ZXTrmr+aSsr13TC6PDWA1GFwrSfIf8NKNP4i/M7AEz+5KZzQy3dQNPRPbZFm4bx8wuNrO1ZrZ2586dcbuISM7yXuc6rs+h0GG88OLe0eChleQao9FB4p+A1wE9wJPAZ8LtFrNv7Cw/d7/J3ee7+/xZs7RQuUgzVDPfoJJ0GHEdzAfsN5WhkfTJv5rvUH8NTcvh7juKj83sC8C3w6fbgCMiux4ObG9g0USkApWu5FZN81Rpk9RRvXdnKlfS52utiOo09E7CzA6NPH0v8GD4+E7gfDPb38yOAo4G7m9k2UQku0rnG2RtnqplbkTS55cbTivpcgsSZnYbQcfzXDPbZmYXAZ82s01m9gBwGnAZgLtvBm4HfgZ8D7hEI5tEWlel8w2yNE9VMzei2E6d9vl5959MdHmObrogZvMXU/a/Hrg+r/KISH1VMt8gS/NUJXMjKmk2Ur6m2ihVuIjkLssEuiyVeTUT4SrtP5GxlJZDZIJqpcV1sjRP5ZV8T/maaqM7CZEJqNrJbnmOAip3F5DlbqPaz4XKm6kkoCAh0saSKvVy7ftJx0oLLHkPI82zMle+puppZTqRNhW3OltxVbbLVmyInY1qwCPL3hl7vKRV22ZOLwCwa8/QmO2FKcaB06ZWvbqcNE8lK9PpTkKkTaXdLWTprC29M4jbH8YHh6KhER99TbmTJi51XIu0qbTRQOU6a+PmJMTlxqlEq889aKWO/HaiOwmRNpV2t1CufT/uLsRhTGbValQy96CRqTJqzVo7mSlIiLSpcqOB0jprkypzJ+iDKPYz7H5pLwOD8c1NcbIOV210pV1NR74E1Nwk0qZqWYozrTJ/4cW9dE0vsH1gELOgg7pUZ2EKhQ4r2ZZ9uGqjU2Vo1nX1dCch0saqHdoZdxdSFO2Q3rVniEKH0dVZ4NnBsaOYamkuanSlrVnX1VOQEJmEipX5pSs2lN13aNg5YP+pbLj6jHHHqCRARYPKlHBN6lIzOoOV5+rdT5HXRL3JQM1NIpPUonnddGe8kq71Cr90NFVcgChMMXa/vDeXlN61NM1NdrqTEJnE0pqdomptlonrgwDoMGPEncO6Otnz8t5xczLq2bmsWdfVUZAQmcRKh8rO6Cyw++W9DA3vu9I34LRjalsqOOlOZMR9dAZ40spz6lxuLgUJkUmu9Ar7yr5N3HLf46PzJRxYua6f+UceVPWVeJaO47R9tPxo86hPQkTGWL1l57gJdbUOT82Srjtpn9OOmaXlR5tIQUJkEsmSmiKP4alZOo6T9lm9ZaeWH22i3LLAmtmXgHcBT7n7ceG25cC7gZeBh4E/dvcBM5sDPAQU/9Xvc/c/K/cZygIrk00tzS5xWWMhmGF99buPHT1OUjbY7vDzGt3sc1Tv3RVntJV0lWSBzfNO4svA20u2/TtwnLv/DvBzYGnktYfdvSf8KRsgRCabuKR8lTS7JI0w2rVnaMxxWq3ZJ68V6ySb3IKEu/8QeKZk2z3uvjd8eh9weF6fLzLR1JrKIq25qPQ40wr7qoauzgLnnNjNbT95oinNPlp+tLma2SfxYeC7kedHmdl6M/tPMzsl6U1mdrGZrTWztTt37sy/lCItota+gnJX3tsHBkfvVqLzFXa/vJcV9z8ROwGuks+vlibCNVdThsCa2SeAvcAt4aYngdnu/rSZnQj0mdmx7v5c6Xvd/SbgJgj6JBpVZpFmqzX/ULmJc4d1dcberUTnTCS9L2+aCNc8Db+TMLMLCTq03+9hr7m7v+TuT4eP1xF0ar++0WUTaWW1NrsUr8i7OgvjXisep9K7AjX7THwNDRJm9nbg48BZ7r4nsn2WmXWEj18LHA38qpFlE2l1pc0uXZ0FphWmcNmKDWOGs6YNc100r5sNV5/B5xb3xDbfVHJX0GGmZp9JINMQWDP7NHAdMAh8DzgBuNTdb055z23AqcDBwA7gaoLRTPsDT4e73efuf2Zm5wCfJGiCGgaudve7ypVLQ2BlMogb9grEZjU958RuVq7rH7c9a2UeN0y2Y4oxPDK+nvjAybO5btHxtZyaNEklQ2CzBokN7t5jZu8FFgGXAavd/YTailobBQmZ6OIq7aDJyRkcGhm3f0dCCu7urk7W9J6e+TOjQSlpdbpKjimtpZIgkbXjutiIeSZwm7s/Y1brsukiUk7SsNck9RiBVNpJrMR7k1vWIHGXmW0haG76czObBbyYX7FEBCqviJPuJKaYcVTv3VXNkq5kVJUS8U08WTuurwbeDMx39yFgD3BWbqUSEaDy4aUXvOmIcSOgILjDqHaWdNZRVbXOCJfWlDVI/Njdd7n7MIC772bsRDgRycGShXMpdGRr2p05vcB1i47nnBO76UhpDq50lnTWyWy1zgiX1pTa3GRmrwG6gU4zm0eQUwvglcD0nMsmMuktmtfNNXduju04juosdHD1u4+lb30/K9f1J/ZNFFXajJVlMlse2WOl+cr1SSwEPkSQY+mzke3PA1fkVCaRSSOtDb/4WlqAMBjzvgXL7i27FCnkM0u61hnh0ppSg4S7fwX4ipmd4+4rG1QmkUmhdHhrsQ2/qNza0x1mPHzDmWO2Zb1qr3U50jhxaT80I7v9lWtu+kA4YW6OmX2s9HV3/2zM20Qkg3Jt+OXuCIbd6VvfP6YZKOlqvtTqLZUnxyw3cql0vWyNbpoYyjU3HRD+PjDvgoi0s6xDP6P7JfUaVNKGX7zzKH5WuSR+1XwGpN/1lAYKBYWJpVxz07+Ev69tTHFEatOMcfpZK9CkleFKTTHjFdOmlu2shn13HsXPKb2an5Iwb6LSfoK0ux4FhYmtXHPT36e97u4frW9xRKqXtbKut6QK9Nq7No9Lb5GlU3nYnd0v76UwxRiKyZlUqvSuIHo1n5TWo9J+Ao1cmrzKNTetizy+lmBSnUhLatbVblJFuWvP0OjiPVn6CaKGhp2Z0wtM32/qaJDZ8/LeMYsBFaXdFdSrn6DWkUuaid2+soxuAsDMLo0+F2k1zbrazdpZXKmBPUOsv+qM0efV3hVU2k8QV6HXMnKpWXd4Uh+VrCehVeCkpSVd1eY9Tj8ubUU9FPMtFdeEaMQynkmpNYCqP1szsdtbU5YvFclDs8bpxzXpJKXXhmACXJYrrmKHc+mVdzP6V5av2sqa3tOr+mz1Z7S3ch3Xz7Pv73m6mRXXnDbA3f2VeRZOpBK1tL9X02ZebrZ00kgmJ7gSh+x9FY0aSZRHha6Z2O2tXJ/EKxpVEJF6qOZKu5o283LvKb7v0hUbYt+/fWCQGxf3jAskaXcZ5SrqenQO51GhayZ2e2voGtciraiaNvMs71k0r3v0jqHUYV2dsX0MN4ZrTye9J0m90nRnTQteiUb0pUh+cuuTMLMvAe8CnnL348JtBwErgDnAo8B57r4rfG0pcBHBGtcfdfdVeZVNJKqaJpas7yl3FR1357P2sWe45b7Hx9xRlKuo6zX8N6/UGpqJ3b7y7Lj+MvAPwFcj23qB77v7MjPrDZ9/3MzeAJwPHAscBvyHmb2+uH6FSJ6qaWLJ+p5KK91iqu9ogDDgnBPTK9m0oFVpM5QqdInKLUi4+w/NbE7J5vcAp4aPvwL8APh4uP1r7v4S8IiZ/RI4CfhxXuUTKaqmzTzuPUZ8dtVKKt24OwKnfEK+pKA1o7OgOQpSk0b3SRzi7k8ChL9fHW7vBp6I7Lct3CYyTt/6fhYsu3fMHIJaVNNmvmheN+ec2E10/TcHVq7rr6k81Y4uSupLMBufTVZzFKQSrTJPIm6txdhBHmZ2MXAxwOzZs/Msk7SgvGbvVtPEsnrLznF/pNEKuJp2/WpHFyU1a12WMrpKJItGB4kdZnaouz9pZocCT4XbtwFHRPY7HNgedwB3vwm4CWD+/PmaBT7JtFI20qSKthi4Kg1kfev72fPy3nHbs44uigt0y1dtjQ06U8zGrUUhEqfRzU13AheGjy8EvhXZfr6Z7W9mRwFHA/c3uGzSBlpp9m7S1X2HWcVNPMU7pNIEfl2dhZqGiyalDBl2r2qIrEw+uQUJM7uNoON5rpltM7OLgGXA28zsF8Dbwue4+2bgduBnwPeASzSySeI0Kz9TnCUL51LoGNtSWuiIX78B0mdXx90hARyw/9Sam9FuOPt4Omx8i676JiSL3IKEu1/g7oe6e8HdD3f3L7r70+7+Vnc/Ovz9TGT/6939de4+192/m1e5pL3lMdmrJqXxwKGzEP/fyiDxyj3PO6RF87oZSQhc6puQcjTjWtpKK83eXb5q67hFgYZGnMGhkdj9Hbj2rs2xr+V9h9RKd2DSXlpldJNIZq0y2auaq/Bde4ZiO4zzzm+k/ElSLd1JiFSp2qvwuH6A4h3SzOmF0W37T63ff89WugOT9qI7CZEqnXbMLG6+7/GK35d2B/JipKlqYHCorrOjW+UOTNqLgoRIxJV9m7jtJ08w7E6HGRe86QiuW3R87L5JqTLKLSqUdAfSSnNARIoUJGTSKk18N+dVnax5eHTAHcPuo3cKcYEi6Y6guKhQ/8DguICR1g/QSnNARIoUJKQt1brATlx6j6R5DLf+5HFWb9k57rOSUmh0d3Wypvf0isupFdykFSlISFMVK9H+gUE6LJiI1p0hnXZS2gvIljMpafJanBHfNxGuf2CQS1ds4Io7HuDsEw9n5br+1BFDWfsBak3JIZIXBQlpmiv7No1ZXKc4Uzkt11Hf+n4uv33juFnNg0PDXHvXZl4cGsmUM6nWJpw9QyOsuP8JFp90ROxdRiWS1sPu6ixwzVnHqj9CmkpBQhqitNnltGNmjVt9LSquw7ZYmSalvSjNe5R0HEhu2qnE0Ihz9wNPsv6qM2o6Tl4pOUTqQfMkJHdx6y+nBYii0qv9a+/anLmJKO04kJz4rlLFyXG1UIe1tDIFCcld0mpr5UQ7bK/sG58hNaqz0EFXZyH2tehxigsWXbZiA9MKU0bfE7egSVa1JslTygxpZQoSkrtqroijHbZ96/u5JWXSWocZN5x9PNecdWxq8r/SO5pde4Z4ae8IXZ2F1KCVkK9vVK1X/C2XtFAkQkFCcu//duAAABPKSURBVJd0RZx09V6aMmL5qq2plfhnzjthdBRRWuqJpMlqA4PJdyiFKcbyc3vGpMsoVesVv1JmSCtTx7XkLim53Dkndo8bGQRBZX7Zig0sX7WVJQvnpl6pd3UWxlSmaUNOK73i7zBj+bknjB5vyTc2MjQ8NlwVplhdrviVMkNalYKENMT+U6eMBomZ0wtc/e7xQzuT5j/M6CzEXu0bcM1Zx2YuQ9KIppnTC2OGzkIQxKJX88Xf1961ebRvRENUZTIwTxhO2A7mz5/va9eubXYxJEXcHIBiqorSSXMLlt2buRI34P0nz07Mq5S1LBBU9u864dCa5zuItAszW+fu87PsqzsJqVla6om0kU2lk92SmoMG9gxx4+KesjOpy6XAiLsbgCDb6sp1/eoHEImhjmupSdwciKV3bBqdO1CuHyC6znJSB/CMzsJo6o4pZvQPDLJ81dYx8xOu7NvEZSs2JJajaNG8bqbvN/7aSOs9i8RreJAws7lmtiHy85yZXWpm15hZf2T7mY0um1QuLb01ZBv5UwwkSRPcnn9p72gzVDR1x2UrNjCn927mffIebo6ZnJdU8Wvymkh2DQ8S7r7V3XvcvQc4EdgDfDN8+cbia+7+nUaXTSpXrsLNMrO5GEgWzevmnBO7xw2NHR6J7zcrbk2bZNc/MDjubkKT10Sya3Zz01uBh939sSaXQ6pUrsKNzgGA8XMjSieNrd6yM9Ns7EqUNjtp8ppIds0OEucDt0We/4WZPWBmXzKzmXFvMLOLzWytma3duTN+ZTBpnCUL51KYMrbqL507sGheN2t6T+fRZe/kxsU9qZPG8mjyKW12KgauaBqPaeWmVYtMUk0b3WRm+wFnAUvDTf8EfIqgFeFTwGeAD5e+z91vAm6CYAhsQworqUYSnieNNkobQVSP7Kxx4oLPS3v3lXzXnvquJy0yUTTz8ukdwE/dfQeAu+9w92F3HwG+AJzUxLJJRtfetXlcn8HwiPOJb25KHfWUpF7ZWUuVNouV63AXkUAzg8QFRJqazOzQyGvvBR5seImkYkmdxrtfHq6qEi52XndYcl7Wchlbs/Q3aISTSDZNCRJmNh14G3BHZPOnzWyTmT0AnAZc1oyySXbVrKNQrhLuW9/PynX9iQsLQXBX0J3QYV7s5yiXLE8jnESyaUqfhLvvAV5Vsu2DzSiLJEubwVycRJekmHqjVFIlHF3rOk30riAuaWBcv0dxDYnoeSQlHdQIJ5GxNKRDYpWbSZ205CYEo5vef/Lscc0+Bpx2zKzUz0oTvSvIOkIp6TwApecWyUC5myRWWsduWp4lYEx67egypQ6sXNfP/CMPGlMZpwWcou6uTtb0nj5ue7kRSmnnsab3dAUFkTJ0JyGxynXsJjUbdXd1jla8cRPj4jqvy/VTJDUDZRmhpA5qkdroTkJi+x6S1nAoBocsbfpZK+i0uRGl6cTTjlPUPzA42gcxxSy2E1wd1CLZ6E5ikotrs1/yjY089+L4ABGdSZ1lyc2sI4iS0mR8bnFPapNQ2rKoxfOJCxDqoBbJTncSk1jf+n4uv33juIq0dInOogOnTc28VChku9soHgcou15EluMnjarqMGPEXQsKiVRIQaLNlVtoJ+19S+/YlDofodSuPUP0re/PXMFGK//+gUE6zMb0GVQScModv3j+Sc1WI+48suydFR1fRBQk2lrSmtBQPv9QlhFFcSrNb1Tcr9pyZjl+9BhJS6CqD0KkOuqTaGO15B9KG91T6LBxmV0rPX69ylkppQEXqS8FiTZWy/DOrumF2O1TDJa/7wSWn3tCxZ9b6f7FUUjVpPdIkqVDXUSyU3NTG0tqg8/StJLUFfHKaYXRCvWaOzenDoPNqmt6ITERYD2bnoqq6d8QkXi6k2hj5ZpWijmLjuq9e9wV+7MxlX90e9/6fp5/aW/sPnGpNZL0re/nhRfjj1OkFN0irUt3Em0sbehouU7tcnchy1dtTVxbevWWYEXALCOrlq/aylDCcaI0A1qkNSlItLlo00qx0r5sxYbYmcbR3EtJcwyKdwlpyfa2DwxmHlmVtfIv14RV7VBfEamNgkQbyFJBllbaSfMfipX2onndrH3smdgEfI/sfKFsmS67fcO4fo1oECrKshxpudFHtQz1FZHaqE+ixZVL2V2Udd5D9Io9KQHfmoefST2Gk9zxXXrnENdvUphizJxeyDz6SEuNijSP7iRaXLmU3UVZmnWKV+xZF/ipRmmzUbUpN6KUyVWkeRQkWlzaHIOjeu/msK5OTjtmVmK209KcRTB+Rbd6SWo2qnVIai1DfUWkNk0JEmb2KPA8MAzsdff5ZnYQsAKYAzwKnOfuu5pRvlaS1qZfbH66+b7HY1/vLHSMa8pZsOzeXAJEh1luk9a01KhI8zSzT+I0d+9x9/nh817g++5+NPD98PmkF9emn0VSpZ1HE01noYPPnHdCbp3ImkUt0jyt1Nz0HuDU8PFXgB8AH29WYZotOqJpRmeBaYUpDOwZik2DHWfEPbYSTVpMqDTFdmGKceC0qQzsGSo7QqkRFbZmUYs0R7PuJBy4x8zWmdnF4bZD3P1JgPD3q5tUtqYrHdE0MDjEi0Mj3Li4h+6M7fBTzMaNgLqyb1NsgAD4rVcfQIcFSf06zFh80hGsv+oMHln2Ttb0np74udHlSkVk4mlWkFjg7m8E3gFcYmZvyfpGM7vYzNaa2dqdO3fmV8ImShvRtGTh3MQMrVHD7mOGyvat7+eWhL4LgF88tXu043vYnVvue5w5kXQeyq4qMjk1JUi4+/bw91PAN4GTgB1mdihA+PuphPfe5O7z3X3+rFnZcwi1k7Qhn4vmdXPgtGythINDw1x712YgCDzZlxfa1/QUnbimfgGRyafhfRJmdgAwxd2fDx+fAXwSuBO4EFgW/v5Wo8vWKsoN+RxIyKgap7iaXC0d1sW7mLT1pkVkYmpGx/UhwDctaP+eCtzq7t8zs/8Bbjezi4DHgXObULaGi0u5sWThXJZ8Y+OYtaYLHTbatJMl1UXU5bdvTOywzqqSIKM8SyITR8ODhLv/Chi3oo27Pw28tdHlaaaknETnnNjNuLahyPO4eQOFDhsTVKKG3dn9cnq67nKyTlxTniWRiUW5m5ooqYP6tp88MS699tCIj+Yqips3sPx9J9DVGb/aHMDQsDO9MIW4Lu+uzgIfOHn26Aim0n0q6aBWniWRiaWV5klMOklNOOUyuELyvIG0lBt7hkb43OKeTBllq20uUp4lkYlFQaKJKu1bKNfkU6zIL12xIfb1DrNMk9JqmbimPEsiE4uamxrgyr5NvG7pd5jTezevW/odruwL2ugrSbnRWejgtGNmJS5HWpRWuSfdodST5lOITCy6k8jZlX2bxiTgG3YffX7douMBuPauzewqM6z1nBO7WbmuP1OHcHfC1Xx3V2fuI4/qkRpcRFqHeQOuLvMyf/58X7t2bbOLkep1S7+TmML74RvOBKDn2ntSh6cWO5STKv41vaeP2VY6wgiCq/nSQFPcrklxIpOLma2LJFdNpeamHPSt72fBsnuZ03t3YhPPsDt96/vLBohiU00lHcJJWVNXb9mpkUciUhHdSdQgrukGYMnXN44bwlpqisH+UzvKru3wucU9LJrXzYJl92a+k0hyVO/diak5urs61TwkMknoTqIBktaevuKOB8oGCIARJ9PiP8XKuh4dwkkjjAzKrqEtIpOTgkSCYpNR0kiipElje4ZG6laGaHrueiy8ExdoSteRADVBicg+Gt0UI0tqiUZMDtvz8l761vePfmatC+/EjTxKmqehyW8iAgoSsdJSSxQr2konwlVj156huuc9Kg00SX0dmvwmIqDmplhJV9H9A4NlF+E5YL/kyXH7dZRfLKhU3k0/mvwmImkUJGKkXUWXW4Rn98vJndEOmVaVK5Vn0089+jpEZOJSc1OJvvX97H4pPa324NAwl67YQIcZF7zpiNGZ0xCs3ZA0N2Jo2Jk5vcD0/aaO9gmcdswsvr3xydS5Enk3/dTa1yEiE9ekDBJJ8xs+8c1NqXcCpeJSbJTLjzSwZ4j1V50xZlvxvUkzpdX0IyLNMumCRNzIpSVf38gIMJxhfkOcm+97nNVbdrJk4VxmTi+k5mFKuytQ3iMRaTWTLkjEjVzKMvmtnP6BQZZ8YyPDCavDQba7AjX9iEgrmXQd19V0And1FsZMbEsyNOwkTaXrMBsdqaTZzCLSLhoeJMzsCDNbbWYPmdlmM/urcPs1ZtZvZhvCnzPz+PxKO4ENuOasY1nTezofOHl21Z9b7KtQ2gsRaSfNuJPYC1zu7r8NnAxcYmZvCF+70d17wp/v5PHhcfMCClOMjoShqe8/efZo8891i47nAyfPpsMqH8YapbQXItIuGh4k3P1Jd/9p+Ph54CGgYY3wcfMClp97Ap859wRmTi+M7tfVWeBzi3vGDG+FIFA8fMOZfG5xz/hg02GZ50Eo7YWItIOmpgo3sznAD4HjgI8BHwKeA9YS3G3sinnPxcDFALNnzz7xsccea1Bpx0saShvdtvulvbFzICpJ8S0iUk+VpApvWpAwswOB/wSud/c7zOwQ4DcEE5M/BRzq7h9OO0az15PIImnug2Y1i0izVBIkmjIE1swKwErgFne/A8Ddd0Re/wLw7WaUrd4090FE2lnDg4SZGfBF4CF3/2xk+6Hu/mT49L3Ag40uW14090FE2lUz7iQWAB8ENpnZhnDbFcAFZtZD0Nz0KPCnTSibiIhENDxIuPuPCKYflMplyKuIiFRv0s24FhGR7BQkREQkkYKEiIgkaupkulqZ2U6g1tl0BxPMz5godD6tbaKdD0y8c5oM53Oku8/K8ua2DhL1YGZrs04qaQc6n9Y20c4HJt456XzGUnOTiIgkUpAQEZFEChJwU7MLUGc6n9Y20c4HJt456XwiJn2fhIiIJNOdhIiIJFKQEBGRRBM+SJjZl8zsKTN7MLLtIDP7dzP7Rfh7ZuS1pWb2SzPbamYLm1PqeCnrg7fl+QCY2TQzu9/MNobndG24vZ3PqcPM1pvZt8PnbXsuAGb2qJltCteeXxtua9tzMrMuM/uGmW0J/y+9uV3Px8zmhv8uxZ/nzOzSup6Pu0/oH+AtwBuBByPbPg30ho97gb8NH78B2AjsDxwFPAx0NPscIuU+FHhj+PgVwM/DMrfl+YRlNODA8HEB+AnB2uftfE4fA24Fvt3Of2+R83kUOLhkW9ueE/AV4CPh4/2ArnY+n8h5dQC/Bo6s5/k0/cQa9OXNKQkSWwlWvitWvFvDx0uBpZH9VgFvbnb5U87rW8DbJtD5TAd+CrypXc8JOBz4PnB6JEi05blEyhUXJNrynIBXAo8QDtpp9/MpOYczgDX1Pp8J39yU4BAPFzgKf7863N4NPBHZb1u4reWE64PPI7jybuvzCZtnNgBPAf/u7u18Tp8D/g8wEtnWrudS5MA9ZrYuXGMe2vecXgvsBP4tbBL8VzM7gPY9n6jzgdvCx3U7n8kaJJLErXPRcmOEw/XBVwKXuvtzabvGbGu583H3YXfvIbgKP8nMjkvZvWXPyczeBTzl7uuyviVmW0ucS4kF7v5G4B3AJWb2lpR9W/2cphI0P/+Tu88DdhM0xyRp9fMBwMz2A84Cvl5u15htqeczWYPEDjM7FIJlUwmuYCGIqkdE9jsc2N7gsqWKWx+cNj6fKHcfAH4AvJ32PKcFwFlm9ijwNeB0M7uZ9jyXUe6+Pfz9FPBN4CTa95y2AdvCu1WAbxAEjXY9n6J3AD919x3h87qdz2QNEncCF4aPLyRo2y9uP9/M9jezo4CjgfubUL5YZvHrg9Om5wNgZrPMrCt83An8AbCFNjwnd1/q7oe7+xyCW/973f0DtOG5FJnZAWb2iuJjgnbvB2nTc3L3XwNPmNnccNNbgZ/RpucTcQH7mpqgnufT7M6WBnTm3AY8CQwRRNGLgFcRdC7+Ivx9UGT/TxD0+G8F3tHs8pecy+8R3Bo+AGwIf85s1/MJy/c7wPrwnB4Ergq3t+05hWU8lX0d1217LgRt+BvDn83AJybAOfUAa8O/uT5gZpufz3TgaWBGZFvdzkdpOUREJNFkbW4SEZEMFCRERCSRgoSIiCRSkBARkUQKEiIikkhBQiSBmb3XzNzMjols6zGzMxP2P9XMng2zcT5gZv9hZq+O2zfL8URagYKESLILgB8RTIwr6iGYm5Lkv9y9x91/B/gf4JIyn1HueCJNpSAhEiPMj7WAYPLl+eG2/YBPAovDu4XFKe83gnTuu8LnJ5nZf4dJ5f47XAdg3PHM7PcjawOsL852FmmWqc0ugEiLWgR8z91/bmbPmNkb3f2nZnYVMN/d/yLhfaeEGW1fRZA87opw+xbgLe6+18z+APgbdz+n9HhmdhdwibuvCQPVi3mepEg5upMQiXcBQZI+wt8XZHxfsbnpCODfCBZ/AZgBfN2CFRJvBI5NeP8a4LNm9lGgy933VlV6kTpRkBApYWavIlg06F/DjK5LCJqE4tIsp7mTYGVEgE8Bq939OODdwLS4N7j7MuAjQCdwX7TTXKQZFCRExnsf8FV3P9Ld54R3BY8QJFh8nqCvIYvfI0ikBsGdRH/4+EORfcYcz8xe5+6b3P1vCZLQKUhIUylIiIx3AcG6CVErgT8EVgNvSOm4PiV8bSPwQeDycPungRvMbA3BWsRFpce71MweDN8/CHy3fqclUjllgRURkUS6kxARkUQKEiIikkhBQkREEilIiIhIIgUJERFJpCAhIiKJFCRERCTR/wd+Jop93nTefQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test scatterplot 2\n",
    "plt.xlabel(\"At Bats\")\n",
    "plt.ylabel(\"Hits\")\n",
    "plt.title(\"AL Batters Batting Average, 2019\")\n",
    "plt.scatter(al_sluggers.AB, al_sluggers.H)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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>0</th>\n",
       "      <td>abercda01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>TRO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>addybo01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>RC1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>118</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>allisar01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>CL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>137</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>allisdo01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>WS3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>133</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ansonca01</td>\n",
       "      <td>1871</td>\n",
       "      <td>1</td>\n",
       "      <td>RC1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>120</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108784</th>\n",
       "      <td>zimmebr02</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108785</th>\n",
       "      <td>zimmejo02</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>DET</td>\n",
       "      <td>AL</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108786</th>\n",
       "      <td>zimmeky01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108787</th>\n",
       "      <td>zuberty01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108788</th>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>TBA</td>\n",
       "      <td>AL</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108789 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID   G   AB   R   H  2B  3B  HR  \\\n",
       "0       abercda01    1871      1    TRO  NaN   1    4   0   0   0   0   0   \n",
       "1        addybo01    1871      1    RC1  NaN  25  118  30  32   6   0   0   \n",
       "2       allisar01    1871      1    CL1  NaN  29  137  28  40   4   5   0   \n",
       "3       allisdo01    1871      1    WS3  NaN  27  133  28  44  10   2   2   \n",
       "4       ansonca01    1871      1    RC1  NaN  25  120  29  39  11   3   0   \n",
       "...           ...     ...    ...    ...  ...  ..  ...  ..  ..  ..  ..  ..   \n",
       "108784  zimmebr02    2020      1    BAL   AL   2    0   0   0   0   0   0   \n",
       "108785  zimmejo02    2020      1    DET   AL   3    0   0   0   0   0   0   \n",
       "108786  zimmeky01    2020      1    KCA   AL  16    0   0   0   0   0   0   \n",
       "108787  zuberty01    2020      1    KCA   AL  23    0   0   0   0   0   0   \n",
       "108788  zuninmi01    2020      1    TBA   AL  28   75   8  11   4   0   4   \n",
       "\n",
       "         RBI   SB   CS  BB    SO  IBB  HBP   SH   SF  GIDP  \n",
       "0        0.0  0.0  0.0   0   0.0  NaN  NaN  NaN  NaN   0.0  \n",
       "1       13.0  8.0  1.0   4   0.0  NaN  NaN  NaN  NaN   0.0  \n",
       "2       19.0  3.0  1.0   2   5.0  NaN  NaN  NaN  NaN   1.0  \n",
       "3       27.0  1.0  1.0   0   2.0  NaN  NaN  NaN  NaN   0.0  \n",
       "4       16.0  6.0  2.0   2   1.0  NaN  NaN  NaN  NaN   0.0  \n",
       "...      ...  ...  ...  ..   ...  ...  ...  ...  ...   ...  \n",
       "108784   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108785   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108786   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108787   0.0  0.0  0.0   0   0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "108788  10.0  0.0  0.0   6  37.0  0.0  3.0  0.0  0.0   0.0  \n",
       "\n",
       "[108789 rows x 22 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slugpct.yearID\n",
    "# 150 years total\n",
    "yr = 1871 # First year statistics were recorded in baseball\n",
    "slugpct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # mean_rows = ['year', 'AB', 'H']\n",
    "# avg_ba = [None] * 150\n",
    "# # avg_ba\n",
    "# yr_ct = 0\n",
    "# add_ab = 0\n",
    "# add_h = 0\n",
    "# yr_counter = 0\n",
    "\n",
    "# for a in slugarr:\n",
    "#     if a[1] == yr:\n",
    "#         yr_ct += 1\n",
    "#         add_ab += a[6]\n",
    "#         print(a[1])\n",
    "#     else:\n",
    "#         avg_ba[yr_counter] = add\n",
    "#         yr += 1\n",
    "#         yr_ct = 0\n",
    "#         add_ab = 0\n",
    "#         add_h = 0\n",
    "\n",
    "\n",
    "# print(year_ba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcb347f9d30>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Batting Average')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(slugpct.yearID, slugpct.H/slugpct.AB)\n",
    "plt.title(\"Batting Average\")\n",
    "plt.show()\n"
   ]
  },
  {
   "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": [
    "<h1>Slugging</h1>\n",
    "\n",
    "Slugging percentage is a batter's average of getting runners left on base home plate per at-bat. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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    "On-Base Percentage Plus Slugging"
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