{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing champion difficulty and hyper-carriness\n",
    "\n",
    "This notebook explores which League of Legends champions are easiest and most challenging to play; and which champions are best in the early game versus the late game. It is part of my [project to analyze LoL using maching learning](http://github.com/map222/lolML). My summoner IDs are Umiy and lemmingo.\n",
    "\n",
    "First, we need to import some libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import re\n",
    "import numpy as np\n",
    "import src.plotting as lol_plt\n",
    "import pdb\n",
    "import ast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to load a list of champions; I output it here so you can remake it yourself. Note that Wukong is the 'monkeyking.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ahri', 'akali', 'alistar', 'amumu', 'anivia', 'annie', 'ashe', 'azir', 'bard', 'blitzcrank', 'brand', 'braum', 'caitlyn', 'cassiopeia', 'chogath', 'corki', 'darius', 'diana', 'drmundo', 'draven', 'ekko', 'elise', 'evelynn', 'ezreal', 'fiddlesticks', 'fiora', 'fizz', 'galio', 'gangplank', 'garen', 'gnar', 'gragas', 'graves', 'hecarim', 'heimerdinger', 'irelia', 'janna', 'jarvaniv', 'jax', 'jayce', 'jinx', 'kalista', 'karma', 'karthus', 'kassadin', 'katarina', 'kayle', 'kennen', 'khazix', 'kindred', 'kogmaw', 'leblanc', 'leesin', 'leona', 'lissandra', 'lucian', 'lulu', 'lux', 'malphite', 'malzahar', 'maokai', 'masteryi', 'missfortune', 'mordekaiser', 'morgana', 'nami', 'nasus', 'nautilus', 'nidalee', 'nocturne', 'nunu', 'olaf', 'orianna', 'pantheon', 'poppy', 'quinn', 'rammus', 'reksai', 'renekton', 'rengar', 'riven', 'rumble', 'ryze', 'sejuani', 'shaco', 'shen', 'shyvana', 'singed', 'sion', 'sivir', 'skarner', 'sona', 'soraka', 'swain', 'syndra', 'tahmkench', 'talon', 'taric', 'teemo', 'thresh', 'tristana', 'trundle', 'tryndamere', 'twistedfate', 'twitch', 'udyr', 'urgot', 'varus', 'vayne', 'veigar', 'velkoz', 'vi', 'viktor', 'vladimir', 'volibear', 'warwick', 'monkeyking', 'xerath', 'xinzhao', 'yasuo', 'yorick', 'zac', 'zed', 'ziggs', 'zilean', 'zyra']\n"
     ]
    }
   ],
   "source": [
    "champ_list = pd.read_csv('../champ list.csv', header=None)\n",
    "champ_list.columns = ['Champion']\n",
    "champ_list.Champion = champ_list.Champion.str.lower()\n",
    "champ_list.Champion = champ_list.Champion.str.replace('[^\\w]','')\n",
    "print(champ_list.Champion.tolist(), )\n",
    "champ_list.drop(49, inplace=True) # drop kindred due to lack of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the champion data, we're going to query [League of Graphs](http://www.leagueofgraphs.com/). For each champion, we're going to create a URL for that champions webpage, and then make a list of html pages that we can use later. The html pages will have all the numbers we care about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "champ_html = [requests.get('http://www.leagueofgraphs.com/champions/stats/'+ cur_champ) for cur_champ in list(champ_list['Champion'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load / save from pickle\n",
    "\n",
    "To save time (and League of Graph's bandwidth), we can save the html to our computer, or load it from the computer. Note that we have to delete Kindred at the moment because there isn't enough data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('champ_html.pickle', 'wb') as champ_pickle:\n",
    "    pickle.dump(champ_html, champ_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('../champ_html.pickle', 'rb') as champ_pickle:\n",
    "    champ_html = pickle.load(champ_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del champ_html[49] # delete kindred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What champions are easiest and most difficult?\n",
    "\n",
    "Now that we have the data loaded into our html list, we can begin analyzing it. First, we need to extract the data for win rates versus time. To do this, I use a regular expression to find the variable d8, and then use eval to get it. (There is probably a more elegant Pythonic way to convert strings to lists.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_wins_by_games(html):\n",
    "    win_by_games_str = re.search('(?<=var d8 = ).*?]]', html).group(0)\n",
    "    win_by_games_list = ast.literal_eval(win_by_games_str)\n",
    "    return win_by_games_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the get_wins_by_games function to the html list using list comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wins_by_games = np.array([get_wins_by_games(x.text) for x in champ_html])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a sense of what these look like, we can plot the win rates versus time for random champions. First we define the plot function (which we'll reuse), and then apply it to wins_by_games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_wins_by_index( wins_by_index):\n",
    "    for cur_game in wins_by_index:\n",
    "        win_index, percent = zip(*cur_game)\n",
    "        plt.plot(win_index, percent, 'grey')\n",
    "    plt.ylabel('Win rate', fontsize = 18)\n",
    "    lol_plt.prettify_axes(plt.gca())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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73Q6FQsHUV+kQPo6IiHisydjtdmN4eBhDQ0MYGRlh5OFwOBhZBJNGQkJCSILz+XwBgcKh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\nqWLq3APSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ce7198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wins_by_index( wins_by_games[np.random.randint(0, 125, 20)])\n",
    "plt.xlabel('Number of games played', fontsize = 18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The win rates for most champions increases as people play them more. To create a metric for the learning curve, we can subtract the maximum winrate from the minimum winrate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_learn_delta(game):\n",
    "    _, percent = zip(*game)\n",
    "    return max(percent) - min(percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can apply it using a list comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "learn_deltas = [get_learn_delta(x) for x in wins_by_games]\n",
    "learn_deltas = np.array(learn_deltas)\n",
    "champ_list['learn_delta'] =learn_deltas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally,  we can plot the histogram of the change in win rate with experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W+AxwOLAdMBO4UdIKJZdjZmbDZKhE8iKwUovzWinXb1lEXB8RX4iIK4GB4jRJAo4GTouI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1c32b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(learn_deltas, bins=range(1,12), color = 'grey');\n",
    "plt.xlabel('Change in win rate with experience (%)', fontsize = 18)\n",
    "plt.ylabel('# Champions', fontsize = 18)\n",
    "lol_plt.prettify_axes(plt.gca())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everyone gets at least 2-3 percent better with experience, with a few notable outliers at 10%! Now we can investigate which champions have the highest learning curve. That is they have the biggest change in winrate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Champion</th>\n",
       "      <th>learn_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>azir</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bard</td>\n",
       "      <td>8.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>kalista</td>\n",
       "      <td>10.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>reksai</td>\n",
       "      <td>10.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>rengar</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>ryze</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>thresh</td>\n",
       "      <td>8.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Champion  learn_delta\n",
       "7      azir        10.45\n",
       "8      bard         8.59\n",
       "41  kalista        10.22\n",
       "77   reksai        10.08\n",
       "79   rengar        10.25\n",
       "82     ryze         8.90\n",
       "99   thresh         8.57"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_skill_champs = np.where(learn_deltas > 8.5)\n",
    "champ_list.iloc[high_skill_champs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list looks pretty intuitive! Many highly mechanical champions like Azir and Rengar, and strategically different champions like Bard and Rek'Sai. Ryze is a surprisingly difficult champion apparently! Let's plot these champions win rate as a function of games played."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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POm7/0h9PCBOdO5ezR84HoSQqitIM6IFLwA+EEA13jh0GHlMU5XdCiFuKojwIZAD/5KSy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5c83908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wins_by_index(wins_by_games[high_skill_champs])\n",
    "plt.xlabel('Number of games played', fontsize = 18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those are indeed steep learning curves.\n",
    "\n",
    "Now we can turn to low skill champs, and repeat the same procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Champion</th>\n",
       "      <th>learn_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>galio</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>karma</td>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>kayle</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>malphite</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>mordekaiser</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>taric</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Champion  learn_delta\n",
       "27        galio         2.09\n",
       "42        karma         2.87\n",
       "46        kayle         2.65\n",
       "58     malphite         2.91\n",
       "63  mordekaiser         2.26\n",
       "97        taric         2.64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_skill_champs = np.where(learn_deltas < 3)\n",
    "champ_list.iloc[low_skill_champs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results look intuitive as well. What about their learning curves?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vFphmwVGr1bi5uemSNDYvxkrnAk1i+ujRo1Y9mcrKSoYPH97KXWbKgf/GxsY2\nQYfNyTWbhaM5hb7+hIXeiknqCMXFxRw6dAhra2tWrFjRYXdOZGSk7uZGn+aeTFdERt8F5uDgwM2b\nNzl//jwrVqzoVraHqKgoSkpKDNrcEYQQfPHFF1RUVPDuu++aPE9ZT1JWVsbu3bsJDg7uTmVZKSj6\ndFRQSkpK+Oqrr9i6dWuXotKFEHz77beYm5szYcIERo8ejZWVFZcuXSI2NpZFixZ1qjKeSqUiOjqa\ne/fuMXbsWEJDQ40SLV1bW9uqJ9PsurG2tm4jNI6OjkbrVajVal1cRfOjRqNpJTAeHh5d/uzVarXB\nqPTq6mpqa2vbrG9sbGyTGqU5f1mzcPTllDJCCOLj4zl37hxz585l2rRpnRLnuro6duzYwSuvvNKh\nbMWGRObRo0fY29u3ERkLCwtu3LjB+fPnsbe3p7GxkfXr1+Ps7Nzl91tfX89f/vIXtmzZgouLS5fb\nSUpK4sSJE9jZ2fHGG2/0q15zRykpKWH37t0899xz3Y2hk4KiT0cFZf/+/bi5uTFnzpwunScmJoY7\nd+7w+uuvY2ZmRlFREYcPH8bKyoqVK1d2aABRCEFaWhrR0dEUFBQQEhJCSEhIjw8iarVaSktLWwnN\no0ePqK6u1t2ZN4uMp6en0Vw7FRUVOnFpFhorK6tWYzFarfapAlFbW4ulpaXBvFmGEjhaW1tjaWmJ\noii62ttpaWnk5+fj5+dHYGBgn55oUFNTw7FjxygtLWXNmjVdvsDGxsaSmJjIq6++2qWe4tNEpqCg\nALVaDUBERAQTJkzoco80OjqarKysbo9tCiH461//CsD48eO7/H/vq6hUKvbs2cO8efOMEXcjBUWf\njghKs3+3q3XiS0tL+fLLL3njjTdwcnIiJiaGixcvMn/+fEJCQp76J2poaOD27dtER0djbm5OaGgo\ngYGBJk+hUldX12pspjm7sJubG97e3nh7ezNy5EijCYwQguLiYp3APHr0CHNz86eKg5WVVYdnTAkh\nKCkpIT09nfT0dDIyMrC3t8fX1xd3d3cePHhASkoKo0ePJigoCG9v7z41gJuens6RI0eYOHEi4eHh\n3fqNaLVadu7cSURERLeCHVvSUmTMzMyYPHkyWVlZnDp1CktLSxYtWtTp+i2NjY18+umnvPzyy3h4\neHTbxubKq5WVlWzcuNEobfYF8vPz2bt3LxEREUyaNKlbbWVmZjJq1CgpKPo8TVCEEPzjH/8gKCiI\noKCgTregoGzIAAAgAElEQVQvhGD37t0EBAQwceJEjhw5QkNDA6tWrXrqwH5lZSUxMTHExcUxYsQI\nwsLC+twFTJ+GhgZycnJ4+PAhDx8+JD8/v8cExljU1NSQkZFBWloa6enpaLVafH198fPzw8fHp00P\nsKamhtu3bxMfH09DQwNBQUFMmTKlU9NUjU1jYyPnzp0jMTGRVatW4evra5R2Hzx4YJRgx6eh1WqJ\nj4/n/PnzjBkzhvDw8A67N2/evElSUhIbN240ii1CCHbu3Mno0aNJSkri3Xff7fezEnNycvjxxx9Z\nunQp48aN61ZbDx8+ZP/+/fzud7+TgqLP0wQlKSmJ8+fP8+6773bpDxUbG8utW7d4/fXX+fbbb/Hy\n8iI8PPyJbeXl5REdHU1KSgqBgYGEhoZ2e1aZqeiLAtPY2Eh2drauF1JcXIyXl5dORJydnTsk2kII\ncnNziY+P5969e4waNYqgoCACAgJ61fdeWFjIoUOHcHBwYPny5UYd2xFCsGfPHqMFOz6N5tout2/f\nZtasWUyfPv2JvUutVsuOHTtYuXJlp4rKPY27d+8SHR2Ng4MDQ4YM6fQ05L5EZmYm+/btY9WqVQQE\nBHSrrezsbH788UdeeuklfHx8TCcoiqLYAM/RVMPknBCiwFjGdIcnCYpGo2Hnzp0sWrSo09N4oWkm\nxZdffsmWLVsoLCzk8uXLvP322wYvNlqtluTkZKKjoykrK2P69OkEBwf3aV99VzCFwAghKCws1AlI\nVlYWLi4uOgEZMWJEt4MI6+vrSUxMJC4ujrKyMqZMmUJQUFCPzhRqGd/x/PPPExQU1CO912ZXybZt\n24w62+9JqFQqTp8+TXl5OQsXLmz3/3fnzh1iY2N5/fXXjXr+ZndfeHg4p0+fZunSpd2+GJuCtLQ0\nDh06xJo1a7rda83NzWXv3r2sXr26+fswjaAoivIBTTVKbAEBRAghIhVFcaOpCNZ2IcTfjGVcZ3iS\noNy4cYOkpCQ2bdrU6T9qc3psHx8fwsLC+Pzzz1mxYkWbynJ1dXXExcURExODjY0NYWFhjB079pnJ\n/NtSYDIzM8nLyzOKwFRWVuoEJD09HQsLC3x9ffH19cXHx6dHhbqwsJD4+Hhu376Nq6srwcHBjBs3\nzqhjXlVVVfz8889UV1fz4osv9ngP9siRI9jZ2REeHt6j52mJEIKUlBROnz6Ns7MzCxYsaPU+m11T\nCxYs6NIN39O4c+cON27cYP78+Rw+fJj33nuvT8/s0yc5OZljx46xbt06vLy8utVWc6mO5cuXt5za\n3fuCoijKGmA/cBQ4BnwFvCCEiHy8/QhgIYQwSZ+yPUHp7LRJfW7evElcXBxvvvkmV69eJScnh5df\nflm3vbS0lOvXr5OQkICfnx9hYWEdSss90OmqwNTX15OZmakTkIqKCnx8fHS9kK4EunWXxsZGkpOT\niY+PJy8vj8DAQIKDgzudTkSfBw8ecOzYMaZMmcK8efN65ebD1JVJr1+/zpUrVwgKCmLOnDkMHjyY\npKQkoqKiePvtt3ukZ6bVavniiy9YunQpKSkplJaWsm7duj49htlMYmIiJ0+e5JVXXun2pILCwkL2\n7NnD4sWL9ZPhmkRQooEaIUS4oijOQCGtBeUPwFtCCOM5QDtBe4ISGRlJeXk5q1ev7nSb5eXl/O1v\nf+O1115j6NChfPHFF7z55ps4Ojq2SosSFBTE9OnTn1jC9FnnSQLj6emJSqUiPT2dvLw83N3ddb0Q\nDw+PPhVDUFZWRnx8PLdu3cLGxoagoCACAwM75UJqaGjQ5blavXq1UccMOsKTgh17g6qqKs6dO0dq\nairh4eHcuHGDWbNm9WjG74SEBG7dusXGjRv56quvCA0N7dLknN4kISGBs2fPsmnTpm7fvBQVFfHt\nt9+yYMECQ0k6TSIo1cDvhRA72hGUt4AdQogOhRcrivIJ8K96qwuEEB6Pt2vbOfQLIcQ2A+21EZTK\nykp27tzJu+++2+mLvRCC77//Hi8vL+bMmcOxY8ewtLRkwYIFHD58mNzc3FZpUSSdo6XA5Obm4uTk\nhJ+fH6NGjeo1/3530Gq1pKWlER8fT3p6OuPGjSMoKIiRI0c+8c63oKCAgwcPMnz4cJYuXWqSaPzu\n9tqNRW5uLkePHqW0tJTNmzd3253zJFoO+g8ZMoTdu3fz1ltvmaTH2xFiY2O5dOkSmzdv7lagKDQF\nQH7zzTeEh4e3V9HSaILSGWewBnjSraI7UN3J8ycB8/TO0Yx+uPg0mlxtP3W08fPnzxMUFNSlnsOt\nW7eorq5m5syZPHr0iKSkJLZt28a5c+coKyvj/fffN3n8SH/GwsICHx+fNmNR/QUzMzMCAgIICAig\nqqqKhIQEfv75ZxRFISgoiMmTJ7eaLiuE4Nq1a1y5coWFCxcSGBhoMpfL4MGDmTt3Lr/++muXgx2N\ngaenJ0OHDsXLy4sDBw7g7e3NCy+80COuODMzM2bPns3Fixd59dVXmTVrFocPH2bLli19qgcMcO3a\nNWJiYtiyZUu3Ba85NUtvlUfuzCd5G1hoaIOiKGbAWuBGJ8+vEUIUtliKmzforS8EVgHJQohLHWm4\nsLCQBw8eMHv27E6a1ORnPnv2LKtWrcLMzIxff/2VOXPmcO/ePe7fv8/LL78sxUSiw8bGhpkzZ7J1\n61aWL19OYWEhn332Gfv37yc1NZXy8nL27NlDUlISb731FpMmTTK5/z44OJiqqipSUlJMZkN2djbl\n5eUsXryYbdu2YW9vz65du4iKiqKxsdHo55s0aRIlJSVkZWURFhbGoEGDuHLlitHP01VUKhXHjh0j\nNjbWKGJSUVHB7t27mTFjRq8lyeyMoHwGLFYU5X8DzXMozRVFGQscACYCn3by/L6KouQqipKuKMoP\niqIYvF19PFX5ZeDLjjZ89uxZZs2a1WmXghCC48ePM23aNNzc3EhJSaG8vBxHR0fOnz/PK6+80q9m\niEh6D0VR8PLyYtWqVfzmN7/Bx8eHyMhIPv30U0aNGmWUi4SxMDMzIyIigjNnzqDVtudd7lkuXbrE\njBkzMDc3x9LSkvDwcN5++20KCgr4/PPPuX//PsaMkzM3N2f27NlERUWhKAorV64kOjqavLw8o52j\ns2g0Gu7evcs333zD7t27sbGx4Y033uj2eGxlZSXffvstISEhTJ8+vd39rl271q3z6NPh22whxE+K\nogQC/wz8j8erT/Ff/rdPhBAnOnHuaOA1mtxebsC/AFcVRZkghCjR2/cVwAL4tiMNZ2RkUFRUxPr1\n6zthThO3b9+moqKC9evX64oETZ8+nSNHjrB+/fp+G5go6V2GDBmiy9FWX1/fJ8fZAgICiI6OJi4u\nrleCHVtSUFBAfn5+m5xdDg4OrFu3joyMDE6dOkVMTAyLFi3q9qB0M5MnTyYqKoqcnBxGjBjBokWL\nOHz4MO+8806vRtGXlZVx8+ZN4uPjcXV1Zfr06YwZM8YoM/2qq6vZvXs3kydPZsaMGQb3EUIQGRlJ\nUlISzz33XLfP2UxXAhuDgY3AOJrE5AGwRwgR2y1DFGUokAH8UQjxZ71tN4A0IcTLBg9u2kd8/PHH\nCCG4efMma9as6XSQVGVlJbt27WLTpk24u7sTExNDYmIiZWVlvPDCC50uYSqR9HVMEewIcODAATw8\nPNq94EHTQHpsbCwXL15k/PjxzJ8/3yjegRs3bpCSksIrr7wCwMGDB7GysupyOYuOotVqSU1NJTY2\nlpycHCZNmkRISEi3B91bUlNTw+7duxkzZgzz5883uI8QgpMnT5KTk8OmTZsYOnTowEy9oihKJHBf\nCLG1xbopQBxNQZTnnnCsEEJw+/ZtYmJiePPNNzvlpxZC8NNPP+Hm5sb8+fOpra1lx44dDB06lMDA\nwAGXrVQiaaa3gx2Li4v5+uuv+eijjzrUc6upqeHChQskJiYyd+5cQkJCujWQrp+Esra2ll27drF8\n+fIeCaysrq4mLi6OmzdvYm1tTUhICBMnTjR6j0itVrN79258fHx44YUXDF7/tFotR48epaysjA0b\nNjQPCRhNUDr8rSiKkqEoyoonbF+uKEp6Vw1RFGUITb2efL1N7wDpTxKTZhobG4mMjOxKCUzu3r1L\naWmpTjiioqKwsLBgxIgRXRrYl0j6C+Hh4cTGxlJRUdEr57t8+TLTpk3rsBtw6NChLFmyhNdee42k\npCT+/Oc/c+DAAWJiYnj06FGnx1kGDRrEzJkziYqKAsDKyopVq1bx888/U1NT0+n3YwghBJmZmRw8\neJAdO3boginffvttgoKCjC4mdXV1fP/994wcObJdMWlsbGTfvn3U1NSwadOmHpmy3pmpSqOAJxXs\nsAa8O9qYoij/AfwMZNOUF+wPgBUtxkkeu8E2An/sSJvXr19n+PDhnQ4Uq6qq4vTp07zyyiuYm5tT\nXFxMbGwsHh4eLFu2zOQzciSSnsTOzo6QkBAiIyN7PNixvLycpKQkPvzww04f6+rqyubNmykrKyMz\nM5PMzEyuX79OTU0NXl5eeHl5MWrUKNzd3Z86FhEcHMzly5cpKChg+PDh+Pj4MGHCBI4fP87atWu7\n/J9Xq9Xcvn2b2NhYhBCEhIT0eLxRfX09e/fuxc3NjUWLFhm0vb6+nh9//BErKyvWrl3bY1kZjDn3\n1RXojLx7Aj8AzoAKuAaECSGyW+yzniaR+UdHGrx69Wqnx02EEPzyyy8EBQXpUhvs37+fwYMHs2HD\nhmcmF5fk2WbmzJns2LGD/Pz8Hg12vHLlSreSpSqKgoODAw4ODrq4isrKSrKyssjMzOT48eOUlpbi\n6empE5kRI0a06Q1ZWFgwY8YMoqKidBMDnn/+eb788ksSEhI6HbORn5/PjRs3uH//Pn5+fixZsoRR\no0b1+M1oQ0MDP/74I46OjixdutTg+Wpra9m7dy8uLi4sW7asR+NunigoiqLMBebyXz62FxVFMeRk\ndKJpWu+tjp5YCLGhA/v8gw6KCcC4ceM6PcCVmJhIcXExa9asAZpcXYWFhXzwwQd9qqa4RNKT9Eaw\nY1VVFXfu3GHr1q1P37kT2NraMmHCBCZMmAA09RKysrLIysri/PnzPHr0CFdXV0aNGqUTGSsrK0JC\nQrhy5YqumNygQYN48cUX2b17N6NGjXrqFO+GhgYSExOJjY2lqqqK4OBgtm7d2uOVV5tpdmHZ2Niw\nfPlyg99ZVVWVLrntggULelzgnjgo3056lPZIBTYKITob3GgUFEURlZWVnfoyq6ur2blzJxs2bMDT\n05OcnBy+/vpr5s2bJwfhJc8cPVHZsSVnz56lrq6u12uSNKf5aRaZnJwc7O3t8fLyQq1WU19fz4YN\n/3V/e/XqVZKTk3nttdcM3s03u8QTEhLw9PQkJCSk12vnaDQa9u3bh7m5OS+99JLBc5eVlbFnzx4m\nTZrEnDlzniQmvTPLS1GUYUCzTKcDv6Up23BLBFDVMsrdFHS0pnxL9u/fj729PREREZSXl7Nr1y6s\nra3ZunWrHDeRPJP0VGXH2tpaPvvsM9555x3s7e2N1m5X0Gg0FBQUkJmZycOHD0lJScHOzg5fX19d\nD+bYsWP4+/sza9Ys3THJycnExsZSWFjIlClTmDp1qkkCVbVaLQcOHECj0bBu3TqDbvmioiK+++47\nwsLCCAsLe1qTvZPLSwhRDpQDKIoSDtx7nAal33Pv3j0KCwtZvXo1dXV1fPfddwghWLVqlRQTyTNL\nTwU7xsTEMHr0aJOLCTRFzHt6euLp6cmMGTO4dOkS2dnZeHh4kJ6ezvnz59FqteTk5FBbW8ugQYOI\nj4/HwcGBkJAQo9fE6QxarVZXfnz9+vUGxaSgoIDvv/+e8PDwXs+o3JlI+Qs9aEevUlNTw8mTJ3U1\nEfbv34+FhQUBAQGylonkmUZRFCIiIti7d2+n0/K3R319PTExMUavxmgspk+fTnR0NAsWLGDatGkI\nISgrK+Py5cvcvHmTiRMnsnHjRqNF63cVIQTHjh2jqqqKDRs2GBS17OxsfvrpJ5YsWdKj5QDao1My\nqyiKBU1JGqfT5Apr0ycWQrxhHNN6jpMnTxIYGMiIESM4fvw4Go2GsrKyLqVqkUgGGu7u7vj5+XHl\nyhWjBDvevHkTb29vo0aEG5PBgwczffp0Ll++rPNQODg4sGzZMurr6ykpKTFZvrNmmmejlpSUsHHj\nRoNxLM1lgluU9n0iKpWKO3fuGDWgtcOCoiiKI3CBpiSQT6JPC8r9+/fJy8vjvffe4+rVq+Tm5uLs\n7ExISIgskCWRPCY8PJxdu3YREhLSrXTyjY2NXLt2rdWgd18kNDSUTz/9lJKSEhwdm3LfNieQjIuL\nY+/evXh5eTFv3jxcXFx61TYhBKdOnaKgoIDNmzcbDAi9f/8+x48fZ/369U+sK1NeXs7du3e5c+cO\nNTU1uplxxqIzBba+AN4E3gfOA2nAIppqyf8LMBpYKIQoNaqFHaQjg/I1NTXs3LmTtWvXUllZya+/\n/srSpUs5duxYr+cykkj6Osao7BgbG0tycjIbN240omU9w4ULFygvL2flypVttjW77a5du0ZAQABz\n587tlQF5IQRnz54lIyODV1991WAoQ3Nlx/YKplVXV3Pv3j3u3r2LSqVi3LhxBAYG4uXl1TzxwiQV\nGzOBM0KIt/QrNipNo9gXaMrD9Z6xjOsMHRGUw4cPY2VlxYQJE/jxxx/ZtGkTJ0+eZMqUKQQHB/eS\npRKNRkNhYSH5+fnk5eWhUqlwcnLC29ubUaNGyZ5iH6G7lR21Wi2fffYZq1ev7tFqjMaieSba22+/\n3a5YqNVqrl27xo0bN5gwYQJz5szB1ta2x2w6f/48ycnJvPrqqwYTY8bExHDlypU2lR3r6upISkri\n7t27ZGdnExAQwMSJE/H39zc0kG8SQakDtgsh/qYoij1QAixrTlmvKMpvgd81l/DtbZ4mKMnJyZw+\nfZqXX36ZPXv2sGLFCurr67l06RLvvPNOn6vaNlDQF4/8/HxUKhX29vZ4eHjg7u6Oi4sLRUVFummc\ngwcPxtvbWwpMHyA2NpbExMQuBTvevn2buLg4tmzZ0jPG9QCRkZFUV1ezfPnyJ+5XU1PD5cuXiY+P\nJygoiFmzZhm9TlJUVBR3797ltddea1X9E5p6LpcuXeLWrVu8+uqr2Nvb09jYSGpqKnfv3iU1NRUv\nLy8CAwMZM2bM0/KmmURQ8oF/F0L8WVEUc5rSrHwohPjr4+3bgP9PCNG1nArd5EmCUltby86dO1m6\ndClnzpwhNDSUoKAgPv/8c1asWNFvy9D2NToiHu7u7gwfPrzdH7gQApVKxcOHDweMwAgh+u1U9K4G\nOwoh2LlzJwsXLsTPz68HLTQuNTU17Nixg3fffbdDv7PKykqioqJITExk2rRpPPfcc0bJsHH16lVu\n3rzJli1b2vSAhBCcOXOGtLQ0XnnlFYqLi7lz5w5JSUm4ubkxceJExo8f3xmBM4mgRNFUgvftx6+v\nA1pgPmAORAI2QgjjjvJ0kCcJypEjR7CwsEClUuHu7s7ChQu5cuUK2dnZvPxyuyVWJE/AGOLREfqL\nwGi1WsrLyykqKqK4uLjVUl1djZeXl64GvZOTU78SmK4EOyYlJREVFcXbb7/dr94rdC2iv7S0lIsX\nL5KSksJzzz3H9OnTu/y7v379OtevX2fLli1tJkRotVqOHz9OTk4OXl5eJCcnY2try8SJE5kwYUJX\n/wsmEZT/CfwTMFwIUacoyjrgR6D28S5WwDtCiK+MZVxnaE9QHjx4wMmTJ/H09NRFltbU1PD555/z\n5ptvygqMHcCQeBQWFuLg4GBU8egIphQYIQQ1NTUUFxfrhKOkpISioiLKysqwtrbGycmpzWJlZaWL\nyE5NTcXc3Bx/f38CAgLw9vbu1UqBXUEIwZ49exg/fnyHgh2FEHz11VfMmjWLcePG9YKFxqW6upod\nO3bw/vvvd3qGm0ql4sKFC2RlZTFr1iymTp361CDIuro6VCoVhYWF5ObmkpaWxpYtW9oEgT569IiD\nBw9SWlqKra0tgYGBBAYGGmM6tkkERQEGCyHULda9CGwGNMB+IcRPxjKssxgSFLVazc6dO/H29qao\nqIgtW7ZgYWHB8ePHGTRoEIsWLTKRtX2X+vp6ioqKKCgoaCMe7u7uOgHpDfHoCC0FpllkuiswzbEH\n+j2N4uKm7ELOzs5tRMPR0bFDwiCEoLCwkJSUFFJSUigoKGDUqFG63ktfiCQ3RGcqO6alpXHq1Ck+\n+OCDftc7aebXX39Fo9GwePHiLh2fn5/P+fPnKSwsZM6cOUyZMgWNRoNKpdKJR/NjTU0Nzs7OuLq6\n4uLiwsSJE3W/2eZpvrdv36a4uBg7OztWrVrFyJEjjfnZDsyKjd3BkKAcPXqU8vJySktLefPNN7Gx\nsaGwsJBvv/2Wbdu2dTmF9kCgpqYGlUpFUVGR7rGoqIjq6mqcnJxwc3Prc+LREToqMFqtlrKyMoOi\nUVNTg4ODA87Ozjg6OrYSEGMPvNbW1pKWlkZqaiopKSkMHTpUJy5eXl59qnxCRys7fvPNNwQFBTF5\n8uRessz4VFVV8fnnn/PBBx90ehZXY2MjxcXFFBYW6r7Xuro6gFbC4erqiqurK/b29q1cifrTfEeP\nHk1BQQEuLi6sWrWqJ34TvSsoiqLY0pTT62MhxP8y1smNib6gpKamcuTIEbRaLa+//jouLi4IIfj+\n++8JCAggNDTUhNb2DkIIKioqDAqHRqPBxcUFZ2dnnJ2ddc/1f9z9HUMCY2ZmhlqtNuiicnZ2xs7O\nziSfgRCCvLw8Xe+luLgYX19fnXusJ6endoSKigp27drFe++9164rKCsri8OHD7N9+/Y++zsSQqDR\naKirq6O+vl732PJ5XV0diYmJaLVaRo4ciVarRQihW5pf19bWolarWy0NDQ1YWFhgaWmJhYUFFhYW\nujE2gGHDhjFkyBCD7Wm1WiorK3XTfD08PPjxxx9xd3dvt96JETCJy6sE+B/Ns7r6Gi0FpXn+fGNj\nI2vXrsXX1xeAlJQUTp8+zfvvv9+n7vy6i0aj0fnyW4pGUVERgwcPNigcNjY2/dYd0R2EEJSXl2Nt\nbd3nxy6qqqpIS0sjJSWFtLQ07O3tdb0XT09Pk1ywz549S0lJCbNmzaK6upqqqirq6upQFAVFUYiN\njWX48OF4e3ujKApmZma6bfqvn7TtSftqtVqDAqC/7kn7KIqCpaUlgwcPxtLSstXzwYMH634b8fHx\nzJw5EyEEVVVVbRYrKyvs7Oyws7Nj2LBhusdBgwa1sRsgNzeXW7duYW5uTkhIiO57bLmvvb09lpaW\nVFZWsmfPHgICAtot62skTCIoB4B6IcQrxjq5MWkpKIcPHyYpKYlFixbpsm1qNBp27drFCy+8wJgx\nY0xpapdpHt/QF47S0lLs7OwMCocsEjYw0Gq1ZGdn63ovlZWV+Pv765buuOIaGxt14vC0x/r6eoQQ\nODo6Ym9vj7W1te5uu7q6mpSUFF06j5Z34C3vwg29ftI2/df6YtBSFJ4kEi3XdfSG8tSpU8TExGBn\nZ9fKVeXi4oKLi0uXbkqEECQmJnL+/HmdC3HkyJGt9iktLWXPnj0EBwfrUuj3ICYRFB/gIvAN8B9C\niIpundhw8a6CloGRiqKMpqme/HzAEkiiqYhXkoH2hBCCBw8esG/fPqZPn86CBQt025vLc27evLlf\n3ZlXVlYSGRlJRkaGbnxDXzScnJxMlk5bYhrKy8t1/vmHDx/i4uKi670MHz6cxsbGdoVBf11DQwPW\n1tbY2Ni0ejS0bujQody8edNgsOP+/ft1KeEHCkII6uvreyQtk1arJSEhgYsXL+Lq6sr8+fNxd3dH\npVLx3XffMWvWLKZNm2b08xrAJIKSAdjQVO5X0FQHvmUNeQUQQgjfDrb3CbAOmNditaa5UNdjAYuh\nScC+B8qAscBdIUSOgfZEbW0tf/rTn/Dw8OC1117T/djVajU7duxg06ZNDB8+vEPv19RoNBpiYmK4\ndOkSwcHBBAcHD7jxDYlxaGxsJDMzU9d7afbVd0QgbGxsGDJkSKdusgwFOxYVFfGPf/yDjz76qN9M\n4OgrNDY2EhcXx6VLlxgxYgQ5OTlEREQwadKk3jLBJIJygSYhedLJhRBifgfb+wRYI4QIbGf7XpoE\nZnMH2xO7du2isrKS3/zmN63u2H/99VfUajUrVqzoSFMm5+HDh5w4cQJbW1sWL17cZ9N+S/om9fX1\nWFhY9GhPXD/Y8ejRo9jb2zN37tweO+dAp6GhgZs3b+Ls7Nyh9PNGpHcqNrZECDHPWCdtga+iKLlA\nHXAd+GchRIaiKGbAMuCPiqKcAoKBhzS52va111hhYSFbt25tJSYlJSXcunWLDz74oAfMNy6VlZWc\nOXOGzMxMFi5cyLhx4/qVe07SN+iNHkLLyo7+/v4kJyezffv2Hj/vQMbCwqIj5Xr7NCaLQ1EUZRFN\nLrQkwI2mFPhjgQk0jZfk0eRS+xea0ro8D/w7sLI5IaVeeyI2NpapU6e2Wr9v3z7c3d2ZPXt2D76b\n7tHSvTV16lRmz54t3QaSPk9zsKOfnx/W1tZERESY2iRJ1xh4gY2KogwFMmgahP8RyAX2CiE2tdjn\ne8BBCLHEwPHi448/1r2eN28e3t7eHDlyhK1bt/bZKaLSvSXpzxw5coTExEQ++ugjbGxsTG2OpGv0\nvsurpxFC1CiKkgj4A0VAI3BPb7ckoN06vZ988knL9vjyyy95/vnn+6SYSPeWZCAQERHB2LFjpZhI\ngD4kKIqiDAHGAZFCiAZFUW7Q5AJryWiaxlKeSkJCAubm5kyc+LSKxb2Lvntr69at0r0l6bdYW1sz\ndqz+31TyrGIyQVEU5T+An4FswBX4A00Zi799vMu/A/sURblEU8nh+TT1TtrW59Sjvr6eyMhI1q5d\n26fu+lu6t9544w3p3pJIJAMKU/ZQPIEfAGeaYlquAWFCiGwAIcRRRVHeAf4Z+AvwANgshDj5tIav\nXrVwfFMAACAASURBVL3KqFGj2kSfmgrp3pJIJM8CfWZQvrs0R8o3J7B75513TJ4KXD84cc6cOdK9\nJZFI+hqmG5R/nA7Fn6aI+TaGCCF2G8GuLhMZGcnUqVNNLibSvSWRSJ41OiwoiqK4AbuBJ002F4/3\nMQl5eXmkpaWxbds2U5kg3VsSieSZpTM9lB3AC8AXNA2SF/eIRd3g9OnTzJ8/v0cSuT0NffeWnL0l\nkUieNTojKBHAX4UQprv9fwp1dXVMmTKl188r3VsSiUTSOUExA271lCHGYMGCBb2ajVe6tyQSieS/\n6Ey24V+Ah0KIrT1rUtcwVFO+p5CztyQSyQDCJOnrx9I0drJdCHHAWAYYi94UlCNHjlBeXs7SpUul\ne0sikfR3TDJteCdQSVP0ei6QDmj0dxJChBvJtj5Jfn6+biaZKQb/JRKJpK/SGUHxoWlacNbj16MM\n7DMwoiSfwLlz55g9e7YUE4lEItGjMwW2vHvQjn5Beno6JSUlbWquSCQSiaRp5pakAwghOHv2LOHh\n4Zibm5vaHIlEIulzSEHpIImJiSiKwoQJE0xtikQikfRJ2nV5KYpynqYxkQVCiMYWr5/IQByU12g0\nREZGsnz5chln0s9paGggOzsbd3d3rKysTG2ORDKgeNIYSvMgvNLOa0MMyEH52NhYnJyc8PHxMbUp\nki4ghCArK4uEhATu37+Po6MjxcXFeHp6MnbsWMaOHYutra2pzZRI+j0DLn29samrq+Ozzz5j06ZN\nDB8+3OjtS3qO0tJSEhISSEhIwMLCgsmTJzNp0iRsbW2pr68nLS2N+/fvk5KSgouLC2PHjmXcuHE4\nODiY2vR+gRCCR48eodFo8PT0NLU5kq7TO4GNiqI4CiFKjHWynqSnBOX8+fOUlZWxevVqo7ctMT5q\ntZp79+6RkJBAUVEREydOZPLkybi7u7frrtRoNGRkZHD//n2Sk5OxtbXViYuLi4t0c7agrq6OjIwM\nUlJSSElJYdCgQdTX1/P8888TFBRkavMkXaPXBEUD3AEu0BQlf1EIUWaskxuTnhCUqqoqvvjiiz5R\nrEvSPlqtlvT0dBISEkhJScHHx4fJkycTEBDQ6Rl5Wq2W7Oxs7t+/z/3797GwsNCJi4eHxzMnLkII\niouLdQKSm5vLiBEjCAgIICAgACcnJ4qKitizZw8zZswgNDTU1CZLOk+vCcopYBYw9PEqLXCbJnG5\nAEQJIcq7fHJF+QT4V73VBUIIj8fbvwFe1dseLYSYYaAtowvKL7/8wqBBg1i4cKFR25UYh8LCQhIS\nErhz5w62trZMnjyZiRMnMnTo0Kcf3AGEEOTn5+vEpaGhQScuXl5evZqItDdpaGggMzNTJyIajQZ/\nf38CAgL4/9s79/CqqjP/f14BBYIFuSUQkkASuYoRo1TrLeANsN5tsWrV1npHO7+Ztk6nddCnjr92\nar1UaofaqggVBEfRtiKIgKiAIlaQS4QQSCAhF8FAIIGQ5J0/1k442ZzcT3LOSd7P8+zn5Ky99trv\nu8/O/u611rvWSk5ODjpvXUlJCS+//DLjxo3jggsuCIPVRitov7m8RKQrcDaQ4W3ncUxgqnAzEK/A\nicwHqlra5JM7QfmuV24NVaq619v/IjAY+H7A/opgtaRQC8revXv5y1/+wrRp00L2gDJaz6FDh9i4\ncSPr16/n4MGDnH766aSlpTFgwIA2P3dxcTFbtmwhMzOT/fv3M2LECEaNGsWwYcPo2rXZi59GFCUl\nJbUCkpOTQ1xcXG0tZODAgU2qmZWWljJ79mxGjBjBxIkTO11tLopp/8khaw9oWGAqVbXJ0+56gnK9\nqo6tZ/9LQD9VvbIJZYVUUBYsWEBcXJy9bUUAVVVVbN26lfXr17Nz506GDx9OWloaw4YNC1stoaSk\npFZcioqKSE1NZdSoUaSmpkbFzNNVVVXk5uaybds2srKyOHToUK2AJCcntzikuqysjDlz5pCQkMCk\nSZNMVKKD8AlKnYNFYnCi8lPgQgBVbfJ/uCcoPwVKgCPAx8B/qOoOb/+LwDVAhZfnfeAXqlocpKyQ\nCcru3buZP38+DzzwAN26dQtJmUbzUFXy8/NZv349mzZtYsCAAaSlpTF69OiIm0ft4MGDZGZmkpmZ\nya5duxg2bBgjR45kxIgRETXWpbS0lKysLLZt20Z2djb9+vWrFZFQ9g8dPnyYV155hX79+nHllVd2\n2KbBDkR4BEVEeuD6VDKACcBZuLEse4EPcJ32zzSjvElALyATiAV+CYwExqjqPhGZChwCduDGwTwG\ndAHSVbXCV1ZIBEVVmTVrFqeffjpnnnlmq8szmseBAwfYsGED69evp6qqqjbUN1pCecvLy9m6dSuZ\nmZlkZ2czZMgQUlNT6dmzJyeeeCLdunVrcAvlG311dTV5eXm1tZCvv/6alJQUTj31VFJSUujVq1fI\nzuWnoqKCefPm0aNHD6677jqbriiyabdO+R7AtzgmIGcD3YAiYKW3vQ9sDMXTXER64sTj16r6VJD9\ng4AcYKqqvuHbp9OnT6/9npGRQUZGRrNt2LZtG0uWLOHee++1N6t2oqKigszMTNavX09+fj6jR48m\nLS2NhISEqG4yqaioICsrix07dnDkyBGOHj1a71ZRUUFlZSVdu3alW7duTRKfms2ft2aMTVZWFief\nfHJtLSQhIaFd7+nKykpee+01qqur+c53vmO1/cil3QTlME5ACnHC8T6uFrIlVAYEOecyYEt9K0OK\nSDbwR1X9rS+91ZpWXV3NzJkzmTBhAiNHjmxVWUb9VFRUsHv3bnJycsjNzSU/P5/ExETS0tIYMWJE\np33wqCqVlZW1AtOYANW374QTTiA5OZnU1FR69+4dVp+qqqpYuHAhBw8e5Hvf+15U9C91Qtptga0T\ngUrgn8Bn3vZlqE7uR0S6A6OAZfXsHwDEA3va4vwbNmzgpJNOYsSIEW1RfKelrKyM3NxccnNzycnJ\nobi4mEGDBpGYmMh5551HQkJCxPWLhAMRqa1ldJTIwi5dunDttdfy97//ndmzZ3PTTTdFVL+SEVoa\nq6Gcg2vqysA1fcXgVm1cxbEay1pVrWzRyUWeAN4CdgEDgYdxfTRjgX3Ao8BrQAEwFPj/OEEZpaqH\nfGW1qoZSWVnJs88+y/XXX09iYmKLyzFcP0hN7SMnJ4cDBw4wZMgQEhMTSUpKIj4+PurDbI3moaos\nXryYnJwcbrnlFmJiYsJtknGMsKwpX1+4cDkBAqOqHzT55CJzcdFh/YFiYDXwsKpmerWVhcA4oA+u\nVrLM258XpKxWCcqqVavIzc3lxhtvbHEZnRFVZd++fXUEpKKigsTExFoBiYuLs/4oA1VlxYoVbN68\nme9///t84xvfCLdJhiP8YcM+gbkCV4NRVQ1LOEdrBKW8vJwZM2Zw++23t8sAuWimurqaoqKiOgLS\npUsXkpKSagWkf//+Ud2ZbrQtH330EZ9++im33npr1ETvdXDCKygB4cMTvC0drz+mOeNQQklrBGXp\n0qWUlZVx1VVXhdiq6Keqqor8/PxaAdm1axe9evWqUwOxec6M5rJ27Vo+/PBDbrnlFnuJCz/tKyie\ngJxH3fEnNaE4h3FNVcuB5ar6UaiMaw4tFZT9+/czc+ZM7rnnnk5fBVdVDh06REFBAbt27SI3N5e8\nvDz69+9fKx6JiYnW/m2EhPXr17N06VJuvvlmWxoivLRb2PCvgIuA8biIL3Cj1j/B9Wcsx03WeCRU\nBrWUlgrKm2++SUxMDJdcckkbWBW5VFZWUlxcTGFhYZ1NVYmNjWXIkCEkJSVZBJbRpmzevJm3336b\nqVOnkpCQEG5zOivtJijVuLDhTzkmIKtUtTxUBoSKlghKUVERs2bN4oEHHqB79+5tZFl4UVUOHDhQ\nKxhFRUUUFBRQUlJC3759iY2NrbP16tXL+j+MdmXbtm0sXLiQG264wVZFDQ/tJiiTcDMIH6o3U4TQ\nEkGZO3cuQ4cO5dxzz20jq9qXiooKioqK6ohHYWEhXbt2PU44+vfvb9NhGBHDzp07WbBgAVdffTXD\nhw8PtzmdjfBHeUUazRWUnJwc3njjDaZNmxZ1YyJUlZKSkuOaqw4cOMCAAQOIjY1l4MCBteJhfR5G\nNLB7927mzZvH5MmTGTNmTLjN6UyYoPhpjqCoKi+88AJnnXUWaWlpbWxZ6yksLCQ3N7dOzaNHjx7H\nCUe/fv1svIcR1RQWFjJnzhwmTpxoSwq3H+029UqHJDMzk6NHjzJ2bNBlWCKCiooKNm7cyLp16zh0\n6BApKSnExsYyduxYYmNjO2yfj9G5iY2N5bbbbmP27NlUVFSEZUnhyspKCgsLycvLIz8/n8OHD9Or\nVy9OPvnkOluvXr2IiYmxPscAOl0Npbq6mueee47LL7+cU089tR0sax579uxh3bp1bNq0iaSkJNLT\n00lJSbGah9GpaK8lhVWVr776ivz8fPLy8sjLy6OoqIh+/foxePBg4uPj6dmzJ6WlpZSWlnLw4ME6\nfx8+fJiYmJg6IhOFwmNNXn6aKijr1q1j48aN3HrrrRHzA/trI+PGjWPcuHGdflyM0bkJ9ZLCqkpp\naWmtcOTn55Ofn0+PHj2Ij4+v3eLi4po8K3JVVVUdkYlS4TFB8dMUQamoqGDGjBlMnTqV+Pj4drKs\nfqw2YhgN05olhcvLy2trHjWf1dXVxMfH19Y+amogbU1zhGfgwIGkpKSQmppKQkJCe0RjmqD4aYqg\nfPDBBxQWFnLDDTe0k1XHY7URw2geTVlSuLKykoKCgjq1j9LSUgYNGlRHPHr37h0xLRPBqKysJD8/\nn6ysLLKysti3bx/Dhg2rFZg2mubIBMVPY4JSVlbGjBkzuOOOO+jXr187Wuaw2ohhtJyKigpeffVV\nevTowTXXXMO+ffvqiEdxcTH9+/evIx4DBgyI+v+vQ4cOsX379tqte/futeIydOjQUC1GZ4LipzFB\neeedd6iqquKKK65oN5usNmIYoaNmSeFt27bRp0+fOk1XcXFxHX6lT1WloKCArKwstm/fzp49exgy\nZAipqamkpqa2ZpZvExQ/DQnK119/zfPPP899991Hr1692twWq40YRtugqhw5csTC5nFNgTt27KgV\nGFWtrb0kJyc35xqZoPhpSFBef/11+vbtS0ZGRpud32ojhmGEC1Vl7969teKSm5tLbGxsbe1l0KBB\nDdVeTFD81Ccoe/bs4ZVXXmHatGltMmuu1UYMw4g0jh49Sk5ODtu3bycrK4uysjJSUlJqN19LTfQL\niog8AvynL7lAVQcHyTsTuBP4qar+rp7yggrKnDlzGD58OOPHj2+90R5Hjx7liy++sNqIYRhRQUlJ\nSW3HfnZ2Nqecckpt7SUpKanDTL2SiVu0q4YqfwYRuQG31HA+0Cz1y87OZt++faSnp7fGxjqoKvPn\nz0dVycjIsNqIYRgRT58+fUhPTyc9PZ2qqip2797N9u3bWbx4MXfddVfIzhNuQalS1aL6dopIEvA0\ncDHwTnMKVlWWLl3KxRdfHNKBQV9++SUlJSXcc889Nv27YRhRR5cuXUhKSiIpKYmJEyeGtOxwv1on\ni0ieiGSLyFwRqV1dR0S6AnOBX6nql80teNOmTYgIo0ePDpmxR48eZfHixUyZMsXExDAMw0c4BWUN\ncBtwOa5/JA5YJSJ9vf2PAkWqOrO5BVdVVbFs2TIuueSSkI6K/eijj4iPj7dV5QzDMIIQtiYvVQ1s\nwtooIquBHcBtIvJPnNic4TusQXV45JFHAMjLy2PgwIEhffB//fXXfPLJJ9x9990hK9MwDKMjEVFh\nwyKyDNdRX4iLAKsO2N3F+56vqolBjtWaQU/PPvsst9xyC3FxcSGzbd68ecTHx7fpVNqGYRhhIGTN\nOOHuQ6lFRLoDo3DRXM8BY4E0bzvDS38S10FfL6tWrSIlJSWkYrJt2zaKi4s7zNrzhmEYbUHYmrxE\n5AngLWAXMBB4GOgBzFLVYqDYl/8obpzKtvrKPHjwIGvXrg1pGFxlZSXvvPMOkydPjrq15w3DMNqT\ncD4h43FRXP1x4rEaOEdVd7W0wPfff5+0tLSQTvG8evVqBg4cSGpqasjKNAzD6IiEs1P+e83M32gP\n+6ZNm5g2bVrLjfKxf/9+Vq9ezZ133hmyMg3DMDoqEdOHEgrOPffckK6+tmTJEsaPH88pp5wSsjIN\nwzA6Kh1KUM4555yQlZWdnU1+fj7nnXdeyMo0DMPoyHQoQQnVAjtVVVUsWrSISZMmdfhFewzDMEJF\nhxKUUPHxxx/Tp08fhg8fHm5TDMMwogYTFB+lpaV8+OGHTJo0KaTTthiGYXR0TFB8vPvuu6Snp9Ov\nX79wm2IYhhFVmKAEkJOTQ25urk2vYhiG0QJMUDyqq6t5++23ueyyyzjxxBPDbY5hGEbUYYLisXbt\nWmJiYhg1alS4TTEMw4hKTFBwc4CtXLmSyZMnW0e8YRhGCzFBAd577z3S0tIYMGBAuE0xDMOIWjq9\noOzatYvt27dz0UUXhdsUwzCMqKZTC0p1dTWLFi3i0ksv5aSTTgq3OYZhGFFNpxaUzz77jG7dunHa\naaeF2xTDMIyop9MKSllZGStWrGDKlCnWEW8YhhECOq2gvPfee4wZM4bY2Nhwm2IYhtEh6JSCkp+f\nz9atW5kwYUK4TTEMw+gwhFVQROQREan2bfkB+38lIltE5KCI7BORpSJybmvOqaq8/fbbXHzxxXTv\n3r31ThiGYRhAZNRQMoG4gG2sb999wGnA+cAOYLGItLid6vPPP0dESEtLa7nFhmEYxnGEbU35AKpU\ntSjYDlX9a+B3Efk34A7gdODd5p6ovLyc9957j5tuusk64g3DMEJMJNRQkkUkT0SyRWSuiAwLlklE\nTgTuAvYC61pyouXLlzNy5EgGDx7cCnMNwzCMYIRbUNYAtwGXA3fimrxWiUjfmgwi8m0RKQXKgZ8A\nV6jqvuaeqKCggM2bNzNx4sTQWG4YhmHUQVQ13DbUIiI9cf0kv1bVpwLS4oD+uBrKlcB4Vc3xHavT\np0+v/Z6RkUFGRgbgOuJfeuklxo4dy1lnndUerhiGYUQLIWv/jyhBARCRZcAWVb2/nv1bgb+q6qO+\ndK3Plw0bNrBmzRp+9KMfccIJ4a6UGYZhRBQhE5SIerqKSHdgFLCngWxdaIbdR44cYenSpUyZMsXE\nxDAMow0Ja5SXiDwBvAXsAgYCDwM9gFkicjLwkLe/ABgA3A8MBuY39RwrVqwgJSWFIUOGhNh6wzAM\nI5Bwv7LHA3Nx403+F9fxfo6q7gIqgdHAG8BWnLCcAlygqpuaUnhRUREbNmzgkksuaQvbDcMwjAAi\nrg+lpfj7UFSVl19+mZEjR/LNb34zjJYZhmFENB2zDyWUbN68mfLycs4+++xwm2IYhtEp6JCCUlFR\nwZIlS5g8ebJ1xBuGYbQTHfJpu3LlSpKSkkhKSgq3KYZhGJ2GDicoe/fu5bPPPuPSSy8NtymGYRid\nig4lKKrKokWLOP/88zn55JPDbY5hGEanokMJypdffsn+/fstqsswDCMMdChBWbx4MZMnT6ZLly7h\nNsUwDKPT0aEEZfDgwSQnJ4fbDMMwjE5JhxrYWFJSQu/evcNtimEYRjTRcWcbbikNzTZsGIZh1IuN\nlDcMwzAiCxMUwzAMIySYoBiGYRghwQTFMAzDCAkmKIZhGEZIMEExDMMwQkJYBUVEHhGRat+W7+3r\nKiK/EZH1InJQRPJF5K8ikhBOmw3DMIzgREINJROIC9jGeukxwDjgMe/zaiABeEdEbG4VwzCMCCMS\nBKVKVYsCtr0AqrpfVS9T1QWquk1V1wJ3A6OAkWG1OAysWLEi3Ca0KR3Zv47sG5h/0Y6IZISqrEgQ\nlGQRyRORbBGZKyLDGshbM6/K1+1hWCTR0W/qjuxfR/YNzL8OQEaoCgq3oKwBbgMuB+7ENXmtEpG+\n/owiciLwO+AtVc1vVysNwzCMRukazpOr6jsBXzeKyGpgB05knqrZISJdgTnAN4Bvt6uRhmEYRpOI\nuMkhRWQZsEVV7/e+dwXmAmOADFUtque4yHLEMAwjSlDVkEwQGdYaih8R6Y7rdF/mfe8GzANG04CY\nQOguiGEYhtEywiooIvIE8BawCxgIPAz0AGZ5NZMFwFnAlS67xHmHlqjq4TCYbBiGYdRDuGso8bjm\nrP5AMbAaOEdVd4nIUOAqQIF1vuNuB15uNysNwzCMRom4PhTDMAwjOgl32HBIEJH7RGSHiJSLyKci\ncn64bWouIvJzEVkrIvtFpEhE3hKRMUHyPeKN2ykTkeUiMjoc9rYWz99qEXnWlx61/onIIBGZ5f1+\n5SKySUQu9OWJOv+8aZAe98aKlXufv/LPWBEtvonIhd7/127vHrwtSJ4GfRGRk0TkWREp9qaGelNE\n4tvPi/ppyL+mTmnVUv+iXlBEZCrwNG6KljOAVcCiKJzz6yJgBnAuMBGoBJaKyCk1GUTkIeBfgWnA\n2UAR8K6I9Gp/c1uOiJyDG3e0AdekWZMetf6JSB/gI5w/U3CzOUzD+VCTJ1r9+w/cLBUPACOAHwP3\nAT+vyRBlvsXg7r0fA+UE3IPQZF+eBq4DbgQuwA1p+LuIRMIztSH/mjqlVcv8U9Wo3oCPgZm+tK3A\n4+G2rZV+xeBE5QrvuwB7gJ8H5OkOHADuCre9zfCrN5CFE9DlwO87gn/A48AHDeyPWv+AvwEv+tJm\nAX/rAL6VArc253fy7uEjwPcC8gwBqoDLwu1TQ/7Vk2cUUA2Maa1/kaCmLcYbPX8msMS3awnwrfa3\nKKR8A1eDrJlmZhgQS4Cv6iLdVhJdvv4JWKCq7+P+eWuIdv+uAT4RkVdFpFBE/iki9wfsj2b/FgET\nRWQEgNf8MwH4h7c/mn3z0xRf0oFuvjy7gS1En79w/JRWLfYv3FFeraU/0AUo9KUX4aZxiWaeAf6J\ni3yDY/4E83VwexnVGkTkTiAZuMlLCqyKR7t/ybhmoCdxtZVxwLMigqr+gSj2T1WfE5EhwBYRqcQ9\nNx5T1f/xskStb0Foii9xuElt9/ryFOLEKGqoZ0qrFvsX7YLSIRGRJ3FvAuerV99shIgP1fPebv8L\n51NVTTJ1ayn1EfH+4WqTn6jqL7zv60XkVOB+4A+NHBvR/onIg8APcO3pm3Bi+YyI7FTVFxo5PKJ9\nayYdyZc2mdIqqpu8gK9w7Xp+1YzFtYNGHSLyFDAVmKiqOwN2FXifwXwtIPI5F1ej3CQiR0XkKHAh\ncJ+IVOB+S4he//KBzb60TCDR+zuaf79f4Pok56vqJlWdg6uJ1XTKR7NvfpriSwHQRUT6+fLEESX+\nyrEprU4DLlbVwBncW+xfVAuKqlbgBj1e5tt1KS7aK6oQkWc4JiZbfbt34H7MywLydwfOJzp8fQN3\n86Z52xnAp7ib+gxgG9Ht30ccv07PcGCn93c0/36C67QNpJpjtcto9s1PU3xZBxz15RmC+/0j3l9x\nU1q9ivt/nKDHT2nVcv/CHYUQgiiG7+IiEu7ARSs8g4vISAi3bc304w/AflxnZ+AKljEBeX4GlADX\nejfDPGB3YJ5o2oAVwLMdwT/cFEEVuBDbVOA7ni/3Rrt/uECKXbhw6KGe/UXAb6PRN1wE5Rnedgg3\n5dMZNc+MpvgCPOddk4txTYDLgc/wBotHqn+4PueFnj/jfM+a7q31L+w/bogu4L24N4vDwFpcO33Y\n7WqmD9W45rtq3/afvnzTcc0r5d6PPDrctrfC59qw4Y7gn/fA/dyzPROYFiRP1PnnPaCe8P7HyoDt\nuHEMJ0ajb7gFpWr+vwL/515oqi/AicDvcU21h4A3gfhw+9aYf0BSA8+awPDpFvlnU68YhmEYISGq\n+1AMwzCMyMEExTAMwwgJJiiGYRhGSDBBMQzDMEKCCYphGIYREkxQDMMwjJBggmIYhmGEBBMUI6R4\nK8S9GG47WoKI9BSR34tIrohUisiOcNvU0RCRjPpWSYxURGSniCwPtx3RgAlKFBDwT1gtIj+qJ0+1\niPytvW2rh2gdLfsQbpW+ucBtuBXvjLYhmu4RJbrsDRs2fX308YiIzFG36I8fu+lbx6XABlV9KNyG\nGBFFU5ZYMLAaSrTxKW6Rn38JtyGRgIh0EZEeISwyjmOr1hmG0UxMUKKL+bippR8Skb6NZa6vP0NE\nbvf2XRiQ9oiXNkpEnhSRfBE5KCLLRGSUl+d6EflMRMpEZIe3AmN9575ERNaIyCER2SMiT4tITJB8\nvUXkNyKSJSKHRaRIRF4RkWH12HyxiDwsIttxE/d9t5Fr0FVEHhKRzSJSLiJficjrInKav2zcTLoX\nBTQvTm+k7J7etdrjXZPVIjJRRF7yygvMO95L3+pdkwMi8qGIXBOk3Je88/cVkRdEpNjL/6aIDPLy\n3C0iWzyftojIVfXYONU7zwHvvGtE5Pog+a4Qkfe9c5WJSI6I/K+4RcIapKaPQUTO9O6XUhHZ6/kx\noAnHi4j8QkRWetfyiHf+5wLvcxEZKCIVIjKnnnL+ICJVIpIYkNak+8vLmyAi80Vkv7e9JSIpjdlv\nHMOavKKLauDfgXdxix79WxOOaW4z2CygFLe64kDvHEu8h+tjuGmt9wE/AmaKyGZV/chXRjpu+vY/\nAS8BE4EHgdNE5FL1ZiQVkd649RUSgL/gVgMcjFtK92MROUtVc31lP4G7b2filinIbMSfv3q2LMEt\nETAIt4riahG5QFU/B94Hvg88BRR7vgNsaKTsBcBk3FovS3HLAL+OWwPFf92vwa2PMg/IwS02dhvw\nuojcrKpzg5T/Dm4K8YeBU3HX8C0ReQO4HfgzbumGB4HXRGS4BizKJiKP4abTXwT8Enf/XAcsEJFp\nqvqcl+8i4C3P38dxU7fH46YuT8GtVdMQCgzxrsFruBefdOCHwFkicraqljdw/EnAT7xj38DNbjse\ntyTF+SKSrqpHVbVIRN4ErhOR3qq6P8DX7rilpd+tuWeac3+JSB/cuvFDgD/iFkvLAJYBoawF1IV3\nYgAAB01JREFUd2zCPdWybc2ajvpfve+LcW/niQF5qnHrQuNLeyFIebd7+y4MSHvES3vTl/cBL/0A\nAdNX4x6I5cArQc5ZDVzlS3/aS58akPYM7uEx1pc3Ebc2zItBbN5CwLoNjVy3S71j5vrST8ctILTS\nl74TWNbEsqd4Zc/0pU/20qt86T2DlNEDJ4ibfOkveWU860v/nZeeA/QKSB/rpT8ekHaml/ZYkPO+\n4V3fGO/7k17e/i28P3d6xz/oS/8XL/2hIPfyrb68JwUp94de3u8E+U3v9eW92Uu/oYX31+Pe8bf5\n8j7lpTfpvujsmzV5RScP4dYr+FUblP173/cPvc+FqppXk6iqXwFf4haT8pOpqm/50n7tfV4LrpkD\n9xBYCeSLSP+aDbfmxsccvxInwB81eEBCMK71Pv8rMFFVNwB/w7399m9iWX6u9D6f9JW9iCC1JlUt\nq/nbayrrh1tnZDkwSkR6BTnH077vNb/FLFU9GFD2FzjBD/wtbsbVHF4OvLaev38DTsYtywyuRgJw\ng7ilYVvCflztNZDnPLuuPT57XVT1CNT2i/Xx7KwJ1R0fkO9d3Losd/iKuAO3dsdCr5zm3l/X4FZq\nfNlX7m8as904hglKFKKumWYucLOIjA1x8dm+7zWd1MHGZJQA/nWnwdUi6qCqBbiHTk3b9QCgL3A5\nrpmpyLddgmty8+NfGrkhhuEWEjrOHo6t/z60GeUFKzsryL4v/Qle+/+fRKQQOMgxn+/GPfj7BCmn\nNb/FKFx0UibHX9s/e+esWTd9BvBPnADsFZF/iMgDzRTbbFWtDExQt0T3Do795vUiIt8VkY9xD/t9\nnp3bvd2n+LL/GThTRNK8Y5OBi4DZATY09/5KBrapVy0J8KHmvjWagPWhRC+/BG7AvUFNaeaxDf3u\nVc1Mb2lIZc1x79K8t8CyxrO0K432UXlvy0twa3I/jYvW24+7pj/Etf0f93Lnf7gF0JTfQjzbJjWQ\nf7N3nn0icjZwAa5J6UJcU8+jIjJFVdfU61wIEJHrcH1LH+P6g3bhVl/tiutH8l+bF4BHcbWSB3HX\nUHBCU1us99nc+8toBSYoUYqq7hSRPwI/9jpVg7EP95bmJ7ntLAPc23EdvOik3hx76y7GvVX3VtVl\nbWRHNm4N7dHAF759o3EP3JaOht/plT2c45u4Rvi+n+5tj6rqo4E7ROSuFp6/Mbbi3s53qWpjgQuo\najUuOOF9z66xuIjCXwLfbsL5kkWkm6oerUkQkZNw99rm+g8DXEBEOTAhsDlTREbWY2uhuEG8N4vI\nv+P619aoamBNtLn3VzYwXERO8K5FjQ01963RBKzJK7p5DNdG/d/17N8KfEsCxmqIyCnAD2jbQZAj\nRORqX1rNYMGFUPsA+yswPlgYK7hmolba8Yb3+XNfuacBVwEfqureFpZd00f0/3xlT8HVRAKpqSGc\n4Mt7Gq5/Idhv0drfZ7b3+biIHPd/LiKxAX8Ha7b8EldL8Dc31UdvXPRUIPfh+moWNnJszfXpEmCT\n4MSsPp73bJuJi9wKrJ205P5aiGsCvNWXzQa5NgOroUQxqrpXRH5L/Z3zM4A5wDIvdr8PLtx3J8fa\nz1tLsCavjcAcEXke18cwAbgeWKGqrwbk+wVwHjBfRObjmjwqgCRcM96nOPFrEaq61Cv3Rk9I/4Eb\nvHg/runswVaU/baILAbu9Poa3sP1FdyFC78N7NvajAtZ/ZmI9MQJ/fCAvOlBTtGq0dmq+qmIPIKL\n3vtcRBYAe3Bh0+m4aLSTvOx/FpF4XLNcLi76bCouaMDfSV0f24Hpnkh+5p3jB7j+K3+gh58FuHDm\nZSIyG+iG6yRvKFx3MS7a7WZcmPu8IHmac3/9N67p8XkRSedY2PA5uM5+Gy3fFMIdZmZb4xu+sGHf\nvh5AHu4t760g+3+CE5DDuIfa7bjxD1XUDRue7qUl+o4f6p37P4OUvRzXGRuYVo1r454IrME9uPfg\nQjhj6rH/l7gHaxmuxrUJ9+Z5dkC+2/02N/HadQF+hntAHMY9HF4HxgTJu4NmhIcCPXF9DQWe7Wtw\nYzdeAw768ibixmcU4UJZ1wBXB7vuwIv4wo4D7oMqfCG3DdmOe3C+A+z1/M/BCetdAXmuBd7kWN9F\nkffbXtvE67ATN17jDJywHvTONwsY0BQfcC86m3BNX/nA/+BqIEFD371jasbWPN+AbU26v7y8CThx\n2+9tb+Ka7Jp1X3TmTbwLaRhGiBCRL4Auqjo63La0ByKyE/diMbGdz/szXDj6uar6cXue2wiO9aEY\nRgvxRmf7064AxuCii4w2whsvczduMk8TkwjB+lAMo+VMF5EzcM1DB3BNPj/ENatZqGobICJDgW/h\nmguHATeG0x6jLiYohtFyVuIebj/FRTntxbXBP6yq+eE0rJ1pz3bzDFwfXTEuDHt+O57baATrQzEM\nwzBCgvWhGIZhGCHBBMUwDMMICSYohmEYRkgwQTEMwzBCggmKYRiGERJMUAzDMIyQ8H+HMUcL70Gt\nywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8c79588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wins_by_index(wins_by_games[low_skill_champs])\n",
    "plt.xlabel('Number of games played', fontsize = 18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of these champions can be effectively mastered in twenty games. If you're looking to pick up ELO late in the season, you might try one of these. The high one here is Kayle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Which champions are best early in the game, and late in the game?\n",
    "\n",
    "We can perform a similar analysis to see which champions are late game carries, and which are best early in the game. First, we again use regular expressions on the html to get the win rates as a function of time.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_wins_by_time(html):\n",
    "    wins_by_time_str = re.search('(?<=var d7 = ).*?]]', html).group(0)\n",
    "    wins_by_time_list = ast.literal_eval(wins_by_time_str)\n",
    "    return wins_by_time_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wins_by_time =  np.array([get_wins_by_time(x.text) for x in champ_html])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the win rate as a function of time for some random champions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x48YN6uvr2bp1K7m5uXPeU1VVBgYGaG9vp6+vTwYwhR9b+IQFoYbDYdavX09l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\nFEsgxhhjgvL/AZEElSx2COuKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb237780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wins_by_index( wins_by_time[np.random.randint(0, 125, 20)])\n",
    "plt.xlabel('Time in game (minutes)', fontsize = 18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Id you ignore the variation at 10 minutes (due to small sample sizes), the curves seem pretty flat.\n",
    "\n",
    "This time, let's define the late game criterion as the change in win rate between 20 and 35 minutes (over 75% of games end by around 35-40 minutes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_time_delta(game):\n",
    "    _, percent = zip(*game)\n",
    "    return percent[2] - percent[5] # win rate at 20 vs win rate at 35"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply this function to the list, and add it to our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_deltas = [get_time_delta(x) for x in wins_by_time]\n",
    "time_deltas = np.array(time_deltas)\n",
    "champ_list['time_delta'] = time_deltas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the distribution of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KgzQvwFzgrlLZMaSz+/eQ6kPnA58oli1L+qL7S812ngIm1CmbD3y+pvxnRflG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\ny+bkYWZm2Zw8zMws2/8Cb1rtQspf0sQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb252a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(time_deltas, bins=range(-12, 10, 2), color = 'grey');\n",
    "plt.xlabel('Win rate at 20 - win rate at 35', fontsize = 18)\n",
    "plt.ylabel('# Champions', fontsize = 18)\n",
    "lol_plt.prettify_axes(plt.gca())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most champions are with +/-5% win rate over the course ofo the game. Notably, there are some deep outliers around -10%!\n",
    "\n",
    "Now we can look at the early game champions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Champion</th>\n",
       "      <th>learn_delta</th>\n",
       "      <th>time_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anivia</td>\n",
       "      <td>5.68</td>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>blitzcrank</td>\n",
       "      <td>5.21</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>leblanc</td>\n",
       "      <td>7.12</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>leesin</td>\n",
       "      <td>5.51</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>pantheon</td>\n",
       "      <td>3.77</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Champion  learn_delta  time_delta\n",
       "4       anivia         5.68        6.37\n",
       "9   blitzcrank         5.21        5.56\n",
       "51     leblanc         7.12        5.21\n",
       "52      leesin         5.51        5.84\n",
       "73    pantheon         3.77        6.67"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_champs = np.where(time_deltas >5)\n",
    "champ_list.iloc[early_champs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list looks pretty good. Blitzcrank, Lee Sin, and Pantheon are all strong in the early game. The presence of Anivia is somewhat surprising here, as people often build Rod of Ages on her. Let's plot their win rate versus time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1DhBjTA/gIvBARBa7W5iu0ABpm4i4RrPHx8fz4osvdnlxqbt375Kenk5tbS2pqamMGzdO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5d20f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wins_by_index( wins_by_time[early_champs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just what we expected, these champions win  the most in the early game, and peter out later.\n",
    "\n",
    "Now we can turn to the late game champs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Champion</th>\n",
       "      <th>learn_delta</th>\n",
       "      <th>time_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>azir</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-11.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cassiopeia</td>\n",
       "      <td>7.38</td>\n",
       "      <td>-8.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>jax</td>\n",
       "      <td>4.59</td>\n",
       "      <td>-6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>poppy</td>\n",
       "      <td>3.82</td>\n",
       "      <td>-8.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>warwick</td>\n",
       "      <td>4.39</td>\n",
       "      <td>-11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>yorick</td>\n",
       "      <td>4.11</td>\n",
       "      <td>-8.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Champion  learn_delta  time_delta\n",
       "7          azir        10.45      -11.35\n",
       "13   cassiopeia         7.38       -8.08\n",
       "38          jax         4.59       -6.91\n",
       "74        poppy         3.82       -8.53\n",
       "115     warwick         4.39      -11.81\n",
       "120      yorick         4.11       -8.22"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "late_champs = np.where(time_deltas < -6)\n",
    "champ_list.iloc[late_champs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list also looks intuitive, with notorious hyper-scaling carries like Azir, Cassiopeia, Jax, and Poppy. I was kinda surprised by the presence of Warwick given his strong level 6 ganking, perhaps people take a long time to stack his Devourer enchantment.\n",
    "\n",
    "Let's plot their winrate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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loijjaC3s1F5Afgu8K4Ton0fIPmL2gTx48ICjR4+yYsUKIiMje/VLOnPmDC0t\nLWzbtm0w/VKHPU1NTezfv5+oqCgWLFjwXG09efKExMRESkpKWLZsGYsWLerVdKZGo7EIQ+eXecqp\n82vs2LGWtoUQ3Llzh8TERBwdHYmNjcXNzY1z585hMBhYv369HN1aiV6vJzMzk2vXruHm5sby5cuZ\nOXPmkP0fNfdPly9ftksQx1MYEAFpBv5aCPGvPQjIu8C/CiHs8hjf3oleX1/P4cOH8fHxYePGjc/s\nSAwGA59++imLFy9m4cKFA2HuC09LSwsHDx60FGCyltraWhITEyksLGTp0qVERER0yQFlMBi6iIT5\ns06n6yAM7YXiaSMGIQR3794lMTERBwcHYmNjmThxIgkJCeTk5BAXF8eCBQsG43TFoKelpYXr16+T\nnp5OYGAgy5YtY9KkSfY2q99oH0auKApxcXFMnz7dnkIyIALSCPy9EOJPTxmB/F9CCNvnt+7evg5R\nWHq9npMnT1JTU8OuXbueWejHXAr3rbfesntN7eGOXq/nyy+/xN/fnzVr1lj1j1NRUUFqaioFBQVE\nRkYSERFBS0tLt6MJlUqFl5dXtyLh5ubWp/ubFx8mJiYyYsQIYmNjmT59Orm5uVy8eJFp06axatUq\nGR5uBfX19aSmppKVlWVZdT9QWY/tgflvKT4+HicnJ+Li4pgyZYo9hGRABOQK0CCE2NBZQBRFGQFk\nAo86VwocKLoL4xVCcO3aNa5du8a2bduemZ4iMzOTlJQU3nvvveG+8tRumEwmDh8+jJOTE6+88kqf\nO++CggJSU1Oprq4mMDAQg8Fgydjq5ubW7WjC09PzuUcCQgjy8/NJTEwEIDY2lpkzZ1JdXc3Zs2dp\naWlhw4YNA5o7a7hQWVlJSkoK9+7dY/78+URGRr5Q+epMJhO5ubkkJibi5uZGXFxcvwWT9JIBEZBd\nwCHgd8AXtJahXUNrKvbfAVuAjUKIs9Ya8zw8bR3I/fv3OX78ONHR0SxevLjHTksIwQ8//CBL4doI\nIQQnT55EpVLx6quv9trhqdPpyMzMJC0tDWdnZxYuXMj9+/epq6tjxYoVjBs3Di8vL5uIvnm6ISEh\nAZPJRExMDLNmzUKv15OYmEhmZqbl70pOV/Uec0RVSkoKFRUVLFmyhIULF77QgSwmk4ns7GwSExPx\n9vYmLi5uoPxnAxbG+4+01ucQbTc1vwN8JIT4f6w15Hl51kLCuro6vv32W/z9/dmwYUOPkTlarZZP\nPvmEuLjseY9YAAAgAElEQVS4AS9cNNy5ePEipaWlvPHGG72qVdHQ0EBaWhqZmZkEBQWxZMkSnJ2d\n+f7775k4cSLr16+3aZ6swsJCEhISMBgMxMTEMHv2bADu3r3L+fPnmTx5MqtXr8bd3d0mNgxHTCYT\neXl5XL16Fa1Wy7JlywgPD5fFodphNBotdWgmTJhAXFycrevQDOhCwgXAa8DsthsXAF8KITKsNaI/\n6M1KdJ1Ox4kTJ6ivr2fXrl09DpMfP37MV199JUvh9iMpKSncunWLffv24eLi8tRzy8rKSEtLo6io\niLlz5xIREYGXlxe5ubmcPXuWVatWMX/+fJvYKYTg/v37JCQkoNPpiImJISQkBEVRqKmp4dy5czQ2\nNrJ+/foBzdg71NHr9WRlZZGSkoKrqyvLly8nODh4MEQgDVoMBgM3btzgypUrtq6EKVOZ9DaViRCC\nq1evkpaWxo4dO3qM7jBXenv77bcHbWz5UCErK4v4+Hj27dvXYzCDOeVHamoqzc3NREZGMn/+fJyc\nnDAajVy8eJH8/Hx27txpkxoY5jxZCQkJaDQaYmJiLLH6er2eK1eucP36dZYvX86SJUvk30QvaR9R\nFRAQwPLly4dVRNVAoNfrSU9PJyUlhWnTphETE4O3d7/GKg2ID+QB8H8KIU72cHwT8EchRO9W8PUz\nfc2FVVhYyA8//EBMTAyLFi3q8iQkhODw4cN4eXmxZs2a/jb3haGgoICTJ0/y1ltvdfv01NLSws2b\nN0lPT8fLy4vIyEiCg4Mt/oSmpia+//57nJ2deeWVV/p9MZ45Nj8hIQG1Wm0RDvP98/Pz+fHHHy0R\nYy+Sc/d5aGho4Nq1ay9MRNVAoNVqSUtLIy0tjZkzZxITE9NftWEGREBMwOtCiG96OP4q8I0Qwi6e\nxL4KCLSuIfj2228JDAzsdj5drVbz8ccfs2HDBmbOnNmf5r4QlJWV8e2337J7924CAwM7HKupqSEt\nLY2cnBxmzpxJZGRkl7KxxcXFHD16lMWLFxMVFdXv0x1m4VCpVMTExBAaGmoRjrq6On788Ueqq6tZ\nv34906ZN69d7D1faR1TNmzePJUuWSNHtZ1paWrh27RoZGRmEhIQQHR39vD/jQSEgvwR+J4To3xzc\nvcQaAYFWVT9x4gRNTU3s3Lmzi0NUlsK1jqqqKr744gu2bNnC9OnTgT9H3qSmpvLw4UMWLlzI4sWL\nu/zMhRCkpKRw7do1XnnllX7vvIuLi0lISKCxsbFLgkWDwUBKSgqpqaksWbKEZcuWSQfvMxBCUFJS\nwtWrV6moqCAyMpJFixa90BFVA4Farebq1avcunWL8PBwVqxYYW0JBNsIiKIoMUBM2w0+BI4B2d2c\n6g28CtwTQli/rPg5sFZAoPUfIDk5mYyMDHbs2NEllj8pKYmioiLefPNNGarZC+rr6zlw4ACrVq0i\nLCwMg8FATk4OaWlpGI1GlixZQnh4eLdhtxqNhhMnTtDY2MjOnTufuQC0L5SUlJCQkGDJzNs5sd39\n+/c5e/Ys48ePZ+3atbJ07DOQEVWDA5VKRXJyMjk5OcyfP5/ly5c/M1ClEzYTkI+Af+hlW4XAa0KI\n69Ya8zw8j4CYKSgo4MSJE6xcubJDShOTycRXX31liYSQ9ExzczMHDhxg8eLFhIaGcv36dTIyMpgw\nYQJLlixh2rRpPU5FVVVVcfjwYaZOncqaNWv6rSMqKysjISGB2tpaoqKiumTmbWxs5Pz585aMuXK6\n8unIiKrBSUNDA8nJydy5c4fFixezdOnS3o4CbSYgHoC5sk8R8Ctas/G2RwAqIUSNtUa03etvgf8O\n/JsQ4hdt+w4Cb3Y6NVUI0aWAR38ICLSmNDl8+DCTJ09m3bp1lo6mqamJTz75pFcr2l9UtFotX3zx\nBRMmTLA8nYaEhLBkyZJnOlCzs7M5f/48L7/8MnPnzu0Xex4+fEhCQgLV1dVERUV1qclgNBpJTU3l\n6tWrLF68mBUrVsgMBE+hu4iqiRMnSuEYZNTV1ZGUlGRJ8xMZGfmsomUD4gOJBe4IIaqsvdlT2l4C\nfAM0AklCiF+27T8A+ANvtDtdJ4So76aNfhEQaO0Ijx8/jlqtZufOnZZ5xcLCQk6dOsUHH3zQ1yHi\nsMeckFKtVmMymYiIiGDRokXP/DkZjUbOnz/P/fv32blzJ76+vs9tS3l5OQkJCVRVVVlSuncOuy0u\nLubMmTN4enqybt06ud7nKXSOqFq6dKnMFzcEqK6uJjExkQcPHrBs2TIWL17c0wPS0F0H0jbKuQG8\nA3wE5LQTkIOAtxBiUy/a6TcBgVa/SGJiIrdu3WLnzp2WlAIXL17kyZMn7N69Wz558ec0Iz/99BOK\norB27VrCwsJ6tU6ioaGB77//Hjc3N7Zs2fLcTtdHjx6RkJBAZWUlK1as6LYWSFNTExcvXqSkpIS1\na9cOlnTagxIZUTU8qKqqIiEhgbKyMlasWMHChQs7/18MWCqTUbTmvIqgdWqri0dZCPF2nwxQlMNA\nkRDibxVFSQCyO41AtgA6oB5IBH4jhHjSTTv9KiBm8vLyOHXqlGX1s9Fo5MCBA8yZM4elS5f2+/2G\nCg0NDaSnp3Pz5k1LQZ933nmn11NARUVFHD9+nMjISJYvX/5cnfjjx49JSEjg8ePHrFixggULFnQR\nDpPJRHp6OklJSSxYsIDo6OhepVN50ZARVcMX8/9JRUVF5zK7AzKFNRZIAJ6aIKov60AURXkPeB9Y\nIoQwKooST8cRyC6gGXgATAH+ERgJLBRC6Dq1ZRMBgdZiRe2du42NjXz66ae89tprXdYuDHfKy8tJ\nTU2lsLCQuXPnoigKDx48YO/evb3qZIQQXLlyhfT0dLZu3cqUKVOstuXRo0ckJSVRXl7e05MV0OpE\nP3PmDC4uLqxbt04uaOsGg8FAfn4+KSkpMqJqmNO5zO68efMGRED+F63TTH8JxAP3gbW01kL/e2Am\nsEYIUdfL9oKBZGCFEKKgbV8CrQLyix6u8QNKgF1CiOOdjokPP/zQ8jk2NrZfI6Y0Gg3Hjh1Dp9Ox\nY8cOSkpKXphSuOaSomlpaTQ2NlrSjNy+fZuUlBTefvvtXsWft7S08MMPP6BWq9mxY4dV0yFGo5G7\nd++Snp5OQ0MDS5cuZeHChd2OfJqbm7l06RL379/n5ZdfHmxlRO2OEIKysjKys7O5c+cOPj4+REZG\nymm9F4SSkhIuX77Mvn37BkRASoCLQoh3u6kHotA6OrkrhPg/etneXmA/YGy3eyStUV1GwFUIoe/m\nuiLgfwsh/rnTfpuNQMwIIYiPjyc7O5udO3dy8+ZNNBrNsC2Fq9FoLGlGxowZw5IlS5g1axYjRozg\nzp07/Pjjj+zdu7dXDuiKigq+++47ZsyYwcsvv9znXFLNzc3cuHGDjIwMxo4dS0REhMWWzphMJm7c\nuEFCQgLh4eHExsY+KwrlhaK2tpasrCxycnIYOXIk4eHhhIWFyXUvLyBCCJTn6Lz6IiBa4BdCiE8U\nRfEEamlX/0NRlF8BfyWE6NWcTpvzvH2yewU4QGt2398JIe50c8144CHwjhDiq07HbC4gZu7evcvp\n06d56aWXSEtLIzIy8rlreg8WTCYTFRUVZGVlkZ2dzYwZM4iMjOxQl+DBgwccOXKE119/vVeJDTMz\nM7l48SLr1q3rc4r8R48ekZ6ebgkJjoiIeGpq6/Lycs6ePYuDgwPr16/vl6iu4YBarSY3N5fs7Gzq\n6uqYM2cOc+fOxc/Pb1g+/Ej6hNV/AH2Z4KwFzHU6mwA90H7Jtp4/rxl5JkKIBqCh/T5FUdRAnRDi\njqIobrRGZR0BKoAg4PdAJdBh+mqgmT17Nt7e3hw+fJjAwEAuXbpEYGDgkAxtNBqNPH78mOLiYkpK\nSigrK2PMmDEEBwfzl3/5l12mmR4/fsyRI0fYsWPHM8XDYDBw7tw5SkpK+lQquPM01eLFi/nlL3/5\n1JDglpYWfvrpJ/Lz81m1ahXh4eEvfMdoMBgoKCggOzub4uJiZsyYQXR0NFOnTpXZhCX9Ql9GIElA\nvhDivbbPaYAJiKN16uky4CaEmGO1Me2c6IqiOAM/APMBT+Bx2z1+K4Qo7+baARuBmGlpaeHYsWPU\n19cjhOCDDz4Y9AvRDAYD5eXllJSUUFJSwsOHD/Hy8mLy5MmWV0/1vGtqajh48CDr16+3FFfqifr6\ner777ju8vLzYvHlzr6aQ+jJNZUYIYQkjDgkJYeXKlcPeJ/U0hBCUlpaSnZ3N3bt38fX1JTw8nJCQ\nEDmNJ+mJAZnC+g3wa2CCEEKrKMpO4Fugpe2U0cD7QohPrTXmebCHgEDrlM/ly5dJT09n2rRp7Nq1\na8BteBp6vZ6HDx9aBKO8vJxx48YxefJkgoKCmDRpUq9SpDc1NbF//35LtNPTMKfKN9fOeNZIwDxN\nlZ+fz+zZs7udpjIajWi12g4vtVpNSkoKQgg2bNhgkzohQ4WamhqLX2PUqFEWv0Z/5hKTDFsGREAU\nwEkIoWm3byutq8SNwPdCiMPWGvK82EtAzGRmZnLy5EkWL17MunXr7GaHTqejrKzMMiVVUVGBr6+v\nZXQxceLEPj+hazQaDh48yJw5c4iKiurxPPPiy5s3b7Jt2zYmTZpk6fg1Gk2Hzr+lpYXS0lKKiorQ\narWMGzcODw+PboVCq9ViNBot603ML0dHR2bNmsWCBQteyOmq5uZmbt++TU5ODg0NDYSGhhIeHs6E\nCRNeyJ+HxGqG7kr0/sLeAgKQk5PD8ePHmTdvHhs3bhyQzL0ajYbS0lLLCKOqqgo/P78OgtHXBXNC\nCPR6PVqtlubmZk6cOIGnpydhYWHodLpuO3i1Ws2jR48wGo2MHj3acr2iKB06fQcHB1paWmhoaGD0\n6NEEBATg5+fXRRw6vxwcHGSnSOuI0uzXKCkpYebMmYSHhzN16lSZKVpiLbYVEEVR3Gl1eH8ohPhv\n1t7MlgwGAYHW1O8pKSn4+/uzffv2fs+ZpVarKS0tpbi4mNLSUqqrqwkICLBMSQUEBDy3H+b06dNk\nZWXh5OSETqfDwcEBX1/fHjt5tVpNeno6QUFBLF26FBcXF8sIwbwQrfM0VWRkpIyQ6iXm1eFmv4a/\nvz/h4eHMmjVL+jUk/cGATGHVAn8rhPjY2pvZksEiIEIIDh06hEqloqWlhV27dj017PRZNDc3U1JS\nYpmSqq+vZ+LEiZYRhr+/f7+uFr59+zbx8fG89957nD9/nqamJnbv3t1t1I4Qgps3b3L58mU2bNhA\nSEhIh+PmaKq0tDSamppYvHgx8+fPl4koe8mTJ0/Izs4mJycHZ2dnwsPDCQ0NlfmoJP3NgAjIEVoz\n4e6x9ma2ZLAICPy5FG5oaCiZmZmsX7+eOXN6F5zW1NRkEYuSkhKampqYNGmSRTD8/PxsFoJZV1fH\np59+yuuvv05ubi7FxcW8+eab3U6B6fV6zp49S3l5OTt37mTcuHGWYyqVihs3bnDjxg28vb2JiIjo\nUOdc0jPNzc3k5OSQnZ2NSqUiNDSUuXPnytGaxJYMiIBMoTWZ4UHgX4QQjdbe1BYMJgGB1jQB33//\nPVu2bOHMmTPMmTOHlStXdulE6+vrO4wwNBoNkydPZtKkSQQFBeHr6zsgHa85SWRoaCiKopCRkcG+\nffu6HS3U1dXx3XffMW7cODZt2mQRmPLyctLT0ykoKLAs+pMd37PR6/Xk5eWRk5NDWVkZwcHBhIeH\nExQUJEVXMhAMiIA8ANxoLV8rgCeAupMRQggx1VpjnofBJiAAiYmJFBcXs23bNo4ePcrIkSNZtWoV\njx49svgxDAZDhzUYPj4+dnEWm9PUL1iwgDNnzvDOO+90m9qioKCAkydPEhUVRUREBCaTiTt37pCe\nnm6ZplqwYEGvQoNfZMz14bOzs8nLyyMgIMDi15BZgiUDzIAISAKtwvG0mwkhRJy1xjwPg1FATCYT\nX375JUFBQURFRXHx4kXu3r1r8WEEBQXh7e1t9+ii+/fvc+LECV555RWOHDnC7t27CQwM7HCOyWQi\nISGBrKwstm/fjpeXl2XR37hx4+Q0VS+pqqoiKyuL27dv4+LiYvFruLu729s0yYuLDOMdjAICfy6F\nu337diZPnmxvc7qgUqn4+OOPWbt2LRcuXLBkrW1Pc3Mzx44dw2QysXz5cnJycuQ0VR9QqVQWv4Za\nrSYsLIzw8PAhmfpGMiyRAjJYBQTg3r17nD59etCVwhVC8PXXX+Pr68uDBw+YPXt2l4WCDx8+5Pvv\nv2fChAk0NzejUqnkNFUvaW5uJjk52VIK1uzXsPeIUyLphBSQwSwgABcuXKCmpoZXX3110HQgV69e\nJS8vD1dXV0aPHs3mzZsttgkhuHr1KklJSYwcOZIJEyYQGRnJzJkz5TTVM2hpaSElJYUbN24QFhZG\nVFRUr+qlSCR2QgrIYBcQc5STt7c3Hh4eHUTEvN3dvmcdt/aa+vp6MjIy8PX1pbm5mcjISEt4sNFo\n5Nq1azQ0NDB79myio6PlNFUv0Ol0pKWlkZqaysyZM4mJiZE1NiRDASkgg11AABobG8nMzMRsZ3t7\nu9u21T6j0Uhubi5jxoyhqamJ4OBgHBwcEEIghODBgwc4OzuzZ88euWitFxgMBjIyMrh69SqTJ08m\nNja2w7oYiWSQIwVkKAjIYEAIwbFjx9BqtTx69Ih9+/bh7e1tOX7t2jVyc3PZt2+frBnxDIxGI1lZ\nWSQmJjJhwgTi4uKeK+uARGInBqSglGQYkJmZSXl5OVqtlh07dnQQj/Lycq5cucK7774rxeMpCCG4\nffs2CQkJeHh4sGPHji5hzxLJi4AUkBeIJ0+ecOHCBRwdHVm9ejVBQUGWYxqNhiNHjrBhwwa8vHpd\nWPKFQghBQUEBly9fZtSoUWzYsIGpU+2yblYiGRT0WUAURZkJTKd1RXqXoY8Q4ot+sEvSzxgMBo4c\nOcLo0aMJDQ1l3rx5lmNCCE6dOsWMGTO6JESUtFJUVMTly5fR6/WsXLmSmTNnDppoOonEXvRaQBRF\n8QW+AFY/5TTRdo5dMBgM/ZqZdjhx/vx5NBoNgYGBxMV1TBaQkZFBbW0tr7zyip2sG7yUlZVx+fJl\nGhsbiYuLY86cOVI4JJI2+tLb/iuwCvhfQDxQYxOLnoOvvvqKXbt2yQVunTAn6hs7dixbtmzp0AFW\nVFSQkJDA22+/LcW3HRUVFcTHx1NZWUl0dDTz5s2T618kkk70pcdYDXwshPi5rYx5Xvz8/Ni/fz+v\nvfaajL9vo7GxkR9++IFRo0axZ8+eDsWmtFot33//PWvXru3gTH+Rqa6uJiEhgZKSElasWMGOHTuk\nsEokPdCX/4wRQKatDOkP1qxZg6enJ/v37+fVV1/F39/f3ibZFZPJxDfffIPJZOKNN97osBpaCMGZ\nM2eYPHkyYWFhdrRycFBfX09iYiIFBQUsWbKEzZs3y6y4Eskz6MuYPBmYaytDFEX5W0VRTIqi/M9O\n+z9SFKVcURS1oijxiqI81csbGRnJ+vXr+frrrykoKLCVuUOC8+fPU11dzY4dO7ok7rt16xaVlZWs\nW7fOTtYNDlQqFWfPnuWTTz7B3d2dX/ziF0RFRUnxkEh6QV9GIP83EK8oSrwQ4kh/GqEoyhLgPSCb\nVke8ef9fA/8ZeAsoAP4BuKgoSrAQQtVTe7NmzcLNzY3Dhw8TGxvLwoUL+9PcIUFBQQHXr19n5cqV\nzJgxo8OxqqoqfvrpJ/bu3fvc9dOHKi0tLVy9epWbN28SHh7Oz372M1xdXe1tlkQypOhLPZB4IIDW\nEN5yoAgwdj5PCLGyTwYoigdwA3gH+AjIEUL8Umn19D4C/iSE+H3buc5AFfBrIcQnndrpshK9traW\nr7/+mpCQEFauXPnCRM80NTXxxz/+kenTp/Pqq692OKbT6fj0009ZtmxZh1DeFwWtVktqaippaWmW\nPF8eHh72NksisScDUlCqmN4VlJrSJwMU5TBQJIT427aiVdltAjIVKAQWCyFutDv/NFAthNjbqZ1u\nU5mo1WoOHTqEl5cXmzdvHvYOUZPJxJ/+9CcUReEXv/hFl8ihEydOYDKZukRjDXf0er0lX9XUqVOJ\njY1l7Nix9jZLIhkM2D6ViRAiyNqb9ISiKO8BU4E95tu0O2xOKlTZ6bIqoNfecRcXF958802OHTv2\nQoT5fvfddzQ3N/OrX/2qi3hkZWVRVlbG+++//8KIh9Fo5NatWyQnJ+Pv78+bb74pCzlJJP2E3R7H\nFUUJBv47sEIIYZ4KU+idGnY7bProo48s27GxscTGxgIwatQoduzYwYULFzhw4AB79uwZlmG+KSkp\nFBQU8NZbb3UpXFVdXc2FCxd48803XwgHsclksuSr8vLyYufOnQQEBNjbLIlkWGG3bLyKouwF9tPR\njzKSVnEwAqFAHl2nsM4AVUKIfZ3a61U23tTUVFJSUti9ezd+fn7P/T0GCyUlJXz++efExMQQExPT\n4Zher+ezzz5j0aJFLFq0yE4WDgxCCPL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JTtZXAL92zv0sZnmfTqJ3xDnHgQMH2L9/P6tX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8d8c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wins_by_index( wins_by_time[late_champs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of these champions gain in win rate as the game goes on. They all have extremely mediocre early games to boot. You probably don't want to get too many of these on one team. That's all for this notebook!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus table\n",
    "I didn't want to clutter the main notebook with a large table. Here are the champions sorted by difficulty from hardest to easiest (there is a gap for middle difficulty champions since the list is large.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Champion</th>\n",
       "      <th>learn_delta</th>\n",
       "      <th>time_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>azir</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-11.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>rengar</td>\n",
       "      <td>10.25</td>\n",
       "      <td>-3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>kalista</td>\n",
       "      <td>10.22</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>reksai</td>\n",
       "      <td>10.08</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>ryze</td>\n",
       "      <td>8.90</td>\n",
       "      <td>-3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bard</td>\n",
       "      <td>8.59</td>\n",
       "      <td>-4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>thresh</td>\n",
       "      <td>8.57</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>evelynn</td>\n",
       "      <td>8.25</td>\n",
       "      <td>-1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>syndra</td>\n",
       "      <td>8.19</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>nidalee</td>\n",
       "      <td>8.19</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>khazix</td>\n",
       "      <td>8.14</td>\n",
       "      <td>-0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>shaco</td>\n",
       "      <td>7.96</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>rumble</td>\n",
       "      <td>7.94</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>gnar</td>\n",
       "      <td>7.86</td>\n",
       "      <td>-2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>zed</td>\n",
       "      <td>7.76</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>yasuo</td>\n",
       "      <td>7.76</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>udyr</td>\n",
       "      <td>7.60</td>\n",
       "      <td>-2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cassiopeia</td>\n",
       "      <td>7.38</td>\n",
       "      <td>-8.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>orianna</td>\n",
       "      <td>7.30</td>\n",
       "      <td>-3.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>viktor</td>\n",
       "      <td>7.29</td>\n",
       "      <td>-5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>hecarim</td>\n",
       "      <td>7.17</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>leblanc</td>\n",
       "      <td>7.12</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>jinx</td>\n",
       "      <td>7.10</td>\n",
       "      <td>-0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>gragas</td>\n",
       "      <td>7.05</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>tahmkench</td>\n",
       "      <td>7.02</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>draven</td>\n",
       "      <td>6.98</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>xerath</td>\n",
       "      <td>6.98</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>singed</td>\n",
       "      <td>6.94</td>\n",
       "      <td>-2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>vi</td>\n",
       "      <td>6.87</td>\n",
       "      <td>-3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>elise</td>\n",
       "      <td>6.70</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>garen</td>\n",
       "      <td>4.12</td>\n",
       "      <td>-0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>yorick</td>\n",
       "      <td>4.11</td>\n",
       "      <td>-8.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>ziggs</td>\n",
       "      <td>4.07</td>\n",
       "      <td>-3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>shen</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>teemo</td>\n",
       "      <td>3.94</td>\n",
       "      <td>-5.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>poppy</td>\n",
       "      <td>3.82</td>\n",
       "      <td>-8.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>morgana</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>zyra</td>\n",
       "      <td>3.79</td>\n",
       "      <td>-1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>pantheon</td>\n",
       "      <td>3.77</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>chogath</td>\n",
       "      <td>3.61</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>skarner</td>\n",
       "      <td>3.61</td>\n",
       "      <td>-5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>kennen</td>\n",
       "      <td>3.60</td>\n",
       "      <td>-2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>volibear</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>nami</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>trundle</td>\n",
       "      <td>3.57</td>\n",
       "      <td>-2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>lux</td>\n",
       "      <td>3.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>brand</td>\n",
       "      <td>3.48</td>\n",
       "      <td>2.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>heimerdinger</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>varus</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ashe</td>\n",
       "      <td>3.35</td>\n",
       "      <td>-0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>sona</td>\n",
       "      <td>3.18</td>\n",
       "      <td>-3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>lulu</td>\n",
       "      <td>3.10</td>\n",
       "      <td>-1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>soraka</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>missfortune</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>malphite</td>\n",
       "      <td>2.91</td>\n",
       "      <td>-0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>karma</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>kayle</td>\n",
       "      <td>2.65</td>\n",
       "      <td>-4.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>taric</td>\n",
       "      <td>2.64</td>\n",
       "      <td>-5.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>mordekaiser</td>\n",
       "      <td>2.26</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>galio</td>\n",
       "      <td>2.09</td>\n",
       "      <td>-1.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Champion  learn_delta  time_delta\n",
       "7            azir        10.45      -11.35\n",
       "79         rengar        10.25       -3.23\n",
       "41        kalista        10.22       -0.54\n",
       "77         reksai        10.08        1.30\n",
       "82           ryze         8.90       -3.29\n",
       "8            bard         8.59       -4.20\n",
       "99         thresh         8.57        2.10\n",
       "22        evelynn         8.25       -1.31\n",
       "94         syndra         8.19        3.23\n",
       "68        nidalee         8.19       -0.56\n",
       "48         khazix         8.14       -0.30\n",
       "84          shaco         7.96        2.36\n",
       "81         rumble         7.94        0.98\n",
       "30           gnar         7.86       -2.31\n",
       "122           zed         7.76        0.13\n",
       "119         yasuo         7.76       -0.35\n",
       "105          udyr         7.60       -2.15\n",
       "13     cassiopeia         7.38       -8.08\n",
       "72        orianna         7.30       -3.63\n",
       "112        viktor         7.29       -5.34\n",
       "33        hecarim         7.17        2.70\n",
       "51        leblanc         7.12        5.21\n",
       "40           jinx         7.10       -0.64\n",
       "31         gragas         7.05        0.11\n",
       "95      tahmkench         7.02       -0.31\n",
       "19         draven         6.98       -0.10\n",
       "117        xerath         6.98        2.93\n",
       "87         singed         6.94       -2.89\n",
       "111            vi         6.87       -3.22\n",
       "21          elise         6.70        3.13\n",
       "..            ...          ...         ...\n",
       "29          garen         4.12       -0.24\n",
       "120        yorick         4.11       -8.22\n",
       "123         ziggs         4.07       -3.69\n",
       "85           shen         4.03        3.43\n",
       "98          teemo         3.94       -5.31\n",
       "74          poppy         3.82       -8.53\n",
       "64        morgana         3.82        1.43\n",
       "125          zyra         3.79       -1.65\n",
       "73       pantheon         3.77        6.67\n",
       "14        chogath         3.61        4.33\n",
       "90        skarner         3.61       -5.66\n",
       "47         kennen         3.60       -2.50\n",
       "114      volibear         3.60        2.94\n",
       "65           nami         3.58        0.75\n",
       "101       trundle         3.57       -2.87\n",
       "57            lux         3.53        1.73\n",
       "10          brand         3.48        2.44\n",
       "34   heimerdinger         3.45        1.98\n",
       "107         varus         3.43        2.73\n",
       "6            ashe         3.35       -0.52\n",
       "91           sona         3.18       -3.30\n",
       "56           lulu         3.10       -1.34\n",
       "92         soraka         3.10        1.13\n",
       "62    missfortune         3.03        2.49\n",
       "58       malphite         2.91       -0.70\n",
       "42          karma         2.87        0.83\n",
       "46          kayle         2.65       -4.89\n",
       "97          taric         2.64       -5.27\n",
       "63    mordekaiser         2.26        3.17\n",
       "27          galio         2.09       -1.27\n",
       "\n",
       "[125 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_list.sort('learn_delta', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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