susanli2016/Baseball_data.ipynb

## Baseball_data.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 2: Investigate a Dataset (Baseball Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Author: Susan Li\n",
    "#### Date: January 12 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This analysis is based on [Lahman's Baseball Database](http://www.seanlahman.com/baseball-archive/statistics/).  The database contains pitching, hitting, and fielding statistics for Major League Baseball from 1871 through 2015. \n",
    "\n",
    "The questions I would like to answer throughout my analysys are :\n",
    "\n",
    "**Question 1**: What is the relationship between OBP (On-Base-Percentage) and team's success?\n",
    "\n",
    "**Question 2**: What is the relationship between team's payroll and wins in 2015?\n",
    "\n",
    "**Question 3**: What is the relationship between pitcher's ERA (Earned Run Average) and his salary?\n",
    "\n",
    "**Question 4**: What is the relationship between batter's home runs and his salary?\n",
    "\n",
    "**Question 5**: Who is the highest paid player? Who are the top 10s?\n",
    "\n",
    "For this project, I will be using the following tables:\n",
    "\n",
    "- Teams.csv\n",
    "- Batting.csv\n",
    "- Pitching.csv\n",
    "- Salaries.csv\n",
    "- Master.csv\n",
    "\n",
    "Because of the high level of inflation in Baseball salaries, in order to make sure my analysis that is most recent and relevant, I will be only using the data from 2010 to 2015 from each table, except Master table.\n",
    "\n",
    "\n",
    "---------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Primary Moduls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import all necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Extract dataframe year start from 2010\n",
    "\n",
    "teams = pd.read_csv('Teams.csv')\n",
    "teams_since_2010 = teams.loc[teams.yearID >= 2010]\n",
    "\n",
    "pitching = pd.read_csv('Pitching.csv')\n",
    "pitching_since_2010 = pitching.loc[pitching.yearID >= 2010]\n",
    "\n",
    "batting = pd.read_csv('Batting.csv')\n",
    "batting_since_2010 = batting.loc[batting.yearID >= 2010]\n",
    "\n",
    "salaries = pd.read_csv('Salaries.csv')\n",
    "salaries_since_2010 = salaries.loc[salaries.yearID >= 2010]\n",
    "\n",
    "master = pd.read_csv('Master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In order to find the answer to my first question, I will need to do data wrangling for the table I am using first - teams_since_2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if any missing value in the datafrme I will be using. Good\n",
    "\n",
    "teams_since_2010.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yearID              int64\n",
       "lgID               object\n",
       "teamID             object\n",
       "franchID           object\n",
       "divID              object\n",
       "Rank                int64\n",
       "G                   int64\n",
       "Ghome             float64\n",
       "W                   int64\n",
       "L                   int64\n",
       "DivWin             object\n",
       "WCWin              object\n",
       "LgWin              object\n",
       "WSWin              object\n",
       "R                   int64\n",
       "AB                  int64\n",
       "H                   int64\n",
       "2B                  int64\n",
       "3B                  int64\n",
       "HR                  int64\n",
       "BB                  int64\n",
       "SO                float64\n",
       "SB                float64\n",
       "CS                float64\n",
       "HBP               float64\n",
       "SF                float64\n",
       "RA                  int64\n",
       "ER                  int64\n",
       "ERA               float64\n",
       "CG                  int64\n",
       "SHO                 int64\n",
       "SV                  int64\n",
       "IPouts              int64\n",
       "HA                  int64\n",
       "HRA                 int64\n",
       "BBA                 int64\n",
       "SOA                 int64\n",
       "E                   int64\n",
       "DP                float64\n",
       "FP                float64\n",
       "name               object\n",
       "park               object\n",
       "attendance        float64\n",
       "BPF                 int64\n",
       "PPF                 int64\n",
       "teamIDBR           object\n",
       "teamIDlahman45     object\n",
       "teamIDretro        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if data types are correct, so that I can perform vectorized operation\n",
    "\n",
    "teams_since_2010.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate OBP (On-Base-Percentage), add OBP column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Add a column - OBP (On-Base-Percentage)\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "# Caculate the value for OBP\n",
    "\n",
    "teams_since_2010['OBP'] = (teams_since_2010['H'] + teams_since_2010['BB']) / teams_since_2010['AB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>franchID</th>\n",
       "      <th>divID</th>\n",
       "      <th>Rank</th>\n",
       "      <th>G</th>\n",
       "      <th>Ghome</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>...</th>\n",
       "      <th>FP</th>\n",
       "      <th>name</th>\n",
       "      <th>park</th>\n",
       "      <th>attendance</th>\n",
       "      <th>BPF</th>\n",
       "      <th>PPF</th>\n",
       "      <th>teamIDBR</th>\n",
       "      <th>teamIDlahman45</th>\n",
       "      <th>teamIDretro</th>\n",
       "      <th>OBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2625</th>\n",
       "      <td>2010</td>\n",
       "      <td>NL</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>65</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983</td>\n",
       "      <td>Arizona Diamondbacks</td>\n",
       "      <td>Chase Field</td>\n",
       "      <td>2056941.0</td>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>0.357208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>2010</td>\n",
       "      <td>NL</td>\n",
       "      <td>ATL</td>\n",
       "      <td>ATL</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>91</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Atlanta Braves</td>\n",
       "      <td>Turner Field</td>\n",
       "      <td>2510119.0</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>ATL</td>\n",
       "      <td>ATL</td>\n",
       "      <td>ATL</td>\n",
       "      <td>0.374336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627</th>\n",
       "      <td>2010</td>\n",
       "      <td>AL</td>\n",
       "      <td>BAL</td>\n",
       "      <td>BAL</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>66</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>0.982</td>\n",
       "      <td>Baltimore Orioles</td>\n",
       "      <td>Oriole Park at Camden Yards</td>\n",
       "      <td>1733018.0</td>\n",
       "      <td>101</td>\n",
       "      <td>102</td>\n",
       "      <td>BAL</td>\n",
       "      <td>BAL</td>\n",
       "      <td>BAL</td>\n",
       "      <td>0.335614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2628</th>\n",
       "      <td>2010</td>\n",
       "      <td>AL</td>\n",
       "      <td>BOS</td>\n",
       "      <td>BOS</td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>89</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.982</td>\n",
       "      <td>Boston Red Sox</td>\n",
       "      <td>Fenway Park II</td>\n",
       "      <td>3046443.0</td>\n",
       "      <td>106</td>\n",
       "      <td>105</td>\n",
       "      <td>BOS</td>\n",
       "      <td>BOS</td>\n",
       "      <td>BOS</td>\n",
       "      <td>0.371591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2629</th>\n",
       "      <td>2010</td>\n",
       "      <td>AL</td>\n",
       "      <td>CHA</td>\n",
       "      <td>CHW</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>88</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983</td>\n",
       "      <td>Chicago White Sox</td>\n",
       "      <td>U.S. Cellular Field</td>\n",
       "      <td>2194378.0</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>CHW</td>\n",
       "      <td>CHA</td>\n",
       "      <td>CHA</td>\n",
       "      <td>0.352662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      yearID lgID teamID franchID divID  Rank    G  Ghome   W   L    ...     \\\n",
       "2625    2010   NL    ARI      ARI     W     5  162   81.0  65  97    ...      \n",
       "2626    2010   NL    ATL      ATL     E     2  162   81.0  91  71    ...      \n",
       "2627    2010   AL    BAL      BAL     E     5  162   81.0  66  96    ...      \n",
       "2628    2010   AL    BOS      BOS     E     3  162   81.0  89  73    ...      \n",
       "2629    2010   AL    CHA      CHW     C     2  162   81.0  88  74    ...      \n",
       "\n",
       "         FP                  name                         park attendance  \\\n",
       "2625  0.983  Arizona Diamondbacks                  Chase Field  2056941.0   \n",
       "2626  0.980        Atlanta Braves                 Turner Field  2510119.0   \n",
       "2627  0.982     Baltimore Orioles  Oriole Park at Camden Yards  1733018.0   \n",
       "2628  0.982        Boston Red Sox               Fenway Park II  3046443.0   \n",
       "2629  0.983     Chicago White Sox          U.S. Cellular Field  2194378.0   \n",
       "\n",
       "      BPF  PPF  teamIDBR  teamIDlahman45  teamIDretro       OBP  \n",
       "2625  104  105       ARI             ARI          ARI  0.357208  \n",
       "2626   98   97       ATL             ATL          ATL  0.374336  \n",
       "2627  101  102       BAL             BAL          BAL  0.335614  \n",
       "2628  106  105       BOS             BOS          BOS  0.371591  \n",
       "2629  105  105       CHW             CHA          CHA  0.352662  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure the new column has been added\n",
    "\n",
    "teams_since_2010.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the relationship of OBP and teams' wins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of On-Base-Percentage to wins 2010-2015\n",
      "0.5220304596244463\n"
     ]
    }
   ],
   "source": [
    "# Define a function to compute the correlation between the two input variables\n",
    "\n",
    "def correlation(x,y):\n",
    "    return ((x-x.mean())*(y-y.mean())).mean() / (x.std(ddof=0) * y.std(ddof=0))\n",
    "\n",
    "wins=teams_since_2010['W']\n",
    "on_base_percentage = teams_since_2010['OBP']\n",
    "\n",
    "print('Correlation of On-Base-Percentage to wins 2010-2015')\n",
    "print(correlation(on_base_percentage, wins))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Then visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x179e0375320>"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGUCAYAAABDbg9eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiI5KKkQkREREREcvkPwZVeQhhWiAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179e036d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Then plot a regression model plot\n",
    "\n",
    "sns.lmplot('OBP', \"W\", teams_since_2010, scatter_kws={'marker':'o', 'color': 'indianred'}, \n",
    "           line_kws={'linewidth':1,'color':'blue'}, size = 4, aspect = 2)\n",
    "\n",
    "plt.title('Correlation of On-Base-Percentage to Wins from 2010 to 2015: 0.522')\n",
    "plt.xlabel('On-Base-Percentage')\n",
    "plt.ylabel('Number of Wins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above analysis, it appears that OBP(On-Base-Percentage) has a medium correlation with team's winning from 2010 to 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In order to find the answer to my second question, I will need two tables - salaries_since_2010 and teams_since_2010. So let's do data wrangling for these two tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check whether there is NaN value in salaries table\n",
    "\n",
    "salaries_since_2010.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [yearID, teamID, lgID, playerID, salary]\n",
       "Index: []"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check whether there is \"zero\" value in salaries table\n",
    "\n",
    "salaries_since_2010[salaries_since_2010[\"salary\"] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yearID       int64\n",
       "teamID      object\n",
       "lgID        object\n",
       "playerID    object\n",
       "salary       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure the data types are all correct, in order to perform vectorized operation\n",
    "\n",
    "salaries_since_2010.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I will only need column 'W' in teams table, so I want to make sure there is no missing value in this column\n",
    "\n",
    "teams_since_2010['W'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x179e03e0780>"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAGJCAYAAADFSDosAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179e0375b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, I want to take a look of the team payroll's distribution in 2015, so plot a histogram\n",
    "\n",
    "salaries_by_yearID_teamID = salaries_since_2010.groupby(['yearID', 'teamID'])['salary'].sum()/1000000\n",
    "salaries_by_yearID_teamID.loc[2015].hist()\n",
    "\n",
    "plt.title(\"Histogram of Team's Payroll in 2015\")\n",
    "plt.xlabel('Payroll in Millions')\n",
    "plt.ylabel('Number of Teams')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So, the most frequent team payroll is around 110 million to 120 million (approx 11 teams in this range). Two teams pay 200 million to 220 millions to their players. I think they are New York Yankees and Los Angeles Dodgers. Let me double check.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x179e03d7a90>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhoAAAGbCAYAAACCvTnGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0pr/AQJdduYFVMUZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179e0456cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar chart\n",
    "\n",
    "salaries_by_yearID_teamID.loc[2015].sort_values(ascending=False).plot(kind='bar')\n",
    "plt.title(\"Bar Chart for Team's Payroll in 2015\")\n",
    "plt.ylabel(\"Payroll in Millions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yes, they are.\n",
    "\n",
    "### Now, I want to create a function, so that I will be able to get the maximum and minimum player's salary for each year from 2010 to 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max and Min player's salary for 2014\n",
      "(26000000, 500000)\n"
     ]
    }
   ],
   "source": [
    "# Set the year as index\n",
    "salaries_indexed = salaries_since_2010.set_index('yearID')\n",
    "\n",
    "# Create a function, calculate the max and min salary for any year from 2010 to 2015\n",
    "\n",
    "def min_and_max_salary_per_year(salaries):\n",
    "    \n",
    "    max_salary_per_year = salaries.max()  \n",
    "    min_salary_per_year = salaries.min()\n",
    "    \n",
    "    return (max_salary_per_year, min_salary_per_year)\n",
    "\n",
    "print(\"Max and Min player's salary for 2014\")\n",
    "print(min_and_max_salary_per_year(salaries_indexed.loc[2014]['salary'])) # I want to get max and min for 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Good, with this function, I can get any year's Max and Min from 2010 - 2015. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In order to find the correlation between team's payroll and wins, I will need to merge these two tables - teams_since_2010 & salaries_since_2010. Then groupby teamID and yearID, extract total wins and payroll for each team, then get the year I am interested in - 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>franchID</th>\n",
       "      <th>divID</th>\n",
       "      <th>Rank</th>\n",
       "      <th>G</th>\n",
       "      <th>Ghome</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>...</th>\n",
       "      <th>park</th>\n",
       "      <th>attendance</th>\n",
       "      <th>BPF</th>\n",
       "      <th>PPF</th>\n",
       "      <th>teamIDBR</th>\n",
       "      <th>teamIDlahman45</th>\n",
       "      <th>teamIDretro</th>\n",
       "      <th>OBP</th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>NL</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>65</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>Chase Field</td>\n",
       "      <td>2056941.0</td>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>0.357208</td>\n",
       "      <td>abreuto01</td>\n",
       "      <td>407000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>NL</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>65</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>Chase Field</td>\n",
       "      <td>2056941.0</td>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>0.357208</td>\n",
       "      <td>boyerbl01</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>NL</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>65</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>Chase Field</td>\n",
       "      <td>2056941.0</td>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>ARI</td>\n",
       "      <td>0.357208</td>\n",
       "      <td>drewst01</td>\n",
       "      <td>3400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearID lgID teamID franchID divID  Rank    G  Ghome   W   L   ...     \\\n",
       "0    2010   NL    ARI      ARI     W     5  162   81.0  65  97   ...      \n",
       "1    2010   NL    ARI      ARI     W     5  162   81.0  65  97   ...      \n",
       "2    2010   NL    ARI      ARI     W     5  162   81.0  65  97   ...      \n",
       "\n",
       "          park attendance  BPF  PPF  teamIDBR  teamIDlahman45  teamIDretro  \\\n",
       "0  Chase Field  2056941.0  104  105       ARI             ARI          ARI   \n",
       "1  Chase Field  2056941.0  104  105       ARI             ARI          ARI   \n",
       "2  Chase Field  2056941.0  104  105       ARI             ARI          ARI   \n",
       "\n",
       "        OBP   playerID   salary  \n",
       "0  0.357208  abreuto01   407000  \n",
       "1  0.357208  boyerbl01   725000  \n",
       "2  0.357208   drewst01  3400000  \n",
       "\n",
       "[3 rows x 51 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to find the correlation between team's win and payroll, I need to merge team table and salaries table\n",
    "\n",
    "teams_and_salaries = teams_since_2010.merge(salaries_since_2010, on=['yearID', 'teamID', 'lgID'], how='inner')\n",
    "\n",
    "# Double check the two tables has been merged\n",
    "\n",
    "teams_and_salaries.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teamID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARI</th>\n",
       "      <td>2212</td>\n",
       "      <td>56959000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATL</th>\n",
       "      <td>1608</td>\n",
       "      <td>71781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAL</th>\n",
       "      <td>2268</td>\n",
       "      <td>115044833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>2496</td>\n",
       "      <td>181103400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHA</th>\n",
       "      <td>2052</td>\n",
       "      <td>112373700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           W     salary\n",
       "teamID                 \n",
       "ARI     2212   56959000\n",
       "ATL     1608   71781250\n",
       "BAL     2268  115044833\n",
       "BOS     2496  181103400\n",
       "CHA     2052  112373700"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then group yearID and teamID, extract total wins and payrolls for each team \n",
    "\n",
    "teams_and_salaries_grouped = teams_and_salaries.groupby(['yearID', 'teamID']).sum()[['W', 'salary']]\n",
    "\n",
    "# And get the numbers for 2015\n",
    "\n",
    "teams_and_salaries_grouped.loc[2015].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of team's payroll to wins 2015\n",
      "0.3370214688460138\n"
     ]
    }
   ],
   "source": [
    "# Using the function defined earlier to compute the correlation\n",
    "\n",
    "print(\"Correlation of team's payroll to wins 2015\")\n",
    "print(correlation(teams_and_salaries_grouped.loc[2015]['W'], teams_and_salaries_grouped.loc[2015]['salary']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Then visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x179e05391d0>"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGUCAYAAABDbg9eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBERERGRuvx/Wu6uR/zrX28AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179e03793c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply to a regression model plot\n",
    "\n",
    "sns.lmplot('salary', \"W\", teams_and_salaries_grouped.loc[2015], scatter_kws={'marker':'o', 'color': 'indianred'}, \n",
    "           line_kws={'linewidth':1,'color':'blue'}, size = 4, aspect = 2)\n",
    "\n",
    "plt.title(\"Correlation of Team's Payroll to Wins in 2015: 0.337\")\n",
    "plt.xlabel(\"Team's Payroll\")\n",
    "plt.ylabel('Number of Wins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above analysis, it appears that the amount of money spent on team's payroll does have a positve correlation with the winning in 2015. However, this relationship is not strong. There are teams with similar budget on payroll but have a huge difference in number of wins (range from approx 1600 to more than 2700), and there are teams with small budget has won as many times as big budget teams. As a matter of fact, from the above graph, the team that had the most successes is a small budget team. Therefore, from the above analysis, we can't say that payroll is a predictor of a team's success."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In order to answer the third question (correlation of ERA and salary), I will need two tables - pitching_since_2010 &  salaries_since_2010. Let me do data wrangling for pitching table first, then merge these two tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "playerID       0\n",
       "yearID         0\n",
       "stint          0\n",
       "teamID         0\n",
       "lgID           0\n",
       "W              0\n",
       "L              0\n",
       "G              0\n",
       "GS             0\n",
       "CG             0\n",
       "SHO            0\n",
       "SV             0\n",
       "IPouts         1\n",
       "H              0\n",
       "ER             0\n",
       "HR             0\n",
       "BB             0\n",
       "SO             0\n",
       "BAOpp          4\n",
       "ERA            7\n",
       "IBB            0\n",
       "WP             0\n",
       "HBP            1\n",
       "BK             0\n",
       "BFP            0\n",
       "GF             0\n",
       "R              0\n",
       "SH             0\n",
       "SF             0\n",
       "GIDP        3649\n",
       "dtype: int64"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I am interested in \"ERA' column, there are seven missing valuse there, I decided to remove them.\n",
    "\n",
    "pd.isnull(pitching_since_2010).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# I used numpy's isfinite() to take rows where 'ERA' is finite\n",
    "\n",
    "pitching_since_2010 = pitching_since_2010[np.isfinite(pitching_since_2010['ERA'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "playerID       0\n",
       "yearID         0\n",
       "stint          0\n",
       "teamID         0\n",
       "lgID           0\n",
       "W              0\n",
       "L              0\n",
       "G              0\n",
       "GS             0\n",
       "CG             0\n",
       "SHO            0\n",
       "SV             0\n",
       "IPouts         0\n",
       "H              0\n",
       "ER             0\n",
       "HR             0\n",
       "BB             0\n",
       "SO             0\n",
       "BAOpp          1\n",
       "ERA            0\n",
       "IBB            0\n",
       "WP             0\n",
       "HBP            0\n",
       "BK             0\n",
       "BFP            0\n",
       "GF             0\n",
       "R              0\n",
       "SH             0\n",
       "SF             0\n",
       "GIDP        3644\n",
       "dtype: int64"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double check there is no missing value in ERA column anymore\n",
    "\n",
    "pd.isnull(pitching_since_2010).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Merge pitching table and salaries table\n",
    "\n",
    "pitching_and_salaries = pitching_since_2010.merge(salaries_since_2010, on=['playerID', 'yearID', 'teamID', 'lgID'], \n",
    "                                                  how='inner')\n",
    "\n",
    "# Group players and get the average ERA and salary for each player from 2010 to 2015\n",
    "\n",
    "pitching_and_salaries_grouped = pitching_and_salaries.groupby('playerID').mean()[['ERA', 'salary']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After group players and get average ERA and salary for each player from 2010 to 2015, compute the correlation of pitcher's ERA and his salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of pitcher's ERA to salary 2010-2015\n",
      "-0.10720614965930884\n"
     ]
    }
   ],
   "source": [
    "# Apply the function defined earlier and get the correlation of ERA and salary\n",
    "\n",
    "print(\"Correlation of pitcher's ERA to salary 2010-2015\")\n",
    "print(correlation(pitching_and_salaries_grouped['ERA'], pitching_and_salaries_grouped['salary']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x179e05ded30>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGMCAYAAACs64+oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1BSDCkmSJElNMaiQJEmS1JT/Dzf9BVssUS+/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179e0530e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I like to use regression model plot for this kind of correlation analysis\n",
    "\n",
    "sns.lmplot('ERA', \"salary\", pitching_and_salaries_grouped, scatter_kws={'marker':'o', 'color': 'indianred'}, \n",
    "           line_kws={'linewidth':1,'color':'blue'}, size = 4, aspect = 2)\n",
    "\n",
    "plt.title(\"Correlation of Pitcher's ERA to Salary from 2010 to 2015: -0.107\")\n",
    "plt.xlabel('Earned-Run-Average')\n",
    "plt.ylabel(\"Pitcher's Salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The low correlation of ERA and salary shows that ERA is not a indicator of how valuable a pitcher is. Starting pitchers tend to get less salary for the same ERA. In another word, we can't predicate salary from ERA.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To answer my fourth question - correlation of batter's home runs and salary, I will need two tables - battings_since_2010 & salaries_since_2010. First, check whether there are missing values in batting table, then merge these two tables, then groupby ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "playerID    0\n",
       "yearID      0\n",
       "stint       0\n",
       "teamID      0\n",
       "lgID        0\n",
       "G           0\n",
       "AB          0\n",
       "R           0\n",
       "H           0\n",
       "2B          0\n",
       "3B          0\n",
       "HR          0\n",
       "RBI         0\n",
       "SB          0\n",
       "CS          0\n",
       "BB          0\n",
       "SO          0\n",
       "IBB         0\n",
       "HBP         0\n",
       "SH          0\n",
       "SF          0\n",
       "GIDP        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No missing values, perfect\n",
    "\n",
    "pd.isnull(batting_since_2010).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In order to better understand batter's home run with his salary, I decided to categorize batter's salary to three categories - High (higher than 10 million), Medium (between 1 million and 10 million) and Low (lower than 1 million), and add a \"Class\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define a function for the new class column\n",
    "\n",
    "def classifier(row):\n",
    "    if row[\"salary\"] > 10000000:\n",
    "        return 'High'\n",
    "    elif row[\"salary\"] >= 1000000:\n",
    "        return \"Medium\"\n",
    "    else:\n",
    "        return \"Low\"\n",
    "    \n",
    "salaries_since_2010['Class'] = salaries_since_2010.apply(classifier, axis=1) # Add a new column - Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20624</th>\n",
       "      <td>2010</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>abreuto01</td>\n",
       "      <td>407000</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>2010</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>boyerbl01</td>\n",
       "      <td>725000</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>2010</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>drewst01</td>\n",
       "      <td>3400000</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20627</th>\n",
       "      <td>2010</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>gutieju01</td>\n",
       "      <td>411000</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20628</th>\n",
       "      <td>2010</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>harenda01</td>\n",
       "      <td>8250000</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       yearID teamID lgID   playerID   salary   Class\n",
       "20624    2010    ARI   NL  abreuto01   407000     Low\n",
       "20625    2010    ARI   NL  boyerbl01   725000     Low\n",
       "20626    2010    ARI   NL   drewst01  3400000  Medium\n",
       "20627    2010    ARI   NL  gutieju01   411000     Low\n",
       "20628    2010    ARI   NL  harenda01  8250000  Medium"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double check the column has been added\n",
    "\n",
    "salaries_since_2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Merge two tables - batting table and salaries table\n",
    "\n",
    "batting_and_salaries = batting_since_2010.merge(salaries_since_2010, on=['yearID', 'teamID', \"playerID\", 'lgID'], \n",
    "                                                how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>...</th>\n",
       "      <th>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>salary</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>SEA</td>\n",
       "      <td>AL</td>\n",
       "      <td>53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2750000</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>LAA</td>\n",
       "      <td>AL</td>\n",
       "      <td>154</td>\n",
       "      <td>573.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9000000</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abreuto01</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>ARI</td>\n",
       "      <td>NL</td>\n",
       "      <td>81</td>\n",
       "      <td>193.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>407000</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>accarje01</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>TOR</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1080000</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aceveal01</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>NYA</td>\n",
       "      <td>AL</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>435650</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  yearID  stint teamID lgID    G     AB     R      H    2B  \\\n",
       "0  aardsda01    2010      1    SEA   AL   53    0.0   0.0    0.0   0.0   \n",
       "1  abreubo01    2010      1    LAA   AL  154  573.0  88.0  146.0  41.0   \n",
       "2  abreuto01    2010      1    ARI   NL   81  193.0  16.0   45.0  11.0   \n",
       "3  accarje01    2010      1    TOR   AL    5    0.0   0.0    0.0   0.0   \n",
       "4  aceveal01    2010      1    NYA   AL   10    0.0   0.0    0.0   0.0   \n",
       "\n",
       "    ...      CS    BB     SO  IBB  HBP   SH   SF  GIDP   salary   Class  \n",
       "0   ...     0.0   0.0    0.0  0.0  0.0  0.0  0.0   0.0  2750000  Medium  \n",
       "1   ...    10.0  87.0  132.0  3.0  2.0  0.0  5.0  13.0  9000000  Medium  \n",
       "2   ...     1.0   4.0   47.0  0.0  0.0  0.0  4.0   8.0   407000     Low  \n",
       "3   ...     0.0   0.0    0.0  0.0  0.0  0.0  0.0   0.0  1080000  Medium  \n",
       "4   ...     0.0   0.0    0.0  0.0  0.0  0.0  0.0   0.0   435650     Low  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double check two tables have been merged\n",
    "\n",
    "batting_and_salaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Extract the metrics I am interested in - mean HR (home run) and salary for each batter, grouped by salary class\n",
    "\n",
    "batting_and_salaries_grouped = batting_and_salaries.groupby(['Class', 'playerID']).mean()[['HR', 'salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of home run to salary for players who earn more than $10 million per year\n",
      "0.1428293364264446\n",
      " \n",
      "Correlation of home run to salary for players who make $1 million to $10 million per year\n",
      "0.275663782707538\n",
      " \n",
      "Correlation of home run to salary for players who earn below $1 million per year\n",
      "-0.021231869954056466\n"
     ]
    }
   ],
   "source": [
    "# Apply the function defined earlier, calculate the correlation these two variables in different salary class groups\n",
    "\n",
    "print('Correlation of home run to salary for players who earn more than $10 million per year')\n",
    "print(correlation(batting_and_salaries_grouped.loc['High']['HR'], batting_and_salaries_grouped.loc['High']['salary']))\n",
    "print(' ')\n",
    "print('Correlation of home run to salary for players who make $1 million to $10 million per year')\n",
    "print(correlation(batting_and_salaries_grouped.loc['Medium']['HR'], batting_and_salaries_grouped.loc['Medium']['salary']))\n",
    "print(' ')\n",
    "print('Correlation of home run to salary for players who earn below $1 million per year')\n",
    "print(correlation(batting_and_salaries_grouped.loc['Low']['HR'], batting_and_salaries_grouped.loc['Low']['salary']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above batting statistics analysis, we can see that players earning the lowest\tsalaries show the lowest correlations, and a high salary does not necessarily indicate a strong home run performance. However, for players who I categorized as medium salary players, it seems that salary has a relatively higher correlation with home run performance when compare with low salary and high salary players. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, its time to use salaries table and master tabel to get the answers to my 5th question - who is the highest paid, and who are the top 10s. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Extract the top 10 highest paid on average from 2010-2015\n",
    "\n",
    "top_ten = salaries_since_2010.groupby('playerID').mean()['salary'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "playerID\n",
       "rodrial01    2.920000e+07\n",
       "sabatcc01    2.364286e+07\n",
       "wellsve01    2.267634e+07\n",
       "teixema01    2.260417e+07\n",
       "tanakma01    2.200000e+07\n",
       "santajo01    2.164481e+07\n",
       "howarry01    2.150000e+07\n",
       "mauerjo01    2.125000e+07\n",
       "cabremi01    2.100000e+07\n",
       "fieldpr01    2.008333e+07\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is the top 10 salary list\n",
    "top_ten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I want to have a new column in master table that combine Firstname and Lastname in the format of 'Firstname Lastname', then drop the previous 'nameFirst' & 'nameLast' columns. Let's do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# From master table, I want to conbime nameFirst and nameLast columns to one column - fullName, \n",
    "# and the name in the format of 'Firstname Lastname'\n",
    "\n",
    "master['fullName'] = master['nameFirst'] + ' ' + master['nameLast']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# After that, I do not need these two old columns anymore\n",
    "\n",
    "master.drop(['nameFirst', 'nameLast'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There are missing values in player's weight and height columns. Let's fill them by taking the average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are NaN values in weight and height columns\n",
    "\n",
    "master[['weight', 'height']].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill the missing values in weight and height columns by the average \n",
    "\n",
    "master['weight'].fillna(master['weight'].mean(), inplace = True)\n",
    "master['height'].fillna(master['height'].mean(), inplace = True)\n",
    "\n",
    "# Double check the missing values have been filled\n",
    "\n",
    "master[['weight', 'height']].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set playerID to index\n",
    "\n",
    "master.set_index('playerID', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here it is, the top 10 highest paid baseball players on average from 2010 to 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>birthMonth</th>\n",
       "      <th>birthDay</th>\n",
       "      <th>birthCountry</th>\n",
       "      <th>birthState</th>\n",
       "      <th>birthCity</th>\n",
       "      <th>deathYear</th>\n",
       "      <th>deathMonth</th>\n",
       "      <th>deathDay</th>\n",
       "      <th>...</th>\n",
       "      <th>nameGiven</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bats</th>\n",
       "      <th>throws</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>retroID</th>\n",
       "      <th>bbrefID</th>\n",
       "      <th>fullName</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rodrial01</th>\n",
       "      <td>2.920000e+07</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Alexander Enmanuel</td>\n",
       "      <td>225.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1994-07-08</td>\n",
       "      <td>2015-10-04</td>\n",
       "      <td>rodra001</td>\n",
       "      <td>rodrial01</td>\n",
       "      <td>Alex Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sabatcc01</th>\n",
       "      <td>2.364286e+07</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>Vallejo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Carsten Charles</td>\n",
       "      <td>285.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2001-04-08</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>sabac001</td>\n",
       "      <td>sabatc.01</td>\n",
       "      <td>CC Sabathia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wellsve01</th>\n",
       "      <td>2.267634e+07</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>LA</td>\n",
       "      <td>Shreveport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Vernon M.</td>\n",
       "      <td>230.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1999-08-30</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>wellv001</td>\n",
       "      <td>wellsve01</td>\n",
       "      <td>Vernon Wells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teixema01</th>\n",
       "      <td>2.260417e+07</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>MD</td>\n",
       "      <td>Annapolis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Mark Charles</td>\n",
       "      <td>225.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>B</td>\n",
       "      <td>R</td>\n",
       "      <td>2003-04-01</td>\n",
       "      <td>2015-08-26</td>\n",
       "      <td>teixm001</td>\n",
       "      <td>teixema01</td>\n",
       "      <td>Mark Teixeira</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tanakma01</th>\n",
       "      <td>2.200000e+07</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Hyogo</td>\n",
       "      <td>Itami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Masahiro</td>\n",
       "      <td>210.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>tanam001</td>\n",
       "      <td>tanakma01</td>\n",
       "      <td>Masahiro Tanaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>santajo01</th>\n",
       "      <td>2.164481e+07</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Merida</td>\n",
       "      <td>Tovar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Johan Alexander</td>\n",
       "      <td>210.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2000-04-03</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>santj003</td>\n",
       "      <td>santajo02</td>\n",
       "      <td>Johan Santana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>howarry01</th>\n",
       "      <td>2.150000e+07</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>MO</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Ryan James</td>\n",
       "      <td>250.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2004-09-01</td>\n",
       "      <td>2015-09-14</td>\n",
       "      <td>howar001</td>\n",
       "      <td>howarry01</td>\n",
       "      <td>Ryan Howard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mauerjo01</th>\n",
       "      <td>2.125000e+07</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>MN</td>\n",
       "      <td>St. Paul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Joseph Patrick</td>\n",
       "      <td>225.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>L</td>\n",
       "      <td>R</td>\n",
       "      <td>2004-04-05</td>\n",
       "      <td>2015-10-03</td>\n",
       "      <td>mauej001</td>\n",
       "      <td>mauerjo01</td>\n",
       "      <td>Joe Mauer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cabremi01</th>\n",
       "      <td>2.100000e+07</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Aragua</td>\n",
       "      <td>Maracay</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Jose Miguel</td>\n",
       "      <td>240.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2003-06-20</td>\n",
       "      <td>2015-10-03</td>\n",
       "      <td>cabrm001</td>\n",
       "      <td>cabremi01</td>\n",
       "      <td>Miguel Cabrera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fieldpr01</th>\n",
       "      <td>2.008333e+07</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Prince Semien</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>L</td>\n",
       "      <td>R</td>\n",
       "      <td>2005-06-13</td>\n",
       "      <td>2015-10-04</td>\n",
       "      <td>fielp001</td>\n",
       "      <td>fieldpr01</td>\n",
       "      <td>Prince Fielder</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 salary  birthYear  birthMonth  birthDay birthCountry  \\\n",
       "playerID                                                                \n",
       "rodrial01  2.920000e+07     1975.0         7.0      27.0          USA   \n",
       "sabatcc01  2.364286e+07     1980.0         7.0      21.0          USA   \n",
       "wellsve01  2.267634e+07     1978.0        12.0       8.0          USA   \n",
       "teixema01  2.260417e+07     1980.0         4.0      11.0          USA   \n",
       "tanakma01  2.200000e+07     1988.0        11.0       1.0        Japan   \n",
       "santajo01  2.164481e+07     1979.0         3.0      13.0    Venezuela   \n",
       "howarry01  2.150000e+07     1979.0        11.0      19.0          USA   \n",
       "mauerjo01  2.125000e+07     1983.0         4.0      19.0          USA   \n",
       "cabremi01  2.100000e+07     1983.0         4.0      18.0    Venezuela   \n",
       "fieldpr01  2.008333e+07     1984.0         5.0       9.0          USA   \n",
       "\n",
       "          birthState   birthCity  deathYear  deathMonth  deathDay  \\\n",
       "playerID                                                            \n",
       "rodrial01         NY    New York        NaN         NaN       NaN   \n",
       "sabatcc01         CA     Vallejo        NaN         NaN       NaN   \n",
       "wellsve01         LA  Shreveport        NaN         NaN       NaN   \n",
       "teixema01         MD   Annapolis        NaN         NaN       NaN   \n",
       "tanakma01      Hyogo       Itami        NaN         NaN       NaN   \n",
       "santajo01     Merida       Tovar        NaN         NaN       NaN   \n",
       "howarry01         MO   St. Louis        NaN         NaN       NaN   \n",
       "mauerjo01         MN    St. Paul        NaN         NaN       NaN   \n",
       "cabremi01     Aragua     Maracay        NaN         NaN       NaN   \n",
       "fieldpr01         CA     Ontario        NaN         NaN       NaN   \n",
       "\n",
       "                ...                  nameGiven weight height bats  throws  \\\n",
       "playerID        ...                                                         \n",
       "rodrial01       ...         Alexander Enmanuel  225.0   75.0    R       R   \n",
       "sabatcc01       ...            Carsten Charles  285.0   79.0    L       L   \n",
       "wellsve01       ...                  Vernon M.  230.0   73.0    R       R   \n",
       "teixema01       ...               Mark Charles  225.0   75.0    B       R   \n",
       "tanakma01       ...                   Masahiro  210.0   74.0    R       R   \n",
       "santajo01       ...            Johan Alexander  210.0   72.0    L       L   \n",
       "howarry01       ...                 Ryan James  250.0   76.0    L       L   \n",
       "mauerjo01       ...             Joseph Patrick  225.0   77.0    L       R   \n",
       "cabremi01       ...                Jose Miguel  240.0   76.0    R       R   \n",
       "fieldpr01       ...              Prince Semien  275.0   71.0    L       R   \n",
       "\n",
       "                debut   finalGame   retroID    bbrefID         fullName  \n",
       "playerID                                                                 \n",
       "rodrial01  1994-07-08  2015-10-04  rodra001  rodrial01   Alex Rodriguez  \n",
       "sabatcc01  2001-04-08  2015-10-01  sabac001  sabatc.01      CC Sabathia  \n",
       "wellsve01  1999-08-30  2013-09-29  wellv001  wellsve01     Vernon Wells  \n",
       "teixema01  2003-04-01  2015-08-26  teixm001  teixema01    Mark Teixeira  \n",
       "tanakma01  2014-04-04  2015-09-30  tanam001  tanakma01  Masahiro Tanaka  \n",
       "santajo01  2000-04-03  2012-08-17  santj003  santajo02    Johan Santana  \n",
       "howarry01  2004-09-01  2015-09-14  howar001  howarry01      Ryan Howard  \n",
       "mauerjo01  2004-04-05  2015-10-03  mauej001  mauerjo01        Joe Mauer  \n",
       "cabremi01  2003-06-20  2015-10-03  cabrm001  cabremi01   Miguel Cabrera  \n",
       "fieldpr01  2005-06-13  2015-10-04  fielp001  fieldpr01   Prince Fielder  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate a Series and DataFrame, return a DataFrame with all top_10 highest paid players informaton.\n",
    "\n",
    "pd.concat([top_ten, master.loc[top_ten.index]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "### In recent years, it has been the common belief that teams with the highest overall salaries have the highest winning percentage. From the above analysis, payroll is not the most significant factor, at least not for the 2015 season. There are countless factors that contribute to a winning team from owners to managers to players to fan attendance. To explore the best model to predict winning, rigorous hypothesis testings are needed (i.e. one sample hypothesis testing for salary to winning correlation).\n",
    "\n",
    "### In addition, for individual players,  the correlation between performance and salary in baseball has been studied extensively. However, there is no single factor that can predict a player's pay. To proper understand the relationship between salary and on-field performance, not only we need to perform rigorous hypothesis testings on many variables, but also we need to understand how wage are determined in baseball. Those are the limitations for this analysis. \n",
    "\n",
    "### For the future research, I am interested in exploring the best way to field a team, and the best way to allocate resources to secure the most possible wins.  \n",
    "\n",
    "### Anyway, Spring training is five weeks away, Jose Bautista doesn't have a deal and Toronto Blue Jays still needs another batter. Let's see what's going to happen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "#### Adam Houser, [Which Baseball Statistic Is the Most Important When Determining Team Success?](https://www.iwu.edu/economics/PPE13/houser.pdf) \n",
    "#### Matt Butts, Miranda Edwards, [Payroll and Wins in Major League Baseball](https://courses.cs.washington.edu/courses/cse140/13wi/projects/mirae-report.pdf)\n",
    "#### Mikhail Averbukh, Baseball Pay and Performance MIS 480/580, 2015\n",
    "#### [Wikipedia](https://en.wikipedia.org/wiki/Baseball)\n",
    "#### [Toronto Blue Jays](http://m.bluejays.mlb.com/news/article/213066364/blue-jays-are-best-fit-for-jose-bautista/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook created by: [Susan Li](https://www.linkedin.com/in/susanli/)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}