{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# LFC Data Analysis: From Rafa to Rodgers\n",
    "#### Lies, Damn Lies and Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Terry's blog [LFC: From Rafa To Rodgers](http://terrydolan.blogspot.co.uk/2015/05/lfc-from-rafa-to-rogers.html) for a discussion of of the data generated by this analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook analyses Liverpool FC's premier league performance from Rafael Benitez to Brendan Rodgers, a period of 11 years covering season 2004-5 to 2014-15. The analysis uses [IPython Notebook](http://ipython.org/notebook.html), [python](https://www.python.org/), [pandas](http://pandas.pydata.org/) and [matplotlib](http://matplotlib.org/) to analyse the data sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<! left align the change log table in next cell >\n",
       "<style>\n",
       "table {float:left}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<! left align the change log table in next cell >\n",
    "<style>\n",
    "table {float:left}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook Change Log\n",
    "\n",
    "| Date          | Change Description |\n",
    "| :------------ | :----------------- |\n",
    "| 25th May 2015 | Initial baseline |\n",
    "| 28th May 2015 | Fixed typos in text and graphs |\n",
    "| 5th June 2015 | Fixed draw and loss % on LFC points per game graph |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the modules needed for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sys \n",
    "from datetime import datetime\n",
    "from __future__ import division\n",
    "\n",
    "# enable inline plotting\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print version numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version: 2.7.8 |Anaconda 2.2.0 (64-bit)| (default, Jul  2 2014, 15:12:11) [MSC v.1500 64 bit (AMD64)]\n",
      "pandas version: 0.15.2\n",
      "matplotlib version: 1.4.3\n",
      "numpy version: 1.9.2\n"
     ]
    }
   ],
   "source": [
    "print 'python version: {}'.format(sys.version)\n",
    "print 'pandas version: {}'.format(pd.__version__)\n",
    "print 'matplotlib version: {}'.format(mpl.__version__)\n",
    "print 'numpy version: {}'.format(np.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data files are located in the *data* sub-directory.\n",
    "\n",
    "Match data\n",
    "\n",
    "The E0_&lt;season&gt;.csv files were downloaded from [english premiership stats](http://www.football-data.co.uk/englandm.php). These files contain the premier league match data from season 2004-5 to season 2014-15. The csv structure is described in the [notes](http://www.football-data.co.uk/notes.txt).\n",
    "\n",
    "LFC top scorers data\n",
    "\n",
    "The LFC_PL_scorers_2004-05_2014-15.csv file was constructed from data held at the excellent [lfchistory.net](http://www.lfchistory.net/). This file contains the LFC top scorers in the premier league from 2004-5 to 2014-15.\n",
    "\n",
    "LFC top scorers appearance data\n",
    "\n",
    "The LFC_PL_top_apps.csv file was also constructed from data held at [lfchistory.net](http://www.lfchistory.net/). This file contains the premier league appearances of the LFC top 5 scorers from 2004-5 to 2014-15. \n",
    "\n",
    "Rich list data\n",
    "\n",
    "The Rich_list_2015.csv file was extracted from [Forbes' list of the most valuable football clubs on wikipedia](http://en.wikipedia.org/wiki/Forbes%27_list_of_the_most_valuable_football_clubs). This file contains the list of the 10 richest clubs in the world, as ranked by Forbes magazine.\n",
    "\n",
    "LFC champion hotshots\n",
    "\n",
    "The LFC_champ_hotshots.csv file was also constructed from data held at [lfchistory.net](http://www.lfchistory.net/). This file contains the LFC top scoring partnerships for the 18 title winning seasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Read the match data into a pandas dataframe for each season. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define list of seasons to analyse, corresponding to the csv files\n",
    "SEASON_LIST = ['2004-05', '2005-06', '2006-07', '2007-08', '2008-09', '2009-10', \n",
    "               '2010-11', '2011-12', '2012-13', '2013-14', '2014-15'] \n",
    "MOST_RECENT_SEASON = SEASON_LIST[-1]\n",
    "\n",
    "# defines the selected columns from the csv file to keep \n",
    "COLUMNS_FILTER = ['Date', 'HomeTeam','AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HS', \n",
    "                  'AS', 'HST', 'AST']\n",
    "\n",
    "# define dictionary to hold premier league dataframes\n",
    "# key is the season and value is the dataframe\n",
    "df_dict = {} \n",
    "\n",
    "# read the selected data in each csv into the dictionary\n",
    "for season in SEASON_LIST:\n",
    "    df_dict[season] = pd.read_csv('data/E0_{}.csv'.format(season), usecols=COLUMNS_FILTER)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of rows and columns for each season's dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season=2004-05, shape=(380, 10)\n",
      "season=2005-06, shape=(380, 10)\n",
      "season=2006-07, shape=(380, 10)\n",
      "season=2007-08, shape=(380, 10)\n",
      "season=2008-09, shape=(380, 10)\n",
      "season=2009-10, shape=(380, 10)\n",
      "season=2010-11, shape=(380, 10)\n",
      "season=2011-12, shape=(380, 10)\n",
      "season=2012-13, shape=(380, 10)\n",
      "season=2013-14, shape=(380, 10)\n",
      "season=2014-15, shape=(380, 10)\n"
     ]
    }
   ],
   "source": [
    "for season, df in sorted(df_dict.items()):\n",
    "    print 'season={}, shape={}'.format(season, df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11 seasons, each with 380 games per season - as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the data - display the final 5 matches from the most recent season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season: 2014-15\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HS</th>\n",
       "      <th>AS</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td> 24/05/15</td>\n",
       "      <td>      Hull</td>\n",
       "      <td>  Man United</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> D</td>\n",
       "      <td> 16</td>\n",
       "      <td>  7</td>\n",
       "      <td> 6</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td> 24/05/15</td>\n",
       "      <td> Leicester</td>\n",
       "      <td>         QPR</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1</td>\n",
       "      <td> H</td>\n",
       "      <td> 22</td>\n",
       "      <td> 18</td>\n",
       "      <td> 7</td>\n",
       "      <td> 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td> 24/05/15</td>\n",
       "      <td>  Man City</td>\n",
       "      <td> Southampton</td>\n",
       "      <td> 2</td>\n",
       "      <td> 0</td>\n",
       "      <td> H</td>\n",
       "      <td> 15</td>\n",
       "      <td> 13</td>\n",
       "      <td> 6</td>\n",
       "      <td> 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td> 24/05/15</td>\n",
       "      <td> Newcastle</td>\n",
       "      <td>    West Ham</td>\n",
       "      <td> 2</td>\n",
       "      <td> 0</td>\n",
       "      <td> H</td>\n",
       "      <td> 17</td>\n",
       "      <td>  4</td>\n",
       "      <td> 4</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td> 24/05/15</td>\n",
       "      <td>     Stoke</td>\n",
       "      <td>   Liverpool</td>\n",
       "      <td> 6</td>\n",
       "      <td> 1</td>\n",
       "      <td> H</td>\n",
       "      <td> 15</td>\n",
       "      <td> 13</td>\n",
       "      <td> 9</td>\n",
       "      <td> 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   HomeTeam     AwayTeam  FTHG  FTAG FTR  HS  AS  HST  AST\n",
       "375  24/05/15       Hull   Man United     0     0   D  16   7    6    1\n",
       "376  24/05/15  Leicester          QPR     5     1   H  22  18    7    2\n",
       "377  24/05/15   Man City  Southampton     2     0   H  15  13    6    4\n",
       "378  24/05/15  Newcastle     West Ham     2     0   H  17   4    4    1\n",
       "379  24/05/15      Stoke    Liverpool     6     1   H  15  13    9    4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'season: {}'.format(MOST_RECENT_SEASON)\n",
    "df_dict[MOST_RECENT_SEASON].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stoke 6 (SIX!), what a debacle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Read the LFC scorers data into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dflfc_scorers = pd.read_csv('data/LFC_PL_scorers_2004-05_2014-15.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the data - show the top 3 scorers for the most recent season. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Season            Player  LeagueGoals\n",
      "143  2014-15    Steven Gerrard            9\n",
      "144  2014-15   Raheem Sterling            7\n",
      "145  2014-15  Jordan Henderson            6\n"
     ]
    }
   ],
   "source": [
    "print dflfc_scorers[dflfc_scorers['Season'] == MOST_RECENT_SEASON].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "####Read the LFC top scorer appearances data into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dflfc_apps = pd.read_csv('data/LFC_PL_top_apps.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the data - show Suarez' appearances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Player</th>\n",
       "      <th>Appearances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td> 2010-2011</td>\n",
       "      <td> Luis Suarez</td>\n",
       "      <td> 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td> 2011-2012</td>\n",
       "      <td> Luis Suarez</td>\n",
       "      <td> 31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td> 2012-2013</td>\n",
       "      <td> Luis Suarez</td>\n",
       "      <td> 33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td> 2013-2014</td>\n",
       "      <td> Luis Suarez</td>\n",
       "      <td> 33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Seasons       Player  Appearances\n",
       "13  2010-2011  Luis Suarez           13\n",
       "14  2011-2012  Luis Suarez           31\n",
       "15  2012-2013  Luis Suarez           33\n",
       "16  2013-2014  Luis Suarez           33"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_apps[dflfc_apps['Player'] == 'Luis Suarez']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Read the Forbes rich list into a dataframe.\n",
    "Note that the data is restricted to the top 10 in 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rich_list_2015 = pd.read_csv('data/Rich_list_2015.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the data - show the top 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Country</th>\n",
       "      <th>Value($M)</th>\n",
       "      <th>Revenue($M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td>  Real Madrid</td>\n",
       "      <td>   Spain</td>\n",
       "      <td> 3260</td>\n",
       "      <td> 746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2</td>\n",
       "      <td> FC Barcelona</td>\n",
       "      <td>   Spain</td>\n",
       "      <td> 3160</td>\n",
       "      <td> 657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 3</td>\n",
       "      <td>   Man United</td>\n",
       "      <td> England</td>\n",
       "      <td> 3100</td>\n",
       "      <td> 703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank          Team  Country  Value($M)  Revenue($M)\n",
       "0     1   Real Madrid    Spain       3260          746\n",
       "1     2  FC Barcelona    Spain       3160          657\n",
       "2     3    Man United  England       3100          703"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rich_list_2015.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Read the LFC title winning scoring partnerships into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dflfc_champ_hotshots = pd.read_csv('data/LFC_champ_hotshots.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's show the hotshots from season 1900-01, Liverpool's first title winning season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1900-01</td>\n",
       "      <td> Sam Raybould</td>\n",
       "      <td> 17</td>\n",
       "      <td> Tom Watson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1900-01</td>\n",
       "      <td>     Jack Cox</td>\n",
       "      <td> 10</td>\n",
       "      <td> Tom Watson</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Season        Player  LeagueGoals     Manager\n",
       "0  1900-01  Sam Raybould           17  Tom Watson\n",
       "1  1900-01      Jack Cox           10  Tom Watson"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_champ_hotshots[dflfc_champ_hotshots['Season'] == '1900-01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Munge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's munge the dataframes to provide a reworked view for the LFC match results only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def assign_points(row):\n",
    "    \"\"\"Return the points associated with given win, draw or loss result.\"\"\"\n",
    "    \n",
    "    POINTS_MAPPER = {'W': 3, 'D': 1, 'L': 0}\n",
    "    return POINTS_MAPPER[row['R']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def prem_munge(df, team='Liverpool'):\n",
    "    \"\"\"Return new dataframe for given team.\n",
    "    \n",
    "        input dataframe columns: see http://www.football-data.co.uk/englandm.php\n",
    "        with \n",
    "        \n",
    "        output dataframe columns:\n",
    "        'Date', 'Opponent', 'R', 'HA', GF', 'GA', SF', 'SA', 'SFT', 'SAT', 'PTS', 'CUMPTS'\n",
    "        \n",
    "        Date is match Date (datetime), Opponent is opponent (str)\n",
    "        HA is Home or Away (str: 'H' or 'A')\n",
    "        R is Result (str: 'W' or 'D' or 'L')\n",
    "        GF is Goals For (int), GA is Goals Against (int)\n",
    "        SF is Shots For (int), SA is Shots Against (int)\n",
    "        SFT is Shots For on Target (int), SAT is Shots Against on Target (int)       \n",
    "        PTS is PoinTS (int), CUMPTS is CUMlative PoinTS (int)\n",
    "    \"\"\"\n",
    " \n",
    "    DATE_FORMAT = '%d/%m/%y' # input dataframe's Date column format\n",
    "    ALT_DATE_FORMAT = '%d/%m/%Y' # used for 2004-5 dataframe\n",
    "    \n",
    "    # define column order for the output dataframe\n",
    "    COLUMN_ORDER = ['Date', 'Opponent', 'HA', 'R', 'GF', 'GA', \n",
    "                    'SF', 'SA', 'SFT', 'SAT', 'PTS', 'CUMPTS']\n",
    "\n",
    "    # filter dataframe for home team \n",
    "    df_home = df[(df['HomeTeam'] == team)].copy()\n",
    "    df_home['HA'] = 'Home'\n",
    "    df_home.drop('HomeTeam', inplace=True, axis=1)\n",
    "    \n",
    "    ########################\n",
    "    # rebuild home dataframe \n",
    "    df_home.rename(columns={'AwayTeam': 'Opponent', 'FTHG': 'GF', 'FTAG': 'GA', 'FTR': 'R',\n",
    "                            'HS': 'SF', 'AS': 'SA', 'HST': 'SFT', 'AST': 'SAT'}, inplace=True)\n",
    "\n",
    "    # rework home result and assign points\n",
    "    \n",
    "    # define mapping dictionary, Home win is win for home team, Away win is loss\n",
    "    HOME_MAPPER = {'H': 'W', 'D': 'D', 'A': 'L'} \n",
    "    f_home = lambda x: HOME_MAPPER[x]\n",
    "    df_home['R'] = df_home['R'].map(f_home)\n",
    "    df_home['PTS'] = df_home.apply(assign_points, axis=1) \n",
    "\n",
    "    # filter dataframe for away team \n",
    "    df_away = df[(df['AwayTeam'] == team)].copy()\n",
    "    df_away['HA'] = 'Away'\n",
    " \n",
    "    ########################\n",
    "    # rebuild away dataframe \n",
    "    df_away.rename(columns={'HomeTeam': 'Opponent', 'FTHG': 'GA', 'FTAG': 'GF', 'FTR': 'R',\n",
    "                           'HS': 'SA', 'AS': 'SF', 'HST': 'SAT', 'AST': 'SFT'}, inplace=True)\n",
    "\n",
    "    # rework away result and assign points\n",
    "    \n",
    "    # define mapping dictionary, Away win is win for away team, Home win is loss\n",
    "    AWAY_MAPPER = {'H': 'L', 'D': 'D', 'A': 'W'} \n",
    "    f_away = lambda x: AWAY_MAPPER[x]\n",
    "    df_away['R'] = df_away['R'].map(f_away)\n",
    "    df_away['PTS'] = df_away.apply(assign_points, axis=1)   \n",
    "    df_away.drop('AwayTeam', inplace=True, axis=1)\n",
    "    \n",
    "    \n",
    "    ########################\n",
    "    # create new dataframe by combining home and away dataframes\n",
    "    df_new = pd.concat([df_home, df_away])    \n",
    "\n",
    "    # convert Date column to datetime (uses numpy datetime64) and sort by date\n",
    "    try:\n",
    "        df_new['Date'] = pd.to_datetime(df_new['Date'], format=DATE_FORMAT)\n",
    "    except ValueError:\n",
    "        df_new['Date'] = pd.to_datetime(df_new['Date'], format=ALT_DATE_FORMAT)            \n",
    "    df_new.sort(columns='Date', inplace=True)\n",
    "    \n",
    "    # add new CUMPTS column of cumulative points\n",
    "    df_new['CUMPTS'] = df_new['PTS'].cumsum()\n",
    "    \n",
    "    # reset index to give match number, starting at 0\n",
    "    df_new.reset_index(inplace=True, drop=True)\n",
    "    \n",
    "    # re-work columns to match required column order\n",
    "    df_new = df_new[COLUMN_ORDER] \n",
    "\n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the munge with the most recent LFC season - display the first 5 matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>HA</th>\n",
       "      <th>R</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>SF</th>\n",
       "      <th>SA</th>\n",
       "      <th>SFT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>PTS</th>\n",
       "      <th>CUMPTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-08-17</td>\n",
       "      <td> Southampton</td>\n",
       "      <td> Home</td>\n",
       "      <td> W</td>\n",
       "      <td> 2</td>\n",
       "      <td> 1</td>\n",
       "      <td> 12</td>\n",
       "      <td> 12</td>\n",
       "      <td> 5</td>\n",
       "      <td> 6</td>\n",
       "      <td> 3</td>\n",
       "      <td> 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-08-25</td>\n",
       "      <td>    Man City</td>\n",
       "      <td> Away</td>\n",
       "      <td> L</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 11</td>\n",
       "      <td>  9</td>\n",
       "      <td> 3</td>\n",
       "      <td> 4</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>   Tottenham</td>\n",
       "      <td> Away</td>\n",
       "      <td> W</td>\n",
       "      <td> 3</td>\n",
       "      <td> 0</td>\n",
       "      <td> 17</td>\n",
       "      <td>  7</td>\n",
       "      <td> 8</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-13</td>\n",
       "      <td> Aston Villa</td>\n",
       "      <td> Home</td>\n",
       "      <td> L</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 18</td>\n",
       "      <td>  5</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>    West Ham</td>\n",
       "      <td> Away</td>\n",
       "      <td> L</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 11</td>\n",
       "      <td> 13</td>\n",
       "      <td> 5</td>\n",
       "      <td> 7</td>\n",
       "      <td> 0</td>\n",
       "      <td> 6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     Opponent    HA  R  GF  GA  SF  SA  SFT  SAT  PTS  CUMPTS\n",
       "0 2014-08-17  Southampton  Home  W   2   1  12  12    5    6    3       3\n",
       "1 2014-08-25     Man City  Away  L   1   3  11   9    3    4    0       3\n",
       "2 2014-08-31    Tottenham  Away  W   3   0  17   7    8    1    3       6\n",
       "3 2014-09-13  Aston Villa  Home  L   0   1  18   5    1    1    0       6\n",
       "4 2014-09-20     West Ham  Away  L   1   3  11  13    5    7    0       6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = prem_munge(df_dict[MOST_RECENT_SEASON])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create dictionary to hold the new LFC dataframes, with key of season and value of dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dflfc_dict = {}\n",
    "for season, df in sorted(df_dict.items()):\n",
    "    dflfc_dict[season] = prem_munge(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the last 5 rows of munged dataframe for the most recent season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>HA</th>\n",
       "      <th>R</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>SF</th>\n",
       "      <th>SA</th>\n",
       "      <th>SFT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>PTS</th>\n",
       "      <th>CUMPTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2015-04-28</td>\n",
       "      <td>           Hull</td>\n",
       "      <td> Away</td>\n",
       "      <td> L</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 12</td>\n",
       "      <td>  7</td>\n",
       "      <td> 9</td>\n",
       "      <td> 4</td>\n",
       "      <td> 0</td>\n",
       "      <td> 58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2015-05-02</td>\n",
       "      <td>            QPR</td>\n",
       "      <td> Home</td>\n",
       "      <td> W</td>\n",
       "      <td> 2</td>\n",
       "      <td> 1</td>\n",
       "      <td> 25</td>\n",
       "      <td> 10</td>\n",
       "      <td> 8</td>\n",
       "      <td> 2</td>\n",
       "      <td> 3</td>\n",
       "      <td> 61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2015-05-10</td>\n",
       "      <td>        Chelsea</td>\n",
       "      <td> Away</td>\n",
       "      <td> D</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 15</td>\n",
       "      <td>  8</td>\n",
       "      <td> 4</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2015-05-16</td>\n",
       "      <td> Crystal Palace</td>\n",
       "      <td> Home</td>\n",
       "      <td> L</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 18</td>\n",
       "      <td> 15</td>\n",
       "      <td> 3</td>\n",
       "      <td> 6</td>\n",
       "      <td> 0</td>\n",
       "      <td> 62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2015-05-24</td>\n",
       "      <td>          Stoke</td>\n",
       "      <td> Away</td>\n",
       "      <td> L</td>\n",
       "      <td> 1</td>\n",
       "      <td> 6</td>\n",
       "      <td> 13</td>\n",
       "      <td> 15</td>\n",
       "      <td> 4</td>\n",
       "      <td> 9</td>\n",
       "      <td> 0</td>\n",
       "      <td> 62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Opponent    HA  R  GF  GA  SF  SA  SFT  SAT  PTS  CUMPTS\n",
       "33 2015-04-28            Hull  Away  L   0   1  12   7    9    4    0      58\n",
       "34 2015-05-02             QPR  Home  W   2   1  25  10    8    2    3      61\n",
       "35 2015-05-10         Chelsea  Away  D   1   1  15   8    4    3    1      62\n",
       "36 2015-05-16  Crystal Palace  Home  L   1   3  18  15    3    6    0      62\n",
       "37 2015-05-24           Stoke  Away  L   1   6  13  15    4    9    0      62"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_dict[MOST_RECENT_SEASON].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season=2004-05, shape=(38, 12)\n",
      "season=2005-06, shape=(38, 12)\n",
      "season=2006-07, shape=(38, 12)\n",
      "season=2007-08, shape=(38, 12)\n",
      "season=2008-09, shape=(38, 12)\n",
      "season=2009-10, shape=(38, 12)\n",
      "season=2010-11, shape=(38, 12)\n",
      "season=2011-12, shape=(38, 12)\n",
      "season=2012-13, shape=(38, 12)\n",
      "season=2013-14, shape=(38, 12)\n",
      "season=2014-15, shape=(38, 12)\n"
     ]
    }
   ],
   "source": [
    "for season, df in sorted(dflfc_dict.items()):\n",
    "    print 'season={}, shape={}'.format(season, df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected each season's dataframe contains 38 matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total LFC matches analysed: 418\n"
     ]
    }
   ],
   "source": [
    "print 'total LFC matches analysed: {}'.format(sum(dflfc_dict[season].shape[0] for season in SEASON_LIST))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now analyse the data... Think of a question and produce the answer!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare key stats (totals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Total</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>SF</th>\n",
       "      <th>SA</th>\n",
       "      <th>SFT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>PTS</th>\n",
       "      <th>GD</th>\n",
       "      <th>SPG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</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>2004-05</th>\n",
       "      <td>  52</td>\n",
       "      <td> 41</td>\n",
       "      <td> 490</td>\n",
       "      <td> 285</td>\n",
       "      <td> 241</td>\n",
       "      <td> 156</td>\n",
       "      <td> 58</td>\n",
       "      <td> 11</td>\n",
       "      <td>  9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06</th>\n",
       "      <td>  57</td>\n",
       "      <td> 25</td>\n",
       "      <td> 563</td>\n",
       "      <td> 245</td>\n",
       "      <td> 283</td>\n",
       "      <td> 126</td>\n",
       "      <td> 82</td>\n",
       "      <td> 32</td>\n",
       "      <td>  9.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07</th>\n",
       "      <td>  57</td>\n",
       "      <td> 27</td>\n",
       "      <td> 592</td>\n",
       "      <td> 265</td>\n",
       "      <td> 286</td>\n",
       "      <td> 133</td>\n",
       "      <td> 68</td>\n",
       "      <td> 30</td>\n",
       "      <td> 10.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08</th>\n",
       "      <td>  67</td>\n",
       "      <td> 28</td>\n",
       "      <td> 607</td>\n",
       "      <td> 278</td>\n",
       "      <td> 320</td>\n",
       "      <td> 133</td>\n",
       "      <td> 76</td>\n",
       "      <td> 39</td>\n",
       "      <td>  9.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09</th>\n",
       "      <td>  77</td>\n",
       "      <td> 27</td>\n",
       "      <td> 609</td>\n",
       "      <td> 266</td>\n",
       "      <td> 311</td>\n",
       "      <td> 142</td>\n",
       "      <td> 86</td>\n",
       "      <td> 50</td>\n",
       "      <td>  7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10</th>\n",
       "      <td>  61</td>\n",
       "      <td> 35</td>\n",
       "      <td> 519</td>\n",
       "      <td> 328</td>\n",
       "      <td> 288</td>\n",
       "      <td> 180</td>\n",
       "      <td> 63</td>\n",
       "      <td> 26</td>\n",
       "      <td>  8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11</th>\n",
       "      <td>  59</td>\n",
       "      <td> 44</td>\n",
       "      <td> 475</td>\n",
       "      <td> 409</td>\n",
       "      <td> 268</td>\n",
       "      <td> 209</td>\n",
       "      <td> 58</td>\n",
       "      <td> 15</td>\n",
       "      <td>  8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>  47</td>\n",
       "      <td> 40</td>\n",
       "      <td> 612</td>\n",
       "      <td> 376</td>\n",
       "      <td> 330</td>\n",
       "      <td> 199</td>\n",
       "      <td> 52</td>\n",
       "      <td>  7</td>\n",
       "      <td> 13.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-13</th>\n",
       "      <td>  71</td>\n",
       "      <td> 43</td>\n",
       "      <td> 669</td>\n",
       "      <td> 383</td>\n",
       "      <td> 385</td>\n",
       "      <td> 217</td>\n",
       "      <td> 61</td>\n",
       "      <td> 28</td>\n",
       "      <td>  9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-14</th>\n",
       "      <td> 101</td>\n",
       "      <td> 50</td>\n",
       "      <td> 650</td>\n",
       "      <td> 475</td>\n",
       "      <td> 259</td>\n",
       "      <td> 150</td>\n",
       "      <td> 84</td>\n",
       "      <td> 51</td>\n",
       "      <td>  6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-15</th>\n",
       "      <td>  52</td>\n",
       "      <td> 48</td>\n",
       "      <td> 590</td>\n",
       "      <td> 415</td>\n",
       "      <td> 194</td>\n",
       "      <td> 143</td>\n",
       "      <td> 62</td>\n",
       "      <td>  4</td>\n",
       "      <td> 11.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Total     GF  GA   SF   SA  SFT  SAT  PTS  GD    SPG\n",
       "Season                                              \n",
       "2004-05   52  41  490  285  241  156   58  11   9.42\n",
       "2005-06   57  25  563  245  283  126   82  32   9.88\n",
       "2006-07   57  27  592  265  286  133   68  30  10.39\n",
       "2007-08   67  28  607  278  320  133   76  39   9.06\n",
       "2008-09   77  27  609  266  311  142   86  50   7.91\n",
       "2009-10   61  35  519  328  288  180   63  26   8.51\n",
       "2010-11   59  44  475  409  268  209   58  15   8.05\n",
       "2011-12   47  40  612  376  330  199   52   7  13.02\n",
       "2012-13   71  43  669  383  385  217   61  28   9.42\n",
       "2013-14  101  50  650  475  259  150   84  51   6.44\n",
       "2014-15   52  48  590  415  194  143   62   4  11.35"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_seasons = pd.DataFrame()\n",
    "for season, dflfc in sorted(dflfc_dict.items()):\n",
    "    dflfc_summary = pd.DataFrame(dflfc.sum()).transpose()\n",
    "    dflfc_summary.drop('CUMPTS', axis=1, inplace=True)\n",
    "    dflfc_summary['Season'] = season\n",
    "    dflfc_summary['GD'] = (dflfc_summary['GF'] - dflfc_summary['GA']) # goal difference\n",
    "    dflfc_summary['SPG'] = (dflfc_summary['SF']/dflfc_summary['GF']).round(2) # shots per goal\n",
    "    dflfc_seasons = pd.concat([dflfc_seasons, dflfc_summary], axis=0)\n",
    "dflfc_seasons.set_index('Season', inplace=True)\n",
    "dflfc_seasons.columns.name = 'Total'\n",
    "dflfc_seasons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at goal difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Total</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>SF</th>\n",
       "      <th>SA</th>\n",
       "      <th>SFT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>PTS</th>\n",
       "      <th>GD</th>\n",
       "      <th>SPG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</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>2013-14</th>\n",
       "      <td> 101</td>\n",
       "      <td> 50</td>\n",
       "      <td> 650</td>\n",
       "      <td> 475</td>\n",
       "      <td> 259</td>\n",
       "      <td> 150</td>\n",
       "      <td> 84</td>\n",
       "      <td> 51</td>\n",
       "      <td>  6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09</th>\n",
       "      <td>  77</td>\n",
       "      <td> 27</td>\n",
       "      <td> 609</td>\n",
       "      <td> 266</td>\n",
       "      <td> 311</td>\n",
       "      <td> 142</td>\n",
       "      <td> 86</td>\n",
       "      <td> 50</td>\n",
       "      <td>  7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08</th>\n",
       "      <td>  67</td>\n",
       "      <td> 28</td>\n",
       "      <td> 607</td>\n",
       "      <td> 278</td>\n",
       "      <td> 320</td>\n",
       "      <td> 133</td>\n",
       "      <td> 76</td>\n",
       "      <td> 39</td>\n",
       "      <td>  9.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06</th>\n",
       "      <td>  57</td>\n",
       "      <td> 25</td>\n",
       "      <td> 563</td>\n",
       "      <td> 245</td>\n",
       "      <td> 283</td>\n",
       "      <td> 126</td>\n",
       "      <td> 82</td>\n",
       "      <td> 32</td>\n",
       "      <td>  9.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07</th>\n",
       "      <td>  57</td>\n",
       "      <td> 27</td>\n",
       "      <td> 592</td>\n",
       "      <td> 265</td>\n",
       "      <td> 286</td>\n",
       "      <td> 133</td>\n",
       "      <td> 68</td>\n",
       "      <td> 30</td>\n",
       "      <td> 10.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-13</th>\n",
       "      <td>  71</td>\n",
       "      <td> 43</td>\n",
       "      <td> 669</td>\n",
       "      <td> 383</td>\n",
       "      <td> 385</td>\n",
       "      <td> 217</td>\n",
       "      <td> 61</td>\n",
       "      <td> 28</td>\n",
       "      <td>  9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10</th>\n",
       "      <td>  61</td>\n",
       "      <td> 35</td>\n",
       "      <td> 519</td>\n",
       "      <td> 328</td>\n",
       "      <td> 288</td>\n",
       "      <td> 180</td>\n",
       "      <td> 63</td>\n",
       "      <td> 26</td>\n",
       "      <td>  8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11</th>\n",
       "      <td>  59</td>\n",
       "      <td> 44</td>\n",
       "      <td> 475</td>\n",
       "      <td> 409</td>\n",
       "      <td> 268</td>\n",
       "      <td> 209</td>\n",
       "      <td> 58</td>\n",
       "      <td> 15</td>\n",
       "      <td>  8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-05</th>\n",
       "      <td>  52</td>\n",
       "      <td> 41</td>\n",
       "      <td> 490</td>\n",
       "      <td> 285</td>\n",
       "      <td> 241</td>\n",
       "      <td> 156</td>\n",
       "      <td> 58</td>\n",
       "      <td> 11</td>\n",
       "      <td>  9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>  47</td>\n",
       "      <td> 40</td>\n",
       "      <td> 612</td>\n",
       "      <td> 376</td>\n",
       "      <td> 330</td>\n",
       "      <td> 199</td>\n",
       "      <td> 52</td>\n",
       "      <td>  7</td>\n",
       "      <td> 13.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-15</th>\n",
       "      <td>  52</td>\n",
       "      <td> 48</td>\n",
       "      <td> 590</td>\n",
       "      <td> 415</td>\n",
       "      <td> 194</td>\n",
       "      <td> 143</td>\n",
       "      <td> 62</td>\n",
       "      <td>  4</td>\n",
       "      <td> 11.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Total     GF  GA   SF   SA  SFT  SAT  PTS  GD    SPG\n",
       "Season                                              \n",
       "2013-14  101  50  650  475  259  150   84  51   6.44\n",
       "2008-09   77  27  609  266  311  142   86  50   7.91\n",
       "2007-08   67  28  607  278  320  133   76  39   9.06\n",
       "2005-06   57  25  563  245  283  126   82  32   9.88\n",
       "2006-07   57  27  592  265  286  133   68  30  10.39\n",
       "2012-13   71  43  669  383  385  217   61  28   9.42\n",
       "2009-10   61  35  519  328  288  180   63  26   8.51\n",
       "2010-11   59  44  475  409  268  209   58  15   8.05\n",
       "2004-05   52  41  490  285  241  156   58  11   9.42\n",
       "2011-12   47  40  612  376  330  199   52   7  13.02\n",
       "2014-15   52  48  590  415  194  143   62   4  11.35"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_seasons.sort(['GD'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot points per season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R8ceS3irpq5LeLukFko6UdHZEPFazsYqHrSoDAAAAsF0rvY5yVZ1RVXeoqjtV1alVdUVV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\noCxJkiRN8F+BCi5x1QCncAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd927fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = dflfc_seasons['PTS'].plot(kind='bar', title='LFC Total Points per Season', color='red')\n",
    "ax.set_ylabel(\"Total Points\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot goals per season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iK8oDAAAA2B1qJ/kUScdX1WdG119O0j/zfZJnzBa7Xs3m0rndXLG32XQ2ndvN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\nWZIkSer5D1w9gKltmd/kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcfb1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = dflfc_seasons['SPG'].plot(kind='bar', title='LFC Shots per Goal per Season', color='red')\n",
    "ax.set_ylabel(\"Shots per Goal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot goal difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WbrU95659hwEAAKhFxk6ZHfLGYfRixhDnY7pG5zrQuQ61da6tr0TnWpTsAAmGeJw7f0bZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb40358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = (dflfc_seasons['GF'] - dflfc_seasons['GA']).plot(kind='bar', title='LFC Goal Difference per Season', color='red')\n",
    "ax.set_ylabel(\"Total Goal Difference\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VJKWUPtq43c2SPpxSumvQ46XE7A4AAGhjZubyKRwmiT6qecP1nVWPcFwn6bca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\nERERA0gDHRERERExgDTQERERERED+B8kqEx5HxbusAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdbc6780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = dflfc_seasons['GF'].plot(kind='bar', label='Goals For', title='LFC Goals per Season', color='red')\n",
    "ax = dflfc_seasons['GA'].plot(kind='bar', stacked=True, color='green', label='Goals Against')\n",
    "ax.set_ylabel(\"Total Goals\")\n",
    "ax.legend(loc='upper left', fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rafa achieved the best defensive performance, in particular from 2005-06 to 2008-09. Let's find the average goals conceded across these 4 season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "2004-05    41\n",
       "2005-06    25\n",
       "2006-07    27\n",
       "2007-08    28\n",
       "2008-09    27\n",
       "2009-10    35\n",
       "2010-11    44\n",
       "2011-12    40\n",
       "2012-13    43\n",
       "2013-14    50\n",
       "2014-15    48\n",
       "Name: GA, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_seasons['GA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.800000000000001"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RAFA_BEST_GA = ['2005-06', '2006-07', '2007-08', '2008-09']\n",
    "dflfc_seasons['GA'].loc[RAFA_BEST_GA].mean().round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what is best goal difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "2013-14    51\n",
       "2008-09    50\n",
       "2007-08    39\n",
       "2005-06    32\n",
       "2006-07    30\n",
       "2012-13    28\n",
       "2009-10    26\n",
       "2010-11    15\n",
       "2004-05    11\n",
       "2011-12     7\n",
       "2014-15     4\n",
       "Name: GD, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = dflfc_seasons['GD'].copy()\n",
    "s.sort('GD', ascending=False)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add the LFC top goal scorers to this LFC Goals per Season chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def lfc_top_scorers(df, season, n):\n",
    "    \"\"\"Return list of names of top n goal scorers for given season in given dataframe.\n",
    "    \n",
    "       Exclude own goals.\n",
    "       If there are multiple scorers on same number of goals then return them all.\n",
    "       \n",
    "        Input:\n",
    "           df - pandas dataframe containing cols: 'Season', 'Player', 'LeagueGoals'\n",
    "           season - str containing season e.g. '2014-15'\n",
    "           n - integer containing number of top goal scorers to return\n",
    "        Output\n",
    "           top_scorer_list - list of (player, goals) tuples \n",
    "    \"\"\"\n",
    "\n",
    "    target = n # target number of scorers\n",
    "    top_scorer_list = [] # holds top player list, containing (player, goals)\n",
    "    count = 0\n",
    "    prev_tot = None\n",
    "    for player, goal_tot in df[['Player', 'LeagueGoals']][(df['Season'] == season) &\n",
    "                                                         (df['Player'] != 'Own goals')].values:\n",
    "        if goal_tot != prev_tot: \n",
    "            # goal tot not same as before so increment count\n",
    "            count += 1\n",
    "            prev_tot = goal_tot\n",
    "                \n",
    "        if count > target:\n",
    "            break\n",
    "        else:\n",
    "            top_scorer_list.append((player, goal_tot))\n",
    "\n",
    "    return top_scorer_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Dirk Kuyt', 12L), ('Peter Crouch', 9L), ('Craig Bellamy', 7L), ('Steven Gerrard', 7L)]\n"
     ]
    }
   ],
   "source": [
    "# test\n",
    "L = lfc_top_scorers(dflfc_scorers, '2006-07', 3)\n",
    "print L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dirk Kuyt (12)\n",
      "Peter Crouch (9)\n",
      "Craig Bellamy (7)\n",
      "Steven Gerrard (7)\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# test join\n",
    "L2 = '\\n'.join(['{} ({})'.format(player, goals) for player, goals in L])\n",
    "print L2\n",
    "print len(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SR2uWKtJvmSrJOaVzVumcVTpnlcY5pXNW6ZxVuqJl5QKvE0ycOPHB3r17r1+0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Ny1eb/BrL7IimvzptJVX7jARVuNctVvq81Q/d90iWtqoWNfDdvKzK4n6TVqn1evKel/RsSbzOxx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\niqQvqA0o5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5R0b0nP2MkeXEf3u39/Lmk6bTPUBqe/oHb37R8i4mBJh/WN0N+kbZunP1DS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\nMSL+H9FhaN/excnKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe0f77b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NUM_TOP_SCORERS = 3\n",
    "FIG_SIZE = (15, 12)\n",
    "WIDTH = 0.7\n",
    "\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = dflfc_seasons['GF'].plot(kind='bar', label='Goals For', color='red', width=WIDTH)\n",
    "ax = dflfc_seasons['GA'].plot(kind='bar', label='Goals Against', color='blue', width=WIDTH, stacked=True)\n",
    "\n",
    "for season, gf in dflfc_seasons['GF'].iteritems():    \n",
    "    # determine top goal scorers and form string to print\n",
    "    top_scorer_list = lfc_top_scorers(dflfc_scorers, season, NUM_TOP_SCORERS)\n",
    "    top_scorer_str = '\\n'.join(['{} ({})'.format(player, goals) for player, goals in top_scorer_list])\n",
    "    \n",
    "    # calculate position of annotation\n",
    "    sidx = SEASON_LIST.index(season)\n",
    "    x, y = (sidx, gf + len(top_scorer_list) - 2)\n",
    "\n",
    "    # annotate above GF bar the names of top scorers and number of goals\n",
    "    ax.annotate(top_scorer_str, xy=(x,y), xytext=(x,y), va=\"bottom\", ha=\"center\", fontsize=8.5)\n",
    "    \n",
    "ax.set_ylabel(\"Total Goals\")\n",
    "ax.set_title('LFC Goals per Season with Top Goal Scorers')\n",
    "ax.legend(loc='upper left', fancybox=True, shadow=True)\n",
    "plt.show()\n",
    "fig.savefig('SeasonvsGoals.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's find the top scorers across all seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe0f7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Na1IW16MsQ6iHJyhmZmZWHPegTBqn7UE64B4UMzMrnXtQzMzMbGp4gpJJlTuANQxhPXfa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\nK44nKGZmZlYcT1DMzMysOP8/T6SkO32S1IAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd8f4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TITLE = 'Top 5 Scorers Across Total Goals Scored'\n",
    "FIG_SIZE = (9, 6)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "\n",
    "dflfc_scorers_grouped = pd.DataFrame(dflfc_scorers['LeagueGoals'].groupby(dflfc_scorers['Player']).sum())\n",
    "dflfc_topscorers = dflfc_scorers_grouped.sort('LeagueGoals', ascending=False).head(5)\n",
    "\n",
    "ax = dflfc_topscorers.plot(kind='bar', legend='False', color='red', figsize=FIG_SIZE)\n",
    "ax.set_ylabel('Total Goals Scored')\n",
    "ax.set_title(TITLE)\n",
    "ax.legend_.remove()\n",
    "fig = plt.gcf() # save current figure\n",
    "plt.show()\n",
    "#fig.savefig('PlayervsGoals.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add the appearance data for top 5 players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Player</th>\n",
       "      <th>Appearances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2012-2013</td>\n",
       "      <td> Daniel Sturridge</td>\n",
       "      <td> 14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2013-2014</td>\n",
       "      <td> Daniel Sturridge</td>\n",
       "      <td> 29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2014-2015</td>\n",
       "      <td> Daniel Sturridge</td>\n",
       "      <td> 12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2006-2007</td>\n",
       "      <td>        Dirk Kuyt</td>\n",
       "      <td> 34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2007-2008</td>\n",
       "      <td>        Dirk Kuyt</td>\n",
       "      <td> 32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Seasons            Player  Appearances\n",
       "0  2012-2013  Daniel Sturridge           14\n",
       "1  2013-2014  Daniel Sturridge           29\n",
       "2  2014-2015  Daniel Sturridge           12\n",
       "3  2006-2007         Dirk Kuyt           34\n",
       "4  2007-2008         Dirk Kuyt           32"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_apps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PLGames</th>\n",
       "      <th>PLGoals</th>\n",
       "      <th>GPA</th>\n",
       "      <th>APG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top Scorer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fernando Torres</th>\n",
       "      <td> 102</td>\n",
       "      <td>  65</td>\n",
       "      <td> 0.637</td>\n",
       "      <td> 1.569231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Sturridge</th>\n",
       "      <td>  55</td>\n",
       "      <td>  35</td>\n",
       "      <td> 0.636</td>\n",
       "      <td> 1.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis Suarez</th>\n",
       "      <td> 110</td>\n",
       "      <td>  69</td>\n",
       "      <td> 0.627</td>\n",
       "      <td> 1.594203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dirk Kuyt</th>\n",
       "      <td> 208</td>\n",
       "      <td>  51</td>\n",
       "      <td> 0.245</td>\n",
       "      <td> 4.078431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steven Gerrard</th>\n",
       "      <td> 504</td>\n",
       "      <td> 100</td>\n",
       "      <td> 0.198</td>\n",
       "      <td> 5.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  PLGames  PLGoals    GPA       APG\n",
       "Top Scorer                                         \n",
       "Fernando Torres       102       65  0.637  1.569231\n",
       "Daniel Sturridge       55       35  0.636  1.571429\n",
       "Luis Suarez           110       69  0.627  1.594203\n",
       "Dirk Kuyt             208       51  0.245  4.078431\n",
       "Steven Gerrard        504      100  0.198  5.040000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build new dataframe with player, appearances, goals, goals per appearance, appearances per goal\n",
    "dflfc_apps_grouped = dflfc_apps[['Player', 'Appearances']].groupby(dflfc_apps['Player']).sum()\n",
    "dflfc_apps_tot = pd.DataFrame(dflfc_apps_grouped)\n",
    "dflfc_top = dflfc_apps_tot.join(dflfc_topscorers)\n",
    "dflfc_top['GPA'] = dflfc_top['LeagueGoals']/dflfc_top['Appearances']\n",
    "dflfc_top['GPA'] = dflfc_top['GPA'].round(3)\n",
    "dflfc_top.sort('GPA', ascending=False, inplace=True)\n",
    "dflfc_top.rename(columns={'LeagueGoals': 'PLGoals', 'Appearances': 'PLGames'}, inplace=True)\n",
    "dflfc_top.index.name='Top Scorer'\n",
    "dflfc_top['APG'] = dflfc_top['PLGames']/dflfc_top['PLGoals']\n",
    "dflfc_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pxheJ1qB7RvCYTr6Uunr+bGYvL29w93nEKMUv7tFziUxYqpiIVM/KRCvCYsxsc2JY9I+V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\nb1EOcNg6wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3d7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "TITLE = 'Top 5 Scorers Premier League Goal Per Game Ratio \\n(2004-05 to 2014-15)'\n",
    "FIG_SIZE = (9, 6)\n",
    "\n",
    "ax = dflfc_top['GPA'].plot(kind='bar', color='red', figsize=FIG_SIZE, width=0.8)\n",
    "\n",
    "# annotate bars with values\n",
    "for scorer_ix, (PLGames, PLGoals, GPA, APG) in enumerate(dflfc_top.values):\n",
    "    x, y = scorer_ix, GPA+0.02\n",
    "    annotate_str = str(GPA) + '\\n\\ngames={}\\n'.format(str(int(PLGames))) + 'goals={}'.format(str(int(PLGoals)))\n",
    "    ax.annotate(annotate_str, xy=(x,y), xytext=(x,y), va=\"top\", ha=\"center\", fontsize=10)\n",
    "\n",
    "ax.set_title(TITLE)\n",
    "ax.set_ylabel('Goals Per Game Ratio')\n",
    "ax.set_xlabel('Top 5 Scorers')\n",
    "fig = plt.gcf() # save current figure\n",
    "plt.show()\n",
    "fig.savefig('ScorervsGPG.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appearances</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Steven Gerrard</th>\n",
       "      <td> 504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dirk Kuyt</th>\n",
       "      <td> 208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis Suarez</th>\n",
       "      <td> 110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fernando Torres</th>\n",
       "      <td> 102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Sturridge</th>\n",
       "      <td>  55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Appearances\n",
       "Player                       \n",
       "Steven Gerrard            504\n",
       "Dirk Kuyt                 208\n",
       "Luis Suarez               110\n",
       "Fernando Torres           102\n",
       "Daniel Sturridge           55"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_apps_tot.sort('Appearances', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LeagueGoals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Steven Gerrard</th>\n",
       "      <td> 100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis Suarez</th>\n",
       "      <td>  69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fernando Torres</th>\n",
       "      <td>  65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dirk Kuyt</th>\n",
       "      <td>  51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Sturridge</th>\n",
       "      <td>  35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LeagueGoals\n",
       "Player                       \n",
       "Steven Gerrard            100\n",
       "Luis Suarez                69\n",
       "Fernando Torres            65\n",
       "Dirk Kuyt                  51\n",
       "Daniel Sturridge           35"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_topscorers.sort('LeagueGoals', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now examine the shots data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MghloAAAAYArQQBdQxxmcMqWWVyJzKsicBjKnIbXMqeWV6pmZBhoAAAAogBlo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\nleuVs0s5Xm4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe475b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = dflfc_seasons['SF'].plot(kind='bar', label='Shots For', title='LFC Shots per Season', \n",
    "                              ylim=(0, 850), color='red')\n",
    "ax = dflfc_seasons['SA'].plot(kind='bar', stacked=True, color='green', label='Shots Against')\n",
    "ax.set_ylabel(\"Total Shots\")\n",
    "ax.legend(loc='upper left', fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jJgDPA8Dx48dXnjlzZvumTZvuLS4ubq6qqtp+4MCBd+7YseP7O3bs+MGuXbvu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\nSZKkAVygJUmSpAFcoCVJkqQB/j+3jQktcV4PUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105c2d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = dflfc_seasons['SFT'].plot(kind='bar', label='Shots On Target For', title='LFC Shots On Target per Season', \n",
    "                               ylim=(0, dflfc_seasons['SFT'].max()+20), color='red')\n",
    "ax = dflfc_seasons['SAT'].plot(kind='bar', stacked=True, color='green', label='Shots On Target Against')\n",
    "ax.set_ylabel(\"Total Shots on Target\")\n",
    "ax.legend(loc='upper left', fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare key stats (average per game)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Average per game</th>\n",
       "      <th>GFavg</th>\n",
       "      <th>GAavg</th>\n",
       "      <th>SFavg</th>\n",
       "      <th>SAavg</th>\n",
       "      <th>SFTavg</th>\n",
       "      <th>SATavg</th>\n",
       "      <th>PTSavg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</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>2004-05</th>\n",
       "      <td> 1.37</td>\n",
       "      <td> 1.08</td>\n",
       "      <td> 12.89</td>\n",
       "      <td>  7.50</td>\n",
       "      <td>  6.34</td>\n",
       "      <td> 4.11</td>\n",
       "      <td> 1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06</th>\n",
       "      <td> 1.50</td>\n",
       "      <td> 0.66</td>\n",
       "      <td> 14.82</td>\n",
       "      <td>  6.45</td>\n",
       "      <td>  7.45</td>\n",
       "      <td> 3.32</td>\n",
       "      <td> 2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07</th>\n",
       "      <td> 1.50</td>\n",
       "      <td> 0.71</td>\n",
       "      <td> 15.58</td>\n",
       "      <td>  6.97</td>\n",
       "      <td>  7.53</td>\n",
       "      <td> 3.50</td>\n",
       "      <td> 1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08</th>\n",
       "      <td> 1.76</td>\n",
       "      <td> 0.74</td>\n",
       "      <td> 15.97</td>\n",
       "      <td>  7.32</td>\n",
       "      <td>  8.42</td>\n",
       "      <td> 3.50</td>\n",
       "      <td> 2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09</th>\n",
       "      <td> 2.03</td>\n",
       "      <td> 0.71</td>\n",
       "      <td> 16.03</td>\n",
       "      <td>  7.00</td>\n",
       "      <td>  8.18</td>\n",
       "      <td> 3.74</td>\n",
       "      <td> 2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10</th>\n",
       "      <td> 1.61</td>\n",
       "      <td> 0.92</td>\n",
       "      <td> 13.66</td>\n",
       "      <td>  8.63</td>\n",
       "      <td>  7.58</td>\n",
       "      <td> 4.74</td>\n",
       "      <td> 1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11</th>\n",
       "      <td> 1.55</td>\n",
       "      <td> 1.16</td>\n",
       "      <td> 12.50</td>\n",
       "      <td> 10.76</td>\n",
       "      <td>  7.05</td>\n",
       "      <td> 5.50</td>\n",
       "      <td> 1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td> 1.24</td>\n",
       "      <td> 1.05</td>\n",
       "      <td> 16.11</td>\n",
       "      <td>  9.89</td>\n",
       "      <td>  8.68</td>\n",
       "      <td> 5.24</td>\n",
       "      <td> 1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-13</th>\n",
       "      <td> 1.87</td>\n",
       "      <td> 1.13</td>\n",
       "      <td> 17.61</td>\n",
       "      <td> 10.08</td>\n",
       "      <td> 10.13</td>\n",
       "      <td> 5.71</td>\n",
       "      <td> 1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-14</th>\n",
       "      <td> 2.66</td>\n",
       "      <td> 1.32</td>\n",
       "      <td> 17.11</td>\n",
       "      <td> 12.50</td>\n",
       "      <td>  6.82</td>\n",
       "      <td> 3.95</td>\n",
       "      <td> 2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-15</th>\n",
       "      <td> 1.37</td>\n",
       "      <td> 1.26</td>\n",
       "      <td> 15.53</td>\n",
       "      <td> 10.92</td>\n",
       "      <td>  5.11</td>\n",
       "      <td> 3.76</td>\n",
       "      <td> 1.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Average per game  GFavg  GAavg  SFavg  SAavg  SFTavg  SATavg  PTSavg\n",
       "Season                                                              \n",
       "2004-05            1.37   1.08  12.89   7.50    6.34    4.11    1.53\n",
       "2005-06            1.50   0.66  14.82   6.45    7.45    3.32    2.16\n",
       "2006-07            1.50   0.71  15.58   6.97    7.53    3.50    1.79\n",
       "2007-08            1.76   0.74  15.97   7.32    8.42    3.50    2.00\n",
       "2008-09            2.03   0.71  16.03   7.00    8.18    3.74    2.26\n",
       "2009-10            1.61   0.92  13.66   8.63    7.58    4.74    1.66\n",
       "2010-11            1.55   1.16  12.50  10.76    7.05    5.50    1.53\n",
       "2011-12            1.24   1.05  16.11   9.89    8.68    5.24    1.37\n",
       "2012-13            1.87   1.13  17.61  10.08   10.13    5.71    1.61\n",
       "2013-14            2.66   1.32  17.11  12.50    6.82    3.95    2.21\n",
       "2014-15            1.37   1.26  15.53  10.92    5.11    3.76    1.63"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_seasons_avg = pd.DataFrame()\n",
    "for season, dflfc in sorted(dflfc_dict.items()):\n",
    "    dflfc_summary = pd.DataFrame(dflfc.sum()).transpose()\n",
    "    dflfc_summary.drop('CUMPTS', axis=1, inplace=True)\n",
    "\n",
    "    tot_games = len(dflfc)\n",
    "    initial_columns = dflfc_summary.columns.values\n",
    "    for col in initial_columns:\n",
    "        dflfc_summary[col+'avg'] = (dflfc_summary[col]/tot_games).round(2)\n",
    "\n",
    "    dflfc_summary['Season'] = season\n",
    "    dflfc_summary.drop(initial_columns, axis=1, inplace=True)\n",
    "    dflfc_seasons_avg = pd.concat([dflfc_seasons_avg, dflfc_summary], axis=0)\n",
    "dflfc_seasons_avg.set_index('Season', inplace=True)\n",
    "dflfc_seasons_avg.columns.name = 'Average per game'\n",
    "dflfc_seasons_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GAAAAdoVF8xzLOE8zNXruAz33obeee+tXoude0PNyYNEMAAAALMBMMwAAACBmmgEAAIBd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\nERERERFD/B+Qg3CY2GlNQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe6099e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "ax = dflfc_seasons_avg['PTSavg'].plot(kind='bar', title='LFC Average Points per Match per Season', color='red')\n",
    "ax.set_ylabel(\"Average Points per Match\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare results (wins, draws, losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Result</th>\n",
       "      <th>W</th>\n",
       "      <th>D</th>\n",
       "      <th>L</th>\n",
       "      <th>W%</th>\n",
       "      <th>D%</th>\n",
       "      <th>L%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</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>2004-05</th>\n",
       "      <td> 17</td>\n",
       "      <td>  7</td>\n",
       "      <td> 14</td>\n",
       "      <td> 44.7</td>\n",
       "      <td> 18.4</td>\n",
       "      <td> 36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06</th>\n",
       "      <td> 25</td>\n",
       "      <td>  7</td>\n",
       "      <td>  6</td>\n",
       "      <td> 65.8</td>\n",
       "      <td> 18.4</td>\n",
       "      <td> 15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07</th>\n",
       "      <td> 20</td>\n",
       "      <td>  8</td>\n",
       "      <td> 10</td>\n",
       "      <td> 52.6</td>\n",
       "      <td> 21.1</td>\n",
       "      <td> 26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08</th>\n",
       "      <td> 21</td>\n",
       "      <td> 13</td>\n",
       "      <td>  4</td>\n",
       "      <td> 55.3</td>\n",
       "      <td> 34.2</td>\n",
       "      <td> 10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09</th>\n",
       "      <td> 25</td>\n",
       "      <td> 11</td>\n",
       "      <td>  2</td>\n",
       "      <td> 65.8</td>\n",
       "      <td> 28.9</td>\n",
       "      <td>  5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10</th>\n",
       "      <td> 18</td>\n",
       "      <td>  9</td>\n",
       "      <td> 11</td>\n",
       "      <td> 47.4</td>\n",
       "      <td> 23.7</td>\n",
       "      <td> 28.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11</th>\n",
       "      <td> 17</td>\n",
       "      <td>  7</td>\n",
       "      <td> 14</td>\n",
       "      <td> 44.7</td>\n",
       "      <td> 18.4</td>\n",
       "      <td> 36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td> 14</td>\n",
       "      <td> 10</td>\n",
       "      <td> 14</td>\n",
       "      <td> 36.8</td>\n",
       "      <td> 26.3</td>\n",
       "      <td> 36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-13</th>\n",
       "      <td> 16</td>\n",
       "      <td> 13</td>\n",
       "      <td>  9</td>\n",
       "      <td> 42.1</td>\n",
       "      <td> 34.2</td>\n",
       "      <td> 23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-14</th>\n",
       "      <td> 26</td>\n",
       "      <td>  6</td>\n",
       "      <td>  6</td>\n",
       "      <td> 68.4</td>\n",
       "      <td> 15.8</td>\n",
       "      <td> 15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-15</th>\n",
       "      <td> 18</td>\n",
       "      <td>  8</td>\n",
       "      <td> 12</td>\n",
       "      <td> 47.4</td>\n",
       "      <td> 21.1</td>\n",
       "      <td> 31.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Result    W   D   L    W%    D%    L%\n",
       "Season                               \n",
       "2004-05  17   7  14  44.7  18.4  36.8\n",
       "2005-06  25   7   6  65.8  18.4  15.8\n",
       "2006-07  20   8  10  52.6  21.1  26.3\n",
       "2007-08  21  13   4  55.3  34.2  10.5\n",
       "2008-09  25  11   2  65.8  28.9   5.3\n",
       "2009-10  18   9  11  47.4  23.7  28.9\n",
       "2010-11  17   7  14  44.7  18.4  36.8\n",
       "2011-12  14  10  14  36.8  26.3  36.8\n",
       "2012-13  16  13   9  42.1  34.2  23.7\n",
       "2013-14  26   6   6  68.4  15.8  15.8\n",
       "2014-15  18   8  12  47.4  21.1  31.6"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_result = pd.DataFrame() # new dataframe for results\n",
    "for season, dflfc in sorted(dflfc_dict.items()):\n",
    "    w = dflfc['R'][dflfc['R'] == 'W'].count()\n",
    "    dflfc_result.set_value(season, 'W', w)\n",
    "    \n",
    "    d = dflfc['R'][dflfc['R'] == 'D'].count()\n",
    "    dflfc_result.set_value(season, 'D', d)\n",
    "    \n",
    "    l = dflfc['R'][dflfc['R'] == 'L'].count()\n",
    "    dflfc_result.set_value(season, 'L', l)\n",
    "    \n",
    "    total_games = len(dflfc_dict[season])\n",
    "    dflfc_result.set_value(season, 'W%', 100*(w/total_games).round(3))\n",
    "    dflfc_result.set_value(season, 'D%', 100*(d/total_games).round(3))\n",
    "    dflfc_result.set_value(season, 'L%', 100*(l/total_games).round(3))\n",
    "\n",
    "dflfc_result.columns.name = 'Result'\n",
    "dflfc_result.index.name = 'Season'\n",
    "dflfc_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "2004-05    17\n",
       "2005-06    25\n",
       "2006-07    20\n",
       "2007-08    21\n",
       "2008-09    25\n",
       "2009-10    18\n",
       "2010-11    17\n",
       "2011-12    14\n",
       "2012-13    16\n",
       "2013-14    26\n",
       "2014-15    18\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_result['W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0a6+99s0DAwMbi6ctXLhwxSOPPLJoy5Ytz5w6deqDp5566hcHBgaezK/njAkTJuxcsmTJ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\ngP8Ddb1SRe8VtUYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf5abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "dflfc_result[['W%', 'D%', 'L%']].plot(kind='bar', title='Wins, Draws and Losses % per Season', \n",
    "                                   color=['red', 'green', 'blue'], figsize=FIG_SIZE)\n",
    "plt.ylabel('Total Result')\n",
    "plt.legend(loc='upper left', fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##What does the graph comparing performance look like?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create event data structure using a dictionary with key of season and value of a tuple with match number and event description. This is used for plotting annotations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def key_event(df, event_date):\n",
    "    \"\"\"Return match number on or after given event_date.\n",
    "    \n",
    "        input:  matches, pandas dataframe in munged format\n",
    "                event_time, string of date in form 'mm/dd/yy'\n",
    "        output: match_number, integer starting at 0 (none if no natch)\n",
    "    \"\"\"\n",
    "    DATE_FORMAT = '%d/%m/%y'\n",
    "       \n",
    "    # convert event date to numpy datetime64, for comparison\n",
    "    event_date = np.datetime64(datetime.strptime(event_date, DATE_FORMAT))\n",
    "    \n",
    "    # find match\n",
    "    for match_date in df['Date'].values:\n",
    "        if match_date >= event_date:\n",
    "            # match found, return match number (the index)\n",
    "            return int(df[df['Date'] == match_date].index.tolist()[0])\n",
    "    \n",
    "    # match not found\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2010-11': (20,\n",
       "  \"Roy Hodgson's final game in season 2010-11, \\nhe leaves 8/1/2011 (thank heavens)\"),\n",
       " '2013-14': (35,\n",
       "  'That game against Chelsea 24/04/14, \\nMourinho parks the bus and gets lucky')}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_event_dict = {}\n",
    "# use key_event() function to determine match at which event took place\n",
    "# dates given are from wikipedia\n",
    "key_event_dict['2010-11'] = (key_event(dflfc_dict['2010-11'], '08/01/11'), \n",
    "                             \"Roy Hodgson's final game in season 2010-11, \\nhe leaves 8/1/2011 (thank heavens)\")\n",
    "key_event_dict['2013-14'] = (key_event(dflfc_dict['2013-14'], '24/04/14'), \n",
    "                             \"That game against Chelsea 24/04/14, \\nMourinho parks the bus and gets lucky\")\n",
    "key_event_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date        2011-01-05 00:00:00\n",
      "Opponent              Blackburn\n",
      "HA                         Away\n",
      "R                             L\n",
      "GF                            1\n",
      "GA                            3\n",
      "SF                           13\n",
      "SA                           10\n",
      "SFT                           9\n",
      "SAT                           4\n",
      "PTS                           0\n",
      "CUMPTS                       25\n",
      "Name: 19, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Roy Hodgson's last game\n",
    "print dflfc_dict['2010-11'].ix[20-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dictionary to hold the season specific matplotlib plot options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "season_dict = {}\n",
    "season_dict['2014-15'] = {'label': '2014-15: Brendan Rodgers season 3', 'ls': '-', 'marker': '', 'lw': 2}\n",
    "season_dict['2013-14'] = {'label': '2013-14: Brendan Rodgers season 2', 'ls': '-', 'marker': '', 'lw': 2}\n",
    "season_dict['2012-13'] = {'label': '2012-13: Brendan Rodgers season 1', 'ls': '-', 'marker': '', 'lw': 2}\n",
    "season_dict['2011-12'] = {'label': '2011-12: Kenny Dalglish season 2', 'ls': '-.', 'marker': 'o', 'lw': 1}\n",
    "season_dict['2010-11'] = {'label': '2010-11: Roy Hodson / Kenny Dalglish season', 'ls': '-.', 'marker': '*', 'lw': 1}\n",
    "season_dict['2009-10'] = {'label': '2009-10: Rafa Benitez season 6', 'ls': ':', 'marker': '', 'lw': 1}\n",
    "season_dict['2008-09'] = {'label': '2008-09: Rafa Benitez season 5', 'ls': ':', 'marker': '', 'lw': 1}\n",
    "season_dict['2007-08'] = {'label': '2007-08: Rafa Benitez season 4', 'ls': ':', 'marker': '', 'lw': 1}\n",
    "season_dict['2006-07'] = {'label': '2006-07: Rafa Benitez season 3', 'ls': ':', 'marker': '', 'lw': 1}\n",
    "season_dict['2005-06'] = {'label': '2005-06: Rafa Benitez season 2', 'ls': ':', 'marker': '', 'lw': 1}\n",
    "season_dict['2004-05'] = {'label': '2004-05: Rafa Benitez season 1', 'ls': ':', 'marker': '', 'lw': 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2004-05: Rafa Benitez season 1'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season_dict['2004-05']['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the match vs cumlative points for all seasons, with annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IgiAIgqAFQoEOgiAIgiAIghYIBToIehCSzk/xT4eV0jeUdKekIr7sNEk7dZOYwUwgqZ+k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cCpeLBZ0at2FvB/tPCjlQcXFIlmFisdsCNe41qQDK2rcYNpZ/MpqSLP4VPfxbYD8Cmi56gCSUvh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\nEqFxoeHXcoFAIBAoA168JP36DS++N4BTtxQYilmW4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bI+0547FngL1dcL3sLE7kJ1rskP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\nOtcHCRcHdyj8tv5k4S7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GJgrM2QzDMAzDMLKwvvVA76OqX4tIF+AFEflIVV9d\nmwaoFB0O/ENcnu5nQsPRqE7Idrz4vtCqz6NstWQUM9qOoNovEp8rgMtxvenPAScyvnIfGc+emlgV\n2CrBeyDCqaCoMjdNvh2wv+/Lk5WVnEbj4L32wCgReRSo3RLOmgFTUH0T1QbgHREZBYxT1UdW2eoG\nAF6HuzYBrhORy0gZfKeqj4XdH6mSdsC9wDEpm79h9YD5EwuYDcMwDMP4MVJwAbSqfh38nS8iY4F+\nuFkhABCRESmH+6rqN9vJRUqBa6SxBPZYYBiqC0Nt9jwV/K3V8+rE97viBr/tj2oDiyY/QIedhqnn\nNch46nAFTly7YGq5gHE0zh+N70uZ52mN5+l8EZkGnAPcipuH+cbgsIXAmcB/3ofdcdUG30zRPBOX\nNpLKPcBiXBDdA9fT/yciTkMnVbITrthJn0BnRGD7NAuYDcMwDMNYXxERD/ereu5jCymFI5i6rFhV\nF4tIG+B5oEpVnw/2t1wKh8jmwMPAPgp1DZRdVkzNyNSUDZEdD4Ee50K71hQvreW8/nDogMPUc5UG\n5eGLh9Ol8kaKW3cEvuHrp//M9JFTNKHVadcZ5B/XPAOl01T5VkSKgV2BiupqyoBTPc/NPCEi3YEV\nqvpD+sC5Uhheozom3qXmN/hOquQUXM5yOTAJOEoTOj3OuQ3DMAzDMNYH1qcUjm7AWBdfUgKMTgbP\nLYrIgQ2UPlZEbTvgK4FjinXl66sfsuMh0L+xCEo9cOevv2HC0L8Ij/wauIhuB1+NC4RfBY7V426Y\nAzdkOmNQ/OPKHlB1m4j0Bp7F5Q3fiyvS8m7yYFVNTetYNXDucvjnR26AY6wAmpiD76RKyoHbgeHB\nplHA2ZrQZTHPaxiGYRiGsd5TUD3QuWj2HmgX7P4Rl8IgdVS8VsKyI1Bdo7CJyJBx8OxB6du37zik\nZurYi8cBvwA47LF3fvjPkXt2Uc+ru0r+0HdP3vnvYH2+N8Am0u2PRSy+fA7LvwD6FMNnm0Dn2XD5\n9dcz1+tE0Z6n8XNUTw5O2gUYGbK+EbAcjVy1LzZSJdvgAvSdgRXAWZrQ+1rqfIZhGIZhGIVAWNyZ\ncXLgDQKRrnWUjwcSwZZECcsGpgbP4vs9xPfPcWvtWq+h0WcxH911VhEueP5hoznLj9v/9plXqefV\nAdRTPO9jtr0zefjxHHf7AhoWAm3dfsoGuN7mO/bck7m1HVkA3J9yhiWh66oLWjh4Pgp4Dxc8fwLs\nZcGzYRiGYRgbOhtmD7TIANzczJs2ULygiPpjUH1RfL8DcJ563hUA4vudgSMrTxr/VPWsN0c19kAr\n/GIOnPUplCm4dItj1PNmRLiGU3BBs8thLmV4TU28HOaWRqqkDJd7cm6waQxwqiZ00bqzyjAMwzAM\nY+1hPdBJRASR3wE+sKnC/35y712PSHW1HxyxFFgsvu9uVqW3YItDSwd/N/fAOTDrVjj9U1rXweXT\n4LefuOB5xpz/AgOiBM8B3XH5xzuedRbv9u3LkGa9xiYiVdITeAUXPNfips07xoJnwzAMwzAMx4bT\nAy3SZof77pv1ZffuGwHUlpTU1pSU1CBSwppTvTlqpBUNUkRpfQ3FUk99fTFKK0qKhZW19a1fe/u2\nZwdd/pHn6V0Avi/tgeOS69tuK3tdfDF7Dx+uN2Xa7/tS7Hlan9f1tABSJUOAf+HKbs8CjtaEvrVu\nrTIMwzAMw1j7rE+zcLQky2tKS0uWlpcn10uDF0CrjC3KFFfrRFz+c3ExAELDh9qqdOizgy6fi5uf\nOUkR0BVcqoYIIydO5KlM+wEKJXiWKikBqoDLgk3PAr/ShH637qwyDMMwDMMoTDacHmjg4UGDenRe\ntKjD4JEjvwg7bujlfz2x6/JvD77txsSJ6fs25SuZw2ZL1POy3jgR2QVXTOUYVZ2Sr71rA6mS7sBD\nuInDG3CzklyrCW0Ia2cYhmEYhvFjJizu3KAC6DAG9azaBeDlmYkPUrf7vvQGLgbO8LzoN0tESlQ1\nc2pIgSBVMhBXPKY7MA84Lr3oi2EYhmEYxoaIpXBEoGjxpuOLSxd/C/RO2/Ul8ECc4BmgJYJnqZLW\nQH+gEtgFKG6CXAlwIC6t5BXgWE24MuqGYRiGYRhGdqwHOmD/za8sf2n25csBfF86Aj08TydFsKkM\nN6vGParNm/YQTCf3U1zAXIkLnjPna+fPtcAfNVHYveWGYRiGYRhrE+uBzsJdvbaa836r3iPvmvrC\nTcngOaAfsBeQM4DGjTLsAZTjpsHLG6mSUmAPGgPmfQLdVD4AqoE3gKaW0v5CEzq5iRqGYRiGYRgb\nFBtMD7TIjodAj3NBt4OKL+Gnr5693TOtvqfTnaOnjv3U90UAcqVqiIgAVwOXaRNvXjD7xa40BswD\nCKoUpjAFFzBXA+NtZgzDMAzDMIyWZ4PvgXbBc/9b4J5kfnNPOK/v7dO+O0V1/KfBtkuBb4G7s+pU\nyZb8kguZx2lsxSZSJfn23rbC9XDvB7RP2zedxoDZ14TOy/MchmEYhmEYRgtQUD3QInIwcDNucNy9\nqnpd2v68eqBFBk6G8TusuWffz1Rf7Q3g+9INWFxZyYvAmar6gVTJ5iR7hxfyKzq0yBeOz2kMmKs1\noXNa4ByGYRiGYRhGDNaLUt4iUgzcDhwMbA8cJyLbNY96128bl/1VS506dVzp+7IxgOfpvMrxtOdi\n7iLB2VIln+Cq8d0PDKMDJdSxjE9Yzn+Bt1nEbJ5EuRFiv0byKtcCW2hCe2lCT9OEjo4TPIuIl+fN\n+NFpFIINhaJRCDYUikYh2FAoGoVgQ3NoFIINhaJRCDYUikYh2FAoGoVgQyFptCSFlMLRD/hUVb8A\nEJGHgcOAaU2XXrKicdnH1QyBsk7zS8+dwPGTxsu2wCCgb9qQvcW4Kd5cD/G19KKOe4ElQGvgn6r6\neD4WicgIfVFn5tM2wCP128CGrVEINhSKRiHYUCgahWBDoWgUgg3NoVEINhSKRiHYUCgahWBDoWgU\ngg2FpNFiFFIAvRmuxzfJbNwUbs3ArFvhpD1ofUtn2nwEe54HW/+zfm7XhdvMXcQtKQcuBf5HY0rF\n+6nTu8kIGYybsm5n4ENgm+axzzAMwzAMw1hfKKQAusWSsVUnPyNFO5zEeV3H8mZdGXsBLs96BfA6\nLlh+GXhHE1qbXUevBRCRnfPteTYMwzAMwzDWbwpmEKGI7AWMUNWDg/VLgYbUgYQiUhjGGoZhGIZh\nGD96sg0iLKQAugT4GNgfmAO8DRynqs2QA20YhmEYhmEYzUPBpHCoap2InA08h0uv+LsFz4ZhGIZh\nGEahUTA90IZhGIZhGIaxPlAw80C3NCJyn4jME5FJTdDoISLVIjJFRCaLyLkx27cWkbdEZKKITBWR\na5pgS7GITBCRp/Js/4WIfBhovJ1H+44iMkZEpgXXslfM9tsG506+Fsa9n4HOpcH7MUlEHhSRVnlo\nnBe0nywi50U4fg1fEpFOIvKCiEwXkTEU35wAAAzRSURBVOdFpGMeGkcH11IvIrvlaccNwXvygYg8\nLiId8tD4c9B+ooi8JCI94rRP2XehiDSISKc8bBghIrNT/OPguBrB9nOC+zFZRK7L1j7EjodTbJgh\nIhNitu8nIm8H7d8RkT3zsGEXEXkj+H99UkTa5dDI+JyK6qMh7SP7Z4hGZP8M0Yjjn6HP7Fw+GmJD\nZP8MsyGqf4bY8UgM/8ymEclHQ9pH9k/J8hkY1TdzaMTxz2wakfwzpH0c3wyNB3L5Zg47IvlnmA0x\nfDObDXF8M5tGrOfnWkdVN4gXsC+wKzCpCRrdgZ8Ey21xOdvbxdSoCP6WAG8CA/K05QJgNPBknu1n\nAJ2acC/+CQxPuZYOTdAqAr4GesRstyWukmOrYP0R4OSYGjsCk3DzehcDLwC94voScD3w+2D5YuDa\nPDT6An1ws8LsFsH2TBoHAkXB8rV52tEuZfkcXFXQWP9XQA9gXBQ/y2JDArggxvuYSaMyeD9Lg/Uu\ncTXS9o8ELo9pgw8cFCwPAarzuI53gH2D5WHAFTk0Mj6novpoSPvI/hmiEdk/QzTi+GfWZ3YUHw2x\nIbJ/hmhE9s+w64jhn9nsiOSjIe3j+ucan4FRfTOHRtznZyaNOP6ZqX1k38ymEdU3c9gRxz8ztY/7\n7AyNa3L5Zogd1VF8c129NpgeaFV9FVjQRI25qjoxWF6CK/KyaUyNZcFiGS5g+z6uHSKyOXAIcC8Q\nu7R5qlRejdy38n1V9T5w+euqurAJdhwAfKaqs3IeuTqLgFqgQtwg1Argq5gafYG3VHWFqtYD44Ff\nhjXI4ku/wH2pIPh7eFwNVf1IVadHNTyLxguq2hCsvgVsnofG4pTVtsC3ZCHk/+om4Pdh546gEdk/\ns2icCVyj6qamVNX5edqBiAhwDPBQzPZfA8lerI7k8M8sGtsE2wFeBI7MoZHpObUZEX0023Mujn+G\naET2zxCNOP4Z9szO6aMh9xIi+meIxq+J6J+5Pnsi+mc2OyL5aEj7uP6Z/hm4gPjPzzU+R/N4fmbS\niOOfmdpH9s1sGsF6nOdnpvsJ0f0zU/vIvplFY1VcE8U3Q+yYS4zn59pmgwmgmxsR2RLXU/RWzHZF\nIjIRmIf7NjU1j9P/BbgIaMh1YAgKvCgi74rI6THbbgXMF5FRIvK+iNwjIhVNsOVY4MG4jVT1e1xp\n9Jm4mVt+UNUXY8pMBvYNfkKsAH5GjqAzC91UdV6wPA/olodGczMceCafhiJylYjMBE7G9cTEaXsY\nMFtVP8zn3CmcE/wc+vewn3RD2AbYT0TeFBFfRPZogi37AvNU9bOY7S4Bbgzu5Q3ApXmce0pwTwGO\nxvVORSLtORXbR/N9zkXUiOyf6Rr5+GeqRj4+mtL+zWB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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe61b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (12, 8)\n",
    "\n",
    "# calculate limits\n",
    "max_played = 38\n",
    "max_points = int(dflfc_seasons['PTS'].max())\n",
    "seasons_analysed = ', '.join(dflfc_seasons.index.values)\n",
    "\n",
    "# plot\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "                    \n",
    "for season, dflfc in sorted(dflfc_dict.items()):\n",
    "    team_cum_points_list = dflfc['CUMPTS']\n",
    "    team_match_list = range(1, len(team_cum_points_list)+1)\n",
    "    # plot x vs y, with selected season options\n",
    "    plt.plot(team_match_list, team_cum_points_list, **season_dict[season])\n",
    "\n",
    "    # if there is a key event then annotate\n",
    "    if season in key_event_dict:\n",
    "        # get match number and event description\n",
    "        event_match, event_desc = key_event_dict[season]\n",
    "        \n",
    "        # calculate position of annotation\n",
    "        x, y = team_match_list[event_match-1], team_cum_points_list[event_match-1] \n",
    "        if y > 50:\n",
    "            # set text position above point and to left\n",
    "            xtext = x - 8\n",
    "            ytext = y + 10\n",
    "        else:\n",
    "            # set text position below point and to right\n",
    "            xtext = x + 1\n",
    "            ytext = y - 15\n",
    "            \n",
    "        # annotate with arrow below event\n",
    "        plt.annotate(event_desc, xy=(x,y), xytext=(xtext, ytext), va=\"bottom\", ha=\"center\",\n",
    "                     arrowprops=dict(facecolor='black', width=.5, shrink=.05, \n",
    "                                     headwidth=4, frac=.05))\n",
    "\n",
    "plt.xticks(range(1, max_played+1))\n",
    "plt.yticks(range(0, max_points+1 + 20, 5))\n",
    "plt.xlabel('Match Number')\n",
    "plt.ylabel('Cumulative Points')\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('LFC Match Number vs Cumulative Points\\n(for seasons: {} to {})'.format(SEASON_LIST[0], \n",
    "                                                                                  SEASON_LIST[-1]), \n",
    "          fontsize=16, fontweight='bold')\n",
    "plt.show()\n",
    "fig.savefig('MatchvsPTS.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##What was the best winning run and worst losing run?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find best winning run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season=2004-05, best winning run: 3 winning games\n",
      "season=2005-06, best winning run: 10 winning games\n",
      "season=2006-07, best winning run: 5 winning games\n",
      "season=2007-08, best winning run: 5 winning games\n",
      "season=2008-09, best winning run: 5 winning games\n",
      "season=2009-10, best winning run: 4 winning games\n",
      "season=2010-11, best winning run: 4 winning games\n",
      "season=2011-12, best winning run: 2 winning games\n",
      "season=2012-13, best winning run: 3 winning games\n",
      "season=2013-14, best winning run: 11 winning games\n",
      "season=2014-15, best winning run: 5 winning games\n"
     ]
    }
   ],
   "source": [
    "for season in SEASON_LIST:\n",
    "    best_run = 0\n",
    "    this_run = 0\n",
    "    prev_pts = 0\n",
    "    for pts in dflfc_dict[season]['PTS']:\n",
    "        if pts == 3:\n",
    "            this_run += 1\n",
    "        else:\n",
    "            if this_run > best_run:\n",
    "                best_run = this_run\n",
    "            this_run = 0 # reset\n",
    "    print 'season={}, best winning run: {} winning games'.format(season, best_run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find worst losing run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season=2004-05, worst losing run: 2 losing games\n",
      "season=2005-06, worst losing run: 2 losing games\n",
      "season=2006-07, worst losing run: 2 losing games\n",
      "season=2007-08, worst losing run: 2 losing games\n",
      "season=2008-09, worst losing run: 1 losing games\n",
      "season=2009-10, worst losing run: 2 losing games\n",
      "season=2010-11, worst losing run: 2 losing games\n",
      "season=2011-12, worst losing run: 3 losing games\n",
      "season=2012-13, worst losing run: 1 losing games\n",
      "season=2013-14, worst losing run: 2 losing games\n",
      "season=2014-15, worst losing run: 3 losing games\n"
     ]
    }
   ],
   "source": [
    "for season in SEASON_LIST:\n",
    "    worst_run = 0\n",
    "    this_run = 0\n",
    "    prev_pts = 0\n",
    "    for pts in dflfc_dict[season]['PTS']:\n",
    "        if pts == 0:\n",
    "            this_run += 1\n",
    "        else:\n",
    "            if this_run > worst_run:\n",
    "                worst_run = this_run\n",
    "            this_run = 0 # reset\n",
    "    print 'season={}, worst losing run: {} losing games'.format(season, worst_run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How has league position changed?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's produce a dataframe that shows league position for each season.\n",
    "\n",
    "data: season | Champions | Champion Points | Champion PPG | LFC Position | LFC Points | LFC PPG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by creating premier league table for each season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prem_table(df, season):\n",
    "    \"\"\"Return premier league table dataframe for given match dataframe for given season.\"\"\"\n",
    "    \n",
    "    results = [] # create results list\n",
    "\n",
    "    for team in df['HomeTeam'].unique():\n",
    "        home_results = df[df['HomeTeam'] == team]\n",
    "        home_played = len(home_results.index)\n",
    "        home_win = home_results.FTR[home_results.FTR == 'H'].count()\n",
    "        home_draw = home_results.FTR[home_results.FTR == 'D'].count()\n",
    "        home_lose = home_results.FTR[home_results.FTR == 'A'].count()\n",
    "        home_goals_for = home_results.FTHG.sum()\n",
    "        home_goals_against = home_results.FTAG.sum()\n",
    "\n",
    "        away_results = df[df['AwayTeam'] == team]\n",
    "        away_played = len(away_results.index)\n",
    "        away_win = away_results.FTR[away_results.FTR == 'A'].count()\n",
    "        away_draw = away_results.FTR[away_results.FTR == 'D'].count()\n",
    "        away_lose = away_results.FTR[away_results.FTR == 'H'].count()\n",
    "        away_goals_for = away_results.FTAG.sum()\n",
    "        away_goals_against = away_results.FTHG.sum()\n",
    "\n",
    "        result_d = {} # create dictionary to hold team results\n",
    "        result_d['Team'] = team\n",
    "        result_d['P'] = home_played + away_played\n",
    "        result_d['W'] = home_win + away_win\n",
    "        result_d['D'] = home_draw + away_draw\n",
    "        result_d['L'] = home_lose + away_lose\n",
    "        result_d['GF'] = home_goals_for + away_goals_for\n",
    "        result_d['GA'] = home_goals_against + away_goals_against\n",
    "        result_d['GD'] = result_d['GF'] - result_d['GA']\n",
    "        result_d['PTS'] = result_d['W']*3 + result_d['D']\n",
    "\n",
    "        results.append(result_d) # append team result dictionary to list of results\n",
    "\n",
    "    # create DataFrame from results and sort by points (and then goal difference)\n",
    "    PLtable = pd.DataFrame(results, columns=['Team', 'P', 'W', 'D', 'L', 'GF', 'GA', 'GD', 'PTS'])\n",
    "    PLtable.sort(columns=['PTS', 'GD'], ascending=False, inplace=True)\n",
    "    PLtable['Position'] = range(1, len(PLtable)+1) # add new column for position, with highest points first\n",
    "    PLtable.set_index(['Position'], inplace=True, drop=True) \n",
    "    return PLtable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Champions</th>\n",
       "      <th>ChampPoints</th>\n",
       "      <th>ChampPPG</th>\n",
       "      <th>LFCPos</th>\n",
       "      <th>LFCPoints</th>\n",
       "      <th>LFCPPG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</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>2004-05</th>\n",
       "      <td>    Chelsea</td>\n",
       "      <td> 95</td>\n",
       "      <td> 2.50</td>\n",
       "      <td> 5</td>\n",
       "      <td> 58</td>\n",
       "      <td> 1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06</th>\n",
       "      <td>    Chelsea</td>\n",
       "      <td> 91</td>\n",
       "      <td> 2.39</td>\n",
       "      <td> 3</td>\n",
       "      <td> 82</td>\n",
       "      <td> 2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07</th>\n",
       "      <td> Man United</td>\n",
       "      <td> 89</td>\n",
       "      <td> 2.34</td>\n",
       "      <td> 3</td>\n",
       "      <td> 68</td>\n",
       "      <td> 1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08</th>\n",
       "      <td> Man United</td>\n",
       "      <td> 87</td>\n",
       "      <td> 2.29</td>\n",
       "      <td> 4</td>\n",
       "      <td> 76</td>\n",
       "      <td> 2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09</th>\n",
       "      <td> Man United</td>\n",
       "      <td> 90</td>\n",
       "      <td> 2.37</td>\n",
       "      <td> 2</td>\n",
       "      <td> 86</td>\n",
       "      <td> 2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10</th>\n",
       "      <td>    Chelsea</td>\n",
       "      <td> 86</td>\n",
       "      <td> 2.26</td>\n",
       "      <td> 7</td>\n",
       "      <td> 63</td>\n",
       "      <td> 1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11</th>\n",
       "      <td> Man United</td>\n",
       "      <td> 80</td>\n",
       "      <td> 2.11</td>\n",
       "      <td> 6</td>\n",
       "      <td> 58</td>\n",
       "      <td> 1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>   Man City</td>\n",
       "      <td> 89</td>\n",
       "      <td> 2.34</td>\n",
       "      <td> 8</td>\n",
       "      <td> 52</td>\n",
       "      <td> 1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-13</th>\n",
       "      <td> Man United</td>\n",
       "      <td> 89</td>\n",
       "      <td> 2.34</td>\n",
       "      <td> 7</td>\n",
       "      <td> 61</td>\n",
       "      <td> 1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-14</th>\n",
       "      <td>   Man City</td>\n",
       "      <td> 86</td>\n",
       "      <td> 2.26</td>\n",
       "      <td> 2</td>\n",
       "      <td> 84</td>\n",
       "      <td> 2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-15</th>\n",
       "      <td>    Chelsea</td>\n",
       "      <td> 87</td>\n",
       "      <td> 2.29</td>\n",
       "      <td> 6</td>\n",
       "      <td> 62</td>\n",
       "      <td> 1.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Champions  ChampPoints  ChampPPG  LFCPos  LFCPoints  LFCPPG\n",
       "Season                                                               \n",
       "2004-05     Chelsea           95      2.50       5         58    1.53\n",
       "2005-06     Chelsea           91      2.39       3         82    2.16\n",
       "2006-07  Man United           89      2.34       3         68    1.79\n",
       "2007-08  Man United           87      2.29       4         76    2.00\n",
       "2008-09  Man United           90      2.37       2         86    2.26\n",
       "2009-10     Chelsea           86      2.26       7         63    1.66\n",
       "2010-11  Man United           80      2.11       6         58    1.53\n",
       "2011-12    Man City           89      2.34       8         52    1.37\n",
       "2012-13  Man United           89      2.34       7         61    1.61\n",
       "2013-14    Man City           86      2.26       2         84    2.21\n",
       "2014-15     Chelsea           87      2.29       6         62    1.63"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new dataframe for positions\n",
    "col_names = ['Champions', 'ChampPoints', 'ChampPPG', 'LFCPos', 'LFCPoints', 'LFCPPG']\n",
    "df_position = pd.DataFrame(columns=col_names)\n",
    "\n",
    "for season, df in sorted(df_dict.items()):\n",
    "    PLTdf = prem_table(df, season)\n",
    "    champions, champ_pts, champ_games = PLTdf[['Team', 'PTS', 'P']].iloc[0]\n",
    "    champ_ppg = round(champ_pts/champ_games, 2)\n",
    "    lfc_pos = PLTdf[PLTdf['Team'] == 'Liverpool'].index[0]\n",
    "    lfc_pts = PLTdf['PTS'][PLTdf['Team'] == 'Liverpool'].values[0]\n",
    "    lfc_games =  PLTdf['P'][PLTdf['Team'] == 'Liverpool'].values[0]\n",
    "    lfc_ppg = round(lfc_pts/lfc_games, 2)\n",
    "    df_position.loc[season] = [champions, champ_pts, champ_ppg, lfc_pos, lfc_pts, lfc_ppg]\n",
    "\n",
    "df_position.index.name = 'Season'\n",
    "df_position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot position performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Ref: http://stackoverflow.com/questions/739241/date-ordinal-output\n",
    "def n_plus_suffix(n):\n",
    "    \"\"\"Return n plus the suffix e.g. 1 becomes 1st, 2 becomes 2nd.\"\"\"\n",
    "    assert isinstance(n, (int, long)), '{} is not an integer'.format(n)\n",
    "    \n",
    "    if 10 <= n % 100 < 20:\n",
    "        return str(n) + 'th'\n",
    "    else:\n",
    "       return  str(n) + {1 : 'st', 2 : 'nd', 3 : 'rd'}.get(n % 10, \"th\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th 16th 17th 18th 19th 20th 21st 22nd 23rd 24th 25th 26th 27th 28th 29th 30th 31st\n"
     ]
    }
   ],
   "source": [
    "# test\n",
    "for i in range(1, 32):\n",
    "    print n_plus_suffix(i), "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIiIikgiduSuQmX3SzO41s/vM7JNx2elm9oSZ3R0vR5VdThERERERaW6q3G0B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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb45160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TITLE = 'LFC Season Points Comparison'\n",
    "FIG_SIZE = (15, 10)\n",
    "max_points = int(df_position['ChampPoints'].max())\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "\n",
    "ax = df_position['ChampPoints'].plot(kind='bar', color='y', label='Champions', width=0.6)\n",
    "ax = df_position['LFCPoints'].plot(kind='bar', color='red', label='LFC', width=0.6)\n",
    "\n",
    "for sidx, (ch, chpts, chppg, lfcpos, lfcpts, lfcppg) in enumerate(df_position.values):\n",
    "    # annotate description of each season, rotated    \n",
    "    season = SEASON_LIST[sidx]\n",
    "    season_desc = season_dict[season]['label'][len(season)+1:]\n",
    "    x, y = (sidx, 2) # calculate position of annotation\n",
    "    ax.annotate(season_desc, xy=(x,y), xytext=(x,y), va=\"bottom\", ha=\"center\", \n",
    "                rotation='vertical', style='italic', fontsize='9')\n",
    "    # annotate above champions bar the name of champions and winning points total\n",
    "    x, y = (sidx, chpts)\n",
    "    ax.annotate(str(ch)+'\\n'+str(int(chpts)), xy=(x,y), xytext=(x,y), \n",
    "                va=\"bottom\", ha=\"center\")\n",
    "    # annotate below LFC bar the points total and position\n",
    "    x, y = (sidx, lfcpts - 8)\n",
    "    ax.annotate('LFC\\n' + str(n_plus_suffix(int(lfcpos))) + '\\n' + str(int(lfcpts)), \n",
    "                xy=(x,y), xytext=(x,y), va=\"bottom\", ha=\"center\")\n",
    "\n",
    "ax.set_ylabel(\"Total Points\")\n",
    "ax.set_ylim((0, max_points+20))\n",
    "ax.set_title(TITLE)\n",
    "plt.legend(loc='upper left', fancybox=True, shadow=True)\n",
    "plt.show()\n",
    "fig.savefig('SeasonvsPTS.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Champions max points per game: 2.5\n"
     ]
    }
   ],
   "source": [
    "print 'Champions max points per game: {}'.format(df_position['ChampPPG'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RIxZvtdVWM0eNGrWiu/XMzMzMzMxql6lb18Dg/eElL3nJufDsNnU92XzzzRcd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\n/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nvH7IN9yvM1bHPRXnexb/oTtlrPA9gvth2wOQtvavuZl7Ex5z9sey6WTytFRCQ++t0n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\naqpM8lQmeYqILhu1ZiXKpKnrg3YdsU8A+wAvoLkU/nPgAuCEvBI7VlRqK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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe4a6080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TITLE = 'LFC Season Points Per Game Comparison with Win Percentage'\n",
    "FIG_SIZE = (15, 10)\n",
    "max_ppg = df_position['ChampPPG'].max()\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "\n",
    "ax = df_position['ChampPPG'].plot(kind='bar', color='y', label='Champions', width=0.75)\n",
    "ax = df_position['LFCPPG'].plot(kind='bar', color='red', label='LFC', width=0.75)\n",
    "ax.set_ylabel(\"Points Per Game\")\n",
    "\n",
    "season_lfcppg = [] #  to hold tuple of (season, LFC points per game)\n",
    "\n",
    "for sidx, (ch, chpts, chppg, lfcpos, lfcpts, lfcppg) in enumerate(df_position.values):\n",
    "    # annotate description of each season, rotated\n",
    "    season = SEASON_LIST[sidx]\n",
    "    season_desc = season_dict[season]['label'][len(season)+1:]\n",
    "    x, y = (sidx, .05) # calculate position of annotation\n",
    "    ax.annotate(season_desc, xy=(x,y), xytext=(x,y), va=\"bottom\", ha=\"center\", \n",
    "                rotation='vertical', style='italic', fontsize='11')\n",
    "    # annotate above champions bar the name of champions and points per game\n",
    "    x, y = (sidx, chppg)\n",
    "    ax.annotate(str(ch)+'\\n'+ str(chppg), xy=(x,y), xytext=(x,y), \n",
    "                va=\"bottom\", ha=\"center\")\n",
    "    # annotate below LFC bar the points total and position\n",
    "    x, y = (sidx, lfcppg - 0.38)\n",
    "    w, d, l = dflfc_result[['W%', 'D%', 'L%']].ix[sidx].values\n",
    "    lfc_pos_str = str(n_plus_suffix(int(lfcpos)))\n",
    "    lfc_ppg_str = '\\n' + str(lfcppg)\n",
    "    result_str = '\\n\\nW%={}\\nD%={}\\nL%={}'.format(w, d, l)\n",
    "    ax.annotate('LFC ' + str(n_plus_suffix(int(lfcpos))) + lfc_ppg_str + result_str, xy=(x,y), xytext=(x,y), \n",
    "                va=\"bottom\", ha=\"center\")\n",
    "    \n",
    "    # append ppg to list \n",
    "    season_lfcppg.append((season, lfcppg))\n",
    "    \n",
    "ax.set_ylim((0, max_ppg+0.5))\n",
    "ax.set_title(TITLE)\n",
    "plt.legend(loc='upper left', fancybox=True, shadow=True)\n",
    "plt.show()\n",
    "fig.savefig('SeasonvsPPG.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RH: matches=20, points=25, ppg=1.25, fcast_pts=47\n",
      "KD: matches=18, points=33, ppg=1.83, fcast_pts=69\n"
     ]
    },
    {
     "data": {
      "image/png": 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maJ5zo0wQv4QNwP2AN1fV/YArgT/rqERJkqTeGZrn2CgTxC/jHOCcqvpye/94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\nIiIi+kjRHBERERHRx/8HKCjwYwS4qdgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf93908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DARKEST_SEASON = '2010-11'\n",
    "DARK_GAMES = 19 # number of games that Hodgson was in charge\n",
    "TOTAL_GAMES = 38\n",
    "rh_season_desc = DARKEST_SEASON + ' Part 1\\n  Roy Hodgson'\n",
    "kd_season_desc = DARKEST_SEASON + ' Part 2\\nKenny Dalglish'\n",
    "\n",
    "# calculate points per game for Roy Hodgson\n",
    "dflfc_201011_rh = dflfc_dict[DARKEST_SEASON][0:DARK_GAMES+1] \n",
    "rh_matches = len(dflfc_201011_rh)\n",
    "rh_points = dflfc_201011_rh['CUMPTS'].values[-1]\n",
    "rh_ppg = round(rh_points/rh_matches, 2)\n",
    "rh_fcast_pts = int(rh_points*(TOTAL_GAMES/rh_matches))\n",
    "print 'RH: matches={}, points={}, ppg={}, fcast_pts={}'.format(rh_matches, rh_points, rh_ppg, rh_fcast_pts)\n",
    "\n",
    "# calculate points per game for Kenny Dalglish\n",
    "dflfc_201011_kd = dflfc_dict[DARKEST_SEASON][DARK_GAMES+1:] \n",
    "kd_matches = len(dflfc_201011_kd)\n",
    "kd_points = dflfc_201011_kd['CUMPTS'].values[-1] - rh_points\n",
    "kd_ppg = round(kd_points/kd_matches, 2)\n",
    "kd_fcast_pts = int(kd_points*(TOTAL_GAMES/kd_matches))\n",
    "print 'KD: matches={}, points={}, ppg={}, fcast_pts={}'.format(kd_matches, kd_points, kd_ppg, kd_fcast_pts)\n",
    "\n",
    "# replace DARKEST SEASON list with 2 ppg entries\n",
    "# one for Hodgson and one for Dalglish\n",
    "season_lfcppg_new = season_lfcppg[:] # copy\n",
    "ppg_201011 = [lfcppg for (season, lfcppg) in season_lfcppg_new if season == DARKEST_SEASON][0]\n",
    "season_lfcppg_new.remove((DARKEST_SEASON, ppg_201011))\n",
    "season_lfcppg_new.append((rh_season_desc, rh_ppg)) \n",
    "season_lfcppg_new.append((kd_season_desc, kd_ppg))\n",
    "\n",
    "# plot ppg as bar chart\n",
    "TITLE = 'LFC Season Points Per Game Comparison\\n \\\n",
    "(with 2010-11 split to show individual performance)'\n",
    "FIG_SIZE = (12, 8)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "season_lfcppg_new.sort() # sort by season, in place\n",
    "season_labels = [s for (s, p) in season_lfcppg_new]\n",
    "x = range(1, len(season_lfcppg_new)+1)\n",
    "y = [p for (s, p) in season_lfcppg_new]\n",
    "ax = plt.bar(x, y, align='center', color='r')\n",
    "\n",
    "# plot ppg as text above bar\n",
    "for xidx, yt in enumerate(y):\n",
    "    xt = xidx + 1\n",
    "    plt.annotate(str(yt), xy=(xt,yt), xytext=(xt, yt), va=\"bottom\", ha=\"center\")   \n",
    "\n",
    "# highlight the low bar with yellow border in black\n",
    "# this is Hodgson's first half of 2010-11\n",
    "season_low, ppg_low = sorted(season_lfcppg_new, key=lambda tup: tup[1], reverse=False)[0]\n",
    "xlow = season_lfcppg_new.index((season_low, ppg_low))\n",
    "ax[xlow].set_color('black')\n",
    "ax[xlow].set_edgecolor('yellow')\n",
    "ax[xlow].set_hatch('/')\n",
    "\n",
    "# and highlight second half of this season with yellow border\n",
    "# this is Dalglish's second half of 2010-11\n",
    "ax[xlow+1].set_edgecolor('yellow')\n",
    "ax[xlow+1].set_hatch('/')\n",
    "\n",
    "# add labels and plot\n",
    "plt.xticks(x, season_labels, rotation='vertical')\n",
    "plt.ylabel(\"Points Per Game\")\n",
    "plt.xlabel(\"\\nSeason\")\n",
    "plt.title(TITLE)\n",
    "plt.show()\n",
    "fig.savefig('SeasonvsPTSdark.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>HA</th>\n",
       "      <th>R</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>SF</th>\n",
       "      <th>SA</th>\n",
       "      <th>SFT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>PTS</th>\n",
       "      <th>CUMPTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2010-12-06</td>\n",
       "      <td> Aston Villa</td>\n",
       "      <td> Home</td>\n",
       "      <td> W</td>\n",
       "      <td> 3</td>\n",
       "      <td> 0</td>\n",
       "      <td> 12</td>\n",
       "      <td>  5</td>\n",
       "      <td> 7</td>\n",
       "      <td> 2</td>\n",
       "      <td> 3</td>\n",
       "      <td> 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2010-12-11</td>\n",
       "      <td>   Newcastle</td>\n",
       "      <td> Away</td>\n",
       "      <td> L</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 16</td>\n",
       "      <td>  8</td>\n",
       "      <td> 7</td>\n",
       "      <td> 5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010-12-29</td>\n",
       "      <td>      Wolves</td>\n",
       "      <td> Home</td>\n",
       "      <td> L</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  9</td>\n",
       "      <td>  7</td>\n",
       "      <td> 5</td>\n",
       "      <td> 3</td>\n",
       "      <td> 0</td>\n",
       "      <td> 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>      Bolton</td>\n",
       "      <td> Home</td>\n",
       "      <td> W</td>\n",
       "      <td> 2</td>\n",
       "      <td> 1</td>\n",
       "      <td> 16</td>\n",
       "      <td> 10</td>\n",
       "      <td> 7</td>\n",
       "      <td> 4</td>\n",
       "      <td> 3</td>\n",
       "      <td> 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>   Blackburn</td>\n",
       "      <td> Away</td>\n",
       "      <td> L</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 13</td>\n",
       "      <td> 10</td>\n",
       "      <td> 9</td>\n",
       "      <td> 4</td>\n",
       "      <td> 0</td>\n",
       "      <td> 25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date     Opponent    HA  R  GF  GA  SF  SA  SFT  SAT  PTS  CUMPTS\n",
       "15 2010-12-06  Aston Villa  Home  W   3   0  12   5    7    2    3      22\n",
       "16 2010-12-11    Newcastle  Away  L   1   3  16   8    7    5    0      22\n",
       "17 2010-12-29       Wolves  Home  L   0   1   9   7    5    3    0      22\n",
       "18 2011-01-01       Bolton  Home  W   2   1  16  10    7    4    3      25\n",
       "19 2011-01-05    Blackburn  Away  L   1   3  13  10    9    4    0      25"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_201011_rh.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Blackburn was his final game - what joy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Who are the richest English clubs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Country</th>\n",
       "      <th>Value($M)</th>\n",
       "      <th>Revenue($M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td>   Real Madrid</td>\n",
       "      <td>   Spain</td>\n",
       "      <td> 3260</td>\n",
       "      <td> 746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2</td>\n",
       "      <td>  FC Barcelona</td>\n",
       "      <td>   Spain</td>\n",
       "      <td> 3160</td>\n",
       "      <td> 657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 3</td>\n",
       "      <td>    Man United</td>\n",
       "      <td> England</td>\n",
       "      <td> 3100</td>\n",
       "      <td> 703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 4</td>\n",
       "      <td> Bayern Munich</td>\n",
       "      <td> Germany</td>\n",
       "      <td> 2350</td>\n",
       "      <td> 661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 5</td>\n",
       "      <td>      Man City</td>\n",
       "      <td> England</td>\n",
       "      <td> 1380</td>\n",
       "      <td> 562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank           Team  Country  Value($M)  Revenue($M)\n",
       "0     1    Real Madrid    Spain       3260          746\n",
       "1     2   FC Barcelona    Spain       3160          657\n",
       "2     3     Man United  England       3100          703\n",
       "3     4  Bayern Munich  Germany       2350          661\n",
       "4     5       Man City  England       1380          562"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rich_list_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Value($M)</th>\n",
       "      <th>Revenue($M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>  3</td>\n",
       "      <td> Man United</td>\n",
       "      <td> 3100</td>\n",
       "      <td> 703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>  5</td>\n",
       "      <td>   Man City</td>\n",
       "      <td> 1380</td>\n",
       "      <td> 562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>  6</td>\n",
       "      <td>    Chelsea</td>\n",
       "      <td> 1370</td>\n",
       "      <td> 526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>  7</td>\n",
       "      <td>    Arsenal</td>\n",
       "      <td> 1310</td>\n",
       "      <td> 487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>  8</td>\n",
       "      <td>  Liverpool</td>\n",
       "      <td>  982</td>\n",
       "      <td> 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 13</td>\n",
       "      <td>  Tottenham</td>\n",
       "      <td>  600</td>\n",
       "      <td> 293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 18</td>\n",
       "      <td>  Newcastle</td>\n",
       "      <td>  349</td>\n",
       "      <td> 210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 19</td>\n",
       "      <td>   West Ham</td>\n",
       "      <td>  309</td>\n",
       "      <td> 186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank        Team  Value($M)  Revenue($M)\n",
       "0     3  Man United       3100          703\n",
       "1     5    Man City       1380          562\n",
       "2     6     Chelsea       1370          526\n",
       "3     7     Arsenal       1310          487\n",
       "4     8   Liverpool        982          415\n",
       "5    13   Tottenham        600          293\n",
       "6    18   Newcastle        349          210\n",
       "7    19    West Ham        309          186"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_rich_list = rich_list_2015[['Rank', 'Team', 'Value($M)', 'Revenue($M)'] ][rich_list_2015['Country'] == 'England']\n",
    "eng_rich_list.reset_index(inplace=True, drop=True)\n",
    "eng_rich_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Value($M)</th>\n",
       "      <th>Revenue($M)</th>\n",
       "      <th>PTS</th>\n",
       "      <th>ValperPT</th>\n",
       "      <th>RevperPT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>  3</td>\n",
       "      <td> Man United</td>\n",
       "      <td> 3100</td>\n",
       "      <td> 703</td>\n",
       "      <td> 70</td>\n",
       "      <td> 44.285714</td>\n",
       "      <td> 10.042857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>  5</td>\n",
       "      <td>   Man City</td>\n",
       "      <td> 1380</td>\n",
       "      <td> 562</td>\n",
       "      <td> 79</td>\n",
       "      <td> 17.468354</td>\n",
       "      <td>  7.113924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>  8</td>\n",
       "      <td>  Liverpool</td>\n",
       "      <td>  982</td>\n",
       "      <td> 415</td>\n",
       "      <td> 62</td>\n",
       "      <td> 15.838710</td>\n",
       "      <td>  6.693548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>  7</td>\n",
       "      <td>    Arsenal</td>\n",
       "      <td> 1310</td>\n",
       "      <td> 487</td>\n",
       "      <td> 75</td>\n",
       "      <td> 17.466667</td>\n",
       "      <td>  6.493333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>  6</td>\n",
       "      <td>    Chelsea</td>\n",
       "      <td> 1370</td>\n",
       "      <td> 526</td>\n",
       "      <td> 87</td>\n",
       "      <td> 15.747126</td>\n",
       "      <td>  6.045977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 18</td>\n",
       "      <td>  Newcastle</td>\n",
       "      <td>  349</td>\n",
       "      <td> 210</td>\n",
       "      <td> 39</td>\n",
       "      <td>  8.948718</td>\n",
       "      <td>  5.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 13</td>\n",
       "      <td>  Tottenham</td>\n",
       "      <td>  600</td>\n",
       "      <td> 293</td>\n",
       "      <td> 64</td>\n",
       "      <td>  9.375000</td>\n",
       "      <td>  4.578125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 19</td>\n",
       "      <td>   West Ham</td>\n",
       "      <td>  309</td>\n",
       "      <td> 186</td>\n",
       "      <td> 47</td>\n",
       "      <td>  6.574468</td>\n",
       "      <td>  3.957447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank        Team  Value($M)  Revenue($M)  PTS   ValperPT   RevperPT\n",
       "0     3  Man United       3100          703   70  44.285714  10.042857\n",
       "1     5    Man City       1380          562   79  17.468354   7.113924\n",
       "4     8   Liverpool        982          415   62  15.838710   6.693548\n",
       "3     7     Arsenal       1310          487   75  17.466667   6.493333\n",
       "2     6     Chelsea       1370          526   87  15.747126   6.045977\n",
       "6    18   Newcastle        349          210   39   8.948718   5.384615\n",
       "5    13   Tottenham        600          293   64   9.375000   4.578125\n",
       "7    19    West Ham        309          186   47   6.574468   3.957447"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_rich_list = eng_rich_list.merge(prem_table(df_dict['2014-15'], '2014-15')[['Team', 'PTS']])\n",
    "eng_rich_list['ValperPT'] = eng_rich_list['Value($M)']/eng_rich_list['PTS']\n",
    "eng_rich_list['RevperPT'] = eng_rich_list['Revenue($M)']/eng_rich_list['PTS']\n",
    "eng_rich_list.sort('RevperPT', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf6f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YOyJeP+21ItzzYmZmNhFm+rm+1mUjSftI+gT1Ms1rgNOAh0XEAXMduKzldbdq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\nZmZmNlY8eDEzM7Ox4sGLmZmZjZX/H/djjKn3DX5mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdcd3668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_SIZE = (9, 6)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "\n",
    "# create list of colours corresponding to teams\n",
    "# set LFC to red\n",
    "bar_colours = ['b' for _ in range(len(eng_rich_list))]\n",
    "LFC_idx = int(eng_rich_list[eng_rich_list['Team'] == 'Liverpool'].index.tolist()[0])\n",
    "bar_colours[LFC_idx] = 'r'\n",
    "\n",
    "ax = eng_rich_list.plot(x='Team', y='Value($M)', kind='bar', legend=False, \n",
    "                        figsize=FIG_SIZE, color=bar_colours, title='English Team Value in 2015')\n",
    "ax.set_xlabel('English Team')\n",
    "ax.set_ylabel('Value ($M)')\n",
    "plt.show()\n",
    "fig.savefig('TeamvsValue.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe513c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vD6Q+6+3zgeupC5cvjSAuMzMzs75m63k5E9gAOAE4EbiWxrxKRFw3igDnytNG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5130b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TITLE = 'English Team Value in 2015 with Final League Position'\n",
    "FIG_SIZE = (9, 6)\n",
    "fig = plt.figure(figsize=FIG_SIZE)\n",
    "\n",
    "# create list of colours corresponding to teams\n",
    "# set LFC to red\n",
    "bar_colours = ['b' for _ in range(len(eng_rich_list))]\n",
    "LFC_idx = int(eng_rich_list[eng_rich_list['Team'] == 'Liverpool'].index.tolist()[0])\n",
    "bar_colours[LFC_idx] = 'r'\n",
    "\n",
    "# create list of rich teams\n",
    "rich_teams = list(eng_rich_list['Team'].values)\n",
    "\n",
    "# plot the rich teams\n",
    "ax = eng_rich_list.plot(x='Team', y='Value($M)', kind='bar', legend=False, \n",
    "                        figsize=FIG_SIZE, color=bar_colours)\n",
    "\n",
    "# create new dataframe for positions\n",
    "col_names = ['Champions', 'ChampPoints', 'ChampPPG', 'LFCPos', 'LFCPoints', 'LFCPPG']\n",
    "df_position = pd.DataFrame(columns=col_names)\n",
    "\n",
    "df_PLT2015 = prem_table(df_dict['2014-15'], '2014-15')['Team']\n",
    "\n",
    "# plot the positions\n",
    "for pos, team in df_PLT2015.iteritems():\n",
    "    if team in rich_teams:  \n",
    "        # annotate team's final position\n",
    "        team_idx = rich_teams.index(team)\n",
    "        team_value = eng_rich_list['Value($M)'][eng_rich_list['Team'] == team].values[0]\n",
    "        x, y = (team_idx, team_value + 20)\n",
    "        ax.annotate(str(n_plus_suffix(int(pos))), xy=(x,y), xytext=(x,y), \n",
    "                    va=\"bottom\", ha=\"center\")\n",
    "    \n",
    "ax.set_xlabel('English Team')\n",
    "ax.set_ylabel('Value ($M)')\n",
    "ax.set_title(TITLE)\n",
    "fig = plt.gcf() # save current figure\n",
    "plt.show()\n",
    "fig.savefig('TeamvsValue.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the title winning top scorer partnerships?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's analyse the champion hotshot data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td> 1985-86</td>\n",
       "      <td>                     Jan Molby</td>\n",
       "      <td> 14</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td> 1987-88</td>\n",
       "      <td>                 John Aldridge</td>\n",
       "      <td> 26</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td> 1987-88</td>\n",
       "      <td> Peter Beardsley / John Barnes</td>\n",
       "      <td> 15</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td> 1989-90</td>\n",
       "      <td>                   John Barnes</td>\n",
       "      <td> 22</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td> 1989-90</td>\n",
       "      <td>                      Ian Rush</td>\n",
       "      <td> 18</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season                         Player  LeagueGoals         Manager\n",
       "31  1985-86                      Jan Molby           14  Kenny Dalglish\n",
       "32  1987-88                  John Aldridge           26  Kenny Dalglish\n",
       "33  1987-88  Peter Beardsley / John Barnes           15  Kenny Dalglish\n",
       "34  1989-90                    John Barnes           22  Kenny Dalglish\n",
       "35  1989-90                       Ian Rush           18  Kenny Dalglish"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_champ_hotshots.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the highest scoring partnership?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LeagueGoals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-64</th>\n",
       "      <td> 52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         LeagueGoals\n",
       "Season              \n",
       "1963-64           52"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_partner_goals = dflfc_champ_hotshots['LeagueGoals'].groupby(dflfc_champ_hotshots['Season']).sum()\n",
    "df_partner_goals = pd.DataFrame(data=s_partner_goals)\n",
    "df_partner_goals.sort('LeagueGoals', ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td> 1963-64</td>\n",
       "      <td>  Roger Hunt</td>\n",
       "      <td> 31</td>\n",
       "      <td> Bill Shankly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td> 1963-64</td>\n",
       "      <td> Ian St John</td>\n",
       "      <td> 21</td>\n",
       "      <td> Bill Shankly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season       Player  LeagueGoals       Manager\n",
       "10  1963-64   Roger Hunt           31  Bill Shankly\n",
       "11  1963-64  Ian St John           21  Bill Shankly"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_champ_hotshots[dflfc_champ_hotshots['Season'] == '1963-64']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the lowest scoring partnership?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LeagueGoals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-77</th>\n",
       "      <td> 22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         LeagueGoals\n",
       "Season              \n",
       "1976-77           22"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_partner_goals.sort('LeagueGoals', ascending=False).tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td> 1976-77</td>\n",
       "      <td> Kevin Keegan</td>\n",
       "      <td> 12</td>\n",
       "      <td> Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td> 1976-77</td>\n",
       "      <td> John Toshack</td>\n",
       "      <td> 10</td>\n",
       "      <td> Bob Paisley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season        Player  LeagueGoals      Manager\n",
       "18  1976-77  Kevin Keegan           12  Bob Paisley\n",
       "19  1976-77  John Toshack           10  Bob Paisley"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_champ_hotshots[dflfc_champ_hotshots['Season'] == '1976-77']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the average total goals of title winning partnership? And what is highest and lowest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average partnership goals: 36.5\n",
      "max partnership goals: 52\n",
      "min partnership goals: 22\n"
     ]
    }
   ],
   "source": [
    "print 'average partnership goals: {}'.format(df_partner_goals['LeagueGoals'].mean())\n",
    "print 'max partnership goals: {}'.format(df_partner_goals['LeagueGoals'].max())\n",
    "print 'min partnership goals: {}'.format(df_partner_goals['LeagueGoals'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the average total goals of title winning top striker?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top scorer average: 21.78\n"
     ]
    }
   ],
   "source": [
    "df_scorer_first = dflfc_champ_hotshots.groupby('Season').first()\n",
    "print 'top scorer average: {}'.format(df_scorer_first['LeagueGoals'].mean().round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the average total goals of title winning partner?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "partner scorer average: 14.72\n"
     ]
    }
   ],
   "source": [
    "df_scorer_second = dflfc_champ_hotshots.groupby('Season').last()\n",
    "print 'partner scorer average: {}'.format(df_scorer_second['LeagueGoals'].mean().round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which top striker scored most goals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1983-84</th>\n",
       "      <td> Ian Rush</td>\n",
       "      <td> 32</td>\n",
       "      <td> Joe Fagan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player  LeagueGoals    Manager\n",
       "Season                                   \n",
       "1983-84  Ian Rush           32  Joe Fagan"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorer_first.sort('LeagueGoals', ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which top striker scored least goals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-77</th>\n",
       "      <td> Kevin Keegan</td>\n",
       "      <td> 12</td>\n",
       "      <td> Bob Paisley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Player  LeagueGoals      Manager\n",
       "Season                                         \n",
       "1976-77  Kevin Keegan           12  Bob Paisley"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorer_first.sort('LeagueGoals', ascending=False).tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which partner striker scored most goals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1946-47</th>\n",
       "      <td> Jack Balmer</td>\n",
       "      <td> 24</td>\n",
       "      <td> George Kay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Player  LeagueGoals     Manager\n",
       "Season                                       \n",
       "1946-47  Jack Balmer           24  George Kay"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorer_second.sort('LeagueGoals', ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's high, let's look at the partnership."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 1946-47</td>\n",
       "      <td> Albert Stubbins</td>\n",
       "      <td> 24</td>\n",
       "      <td> George Kay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 1946-47</td>\n",
       "      <td>     Jack Balmer</td>\n",
       "      <td> 24</td>\n",
       "      <td> George Kay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Season           Player  LeagueGoals     Manager\n",
       "8  1946-47  Albert Stubbins           24  George Kay\n",
       "9  1946-47      Jack Balmer           24  George Kay"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_champ_hotshots[dflfc_champ_hotshots['Season'] == '1946-47']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which partner striker scored least goals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1983-84</th>\n",
       "      <td> Kenny Dalglish / Graeme Souness</td>\n",
       "      <td> 7</td>\n",
       "      <td> Joe Fagan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Player  LeagueGoals    Manager\n",
       "Season                                                          \n",
       "1983-84  Kenny Dalglish / Graeme Souness            7  Joe Fagan"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorer_second.sort('LeagueGoals', ascending=False).tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who were hotshots in 70s and 80s?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Player</th>\n",
       "      <th>LeagueGoals</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td> 1972-73</td>\n",
       "      <td>                    John Toshack</td>\n",
       "      <td> 13</td>\n",
       "      <td>   Bill Shankly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td> 1972-73</td>\n",
       "      <td>                    Kevin Keegan</td>\n",
       "      <td> 13</td>\n",
       "      <td>   Bill Shankly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td> 1975-76</td>\n",
       "      <td>                    John Toshack</td>\n",
       "      <td> 16</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td> 1975-76</td>\n",
       "      <td>                    Kevin Keegan</td>\n",
       "      <td> 12</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td> 1976-77</td>\n",
       "      <td>                    Kevin Keegan</td>\n",
       "      <td> 12</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td> 1976-77</td>\n",
       "      <td>                    John Toshack</td>\n",
       "      <td> 10</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td> 1978-79</td>\n",
       "      <td>                  Kenny Dalglish</td>\n",
       "      <td> 21</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td> 1978-79</td>\n",
       "      <td>                   David Johnson</td>\n",
       "      <td> 16</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td> 1979-80</td>\n",
       "      <td>                   David Johnson</td>\n",
       "      <td> 21</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td> 1979-80</td>\n",
       "      <td>                  Kenny Dalglish</td>\n",
       "      <td> 16</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td> 1981-82</td>\n",
       "      <td>                        Ian Rush</td>\n",
       "      <td> 17</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td> 1981-82</td>\n",
       "      <td>                 Terry McDermott</td>\n",
       "      <td> 14</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td> 1982-83</td>\n",
       "      <td>                        Ian Rush</td>\n",
       "      <td> 24</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td> 1982-83</td>\n",
       "      <td>                  Kenny Dalglish</td>\n",
       "      <td> 18</td>\n",
       "      <td>    Bob Paisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td> 1983-84</td>\n",
       "      <td>                        Ian Rush</td>\n",
       "      <td> 32</td>\n",
       "      <td>      Joe Fagan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td> 1983-84</td>\n",
       "      <td> Kenny Dalglish / Graeme Souness</td>\n",
       "      <td>  7</td>\n",
       "      <td>      Joe Fagan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 1985-86</td>\n",
       "      <td>                        Ian Rush</td>\n",
       "      <td> 22</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td> 1985-86</td>\n",
       "      <td>                       Jan Molby</td>\n",
       "      <td> 14</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td> 1987-88</td>\n",
       "      <td>                   John Aldridge</td>\n",
       "      <td> 26</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td> 1987-88</td>\n",
       "      <td>   Peter Beardsley / John Barnes</td>\n",
       "      <td> 15</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td> 1989-90</td>\n",
       "      <td>                     John Barnes</td>\n",
       "      <td> 22</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td> 1989-90</td>\n",
       "      <td>                        Ian Rush</td>\n",
       "      <td> 18</td>\n",
       "      <td> Kenny Dalglish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Season                           Player  LeagueGoals         Manager\n",
       "14  1972-73                     John Toshack           13    Bill Shankly\n",
       "15  1972-73                     Kevin Keegan           13    Bill Shankly\n",
       "16  1975-76                     John Toshack           16     Bob Paisley\n",
       "17  1975-76                     Kevin Keegan           12     Bob Paisley\n",
       "18  1976-77                     Kevin Keegan           12     Bob Paisley\n",
       "19  1976-77                     John Toshack           10     Bob Paisley\n",
       "20  1978-79                   Kenny Dalglish           21     Bob Paisley\n",
       "21  1978-79                    David Johnson           16     Bob Paisley\n",
       "22  1979-80                    David Johnson           21     Bob Paisley\n",
       "23  1979-80                   Kenny Dalglish           16     Bob Paisley\n",
       "24  1981-82                         Ian Rush           17     Bob Paisley\n",
       "25  1981-82                  Terry McDermott           14     Bob Paisley\n",
       "26  1982-83                         Ian Rush           24     Bob Paisley\n",
       "27  1982-83                   Kenny Dalglish           18     Bob Paisley\n",
       "28  1983-84                         Ian Rush           32       Joe Fagan\n",
       "29  1983-84  Kenny Dalglish / Graeme Souness            7       Joe Fagan\n",
       "30  1985-86                         Ian Rush           22  Kenny Dalglish\n",
       "31  1985-86                        Jan Molby           14  Kenny Dalglish\n",
       "32  1987-88                    John Aldridge           26  Kenny Dalglish\n",
       "33  1987-88    Peter Beardsley / John Barnes           15  Kenny Dalglish\n",
       "34  1989-90                      John Barnes           22  Kenny Dalglish\n",
       "35  1989-90                         Ian Rush           18  Kenny Dalglish"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflfc_champ_hotshots[(dflfc_champ_hotshots['Season'].str.contains('197')) |\n",
    "                    (dflfc_champ_hotshots['Season'].str.contains('198'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "legends one and all"
   ]
  }
 ],
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