{
 "metadata": {
  "name": "",
  "signature": "sha256:8039267a05cd12aea86382715fc2cfe4bde5f77ed6167f05368c1b469db900e6"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Exploratory Analysis of the Battles of the War of Five Kings Dataset\n",
      "\n",
      "- Author: Chris Albon (@ChrisAlbon)\n",
      "- Date: August 17, 2014\n",
      "- Repo: https://github.com/chrisalbon/war_of_the_five_kings_dataset/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Preliminary"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import required modules\n",
      "import pandas as pd\n",
      "from ggplot import *\n",
      "%matplotlib inline\n",
      "\n",
      "# Set ipython's max row display\n",
      "pd.set_option('display.max_row', 1000)\n",
      "\n",
      "# Set iPython's max column width to 50\n",
      "pd.set_option('display.max_columns', 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Loading the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load the dataset\n",
      "df = pd.read_csv('5kings_battles_v1.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Quick check on the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# View the top five observations\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>name</th>\n",
        "      <th>year</th>\n",
        "      <th>battle_number</th>\n",
        "      <th>attacker_king</th>\n",
        "      <th>defender_king</th>\n",
        "      <th>attacker_1</th>\n",
        "      <th>attacker_2</th>\n",
        "      <th>attacker_3</th>\n",
        "      <th>attacker_4</th>\n",
        "      <th>defender_1</th>\n",
        "      <th>defender_2</th>\n",
        "      <th>defender_3</th>\n",
        "      <th>defender_4</th>\n",
        "      <th>attacker_outcome</th>\n",
        "      <th>battle_type</th>\n",
        "      <th>major_death</th>\n",
        "      <th>major_capture</th>\n",
        "      <th>attacker_size</th>\n",
        "      <th>defender_size</th>\n",
        "      <th>attacker_commander</th>\n",
        "      <th>defender_commander</th>\n",
        "      <th>summer</th>\n",
        "      <th>location</th>\n",
        "      <th>region</th>\n",
        "      <th>note</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    Battle of the Golden Tooth</td>\n",
        "      <td> 298</td>\n",
        "      <td> 1</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>               Robb Stark</td>\n",
        "      <td> Lannister</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     Tully</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  win</td>\n",
        "      <td> pitched battle</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15000</td>\n",
        "      <td>  4000</td>\n",
        "      <td>                                   Jaime Lannister</td>\n",
        "      <td>                              Clement Piper, Vance</td>\n",
        "      <td> 1</td>\n",
        "      <td>    Golden Tooth</td>\n",
        "      <td> The Westerlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   Battle at the Mummer's Ford</td>\n",
        "      <td> 298</td>\n",
        "      <td> 2</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>               Robb Stark</td>\n",
        "      <td> Lannister</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Baratheon</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  win</td>\n",
        "      <td>         ambush</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   120</td>\n",
        "      <td>                                    Gregor Clegane</td>\n",
        "      <td>                                  Beric Dondarrion</td>\n",
        "      <td> 1</td>\n",
        "      <td>   Mummer's Ford</td>\n",
        "      <td>  The Riverlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>            Battle of Riverrun</td>\n",
        "      <td> 298</td>\n",
        "      <td> 3</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>               Robb Stark</td>\n",
        "      <td> Lannister</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     Tully</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  win</td>\n",
        "      <td> pitched battle</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 15000</td>\n",
        "      <td> 10000</td>\n",
        "      <td>                      Jaime Lannister, Andros Brax</td>\n",
        "      <td>                     Edmure Tully, Tytos Blackwood</td>\n",
        "      <td> 1</td>\n",
        "      <td>        Riverrun</td>\n",
        "      <td>  The Riverlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      Battle of the Green Fork</td>\n",
        "      <td> 298</td>\n",
        "      <td> 4</td>\n",
        "      <td>               Robb Stark</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>     Stark</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Lannister</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> loss</td>\n",
        "      <td> pitched battle</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 18000</td>\n",
        "      <td> 20000</td>\n",
        "      <td> Roose Bolton, Wylis Manderly, Medger Cerwyn, H...</td>\n",
        "      <td> Tywin Lannister, Gregor Clegane, Kevan Lannist...</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Green Fork</td>\n",
        "      <td>  The Riverlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Battle of the Whispering Wood</td>\n",
        "      <td> 298</td>\n",
        "      <td> 5</td>\n",
        "      <td>               Robb Stark</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>     Stark</td>\n",
        "      <td> Tully</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Lannister</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  win</td>\n",
        "      <td>         ambush</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1875</td>\n",
        "      <td>  6000</td>\n",
        "      <td>                         Robb Stark, Brynden Tully</td>\n",
        "      <td>                                   Jaime Lannister</td>\n",
        "      <td> 1</td>\n",
        "      <td> Whispering Wood</td>\n",
        "      <td>  The Riverlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                            name  year  battle_number  \\\n",
        "0     Battle of the Golden Tooth   298              1   \n",
        "1    Battle at the Mummer's Ford   298              2   \n",
        "2             Battle of Riverrun   298              3   \n",
        "3       Battle of the Green Fork   298              4   \n",
        "4  Battle of the Whispering Wood   298              5   \n",
        "\n",
        "              attacker_king             defender_king attacker_1 attacker_2  \\\n",
        "0  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
        "1  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
        "2  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
        "3                Robb Stark  Joffrey/Tommen Baratheon      Stark        NaN   \n",
        "4                Robb Stark  Joffrey/Tommen Baratheon      Stark      Tully   \n",
        "\n",
        "  attacker_3 attacker_4 defender_1 defender_2  defender_3  defender_4  \\\n",
        "0        NaN        NaN      Tully        NaN         NaN         NaN   \n",
        "1        NaN        NaN  Baratheon        NaN         NaN         NaN   \n",
        "2        NaN        NaN      Tully        NaN         NaN         NaN   \n",
        "3        NaN        NaN  Lannister        NaN         NaN         NaN   \n",
        "4        NaN        NaN  Lannister        NaN         NaN         NaN   \n",
        "\n",
        "  attacker_outcome     battle_type  major_death  major_capture  attacker_size  \\\n",
        "0              win  pitched battle            1              0          15000   \n",
        "1              win          ambush            1              0            NaN   \n",
        "2              win  pitched battle            0              1          15000   \n",
        "3             loss  pitched battle            1              1          18000   \n",
        "4              win          ambush            1              1           1875   \n",
        "\n",
        "   defender_size                                 attacker_commander  \\\n",
        "0           4000                                    Jaime Lannister   \n",
        "1            120                                     Gregor Clegane   \n",
        "2          10000                       Jaime Lannister, Andros Brax   \n",
        "3          20000  Roose Bolton, Wylis Manderly, Medger Cerwyn, H...   \n",
        "4           6000                          Robb Stark, Brynden Tully   \n",
        "\n",
        "                                  defender_commander  summer         location  \\\n",
        "0                               Clement Piper, Vance       1     Golden Tooth   \n",
        "1                                   Beric Dondarrion       1    Mummer's Ford   \n",
        "2                      Edmure Tully, Tytos Blackwood       1         Riverrun   \n",
        "3  Tywin Lannister, Gregor Clegane, Kevan Lannist...       1       Green Fork   \n",
        "4                                    Jaime Lannister       1  Whispering Wood   \n",
        "\n",
        "            region note  \n",
        "0  The Westerlands  NaN  \n",
        "1   The Riverlands  NaN  \n",
        "2   The Riverlands  NaN  \n",
        "3   The Riverlands  NaN  \n",
        "4   The Riverlands  NaN  "
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# View the bottom five observations\n",
      "df.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>name</th>\n",
        "      <th>year</th>\n",
        "      <th>battle_number</th>\n",
        "      <th>attacker_king</th>\n",
        "      <th>defender_king</th>\n",
        "      <th>attacker_1</th>\n",
        "      <th>attacker_2</th>\n",
        "      <th>attacker_3</th>\n",
        "      <th>attacker_4</th>\n",
        "      <th>defender_1</th>\n",
        "      <th>defender_2</th>\n",
        "      <th>defender_3</th>\n",
        "      <th>defender_4</th>\n",
        "      <th>attacker_outcome</th>\n",
        "      <th>battle_type</th>\n",
        "      <th>major_death</th>\n",
        "      <th>major_capture</th>\n",
        "      <th>attacker_size</th>\n",
        "      <th>defender_size</th>\n",
        "      <th>attacker_commander</th>\n",
        "      <th>defender_commander</th>\n",
        "      <th>summer</th>\n",
        "      <th>location</th>\n",
        "      <th>region</th>\n",
        "      <th>note</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> Second Seige of Storm's End</td>\n",
        "      <td> 300</td>\n",
        "      <td> 34</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>        Stannis Baratheon</td>\n",
        "      <td> Baratheon</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> Baratheon</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> win</td>\n",
        "      <td> siege</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  200</td>\n",
        "      <td>                   Mace Tyrell, Mathis Rowan</td>\n",
        "      <td> Gilbert Farring</td>\n",
        "      <td> 0</td>\n",
        "      <td> Storm's End</td>\n",
        "      <td> The Stormlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>        Siege of Dragonstone</td>\n",
        "      <td> 300</td>\n",
        "      <td> 35</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>        Stannis Baratheon</td>\n",
        "      <td> Baratheon</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> Baratheon</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> win</td>\n",
        "      <td> siege</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2000</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>                Loras Tyrell, Raxter Redwyne</td>\n",
        "      <td>   Rolland Storm</td>\n",
        "      <td> 0</td>\n",
        "      <td> Dragonstone</td>\n",
        "      <td> The Stormlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>           Siege of Riverrun</td>\n",
        "      <td> 300</td>\n",
        "      <td> 36</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>               Robb Stark</td>\n",
        "      <td> Lannister</td>\n",
        "      <td>      Frey</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     Tully</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> win</td>\n",
        "      <td> siege</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 3000</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> Daven Lannister, Ryman Fey, Jaime Lannister</td>\n",
        "      <td>   Brynden Tully</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Riverrun</td>\n",
        "      <td> The Riverlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>          Siege of Raventree</td>\n",
        "      <td> 300</td>\n",
        "      <td> 37</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td>               Robb Stark</td>\n",
        "      <td>   Bracken</td>\n",
        "      <td> Lannister</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> Blackwood</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> win</td>\n",
        "      <td> siege</td>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>              Jonos Bracken, Jaime Lannister</td>\n",
        "      <td> Tytos Blackwood</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Raventree</td>\n",
        "      <td> The Riverlands</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>         Siege of Winterfell</td>\n",
        "      <td> 300</td>\n",
        "      <td> 38</td>\n",
        "      <td>        Stannis Baratheon</td>\n",
        "      <td> Joffrey/Tommen Baratheon</td>\n",
        "      <td> Baratheon</td>\n",
        "      <td>  Karstark</td>\n",
        "      <td> Mormont</td>\n",
        "      <td> Glover</td>\n",
        "      <td>    Bolton</td>\n",
        "      <td> Frey</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5000</td>\n",
        "      <td> 8000</td>\n",
        "      <td>                           Stannis Baratheon</td>\n",
        "      <td>    Roose Bolton</td>\n",
        "      <td> 0</td>\n",
        "      <td>  Winterfell</td>\n",
        "      <td>      The North</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "                           name  year  battle_number  \\\n",
        "33  Second Seige of Storm's End   300             34   \n",
        "34         Siege of Dragonstone   300             35   \n",
        "35            Siege of Riverrun   300             36   \n",
        "36           Siege of Raventree   300             37   \n",
        "37          Siege of Winterfell   300             38   \n",
        "\n",
        "               attacker_king             defender_king attacker_1 attacker_2  \\\n",
        "33  Joffrey/Tommen Baratheon         Stannis Baratheon  Baratheon        NaN   \n",
        "34  Joffrey/Tommen Baratheon         Stannis Baratheon  Baratheon        NaN   \n",
        "35  Joffrey/Tommen Baratheon                Robb Stark  Lannister       Frey   \n",
        "36  Joffrey/Tommen Baratheon                Robb Stark    Bracken  Lannister   \n",
        "37         Stannis Baratheon  Joffrey/Tommen Baratheon  Baratheon   Karstark   \n",
        "\n",
        "   attacker_3 attacker_4 defender_1 defender_2  defender_3  defender_4  \\\n",
        "33        NaN        NaN  Baratheon        NaN         NaN         NaN   \n",
        "34        NaN        NaN  Baratheon        NaN         NaN         NaN   \n",
        "35        NaN        NaN      Tully        NaN         NaN         NaN   \n",
        "36        NaN        NaN  Blackwood        NaN         NaN         NaN   \n",
        "37    Mormont     Glover     Bolton       Frey         NaN         NaN   \n",
        "\n",
        "   attacker_outcome battle_type  major_death  major_capture  attacker_size  \\\n",
        "33              win       siege            0              0            NaN   \n",
        "34              win       siege            0              0           2000   \n",
        "35              win       siege            0              0           3000   \n",
        "36              win       siege            0              1           1500   \n",
        "37              NaN         NaN          NaN            NaN           5000   \n",
        "\n",
        "    defender_size                           attacker_commander  \\\n",
        "33            200                    Mace Tyrell, Mathis Rowan   \n",
        "34            NaN                 Loras Tyrell, Raxter Redwyne   \n",
        "35            NaN  Daven Lannister, Ryman Fey, Jaime Lannister   \n",
        "36            NaN               Jonos Bracken, Jaime Lannister   \n",
        "37           8000                            Stannis Baratheon   \n",
        "\n",
        "   defender_commander  summer     location          region note  \n",
        "33    Gilbert Farring       0  Storm's End  The Stormlands  NaN  \n",
        "34      Rolland Storm       0  Dragonstone  The Stormlands  NaN  \n",
        "35      Brynden Tully       0     Riverrun  The Riverlands  NaN  \n",
        "36    Tytos Blackwood       0    Raventree  The Riverlands  NaN  \n",
        "37       Roose Bolton       0   Winterfell       The North  NaN  "
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exploratory Data Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Which year had the most battles?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Count the number of observations for each value\n",
      "df['year'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "299    20\n",
        "300    11\n",
        "298     7\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Which region had the most battles?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Count the number of observations for each value, then make a bar plot\n",
      "df['region'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c33e810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAFJCAYAAAC//hc8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bf2f6d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### What was the outcomes of all battles?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Count the number of observations for each value, then make a bar plot\n",
      "df['attacker_outcome'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c376710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEICAYAAAB7+s71AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJkiRJkiRJif0fzViGDVtNyt0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c10a410>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### How common was the different types of battles?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Count the number of observations for each value, then make a bar plot\n",
      "df['battle_type'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c46eb10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAE7CAYAAAD5FLv2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c51e650>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Which king attacked the most?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Count the number of observations for each value, then make a bar plot\n",
      "df['attacker_king'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c549350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAF6CAYAAAA9EGI/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzSbSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c72d450>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Which king was the most attacked?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Count the number of observations for each value, then make a bar plot\n",
      "df['defender_king'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c90c7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAF6CAYAAAA9EGI/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQa4gAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c9213d0>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Which kings were most active in the war?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "war_action = df['attacker_king'].value_counts() + df['defender_king'].value_counts()\n",
      "war_action.fillna(1).plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c9c8610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAF6CAYAAAA9EGI/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYJFWZ7/FvdTfKWpQNDbQIFCI7aiHiyIBSuCCOCgoK\ng8tQ7jpeEK9zXa/SKkrjgldcBgGhQQER2WXYRGIAEYGmF5pVWkoUhXYAoVpFBer+8UaQS1V1Vlac\nyJPnzd/nefLJjMyqzPdURJ2MeM8GIiIiIiIiIiIiIiIiIiIiIiIiIpKItYFfAkuB24Fj8ufnAlcC\ndwNXAANRohMRkQbr5vdzgBuAvYAvAx/Ln/84sDBCXCIiMoV1gZuAnYE7gU3z5zfLt0VEJLJZWEpk\nDDuzBnik7vW+pm0REanAnGn8zFPAELAhcDmwT9Pr4/mtwTbbbDO+cuXK0gGKiPSYZVidO8GsNt7k\nUeASYDfgQSwVAjAfWNX8wytXrmR8fNzd7aijjooeg8rVu2XyWi6PZZppuYAXTlUJt6qwN6bWA2Qd\n4NXAEuAi4LD8+cOAC6ZV5YuIyIy1SonMB07DKvZZwPeBq7BK+0fAu4FR4ODqQuwuo6OjsUOohMdy\neSwT+CyXxzJB+HK1qrBvBV40yfMPA68KGkkihoYmTS0lz2O5PJYJfJbLY5kgfLn6gr5bo/E8HyMi\nItPU19cHU9TN7TQ6iohIRKqw25RlWewQKuGxXB7LBD7L5bFMEL5cqrBFRBKhHLaISBdRDltExAFV\n2G1Sri0dHssEPsvlsUygHLaISM9SDltEpIsohy0i4oAq7DYp15YOj2UCn+XyWCZQDltEpGcphy0i\n0kWUwxYRcUAVdpuUa0uHxzKBz3J5LBMohy0i0rOUwxYR6SLKYYuIOKAKu03dnmvr759LX19fR279\n/XNjF3eNun1fzZTHcnksEyiHLS2MjT0CjM/gdnXbv2OfJSKdohy2M5b/6tTfvQ/tY5GwlMMWEXFA\nFXabvObaIIsdQHBe95XHcnksEyiHLSLSs5TDdkY5bJG0KYctIuKAKuw2ec21KYedDo/l8lgmUA5b\nRKRntcphbwGcDmyCJUZPBI4HFgDvAf6Y/9wngcuaflc57AiUwxZJ25py2K0q7M3y21JgfWAx8Ebg\nYGAMOG4Nv6sKOwJV2CJpK9Po+ABWWQOsBu4ANi/eN0RwqfGaa1MOOx0ey+WxTBA3hz0I7ArckG8f\nDiwDvgcMBI1KREQmmO5Z8vrYKdjRwAVYTrvIX38BmA+8u+l3lBKJQCkRkbStKSUyZxq/vxZwLvAD\nrLIGWFX3+snAxZP94sjICIODgwAMDAwwNDTE8PAwULtU0HbY7Zpie7jibUrFq21t9/p2lmUsWrQI\n4On6ciqtzrD7gNOAh4CP1D0/H/hD/vgjwO7AW5t+1+UZdpZlT//Ru9HMz7AzapXxtD+tq8+wu31f\nzZTHcnksE8ysXGXOsPcE3g4sB5bkz30KOBQYwmqGe4H3txWRiIi0TXOJOKMctkjaNJeIiIgDqrDb\nNLFxz4ssdgDBed1XHsvlsUyguURERHqWctjOKIctsfX3z+3YAs0bbPAsHnvs4Y58VqeUmUukDFXY\nEajClth0DJajRseAvObalMNOh89yZbEDqIRy2CIiPUopEWd0OSqx6RgsRykREREHVGG3yWf+EDzm\nEL3uK5/lymIHUAnlsEVEepRy2M4ofyix6RgsRzlsEREHVGG3yWf+EDzmEL3uK5/lymIHUAnlsEVE\nepRy2M4ofyix6RgsRzlsEREHVGG3yWf+EDzmEL3uK5/lymIHUAnlsEVEepRy2M4ofyix6RgsRzls\nEREHVGG3yWf+EDzmEL3uK5/lymIHUAnlsEVEepRy2M4ofyix6RgsRzlsEREHVGG3yWf+EDzmEL3u\nK5/lymIHUAnlsEVEepRy2M4ofyix6RgsRzlsEREHWlXYWwBXA7cBK4Aj8ufnAlcCdwNXAANVBdht\nfOYPwWMO0eu+8lmuLHYAleh0DvsfwEeAnYGXAh8CdgQ+gVXY2wFX5dsiIlKhdnPYFwDfym97Aw8C\nm2Ffjzs0/axy2BEofyix6RgsJ1QOexDYFfglsClWWZPfbzrz8EREZDrmTPPn1gfOBT4MjDW9Ns4U\nX6cjIyMMDg4CMDAwwNDQEMPDw0Att5PadvFct8QzWXym2B6e5vb/A4ba+PnGz+uW8tdvL126lCOP\nPLJr4gm13Xwsxo5nsm0df7X4WtUXWZaxaNEigKfry6lMJyWyFvAT4FLsrwpwJ/aXewCYjzVM9kRK\nJMuyuoOy+8z8cjSj9s8w7U/r6svRbt9XM9Xt5ZrZMZjR/vEHHo/BNaVEWlXYfcBpwENY42Phy/lz\nx2INjgNMbHh0WWF3O+UPJTYdg+WUqbD3Aq4BllPbA58EbgR+BGwJjAIHA39q+l1V2BHon0Vi0zFY\nTplGx+vynxnCGhx3BS4DHgZehXXr25eJlbVbE3PFXmSxAwjO677yWa4sdgCVCL2vNNJRRCQRmkvE\nGV2OSmw6BsvRXCIiIg6owm6Tz/wheMwhet1XPsuVxQ6gEsphi4j0KOWwnVH+UGLTMViOctgiIg6o\nwm6Tz/wheMwhet1XPsuVxQ6gEsphi4j0KOWwnVH+UGLTMViOctgiIg6owm6Tz/wheMwhet1XPsuV\nxQ6gEsphi4j0KOWwnVH+UGLTMViOctgiIg6owm6Tz/wheMwhet1XPsuVxQ6gEsphi4j0KOWwnVH+\nUGLTMViOctgiIg6owm6Tz/wheMwhet1XPsuVxQ6gEsphi4j0KOWwnVH+UGLTMViOctgiIg6owm6T\nz/wheMwhet1XPsuVxQ6gEsphi4j0KOWwnVH+UGLTMViOctgiIg6owm6Tz/wheMwhet1XPsuVxQ6g\nEjFy2KcADwK31j23APgdsCS/7Rc0KhERmWA6OeyXAauB04Hn588dBYwBx63h95TDjkD5Q4lNx2A5\nZXPY1wKPTPa+JWISEZE2lclhHw4sA74HDIQJp/v5zB+Cxxyi133ls1xZ7AAqEXpfzZnh7/0n8Pn8\n8ReArwHvbv6hkZERBgcHARgYGGBoaIjh4WGgVpDUtgvdEs9U8dX+AYanub20zZ9v/LxuKX/99tKl\nS7sqnl7a1vE3/fiyLGPRokUAT9eXU5luWmMQuJhaDns6rymHHYHyhxKbjsFyquiHPb/u8Zto7EEi\nIiIVmE6FfRZwPbA98FvgXcCxwHIsh7038JGqAuw2E1MPXmSxAwjO677yWa4sdgCVCL2vppPDPnSS\n504JGoWIiLSkuUScUf5QYtMxWI7mEhERcUAVdpt85g/BYw7R677yWa4sdgCVCL2vVGGLiCRCOWxn\nlD+U2HQMlqMctoiIA6qw2+Qzfwgec4he95XPcmWxA6iEctgiIj1KOWxnlD+U2HQMlqMctoiIA6qw\n2+Qzfwgec4he95XPcmWxA6iEctgiIj1KOWxnlD+U2HQMlqMctoiIA6qw2+Qzfwgec4he95XPcmWx\nA6iEctgiIj1KOWxnlD+U2HQMlqMctoiIA6qw2+Qzfwgec4he95XPcmWxA6iEctgiIj1KOWxnlD+U\n2HQMlqMctoiIA6qw2+Qzfwgec4he95XPcmWxA6iEctgiIj1KOWxnlD+U2HQMlqMc9hT6++fS19dX\n+a2/f27sooqIAz1dYY+NPYKdCbRzu7rt37HP6XZZ7ACC85nr9VquLHYAlVAOW0SkR00nh30K8Dpg\nFfD8/Lm5wNnAVsAocDDwp6bf6/ocdudybZ3Lsyl/KLHpGCynbA77VGC/puc+AVwJbAdclW+LiEiF\nplNhXws0J2H3B07LH58GvDFkUN0tix1ARbLYAQTnM9frtVxZ7AAq0S057E2BB/PHD+bbIiJSoTkB\n3qPoDjHByMgIg4ODAAwMDDA0NMTw8DBQ++aJvV1TbA9Xst395Smem+7PN35et+zPqf4e3RJPiO3h\n4eGuimeybR1/09/OsoxFixYBPF1fTmW6A2cGgYupNTreif3lHgDmY33ddmj6HTU61j5JjY7SM3QM\nllPFwJmLgMPyx4cBF8zwfRKUxQ6gIlnsAILzmev1Wq4sdgCVCL2vplNhnwVcD2wP/BZ4J7AQeDVw\nN/CKfFtERCrU03OJKCVS+tPcXY5KeToGy9FcIiIiDqjCblsWO4CKZLEDCM5nrtdrubLYAVQiRg5b\nRES6gHLYymGX+TR3+UMpT8dgOcphi4g4oAq7bVnsACqSxQ4gOJ+5Xq/lymIHUAnlsEVEepRy2Mph\nl/k0d/lDKU/HYDnKYYuIOKAKu21Z7AAqksUOIDifuV6v5cpiB1AJ5bBFRHqUctjKYZf5NHf5QylP\nx2A5ymGLiDigCrttWewAKpLFDiA4n7ler+XKYgdQCeWwRUR6lHLYymGX+TR3+UMpT8dgOcphi4g4\noAq7bVnsACqSxQ4gOJ+5Xq/lymIHUInQ+2pO0HcTEXGov38uY2OPxA5DOWzlsEt9mrv8oZTn8Rjs\ndJlQDltEJG2qsNuWxQ6gIlnsAILzmev1Wq4sdgAVyYK+mypsEZFEKIetHHaZT1MOWybweAwqhy0i\nIm1Rhd22LHYAFcliBxCcz1yv13JlsQOoSBb03VRhi4gkQjls5bDLfJpy2DKBx2OwW3LYZUc6jgKP\nAU8C/wBeUvL9RERkCmVTIuPAMLArPVNZZ7EDqEgWO4DgfOZ6vZYrix1ARbKg7xYih11lWkVERHJl\nK9tfA49iKZHvAifVvaYcdu2TlMOWnuHxGPSSw94T+AMwD7gSuBO4tnhxZGSEwcFBAAYGBhgaGmJ4\neBioXdbF3q4ptocr2fZWnuZLvW7Zn9rujm1vx1/tM0PHP5w/XpRvD7ImIdMZRwGrga/l207PsDNq\nf/Rpf1ICZ9gZ3VyumciyrOkfzoduL1fn/q+g+8+wM2byf0UFIx3XBTbIH68H7AvcWuL9RERkDcqc\nYW8NnJ8/ngOcARxT97rTM+wZfVICZ9gz+rSuPsOWODweg92Sw9bAGVXYZT5NFbZM4PEY7JYKW0PT\n25bFDqAiWewAgvPZX9lrubLYAVQkC/puqrBFRBKhlIhSImU+TSkRmcDjMaiUiIiItEUVdtuy2AFU\nJIsdQHA+c71ey5XFDqAiWdB3U4UtIpII5bCVwy7zacphywQej0HlsEVEpC2qsNuWxQ6gIlnsAILz\nmev1Wq4sdgAVyYK+mypsEZFEKIetHHaZT1MOWybweAwqhy0iIm1Rhd22LHYAFcliBxCcz1yv13Jl\nsQOoSBb03VRhi4gkQjls5bDLfJpy2DKBx2NQOWwREWmLKuy2ZbEDqEgWO4DgfOZ6vZYrix1ARbKg\n76YKW0QkEcphK4dd5tOUw5YJPB6DymGLiEhbVGG3LYsdQEWy2AEE5zPX67VcWewAKpIFfTdV2CIi\niVAOWznsMp+mHLZM4PEYVA5bRETaogq7bVnsACqSxQ4gOJ+5Xq/lymIHUJEs6LvNCfpuIhXp75/L\n2NgjHfmsDTZ4Fo899nDln+OxTFIt5bCVwy7zaSpXmU9xWCbwWS7lsEVEpC1lKuz9gDuBXwEfDxNO\nCrLYAVQkix1ABbLYAVQkix1ABbLYAVQkC/puM62wZwPfwirtnYBDgR1DBdXdlsYOoCIey+WxTOCz\nXB7LBKHLNdMK+yXAPcAo8A/gh8ABgWLqcn+KHUBFPJbLY5nAZ7k8lglCl2umFfbmwG/rtn+XPyci\nIhWZaYXd3d0/KjUaO4CKjMYOoAKjsQOoyGjsACowGjuAiowGfbeZdut7KbAAy2EDfBJ4Cji27meW\nAi+ccWQiIr1pGTAU8g3nACuBQeAZWOXcI42OIiLpeS1wF9b4+MnIsYiIiIiIiIiItKHKuUS8OA74\nHnBb7EBkjfqA59DY3dSLvYCjsDajYsK2ceC5sQKqSCcn7KjSnkzcV6eHeGPN1tfaHcCJwFrAKcBZ\nwKNRIwpjE+C9TDyw3hUroAAuBXaJHUQFvgccCdwCPBk5llC+AHymbns28H3grXHCCeYH2BfpUhr3\nVZAKW6ZvB2AhcB9wJrBP3HBK+wXWDfNg4M357aCoEZV3GjYK15tfxg6gAouodVZ4JnAh1lU4dXdQ\nYeZCKZHpmQ28AXgndtn9I+wy9S/AIRHjKmMpgft6doG7gOcBvwH+nD83DrwgWkRhLMSOwfOAv9U9\nf0uccIKYBZwBLAdeAfwX8PWoEYVxDvBh4PdVvLkq7Na+jlXWPwNOBm6se+0uYPsYQQVwNHaWfUns\nQAIanOL50Q7GUIWMyXO7KV7l7UatLGsB3wWux/63IO0vIbB9NYTVE8WX6ziwf4g3V4Xd2ruAs6md\nsdUbIN1Za1YD6wJ/xybwAjuw+qNFFMbLsLPsU4F5wPrAvVEjknoZjV8+zQ2NKX4J1RvO74syFeX7\n7yjR9KgDgK/ltzdEjkWmtgC4GLg7394c+Hm0aMIZwK70Fue3rwEbRo2onNmkm0qcjs2weuL1WOO+\ndNBC4CrsTPvdwJXAMVEjCqf4IvoqPr6IlmG50SV1zy2PFEtI5wGfw3ofbIN9MZ0XM6AAFscOoCIH\nY20op+e3UeAtMQPqNbdiZwSF2flzqfP4RVS0LxQV9nr4qLCXTfO5lCwE/gPYAphbd0vdchrPqufh\n4xhMxnJgo7rtjfCxAzx+Ef0frBHrXuB9wA3AEVEjCuMGLDdf2AtrME7ZKLafmm+pu5XGtsFZBPy/\n0sCZ1o7BWq6zfHtv4BPRoglnHMuNPpRvD5D+KLOvAPsCY8B22MCMK6NGFMYHsMvrIm/9CHBYvHCC\nGIwdQEUuAy7Hxmr0Ybn6S0O9uXqJTM+zgd2xCu1G4IG44QRxKHZZmuXbxRfRD2MFJC31Y/+zHkba\ngo1K3QlYu+651EcE9gEHYsPTAa4Fzg/55jK5HbFRS0W/0eJvNZ7fHsYaF1L2bODF+eOUv4hWM/XV\ngYeuipsBX8R6vRQLX++BDVlP1QLsJGFnbCzAa4HrsBG3qRsEtsWu7tbF0o1jMQPqBSfl9xlw9SS3\n5di8AamaBbwD+Gy+vSXpD+s+Gvh3rILuBz6IzVmRusuwS+ui7WQtYEW8cIJYgVVkRePppsBP44UT\nzPuAm7AFXsBSc1fFC0fqXRE7gBJOAL6DXUWAtdDfHC+cICZrDPbQQFzsl/ruiktjBBLQTfn9Yiw3\n34eNHE7dMmxulPp9FazRcaaL8PaSxcCHgGdN8tq+HY4lpH/CzkYfz7cfxs7cUvZn4O3Ymdts4G1Y\nuiR1q2nsqfRS0s9j34z9T52UP16CDVFP3d9onO9lDuk35idlW+BL2FJoZwOvwUfu/5dYpVacCcyj\n8awgRVsDFwH/k98uxEdvhN2wyuzR/P5X+Frgemv8lOcrwKexq4VXYw2OX4waUY+ahU3gcj82Sf7n\nSLuj/9uxyu1+7AvpbmyUVso2av0jyVoL61WxC+lfCcHkeV0Pud7ZWB77x/ntvQQ8wfNwptgJL8Sm\nVn0ttT6We2GVXspTlO4IvDJ/fBW1fHaqfoXldk/F+r56uhStX8WkKFeKXeDWwXpOXE1toiSwRuLL\nsHnnZQqqsFtbjF2KngycS2N+6nzgTTGCCmQ21mWsvhK4L144pc0CXoUNt98dm7f8VGqTQaVqqlVM\nDo8TTilHYvNFP5vGOaPHsJWdvhUjqIB6ZTm3rrVN7AAqcjiW570da8Uubl68AqsQHsWmtvznuOGU\nUukqJpF4mDJgMndhV+KbAhvX3aRDNsMGKFyWb++ETZaUupX4y/lujJ29LcZWMDkQy/e+mLQXMTgH\nOyP1YHdgft32YVhbyvGk3SZU8LicW1I8DloAyyF6aLyqdzc2EOg5k7yW4vwvF+e3q7GFMq6oe+6i\niHGVsYRaxfxy4A/YWqJHY410qdotvy3EeorsAbyo7haEt8usKtyMnaEtAXbNn0t5PcSP5vc7YQ08\nP8FWnQHLtR0XI6hAZgFPxQ4ioOH8vn5qBOqeS3EVk2XUuvB9G/gjtcV3619LTcaaG7mDrKSj2fpa\n8zZoYQPswLoP6574jPzmwcbAx7D5KYoJhcaxfHaKsvz+y1i56h1LmhX2bOzK7h9YA/H76l5LuT4a\nzu+fC/y66TU1OHaQ10ELk/W5Tr0f9pXAe4A7sYmFTsUqu9RNNqAp1QbiT2P/Rxdh5SpGW2+Lj+Xc\nJltE2OvqOl1nNvAR7Jt/F+D5+DkbnawSSH2kY/HPUj9/SMrzo3wQq5j/QmNPnlHgjHhhlbYH1h12\nvbrntiNgrjeCHbFc/K+xxu6D8vsR4LZQH5LyJUgnPAm8FVsA1UNDI1iXo3/Bpuo8nlpudANqq6en\nqsjFP4AtgPp7Jp8DJhVnYgOAFgIfp7avxqgtPJGiyVbLSb2v/HbYuqgb0rg+6hg22jEINTq29nUs\n53Y2NrlQsWz9ZJc+KXgh1nj6eWxFluIYeAzrjfBIpLhCeAM2YfwWwDex0XMLSLdHRbNNaJzsP+VB\nTl79MxVOYqUKu7WMyVt/g7T6RvQMameknr2E2uK8qdofW93+2cAqYCtsMM3OMYOSSa2DjdPYKX9c\n1B3vihaRuLAd1u/1dmoLoDa3bqdiFpYz/BiW7gHrinkF6c8bDZaT35haG8M+wCnxwglmPnAAdmW0\nWeRYQvkxtmjGr7FBQVdiqUep2BY0rlT9UWyOgM8Cz4sSUVg/x7pVLcfO2BaQ7uosJ2OTVx2DXY6e\nizX0vBEfV5FFL4Nl1Fa6T31hhvdgKZ3T8ttv8DGCuDhBqB9op9GPHfBDGhsP7sIq7c+Sdgt9ocjB\n3zrJc6lZQa172NrYqEBPw+5/ijUKfws7Lo8n/cn+76ZxH21E+g2PUEu/XYv1KptHuleuSWnu4lZ/\naX1dJwOpyPXY2dr5wP/CuiClukRT875KvXtis/WoDTgZwSZOSv0L6XpsKa3CM0n/SwjsymEuNg7g\nXmwk5wdCvbm69U1t7abtV9Y99jD71oexeYmPwFIh/VjOLUU70HilsE3d9jjwgo5HFM4cbPqAfbBu\npouiRlNeMTXCPViq4IJ8+wDST/PMwrrxPYyNQt069Aeowp7aY8D21M46i36vO+SvpWw2NqHVf2AH\n2EjUaMrbMXYAFXoCmx9lAEv1pK6YGmElliooelFcSPoLTjyFNXyfXdUHeGiQqcp+WK7wi9Ryu7th\nQ2s/jE3fmbIbsBFnqf+T9IKLsL7zV2CjHsH2m4c5pTfEypL6SVBhITbPfDFuo/BwiDdXhb1mu2Aj\nzHbKt2/D5qbwMOrxBKxf7zk0VgLnRYtIpjIyyXPjWO+KVO2OdU3sz7f/hPUSSXkqAbBpAyY7CQqS\nHlGF3bsW5ffNB9c7OxyH9KZbgX/HelOALa31HdJub6icKmzxZl2sD32qPV4msx22sn0xeg7SXyew\nfn75wi2kPQFUYRdsX9V3XAiyYLIaHXuXxyG0+2OrfTwTWwR1V+Bz+fMpOxUbtHUcNu/yO6kNoEnN\nbvn9fwPfBc7Ktw8hzfm9my3AuvTtDFyCTbZ2HWmucC9dxOMQ2luw3hT1/bA9tDd4GuSUYZOMXT3F\n49StwL5Ml+Xbm2IDn4LQGXZrm2DTIw7SuGx9ymeiYMPr34z1fz0Nm8oz9QFB/2Bi1zcPS4Y9jlUC\n92CDnH5P41zSKRmOHUDF/or1l38C6wGzCkvRBaEKu7ULgWuwM9Din99DV7hipr5HsSG0D2DDaFN2\nG/A27LjeFuv25mH03JH4GeRUGMDSPC/PtzNsyt+Ul98DuAmbg/0krMfLn/FxDCbDw2xvk3kvFQ6h\njWQ9rHHu5vz2RSaOWJXucB7WvvBcbGTqAvx1Kd0aH8sJJuVo4HWxg5CeNA+ryI7ARgj+J3YVcSHp\nzxi5bJrPpWQOtZ53W2Ipx+aeMKXMav0jPe9I4GIsjziW31IeldWH5RGL/q6HAN/G1q585hS/k4qf\nYpfahbnA5ZFiCeFMbKGJ7bB5N+7FKoGfYFPKpuyvNE5fvBe1AVwpei+Wr/5N/vin2L76IfCJiHFJ\n4r6DDVa4CfgBNvnOB/LHqU8bO1n6KuWUVnHG2cfE5cBSLhfAEDbZ02/y21LSTh/cjuWut8K+eIoJ\n4tbNX5MOOgBboumrNM6RnaI7sApgHWx+g6LhuY/0u8Atxv5hCoOk2/0NGrsnep1CdkOsEbUPu9pL\nVf3+aE7tBNtX6iXS2kJs3oMzsIPqCGyhzU/GDKqEx7FeLn/FzmyeyJ8fJ/1V0z+NXT1ck2+/HHhf\nvHBKey428VMf1oB1cd1rwafu7JD1gfdjDY0rsDltDsAaiO+hwpnuKrYONkqzD0stFiM2i5OjIDQ0\nvbVbscu3J/Pt2djl2/OjRVTO77ARc31Y3rp4TL79nEhxhTIPeCn2BXQDNnNaqobX8No4aY4MPA9r\nA/oFsC/WR/lx7EQo5TRPRq27bx8Tu/4GWbRbFXZry7E/djEf9kbYiKxUJ6lZwJoPrM91NJrwNqc2\nyKko2zVT/rR02nJq/zuzgT9gaay/RosoIUqJtHYMlge9Gqvg9ibtVt8FsQOo0LFYHvR2aldEoAq7\nmzzZ9Ph+VFlPm86w12wW8BYsL7o7dsZ2E3ZWIN3nbixV9bfYgciUnqSx+9461CrscWrzY4vMyOLY\nAci0XYoNMPEm1fYSCUxn2K1VuuSPBHUe1pf3Kmpn2R6W0roO63lwKtZbKfX5NjzbC2s8XQ28Axvp\n+A2sR1ZpqrBbG6XCJX8iWhs4iImzEH4+VkABjEzyXOpLaRW2w2aIfAtwI1Z5XxE1IpnMrVij6guw\nVZ1OBg7G2r5KU4Xduy7HpiJdTGND0NfihCPTMAd4IzZv+aNYG8ungHNjBiUNipV0jsIaVE8m4Eo6\n6iXS2mFMfoad+goSmwOviR1EYB6X0gJL84wAr8em+X09Vgk8G+trrgq7e4xhX6Jvx+ZKmQ2sFTWi\nHvMt4Jv57WRshZYfR40ojBNJty/5VH4OvArr67sV1oXxCzEDCuQa4N+weSma/VuHY5E1mw98lNrE\nVluS/tzlSRsg7RngCndgQ9HvxvJut2IVXco8LaUlMoFSIu37C+k3OIItDuqNp6W0oPGLp9k4/q6Q\nUvZzYE97UYy4AAAIS0lEQVSsd0hzCjVY/3I1OrZWP+HOLCw/+iPg43HCCepl2ET4p2JzcKyPzbmc\nqpdgVw4D1JbS+jKW503RYIvXRzsQg3QRVditDdc9fgLrT/nbOKEEtQDYDdgea6zbHPsi2jNiTCIe\nzMZWS6/PYDTPZz4jSolMbQfgTmwWrrWxy+3CS0n3rK3wJqz7UTGS837SHSV4MXbZOdkJyDiwf2fD\nCe4gbADXptTKqGHc3elwrEvfKhq7y2q0asXqJx1vbrjyMHn8jfl9UZb1SLfR8Y9YOT6GDVDYG7sy\nGibQgIXIVgI7xg5CpmUlNqNnJXSGPT0eU0fnAN/F8r3vw0bRpbpO4Hzg1cCh+e0S4CxswVoPHsBy\n89L97qPCNV9VYfeur2ATyI9hOezPYIMyUvQENvHTpdicG4dik/svwPrRp+5mbC6bC4C/58+NY3On\nSHe5F5uK+RIa99VxId5cFfbUnoMNAe7DGuSKx+TbHlyBrcZdTPY/l3QntVobeB3wr1jvim8A58cM\nKKANsSlI9216XhV297kvvz0jv022SMiMebzUD2WEiQ1ZxbaHCYXej60u8zfgqfy5VIdxfx/YGfgv\n7Ex0Tf2XRcShT2G9KLy6B9g4dhCBPIWldia7VZZP7KDtsSlji5z8C4D/Gy8cWYPtgZOw9OLV+e1n\nUSPqEf+KTY+4FDubPgR4VsyAAruCtEcB9pJrgH+i1qOnDz8Nqt4sBz6I7a8X57fdQr25UiKt9WFn\n2vthPRHmYN+el1HrGpeiF2FfSL+gsXEk9cn+PboZ+8cvpu4EO5EYihaRTGUxASvoZmp0bG0c64d9\nCzZ154ZYxf1e0q6wTwR+iuV7nyJw44gE9UdsCoHCm9G6ot3qYuBDWINw/dqiQRrzdYY9PXtiPQ9m\nU6vYUp8Pu/5sTbrbNtgX7B7YohP3Am9Dc4l0o1EqXKFKFXZrP8B6Tiylcajp4XHCCeZL2LwoF1HB\nmYBUYn3sf3Y1tuzU2XHDkU5Thd3aHdgMfd7SBaP4XKvSk/Wx7pfbACuAE4ADgC9ivXxSnyPFq12w\nOmPtuudSvyJPxjnYUkwinXYe1jD8fmwZsBuxHiNqbOxeC7CufKuwaYsfIOAKVTrDbi3D/kFupJY6\n8DAD3BxsZOBW+eMiNx9kCK0EsZzaIgWzsYbGrbBRj9KdVmBrcN6S328KnIEtXVeaeom0tiC/L9IH\nXnpTXIz94xe9RKT7PNn0+H5UWXe7v2L76gmsR9kqYItQb64Ku7UM2AzYHauob8R2Quo2R0tMdbsX\nYKM1C+vUbWs+7O50MzbA7qT88Z+B66NG1GMOxnpTnJ7fRoG3xAwokK8Cr4kdhIhjW2NpEemg5cAm\nddvzSHei/3oHYgsKP46veTdEYrpqms/NiFIirfVhI80KD+GjsfY4bKmzFSiHLVLWOsC62And3Lrn\n+wk4HbMq7NYuAy4HzsQq6kOwifJTdx82gZAqa5Hy3g98GOsCvLju+TECLqLh4Uyxan1Y+mAvrKHn\nWnxMjH8almO7lApWxhDpUUdgi51UQhV271qQ3zd3V/xclGhE0rY78Dtqk3Idhq12P4r9r2nKh4qt\nxvek+IUN8puIzNwSarnrl2MV90HA0QQc6Si96/nYQVasQbcYmwNBRNq3rO7xt6ldwTa/Jh2yCbBl\n3S11vwD2qdseRh38RWZqBbBW/vguYO+617Q6UAftD/wKG7F0L9arwsMOmOxbX2cCIjPzaeyE5yLs\nynVW/vy2wM9jBdWLlmOL1Rbr6e0DnBIvnNIOzO8vAD6DLcywNbaoq4feLyKx7AG8ica1UrfDluOT\nDin6VC7DZkyDtEc6Fl88c4FvUlv+7Bv4WmRYxB0NnGntEawXxbXYNImrsB4kqXuY9FfNEekp6ofd\n2vrYlImzsHX0+rGK+6GYQZXwF2DlFK+Noxn8RLqWKuz2zMMq6pSHc98G/AtT7/vRzoUiIhLGHthc\n2OdhjQYrsOV+VgGvjRdWaUta/4iISFoWA/tic1//CZvZDmAHbAX1VP0MXVmJiDP1lfIdTa+lfJZ6\nAhb/2cAItpqOiCRAvUSmVr9u4+PRogjvA/n9jlhqZxEwgJ15X4Z18n9y0t8Ukah0aTy1J7EeFWCT\nk9cvfroOvr7s1sUGBL0Wy93vFjccERGpdxywc+wgRGT6Zrf+EXFqS2xo+gew4+BXwN+iRiQiImu0\nA7AQm2L1TBpn8BORLjKr9Y+IY7OxCntHbKHhZcD/xnqQiIhIl/g6cA9wIvCSptfu6nw4IiIylXfR\nOA1kvYFOBiIiIms2C3gH8Nl8e0smnmmLiEgXOAH4DnBnvj0XuDleOCIiMpUlTfegJcJEupp6ifSu\nv9PYD38eaU8bKyLi1tuxBUPvB74E3A0cHDUiEVkjzSXSm2Zhc4Y8DLwyf+4qJs5KKCIiXSDlOb1F\nepLmEuldz8PWp9RZtUgilBLpXauxaVWfpDbf9zhWiYuISBfYM79fO2oUIiLS0uL8/paoUYhI2zyt\nmiLT8wRwEvAc4Hga02LjwBExghKR1lRh957XAa/CVoRfjFXY43X3IiLSJY7N7z8WNQoREWlpBXY2\nvaTVD4pId1FKpPdcCjwCrA+MNb2mbn0iIl3ootgBiEh7NHCmt20K7J4/vhFYFTEWEWlBQ9N718HA\nOVhqZBfgaGzmvttjBiUiIhMtBzap256XPyciXUoLGPSuPuCPddsPoRSZSFdTL5HedRlwOXAmVlEf\ngvUgEZEupTOq3nYgsBfWne864Py44YiISL3VWP/ryW7/A9yADV0XEZEuNgd4IXBb7EBEZCJ165N6\nTwEP5vc3R45FREREREREREREREREREREQvr/bvBhmj4Fr3wAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca20f90>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Is there any relationship between troop size and battle outcome?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a ggplot scatter plot of attacker_size against defender_size (if not NaN), \n",
      "# with the color of each dot being determined by the outcome of the battle\n",
      "ggplot(aes(x='attacker_size', y='defender_size', colour='attacker_outcome'), \n",
      "       data=df[df['attacker_size'].notnull() & df['defender_size'].notnull() & df['attacker_outcome'].notnull()]) + \\\n",
      "    geom_point()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAHzCAYAAAAjGxlzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt4FIW9//HP7CYsS7JrFkmUJIAJSQMit6BiWgSRVo0o\neixY24MVr1Wx/Fr9ndNqa4+19tjaU/VHxaPSHi1qVa7Bg1Wh4sOlVqsQIhGDAbkYCBJMyG7uu8n8\n/qDZEhhwgewlmffreXye7OzOznc+Xbsfd3Z2DNM0TQEAANiUI94DAAAAxBNlCAAA2BplCAAA2Bpl\nCAAA2BplCAAA2BplCAAA2FpSLDdWX1+vZcuWqbGxUZI0btw4XXDBBXr77be1ceNGpaSkSJKmTJmi\n/Px8SdK6detUWloqwzBUXFysvLw8SdLevXtVUlKiUCik/Px8FRcXS5JCoZCWLVum6upqud1uzZgx\nQ2lpabHcTQAA0IPEtAw5HA5deumlGjhwoFpbW/XMM89o6NChMgxDRUVF+upXv9rl8fv371d5eblm\nz54tv9+vBQsWaM6cOTIMQytWrNC0adOUnZ2tF154QZWVlcrPz9fGjRvldrs1Z84clZeXa9WqVZox\nY0YsdxMAAPQgMT1M5vF4NHDgQEmSy+XSgAED5Pf7j/n4rVu3auTIkXI6nfL5fOrfv7+qqqoUCATU\n1tam7OxsSdLo0aNVUVERXmfMmDGSpOHDh2vHjh1R3isAANCTxfSTocPV1dVp3759ys7O1meffab3\n3ntPZWVlyszM1CWXXCK3261AIBAuPJLk9XoVCATkdDrl9XqPWi5JgUAgfJ/T6ZTL5VJTU5NCoZAa\nGhq6zJCamtrleQAAgP3EpQy1trZq4cKFuuyyy+RyuXTuuedq0qRJkqTVq1dr5cqVuuqqq7p1mxs2\nbNCaNWu6LJs0aZImT57crdsBAAA9S8zLUHt7uxYuXKhRo0Zp+PDhkg59QtOpsLBQL730kqRDh9Xq\n6+vD9/n9fnm9Xnk8ni6H1zqXH76O1+tVe3u7Wltb1a9fP40bN04FBQVdZklNTVVdXZ1CoVDU9jdS\nLpdLra2t8R5DSUlJ8vl85GKBbKyRizVysUYu1sjFWmcuUd9O1LdwGNM0tXz5cqWnp6uoqCi8PBAI\nyOPxSJIqKiqUkZEhSSooKNCSJUtUVFSkQCCg2tpaZWVlyTAMuVwuVVVVKSsrS2VlZRo/fnx4nbKy\nMg0aNEhbtmxRTk6OpEOH0qwOidXU1CgYDEZ7179UUlJSQszRKRQKJcQ8iZaLRDbHQi7WyMUauVgj\nl/iIaRnavXu3PvzwQ51xxhl66qmnJB06jX7z5s3at2+fDMNQWlqarrzySklSRkaGRowYoXnz5snh\ncGjq1KkyDEOSNHXqVJWUlCgYDCo/Pz98Kn5hYaGWLl2quXPnyu12a/r06bHcRQAA0MMYpmma8R4i\nnhLlkyG3263m5uZ4j6Hk5GSlp6eTiwWysUYu1sjFGrlYIxdrnblEG79ADQAAbI0yBAAAbI0yBAAA\nbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0y\nBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAA\nbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0y\nBAAAbI0yBAAAbM0wTdOM9xDx0tLSopaWFiVCBA6HQx0dHfEeQ4ZhqE+fPmprayOXI5CNNXKxRi7W\nyMUauVgzDENpaWlR305S1LeQwPr27atAIKBgMBjvUeR2u9Xc3BzvMZScnKy0tDQ1NjaSyxHIxhq5\nWCMXa+RijVysJScnx2Q7HCYDAAC2RhkCAAC2RhkCAAC2RhkCAAC2RhkCAAC2RhkCAAC2MHHiRDkc\nDjkcDr3++uvh5ZQhAADQ6+3atUvr168P337hhRfCf1OGAABAr/fiiy9KksaOHStJWr58uRobGyVR\nhgAAgA10lqF77rlHI0aMUFNTk5YsWSKJMgQAAHq5jRs36uOPP1ZycrKuuOIKXXPNNZL+eaiMMgQA\nAHq1ztIzadIkeb1eXX311ZKkt99+W9XV1fa+NhkAAOjd2tvb9fLLL0uS/vKXv8jhcHS5709/+hNl\nCAAA9F5vvfWW9u3bp6SkJF1xxRXh5Tt27FBZWZmef/55yhAAAOi9XnzxRRmGoWuuuSb8CZEk7dy5\nU3l5edq8eTPfGQIAAL3XH//4xy6HyjqdddZZCoVCam9vpwwBAAB7owwBAABbowwBAABbowwBAABb\nowwBAABbowwBAABbowwBAABbowwBAABbowwBAABbowwBAABbowwBAABbowwBAABbowwBAABbowwB\nAABbowwBAABbowwBAABbS4rlxurr67Vs2TI1NjZKksaNG6cLLrhATU1NWrx4sQ4ePKi0tDTNmDFD\nbrdbkrRu3TqVlpbKMAwVFxcrLy9PkrR3716VlJQoFAopPz9fxcXFkqRQKKRly5apurpabrdbM2bM\nUFpaWix3EwAA9CAx/WTI4XDo0ksv1ezZs3XLLbfo/fffV01NjdavX6/c3FzNmTNHubm5Wr9+vSRp\n//79Ki8v1+zZszVz5ky99tprMk1TkrRixQpNmzZNc+bM0RdffKHKykpJ0saNG+V2uzVnzhwVFRVp\n1apVsdxFSNL+v0gbZ0mbbpYatsV7mogEg0E9+OCDuv766/Xggw8qGAzGeyQAQIzEtAx5PB4NHDhQ\nkuRyuTRgwAD5/X5t3bpVY8aMkSSNHj1aFRUVkqStW7dq5MiRcjqd8vl86t+/v6qqqhQIBNTW1qbs\n7GzLdTqfa/jw4dqxY0csdxFf/FXa8n9lHFglY/8bUukNUuv+eE/1pb7//e9r/vz5Wr16tX7/+9/r\n+9//frxHAgDESEwPkx2urq5O+/btU3Z2thobG5WamipJSk1NDR9GCwQC4cIjSV6vV4FAQE6nU16v\n96jlnet03ud0OuVyudTU1KRQKKSGhoYuM6SmpiopKW4RdOF0OpWcnBzvMcJ5nGwuoeqXpbaa8G2j\neaeML1bJOWTWST1frHLZsmWLOjo6JEnt7e3asmXLUds91Wy6W295zXQ3crFGLtbIxVqi5RL17cRk\nK0dobW3VwoULddlll8nlcnW5zzCMqGxzw4YNWrNmTZdlkyZN0uTJk6OyvZ7O5/Od1Hr13oFq2nvY\nAkeyTkvPlzs9vXsGi5J+/foddTv9GDOfbDa9HblYIxdr5GKNXOIj5mWovb1dCxcu1KhRozR8+HBJ\nUkpKigKBgDwejwKBgFJSUiQdOqxWX18fXtfv98vr9crj8cjv9x+1/PB1vF6v2tvb1draqn79+mnc\nuHEqKCjoMktqaqrq6uoUCoWivdtfyuVyqbW1Nd5jKCkpST6f76RzMQffLVW/I/k3S0YfKX2iAu4i\nNdTUfPnKFmKVyx133KGHHnpI1dXVGjhwoO68807VHDHzqWbT3XrLa6a7kYs1crFGLtYSLZeobyfq\nWziMaZpavny50tPTVVRUFF5eUFCgsrIyTZgwQZs2bdKwYcPCy5csWaKioiIFAgHV1tYqKytLhmHI\n5XKpqqpKWVlZKisr0/jx47s816BBg7Rlyxbl5ORIOnQo7fBDa51qamoS4suySUlJCTFHp1AodJLz\n9JXOXSzVl0nOvpJ3lEKhdkntJzVHrHK56qqrdN5552n79u0aOnSoMjMzj7ndk8+me/We10z3Ihdr\n5GKNXKwlWi7RFtMytHv3bn344Yc644wz9NRTT0mSpkyZogkTJmjRokXauHFj+NR6ScrIyNCIESM0\nb948ORwOTZ06NXwYberUqSopKVEwGFR+fr7y8/MlSYWFhVq6dKnmzp0rt9ut6dOnx3IXIR0qQf3H\nx3uKE5aZmanMzMx4jwEAiDHD7DxX3aYS5ZMht9ut5ubmeI+h5ORkpaenk4sFsrFGLtbIxRq5WCMX\na525nKqysjLt3r37mPcnxtfWAQAAomT37t2aNm3aMe/nchwAAMDWKEMAAMDWKEMAAMDW+M4QAADo\n1QYPHqxXX331mPdThgAAQK82evRojR49+pj3c5gMAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADY\nGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUI\nAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADYGmUIAADY\nGmUIAADYGmUIAADYmmGaphnvIeKlpaVFLS0tSoQIHA6HOjo64j2GDMNQnz591NbWRi5HIBtr5GKN\nXKyRizVysWYYhtLS0qK+naSobyGB9e3bV4FAQMFgMN6jyO12q7m5Od5jKDk5WWlpaWpsbCSXI5CN\nNXKxRi7WyMUauVhLTk6OyXY4TAYAAGyNMgQAAGyNMgQAAGyNMgQAAGyNMgQAAGzN1meTAQCA3u/g\n3jKFGnYf837KEAAA6NVCDbsVfGfaMe/nMBkAALA1yhAAALA1yhAAALA1vjMEAAB6taTUwdJXXz32\n/TGcBQAAIObSMkdLGn3M+zlMBgAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAA\nbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0yBAAAbI0y\nBAAAbI0yBAAAbI0yBAAAbC0plhsrKSlRZWWlUlJSdOedd0qS3n77bW3cuFEpKSmSpClTpig/P1+S\ntG7dOpWWlsowDBUXFysvL0+StHfvXpWUlCgUCik/P1/FxcWSpFAopGXLlqm6ulput1szZsxQWlpa\nLHcRAAD0MDH9ZGjs2LGaOXNml2WGYaioqEi33367br/99nAR2r9/v8rLyzV79mzNnDlTr732mkzT\nlCStWLFC06ZN05w5c/TFF1+osrJSkrRx40a53W7NmTNHRUVFWrVqVSx3DwAA9EAxLUNDhgxR3759\nI3rs1q1bNXLkSDmdTvl8PvXv319VVVUKBAJqa2tTdna2JGn06NGqqKgIrzNmzBhJ0vDhw7Vjx47o\n7AgAAOg1YnqY7Fjee+89lZWVKTMzU5dcconcbrcCgUC48EiS1+tVIBCQ0+mU1+s9arkkBQKB8H1O\np1Mul0tNTU3q16+f/H6/Ghoaumw3NTVVSUkJEYGcTqeSk5PjPUY4D3I5GtlYIxdr5GKNXKyRi7VY\n5RH31M8991xNmjRJkrR69WqtXLlSV111VbdvZ8OGDVqzZk2XZZMmTdLkyZO7fVu9gc/ni/cICYts\nrJGLNXKxRi7WyCU+4l6GUlNTw38XFhbqpZdekiR5PB7V19eH7/P7/fJ6vfJ4PPL7/UctP3wdr9er\n9vZ2tba2ql+/fpKkcePGqaCg4Kht19XVKRQKRW3/IuVyudTa2hrvMZSUlCSfz0cuFsjGGrlYIxdr\n5GKNXKx15hL17UR9C18iEAjI4/FIkioqKpSRkSFJKigo0JIlS1RUVKRAIKDa2lplZWXJMAy5XC5V\nVVUpKytLZWVlGj9+fHidsrIyDRo0SFu2bFFOTk54O16vt8vhtU41NTUKBoMx2NPjS0pKSog5OoVC\noYSYJ9FykcjmWMjFGrlYIxdr5BIfMS1Dixcv1s6dO9XU1KRHH31UF110kXbu3Kl9+/bJMAylpaXp\nyiuvlCRlZGRoxIgRmjdvnhwOh6ZOnSrDMCRJU6dOVUlJiYLBoPLz88NnoBUWFmrp0qWaO3eu3G63\npk+fHsvdAwAAPVBMy5BVOSksLDzm4ydOnKiJEycetTwzMzP8O0WHS0pK0rXXXntqQwIAAFvhF6gB\nAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICt\nUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYA\nAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtUYYAAICtJcV7APRy\nDZ9IH/1fKeSX+mZKo56UktPiPRUAAGF8MoToMU3pw9ky6jfIaKyU8cUaafP/ifdUAAB0QRlC9LQ3\nScG6rsta98VnFgAAjiHiMtTS0qL77rtPubm58nq9kqSVK1fqiSeeiNpw6OGc/aQkb9dlyf3jMwsA\nAMdgmKZpRvLAO+64Q3v27NG9996r4uJiHTx4UHv27NE3vvENbdmyJdpzRkVLS4taWloUYQRR5XA4\n1NHREe8xZBiG+vTpo7a2tm7Jpb32fYXK7pEZ9Mvoe6aSz3tWDvfAiNdPlFyk7s/mVCVKNuRijVys\nkYs1crFmGIbS0qL/PdOIy9CZZ56pbdu2KTU1VT6fT3V1hw5/nHbaaaqvr4/qkNFUU1OjYDAY7zHk\ndrvV3Nwc7zGUnJys9PT07s/F7JCMEz8qmyi5SFHM5iQlSjbkYo1crJGLNXKx1plLtEX87uRyuRQK\nhbosq6mp0YABA7p9KPRCJ1GEAACIhYjfoWbMmKFZs2bp008/lSRVV1frrrvu0nXXXRe14QAAAKIt\n4jL0y1/+Ujk5ORo1apTq6+uVl5engQMH6mc/+1k05wMAAIiqiH900eVy6bHHHtOjjz4aPjzmcHDo\nAwAA9GwRt5nJkydr/vz5MgxDGRkZ4SI0derUqA0HAAAQbRF/MvTOO+/o888/16ZNmzR37lw5nU5J\n0tq1a6M2HAAAQLRF/MlQnz599N5772nXrl36+te/ri+++CKacwEAAMTECX3px+Px6NVXX9UFF1yg\n8847T2VlZdGaCwAAICZO+Kr1DodDDz/8sEaPHq2vf/3ramlpicZcAAAAMRFxGfr973/f5fZ1112n\ngoICLV++vNuHAgAAiJWIy9C3vvWto5aNHTtWY8eO7daBAAAAYum4ZWjYsGGqqKiQJA0aNMjyMYZh\naPfu3d0/GQAAQAwctwzNnz8//Pfzzz8f9WEAAABi7bhl6MILLwz/fdFFF0V7FgAAgJiL+NT63/72\ntyotLZUkvfvuuxo8eLBycnL0zjvvRG04AACAaIu4DD322GPKzc2VJP34xz/W3XffrZ/+9Kf64Q9/\nGLXhAAAAoi3is8n8fr9OO+00+f1+ffjhh3rrrbfkdDp19913R3M+AACAqIq4DA0aNEh//etf9dFH\nH2nixIlyOp2qr68PX6MMAACgJ4q4DP3mN7/R9OnT1adPHy1ZskSStGLFCo0fPz5qwwEAAERbxN8Z\nuvzyy1VdXa1du3bp3HPPlSRde+21evXVV8OPeemll7p/QgAAgCg6oQu1Hik5OVnJycnh27fddtsp\nDwQAABBLp1SGAAAAejrKEAAAsDXKEAAAsDXKEAAAsLWIylBHR4dWr16t1tbW4z5u8ODB3TIUAABA\nrERUhhwOh6ZNmyaXy3Xcx3300UfdMhQAAECsRHyYbOLEifrb3/4WzVkAAABiLuJfoB4yZIiKi4t1\n9dVXa9CgQeHlhmHowQcfjMpwAAAA0RZxGWpubtbVV18tSaqqqpIkmaYpwzCiMxkAAEAMRFyGnnvu\nuSiOAQAAEB8RlyFJ+vjjj7Vo0SJ9/vnnmjdvnioqKtTW1qZRo0ZFtH5JSYkqKyuVkpKiO++8U5LU\n1NSkxYsX6+DBg0pLS9OMGTPkdrslSevWrVNpaakMw1BxcbHy8vIkSXv37lVJSYlCoZDy8/NVXFws\nSQqFQlq2bJmqq6vldrs1Y8YMpaWlncguAgAAm4n4C9SLFi3SxIkTtWfPHi1YsECSFAgEdPfdd0e8\nsbFjx2rmzJldlq1fv165ubmaM2eOcnNztX79eknS/v37VV5ertmzZ2vmzJl67bXXZJqmJGnFihWa\nNm2a5syZoy+++EKVlZWSpI0bN8rtdmvOnDkqKirSqlWrIp4NAADYU8Rl6P7779eqVav09NNPKynp\n0AdKY8aM0aZNmyLe2JAhQ9S3b98uy7Zu3aoxY8ZIkkaPHq2Kiorw8pEjR8rpdMrn86l///6qqqpS\nIBBQW1ubsrOzLdfpfK7hw4drx44dEc+GKGmukj64VvrbZdKmW6RQQ2y3v/MZ6d3LD/3z2fMn9RS1\ntbW6/vrrNWXKFBUXF+vAgQPdPCQAIJ4iPkxWU1NjeTjM4Ti1H7FubGxUamqqJCk1NVWNjY2SDn3q\n1Fl4JMnr9SoQCMjpdMrr9R61vHOdzvucTqdcLpeamprUr1+/U5oRp2DTLTICmw/9Hdgss9yUxvwh\nNtve/xfp08dlhOolSWbzZ1LqVyTf+BN6mjvuuCP8ieXmzZvl9/u1aNGibh8XABAfEZehwsJCPf/8\n87rhhhvCy1555RWdf/753TZMNM9M8/v9amjo+qlEampq+FOueHM6nUpOTo73GOE8uiMXM9Sg9mDX\nT1GMlj1KOoH9PJVc2r/4i8x/FCFJMoK1MmrfljNjwgk9T3V19VG3E+F/q974mukO5GKNXKyRi7VE\nyyXq24n0gb/73e/0jW98Q3/4wx/U1NSkSy65RJ988olWrlx5SgOkpKQoEAjI4/EoEAgoJSVFkuTx\neFRf/883Mr/fL6/XK4/HI7/ff9Tyw9fxer1qb29Xa2tr+FOhDRs2aM2aNV22PWnSJE2ePPmU5u+t\nfD7fKT+HaQ5QjStN7S3/LBN9+g3Q6enpp/zckWjM/pr8exdK7f+4jIyzn07LvlDuE9x+//79tX37\n9vBtn8+n9BjtQ0/SHa+Z3ohcrJGLNXKJj4jL0LBhw1RRUaEVK1boiiuu0ODBgzV16lR5PJ5TGqCg\noEBlZWWaMGGCNm3apGHDhoWXL1myREVFRQoEAqqtrVVWVpYMw5DL5VJVVZWysrJUVlam8ePHd3mu\nQYMGacuWLcrJyQlvZ9y4cSooKOiy7dTUVNXV1SkUCp3SPnQHl8v1pdd+i4WkpCT5fL5uy8Uc9gvp\no/ukUEDqk67Q8EdUU1MT8fqnkovp+xcZZ74t84v1kmHISJ+iQMokNZzA9iXp17/+te666y7V1dVp\nwIAB+s1vfnNC+xAtvfU1c6rIxRq5WCMXa4mWS7QZZucpWjGwePFi7dy5U01NTUpNTdXkyZNVUFCg\nRYsWqb6+/qhT69euXavS0lI5HA7LU+uDwaDy8/N1+eWXSzp0av3SpUu1b98+ud1uTZ8+/UtDrKmp\nUTAYjO6OR8Dtdqu5uTneYyg5OVnp6endm4tpSu2NkjNFOsFDod2SS3vLoe06jn9tveMxTVOtra3K\nycnRgQMHeM0cJiqvmVNALtbIxRq5WEu0XKLtuGXowgsv7Ppgw9DhD+/8js/atWujNF708cLrin8h\nj41srJGLNXKxRi7WyMVarMrQcQ+T3XzzzeG/t2/frmeffVY33HCDBg8erN27d+uPf/yjbrrppqgP\nCQAAEC3HLUOzZs0K/z1+/Hi9+eabGjFiRHjZv/7rv+qmm27iQq0AAKDHivhHgioqKpSbm9tlWU5O\njj7++ONuHwoAACBWIi5DkyZN0o033qhPPvlEzc3N2rp1q2666SZNnDgxmvMBAABEVcRl6Nlnn5Uk\nnXPOOUpJSdHIkSNlmmZ4OQAAQE8U8e8MnX766Xr55ZfV3t6umpoapaeny+l0RnM2AACAqDuh37mu\nr6/X1q1bj7qsxcUXX9ytQwEAAMRKxGXoueee0+zZs5WamnrUhU+5OjwAAOipIi5D9913nxYvXqzi\n4uJozgMAABBTEX+Bur29XZdcckk0ZwEAAIi5iMvQj370I/3iF79QR0dHNOcBAACIqYgPkz366KP6\n/PPP9cgjj+j0008PLzcMQ7t3747KcAAAANEWcRl64YUXojkHAABAXERchi666KIojgEAABAfEX9n\nqKWlRffdd59yc3Pl9XolSStXrtQTTzwRteEAAACiLeIy9MMf/lDl5eV68cUX5XAcWm3EiBF68skn\nozYcAABAtEV8mGzZsmXatm2bUlNTZRiGJCkrK0t79uyJ2nAAAADRFvEnQy6XS6FQqMuympoaDRgw\noNuHAgAAiJWIy9CMGTM0a9Ysffrpp5Kk6upq3XXXXbruuuuiNhwAAEC0RVyGfvnLXyonJ0ejRo1S\nfX298vLyNHDgQP3sZz+L5nwAAABRddwydPiZYp999pkee+wxBQIB7du3T4FAQI8//rhcLlfUhwQA\nAIiW45ah++67L/x3YWGhpEO/OJ2RkRE+owwAAKAnO+7ZZLm5ubrnnnt09tlnKxgM6n/+539kmmb4\nbLLOv2+66aaYDAsAANDdjluGXnnlFT3yyCN66aWXFAwG9fzzz1s+jjIEAAB6quOWoYKCAv3hD3+Q\nJF188cVavXp1TIYCAACIlYi/+LN69WoFg0GtW7dOr7zyiiSpoaFBjY2NURsOAAAg2iIuQ5s3b9ZX\nvvIV3Xrrrbr55pslSWvWrOEQGQAA6NEiLkO33367fv7zn6uiokLJycmSDl3Jft26dVEbDgAAINoi\nLkNbtmzR9ddf32VZv3791Nzc3O1DAQAAxErEZWjIkCH64IMPuix7//33lZ+f3+1DAQAAxErEV61/\n6KGHdMUVV+h73/ue2tra9J//+Z966qmnNH/+/GjOBwAAEFURfzJ0xRVX6I033tCBAwd00UUXaffu\n3Vq2bJkuvfTSaM4HAAAQVcf9ZOj++++XYRgyTVPSoUtxnH766Tr99NMlScuXL9fy5cv14IMPRn9S\nAACAKDhuGfrss8/Cl95oaWnRkiVLdN5552nIkCHatWuX3n//fX3zm9+MyaDR0NLSouTkZCUlRXy0\nMGocDofcbne8x5BhGGpqaiIXC2RjjVyskYs1crFGLtY6O0i0HTfx5557Lvz3ddddp5deeqlL+Vm6\ndKkWLlwYteGirW/fvgoEAgoGg/EeRW63OyHOzEtOTlZaWpoaGxvJ5Qid2fziF7/Q0qVLJUmXXnqp\nfvzjH8d35rDtAAAdJklEQVRlnkTJhteMNXKxRi7WyMVa50/5RFvE9fPPf/6zXnzxxS7LrrzySs2a\nNau7ZwIS1htvvKEnn3xSfr9fkrR3716NGDFCV155ZZwnAwCcrIi/QJ2Xl6cnnniiy7L//u//Vl5e\nXrcPBSSq1atXh4uQdOiSNOvXr4/jRACAUxVxGfrDH/6gRx99VFlZWTr//POVlZWl3/72t5xaD1uZ\nMGGCPB5P+HZKSoouuOCCOE4EADhVER8mGzt2rCorK/Xuu+9q7969GjhwoL761a/G7HgekAimTZum\nG2+8Ua+99ppM09SUKVP0L//yL/EeCwBwCk7oK+t9+vTRxIkTozUL0CP89Kc/1b//+79Lit2ZDgCA\n6In/+XtAD0QJAoDeI+LvDAEAAPRGlCEAAGBrlCEAAGBrlCEAAGBrlCEAAGBrlCEAAGBrlCEAAGBr\nlCEAAGBrlCEAAGBrlCEAAGBrlCEAAGBrXJsMkQk1SjWrJCNZyrhEciTHeyIAALoFZQhfLnhQ+uBa\nGYGPZMohpZ0nnfuy5OgT78kAADhlHCbDl9v+mIzAR5IkQx3Swb9LexfFeSgAALoHZQhfLtTQ5aYh\nUwr64zQMAADdizKELzfkFpl9s8I3zX5DpczpcRwIAIDuw3eG8OU8w6Ux/yNz55OSkSTl/Uhypcd7\nKgAAugVlCJHxniONejLeUwAA0O04TAYAAGyNMgQAAGyNMgQAAGyNMgQAAGyNMgQAAGyNMgQAAGyN\nMgQAAGwtYX5n6LHHHpPL5ZLD4ZDD4dBtt92mpqYmLV68WAcPHlRaWppmzJght9stSVq3bp1KS0tl\nGIaKi4uVl5cnSdq7d69KSkoUCoWUn5+v4uLieO4WAABIcAlThgzD0KxZs9SvX7/wsvXr1ys3N1cT\nJkzQ+vXrtX79en3jG9/Q/v37VV5ertmzZ8vv92vBggWaM2eODMPQihUrNG3aNGVnZ+uFF15QZWWl\n8vPz47hnAAAgkSX0YbKtW7dqzJgxkqTRo0eroqIivHzkyJFyOp3y+Xzq37+/qqqqFAgE1NbWpuzs\n7KPWAQAAsJIwnwxJ0oIFC2QYhs4991yNGzdOjY2NSk1NlSSlpqaqsbFRkhQIBMKFR5K8Xq8CgYCc\nTqe8Xu9RyyXJ7/eroaHr1ddTU1OVlJQYETidTiUnJ8d7jHAe5HI0srFGLtbIxRq5WCMXa7HKIzFS\nl3TzzTfL4/GosbFRCxYs0IABA7rcbxjGKT3/hg0btGbNmi7LJk2apMmTJ5/S8/ZWPp8v3iMkLLKx\nRi7WyMUauVgjl/hImDLk8XgkSSkpKRo+fLj27NmjlJQUBQIBeTweBQIBpaSkhB9bX18fXtfv98vr\n9crj8cjv93dZ3vm848aNU0FBQZdtpqamqq6uTqFQKNq796VcLpdaW1vjPYaSkpLk8/nIxQLZWCMX\na+RijVyskYu1zlyivp2obyECbW1tMk1TLpdLbW1t2r59uyZNmqSCggKVlZVpwoQJ2rRpk4YNGyZJ\nKigo0JIlS1RUVKRAIKDa2lplZWXJMAy5XC5VVVUpKytLZWVlGj9+vKRDh8wOP4TWqaamRsFgMKb7\nayUpKSkh5ugUCoUSYp5Ey0Uim2MhF2vkYo1crJFLfCREGWpsbNTLL78sSero6NCoUaOUl5enzMxM\nLVq0SBs3bgyfWi9JGRkZGjFihObNmyeHw6GpU6eGD6NNnTpVJSUlCgaDys/P50wyAABwXAlRhnw+\nn+64446jlvfr10833HCD5ToTJ07UxIkTj1qemZmpO++8s9tnBAAAvVNCn1oPAAAQbZQhAABga5Qh\nAABga5QhAABga5QhAABga5QhAABga5QhAABgawnxO0NA1AS2Sjt+J8mQ8u6R+p0V74kAAAmGMoTe\nq/FTqfQGGS2fSZLM+o3SuYskd2acBwMAJBIOk6H32v0/4SIkSUbzTmnPi/GbBwCQkChD6L2SPF1u\nmpKUdPTFegEA9kYZQu+VM1umZ5QkyZQhnXauNOi7cR4KAJBo+M4Qeq+kVOn8pTL3r5QMp5TxDcnh\nivdUAIAEQxlC7+Z0SwOvivcUAIAExmEynJqONqlppxRqiPckAACcFD4Zwslr/FQqu1Vq2Scle6Tc\nH0pZ34r3VAAAnBA+GcLJ+/heGQ0VMkIHZTR/Jn36/6T2lnhPBQDACaEM4eSFGrvebm+SQv74zAIA\nwEmiDOHkeYbLPPwl1Heg1GdA/OYBAOAk8J0hnLzh/ykZTpkNWw/9mOGIRySDfg0A6FkoQzh5jmTp\n7F/FewoAAE4J/xkPAABsjTIEAABsjTIEAABsjTIEAABsjTIEAABsjTIEAABsjTIEAABsjTIEAABs\njTIEAABsjTIEAABsjTIEAABszTBN04z3EPHS0tKilpYWJUIEDodDHR0d8R5DhmGoT58+amtrI5cj\nkI01crFGLtbIxRq5WDMMQ2lpaVHfjq0v1Nq3b18FAgEFg8F4jyK3263m5uZ4j6Hk5GSlpaWpsbGR\nXI5ANtbIxRq5WCMXa+RiLTk5OSbb4TAZAACwNcoQAACwNcoQAACwNVt/ZwjWOtr8Cm24SWqqklzp\n0ohHpT794z0WAABRQRnCUerWfFva92cZ/7htlt0qnbckrjMBABAtHCbDUdobdnRd0LxXSoBTPQEA\niAbKEI7iSPZ0XZCUIhmG9YMBAOjhKEM4iveCJ6XUApl90mX2GyoNezDeIwEAEDV8ZwhH6TNgnJwX\nvq1Q0z4pub/kiM2PXgEAEA+UIVgyHEmS64x4jwEAQNRxmAwAANgaZQgAANgaZQgAANgaZQgAANga\nX6AGACCODh48qLlz56qjo0M33HCDcnJy4j2S7VCGAACIk4aGBl177bX66KOPJElvvPGGnnvuOQ0b\nNizOk9kLh8kAAIiT119/PVyEJOmzzz7TE088EceJ7IkyBABAnPTp00fGEZc7SkrioE2sUYYAAIiT\nyy67TOPGjQvfHjp0qP7t3/4tjhPZE/UTXZgdQTV89P/U/sVW6YwZkqcg3iMBQK/lcrn0yiuvaPHi\nxTJNU1dccYV8Pl+8x7IdyhD+yWxX+/szFTiwXlKHtKdEGvWk5Ds/3pMBQK/Vt29f3XjjjUpPT1dN\nTY2CwWC8R7IdDpPhnwIfSbV/l9QhSTJaq6Ud8+I7EwAAUUYZwj+ZptXCmI8BAEAsUYbwT95zJN95\n6nxZmK4zpbPujO9MAABEGd8Zwj8ZTjnPf0n99v9JDV9sk3nmDMl7drynAgAgqihD6MJw9FHqyH9T\nc02NOvgSHwDABjhMBgAAbI0yBAAAbI0yBAAAbI0yhO5ldkgdbfGeAgCAiPEFanSfT5+Q9vxJMkOS\n5xxp9NOSIzneUwEAcFy9sgxVVlbqjTfekGmaKiws1IQJE+I9Uo/R8fmbqtvyutqN/lLODyVn38hW\nbNgq7XpaRrBWkmS2VEvbHpG+8pMoTgsAwKnrdWWoo6NDf/7zn/Xd735XXq9XzzzzjAoKCpSenh7v\n0RLfnlfU8clDavlHodHBjdK4VyQjgqOpgY/DRUiSDHXIbNoRpUEBAOg+ve47Q3v27FH//v3l8/nk\ndDp1zjnnqKKiIt5j9QzVS6TDCo38H0mRFpq0cTJdA8M3TUdf6bRzu3lAAAC6X6/7ZMjv9+u0004L\n3/Z6vdqzZ4/8fr8aGhq6PDY1NVVJSYkRgdPpVHJyfL9fEzKcXW4bjiQ5+6TIiGSu5Fx1jPilOrbP\nlcx2OfoXyZF/lwzDOKWZEiGXTp2vFV4zXZGLNXKxRi7WyMVarPJIjNS70bHefDds2KA1a9Z0WTZp\n0iRNnjw5FmP1CG3nPaS6dTPV0bhbcrrlHnS50oYURv4E6TdKo26M3oAJwufzxXuEhEQu1sjFGrlY\nI5f46HVlyOPxqL6+Pnzb7/fL6/Vq1KhRKigo6PLY1NRU1dXVKRQKxXrMo7hcLrW2tsZ3COcwJRUt\nV1//WjUbGWo7/WLV1NTEdaSEyOUfkpKS5PP5eM0cgVyskYs1crFGLtY6c4n6dqK+hRjLzMxUbW2t\n6urq5PF4VF5erunTp8vr9crr9R71+JqaGgUT4BpcSUlJCTFHsitLKWfPUVNNjUIJME+i5HK4UCiU\nEDMlWjbkYo1crJGLNXKJj15XhpxOpy6//HK98MIL6ujoUGFhIWeSAQCAY+p1ZUiS8vPzlZ+fH+8x\nAABAD9DrTq0HAAA4EZQhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5Qh\nAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABg\na5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga5Qh\nAABga5QhAABga5QhAABga5QhAABga5QhAABga5QhAABga4Zpmma8h4iXlpYWtbS0KBEicDgc6ujo\niPcYMgxDffr0UVtbG7kcgWyskYs1crFGLtbIxZphGEpLS4v6dpKivoUE1rdvXwUCAQWDwXiPIrfb\nrebm5niPoeTkZKWlpamxsZFcjkA21sjFGrlYIxdr5GItOTk5JtvhMBkAALA1yhAAALA1yhAAALA1\nyhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAA\nALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1\nyhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALA1yhAAALC1pHgP\n8Pbbb2vjxo1KSUmRJE2ZMkX5+fmSpHXr1qm0tFSGYai4uFh5eXmSpL1796qkpEShUEj5+fkqLi6W\nJIVCIS1btkzV1dVyu92aMWOG0tLS4rNjAACgR4h7GTIMQ0VFRfrqV7/aZfn+/ftVXl6u2bNny+/3\na8GCBZozZ44Mw9CKFSs0bdo0ZWdn64UXXlBlZaXy8/O1ceNGud1uzZkzR+Xl5Vq1apVmzJgRpz0D\nAAA9QcIeJtu6datGjhwpp9Mpn8+n/v37q6qqSoFAQG1tbcrOzpYkjR49WhUVFeF1xowZI0kaPny4\nduzYEbf5AQBAzxD3T4Yk6b333lNZWZkyMzN1ySWXyO12KxAIhAuPJHm9XgUCATmdTnm93qOWS1Ig\nEAjf53Q65XK51NTUpH79+snv96uhoaHLdlNTU5WUlBARyOl0Kjk5Od5jhPMgl6ORjTVysUYu1sjF\nGrlYi1UeMdnKggULjioiknTxxRfr3HPP1aRJkyRJq1ev1sqVK3XVVVd1+wwbNmzQmjVruiwbMmSI\nvvnNb8rn83X79noqv9+vt99+W+PGjSOXI5CNNXKxRi7WyMUauVg7PJfDPwjpbjEpQ9/97ncjelxh\nYaFeeuklSZLH41F9fX34Pr/fL6/XK4/HI7/ff9Tyw9fxer1qb29Xa2ur+vXrJ0kaN26cCgoKwuvV\n1NRo2bJlamhoiGrAPU1DQ4PWrFmjgoICcjkC2VgjF2vkYo1crJGLtVjlEvfvDHUe4pKkiooKZWRk\nSJIKCgpUXl6uUCikuro61dbWKisrSx6PRy6XS1VVVTJNU2VlZeGSU1BQoLKyMknSli1blJOTE35u\nr9erzMzM8D/p6ekx3EsAAJCo4n5wctWqVdq3b58Mw1BaWpquvPJKSVJGRoZGjBihefPmyeFwaOrU\nqTIMQ5I0depUlZSUKBgMKj8/P3wqfmFhoZYuXaq5c+fK7XZr+vTpcdsvAADQM8S9DF1zzTXHvG/i\nxImaOHHiUcszMzN15513HrU8KSlJ1157bbfOBwAAejfnAw888EC8h4gH0zTVp08fnXXWWXK5XPEe\nJ2GQy7GRjTVysUYu1sjFGrlYi1UuhmmaZtSeHQAAIMHF/TBZd1q5cqU++eST8A81Xn311erbt6+k\n7r20x6ZNm7R27VpJhw7ldf7QY29QWVmpN954Q6ZpqrCwUBMmTIj3SN2qvr5ey5YtU2Njo6RDZxle\ncMEFampq0uLFi3Xw4EGlpaVpxowZcrvdkux1WZiOjg4988wz8nq9+s53vkMu/9Dc3KxXX31VNTU1\nkqSrr75a/fv3t30269at04cffijDMJSRkaGrr75abW1ttsulpKRElZWVSklJCX+FI1b/7iTy+5FV\nLgn7Pm32Itu2bTPb29tN0zTNlStXmitXrjRN0zQ///xz88knnzRDoZBZW1trPv7442ZHR4dpmqb5\n9NNPm5999plpmqb5/PPPm5988olpmqb53nvvmf/7v/9rmqZpbt682Vy4cKFpmqbZ2NhoPv7442ZT\nU5PZ1NQU/rs3aG9vNx9//HGztrbWDIVC5pNPPmnu378/3mN1K7/fb+7du9c0TdNsaWkx586da+7f\nv9988803zXXr1pmmaZrr1q2LymunJ/jrX/9qLl682HzxxRdN0zTJ5R+WLl1qbtiwwTRN0wyFQmZz\nc7Pts6mtrTUfe+wxMxgMmqZpmgsXLjRLS0ttmcvOnTvNvXv3mvPmzQsvi0UOif5+ZJVLor5Px/3U\n+u40dOhQORyHdik7Ozv8e0TdeWmP7du3a+jQoXK73XK73crNzdW2bdtivatRsWfPHvXv318+n09O\np1PnnHNOOI/ewuPxaODAgZIkl8ulAQMGyO/3d/nf+8jXgV0uC1NfX6/KykoVFhaGl5GL1NLSol27\ndoVzcTqd6tu3r+2zcblccjqdCgaDam9vVzAYlMfjsWUuQ4YMCX+60SkWOST6+5FVLon6Pt2rDpMd\nrrS0VOecc44kdeulPQ5ffuQ6PZ3f79dpp50Wvu31erVnz544ThRddXV12rdvn7Kzs9XY2KjU1FRJ\nhy7T0nkYrbsvC5PI3nzzTV1yySVqbW0NLyOXQ6+TlJQUlZSUaN++fcrMzNRll11m+2z69eunoqIi\nPfbYY0pKSlJeXp6GDh1q+1w6xSKHnv5+lEjv0z2uDB3r0h5TpkwJ//ji2rVr5XQ6NWrUqFiP16N1\n/o6THbS2tmrhwoW67LLLjjpDwU45dNq6datSUlI0cODAY/7Xtx1zkQ59j6q6ulqXX365srKy9Prr\nr2v9+vVdHmPHbGpra/Xuu+/qBz/4gVwulxYtWhT+0dtOdszFCjkcLdHep3tcGfqyS3uUlpaqsrKy\ny+O689IeHo9HO3fu7LLO4b903ZMdK6fepr29XQsXLtSoUaM0fPhwSVJKSooCgYA8Ho8CgYBSUlIk\ndf9lYRLVZ599pq1bt6qyslKhUEitra1aunSp7XORDv1XpdfrVVZWliTp7LPP1vr165WammrrbPbu\n3atBgwaF5xw+fLiqqqpsn0unWPy701PfjxLxfbpXfWeosrJS77zzjq677rouV9vtzkt7DB06VNu3\nb1dzc7Oam5vDxyZ7g8zMTNXW1qqurk6hUEjl5eVdrufWG5imqeXLlys9PV1FRUXh5Yf/771p0yYN\nGzYsvLw7LwuTqL7+9a/r7rvv1g9+8ANNnz5dOTk5uuaaa2yfi3To/3C9Xq8OHDggSfr000+Vnp6u\nr3zlK7bOZsCAAaqqqlIwGJRpmuRyhFj8u9MT348S9X26V/3O0Ny5c9Xe3h4+fTE7O1tXXHGFpEMf\nyZWWlsrhcFiestd5aY/LL79c0qFT9pYuXap9+/aFL+3ReSXh0tJSrVu3TlLincp4qjpPre/o6FBh\nYaEuvPDCeI/UrXbt2qVnn31WZ5xxRvij6ylTpigrK0uLFi1SfX39UafBdudrpyfYuXOn3nnnnfCp\n9eQi7du3T6+++qra29vDpwN3dHTYPpv169errKxMhmFo4MCBmjZtmlpbW22Xy+LFi7Vz5041NTUp\nNTVVkydPVkFBQUxySOT3oyNzueiii7R+/fqEfJ/uVWUIAADgRPWqw2QAAAAnijIEAABsjTIEAABs\njTIEAABsjTIEAABsjTIEAABsjTIEAABsjTIEoEc566yz9NZbb8Vl2w8//LBuvfXWuGwbQPRQhgB0\nq+eee+6oXy6fNWuW7r///m55fsMw4nbhy3vvvVfz58+Py7YBRA9lCIDthEKheI8AIIFQhgCclF/9\n6lfKy8uT1+vViBEjVFJSoo8//li33367/va3v8nj8cjn82n+/Pn605/+pEceeUQej0dXXXXVMdc/\n3Pz583X22WeH79+0adNRM3z88cfKzc3VK6+8IklasWKFxowZI5/Pp6997WvavHlz+LFnnXWWHnnk\nEY0aNUoej0cdHR3H3Ldf//rXys7Oltfr1bBhw7R69WpJ0gMPPKDrr79eknTXXXfJ4/GE/0lOTtbP\nf/5zSYeupfTNb35TGRkZys3N1e9+97tTSBpA1JkAcBIWLVpkVldXm6Zpmq+88oqZkpJiVldXm889\n95w5YcKELo+dNWuWef/993/p+vv27TNN0zQXLlxoZmVlmR988IFpmqa5bds2c9euXaZpmuZZZ51l\nvvXWW+aGDRvMwYMHm6+99pppmqa5ceNGMyMjw/z73/9udnR0mH/84x/Ns846y2xrazNN0zSHDBli\njh071qyqqjJbWlqOuV8VFRXmoEGDwrPt2rXL3L59u2mapvnAAw+YM2fOPGqd0tJSMz093dy0aZPZ\n3t5uFhYWmr/4xS/MYDBofvrpp2Zubq755ptvnkC6AGKJT4YAnJTp06frzDPPlCRde+21ys/P19//\n/neZx7j285HLj7W+JP3+97/Xj370I40bN06SNHToUA0ePDi87po1a3TVVVfp+eefD1/B+plnntH3\nvvc9nXfeeTIMQ9/97nflcrn07rvvSjr0XaM5c+YoKytLLpfrmPvldDrV2tqqjz76SMFgUIMHD1Zu\nbq7lPkhSTU2Nrr76aj3xxBMaPXq03n//fR04cEA//elPlZSUpJycHN1yyy16+eWXvzxUAHFBGQJw\nUhYsWKCxY8fK5/PJ5/OpvLxcBw4ciPjLzcdaX5Kqqqo0dOhQy/VM09TTTz+tr33ta5o4cWJ4+a5d\nu/Tb3/42/Hw+n09VVVXau3dv+DGDBg360rny8vL0+OOP64EHHtAZZ5yhb3/726qurrZ8bDAY1PTp\n0zVz5kxde+214Tn27t3bZY6HH35Y+/fvjygXALFHGQJwwnbt2qXbbrtN8+bNU21trerq6nTOOefI\nNE3LMnTksuOtLx0qLdu2bbPctmEYevrpp7Vr1y7dfffd4eWDBw/WT37yE9XV1YX/aWho0Le+9a1j\nznEs3/72t7Vu3Trt2rVLhmHoRz/6keXjvv/97ystLU0PPfRQlzlycnK6zOH3+7VixYqItg0g9ihD\nAE5YY2OjDMPQgAED1NHRoWeffVbl5eUyDENnnHGGqqqqFAwGw48/44wz9Omnn37p+p1uueUW/dd/\n/Zc2btwo0zS1bds27d69O3y/x+PRG2+8obVr1+ree++VJN1666166qmnwofqGhsb9dprr6mhoeGE\n9u2TTz7R6tWr1draKpfLpb59+8rpdB71uKefflpr167VCy+80GX5+eefL4/Ho0ceeUTNzc1qb29X\neXm5PvjggxOaA0DsUIYAnLCzzz5b99xzj4qKinTmmWeqvLxcEyZMkCRdfPHFGjFihM4880xlZGRI\nkm6++WZt2bJFPp9P11xzzXHXlw59n+gnP/mJvvOd78jr9eqaa65RXV1dlxlOO+00rVq1Sq+//rr+\n4z/+Q+PGjdP8+fN11113qX///srPz9eCBQtO+DeJWltbde+99yo9PV0DBw7UgQMH9PDDD0vq+htH\nL7/8snbs2KHMzMzwGWW/+tWv5HA4tGLFCm3atEm5ublKT0/XbbfdJr/ff9J5A4guwzzWtx0BAABs\ngE+GAACArVGGANjO7t27u/xgYuc/Xq9XVVVV8R4PQIxxmAwAANganwwBAABbowwBAABbowwBAABb\nowwBAABb+//c8WSLIZPz/gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cad08d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<ggplot: (281727157)>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    }
   ],
   "metadata": {}
  }
 ]
}