wbc_article_support.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2014 entrant counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country    object\n",
      "entries     int64\n",
      "dtype: object\n",
      "(58, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>entries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belize</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  entries\n",
       "0  Argentina        8\n",
       "1  Australia       80\n",
       "2    Austria       32\n",
       "3    Belgium       89\n",
       "4     Belize        4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entrants2014 = pd.read_csv('data/2014_participating_countries.txt', header=None, names=['country', 'entries'])\n",
    "print entrants2014.dtypes\n",
    "print entrants2014.shape\n",
    "entrants2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4753L"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entry_count = np.sum(entrants2014.entries)\n",
    "entry_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "entrants2014['perOfTotal'] = entrants2014.entries/entry_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>country</th>\n",
       "      <th>entries</th>\n",
       "      <th>perOfTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>3402</td>\n",
       "      <td>0.715758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>249</td>\n",
       "      <td>0.052388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>190</td>\n",
       "      <td>0.039975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>89</td>\n",
       "      <td>0.018725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>80</td>\n",
       "      <td>0.016831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Japan</td>\n",
       "      <td>71</td>\n",
       "      <td>0.014938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>69</td>\n",
       "      <td>0.014517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>63</td>\n",
       "      <td>0.013255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Italy</td>\n",
       "      <td>63</td>\n",
       "      <td>0.013255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>50</td>\n",
       "      <td>0.010520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  entries  perOfTotal\n",
       "0   United States     3402    0.715758\n",
       "1         Germany      249    0.052388\n",
       "2          Canada      190    0.039975\n",
       "3         Belgium       89    0.018725\n",
       "4       Australia       80    0.016831\n",
       "5           Japan       71    0.014938\n",
       "6  United Kingdom       69    0.014517\n",
       "7          Brazil       63    0.013255\n",
       "8           Italy       63    0.013255\n",
       "9     New Zealand       50    0.010520"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topTen2014 = entrants2014.sort_values('entries', axis=0, ascending=False).head(10)\n",
    "topTen2014.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>country</th>\n",
       "      <th>entries</th>\n",
       "      <th>perOfTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>United States</td>\n",
       "      <td>3402</td>\n",
       "      <td>0.715758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Germany</td>\n",
       "      <td>249</td>\n",
       "      <td>0.052388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Canada</td>\n",
       "      <td>190</td>\n",
       "      <td>0.039975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>89</td>\n",
       "      <td>0.018725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>80</td>\n",
       "      <td>0.016831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Japan</td>\n",
       "      <td>71</td>\n",
       "      <td>0.014938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>69</td>\n",
       "      <td>0.014517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>63</td>\n",
       "      <td>0.013255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Italy</td>\n",
       "      <td>63</td>\n",
       "      <td>0.013255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>50</td>\n",
       "      <td>0.010520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  entries  perOfTotal\n",
       "56   United States     3402    0.715758\n",
       "21         Germany      249    0.052388\n",
       "8           Canada      190    0.039975\n",
       "3          Belgium       89    0.018725\n",
       "1        Australia       80    0.016831\n",
       "31           Japan       71    0.014938\n",
       "55  United Kingdom       69    0.014517\n",
       "6           Brazil       63    0.013255\n",
       "30           Italy       63    0.013255\n",
       "38     New Zealand       50    0.010520"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topTen2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.910162002945508"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(topTen2014.perOfTotal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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WrVo5L185m/DwcKWkpCglJUXHjx9XaWmpbrrpJr399tuqU6eO6tSpo969e6tHjx6qX7++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\nT2Cgv7y9vSptKyiwGZr+8lO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B8vHx0YYNG864SrJz504dOXKk0tfqqd8DfHx8Kn0NLViw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc15dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.plt.title(\"2014 World Beer Cup Top 10 Country Entrants\")\n",
    "sns.barplot(topTen2014.country, topTen2014.entries, palette=\"BuGn_d\")\n",
    "sns.plt.xlabel(\"Country\")\n",
    "sns.plt.ylabel(\"Entries\")\n",
    "sns.plt.savefig('img/wbc_2014_top10entrants.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2014 awards by country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country    object\n",
      "awards      int64\n",
      "dtype: object\n",
      "(22, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>awards</th>\n",
       "      <th>perOfTot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>United States</td>\n",
       "      <td>205</td>\n",
       "      <td>0.729537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Germany</td>\n",
       "      <td>27</td>\n",
       "      <td>0.096085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>7</td>\n",
       "      <td>0.024911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>5</td>\n",
       "      <td>0.017794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5</td>\n",
       "      <td>0.017794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Italy</td>\n",
       "      <td>4</td>\n",
       "      <td>0.014235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Japan</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>2</td>\n",
       "      <td>0.007117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2</td>\n",
       "      <td>0.007117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2</td>\n",
       "      <td>0.007117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2</td>\n",
       "      <td>0.007117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Israel</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Poland</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  awards  perOfTot\n",
       "21   United States     205  0.729537\n",
       "7          Germany      27  0.096085\n",
       "4           Canada       7  0.024911\n",
       "2          Belgium       5  0.017794\n",
       "20  United Kingdom       5  0.017794\n",
       "10           Italy       4  0.014235\n",
       "14     Netherlands       3  0.010676\n",
       "0        Australia       3  0.010676\n",
       "5   Czech Republic       3  0.010676\n",
       "11           Japan       3  0.010676\n",
       "6      El Salvador       2  0.007117\n",
       "1          Austria       2  0.007117\n",
       "15     New Zealand       2  0.007117\n",
       "3           Brazil       2  0.007117\n",
       "8          Ireland       1  0.003559\n",
       "9           Israel       1  0.003559\n",
       "12          Latvia       1  0.003559\n",
       "13          Mexico       1  0.003559\n",
       "16          Poland       1  0.003559\n",
       "17     South Korea       1  0.003559\n",
       "18          Sweden       1  0.003559\n",
       "19          Taiwan       1  0.003559"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winners2014 = pd.read_csv('data/2014_award_counts_country.txt', header=None, names=['country', 'awards'])\n",
    "print winners2014.dtypes\n",
    "winners2014['perOfTot'] = winners2014.awards/np.sum(winners2014.awards)\n",
    "print winners2014.shape\n",
    "winners2014.sort_values('awards', axis=0, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>country</th>\n",
       "      <th>awards</th>\n",
       "      <th>perOfTot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>United States</td>\n",
       "      <td>205</td>\n",
       "      <td>0.729537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Germany</td>\n",
       "      <td>27</td>\n",
       "      <td>0.096085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>7</td>\n",
       "      <td>0.024911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>5</td>\n",
       "      <td>0.017794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5</td>\n",
       "      <td>0.017794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Italy</td>\n",
       "      <td>4</td>\n",
       "      <td>0.014235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Japan</td>\n",
       "      <td>3</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  awards  perOfTot\n",
       "21   United States     205  0.729537\n",
       "7          Germany      27  0.096085\n",
       "4           Canada       7  0.024911\n",
       "2          Belgium       5  0.017794\n",
       "20  United Kingdom       5  0.017794\n",
       "10           Italy       4  0.014235\n",
       "14     Netherlands       3  0.010676\n",
       "0        Australia       3  0.010676\n",
       "5   Czech Republic       3  0.010676\n",
       "11           Japan       3  0.010676"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winners2014.sort_values('awards', axis=0, ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9430604982206405"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(winners2014.sort_values('awards', axis=0, ascending=False).head(10)['perOfTot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Unnamed: 0</th>\n",
       "      <th>award</th>\n",
       "      <th>beer</th>\n",
       "      <th>brewery</th>\n",
       "      <th>category</th>\n",
       "      <th>origin</th>\n",
       "      <th>year</th>\n",
       "      <th>city</th>\n",
       "      <th>country_state</th>\n",
       "      <th>country</th>\n",
       "      <th>category_num</th>\n",
       "      <th>entries</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>Veritas 002</td>\n",
       "      <td>Port Brewing Co. and The Lost Abbey</td>\n",
       "      <td>Category 13: Experimental Beer (Lager or Ale),...</td>\n",
       "      <td>San Marcos, CA</td>\n",
       "      <td>2008</td>\n",
       "      <td>San Marcos</td>\n",
       "      <td>CA</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 13</td>\n",
       "      <td>27</td>\n",
       "      <td>Experimental Beer (Lager or Ale)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Hopsquatch '06</td>\n",
       "      <td>Four Peaks Brewing Co.</td>\n",
       "      <td>Category 18: Aged Beer (Ale or Lager), 25 Entries</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>2008</td>\n",
       "      <td>Tempe</td>\n",
       "      <td>AZ</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 18</td>\n",
       "      <td>25</td>\n",
       "      <td>Aged Beer (Ale or Lager)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2268</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Alaskan Smoked Porter 2007</td>\n",
       "      <td>Alaskan Brewing Co.</td>\n",
       "      <td>Category 20: Smoke-Flavored Beer, 27 Entries</td>\n",
       "      <td>Juneau, AK</td>\n",
       "      <td>2008</td>\n",
       "      <td>Juneau</td>\n",
       "      <td>AK</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 20</td>\n",
       "      <td>27</td>\n",
       "      <td>Smoke-Flavored Beer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Saint Arnold Divine Reserve No. 4</td>\n",
       "      <td>Saint Arnold Brewing Co</td>\n",
       "      <td>Category 64: Strong Scotch Ale, 43 Entries</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>2008</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 64</td>\n",
       "      <td>43</td>\n",
       "      <td>Strong Scotch Ale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Hopsquatch '07</td>\n",
       "      <td>Four Peaks Brewing Co.</td>\n",
       "      <td>Category 68: Barley Wine-Style Ale, 61 Entries</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>2008</td>\n",
       "      <td>Tempe</td>\n",
       "      <td>AZ</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 68</td>\n",
       "      <td>61</td>\n",
       "      <td>Barley Wine-Style Ale</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   award                               beer  \\\n",
       "2266         NaN  Bronze                        Veritas 002   \n",
       "2267         NaN  Silver                     Hopsquatch '06   \n",
       "2268         NaN    Gold         Alaskan Smoked Porter 2007   \n",
       "2269         NaN    Gold  Saint Arnold Divine Reserve No. 4   \n",
       "2270         NaN  Silver                     Hopsquatch '07   \n",
       "\n",
       "                                  brewery  \\\n",
       "2266  Port Brewing Co. and The Lost Abbey   \n",
       "2267               Four Peaks Brewing Co.   \n",
       "2268                  Alaskan Brewing Co.   \n",
       "2269              Saint Arnold Brewing Co   \n",
       "2270               Four Peaks Brewing Co.   \n",
       "\n",
       "                                               category          origin  year  \\\n",
       "2266  Category 13: Experimental Beer (Lager or Ale),...  San Marcos, CA  2008   \n",
       "2267  Category 18: Aged Beer (Ale or Lager), 25 Entries       Tempe, AZ  2008   \n",
       "2268       Category 20: Smoke-Flavored Beer, 27 Entries      Juneau, AK  2008   \n",
       "2269         Category 64: Strong Scotch Ale, 43 Entries     Houston, TX  2008   \n",
       "2270     Category 68: Barley Wine-Style Ale, 61 Entries       Tempe, AZ  2008   \n",
       "\n",
       "            city country_state country  category_num  entries  \\\n",
       "2266  San Marcos            CA     USA  Category 13        27   \n",
       "2267       Tempe            AZ     USA  Category 18        25   \n",
       "2268      Juneau            AK     USA  Category 20        27   \n",
       "2269     Houston            TX     USA  Category 64        43   \n",
       "2270       Tempe            AZ     USA  Category 68        61   \n",
       "\n",
       "                         category_name  \n",
       "2266  Experimental Beer (Lager or Ale)  \n",
       "2267          Aged Beer (Ale or Lager)  \n",
       "2268               Smoke-Flavored Beer  \n",
       "2269                 Strong Scotch Ale  \n",
       "2270             Barley Wine-Style Ale  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WBC = pd.read_csv('data/wbc_PDF98_14c.csv')\n",
    "WBC.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(202, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>award</th>\n",
       "      <th>beer</th>\n",
       "      <th>brewery</th>\n",
       "      <th>category</th>\n",
       "      <th>origin</th>\n",
       "      <th>year</th>\n",
       "      <th>city</th>\n",
       "      <th>country_state</th>\n",
       "      <th>country</th>\n",
       "      <th>category_num</th>\n",
       "      <th>entries</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>1940</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Float Trip</td>\n",
       "      <td>Piney River Brewing Co.</td>\n",
       "      <td>Category 1: American-Style Wheat Beer, 23 Entries</td>\n",
       "      <td>Bucyrus, MO</td>\n",
       "      <td>2014</td>\n",
       "      <td>Bucyrus</td>\n",
       "      <td>MO</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>23</td>\n",
       "      <td>American-Style Wheat Beer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>1941</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Red Dawn</td>\n",
       "      <td>Carson�s Brewery</td>\n",
       "      <td>Category 1: American-Style Wheat Beer, 23 Entries</td>\n",
       "      <td>Evansville, IN</td>\n",
       "      <td>2014</td>\n",
       "      <td>Evansville</td>\n",
       "      <td>IN</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>23</td>\n",
       "      <td>American-Style Wheat Beer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>1942</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>DD Blonde</td>\n",
       "      <td>Hop Valley Brewing Co.</td>\n",
       "      <td>Category 1: American-Style Wheat Beer, 23 Entries</td>\n",
       "      <td>Eugene, OR</td>\n",
       "      <td>2014</td>\n",
       "      <td>Eugene</td>\n",
       "      <td>OR</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>23</td>\n",
       "      <td>American-Style Wheat Beer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>1943</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Hefeweizen</td>\n",
       "      <td>Widmer Brothers Brewing Co.</td>\n",
       "      <td>Category 2: American-Style Wheat Beer With Yea...</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>2014</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>25</td>\n",
       "      <td>American-Style Wheat Beer With Yeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>1944</td>\n",
       "      <td>Silver</td>\n",
       "      <td>White Noise</td>\n",
       "      <td>�berbrew</td>\n",
       "      <td>Category 2: American-Style Wheat Beer With Yea...</td>\n",
       "      <td>Billings, MT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Billings</td>\n",
       "      <td>MT</td>\n",
       "      <td>USA</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>25</td>\n",
       "      <td>American-Style Wheat Beer With Yeast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   award         beer                      brewery  \\\n",
       "1939        1940    Gold   Float Trip      Piney River Brewing Co.   \n",
       "1940        1941  Silver     Red Dawn             Carson�s Brewery   \n",
       "1941        1942  Bronze    DD Blonde       Hop Valley Brewing Co.   \n",
       "1942        1943    Gold   Hefeweizen  Widmer Brothers Brewing Co.   \n",
       "1943        1944  Silver  White Noise                     �berbrew   \n",
       "\n",
       "                                               category          origin  year  \\\n",
       "1939  Category 1: American-Style Wheat Beer, 23 Entries     Bucyrus, MO  2014   \n",
       "1940  Category 1: American-Style Wheat Beer, 23 Entries  Evansville, IN  2014   \n",
       "1941  Category 1: American-Style Wheat Beer, 23 Entries      Eugene, OR  2014   \n",
       "1942  Category 2: American-Style Wheat Beer With Yea...    Portland, OR  2014   \n",
       "1943  Category 2: American-Style Wheat Beer With Yea...    Billings, MT  2014   \n",
       "\n",
       "            city country_state country category_num  entries  \\\n",
       "1939     Bucyrus            MO     USA   Category 1       23   \n",
       "1940  Evansville            IN     USA   Category 1       23   \n",
       "1941      Eugene            OR     USA   Category 1       23   \n",
       "1942    Portland            OR     USA   Category 2       25   \n",
       "1943    Billings            MT     USA   Category 2       25   \n",
       "\n",
       "                              category_name  \n",
       "1939              American-Style Wheat Beer  \n",
       "1940              American-Style Wheat Beer  \n",
       "1941              American-Style Wheat Beer  \n",
       "1942   American-Style Wheat Beer With Yeast  \n",
       "1943   American-Style Wheat Beer With Yeast  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa2014 = WBC[(WBC.country==\"USA\") & (WBC.year==2014)]\n",
    "print usa2014.shape\n",
    "usa2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "states2014 = usa2014.groupby('country_state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_state\n",
       "CA    36\n",
       "CO    22\n",
       "OR    17\n",
       "IL     8\n",
       "WA     8\n",
       "Name: award, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states2014.award.count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IBM data science workbench, \"Tutorial \\#2 - Explore & Visualize\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Silver    764\n",
       "Bronze    754\n",
       "Gold      753\n",
       "Name: award, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WBC.award.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    award \n",
       "Argentina  Bronze     1\n",
       "           Gold       2\n",
       "           Silver     1\n",
       "Australia  Bronze    13\n",
       "           Gold      14\n",
       "           Silver     9\n",
       "Austria    Bronze     4\n",
       "           Gold       5\n",
       "           Silver     7\n",
       "Belgium    Bronze    21\n",
       "           Gold      37\n",
       "           Silver    24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "award_by_country = WBC.groupby(['country', 'award']).size()\n",
    "award_by_country.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>award</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "award      Bronze  Gold  Silver\n",
       "country                        \n",
       "Argentina       1     2       1\n",
       "Australia      13    14       9\n",
       "Austria         4     5       7\n",
       "Belgium        21    37      24\n",
       "Bolivia         1     1     NaN"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "award_by_country_df = award_by_country.unstack()\n",
    "award_by_country_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "award_by_country_df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "award\n",
       "Bronze    USA\n",
       "Gold      USA\n",
       "Silver    USA\n",
       "dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "award_by_country_df.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "award_by_country_df.sort('Gold', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>award</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>534</td>\n",
       "      <td>529</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>63</td>\n",
       "      <td>67</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>37</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "award           Gold  Silver  Bronze\n",
       "country                             \n",
       "USA              534     529     532\n",
       "Germany           63      67      64\n",
       "Belgium           37      24      21\n",
       "Canada            21      35      36\n",
       "Japan             14      23      26\n",
       "Australia         14       9      13\n",
       "UK                11       8       6\n",
       "Czech Republic     7       8       6\n",
       "Singapore          5       1       1\n",
       "Austria            5       7       4"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "award_by_country_df = award_by_country_df[['Gold', 'Silver', 'Bronze']]\n",
    "award_by_country_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe3579b0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiL\nGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABjLpyIH9evXT3a7XZLUsmVLjRw5\nUpMmTZKXl5ciIiI0ffp0SdKKFSuUkpIiX19fjRw5UtHR0VU2OAAAAFBZ5cZwUVGRJOnNN990vTZq\n1CjFx8crKipK06dPV2pqqjp06KDk5GStXLlSJ0+e1KBBg9StWzf5+vpW3fQAAABAJZQbwzt37lRB\nQYGGDRum0tJSjR8/Xjt27FBUVJQkqWfPntqwYYO8vLwUGRkpHx8f2e12hYSEKD09XTfccEOVfwgA\nAADgcpQbw/Xr19ewYcMUGxurrKwsjRgxQk6n0/X1gIAAORwO5efnKzAw0PW6v7+/jh8/XjVTAwAA\nAB5QbgyHhISodevWrl83atRIO3bscH09Pz9fDRs2lN1ul8PhOOf1i2nc2F8+Pt6XO7vy8uyX/Wc9\nKSjIruDgwPIPrCXq0mepDNbBjbVwYy3cWAs31sKNtXBjLdxq8lqUG8PvvfeefvrpJ02fPl0HDx6U\nw+FQt27dtHnzZnXu3Fnr169Xly5d1K5dOyUlJamoqEiFhYXKyMhQRETERd87L6+gUsPn5jokWR/E\nubkO5eTUjbPgwcGBdeazVAbr4MZauLEWbqyFG2vhxlq4sRZuNWUtLhTk5cZw//79NXnyZA0ePFhe\nXl6aPXu2GjVqpISEBBUXFys8PFwxMTGy2WyKi4vT4MGD5XQ6FR8fLz8/P49/EAAAAMBTyo1hX19f\nzZs375zXk5OTz3ktNjZWsbGxnpkMAAAAqGI8dAMAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAA\nAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYB\nAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxi\nGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADG\nIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAA\nYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgA\nAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKG\nAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAs\nYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAA\nxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEA\nAGAsYhgAAADGqlAM//LLL4qOjlZmZqays7M1ePBgDRkyRImJia5jVqxYoXvvvVcDBw7UunXrqmpe\nAAAAwGPKjeGSkhJNnz5d9evXlyTNmjVL8fHxWrp0qcrKypSamqrDhw8rOTlZKSkpWrx4sebPn6/i\n4uIqHx4AAACojHJj+JlnntGgQYPUrFkzOZ1O7dixQ1FRUZKknj17auPGjdq+fbsiIyPl4+Mju92u\nkJAQpaenV/nwAAAAQGVcNIbff/99NWnSRN26dZPT6ZQklZWVub4eEBAgh8Oh/Px8BQYGul739/fX\n8ePHq2hkAAAAwDN8LvbF999/XzabTRs2bFB6eromTpyovLw819fz8/PVsGFD2e12ORyOc14vT+PG\n/vLx8b7s4fPy7Jf9Zz0pKMiu4ODA8g+sJerSZ6kM1sGNtXBjLdxYCzfWwo21cGMt3GryWlw0hpcu\nXer69R/+8AclJiZqzpw5SktLU6dOnbR+/Xp16dJF7dq1U1JSkoqKilRYWKiMjAxFRESU+5fn5RVU\navjcXIck64M4N9ehnJy6cSY8ODiwznyWymAd3FgLN9bCjbVwYy3cWAs31sKtpqzFhYL8ojF8PhMn\nTtTUqVNVXFys8PBwxcTEyGazKS4uToMHD5bT6VR8fLz8/PwqPTQAAABQlSocw2+++abr18nJyed8\nPTY2VrGxsZ6ZCgAAAKgGPHQDAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIY\nAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYi\nhgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABg\nLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAA\nAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYB\nAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxi\nGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADG\nIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAA\nYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgA\nAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxvIp\n74CysjIlJCQoMzNTXl5eSkxMlJ+fnyZNmiQvLy9FRERo+vTpkqQVK1YoJSVFvr6+GjlypKKjo6t6\nfgAAAOCylRvDn3/+uWw2m5YtW6bNmzfr2WefldPpVHx8vKKiojR9+nSlpqaqQ4cOSk5O1sqVK3Xy\n5EkNGjRI3bp1k6+vb3V8DgAAAOCSlRvDvXv31q233ipJ2r9/v6644gpt3LhRUVFRkqSePXtqw4YN\n8vLyUmRkpHx8fGS32xUSEqL09HTdcMMNVfsJAAAAgMtUoWuGvby8NGnSJD311FPq06ePnE6n62sB\nAQFyOBzKz89XYGCg63V/f38dP37c8xMDAAAAHlLumeHTZs+erV9++UX9+/dXYWGh6/X8/Hw1bNhQ\ndrtdDofjnNcvpnFjf/n4eF/G2Kfk5dkv+896UlCQXcHBgeUfWEvUpc9SGayDG2vhxlq4sRZurIUb\na+HGWrjV5LUoN4ZXrVqlgwcP6qGHHlK9evXk5eWlG264QZs3b1bnzp21fv16denSRe3atVNSUpKK\niopUWFiojIwMRUREXPS98/IKKjV8bq5DkvVBnJvrUE5O3TgLHhwcWGc+S2WwDm6shRtr4cZauLEW\nbqyFG2vhVlPW4kJBXm4M33777Zo8ebKGDBmikpISJSQkKCwsTAkJCSouLlZ4eLhiYmJks9kUFxen\nwYMHu26w8/Pz8/gHAQAAADyl3Bhu0KCBFixYcM7rycnJ57wWGxur2NhYz0wGAAAAVDEeugEAAABj\nEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAA\nMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwA\nAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHD\nAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAW\nMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAA\nYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAA\nADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEM\nAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMR\nwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAw\nFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABj+VzsiyUlJXriiSe0b98+FRcXa+TIkWrT\npo0mTZokLy8vRUREaPr06ZKkFStWKCUlRb6+vho5cqSio6OrY34AAADgsl00hj/88EM1btxYc+bM\n0bFjx9S3b19de+21io+PV1RUlKZPn67U1FR16NBBycnJWrlypU6ePKlBgwapW7du8vX1ra7PAQAA\nAFyyi8bw7373O8XExEiSSktL5e3trR07digqKkqS1LNnT23YsEFeXl6KjIyUj4+P7Ha7QkJClJ6e\nrhtuuKHqPwEAAABwmS56zXCDBg3k7+8vh8OhRx99VOPHj5fT6XR9PSAgQA6HQ/n5+QoMDHS97u/v\nr+PHj1fd1AAAAIAHXPTMsCQdOHBAY8aM0ZAhQ3TXXXdp7ty5rq/l5+erYcOGstvtcjgc57xensaN\n/eXj432Zo0t5efbL/rOeFBRkV3BwYPkH1hJ16bNUBuvgxlq4sRZurIUba+HGWrixFm41eS0uGsOH\nDx/WsGHDNG3aNHXp0kWSdN111yktLU2dOnXS+vXr1aVLF7Vr105JSUkqKipSYWGhMjIyFBERUe5f\nnpdXUKnhc3MdkqwP4txch3Jy6saZ8ODgwDrzWSqDdXBjLdxYCzfWwo21cGMt3FgLt5qyFhcK8ovG\n8Msvv6xjx45p0aJFevHFF2Wz2TRlyhQ99dRTKi4uVnh4uGJiYmSz2RQXF6fBgwfL6XQqPj5efn5+\nVfJBAAAAAE+5aAxPmTJFU6ZMOef15OTkc16LjY1VbGys5yYDAAAAqhgP3QAAAICxiGEAAAAYixgG\nAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGI\nYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAY\nixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAA\ngLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEA\nAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsY\nBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICx\niGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAA\nGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYA\nAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhh\nAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxKhTD27ZtU1xcnCQpOztbgwcP1pAhQ5SYmOg6ZsWKFbr3\n3ns1cOBArVu3rkqGBQAAADyp3BhevHixEhISVFxcLEmaNWuW4uPjtXTpUpWVlSk1NVWHDx9WcnKy\nUlJStHjUHBeiAAAgAElEQVTxYs2fP991PAAAAFBTlRvDrVu31osvvuj6/Q8//KCoqChJUs+ePbVx\n40Zt375dkZGR8vHxkd1uV0hIiNLT06tuagAAAMADyo3h2267Td7e3q7fO51O168DAgLkcDiUn5+v\nwMBA1+v+/v46fvy4h0cFAAAAPMvnUv+Al5e7n/Pz89WwYUPZ7XY5HI5zXi9P48b+8vHxLve4C8nL\ns1/2n/WkoCC7goMDyz+wlqhLn6UyWAc31sKNtXBjLdxYCzfWwo21cKvJa3HJMXz99dcrLS1NnTp1\n0vr169WlSxe1a9dOSUlJKioqUmFhoTIyMhQREVHue+XlFVzW0Kfl5jokWR/EubkO5eTUjTPhwcGB\ndeazVAbr4MZauLEWbqyFG2vhxlq4sRZuNWUtLhTklxzDEydO1NSpU1VcXKzw8HDFxMTIZrMpLi5O\ngwcPltPpVHx8vPz8/Co9NAAAAFCVKhTDLVq00PLlyyVJISEhSk5OPueY2NhYxcbGenY6AAAAoArx\n0A0AAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAA\nGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYA\nAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsXys\nHgDVr7S0VFlZGef9Wl6eXbm5jrNeCwkJk7e3d3WMBgAAUK2IYQNlZWWo64upUqPm5R985IA2je6t\n8PCIqh8MAACgmhHDpmrUXGp6tdVTAAAAWIoYxsWVlSk7e3eFD+eSCgAAUJsQw3XExa4D/rVLiVsd\nPagBV6RKQY3KPzbziDZljeeSCgAAUGsQw3VEVlaGunbNkRRagaNzpTF+FX/z0EZS26YVOza34m8L\nAABgNWK4TgmV1LYCx2VKOlTFswAAANR87DMMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABj\nEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAA\nMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGP5WD0A4GmlpaXK\nysqo8PEhIWHy9vauwokAAEBNRQyjzsnKylDXrjmSQitwdKY2bZLCwyOqeqw6hW84AAB1BTGMOipU\nUtsKHuuoykFqjQsFbl6eXbm5Z69RdvZuDbjifSm0UflvnHlEm7LG8w0HAKBGIoYBSPrPGfUXU6VG\nzcs/OHu7NLeR1LZpxd48t3KzAQBQVYhhAG6NmktNry7/uLwDkvZU+TgAAFQ1dpMAAACAsYhhAAAA\nGIsYBgAAgLGIYQAAABiLGAYAAICx2E0CACqBB5AAQO1GDANAJWRlZajr0SQeQAIAtRQxDMOVKjt7\n9zmvnu+paxJn9XABoTyABABqK2IYhsvWgNW7pEYVeCTzkQPaNLo3Z/UMcKmPplZQdU0GAPA0Yhio\n6FPXYIxLfjR1h6qfCQBQNYhhoKLKys57ScWF1IRLKi7l5q5L+WxG4NHUAGAEYhioqKMHNeCKVCmo\n9twolZWVoa5dcySFVuDoXGmMX1WPBABAjUIMA5eiVt4oFSqpbQWOy5R0qIpnAQCgZuGhGwAAADAW\nMQwAAABjEcMAAAAwFtcMAzACO2u48QhpAHAjhgEYgZ013C5tLTK1aZMqtDMKkQ2gNiKGARiEnTXc\nKroWklSBJzTqP5F9NOnUrivlqSHbDwIAMQwA8Jxatv0gZ7MBEMMAAGNV1SUjAGoPYhgAcBGl572h\nMC/Prtzcsy+fyM7eLQVV11ye5PlLRgDUHsQwAOAisjVg9S6pUQUiMHu71KHqJ7JOxb8xkOr2JRUX\nurzExLVA7UcMAwAurlFzqenV5R+Xd0DSniofxzqX8I3BkQPaNLp3rbqk4lK3Hzy1Fs3LP7gWrgUu\nT239JokYBgCgoir6jUFZ2SXtV10TouDStx+sm2txqTdVStbPXFUudS1q6zdJxDAAAJ529KAGXJEq\nBdW2beaqYPvBWrYWl/ZNgVSXb6y89LWond8keTSGnU6nZsyYofT0dPn5+enpp5/W1VdXYEEAAKhr\natk2c1Wq1q3FpdxUKdXtGysvZS1q5zdJHo3h1NRUFRUVafny5dq2bZtmzZqlRYsWefKvAAAAQF1Q\nQ75J8mgMb9myRT169JAk3Xjjjfr+++89+fYAAAA1TMV3GSktLZVkk7e3V4Xeua5ei1zTeDSGHQ6H\nAgMD3W/u46OysjJ5eVXs/+mXJ7OCx+2VjvxSsUOP50iZRyr41x+RrqjgCFWOtXBjLdxYCzfWwo21\ncGMt3FiLUyq6DpK0UQOSf5ACd5Z/6IGfpIGHpRaB5R+777g2KbEGXIt8KWtRO/93YXM6nU5Pvdns\n2bPVoUMHxcTESJKio6O1bt06T709AAAA4FEePWXbsWNHffHFF5KkrVu3qm3bS7n4HAAAAKheHj0z\nfOZuEpI0a9YshYZWdDsOAAAAoHp5NIYBAACA2qQq72wDAAAAajRiGAAAAMYihgEAAGAsYhgAAADG\nIoYBAMBlKyoq0o8//ihJSk1NVXFxscUTAZfGo0+gq822bt2qDh06WD2GJYqKiuTn52f1GABquK1b\nt+r99993xc6hQ4f02muvWTxV9UtPT9c111xzzuurVq1S3759LZjIWo899ph69eql6667TpmZmfr4\n4481f/58q8eyTFZWlnbv3q1rrrlGv/nNb2Sz2aweyRJr167VW2+9pZKSEjmdTh05ckSrV6+2eqzz\nMjqGi4qKtHr1ar311lsqKirSRx99ZPVIlrj33nvVpUsXxcbGGv+glO3bt2vNmjUqLCx0vTZjxgzr\nBrLI7t279cknn5wVPTNnzrR4KmscOHBAH3300Vn/mxgzZoyFE1lnxowZGj58uD799FO1bdtWRUVF\nVo9kiXHjxunVV19Vq1atJJ36b0liYqK+++47I2P44MGDuvfeeyVJI0aMUFxcnMUTWWfp0qX67LPP\ndPToUd1zzz3Kzs7WtGnTrB7LEgsWLNDMmTO1fPly3Xzzzdq4caPVI12QkZdJ7N27V88884xuv/12\nLVy4UAkJCcaGsHTqbEb37t31wgsvKC4uTu+8847y8/OtHssSEydO1DXXXKPu3bu7/s9EEyZMkCR9\n88032rt3r44cqeDz4+ugRx99VA6HQ02bNnX9n6kaN26sPn36yG63a+zYsTp48KDVI1ni6aef1iOP\nPKKDBw8qMzNTsbGxatCggd59912rR7OEzWZTZmamJCk7O1tlZWUWT2SdNWvW6PXXX1dgYKAeeOAB\nbdu2zeqRLNOsWTPddNNNkqR+/frV6H8vjDszPHLkSDkcDvXt21cfffSR/vSnP6ljx45Wj2UpLy8v\n9ezZU5L07rvvKjk5We+995769OmjIUOGWDxd9WrdurX69etn9RiW8/f318MPP6ysrCzNmjVLgwcP\ntnokywQEBGj8+PFWj1EjeHl5adeuXTpx4oQyMjJ09OhRq0eyRFRUlKZOnaoHH3xQRUVFmjJliqKj\no60eyzKTJ0/W+PHjdfjwYTVr1kyJiYlWj2QZp9Mpm83mujTC5EsQfX19lZaWppKSEn355ZfKy8uz\neqQLMi6GJcnb21snT55UWVmZsdfynGnOnDlau3atOnfurBEjRqh9+/YqKytTv379jIvhO+64Q+PH\nj1d4eLjrNRN/JG6z2ZSTk6P8/HwVFBSooKDA6pEsExERoTVr1ui6665z/Xth6mPmJ02apF27diku\nLk6PPfaY60fjJrr55puVkJCgBQsWKDIy0upxLHXjjTfqgw8+sHqMGqFPnz66//77tX//fo0YMUK9\ne/e2eiTLJCYmKiMjQ6NGjdJzzz2nUaNGWT3SBRn5OOYDBw7ovffe0+rVq1VQUKCnn35a3bt3l5eX\nkVeNaMWKFbrrrrsUEBBw1ut79+5Vy5YtLZrKGv3799ftt9+uhg0bul4bOHCghRNZIy0tTbt27dJv\nfvMbTZ06VX379tXEiROtHssSv77+0Waz6c0337RoGmuUlJTIx8fnvNcIm3jma8CAAa5vjHbv3q0G\nDRooODhYNptNy5cvt3i66jNu3DgtXLjwvJeTffXVVxZMZL2MjAw5nU799NNPCg0N1bXXXmv1SNXu\n559/1pVXXum6dOZMNfVEgpExfJrT6dSXX36pd999V9u3b9e6deusHskS+/fvP+emMRPPhkrS8OHD\ntXjxYqvHQA1m4u4rEyZM0Pz583XrrbfKZrPp9H82bDab1q5da/F01W/lypWuX+/bt0/169fXHXfc\nIS8vL7Vo0cLCyWC1QYMGadmyZVaPYalZs2Zp8uTJiouLO+ffi5p6IsHoGJakXbt2ydfXV4GBgWrS\npInV41jivvvuU9euXdW8eXPXayaeDZWkxx9/XA0aNND111/vOvMzYMAAi6eqPqdj5zQfHx+VlJSo\nXr16+vvf/27hZNZZvny5Xn/9ddf2QL6+vvr000+tHgsW+vW2YQUFBfr6668VFxen/v37WzRV9YuP\nj7/gpYambq02bNgwhYeHKzQ01PXTZpP+G3KmxYsXa/jw4VaPUSHGXTO8YcMGTZkyRZ999pneffdd\nvfbaawoKClJsbKxiY2OtHs8S3CDk1rp1a0nS4cOHLZ7EGp988omcTqcSExM1cOBAtW/fXjt27NDb\nb79t9WiWeeutt5ScnKyXXnpJMTExWrJkidUjVbszLwv4NZMuCzjt9G4rZyosLDQuhk09aXIxp3dP\n+OWXXyyexHrr16/X0KFD5e3tbfUo5TIuhl988UW988478vX11auvvqrXX39dzZs3V1xcnLExzA1C\nbqbvJHH6x/979uxR+/btJUnXX3/9ea/9MkWzZs3UrFkz5efn6+abb9YLL7xg9UjV7tlnn7V6hBqv\nXr168vX1tXqMatW5c2dJUl5enl5++WVlZWUpIiJCDz30kMWTWWfMmDFat26ddu3apdDQUKNvoMvL\ny1OPHj3UsmVL1w4bNfWbZ+Ni2MfHR8HBwdqzZ498fX1dZwJNvXlOkn788UfXozSlmn1dT1UbP368\nbDabysrKtHfvXrVu3drI678CAwO1YMECtW/fXt9++62Cg4OtHskygYGBSk1Ndf1DbuKey6evg+Vh\nLBeWk5OjEydOWD2GJSZOnKjo6Gjdc889+vrrrzVx4kQtWrTI6rEsMX/+fO3evVsdO3bUBx98oC1b\nthh78/Hzzz9/1jeINXkrRuNi2GazqaSkRP/85z9dd8Dm5+fr5MmTFk9mneTk5LN+b+pTpSQpJSXF\n9etjx45p6tSpFk5jnXnz5mn58uVat26d2rRpo7Fjx1o9kmWeeuop7dmzR/Hx8Xr99deVkJBg9UiW\nmTBhgm677TZ98803atasmbFb7v36WtnCwkL9+OOPmjx5soVTWaewsNC1F/m1115r9DX1aWlprrOf\nf/zjH3XfffdZPFH1y8nJkcPh0MSJEzVnzhw5nU6VlZVp2rRpNfbBNMbF8P/8z//ozjvvVElJiZYs\nWaKffvpJjz/+uNGPj+QGofMLDAzUnj17rB7DEn5+frrpppvUrl07OZ1O/eMf/1CfPn2sHssSPj4+\n+te//qXMzExFREQY/ZAeHsZyyq+vla1fv77CwsJkt9stmsgapy+faty4sT7++GNFRUVp+/btxm3J\neaaSkhKVlZXJy8vL9QAO02zbtk1LlixRZmampk2bJqfTKS8vrxr9RFfjYtjpdGrIkCHy8/PTli1b\nZLPZ9NRTT6ldu3ZWj2YZbhByO32jkNPpVG5urrp27Wr1SJYYM2aMiouLdejQIZWWlqpZs2bGxnB8\nfLzCwsLUo0cPffPNN5o8ebLmzZtn9ViW4GEsp5y+VtZ006ZNc/367bffdt1oa2IAnnbnnXdq0KBB\nuvHGG7V9+3bdeeedVo9U7Xr37q3evXvriy++UK9evawep0KMi+GMjIyzfl9QUKC//e1vxt0FfCZu\nEHI780ahevXqqWnTphZOY528vDylpKRoypQpmjp1qoYOHWr1SJY5cuSIHnvsMUmn/pE39WyodOqb\npNTUVPXt21e9e/dW3759rR4JFvr1JXaQHnzwQXXv3l2ZmZmKjY1VRESE1SNZxtfXV+vXr5fT6dST\nTz6pRx99VL///e+tHuu8jIthtsQ5FzcISe+8845iY2O1fPnyc85qxMfHWzSVderXry9JOnHihOrX\nr2/0mZ42bdpoy5YtioyMVHp6uq666ioVFxfL6XQa9/CN7du3a9iwYZKk//7v/7Z4GtQUv96fPDAw\n0NjHM2dmZiopKUmZmZlq27atJk6caOyDWJKSkjR//nwlJiZq2bJl+tOf/kQM12QmbolzpqeeekrZ\n2dmuG4RMvGnsyiuvlCSFhYVZPEnNcPvtt+uFF17Qtddeq/vuu0/+/v5Wj2SZLVu26KuvvpKvr69r\nF4U77rjDyKevffHFF3rggQdqxb6hqD6ffPKJpFOXIX7//feu35to4sSJGj16tDp27KgtW7Zo0qRJ\nxp5Br1+/vpo0aeLaxasmn1QhhmX2ljjSqX/ADhw4oKysLLVv397IB0706NFD0rnXuvn4+Ojrr79W\nVFSUFWNZ5v7773f9ulevXgoJCbFuGIutWbPG6hFqjNq0byiqz5k/IYmMjDR6X+oGDRq4rpONjo7W\n66+/bvFE1gkICNDw4cM1YMAAvfXWWwoKCrJ6pAsyLobZEudcDz74oMLDw9WwYUNJp4LQxIv+pVPh\nc/LkSXXo0EHbt29XYWGhvL299dvf/lZPPPGE1eNVmx9//FEpKSkqLCx0vTZr1iwLJ7LO2rVr9fbb\nb7sujThy5IhWr15t9ViWqE37hqL6zJ8/3/Xf1UOHDhm9b3/z5s21aNEidenSRT/88IP8/Pz01Vdf\nSVKN3k2hKixcuFDZ2dlq06aNfvrppxr9YDPjYpgtcc4VGBio2bNnWz1GjXB6yz0vLy+VlZVpxIgR\neu2114x77OikSZM0ZMgQ1+UjJluwYIFmzpyp5cuX6+abb9aGDRusHqna1cZ9Q1F9zry87Nprr3X9\npM1ENptNe/bscW3L2bRpU9dPl0yL4VdeeeWc18aMGWPBJOUzLobZEudc3bt317Jly9SmTRvXa506\ndbJwIuscOXJEJSUl8vPzU0lJievMl2kPImnatGmN/i6+OjVr1kw33XSTli9frn79+mnlypVWj1Tt\nauO+oag+MTExSklJce3FffoGXBP9+idohw4dUrNmzSyaxlqnd2NyOp3asWOHysrKLJ7owoyLYZzr\n66+/VlFRkdLS0iSd+s7W1BgePHiwfv/73ysiIkIZGRkaPny4/vrXvxp3pqNFixZ65ZVXdN1117l+\n/Glq+Pj6+iotLU0lJSX68ssvlZeXZ/VI1a427huK6jNhwgT24v6P5557TsuWLVNxcbFOnjypkJAQ\nY+87+PVPVIcPH27RJOUjhqGCggK98cYbVo9RI8TGxqp3797Kzs5Wq1at1LhxY5WWlhp393xxcbEy\nMzNdT5iSzIvh0599xIgR2rlzp0aMGKHZs2frkUcesXgy69SmfUNRfdiL2+3zzz/X+vXr9Ze//EVD\nhw5VYmKi1SNZ5sz/fhw6dEgHDhywcJqLI4ahiIgIrVmz5qyzgKGhoRZPVb1+fWPlmebPn1/N01jv\nfD/qM82ZT9eSTm0flZubq3//+98WTWS92rRvKKoPe3G7BQcHy8/PT/n5+WrdurVrO0YTTZs2zfXf\nVW9v7xr9kB5iGNq5c6d27tzp+r3NZtObb75p4UTVz7Qb5MrDj/rO/3StsrIy3XfffRo7dqwFE1mv\nNu0biurDXtxuV155pd599101aNBA8+fP17Fjx6weyTLJycnavn27li5dqg0bNtTok2zEMNSrV68a\nfS1PdTh9Y6XD4dCrr76qQ4cO6ZZbbtE111xj8WTW4Ed95yotLdWWLVvkdDqtHsUytWnfUFSf098o\n5+XlqVGjRkZ/kzRz5kz9/PPPiomJ0cqVK43cc7moqEhr1qzR22+/LV9fXzkcDq1du7ZG31hJDEPr\n16/X0KFDjbsu9nyeeOIJ9ezZU2lpaWratKmmTJmipUuXWj1WteNHfecqLCzU0qVLjXxC42m1ad9Q\nVJ+0tDQlJiaqtLRUMTExuuqqq4z938aiRYvO+n1qaqq+//573XnnncY86fbWW29Vnz59NHfuXIWE\nhGj48OE1OoQlYhjiqVJnOnLkiPr3768PP/xQHTt2rNFbwVSlM3/UN2/ePKN/1Heav7+/Fi5caPUY\nlqpN+4ai+ixYsEBLly7V2LFjNXLkSA0aNMjYGE5PT1e9evUUFRWlbdu26cCBAwoODtZXX32luXPn\nWj1etfjjH/+o1atXa9++ferfv3+t+GkaMQz99a9/tXqEGuX0DVI///yzkWfLU1JSNG3aNOXk5Cgk\nJEQ7d+408kd9OFdt2jcU1cfLy8t1eUS9evUUEBBg9UiWOXbsmJYsWSLp1L0oDz74oObOnatBgwZZ\nPFn1GTFihEaMGKHNmzfrnXfe0ffff6+5c+eqb9++atu2rdXjnRcxDPn4+Gju3LnKzc1VTEyMrrnm\nGrVo0cLqsSyRkJCgJ554Qj/++KPGjRunGTNmWD1StXr++ee1a9cu3X333WrRooWcTqfeeOMNHT16\nVKNHj7Z6PFisNu0biurTqlUrzZ8/X0eOHNErr7yiq666yuqRLHP8+HHl5uYqKChIeXl5On78uOtG\nZNN07txZnTt31rFjx7Rq1Sr9+c9/1gcffGD1WOdlc9aG89eoUg899JCGDh2qRYsWKTExUZMmTdKK\nFSusHqta7dy5UwsWLFCTJk105513avz48bLZbJo8ebLuueceq8erNrGxsVqxYsVZN8AUFxdr4MCB\neu+99yycDDXBr/cNnTlzpnG7jOBcJSUleuedd/TTTz8pLCxMAwcONOb62F/75z//qaefflp2u10F\nBQVKSEjQzp07FRAQoPvvv9/q8XABnBmGTp48qa5du+qll15SWFiY6tWrZ/VI1W7GjBkaO3asjh49\nqjFjxmjlypUKCgrS8OHDjYphf3//c+4E9/X1NfrHnnCrTfuGonr88MMPCgwMdF0GcPLkSS1YsECP\nP/64xZNZ45ZbblGvXr2Um5urJk2ayGazqWfPnlaPhXIQw1C9evX05ZdfqqysTFu3bjVuk3TpVPB1\n69ZNkvTmm28qJCRE0qk4NEn9+vW1Z88eXX311a7X9uzZY/RWSXCrTfuGouo9+eST2rFjhxwOh0aN\nGqXWrVtr3Lhxrn9LTbRhwwa98cYbKiwsdL1m2r79/9/e/cdEXT9wHH/d9w5DQUcahD8YM4ZjILbV\nGtIfRUomtojDYfAHw02dG6lbEAPKH8uoprZsS0NnjaTNCS1trLYgbGS6NmUO2folOPXuRCDcRJxX\nodAffrmh+E0/11feXp/nY2P78OG4ve52g9e97/15v0MRZRh68803VVFRoR9//FHbt2/XW2+9ZTrS\nuBtd9ka/GbDbBUKvvvqqiouLlZ6erri4OHV1denIkSPasmWL6WgwKBTXDcW9d+LECR08eFADAwNa\nvny5rl69qqqqKqWnp5uOZsw777yj1157TbGxsaajwALKsI11dnZq8+bNqq2tVXd3t+bMmaOzZ8/q\np59+0qxZs0zHG1ednZ0qLS3V8PDwTcd223o3MTFR+/bt06FDh9Tb26uUlBS9/PLLioyMNB0NBoXi\nuqG490b+LkyePFlXrlzRxx9/bLv/HbeaPn26nnzySdMxYBFl2MbefffdwLyu6Ohoffrppzp37pzW\nr1+vRYsWGU43vt5///3A8egr5u24TfPkyZNtNU8adxaK64bi3hv9iVpMTIzti7AkTZs2TRs3blRy\ncnLg+XnppZcMp8KdUIZtzO/3KzU1VdKNAiRJ8fHxunbtmslYRoxsxwxgrFBcNxT3ntfr1Xvvvafh\n4WH5fL6b1iMvKSkxmMyckTcEfX19hpPACsqwjY2e4D96C0mXi5cFgLFCad1Q3Hvr1q277bEddXd3\nKzY2Vs8//7zpKAgCrcfGYmJi1N7ernnz5gXOtbe3Kzo62mAqAPe7KVOmqLCwUIWFhaajwCC32206\nwn2jpqZGlZWVgeUH+/v75XQ6FRkZyWoSIYAybGNlZWUqLi7W/PnzFR8fL6/Xqx9++IHtmQEAsCA7\nO1s5OTmqr69XS0uLNm3apClTprBzZ4hgBzqb+/333/Xtt9/K5/Np+vTpWrhwoe3W1gUA4J8oKipS\nZWWlkpKStGTJEm3btk3x8fFauXKl9u/fbzoe7oCRYZsLDw/XkiVLTMcAAIQon8+nxsZG+f3+wLk1\na9YYTDT+hoaGlJSUpJ6eHvn9fqWkpEgSGxaFiP+YDgAAAEJXaWmp/H6/HnroocCX3YxceP79998H\nNh0ZHBzU1atXTcbCXWJkGAAABC08PNx2I8G3Sk9PV35+vrq7u1VdXS2Px6PNmzfzyWuIYM4wAACw\n7MyZM5KkHTt2KCMjQykpKYFpAbNnzzYZzYjTp08rMjJSDz/8sDwej3799Vc9++yzpmPhLlCGAQCA\nZf9raT2Hw8FyYggplGEAABC0P/74Q6dPn1ZycrKam5v19NNPKywszHQs4K5xAR0AAAhaWVmZfv75\nZ0k3pk5UVFQYTgRYQxkGAABB6+np0dKlSyVJq1atUm9vr+FEgDWUYQAAEDSHwxG4mM7j8WhoaMhw\nIsAa5gwDAICgnTx5Ups2bVJfX59iYmL0xhtvKDU11XQs4K5RhgEAwD8yMDCg8+fPKy4uThEREabj\nAJaw6QYAAAhaY2Ojqqurdf36dS1evFgOh0PFxcWmYwF3jTnDAAAgaDU1Naqvr1dUVJSKi4vV3Nxs\nOmLzL6QAAASKSURBVBJgCWUYAAAEzel0asKECXI4HHI4HJo4caLpSIAllGEAABC0xx9/XKWlperp\n6dHGjRu5eA4hhwvoAADAP3L48GGdOnVKjzzyiBYsWGA6DmAJI8MAACBoRUVFmjNnjlauXKkFCxZo\nxYoVpiMBllCGAQBA0C5cuKC1a9eqs7NTkvTnn38aTgRYQxkGAABBi42N1fbt21VeXq7W1la5XKza\nitDCKxYAAARteHhYs2bN0q5du7RmzRr99ttvpiMBljAyDAAAglZUVCRJio6O1kcffaSMjAyzgQCL\nWE0CAAAEpa6uTkuXLpXL5VJra6s6OjpUUFBgOhZgCSPDAADAsg8++EBHjx7V4OCgpBtzh48ePaqd\nO3caTgZYw8gwAACwLC8vT/X19XI4HIFzg4ODys/P1+eff24wGWANI8MAAMCySZMm3VSEJSksLEwR\nERGGEgHBoQwDAADLwsPD5fV6bzrn9XrHFGTgfsc0CQAAYFlHR4dKSkqUnp6uuLg4dXV16ciRI9qy\nZYuSk5NNxwPuGmUYAAAEZWBgQIcOHVJvb69mzJihjIwMRUZGmo4FWEIZBgAAgG0xZxgAAAC2RRkG\nAACAbVGGAQAAYFuUYQD4F/D5fHr99ddNxwCAkEMZBoB/gfPnz49Z8xUAcGesJgEA42jbtm1qbm5W\nWFiYli1bpqeeekobNmxQf3+/Jk2apPXr12vu3LmqrKxUWlqacnJyJElJSUn65ZdftGPHDvX09Ojs\n2bO6cOGC8vLytHr1amVnZ8vn88ntduu5557T1q1bNTw8rISEBLW2tqqmpkbx8fHy+/3KyspSU1OT\nJkyYYPjZAADzGBkGgHHy9ddfq62tTV999ZU+++wzHThwQKtXr1ZRUZEaGhpUWVmpdevWaXBwcMzv\njt7V69SpU/rkk09UX1+v3bt368qVK4ESvWHDBkmSx+NRbW2ttm7dqtzcXDU0NEiSGhsb9cwzz1CE\nAeC/KMMAME6OHz+urKwsuVwuTZw4Ufv27dOlS5eUmZkpSXr00UcVFRWlM2fO/O39pKWlyel0aurU\nqYqKitLAwMCY28yePVsRERGSJLfbrS+//FKS9MUXX8jtdv+fHxkAhC7KMACME5fLddP3Ho9nzG2G\nhoZ0/fp1SdLILLZbR4pvHdW93Wy3Bx54IHA8c+ZMzZgxQ998840uXryoefPmBfcAAOBfiDIMAOPk\niSeeUFNTk65duya/369XXnlFktTc3CxJamtrU19fnxITE/Xggw+qo6Pjpp//HafTGSjRt5Obm6uq\nqqrAHGQAwA2UYQAYJ5mZmXrsscfkdru1bNkyLV++XPv379fevXv1wgsvqKqqSjt37pTL5VJBQYGO\nHTumF198UW1tbYqJibntfY7MJU5ISNDly5dVXl5+29stWrRI/f39ys7OvmePDwBCEatJAIANfPfd\nd6qrq9OHH35oOgoA3Fdcd74JACCUvf3222ppadGePXtMRwGA+w4jwwAAALAt5gwDAADAtijDAAAA\nsC3KMAAAAGyLMgwAAADbogwDAADAtijDAAAAsK2/AFDIEusH75DAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdefefd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "award_by_country_df[:10].plot(kind='bar', figsize=(12,10), colormap='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1346ae48>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aMmSI/vznP2v69Onur5WUlKh58+YKDw9XcXHxcY/XJSQkRHb7mV/DFxoaesZ/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\nYFv/AJSEhhgCJZUpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf816c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_colors = ['gold', 'silver', 'saddlebrown']\n",
    "award_by_country_df[:10].plot(kind='bar', figsize=(12,10), color=plot_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1996     99\n",
       "1998    118\n",
       "2000    118\n",
       "2002    141\n",
       "2004    157\n",
       "2006    159\n",
       "2008    190\n",
       "2010    203\n",
       "2012    208\n",
       "2014    202\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_award_year = WBC[WBC.country==\"USA\"].groupby('year').size()\n",
    "usa_award_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe37c0f0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QQ/PII49k0qRJGT9+fG644YZ0d3dnw4YN6ejoyNixY7e5VmfnW9s19Ls2bdqUNWs6erVG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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf196e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_award_year.plot(kind='bar', figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IBM data science workbench, \"Tutorial \\#4 - Analyze Data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aafXq1ZKk5ORkNW/evNjyadOmKS8vTwsWLHBuor/QGAAAAFQ+Fb5GNCIiQklJ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\nAez/ybrkkb+6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf3defd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.pylabtools import figsize\n",
    "plt.rcParams['axes.facecolor'] = '#F5F5F5'\n",
    "\n",
    "figsize(11, 6)\n",
    "\n",
    "import scipy.stats as stats\n",
    "a = np.arange(30)\n",
    "poi = stats.poisson\n",
    "lambdas = [2.5, 14.5]\n",
    "colors = [\"#6B6A96\", \"#943A45\"]\n",
    "\n",
    "# plot a Poisson distribution for each lambda\n",
    "for lambda_, color in zip(lambdas, colors):\n",
    "    plt.bar(a, poi.pmf(a, lambda_), color=color,\n",
    "            label=\"$\\lambda = %.1f$\" % lambda_, alpha=0.50,\n",
    "            edgecolor=color, lw=\"1.5\")\n",
    "\n",
    "plt.title(\"Probability mass function of a Poisson random variable\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.ylabel(\"probability of $n$\")\n",
    "plt.xticks(a + 0.4, a)\n",
    "plt.xlabel(\"$n$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# conda install -c https://conda.binstar.org/pymc pymc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pymc as pm\n",
    "import numpy as np\n",
    "\n",
    "def make_model(data):\n",
    "    '''\n",
    "    Create a model for use in predicting lambda_1, lambda_2, and tau,\n",
    "    which combine to create our posterior probability distributions\n",
    "    (one before tau, and one after tau).\n",
    "    '''\n",
    "    # prior lambda estimates\n",
    "    alpha = 1.0 / data.mean() \n",
    "    lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n",
    "    lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n",
    "    # prior tau estimates\n",
    "    tau = pm.DiscreteUniform(\"tau\", lower=0, upper=len(data))\n",
    "\n",
    "    @pm.deterministic\n",
    "    def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):\n",
    "        '''Create random value of lambda, based on prior lambda.'''\n",
    "        out = np.zeros(len(data))\n",
    "        out[:tau] = lambda_1  # lambda before tau\n",
    "        out[tau:] = lambda_2  # lambda after (and including) tau\n",
    "        return out\n",
    "    \n",
    "    # lambda is Poisson distributed\n",
    "    observation = pm.Poisson(\"obs\", lambda_, value=data.values, observed=True)\n",
    "\n",
    "    model = pm.Model([observation, lambda_1, lambda_2, tau])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = make_model(usa_award_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 50000 of 50000 complete in 10.7 sec"
     ]
    }
   ],
   "source": [
    "mcmc = pm.MCMC(model)\n",
    "mcmc.sample(50000, 10000, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lambda_1_samples = mcmc.trace('lambda_1')[:]\n",
    "lambda_2_samples = mcmc.trace('lambda_2')[:]\n",
    "tau_samples = mcmc.trace('tau')[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r1aqkpCSlpKTI7Xbrqaee0u7du1VVVaUxY8aoX79+gSwTAADAFAIa3PLy8uRy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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe198fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# our data\n",
    "data = usa_award_year\n",
    "n_data = len(data)\n",
    "years = data.index.values\n",
    "\n",
    "figsize(10, 12)\n",
    "xticks = np.arange(80, 200, 10)\n",
    "yticks = np.arange(0.0, 1.1, 0.1)\n",
    "\n",
    "# lambda_1\n",
    "ax = plt.subplot(311)\n",
    "ax.set_autoscaley_on(False)\n",
    "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.65,\n",
    "         label=\"posterior of $\\lambda_1$\", color=\"#6B6A96\", normed=True)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xticks(xticks)\n",
    "# plt.yticks(yticks)\n",
    "plt.ylim([0., .06])\n",
    "plt.xlabel(\"$\\lambda_1$ value\")\n",
    "plt.ylabel(\"probability\");\n",
    "plt.title(r\"\"\"Posterior distributions of $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n",
    "plt.grid()\n",
    "\n",
    "# lambda_2\n",
    "ax = plt.subplot(312)\n",
    "ax.set_autoscaley_on(False)\n",
    "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.65,\n",
    "         label=\"posterior of $\\lambda_2$\", color=\"#943A45\", normed=True)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xticks(xticks)\n",
    "# plt.xlim([xticks.min(), xticks.max()])\n",
    "plt.xlim([150, 250])\n",
    "# plt.yticks(yticks)\n",
    "plt.ylim([0., .06])\n",
    "plt.xlabel(\"$\\lambda_2$ value\")\n",
    "plt.ylabel(\"probability\");\n",
    "plt.grid()\n",
    "\n",
    "# tau\n",
    "ax = plt.subplot(313)\n",
    "w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n",
    "plt.hist(tau_samples, bins=n_data, alpha=1,\n",
    "         label=r\"posterior of $\\tau$\",\n",
    "         color=\"#799631\", weights=w, rwidth=2.)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xticks(np.arange(n_data), years, rotation='vertical')\n",
    "plt.yticks(yticks)\n",
    "plt.xlabel(r\"$\\tau$ (year of Cup)\")\n",
    "plt.ylabel(\"probability\");\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:6: FutureWarning: the 'cols' keyword is deprecated, use 'subset' instead\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xf63e9b0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FjnNcTi/C0d6ORx5oRUyPFnfc9uVekVqYwfvxK8nU5dChUTDTuo7HzaFqG3cM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1311e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot medals awarded per games\n",
    "ax = WBC.groupby('year').size()\\\n",
    ".plot(figsize=(10,5), legend=True, label='Medals')\n",
    "ax.set_ylabel('award')\n",
    "\n",
    "# plot events per games\n",
    "events_year = WBC.drop_duplicates(cols=['year', 'category_name'])\\\n",
    ".groupby('year').size()\n",
    "events_year.plot(secondary_y=True, legend=True, label='Events', mark_right=False)\n",
    "ax.right_ax.set_ylabel('Events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## category entries heatmap\n",
    "- https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}