assignment 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Github: https://github.com/Yrzxiong/data-analysis-for-2016-baseball-games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1)\tLoad in the appropriate csv file as a pandas dataframe (batting.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('Batting.csv')  # load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### 2)\tPrint out the dimensions and info about the dataframe you just created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 102816 entries, 0 to 102815\n",
      "Data columns (total 25 columns):\n",
      "playerID     102816 non-null object\n",
      "nameFirst    102816 non-null object\n",
      "nameLast     102816 non-null object\n",
      "birthYear    102816 non-null int64\n",
      "yearID       102816 non-null int64\n",
      "stint        102816 non-null int64\n",
      "teamID       102816 non-null object\n",
      "lgID         102079 non-null object\n",
      "G            102816 non-null int64\n",
      "AB           102816 non-null int64\n",
      "R            102816 non-null int64\n",
      "H            102816 non-null int64\n",
      "2B           102816 non-null int64\n",
      "3B           102816 non-null int64\n",
      "HR           102816 non-null int64\n",
      "RBI          102392 non-null float64\n",
      "SB           101516 non-null float64\n",
      "CS           79360 non-null float64\n",
      "BB           102816 non-null int64\n",
      "SO           94978 non-null float64\n",
      "IBB          66251 non-null float64\n",
      "HBP          100006 non-null float64\n",
      "SH           96478 non-null float64\n",
      "SF           66782 non-null float64\n",
      "GIDP         76706 non-null float64\n",
      "dtypes: float64(9), int64(11), object(5)\n",
      "memory usage: 19.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</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>...</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>Frank</td>\n",
       "      <td>Abercrombie</td>\n",
       "      <td>1850</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>addybo01</td>\n",
       "      <td>Bob</td>\n",
       "      <td>Addy</td>\n",
       "      <td>1842</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>allisar01</td>\n",
       "      <td>Art</td>\n",
       "      <td>Allison</td>\n",
       "      <td>1849</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>allisdo01</td>\n",
       "      <td>Doug</td>\n",
       "      <td>Allison</td>\n",
       "      <td>1846</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ansonca01</td>\n",
       "      <td>Cap</td>\n",
       "      <td>Anson</td>\n",
       "      <td>1852</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst     nameLast  birthYear  yearID  stint teamID lgID   G  \\\n",
       "0  abercda01     Frank  Abercrombie       1850    1871      1    TRO  NaN   1   \n",
       "1   addybo01       Bob         Addy       1842    1871      1    RC1  NaN  25   \n",
       "2  allisar01       Art      Allison       1849    1871      1    CL1  NaN  29   \n",
       "3  allisdo01      Doug      Allison       1846    1871      1    WS3  NaN  27   \n",
       "4  ansonca01       Cap        Anson       1852    1871      1    RC1  NaN  25   \n",
       "\n",
       "    AB  ...    RBI   SB   CS  BB   SO  IBB  HBP  SH  SF  GIDP  \n",
       "0    4  ...    0.0  0.0  0.0   0  0.0  NaN  NaN NaN NaN   NaN  \n",
       "1  118  ...   13.0  8.0  1.0   4  0.0  NaN  NaN NaN NaN   NaN  \n",
       "2  137  ...   19.0  3.0  1.0   2  5.0  NaN  NaN NaN NaN   NaN  \n",
       "3  133  ...   27.0  1.0  1.0   0  2.0  NaN  NaN NaN NaN   NaN  \n",
       "4  120  ...   16.0  6.0  2.0   2  1.0  NaN  NaN NaN NaN   NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>birthYear</th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>102392.000000</td>\n",
       "      <td>101516.000000</td>\n",
       "      <td>79360.000000</td>\n",
       "      <td>102816.000000</td>\n",
       "      <td>94978.000000</td>\n",
       "      <td>66251.000000</td>\n",
       "      <td>100006.000000</td>\n",
       "      <td>96478.000000</td>\n",
       "      <td>66782.000000</td>\n",
       "      <td>76706.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1932.991869</td>\n",
       "      <td>1964.262313</td>\n",
       "      <td>1.077838</td>\n",
       "      <td>51.343439</td>\n",
       "      <td>141.905511</td>\n",
       "      <td>18.815544</td>\n",
       "      <td>37.139930</td>\n",
       "      <td>6.289167</td>\n",
       "      <td>1.293252</td>\n",
       "      <td>2.813599</td>\n",
       "      <td>17.003975</td>\n",
       "      <td>2.976821</td>\n",
       "      <td>1.226008</td>\n",
       "      <td>13.067207</td>\n",
       "      <td>20.529712</td>\n",
       "      <td>1.105870</td>\n",
       "      <td>1.056057</td>\n",
       "      <td>2.299540</td>\n",
       "      <td>1.054101</td>\n",
       "      <td>2.981018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>87.105524</td>\n",
       "      <td>38.856297</td>\n",
       "      <td>0.284366</td>\n",
       "      <td>47.121658</td>\n",
       "      <td>184.654492</td>\n",
       "      <td>28.242983</td>\n",
       "      <td>52.603757</td>\n",
       "      <td>9.662468</td>\n",
       "      <td>2.645770</td>\n",
       "      <td>6.304919</td>\n",
       "      <td>26.352011</td>\n",
       "      <td>7.717174</td>\n",
       "      <td>2.747377</td>\n",
       "      <td>20.746460</td>\n",
       "      <td>28.328542</td>\n",
       "      <td>2.780187</td>\n",
       "      <td>2.276251</td>\n",
       "      <td>4.241095</td>\n",
       "      <td>1.961732</td>\n",
       "      <td>4.735153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1871.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1905.000000</td>\n",
       "      <td>1934.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1945.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1969.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1996.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           birthYear         yearID          stint              G  \\\n",
       "count  102816.000000  102816.000000  102816.000000  102816.000000   \n",
       "mean     1932.991869    1964.262313       1.077838      51.343439   \n",
       "std        87.105524      38.856297       0.284366      47.121658   \n",
       "min         0.000000    1871.000000       1.000000       1.000000   \n",
       "25%      1905.000000    1934.000000       1.000000      13.000000   \n",
       "50%      1945.000000    1973.000000       1.000000      34.000000   \n",
       "75%      1969.000000    1998.000000       1.000000      80.000000   \n",
       "max      1996.000000    2016.000000       5.000000     165.000000   \n",
       "\n",
       "                  AB              R              H             2B  \\\n",
       "count  102816.000000  102816.000000  102816.000000  102816.000000   \n",
       "mean      141.905511      18.815544      37.139930       6.289167   \n",
       "std       184.654492      28.242983      52.603757       9.662468   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         4.000000       0.000000       0.000000       0.000000   \n",
       "50%        49.000000       4.000000       9.000000       1.000000   \n",
       "75%       231.000000      27.000000      58.000000       9.000000   \n",
       "max       716.000000     192.000000     262.000000      67.000000   \n",
       "\n",
       "                  3B             HR            RBI             SB  \\\n",
       "count  102816.000000  102816.000000  102392.000000  101516.000000   \n",
       "mean        1.293252       2.813599      17.003975       2.976821   \n",
       "std         2.645770       6.304919      26.352011       7.717174   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       3.000000       0.000000   \n",
       "75%         1.000000       2.000000      24.000000       2.000000   \n",
       "max        36.000000      73.000000     191.000000     138.000000   \n",
       "\n",
       "                 CS             BB            SO           IBB            HBP  \\\n",
       "count  79360.000000  102816.000000  94978.000000  66251.000000  100006.000000   \n",
       "mean       1.226008      13.067207     20.529712      1.105870       1.056057   \n",
       "std        2.747377      20.746460     28.328542      2.780187       2.276251   \n",
       "min        0.000000       0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.000000       0.000000      1.000000      0.000000       0.000000   \n",
       "50%        0.000000       3.000000      9.000000      0.000000       0.000000   \n",
       "75%        1.000000      18.000000     29.000000      1.000000       1.000000   \n",
       "max       42.000000     232.000000    223.000000    120.000000      51.000000   \n",
       "\n",
       "                 SH            SF          GIDP  \n",
       "count  96478.000000  66782.000000  76706.000000  \n",
       "mean       2.299540      1.054101      2.981018  \n",
       "std        4.241095      1.961732      4.735153  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      0.000000  \n",
       "50%        0.000000      0.000000      0.000000  \n",
       "75%        3.000000      1.000000      4.000000  \n",
       "max       67.000000     19.000000     36.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variables Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "playerInfo = df[['playerID','nameFirst','nameLast','birthYear']].drop_duplicates()     # information of players\n",
    "df['PA']=df['AB']+df['BB']+df['IBB']+df['SH']+df['SF']                                 # plate appearances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3)\tHow many players have hit 40 or more HRs in one single season? (Number only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_SumSeason = df.groupby(['playerID', 'yearID'], as_index = False).sum()            # the summary of each season per player\n",
    "len(g_SumSeason[g_SumSeason['HR']>=40].drop_duplicates('playerID'))                 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4)\tHow many players have hit more than 600 HRs for their career? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>Total_HR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ruthba01</td>\n",
       "      <td>Babe</td>\n",
       "      <td>Ruth</td>\n",
       "      <td>1895</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mayswi01</td>\n",
       "      <td>Willie</td>\n",
       "      <td>Mays</td>\n",
       "      <td>1931</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>Hank</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>1934</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bondsba01</td>\n",
       "      <td>Barry</td>\n",
       "      <td>Bonds</td>\n",
       "      <td>1964</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>griffke02</td>\n",
       "      <td>Ken</td>\n",
       "      <td>Griffey</td>\n",
       "      <td>1969</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sosasa01</td>\n",
       "      <td>Sammy</td>\n",
       "      <td>Sosa</td>\n",
       "      <td>1968</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>thomeji01</td>\n",
       "      <td>Jim</td>\n",
       "      <td>Thome</td>\n",
       "      <td>1970</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>rodrial01</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Rodriguez</td>\n",
       "      <td>1975</td>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst   nameLast  birthYear  Total_HR\n",
       "0   ruthba01      Babe       Ruth       1895       714\n",
       "1   mayswi01    Willie       Mays       1931       660\n",
       "2  aaronha01      Hank      Aaron       1934       755\n",
       "3  bondsba01     Barry      Bonds       1964       762\n",
       "4  griffke02       Ken    Griffey       1969       630\n",
       "5   sosasa01     Sammy       Sosa       1968       609\n",
       "6  thomeji01       Jim      Thome       1970       612\n",
       "7  rodrial01      Alex  Rodriguez       1975       696"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_SumID = df.groupby('playerID', as_index = False).sum()                           # the summary of each player\n",
    "g =g_SumID[g_SumID['HR']>600][['playerID','HR']]                 # players who hit more than 600 HRs for their career\n",
    "g.columns = ['playerID','Total_HR']\n",
    "pd.merge(playerInfo,g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5)\tHow many players have hit 40 2Bs, 10 3Bs, 200 Hits, and 30 HRs (inclusive) in one season? (Number Only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(g_SumSeason[(g_SumSeason['2B']>=40) & (g_SumSeason['3B']>=10) &(g_SumSeason['H']>=200) &(g_SumSeason['HR']>=30)].drop_duplicates('playerID'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6)\tHow many players have had 100 or more SBs in a season? (Dataframe)"
   ]
  },
  {
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wardjo01</td>\n",
       "      <td>John</td>\n",
       "      <td>Ward</td>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lathaar01</td>\n",
       "      <td>Arlie</td>\n",
       "      <td>Latham</td>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nicolhu01</td>\n",
       "      <td>Hugh</td>\n",
       "      <td>Nicol</td>\n",
       "      <td>1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>brownpe01</td>\n",
       "      <td>Pete</td>\n",
       "      <td>Browning</td>\n",
       "      <td>1861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>brownto01</td>\n",
       "      <td>Tom</td>\n",
       "      <td>Brown</td>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>comisch01</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>Comiskey</td>\n",
       "      <td>1859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fogarji01</td>\n",
       "      <td>Jim</td>\n",
       "      <td>Fogarty</td>\n",
       "      <td>1864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hamilbi01</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>1866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>willsma01</td>\n",
       "      <td>Maury</td>\n",
       "      <td>Wills</td>\n",
       "      <td>1932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>brocklo01</td>\n",
       "      <td>Lou</td>\n",
       "      <td>Brock</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>henderi01</td>\n",
       "      <td>Rickey</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>colemvi01</td>\n",
       "      <td>Vince</td>\n",
       "      <td>Coleman</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID nameFirst   nameLast  birthYear\n",
       "0    wardjo01      John       Ward       1860\n",
       "1   lathaar01     Arlie     Latham       1860\n",
       "2   nicolhu01      Hugh      Nicol       1858\n",
       "3   brownpe01      Pete   Browning       1861\n",
       "4   brownto01       Tom      Brown       1860\n",
       "5   comisch01   Charlie   Comiskey       1859\n",
       "6   fogarji01       Jim    Fogarty       1864\n",
       "7   hamilbi01     Billy   Hamilton       1866\n",
       "8   willsma01     Maury      Wills       1932\n",
       "9   brocklo01       Lou      Brock       1939\n",
       "10  henderi01    Rickey  Henderson       1958\n",
       "11  colemvi01     Vince    Coleman       1961"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(playerInfo, g_SumSeason[g_SumSeason['SB']>=100][['playerID']].drop_duplicates('playerID'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7)\tHow many players in the 1960s have hit more than 200 HRs? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Total_HR</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>375</td>\n",
       "      <td>Hank</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>allisbo01</td>\n",
       "      <td>225</td>\n",
       "      <td>Bob</td>\n",
       "      <td>Allison</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bankser01</td>\n",
       "      <td>269</td>\n",
       "      <td>Ernie</td>\n",
       "      <td>Banks</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cashno01</td>\n",
       "      <td>278</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cepedor01</td>\n",
       "      <td>254</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>Cepeda</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>colavro01</td>\n",
       "      <td>245</td>\n",
       "      <td>Rocky</td>\n",
       "      <td>Colavito</td>\n",
       "      <td>1933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>howarfr01</td>\n",
       "      <td>288</td>\n",
       "      <td>Frank</td>\n",
       "      <td>Howard</td>\n",
       "      <td>1936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kalinal01</td>\n",
       "      <td>210</td>\n",
       "      <td>Al</td>\n",
       "      <td>Kaline</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>killeha01</td>\n",
       "      <td>393</td>\n",
       "      <td>Harmon</td>\n",
       "      <td>Killebrew</td>\n",
       "      <td>1936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mantlmi01</td>\n",
       "      <td>256</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Mantle</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>marisro01</td>\n",
       "      <td>217</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Maris</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>matheed01</td>\n",
       "      <td>213</td>\n",
       "      <td>Eddie</td>\n",
       "      <td>Mathews</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mayswi01</td>\n",
       "      <td>350</td>\n",
       "      <td>Willie</td>\n",
       "      <td>Mays</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mccovwi01</td>\n",
       "      <td>300</td>\n",
       "      <td>Willie</td>\n",
       "      <td>McCovey</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>powelbo01</td>\n",
       "      <td>202</td>\n",
       "      <td>Boog</td>\n",
       "      <td>Powell</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>robinfr02</td>\n",
       "      <td>316</td>\n",
       "      <td>Frank</td>\n",
       "      <td>Robinson</td>\n",
       "      <td>1935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>santoro01</td>\n",
       "      <td>253</td>\n",
       "      <td>Ron</td>\n",
       "      <td>Santo</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>willibi01</td>\n",
       "      <td>249</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Williams</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>yastrca01</td>\n",
       "      <td>202</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Yastrzemski</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID  Total_HR nameFirst     nameLast  birthYear\n",
       "0   aaronha01       375      Hank        Aaron       1934\n",
       "1   allisbo01       225       Bob      Allison       1934\n",
       "2   bankser01       269     Ernie        Banks       1931\n",
       "3    cashno01       278      Norm         Cash       1934\n",
       "4   cepedor01       254   Orlando       Cepeda       1937\n",
       "5   colavro01       245     Rocky     Colavito       1933\n",
       "6   howarfr01       288     Frank       Howard       1936\n",
       "7   kalinal01       210        Al       Kaline       1934\n",
       "8   killeha01       393    Harmon    Killebrew       1936\n",
       "9   mantlmi01       256    Mickey       Mantle       1931\n",
       "10  marisro01       217     Roger        Maris       1934\n",
       "11  matheed01       213     Eddie      Mathews       1931\n",
       "12   mayswi01       350    Willie         Mays       1931\n",
       "13  mccovwi01       300    Willie      McCovey       1938\n",
       "14  powelbo01       202      Boog       Powell       1941\n",
       "15  robinfr02       316     Frank     Robinson       1935\n",
       "16  santoro01       253       Ron        Santo       1940\n",
       "17  willibi01       249     Billy     Williams       1938\n",
       "18  yastrca01       202      Carl  Yastrzemski       1939"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df[(df['yearID'] >=1960) & (df['yearID'] <1970)].groupby('playerID',as_index = False).sum()[['playerID','HR']]\n",
    "g.columns=['playerID','Total_HR']\n",
    "g[g['Total_HR']>200].merge(playerInfo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8)\tWho has hit the most HRs in history? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</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>...</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>PA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80752</th>\n",
       "      <td>bondsba01</td>\n",
       "      <td>Barry</td>\n",
       "      <td>Bonds</td>\n",
       "      <td>1964</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>SFN</td>\n",
       "      <td>NL</td>\n",
       "      <td>153</td>\n",
       "      <td>476</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>177</td>\n",
       "      <td>93.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID nameFirst nameLast  birthYear  yearID  stint teamID lgID  \\\n",
       "80752  bondsba01     Barry    Bonds       1964    2001      1    SFN   NL   \n",
       "\n",
       "         G   AB  ...      SB   CS   BB    SO   IBB  HBP   SH   SF  GIDP     PA  \n",
       "80752  153  476  ...    13.0  3.0  177  93.0  35.0  9.0  0.0  2.0   5.0  690.0  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['HR']== max(df['HR'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9)\tWho had the most hits in the 1970s? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 21 elements, new values have 2 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-e2eefca50fb1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'yearID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>=\u001b[0m\u001b[1;36m1970\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'yearID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;36m1980\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'playerID'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'playerID'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Total_H'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplayerInfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\program files (x86)\\python36\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m   3625\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3626\u001b[0m             \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3627\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3628\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3629\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32md:\\program files (x86)\\python36\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m    557\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 559\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    560\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\program files (x86)\\python36\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mset_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m   3072\u001b[0m             raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[0;32m   3073\u001b[0m                              \u001b[1;34m'new values have %d elements'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3074\u001b[1;33m                              (old_len, new_len))\n\u001b[0m\u001b[0;32m   3075\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3076\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 21 elements, new values have 2 elements"
     ]
    }
   ],
   "source": [
    "g = df[(df['yearID']>=1970) & (df['yearID']<1980)].groupby('playerID').sum()\n",
    "g.columns = ['playerID','Total_H']\n",
    "playerInfo.merge(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10)\tTop 5 highest OBP (on base percentage) with at least 500 PAs in 1977?  (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>yearID</th>\n",
       "      <th>PA</th>\n",
       "      <th>OBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>singlke01</td>\n",
       "      <td>Ken</td>\n",
       "      <td>Singleton</td>\n",
       "      <td>1947</td>\n",
       "      <td>1977</td>\n",
       "      <td>662.0</td>\n",
       "      <td>0.563433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>smithre06</td>\n",
       "      <td>Reggie</td>\n",
       "      <td>Smith</td>\n",
       "      <td>1945</td>\n",
       "      <td>1977</td>\n",
       "      <td>611.0</td>\n",
       "      <td>0.559426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tenacge01</td>\n",
       "      <td>Gene</td>\n",
       "      <td>Tenace</td>\n",
       "      <td>1946</td>\n",
       "      <td>1977</td>\n",
       "      <td>578.0</td>\n",
       "      <td>0.556064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hargrmi01</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Hargrove</td>\n",
       "      <td>1949</td>\n",
       "      <td>1977</td>\n",
       "      <td>649.0</td>\n",
       "      <td>0.540952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>carewro01</td>\n",
       "      <td>Rod</td>\n",
       "      <td>Carew</td>\n",
       "      <td>1945</td>\n",
       "      <td>1977</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.534091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst   nameLast  birthYear  yearID     PA       OBP\n",
       "3  singlke01       Ken  Singleton       1947    1977  662.0  0.563433\n",
       "0  smithre06    Reggie      Smith       1945    1977  611.0  0.559426\n",
       "2  tenacge01      Gene     Tenace       1946    1977  578.0  0.556064\n",
       "4  hargrmi01      Mike   Hargrove       1949    1977  649.0  0.540952\n",
       "1  carewro01       Rod      Carew       1945    1977  706.0  0.534091"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = g_SumSeason[(g_SumSeason['yearID'] == 1977) & (g_SumSeason['PA']>= 500)]\n",
    "g.is_copy = False\n",
    "g['OBP'] = (g['H']+g['BB']+g['IBB']+g['SH']+g['SF'])/g['AB']\n",
    "g = g[['playerID','yearID','PA','OBP']].sort_values('OBP')[-5:]\n",
    "pd.merge(playerInfo,g).sort_values('OBP',ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11)\tTop 8 highest averages in 2013 with at least 300 PAs? (Dataframe)"
   ]
  },
  {
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>yearID</th>\n",
       "      <th>PA</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cabremi01</td>\n",
       "      <td>Miguel</td>\n",
       "      <td>Cabrera</td>\n",
       "      <td>1983</td>\n",
       "      <td>2013</td>\n",
       "      <td>666.0</td>\n",
       "      <td>0.347748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ramirha01</td>\n",
       "      <td>Hanley</td>\n",
       "      <td>Ramirez</td>\n",
       "      <td>1983</td>\n",
       "      <td>2013</td>\n",
       "      <td>336.0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cuddymi01</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Cuddyer</td>\n",
       "      <td>1979</td>\n",
       "      <td>2013</td>\n",
       "      <td>543.0</td>\n",
       "      <td>0.331288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mauerjo01</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Mauer</td>\n",
       "      <td>1983</td>\n",
       "      <td>2013</td>\n",
       "      <td>515.0</td>\n",
       "      <td>0.323596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>troutmi01</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Trout</td>\n",
       "      <td>1991</td>\n",
       "      <td>2013</td>\n",
       "      <td>717.0</td>\n",
       "      <td>0.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>johnsch05</td>\n",
       "      <td>Chris</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>1984</td>\n",
       "      <td>2013</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.321012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>freemfr01</td>\n",
       "      <td>Freddie</td>\n",
       "      <td>Freeman</td>\n",
       "      <td>1989</td>\n",
       "      <td>2013</td>\n",
       "      <td>632.0</td>\n",
       "      <td>0.319419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>puigya01</td>\n",
       "      <td>Yasiel</td>\n",
       "      <td>Puig</td>\n",
       "      <td>1990</td>\n",
       "      <td>2013</td>\n",
       "      <td>427.0</td>\n",
       "      <td>0.319372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst nameLast  birthYear  yearID     PA   Average\n",
       "1  cabremi01    Miguel  Cabrera       1983    2013  666.0  0.347748\n",
       "3  ramirha01    Hanley  Ramirez       1983    2013  336.0  0.345395\n",
       "0  cuddymi01   Michael  Cuddyer       1979    2013  543.0  0.331288\n",
       "2  mauerjo01       Joe    Mauer       1983    2013  515.0  0.323596\n",
       "6  troutmi01      Mike    Trout       1991    2013  717.0  0.322581\n",
       "4  johnsch05     Chris  Johnson       1984    2013  550.0  0.321012\n",
       "5  freemfr01   Freddie  Freeman       1989    2013  632.0  0.319419\n",
       "7   puigya01    Yasiel     Puig       1990    2013  427.0  0.319372"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = g_SumSeason[(g_SumSeason['yearID'] == 2013) & (g_SumSeason['PA']>= 300)]\n",
    "g.is_copy = False\n",
    "g['Average'] = g['H']/g['AB']\n",
    "g = g[['playerID','yearID','PA','Average']].sort_values('Average')[-8:]\n",
    "pd.merge(playerInfo,g).sort_values('Average',ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12)\tLeaders in hits from 1940 up to and including 1949. (Dataframe)"
   ]
  },
  {
   "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>playerID</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>H</th>\n",
       "      <th>yearID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cramedo01</td>\n",
       "      <td>Doc</td>\n",
       "      <td>Cramer</td>\n",
       "      <td>1905</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>radclri01</td>\n",
       "      <td>Rip</td>\n",
       "      <td>Radcliff</td>\n",
       "      <td>1906</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mccosba01</td>\n",
       "      <td>Barney</td>\n",
       "      <td>McCosky</td>\n",
       "      <td>1917</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>travice01</td>\n",
       "      <td>Cecil</td>\n",
       "      <td>Travis</td>\n",
       "      <td>1913</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>peskyjo01</td>\n",
       "      <td>Johnny</td>\n",
       "      <td>Pesky</td>\n",
       "      <td>1919</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>musiast01</td>\n",
       "      <td>Stan</td>\n",
       "      <td>Musial</td>\n",
       "      <td>1920</td>\n",
       "      <td>220.0</td>\n",
       "      <td>1943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>stirnsn01</td>\n",
       "      <td>Snuffy</td>\n",
       "      <td>Stirnweiss</td>\n",
       "      <td>1918</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>holmeto01</td>\n",
       "      <td>Tommy</td>\n",
       "      <td>Holmes</td>\n",
       "      <td>1917</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>musiast01</td>\n",
       "      <td>Stan</td>\n",
       "      <td>Musial</td>\n",
       "      <td>1920</td>\n",
       "      <td>228.0</td>\n",
       "      <td>1946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>peskyjo01</td>\n",
       "      <td>Johnny</td>\n",
       "      <td>Pesky</td>\n",
       "      <td>1919</td>\n",
       "      <td>207.0</td>\n",
       "      <td>1947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>musiast01</td>\n",
       "      <td>Stan</td>\n",
       "      <td>Musial</td>\n",
       "      <td>1920</td>\n",
       "      <td>230.0</td>\n",
       "      <td>1948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>musiast01</td>\n",
       "      <td>Stan</td>\n",
       "      <td>Musial</td>\n",
       "      <td>1920</td>\n",
       "      <td>207.0</td>\n",
       "      <td>1949.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID nameFirst    nameLast  birthYear      H  yearID\n",
       "0   cramedo01       Doc      Cramer       1905  200.0  1940.0\n",
       "2   radclri01       Rip    Radcliff       1906  200.0  1940.0\n",
       "3   mccosba01    Barney     McCosky       1917  200.0  1940.0\n",
       "1   travice01     Cecil      Travis       1913  218.0  1941.0\n",
       "9   peskyjo01    Johnny       Pesky       1919  205.0  1942.0\n",
       "4   musiast01      Stan      Musial       1920  220.0  1943.0\n",
       "11  stirnsn01    Snuffy  Stirnweiss       1918  205.0  1944.0\n",
       "8   holmeto01     Tommy      Holmes       1917  224.0  1945.0\n",
       "5   musiast01      Stan      Musial       1920  228.0  1946.0\n",
       "10  peskyjo01    Johnny       Pesky       1919  207.0  1947.0\n",
       "6   musiast01      Stan      Musial       1920  230.0  1948.0\n",
       "7   musiast01      Stan      Musial       1920  207.0  1949.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = pd.DataFrame({'playerID' : []})\n",
    "for i in range(10):\n",
    "    i +=1940\n",
    "    g1 = g_SumSeason[g_SumSeason['yearID'] == i][['playerID','yearID','H']]\n",
    "    g1 = g1[g1['H']==max(g1['H'])]\n",
    "    g = g.append(g1)\n",
    "pd.merge(playerInfo,g).sort_values('yearID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13)\tWho led MLB with the most hits the most times?  And how many times?  (Dataframe, Number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Times_of_most_Hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>cobbty01</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  Times_of_most_Hits\n",
       "22  cobbty01                   7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = pd.DataFrame({'playerID' : []})\n",
    "for i in set(df['yearID']):\n",
    "    g1 = g_SumSeason[g_SumSeason['yearID']==i]\n",
    "    g=g.append(g1[g1['H']==max(g1['H'])][['playerID','yearID','H']])\n",
    "g = g.groupby('playerID',as_index = False).count()[['playerID','H']]\n",
    "g.columns = ['playerID','Times_of_most_Hits']\n",
    "g.sort_values('Times_of_most_Hits')[-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 14) Which players have played the most games for their careers?  Top 5, descending by games played presented as a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>Total_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rosepe01</td>\n",
       "      <td>Pete</td>\n",
       "      <td>Rose</td>\n",
       "      <td>1941</td>\n",
       "      <td>3562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yastrca01</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Yastrzemski</td>\n",
       "      <td>1939</td>\n",
       "      <td>3308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>Hank</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>1934</td>\n",
       "      <td>3298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>henderi01</td>\n",
       "      <td>Rickey</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>1958</td>\n",
       "      <td>3081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cobbty01</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Cobb</td>\n",
       "      <td>1886</td>\n",
       "      <td>3035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst     nameLast  birthYear  Total_played\n",
       "3   rosepe01      Pete         Rose       1941          3562\n",
       "2  yastrca01      Carl  Yastrzemski       1939          3308\n",
       "1  aaronha01      Hank        Aaron       1934          3298\n",
       "4  henderi01    Rickey    Henderson       1958          3081\n",
       "0   cobbty01        Ty         Cobb       1886          3035"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = g_SumID[['playerID','G']]\n",
    "g.columns = ['playerID','Total_played']\n",
    "pd.merge(playerInfo,g.sort_values('Total_played')[-5:]).sort_values('Total_played')[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 15) How many players have had more than 3000 hits for their careers while also hitting 500 or more HRs?  Just a number is okay here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(g_SumID[(g_SumID['H']>3000) & (g_SumID['HR']>=500)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 16) How many HRs were hit during the entire 1988 season?  Just a number is okay here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3180"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['yearID'] == 1988]['HR'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 17) Please filter out and show me the top 3 average seasons by Wade Boggs during his career in seasons in which he had at least 500 ABs.  I would like a dataframe sorted by average"
   ]
  },
  {
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>AB</th>\n",
       "      <th>Average</th>\n",
       "      <th>yearID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boggswa01</td>\n",
       "      <td>Wade</td>\n",
       "      <td>Boggs</td>\n",
       "      <td>1958</td>\n",
       "      <td>653</td>\n",
       "      <td>0.367534</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boggswa01</td>\n",
       "      <td>Wade</td>\n",
       "      <td>Boggs</td>\n",
       "      <td>1958</td>\n",
       "      <td>584</td>\n",
       "      <td>0.366438</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boggswa01</td>\n",
       "      <td>Wade</td>\n",
       "      <td>Boggs</td>\n",
       "      <td>1958</td>\n",
       "      <td>551</td>\n",
       "      <td>0.362976</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst nameLast  birthYear   AB   Average  yearID\n",
       "0  boggswa01      Wade    Boggs       1958  653  0.367534    1985\n",
       "1  boggswa01      Wade    Boggs       1958  584  0.366438    1988\n",
       "2  boggswa01      Wade    Boggs       1958  551  0.362976    1987"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player = playerInfo[(playerInfo['nameFirst']=='Wade') & (playerInfo['nameLast']=='Boggs')]\n",
    "g = g_SumSeason[g_SumSeason['playerID']==str(player['playerID']).split()[1]]\n",
    "g = g[g['AB']>=500]\n",
    "g['Average']= g['H']/g['AB']\n",
    "g = g.sort_values('Average',ascending = False)[:3]\n",
    "pd.merge(playerInfo,g[['playerID','AB','Average','yearID']]).sort_values('Average', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 18) Please filter out the top OBPs for the 1995 season with at least 400 PAs, sorted by OBP.  I would like a dataframe for this"
   ]
  },
  {
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>yearID</th>\n",
       "      <th>PA</th>\n",
       "      <th>OBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>thomafr04</td>\n",
       "      <td>Frank</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>670.0</td>\n",
       "      <td>0.667343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>martied01</td>\n",
       "      <td>Edgar</td>\n",
       "      <td>Martinez</td>\n",
       "      <td>1963</td>\n",
       "      <td>1995</td>\n",
       "      <td>650.0</td>\n",
       "      <td>0.628180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mcgwima01</td>\n",
       "      <td>Mark</td>\n",
       "      <td>McGwire</td>\n",
       "      <td>1963</td>\n",
       "      <td>1995</td>\n",
       "      <td>416.0</td>\n",
       "      <td>0.586751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>bondsba01</td>\n",
       "      <td>Barry</td>\n",
       "      <td>Bonds</td>\n",
       "      <td>1964</td>\n",
       "      <td>1995</td>\n",
       "      <td>652.0</td>\n",
       "      <td>0.583004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>davisch01</td>\n",
       "      <td>Chili</td>\n",
       "      <td>Davis</td>\n",
       "      <td>1960</td>\n",
       "      <td>1995</td>\n",
       "      <td>534.0</td>\n",
       "      <td>0.577830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>magadda01</td>\n",
       "      <td>Dave</td>\n",
       "      <td>Magadan</td>\n",
       "      <td>1962</td>\n",
       "      <td>1995</td>\n",
       "      <td>431.0</td>\n",
       "      <td>0.551724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>thomeji01</td>\n",
       "      <td>Jim</td>\n",
       "      <td>Thome</td>\n",
       "      <td>1970</td>\n",
       "      <td>1995</td>\n",
       "      <td>555.0</td>\n",
       "      <td>0.542035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baineha01</td>\n",
       "      <td>Harold</td>\n",
       "      <td>Baines</td>\n",
       "      <td>1959</td>\n",
       "      <td>1995</td>\n",
       "      <td>472.0</td>\n",
       "      <td>0.524675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>weisswa01</td>\n",
       "      <td>Walt</td>\n",
       "      <td>Weiss</td>\n",
       "      <td>1963</td>\n",
       "      <td>1995</td>\n",
       "      <td>540.0</td>\n",
       "      <td>0.524590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>boggswa01</td>\n",
       "      <td>Wade</td>\n",
       "      <td>Boggs</td>\n",
       "      <td>1958</td>\n",
       "      <td>1995</td>\n",
       "      <td>546.0</td>\n",
       "      <td>0.510870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>naehrti01</td>\n",
       "      <td>Tim</td>\n",
       "      <td>Naehring</td>\n",
       "      <td>1967</td>\n",
       "      <td>1995</td>\n",
       "      <td>521.0</td>\n",
       "      <td>0.510393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>salmoti01</td>\n",
       "      <td>Tim</td>\n",
       "      <td>Salmon</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>634.0</td>\n",
       "      <td>0.510242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>joynewa01</td>\n",
       "      <td>Wally</td>\n",
       "      <td>Joyner</td>\n",
       "      <td>1962</td>\n",
       "      <td>1995</td>\n",
       "      <td>558.0</td>\n",
       "      <td>0.509677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tettlmi01</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Tettleton</td>\n",
       "      <td>1960</td>\n",
       "      <td>1995</td>\n",
       "      <td>545.0</td>\n",
       "      <td>0.508159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>bagweje01</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Bagwell</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>545.0</td>\n",
       "      <td>0.506696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>oneilpa01</td>\n",
       "      <td>Paul</td>\n",
       "      <td>O'Neill</td>\n",
       "      <td>1963</td>\n",
       "      <td>1995</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.495652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>phillto02</td>\n",
       "      <td>Tony</td>\n",
       "      <td>Phillips</td>\n",
       "      <td>1959</td>\n",
       "      <td>1995</td>\n",
       "      <td>646.0</td>\n",
       "      <td>0.491429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>henderi01</td>\n",
       "      <td>Rickey</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>1958</td>\n",
       "      <td>1995</td>\n",
       "      <td>485.0</td>\n",
       "      <td>0.491400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>ramirma02</td>\n",
       "      <td>Manny</td>\n",
       "      <td>Ramirez</td>\n",
       "      <td>1972</td>\n",
       "      <td>1995</td>\n",
       "      <td>572.0</td>\n",
       "      <td>0.489669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>clarkwi02</td>\n",
       "      <td>Will</td>\n",
       "      <td>Clark</td>\n",
       "      <td>1964</td>\n",
       "      <td>1995</td>\n",
       "      <td>539.0</td>\n",
       "      <td>0.488987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>knoblch01</td>\n",
       "      <td>Chuck</td>\n",
       "      <td>Knoblauch</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>622.0</td>\n",
       "      <td>0.488848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>venturo01</td>\n",
       "      <td>Robin</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>1967</td>\n",
       "      <td>1995</td>\n",
       "      <td>587.0</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>olerujo01</td>\n",
       "      <td>John</td>\n",
       "      <td>Olerud</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>587.0</td>\n",
       "      <td>0.483740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>gantro01</td>\n",
       "      <td>Ron</td>\n",
       "      <td>Gant</td>\n",
       "      <td>1965</td>\n",
       "      <td>1995</td>\n",
       "      <td>495.0</td>\n",
       "      <td>0.482927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>biggicr01</td>\n",
       "      <td>Craig</td>\n",
       "      <td>Biggio</td>\n",
       "      <td>1965</td>\n",
       "      <td>1995</td>\n",
       "      <td>652.0</td>\n",
       "      <td>0.481013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>valenjo02</td>\n",
       "      <td>John</td>\n",
       "      <td>Valentin</td>\n",
       "      <td>1967</td>\n",
       "      <td>1995</td>\n",
       "      <td>613.0</td>\n",
       "      <td>0.476923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>gracema01</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Grace</td>\n",
       "      <td>1964</td>\n",
       "      <td>1995</td>\n",
       "      <td>634.0</td>\n",
       "      <td>0.474638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>coninje01</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Conine</td>\n",
       "      <td>1966</td>\n",
       "      <td>1995</td>\n",
       "      <td>566.0</td>\n",
       "      <td>0.474120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>alicelu01</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Alicea</td>\n",
       "      <td>1965</td>\n",
       "      <td>1995</td>\n",
       "      <td>504.0</td>\n",
       "      <td>0.472554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>offerjo01</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Offerman</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>508.0</td>\n",
       "      <td>0.470862</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>steinte01</td>\n",
       "      <td>Terry</td>\n",
       "      <td>Steinbach</td>\n",
       "      <td>1962</td>\n",
       "      <td>1995</td>\n",
       "      <td>440.0</td>\n",
       "      <td>0.362069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>grissma02</td>\n",
       "      <td>Marquis</td>\n",
       "      <td>Grissom</td>\n",
       "      <td>1967</td>\n",
       "      <td>1995</td>\n",
       "      <td>607.0</td>\n",
       "      <td>0.359347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>manwaki01</td>\n",
       "      <td>Kirt</td>\n",
       "      <td>Manwaring</td>\n",
       "      <td>1965</td>\n",
       "      <td>1995</td>\n",
       "      <td>420.0</td>\n",
       "      <td>0.358839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>galaran01</td>\n",
       "      <td>Andres</td>\n",
       "      <td>Galarraga</td>\n",
       "      <td>1961</td>\n",
       "      <td>1995</td>\n",
       "      <td>597.0</td>\n",
       "      <td>0.357401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>blausje01</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Blauser</td>\n",
       "      <td>1965</td>\n",
       "      <td>1995</td>\n",
       "      <td>494.0</td>\n",
       "      <td>0.357309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>greensh01</td>\n",
       "      <td>Shawn</td>\n",
       "      <td>Green</td>\n",
       "      <td>1972</td>\n",
       "      <td>1995</td>\n",
       "      <td>405.0</td>\n",
       "      <td>0.356201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>kentje01</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Kent</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>509.0</td>\n",
       "      <td>0.355932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>hillgl01</td>\n",
       "      <td>Glenallen</td>\n",
       "      <td>Hill</td>\n",
       "      <td>1965</td>\n",
       "      <td>1995</td>\n",
       "      <td>542.0</td>\n",
       "      <td>0.354125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>jordabr01</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>1967</td>\n",
       "      <td>1995</td>\n",
       "      <td>518.0</td>\n",
       "      <td>0.353061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>girarjo01</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Girardi</td>\n",
       "      <td>1964</td>\n",
       "      <td>1995</td>\n",
       "      <td>504.0</td>\n",
       "      <td>0.352814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>zeileto01</td>\n",
       "      <td>Todd</td>\n",
       "      <td>Zeile</td>\n",
       "      <td>1965</td>\n",
       "      <td>1995</td>\n",
       "      <td>470.0</td>\n",
       "      <td>0.349765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>rodriiv01</td>\n",
       "      <td>Ivan</td>\n",
       "      <td>Rodriguez</td>\n",
       "      <td>1971</td>\n",
       "      <td>1995</td>\n",
       "      <td>515.0</td>\n",
       "      <td>0.349593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>lewisda01</td>\n",
       "      <td>Darren</td>\n",
       "      <td>Lewis</td>\n",
       "      <td>1967</td>\n",
       "      <td>1995</td>\n",
       "      <td>519.0</td>\n",
       "      <td>0.349576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>kellyro01</td>\n",
       "      <td>Roberto</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>1964</td>\n",
       "      <td>1995</td>\n",
       "      <td>539.0</td>\n",
       "      <td>0.347222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>durhara01</td>\n",
       "      <td>Ray</td>\n",
       "      <td>Durham</td>\n",
       "      <td>1971</td>\n",
       "      <td>1995</td>\n",
       "      <td>513.0</td>\n",
       "      <td>0.346072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>beckeri01</td>\n",
       "      <td>Rich</td>\n",
       "      <td>Becker</td>\n",
       "      <td>1972</td>\n",
       "      <td>1995</td>\n",
       "      <td>434.0</td>\n",
       "      <td>0.344388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>dunstsh01</td>\n",
       "      <td>Shawon</td>\n",
       "      <td>Dunston</td>\n",
       "      <td>1963</td>\n",
       "      <td>1995</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.343816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>macfami01</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Macfarlane</td>\n",
       "      <td>1964</td>\n",
       "      <td>1995</td>\n",
       "      <td>406.0</td>\n",
       "      <td>0.340659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>sanchre01</td>\n",
       "      <td>Rey</td>\n",
       "      <td>Sanchez</td>\n",
       "      <td>1967</td>\n",
       "      <td>1995</td>\n",
       "      <td>454.0</td>\n",
       "      <td>0.338785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cartejo01</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Carter</td>\n",
       "      <td>1960</td>\n",
       "      <td>1995</td>\n",
       "      <td>605.0</td>\n",
       "      <td>0.336918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>easleda01</td>\n",
       "      <td>Damion</td>\n",
       "      <td>Easley</td>\n",
       "      <td>1969</td>\n",
       "      <td>1995</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.336134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>gomezch02</td>\n",
       "      <td>Chris</td>\n",
       "      <td>Gomez</td>\n",
       "      <td>1971</td>\n",
       "      <td>1995</td>\n",
       "      <td>479.0</td>\n",
       "      <td>0.334107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>claytro01</td>\n",
       "      <td>Royce</td>\n",
       "      <td>Clayton</td>\n",
       "      <td>1970</td>\n",
       "      <td>1995</td>\n",
       "      <td>555.0</td>\n",
       "      <td>0.333988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>colbrgr01</td>\n",
       "      <td>Greg</td>\n",
       "      <td>Colbrunn</td>\n",
       "      <td>1969</td>\n",
       "      <td>1995</td>\n",
       "      <td>558.0</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>walbema01</td>\n",
       "      <td>Matt</td>\n",
       "      <td>Walbeck</td>\n",
       "      <td>1969</td>\n",
       "      <td>1995</td>\n",
       "      <td>423.0</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>mearepa01</td>\n",
       "      <td>Pat</td>\n",
       "      <td>Meares</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>414.0</td>\n",
       "      <td>0.330769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>lansimi01</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Lansing</td>\n",
       "      <td>1968</td>\n",
       "      <td>1995</td>\n",
       "      <td>501.0</td>\n",
       "      <td>0.327623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>gilbe01</td>\n",
       "      <td>Benji</td>\n",
       "      <td>Gil</td>\n",
       "      <td>1972</td>\n",
       "      <td>1995</td>\n",
       "      <td>453.0</td>\n",
       "      <td>0.310843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>cedenan01</td>\n",
       "      <td>Andujar</td>\n",
       "      <td>Cedeno</td>\n",
       "      <td>1969</td>\n",
       "      <td>1995</td>\n",
       "      <td>426.0</td>\n",
       "      <td>0.302564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>guilloz01</td>\n",
       "      <td>Ozzie</td>\n",
       "      <td>Guillen</td>\n",
       "      <td>1964</td>\n",
       "      <td>1995</td>\n",
       "      <td>434.0</td>\n",
       "      <td>0.293976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID  nameFirst    nameLast  birthYear  yearID     PA       OBP\n",
       "112  thomafr04      Frank      Thomas       1968    1995  670.0  0.667343\n",
       "60   martied01      Edgar    Martinez       1963    1995  650.0  0.628180\n",
       "41   mcgwima01       Mark     McGwire       1963    1995  416.0  0.586751\n",
       "33   bondsba01      Barry       Bonds       1964    1995  652.0  0.583004\n",
       "6    davisch01      Chili       Davis       1960    1995  534.0  0.577830\n",
       "39   magadda01       Dave     Magadan       1962    1995  431.0  0.551724\n",
       "127  thomeji01        Jim       Thome       1970    1995  555.0  0.542035\n",
       "4    baineha01     Harold      Baines       1959    1995  472.0  0.524675\n",
       "64   weisswa01       Walt       Weiss       1963    1995  540.0  0.524590\n",
       "9    boggswa01       Wade       Boggs       1958    1995  546.0  0.510870\n",
       "109  naehrti01        Tim    Naehring       1967    1995  521.0  0.510393\n",
       "141  salmoti01        Tim      Salmon       1968    1995  634.0  0.510242\n",
       "36   joynewa01      Wally      Joyner       1962    1995  558.0  0.509677\n",
       "22   tettlmi01     Mickey   Tettleton       1960    1995  545.0  0.508159\n",
       "113  bagweje01       Jeff     Bagwell       1968    1995  545.0  0.506696\n",
       "30   oneilpa01       Paul     O'Neill       1963    1995  550.0  0.495652\n",
       "13   phillto02       Tony    Phillips       1959    1995  646.0  0.491429\n",
       "2    henderi01     Rickey   Henderson       1958    1995  485.0  0.491400\n",
       "158  ramirma02      Manny     Ramirez       1972    1995  572.0  0.489669\n",
       "35   clarkwi02       Will       Clark       1964    1995  539.0  0.488987\n",
       "121  knoblch01      Chuck   Knoblauch       1968    1995  622.0  0.488848\n",
       "87   venturo01      Robin     Ventura       1967    1995  587.0  0.487805\n",
       "84   olerujo01       John      Olerud       1968    1995  587.0  0.483740\n",
       "54    gantro01        Ron        Gant       1965    1995  495.0  0.482927\n",
       "69   biggicr01      Craig      Biggio       1965    1995  652.0  0.481013\n",
       "143  valenjo02       John    Valentin       1967    1995  613.0  0.476923\n",
       "70   gracema01       Mark       Grace       1964    1995  634.0  0.474638\n",
       "95   coninje01       Jeff      Conine       1966    1995  566.0  0.474120\n",
       "65   alicelu01       Luis      Alicea       1965    1995  504.0  0.472554\n",
       "110  offerjo01       Jose    Offerman       1968    1995  508.0  0.470862\n",
       "..         ...        ...         ...        ...     ...    ...       ...\n",
       "47   steinte01      Terry   Steinbach       1962    1995  440.0  0.362069\n",
       "79   grissma02    Marquis     Grissom       1967    1995  607.0  0.359347\n",
       "59   manwaki01       Kirt   Manwaring       1965    1995  420.0  0.358839\n",
       "27   galaran01     Andres   Galarraga       1961    1995  597.0  0.357401\n",
       "48   blausje01       Jeff     Blauser       1965    1995  494.0  0.357309\n",
       "152  greensh01      Shawn       Green       1972    1995  405.0  0.356201\n",
       "138   kentje01       Jeff        Kent       1968    1995  509.0  0.355932\n",
       "80    hillgl01  Glenallen        Hill       1965    1995  542.0  0.354125\n",
       "137  jordabr01      Brian      Jordan       1967    1995  518.0  0.353061\n",
       "78   girarjo01        Joe     Girardi       1964    1995  504.0  0.352814\n",
       "91   zeileto01       Todd       Zeile       1965    1995  470.0  0.349765\n",
       "123  rodriiv01       Ivan   Rodriguez       1971    1995  515.0  0.349593\n",
       "104  lewisda01     Darren       Lewis       1967    1995  519.0  0.349576\n",
       "57   kellyro01    Roberto       Kelly       1964    1995  539.0  0.347222\n",
       "168  durhara01        Ray      Durham       1971    1995  513.0  0.346072\n",
       "147  beckeri01       Rich      Becker       1972    1995  434.0  0.344388\n",
       "25   dunstsh01     Shawon     Dunston       1963    1995  500.0  0.343816\n",
       "58   macfami01       Mike  Macfarlane       1964    1995  406.0  0.340659\n",
       "124  sanchre01        Rey     Sanchez       1967    1995  454.0  0.338785\n",
       "14   cartejo01        Joe      Carter       1960    1995  605.0  0.336918\n",
       "136  easleda01     Damion      Easley       1969    1995  400.0  0.336134\n",
       "151  gomezch02      Chris       Gomez       1971    1995  479.0  0.334107\n",
       "117  claytro01      Royce     Clayton       1970    1995  555.0  0.333988\n",
       "133  colbrgr01       Greg    Colbrunn       1969    1995  558.0  0.333333\n",
       "161  walbema01       Matt     Walbeck       1969    1995  423.0  0.333333\n",
       "155  mearepa01        Pat      Meares       1968    1995  414.0  0.330769\n",
       "154  lansimi01       Mike     Lansing       1968    1995  501.0  0.327623\n",
       "150    gilbe01      Benji         Gil       1972    1995  453.0  0.310843\n",
       "94   cedenan01    Andujar      Cedeno       1969    1995  426.0  0.302564\n",
       "29   guilloz01      Ozzie     Guillen       1964    1995  434.0  0.293976\n",
       "\n",
       "[171 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = g_SumSeason[(g_SumSeason['yearID']==1995) & (g_SumSeason['PA']>=400)]\n",
    "g.is_copy = False\n",
    "g['OBP'] = (g['H']+g['BB']+g['IBB']+g['SH']+g['SF'])/g['AB']\n",
    "g = g[['playerID','yearID','PA','OBP']]\n",
    "pd.merge(playerInfo,g).sort_values('OBP',ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 19) Who had the most 3Bs (in total) in 1922, 1925, 1926, and 1928?  I would like a dataframe with just the leader"
   ]
  },
  {
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>Total_3Bs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>walkecu01</td>\n",
       "      <td>Curt</td>\n",
       "      <td>Walker</td>\n",
       "      <td>1896</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst nameLast  birthYear  Total_3Bs\n",
       "0  walkecu01      Curt   Walker       1896       59.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = pd.DataFrame({'playerID' : []})\n",
    "for i in [1922,1925,1926,1928]:\n",
    "    g = g.append(g_SumSeason[g_SumSeason['yearID'] ==i][['playerID','3B']])\n",
    "g = g.groupby('playerID',as_index = False).sum()\n",
    "g.columns= ['playerID','Total_3Bs']\n",
    "pd.merge(playerInfo,g[g['Total_3Bs']==max(g['Total_3Bs'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 20) How many players have hit 30 or more HRs in season while also stealing (SB) 30 more or bases?  A number is okay here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(g_SumSeason[(g_SumSeason['HR']>=30) & (g_SumSeason['SB']>=30)].drop_duplicates('playerID'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 21) Who had the highest OBP is 1986 with at least 400 PAs? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>yearID</th>\n",
       "      <th>PA</th>\n",
       "      <th>OBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boggswa01</td>\n",
       "      <td>Wade</td>\n",
       "      <td>Boggs</td>\n",
       "      <td>1958</td>\n",
       "      <td>1986</td>\n",
       "      <td>707.0</td>\n",
       "      <td>0.575862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst nameLast  birthYear  yearID     PA       OBP\n",
       "0  boggswa01      Wade    Boggs       1958    1986  707.0  0.575862"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = g_SumSeason[(g_SumSeason['yearID']== 1986)& (g_SumSeason['PA']>=400)]\n",
    "g.is_copy = False\n",
    "g['OBP'] = (g['H']+g['BB']+g['IBB']+g['SH']+g['SF'])/g['AB']\n",
    "pd.merge(playerInfo,g.sort_values('OBP')[['playerID','yearID','PA','OBP']][-1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 22) Same question but for 1997 and only in the NL (check league ID)? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>lgID</th>\n",
       "      <th>OBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>winstda01</td>\n",
       "      <td>1997</td>\n",
       "      <td>NL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID  yearID lgID  OBP\n",
       "614  winstda01    1997   NL  1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df[(df['yearID']== 1997)& (df['lgID']=='NL')]\n",
    "g.is_copy = False\n",
    "g['OBP'] = (g['H']+g['BB']+g['IBB']+g['SH']+g['SF'])/g['AB']\n",
    "pd.merge(playerInfo,g.sort_values('OBP'))[['playerID','yearID','lgID','OBP']][-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 23) Who had more than the league average HRs in 2012 (filter out all players with less 500 PAs)? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>Average_HR</th>\n",
       "      <th>HR</th>\n",
       "      <th>lgID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rodrial01</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Rodriguez</td>\n",
       "      <td>1975</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>18.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jeterde01</td>\n",
       "      <td>Derek</td>\n",
       "      <td>Jeter</td>\n",
       "      <td>1974</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>15.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hunteto01</td>\n",
       "      <td>Torii</td>\n",
       "      <td>Hunter</td>\n",
       "      <td>1975</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>16.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>konerpa01</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Konerko</td>\n",
       "      <td>1976</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>26.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>beltrad01</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Beltre</td>\n",
       "      <td>1979</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>36.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>beltrca01</td>\n",
       "      <td>Carlos</td>\n",
       "      <td>Beltran</td>\n",
       "      <td>1977</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pierzaj01</td>\n",
       "      <td>A. J.</td>\n",
       "      <td>Pierzynski</td>\n",
       "      <td>1976</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>27.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ramirar01</td>\n",
       "      <td>Aramis</td>\n",
       "      <td>Ramirez</td>\n",
       "      <td>1978</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>soriaal01</td>\n",
       "      <td>Alfonso</td>\n",
       "      <td>Soriano</td>\n",
       "      <td>1976</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>furcara01</td>\n",
       "      <td>Rafael</td>\n",
       "      <td>Furcal</td>\n",
       "      <td>1977</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rolliji01</td>\n",
       "      <td>Jimmy</td>\n",
       "      <td>Rollins</td>\n",
       "      <td>1978</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>youngmi02</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Young</td>\n",
       "      <td>1976</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dunnad01</td>\n",
       "      <td>Adam</td>\n",
       "      <td>Dunn</td>\n",
       "      <td>1979</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>41.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>penaca01</td>\n",
       "      <td>Carlos</td>\n",
       "      <td>Pena</td>\n",
       "      <td>1978</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>19.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pujolal01</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Pujols</td>\n",
       "      <td>1980</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>30.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>crispco01</td>\n",
       "      <td>Coco</td>\n",
       "      <td>Crisp</td>\n",
       "      <td>1979</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>11.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>phillbr01</td>\n",
       "      <td>Brandon</td>\n",
       "      <td>Phillips</td>\n",
       "      <td>1981</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>cabremi01</td>\n",
       "      <td>Miguel</td>\n",
       "      <td>Cabrera</td>\n",
       "      <td>1983</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>44.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>dejesda01</td>\n",
       "      <td>David</td>\n",
       "      <td>DeJesus</td>\n",
       "      <td>1979</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>morneju01</td>\n",
       "      <td>Justin</td>\n",
       "      <td>Morneau</td>\n",
       "      <td>1981</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>19.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>peraljh01</td>\n",
       "      <td>Jhonny</td>\n",
       "      <td>Peralta</td>\n",
       "      <td>1982</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>13.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>reyesjo01</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Reyes</td>\n",
       "      <td>1983</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>rossco01</td>\n",
       "      <td>Cody</td>\n",
       "      <td>Ross</td>\n",
       "      <td>1980</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>22.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>teixema01</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Teixeira</td>\n",
       "      <td>1980</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>24.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weeksri01</td>\n",
       "      <td>Rickie</td>\n",
       "      <td>Weeks</td>\n",
       "      <td>1982</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gonzaad01</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Gonzalez</td>\n",
       "      <td>1982</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>15.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>grandcu01</td>\n",
       "      <td>Curtis</td>\n",
       "      <td>Granderson</td>\n",
       "      <td>1981</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>43.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hartco01</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Hart</td>\n",
       "      <td>1982</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hollima01</td>\n",
       "      <td>Matt</td>\n",
       "      <td>Holliday</td>\n",
       "      <td>1980</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>kubelja01</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Kubel</td>\n",
       "      <td>1982</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>alvarpe01</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>1987</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>barneda01</td>\n",
       "      <td>Darwin</td>\n",
       "      <td>Barney</td>\n",
       "      <td>1985</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>castrst01</td>\n",
       "      <td>Starlin</td>\n",
       "      <td>Castro</td>\n",
       "      <td>1990</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>craigal01</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Craig</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>davisik02</td>\n",
       "      <td>Ike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>1987</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>espinda01</td>\n",
       "      <td>Danny</td>\n",
       "      <td>Espinosa</td>\n",
       "      <td>1987</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>freemfr01</td>\n",
       "      <td>Freddie</td>\n",
       "      <td>Freeman</td>\n",
       "      <td>1989</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>heywaja01</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Heyward</td>\n",
       "      <td>1989</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>jacksau01</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>1987</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>16.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>jennide01</td>\n",
       "      <td>Desmond</td>\n",
       "      <td>Jennings</td>\n",
       "      <td>1986</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>13.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>santaca01</td>\n",
       "      <td>Carlos</td>\n",
       "      <td>Santana</td>\n",
       "      <td>1986</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>18.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>smoakju01</td>\n",
       "      <td>Justin</td>\n",
       "      <td>Smoak</td>\n",
       "      <td>1986</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>19.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>stantmi03</td>\n",
       "      <td>Giancarlo</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>1989</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>trumbma01</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Trumbo</td>\n",
       "      <td>1986</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>32.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>vicieda01</td>\n",
       "      <td>Dayan</td>\n",
       "      <td>Viciedo</td>\n",
       "      <td>1989</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>25.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>ackledu01</td>\n",
       "      <td>Dustin</td>\n",
       "      <td>Ackley</td>\n",
       "      <td>1988</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>altuvjo01</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Altuve</td>\n",
       "      <td>1990</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>cozarza01</td>\n",
       "      <td>Zack</td>\n",
       "      <td>Cozart</td>\n",
       "      <td>1985</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>goldspa01</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Goldschmidt</td>\n",
       "      <td>1987</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>hosmeer01</td>\n",
       "      <td>Eric</td>\n",
       "      <td>Hosmer</td>\n",
       "      <td>1989</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>14.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>kipnija01</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Kipnis</td>\n",
       "      <td>1987</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>14.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>lawribr01</td>\n",
       "      <td>Brett</td>\n",
       "      <td>Lawrie</td>\n",
       "      <td>1990</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>11.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>monteje01</td>\n",
       "      <td>Jesus</td>\n",
       "      <td>Montero</td>\n",
       "      <td>1989</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>15.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>moustmi01</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Moustakas</td>\n",
       "      <td>1988</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>20.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>pachejo01</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>Pacheco</td>\n",
       "      <td>1986</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>seageky01</td>\n",
       "      <td>Kyle</td>\n",
       "      <td>Seager</td>\n",
       "      <td>1987</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>20.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>troutmi01</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Trout</td>\n",
       "      <td>1991</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>30.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>aokino01</td>\n",
       "      <td>Nori</td>\n",
       "      <td>Aoki</td>\n",
       "      <td>1982</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>cespeyo01</td>\n",
       "      <td>Yoenis</td>\n",
       "      <td>Cespedes</td>\n",
       "      <td>1985</td>\n",
       "      <td>3.822630</td>\n",
       "      <td>23.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>harpebr03</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Harper</td>\n",
       "      <td>1992</td>\n",
       "      <td>3.228117</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID  nameFirst     nameLast  birthYear  Average_HR    HR lgID\n",
       "0    rodrial01       Alex    Rodriguez       1975    3.822630  18.0   AL\n",
       "1    jeterde01      Derek        Jeter       1974    3.822630  15.0   AL\n",
       "2    hunteto01      Torii       Hunter       1975    3.822630  16.0   AL\n",
       "3    konerpa01       Paul      Konerko       1976    3.822630  26.0   AL\n",
       "4    beltrad01     Adrian       Beltre       1979    3.822630  36.0   AL\n",
       "5    beltrca01     Carlos      Beltran       1977    3.228117  32.0   NL\n",
       "6    pierzaj01      A. J.   Pierzynski       1976    3.822630  27.0   AL\n",
       "7    ramirar01     Aramis      Ramirez       1978    3.228117  27.0   NL\n",
       "8    soriaal01    Alfonso      Soriano       1976    3.228117  32.0   NL\n",
       "9    furcara01     Rafael       Furcal       1977    3.228117   5.0   NL\n",
       "10   rolliji01      Jimmy      Rollins       1978    3.228117  23.0   NL\n",
       "11   youngmi02    Michael        Young       1976    3.822630   8.0   AL\n",
       "12    dunnad01       Adam         Dunn       1979    3.822630  41.0   AL\n",
       "13    penaca01     Carlos         Pena       1978    3.822630  19.0   AL\n",
       "14   pujolal01     Albert       Pujols       1980    3.822630  30.0   AL\n",
       "15   crispco01       Coco        Crisp       1979    3.822630  11.0   AL\n",
       "16   phillbr01    Brandon     Phillips       1981    3.228117  18.0   NL\n",
       "17   cabremi01     Miguel      Cabrera       1983    3.822630  44.0   AL\n",
       "18   dejesda01      David      DeJesus       1979    3.228117   9.0   NL\n",
       "19   morneju01     Justin      Morneau       1981    3.822630  19.0   AL\n",
       "20   peraljh01     Jhonny      Peralta       1982    3.822630  13.0   AL\n",
       "21   reyesjo01       Jose        Reyes       1983    3.228117  11.0   NL\n",
       "22    rossco01       Cody         Ross       1980    3.822630  22.0   AL\n",
       "23   teixema01       Mark     Teixeira       1980    3.822630  24.0   AL\n",
       "24   weeksri01     Rickie        Weeks       1982    3.228117  21.0   NL\n",
       "25   gonzaad01     Adrian     Gonzalez       1982    3.822630  15.0   AL\n",
       "26   grandcu01     Curtis   Granderson       1981    3.822630  43.0   AL\n",
       "27    hartco01      Corey         Hart       1982    3.228117  30.0   NL\n",
       "28   hollima01       Matt     Holliday       1980    3.228117  27.0   NL\n",
       "29   kubelja01      Jason        Kubel       1982    3.228117  30.0   NL\n",
       "..         ...        ...          ...        ...         ...   ...  ...\n",
       "101  alvarpe01      Pedro      Alvarez       1987    3.228117  30.0   NL\n",
       "102  barneda01     Darwin       Barney       1985    3.228117   7.0   NL\n",
       "103  castrst01    Starlin       Castro       1990    3.228117  14.0   NL\n",
       "104  craigal01      Allen        Craig       1984    3.228117  22.0   NL\n",
       "105  davisik02        Ike        Davis       1987    3.228117  32.0   NL\n",
       "106  espinda01      Danny     Espinosa       1987    3.228117  17.0   NL\n",
       "107  freemfr01    Freddie      Freeman       1989    3.228117  23.0   NL\n",
       "108  heywaja01      Jason      Heyward       1989    3.228117  27.0   NL\n",
       "109  jacksau01     Austin      Jackson       1987    3.822630  16.0   AL\n",
       "110  jennide01    Desmond     Jennings       1986    3.822630  13.0   AL\n",
       "111  santaca01     Carlos      Santana       1986    3.822630  18.0   AL\n",
       "112  smoakju01     Justin        Smoak       1986    3.822630  19.0   AL\n",
       "113  stantmi03  Giancarlo      Stanton       1989    3.228117  37.0   NL\n",
       "114  trumbma01       Mark       Trumbo       1986    3.822630  32.0   AL\n",
       "115  vicieda01      Dayan      Viciedo       1989    3.822630  25.0   AL\n",
       "116  ackledu01     Dustin       Ackley       1988    3.822630  12.0   AL\n",
       "117  altuvjo01       Jose       Altuve       1990    3.228117   7.0   NL\n",
       "118  cozarza01       Zack       Cozart       1985    3.228117  15.0   NL\n",
       "119  goldspa01       Paul  Goldschmidt       1987    3.228117  20.0   NL\n",
       "120  hosmeer01       Eric       Hosmer       1989    3.822630  14.0   AL\n",
       "121  kipnija01      Jason       Kipnis       1987    3.822630  14.0   AL\n",
       "122  lawribr01      Brett       Lawrie       1990    3.822630  11.0   AL\n",
       "123  monteje01      Jesus      Montero       1989    3.822630  15.0   AL\n",
       "124  moustmi01       Mike    Moustakas       1988    3.822630  20.0   AL\n",
       "125  pachejo01     Jordan      Pacheco       1986    3.228117   5.0   NL\n",
       "126  seageky01       Kyle       Seager       1987    3.822630  20.0   AL\n",
       "127  troutmi01       Mike        Trout       1991    3.822630  30.0   AL\n",
       "128   aokino01       Nori         Aoki       1982    3.228117  10.0   NL\n",
       "129  cespeyo01     Yoenis     Cespedes       1985    3.822630  23.0   AL\n",
       "130  harpebr03      Bryce       Harper       1992    3.228117  22.0   NL\n",
       "\n",
       "[131 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_MeanLeague = df[df['yearID'] == 2012].groupby('lgID', as_index = False).mean()[['lgID','HR']]\n",
    "g_MeanLeague.columns = ['lgID','Average_HR']\n",
    "player = df[(df['yearID'] == 2012) &(df['PA']>= 500) ][['playerID','lgID','HR']]\n",
    "g = pd.DataFrame({'playerID' : []})\n",
    "for i in range(len(g_MeanLeague)):\n",
    "    g = g.append(player[(player['lgID']==str(g_MeanLeague['lgID'][i:i+1]).split()[1])& (player['HR']>float(g_MeanLeague['Average_HR'][i:i+1]))].merge(g_MeanLeague,how = 'left'))\n",
    "pd.merge(playerInfo,g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 24) Who is the youngest player to hit 50 or more HRs in a single season? (Dataframe)"
   ]
  },
  {
   "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>playerID</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>HR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mcgwima01</td>\n",
       "      <td>Mark</td>\n",
       "      <td>McGwire</td>\n",
       "      <td>1963</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst nameLast  birthYear  HR\n",
       "0  mcgwima01      Mark  McGwire       1963  58"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = g_SumSeason[g_SumSeason['HR']>=50]\n",
    "pd.merge(playerInfo,g[g['birthYear']==max(g['birthYear'])][['playerID','HR']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 25) Who are the five youngest players to hit 300 or more HRs for their career? (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Total_HR</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>fieldpr01</td>\n",
       "      <td>319</td>\n",
       "      <td>Prince</td>\n",
       "      <td>Fielder</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>encared01</td>\n",
       "      <td>310</td>\n",
       "      <td>Edwin</td>\n",
       "      <td>Encarnacion</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cabremi01</td>\n",
       "      <td>446</td>\n",
       "      <td>Miguel</td>\n",
       "      <td>Cabrera</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>gonzaad01</td>\n",
       "      <td>308</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Gonzalez</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>pujolal01</td>\n",
       "      <td>591</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Pujols</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID  Total_HR nameFirst     nameLast  birthYear\n",
       "42   fieldpr01       319    Prince      Fielder       1984\n",
       "38   encared01       310     Edwin  Encarnacion       1983\n",
       "21   cabremi01       446    Miguel      Cabrera       1983\n",
       "53   gonzaad01       308    Adrian     Gonzalez       1982\n",
       "105  pujolal01       591    Albert       Pujols       1980"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = g_SumID[g_SumID['HR']>=300][['playerID','HR']]\n",
    "g.columns = ['playerID','Total_HR']\n",
    "g.merge(playerInfo).sort_values('birthYear',ascending = False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Total_HR</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>birthYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>fieldpr01</td>\n",
       "      <td>319</td>\n",
       "      <td>Prince</td>\n",
       "      <td>Fielder</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>encared01</td>\n",
       "      <td>310</td>\n",
       "      <td>Edwin</td>\n",
       "      <td>Encarnacion</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cabremi01</td>\n",
       "      <td>446</td>\n",
       "      <td>Miguel</td>\n",
       "      <td>Cabrera</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>gonzaad01</td>\n",
       "      <td>308</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Gonzalez</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>pujolal01</td>\n",
       "      <td>591</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Pujols</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bautijo02</td>\n",
       "      <td>308</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Bautista</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>teixema01</td>\n",
       "      <td>409</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Teixeira</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID  Total_HR nameFirst     nameLast  birthYear\n",
       "42   fieldpr01       319    Prince      Fielder       1984\n",
       "38   encared01       310     Edwin  Encarnacion       1983\n",
       "21   cabremi01       446    Miguel      Cabrera       1983\n",
       "53   gonzaad01       308    Adrian     Gonzalez       1982\n",
       "105  pujolal01       591    Albert       Pujols       1980\n",
       "7    bautijo02       308      Jose     Bautista       1980\n",
       "129  teixema01       409      Mark     Teixeira       1980"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I found the 6th, 7th youngest players are the same age as 5th, so I change my code.\n",
    "g = g_SumID[g_SumID['HR']>=300][['playerID','HR']]\n",
    "g.columns = ['playerID','Total_HR']\n",
    "g = g.merge(playerInfo).sort_values('birthYear',ascending = False)\n",
    "for i in range(len(g)):\n",
    "    if i < 5:\n",
    "        pass\n",
    "    else:\n",
    "        if (g.iloc[i]['birthYear'] !=g.iloc[i+1]['birthYear']):\n",
    "            break;\n",
    "        else:\n",
    "            pass\n",
    "g[:i+1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bonus 1: Graph total HRs per season using bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2555d529e10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2555d668048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%pylab inline\n",
    "df.groupby('yearID').sum()[['HR']].plot(kind = 'bar', title = 'Total HRs per season using bar graph')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bonus 2: Using a line graph please graph the average HRs per AB (think about this) per season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2555d7aedd8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2555d760b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = df.groupby('yearID').sum()\n",
    "g['AverageHRs'] = g['HR']/g['AB']\n",
    "g['AverageHRs'].plot()"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}