PCA_Batting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = \"Batting.csv\"\n",
    "df = pd.read_csv(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>107409</td>\n",
       "      <td>yacabji01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107410</td>\n",
       "      <td>yamamjo01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>MIA</td>\n",
       "      <td>NL</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107411</td>\n",
       "      <td>yarbrry01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>TBA</td>\n",
       "      <td>AL</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107412</td>\n",
       "      <td>yardler01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>SDN</td>\n",
       "      <td>NL</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107413</td>\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>22</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107414</td>\n",
       "      <td>yateski01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>SDN</td>\n",
       "      <td>NL</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107415</td>\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>29</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107416</td>\n",
       "      <td>ynoaga01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107417</td>\n",
       "      <td>ynoahu01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107418</td>\n",
       "      <td>youngal01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</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",
       "      <td>107419</td>\n",
       "      <td>zagunma01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>30</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107420</td>\n",
       "      <td>zamorda01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>NYN</td>\n",
       "      <td>NL</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107421</td>\n",
       "      <td>zavalse01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>CHA</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</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",
       "      <td>107422</td>\n",
       "      <td>zeuchtj01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>TOR</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107423</td>\n",
       "      <td>zimmebr01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>AL</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</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",
       "      <td>107424</td>\n",
       "      <td>zimmejo02</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>DET</td>\n",
       "      <td>AL</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107425</td>\n",
       "      <td>zimmeky01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>107426</td>\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>9</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107427</td>\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>5</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107428</td>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>TBA</td>\n",
       "      <td>AL</td>\n",
       "      <td>90</td>\n",
       "      <td>266</td>\n",
       "      <td>30</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID    G   AB    R    H  2B  ...  \\\n",
       "107409  yacabji01    2019      1    BAL   AL   29    0    0    0   0  ...   \n",
       "107410  yamamjo01    2019      1    MIA   NL   16   21    0    1   0  ...   \n",
       "107411  yarbrry01    2019      1    TBA   AL   28    0    0    0   0  ...   \n",
       "107412  yardler01    2019      1    SDN   NL   10    0    0    0   0  ...   \n",
       "107413  yastrmi01    2019      1    SFN   NL  107  371   64  101  22  ...   \n",
       "107414  yateski01    2019      1    SDN   NL   60    0    0    0   0  ...   \n",
       "107415  yelicch01    2019      1    MIL   NL  130  489  100  161  29  ...   \n",
       "107416   ynoaga01    2019      1    BAL   AL   36    0    0    0   0  ...   \n",
       "107417   ynoahu01    2019      1    ATL   NL    2    0    0    0   0  ...   \n",
       "107418  youngal01    2019      1    ARI   NL   17   25    1    1   0  ...   \n",
       "107419  zagunma01    2019      1    CHN   NL   30   36    2    9   3  ...   \n",
       "107420  zamorda01    2019      1    NYN   NL   17    0    0    0   0  ...   \n",
       "107421  zavalse01    2019      1    CHA   AL    5   12    1    1   0  ...   \n",
       "107422  zeuchtj01    2019      1    TOR   AL    5    1    0    0   0  ...   \n",
       "107423  zimmebr01    2019      1    CLE   AL    9   13    1    0   0  ...   \n",
       "107424  zimmejo02    2019      1    DET   AL   23    2    0    0   0  ...   \n",
       "107425  zimmeky01    2019      1    KCA   AL   15    0    0    0   0  ...   \n",
       "107426  zimmery01    2019      1    WAS   NL   52  171   20   44   9  ...   \n",
       "107427  zobribe01    2019      1    CHN   NL   47  150   24   39   5  ...   \n",
       "107428  zuninmi01    2019      1    TBA   AL   90  266   30   44  10  ...   \n",
       "\n",
       "         RBI    SB   CS  BB     SO   IBB  HBP   SH   SF  GIDP  \n",
       "107409   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107410   1.0   0.0  0.0   1    9.0   0.0  0.0  4.0  0.0   1.0  \n",
       "107411   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107412   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107413  55.0   2.0  4.0  32  107.0   1.0  4.0  1.0  3.0   4.0  \n",
       "107414   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107415  97.0  30.0  2.0  80  118.0  16.0  8.0  0.0  3.0   8.0  \n",
       "107416   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107417   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107418   0.0   0.0  0.0   0   18.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107419   5.0   0.0  0.0   4   16.0   0.0  0.0  0.0  0.0   1.0  \n",
       "107420   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107421   0.0   0.0  0.0   0    9.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107422   0.0   0.0  0.0   0    1.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107423   0.0   0.0  0.0   1    7.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107424   0.0   0.0  0.0   0    2.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107425   0.0   0.0  0.0   0    0.0   0.0  0.0  0.0  0.0   0.0  \n",
       "107426  27.0   0.0  0.0  17   39.0   0.0  0.0  0.0  2.0   4.0  \n",
       "107427  17.0   0.0  0.0  23   24.0   0.0  1.0  0.0  2.0   6.0  \n",
       "107428  32.0   0.0  0.0  20   98.0   0.0  3.0  0.0  0.0   4.0  \n",
       "\n",
       "[20 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[-20:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"yearID\"] == 1871]['AB'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"yearID\"] == 2015]['AB'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1486"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"yearID\"] == 2015]['AB'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <td>99865</td>\n",
       "      <td>abreujo02</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>CHA</td>\n",
       "      <td>AL</td>\n",
       "      <td>154</td>\n",
       "      <td>613</td>\n",
       "      <td>88</td>\n",
       "      <td>178</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>140.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99876</td>\n",
       "      <td>ahmedni01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>134</td>\n",
       "      <td>421</td>\n",
       "      <td>49</td>\n",
       "      <td>95</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99887</td>\n",
       "      <td>alonsyo01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>SDN</td>\n",
       "      <td>NL</td>\n",
       "      <td>103</td>\n",
       "      <td>354</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "      <td>48.0</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",
       "      <td>99889</td>\n",
       "      <td>altuvjo01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>HOU</td>\n",
       "      <td>AL</td>\n",
       "      <td>154</td>\n",
       "      <td>638</td>\n",
       "      <td>86</td>\n",
       "      <td>200</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99894</td>\n",
       "      <td>alvarpe01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>PIT</td>\n",
       "      <td>NL</td>\n",
       "      <td>150</td>\n",
       "      <td>437</td>\n",
       "      <td>60</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>131.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>101322</td>\n",
       "      <td>wongko01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>SLN</td>\n",
       "      <td>NL</td>\n",
       "      <td>150</td>\n",
       "      <td>557</td>\n",
       "      <td>71</td>\n",
       "      <td>146</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101334</td>\n",
       "      <td>yelicch01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>MIA</td>\n",
       "      <td>NL</td>\n",
       "      <td>126</td>\n",
       "      <td>476</td>\n",
       "      <td>63</td>\n",
       "      <td>143</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101337</td>\n",
       "      <td>youngch04</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>NYA</td>\n",
       "      <td>AL</td>\n",
       "      <td>140</td>\n",
       "      <td>318</td>\n",
       "      <td>53</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101343</td>\n",
       "      <td>zimmery01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>WAS</td>\n",
       "      <td>NL</td>\n",
       "      <td>95</td>\n",
       "      <td>346</td>\n",
       "      <td>43</td>\n",
       "      <td>86</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101347</td>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>SEA</td>\n",
       "      <td>AL</td>\n",
       "      <td>112</td>\n",
       "      <td>350</td>\n",
       "      <td>28</td>\n",
       "      <td>61</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID    G   AB   R    H  2B  ...  \\\n",
       "99865   abreujo02    2015      1    CHA   AL  154  613  88  178  34  ...   \n",
       "99876   ahmedni01    2015      1    ARI   NL  134  421  49   95  17  ...   \n",
       "99887   alonsyo01    2015      1    SDN   NL  103  354  50  100  18  ...   \n",
       "99889   altuvjo01    2015      1    HOU   AL  154  638  86  200  40  ...   \n",
       "99894   alvarpe01    2015      1    PIT   NL  150  437  60  106  18  ...   \n",
       "...           ...     ...    ...    ...  ...  ...  ...  ..  ...  ..  ...   \n",
       "101322   wongko01    2015      1    SLN   NL  150  557  71  146  28  ...   \n",
       "101334  yelicch01    2015      1    MIA   NL  126  476  63  143  30  ...   \n",
       "101337  youngch04    2015      1    NYA   AL  140  318  53   80  20  ...   \n",
       "101343  zimmery01    2015      1    WAS   NL   95  346  43   86  25  ...   \n",
       "101347  zuninmi01    2015      1    SEA   AL  112  350  28   61  11  ...   \n",
       "\n",
       "          RBI    SB    CS  BB     SO   IBB   HBP   SH    SF  GIDP  \n",
       "99865   101.0   0.0   0.0  39  140.0  11.0  15.0  0.0   1.0  16.0  \n",
       "99876    34.0   4.0   5.0  29   81.0   1.0   1.0  5.0   3.0   4.0  \n",
       "99887    31.0   2.0   5.0  42   48.0   3.0   3.0  0.0   3.0  13.0  \n",
       "99889    66.0  38.0  13.0  33   67.0   8.0   9.0  3.0   6.0  17.0  \n",
       "99894    77.0   2.0   0.0  48  131.0   9.0   2.0  0.0   4.0   6.0  \n",
       "...       ...   ...   ...  ..    ...   ...   ...  ...   ...   ...  \n",
       "101322   61.0  15.0   8.0  36   95.0   2.0  15.0  0.0   5.0  10.0  \n",
       "101334   44.0  16.0   5.0  47  101.0   2.0   2.0  0.0   0.0  13.0  \n",
       "101337   42.0   3.0   1.0  30   73.0   2.0   3.0  3.0   2.0   6.0  \n",
       "101343   73.0   1.0   0.0  33   79.0   0.0   1.0  0.0  10.0  13.0  \n",
       "101347   28.0   0.0   1.0  21  132.0   0.0   5.0  8.0   2.0   6.0  \n",
       "\n",
       "[279 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"yearID\"] == 2015) & (df[\"AB\"] > 240) ]"
   ]
  },
  {
   "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": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols2keep  = ['AB', 'R', 'H','2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO']\n",
    "newdf = df[(df[\"yearID\"] > 2010) & (df[\"AB\"] > 240) ]\n",
    "newdf = newdf[cols2keep]"
   ]
  },
  {
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>94223</td>\n",
       "      <td>502</td>\n",
       "      <td>54</td>\n",
       "      <td>127</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94227</td>\n",
       "      <td>333</td>\n",
       "      <td>39</td>\n",
       "      <td>91</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94249</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>120</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94250</td>\n",
       "      <td>587</td>\n",
       "      <td>96</td>\n",
       "      <td>164</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>56</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94252</td>\n",
       "      <td>380</td>\n",
       "      <td>46</td>\n",
       "      <td>91</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>37.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94253</td>\n",
       "      <td>443</td>\n",
       "      <td>47</td>\n",
       "      <td>97</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        AB   R    H  2B  3B  HR   RBI    SB    CS  BB     SO\n",
       "94223  502  54  127  30   1   8  60.0  21.0   5.0  78  113.0\n",
       "94227  333  39   91  16   7   6  36.0   6.0   0.0  40   79.0\n",
       "94249  457  63  120  22   0   5  36.0  13.0   3.0  41   83.0\n",
       "94250  587  96  164  27   3   5  60.0  37.0  12.0  56   74.0\n",
       "94252  380  46   91  20   0   9  37.0  10.0   3.0  29   96.0\n",
       "94253  443  47   97  20   4  23  78.0   1.0   1.0  36  133.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf['PA'] = newdf['AB']+newdf['BB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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>PA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>94223</td>\n",
       "      <td>502</td>\n",
       "      <td>54</td>\n",
       "      <td>127</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94227</td>\n",
       "      <td>333</td>\n",
       "      <td>39</td>\n",
       "      <td>91</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>79.0</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94249</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>120</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41</td>\n",
       "      <td>83.0</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94250</td>\n",
       "      <td>587</td>\n",
       "      <td>96</td>\n",
       "      <td>164</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>56</td>\n",
       "      <td>74.0</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94252</td>\n",
       "      <td>380</td>\n",
       "      <td>46</td>\n",
       "      <td>91</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>37.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29</td>\n",
       "      <td>96.0</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94253</td>\n",
       "      <td>443</td>\n",
       "      <td>47</td>\n",
       "      <td>97</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36</td>\n",
       "      <td>133.0</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94261</td>\n",
       "      <td>464</td>\n",
       "      <td>63</td>\n",
       "      <td>137</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73</td>\n",
       "      <td>131.0</td>\n",
       "      <td>537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94267</td>\n",
       "      <td>556</td>\n",
       "      <td>71</td>\n",
       "      <td>155</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>59.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31</td>\n",
       "      <td>68.0</td>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94277</td>\n",
       "      <td>305</td>\n",
       "      <td>29</td>\n",
       "      <td>70</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>71.0</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94280</td>\n",
       "      <td>446</td>\n",
       "      <td>47</td>\n",
       "      <td>109</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38</td>\n",
       "      <td>88.0</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94281</td>\n",
       "      <td>529</td>\n",
       "      <td>66</td>\n",
       "      <td>146</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "      <td>67.0</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94282</td>\n",
       "      <td>554</td>\n",
       "      <td>61</td>\n",
       "      <td>136</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>48</td>\n",
       "      <td>98.0</td>\n",
       "      <td>602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        AB   R    H  2B  3B  HR   RBI    SB    CS  BB     SO   PA\n",
       "94223  502  54  127  30   1   8  60.0  21.0   5.0  78  113.0  580\n",
       "94227  333  39   91  16   7   6  36.0   6.0   0.0  40   79.0  373\n",
       "94249  457  63  120  22   0   5  36.0  13.0   3.0  41   83.0  498\n",
       "94250  587  96  164  27   3   5  60.0  37.0  12.0  56   74.0  643\n",
       "94252  380  46   91  20   0   9  37.0  10.0   3.0  29   96.0  409\n",
       "94253  443  47   97  20   4  23  78.0   1.0   1.0  36  133.0  479\n",
       "94261  464  63  137  33   4  19  82.0   3.0   1.0  73  131.0  537\n",
       "94267  556  71  155  33   8  10  59.0  30.0   6.0  31   68.0  587\n",
       "94277  305  29   70  13   0  16  47.0   0.0   0.0  22   71.0  327\n",
       "94280  446  47  109  27   0  12  39.0   3.0   1.0  38   88.0  484\n",
       "94281  529  66  146  23   6   2  43.0   9.0   2.0  22   67.0  551\n",
       "94282  554  61  136  22   3   2  40.0  23.0  10.0  48   98.0  602"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RperPA\n",
      "HperPA\n",
      "2BperPA\n",
      "3BperPA\n",
      "HRperPA\n",
      "BBperPA\n",
      "SBperPA\n"
     ]
    }
   ],
   "source": [
    "statlist = ['R','H','2B','3B','HR', 'BB','SB']\n",
    "\n",
    "\n",
    "for stat in statlist:\n",
    "    print(stat+'perPA')\n",
    "    newdf[stat+'perPA'] = newdf[stat]/newdf['PA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12174585797442794"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf[\"RperPA\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2146422628951747"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf[\"RperPA\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>PA</th>\n",
       "      <th>RperPA</th>\n",
       "      <th>HperPA</th>\n",
       "      <th>2BperPA</th>\n",
       "      <th>3BperPA</th>\n",
       "      <th>HRperPA</th>\n",
       "      <th>BBperPA</th>\n",
       "      <th>SBperPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>94700</td>\n",
       "      <td>583</td>\n",
       "      <td>136</td>\n",
       "      <td>153</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>119.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>85</td>\n",
       "      <td>169.0</td>\n",
       "      <td>668</td>\n",
       "      <td>0.203593</td>\n",
       "      <td>0.229042</td>\n",
       "      <td>0.038922</td>\n",
       "      <td>0.014970</td>\n",
       "      <td>0.061377</td>\n",
       "      <td>0.127246</td>\n",
       "      <td>0.037425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96905</td>\n",
       "      <td>559</td>\n",
       "      <td>129</td>\n",
       "      <td>182</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>83.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>67</td>\n",
       "      <td>139.0</td>\n",
       "      <td>626</td>\n",
       "      <td>0.206070</td>\n",
       "      <td>0.290735</td>\n",
       "      <td>0.043131</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>0.047923</td>\n",
       "      <td>0.107029</td>\n",
       "      <td>0.078275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104453</td>\n",
       "      <td>520</td>\n",
       "      <td>129</td>\n",
       "      <td>180</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>80.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>81</td>\n",
       "      <td>91.0</td>\n",
       "      <td>601</td>\n",
       "      <td>0.214642</td>\n",
       "      <td>0.299501</td>\n",
       "      <td>0.078203</td>\n",
       "      <td>0.008319</td>\n",
       "      <td>0.053245</td>\n",
       "      <td>0.134775</td>\n",
       "      <td>0.049917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AB    R    H  2B  3B  HR    RBI    SB    CS  BB     SO   PA  \\\n",
       "94700   583  136  153  26  10  41  119.0  25.0  10.0  85  169.0  668   \n",
       "96905   559  129  182  27   8  30   83.0  49.0   5.0  67  139.0  626   \n",
       "104453  520  129  180  47   5  32   80.0  30.0   6.0  81   91.0  601   \n",
       "\n",
       "          RperPA    HperPA   2BperPA   3BperPA   HRperPA   BBperPA   SBperPA  \n",
       "94700   0.203593  0.229042  0.038922  0.014970  0.061377  0.127246  0.037425  \n",
       "96905   0.206070  0.290735  0.043131  0.012780  0.047923  0.107029  0.078275  \n",
       "104453  0.214642  0.299501  0.078203  0.008319  0.053245  0.134775  0.049917  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf[newdf[\"RperPA\"]>0.20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>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",
       "      <td>104453</td>\n",
       "      <td>bettsmo01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>136</td>\n",
       "      <td>520</td>\n",
       "      <td>129</td>\n",
       "      <td>180</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>81</td>\n",
       "      <td>91.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID    G   AB    R    H  2B  ...  \\\n",
       "104453  bettsmo01    2018      1    BOS   AL  136  520  129  180  47  ...   \n",
       "\n",
       "         RBI    SB   CS  BB    SO  IBB  HBP   SH   SF  GIDP  \n",
       "104453  80.0  30.0  6.0  81  91.0  8.0  8.0  0.0  5.0   5.0  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[104453:104454]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "      <td>94700</td>\n",
       "      <td>grandcu01</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>NYA</td>\n",
       "      <td>AL</td>\n",
       "      <td>156</td>\n",
       "      <td>583</td>\n",
       "      <td>136</td>\n",
       "      <td>153</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>119.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>85</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  yearID  stint teamID lgID    G   AB    R    H  2B  ...  \\\n",
       "94700  grandcu01    2011      1    NYA   AL  156  583  136  153  26  ...   \n",
       "\n",
       "         RBI    SB    CS  BB     SO  IBB   HBP   SH   SF  GIDP  \n",
       "94700  119.0  25.0  10.0  85  169.0  0.0  12.0  4.0  7.0  12.0  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[94700:94701]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "      <td>96905</td>\n",
       "      <td>troutmi01</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>LAA</td>\n",
       "      <td>AL</td>\n",
       "      <td>139</td>\n",
       "      <td>559</td>\n",
       "      <td>129</td>\n",
       "      <td>182</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>67</td>\n",
       "      <td>139.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  yearID  stint teamID lgID    G   AB    R    H  2B  ...  \\\n",
       "96905  troutmi01    2012      1    LAA   AL  139  559  129  182  27  ...   \n",
       "\n",
       "        RBI    SB   CS  BB     SO  IBB  HBP   SH   SF  GIDP  \n",
       "96905  83.0  49.0  5.0  67  139.0  4.0  6.0  0.0  7.0   7.0  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[96905:96906]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2594, 2594)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.shape[0], newdf.dropna().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RperPA, HperPA, 2BperPA, 3BperPA, HRperPA, SBperPA\n",
    "features = ['HperPA','2BperPA','3BperPA','HRperPA', 'BBperPA','SBperPA']\n",
    "x = newdf.loc[:, features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.21896552, 0.05172414, 0.00172414, 0.0137931 , 0.13448276,\n",
       "        0.0362069 ],\n",
       "       [0.24396783, 0.04289544, 0.01876676, 0.01608579, 0.10723861,\n",
       "        0.01608579],\n",
       "       [0.24096386, 0.04417671, 0.        , 0.01004016, 0.08232932,\n",
       "        0.02610442],\n",
       "       ...,\n",
       "       [0.25062035, 0.05459057, 0.00744417, 0.05210918, 0.07940447,\n",
       "        0.00496278],\n",
       "       [0.28295255, 0.05096661, 0.00527241, 0.07732865, 0.14059754,\n",
       "        0.05272408],\n",
       "       [0.15384615, 0.03496503, 0.0034965 , 0.03146853, 0.06993007,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = newdf.loc[:,['RperPA']].values\n",
    "dfy = pd.DataFrame(y); dfy.columns = [\"RperPA\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HperPA</th>\n",
       "      <th>2BperPA</th>\n",
       "      <th>3BperPA</th>\n",
       "      <th>HRperPA</th>\n",
       "      <th>BBperPA</th>\n",
       "      <th>SBperPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.218966</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0.134483</td>\n",
       "      <td>0.036207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.243968</td>\n",
       "      <td>0.042895</td>\n",
       "      <td>0.018767</td>\n",
       "      <td>0.016086</td>\n",
       "      <td>0.107239</td>\n",
       "      <td>0.016086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.240964</td>\n",
       "      <td>0.044177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010040</td>\n",
       "      <td>0.082329</td>\n",
       "      <td>0.026104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.255054</td>\n",
       "      <td>0.041991</td>\n",
       "      <td>0.004666</td>\n",
       "      <td>0.007776</td>\n",
       "      <td>0.087092</td>\n",
       "      <td>0.057543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.222494</td>\n",
       "      <td>0.048900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022005</td>\n",
       "      <td>0.070905</td>\n",
       "      <td>0.024450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     HperPA   2BperPA   3BperPA   HRperPA   BBperPA   SBperPA\n",
       "0  0.218966  0.051724  0.001724  0.013793  0.134483  0.036207\n",
       "1  0.243968  0.042895  0.018767  0.016086  0.107239  0.016086\n",
       "2  0.240964  0.044177  0.000000  0.010040  0.082329  0.026104\n",
       "3  0.255054  0.041991  0.004666  0.007776  0.087092  0.057543\n",
       "4  0.222494  0.048900  0.000000  0.022005  0.070905  0.024450"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#x = StandardScaler().fit_transform(x)\n",
    "pd.DataFrame(data = x, columns = features).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "principalComponents = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.63069707, -0.08681498, -0.03519041,  0.14357564,  0.74480064,\n",
       "        -0.13455126]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "score = -0.630*HperPA + -0.08681*2BperPA+..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.63069707, -0.08681498, -0.03519041,  0.14357564,  0.74480064,\n",
       "        -0.13455126],\n",
       "       [ 0.73836535,  0.19714164, -0.00082274,  0.23152477,  0.60188155,\n",
       "        -0.0092729 ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>RperPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.045117</td>\n",
       "      <td>0.011568</td>\n",
       "      <td>0.093103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.104558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.006065</td>\n",
       "      <td>-0.005841</td>\n",
       "      <td>0.126506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.015934</td>\n",
       "      <td>0.006178</td>\n",
       "      <td>0.149300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.001395</td>\n",
       "      <td>-0.022639</td>\n",
       "      <td>0.112469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.021449</td>\n",
       "      <td>-0.030024</td>\n",
       "      <td>0.098121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2    RperPA\n",
       "0               0.045117               0.011568  0.093103\n",
       "1               0.012260               0.012594  0.104558\n",
       "2              -0.006065              -0.005841  0.126506\n",
       "3              -0.015934               0.006178  0.149300\n",
       "4              -0.001395              -0.022639  0.112469\n",
       "5               0.021449              -0.030024  0.098121"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])\n",
    "\n",
    "finalDf = pd.concat([principalDf, dfy['RperPA'] ], axis = 1)\n",
    "finalDf[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "\n",
    "\n",
    "\n",
    "ax.scatter(finalDf['principal component 1']\n",
    "               , finalDf['principal component 2'])\n",
    "#ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#colors = ['r', 'g', 'b']\n",
    "\n",
    "\n",
    "# a range of blues \n",
    "#(204,229,255), (153,204,255), (102,178,255), (51,153,255)\n",
    "# (0,128,255),(0,102,204),(0,76,153),(0,51,102),(0,25,51)\n",
    "\n",
    "\n",
    "numDivisions = 8\n",
    "\n",
    "aRange = np.linspace(y.min(),y.max(),numDivisions+1)\n",
    "\n",
    "finalDf['Rness'] = 0\n",
    "\n",
    "for aNum in range(numDivisions):\n",
    "    finalDf.loc[finalDf[\"RperPA\"]> aRange[aNum+1],'Rness']= aNum+1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>RperPA</th>\n",
       "      <th>Rness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.045117</td>\n",
       "      <td>0.011568</td>\n",
       "      <td>0.093103</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.104558</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.006065</td>\n",
       "      <td>-0.005841</td>\n",
       "      <td>0.126506</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.015934</td>\n",
       "      <td>0.006178</td>\n",
       "      <td>0.149300</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.001395</td>\n",
       "      <td>-0.022639</td>\n",
       "      <td>0.112469</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.021449</td>\n",
       "      <td>-0.030024</td>\n",
       "      <td>0.098121</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2    RperPA  Rness\n",
       "0               0.045117               0.011568  0.093103      2\n",
       "1               0.012260               0.012594  0.104558      2\n",
       "2              -0.006065              -0.005841  0.126506      3\n",
       "3              -0.015934               0.006178  0.149300      4\n",
       "4              -0.001395              -0.022639  0.112469      3\n",
       "5               0.021449              -0.030024  0.098121      2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDf[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "\n",
    "\n",
    "targets = [0,1,2,3,4,5,6,7,8]\n",
    "colors = [(204,229,255), (153,204,255), (102,178,255), (51,153,255),\n",
    " (0,128,255),(0,102,204),(0,76,153),(0,51,102),(0,25,51)]\n",
    "\n",
    "colors = [np.array(color)/255 for color in colors]\n",
    "colors = [ [list(color)] for color in colors]\n",
    "\n",
    "#colors = ['lightgray', 'silver', 'darkgray', 'gray','dimgray','black']\n",
    "\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf['Rness'] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
    "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "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": "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}