{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zoeolson1/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dob</th>\n",
       "      <th>name</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>wins</th>\n",
       "      <th>fouls</th>\n",
       "      <th>pl_goals</th>\n",
       "      <th>losses</th>\n",
       "      <th>...</th>\n",
       "      <th>last_man_tackle</th>\n",
       "      <th>clearance_off_line</th>\n",
       "      <th>saves</th>\n",
       "      <th>punches</th>\n",
       "      <th>goal_kicks</th>\n",
       "      <th>penalty_save</th>\n",
       "      <th>keeper_throws</th>\n",
       "      <th>good_high_claim</th>\n",
       "      <th>total_keeper_sweeper</th>\n",
       "      <th>stand_catch_dive_catch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3452</td>\n",
       "      <td>11/26/90</td>\n",
       "      <td>Danny Welbeck</td>\n",
       "      <td>England</td>\n",
       "      <td>185cm</td>\n",
       "      <td>73kg</td>\n",
       "      <td>87</td>\n",
       "      <td>89</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5001</td>\n",
       "      <td>12/1/94</td>\n",
       "      <td>Emre Can</td>\n",
       "      <td>Germany</td>\n",
       "      <td>184cm</td>\n",
       "      <td>82kg</td>\n",
       "      <td>29</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id       dob           name nationality height weight  wins  fouls  \\\n",
       "0  3452  11/26/90  Danny Welbeck     England  185cm   73kg    87     89   \n",
       "1  5001   12/1/94       Emre Can     Germany  184cm   82kg    29     68   \n",
       "\n",
       "   pl_goals  losses           ...            last_man_tackle  \\\n",
       "0        34      35           ...                        NaN   \n",
       "1         2      16           ...                        NaN   \n",
       "\n",
       "   clearance_off_line  saves  punches  goal_kicks  penalty_save  \\\n",
       "0                 NaN    NaN      NaN         NaN           NaN   \n",
       "1                 NaN    NaN      NaN         NaN           NaN   \n",
       "\n",
       "   keeper_throws  good_high_claim total_keeper_sweeper  stand_catch_dive_catch  \n",
       "0            NaN              NaN                  NaN                     NaN  \n",
       "1            NaN              NaN                  NaN                     NaN  \n",
       "\n",
       "[2 rows x 56 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "player_data = pd.read_csv('/Users/zoeolson1/player_data.csv')\n",
    "player_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to look at how players score goals, and weather goals are more frequently made with the left foot, right foot, or head. First, we will clean our data by replacing all of the NaN values with '0', because this is how they appear in the original CSV. Below we have a list of all of the head, left foot, and right foot goals of each player. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>att_hd_goal</th>\n",
       "      <th>att_lf_goal</th>\n",
       "      <th>att_rf_goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   att_hd_goal  att_lf_goal  att_rf_goal\n",
       "0          9.0          7.0         18.0\n",
       "1          0.0          0.0          2.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(player_data, columns = ['att_hd_goal', 'att_lf_goal', 'att_rf_goal'])\n",
    "df2 = df.fillna(0)\n",
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will format the dataframe to make it suitable for a pie chart. We will do so by aggregating each column to get the total head, right foot, and left foot goals. Then creating leables for each of these categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>header</th>\n",
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       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>655.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>2067.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       header  left-foot  right-foot\n",
       "0         9.0        7.0        18.0\n",
       "1         0.0        0.0         2.0\n",
       "2         3.0        6.0         1.0\n",
       "3         0.0        0.0         0.0\n",
       "4         0.0        0.0         0.0\n",
       "5         0.0        0.0         0.0\n",
       "6         1.0        2.0         5.0\n",
       "7         0.0        0.0         0.0\n",
       "8         0.0        0.0         0.0\n",
       "9         1.0        5.0         1.0\n",
       "10        0.0        0.0         0.0\n",
       "11        0.0        0.0         0.0\n",
       "12        0.0        0.0         0.0\n",
       "13        0.0        0.0         0.0\n",
       "14        0.0        0.0         0.0\n",
       "15        3.0        0.0         1.0\n",
       "16        1.0        6.0        10.0\n",
       "17        3.0        2.0        13.0\n",
       "18        7.0        2.0        13.0\n",
       "19        0.0       19.0         5.0\n",
       "20        4.0        4.0        20.0\n",
       "21        0.0        1.0         0.0\n",
       "22        1.0        0.0         0.0\n",
       "23        0.0        0.0         0.0\n",
       "24        1.0        0.0         3.0\n",
       "25        1.0        0.0         6.0\n",
       "26        0.0        0.0         0.0\n",
       "27        2.0        3.0         1.0\n",
       "28        0.0        0.0         0.0\n",
       "29        0.0        1.0         0.0\n",
       "...       ...        ...         ...\n",
       "538       0.0        0.0         0.0\n",
       "539       3.0        0.0        13.0\n",
       "540       0.0        0.0         0.0\n",
       "541       1.0       18.0        81.0\n",
       "542       0.0        0.0         0.0\n",
       "543       2.0       12.0         3.0\n",
       "544       0.0        0.0         0.0\n",
       "545       0.0        0.0         0.0\n",
       "546       0.0        2.0        14.0\n",
       "547       0.0        2.0         2.0\n",
       "548       0.0        0.0         0.0\n",
       "549       0.0        0.0         0.0\n",
       "550       0.0        0.0         0.0\n",
       "551       0.0        0.0         0.0\n",
       "552       1.0        0.0         1.0\n",
       "553       0.0        3.0         1.0\n",
       "554       0.0        0.0         1.0\n",
       "555       0.0        0.0         0.0\n",
       "556       0.0        0.0         0.0\n",
       "557       0.0        0.0         0.0\n",
       "558       0.0        0.0         1.0\n",
       "559       0.0        0.0         0.0\n",
       "560       2.0        2.0        17.0\n",
       "561       0.0        0.0         0.0\n",
       "562       3.0        1.0         1.0\n",
       "563       0.0        0.0         0.0\n",
       "564       0.0        1.0         1.0\n",
       "565       3.0        5.0         5.0\n",
       "566       0.0        0.0         0.0\n",
       "Total   655.0     1048.0      2067.0\n",
       "\n",
       "[568 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns = ['header', 'left-foot', 'right-foot']\n",
    "df2.loc['Total']= df2.sum()\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, we have a total row. Now lets remove all of the data except for the column headers and totals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "goal_types = df2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>558</th>\n",
       "      <th>559</th>\n",
       "      <th>560</th>\n",
       "      <th>561</th>\n",
       "      <th>562</th>\n",
       "      <th>563</th>\n",
       "      <th>564</th>\n",
       "      <th>565</th>\n",
       "      <th>566</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>header</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left-foot</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right-foot</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2067.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 568 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0    1    2    3    4    5    6    7    8    9   ...    558  \\\n",
       "header       9.0  0.0  3.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0   ...    0.0   \n",
       "left-foot    7.0  0.0  6.0  0.0  0.0  0.0  2.0  0.0  0.0  5.0   ...    0.0   \n",
       "right-foot  18.0  2.0  1.0  0.0  0.0  0.0  5.0  0.0  0.0  1.0   ...    1.0   \n",
       "\n",
       "            559   560  561  562  563  564  565  566   Total  \n",
       "header      0.0   2.0  0.0  3.0  0.0  0.0  3.0  0.0   655.0  \n",
       "left-foot   0.0   2.0  0.0  1.0  0.0  1.0  5.0  0.0  1048.0  \n",
       "right-foot  0.0  17.0  0.0  1.0  0.0  1.0  5.0  0.0  2067.0  \n",
       "\n",
       "[3 rows x 568 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goal_types.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>header</th>\n",
       "      <td>655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left-foot</th>\n",
       "      <td>1048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right-foot</th>\n",
       "      <td>2067.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total\n",
       "header       655.0\n",
       "left-foot   1048.0\n",
       "right-foot  2067.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goal_types = pd.DataFrame(goal_types['Total'])\n",
    "\n",
    "goal_types.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now our data frame is ready for a pie chart display!  \n",
    "For our pie chart, we will choose colors using iWantHue ( http://tools.medialab.sciences-po.fr/iwanthue/ ).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iZJKepCzBrcA1hw/Cu/MovKKcsFMZk75y8yJcedcAXXRzH/xAh/u+XpBUPexcxpQHK04m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\nJxEREZEaUnESERERqaH/B0UQuzJNWlPfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133eed50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"#e49300\", \"#0263f3\", \"#dbff62\", \"#ff004e\",\"#a14400\"]\n",
    "\n",
    "plt.pie(goal_types['Total'], labels = goal_types.index, shadow= False, colors = colors, explode = (0, 0, 0), startangle = 90, autopct='%1.1f%%',)\n",
    "\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay! We have our pie chart. Now lets look at what other factors go into goal scoring.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>nationality</th>\n",
       "      <th>id</th>\n",
       "      <th>pl_goals</th>\n",
       "      <th>appearances</th>\n",
       "      <th>blocked_scoring_att</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>England</td>\n",
       "      <td>3452</td>\n",
       "      <td>34</td>\n",
       "      <td>154</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>5001</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>4805</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nationality    id  pl_goals  appearances  blocked_scoring_att\n",
       "0     England  3452        34          154                 84.0\n",
       "1     Germany  5001         2           60                 13.0\n",
       "2       Spain  4805        10           39                 13.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ng = pd.DataFrame(player_data, columns = ['nationality', 'id', 'pl_goals', 'appearances', 'blocked_scoring_att'])\n",
    "nat_goals = ng.fillna(0)\n",
    "nat_goals.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>nationality</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nationality  id\n",
       "0           0   1\n",
       "1     Algeria   4\n",
       "2   Argentina  16\n",
       "3     Armenia   1\n",
       "4   Australia   5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nat_goals2 = (nat_goals.groupby(['nationality'],as_index=False).id.count())\n",
    "nat_goals2.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>nationality</th>\n",
       "      <th>pl_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nationality  pl_goals\n",
       "0           0         0\n",
       "1     Algeria        25\n",
       "2   Argentina       152\n",
       "3     Armenia         0\n",
       "4   Australia         0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nat_goals3 = (nat_goals.groupby(['nationality'],as_index=False).pl_goals.sum())\n",
    "nat_goals3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>nationality</th>\n",
       "      <th>id</th>\n",
       "      <th>pl_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>16</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Austria</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>23</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bosnia And Herzegovina</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>12</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Congo</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cote D'Ivoire</td>\n",
       "      <td>7</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Curacao</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>England</td>\n",
       "      <td>207</td>\n",
       "      <td>1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>France</td>\n",
       "      <td>30</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Germany</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Japan</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Mali</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Northern Ireland</td>\n",
       "      <td>7</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Norway</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Poland</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Romania</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Scotland</td>\n",
       "      <td>17</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>7</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Spain</td>\n",
       "      <td>41</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>United States</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Wales</td>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               nationality   id  pl_goals\n",
       "0                        0    1         0\n",
       "1                  Algeria    4        25\n",
       "2                Argentina   16       152\n",
       "3                  Armenia    1         0\n",
       "4                Australia    5         0\n",
       "5                  Austria    7        23\n",
       "6                  Belgium   23       289\n",
       "7   Bosnia And Herzegovina    2         0\n",
       "8                   Brazil   12        96\n",
       "9                 Cameroon    2         0\n",
       "10                  Canada    1         2\n",
       "11                   Chile    2        33\n",
       "12                Colombia    3         1\n",
       "13                   Congo    3        20\n",
       "14              Costa Rica    1         2\n",
       "15           Cote D'Ivoire    7       107\n",
       "16                 Croatia    1         3\n",
       "17                 Curacao    1         1\n",
       "18          Czech Republic    1         0\n",
       "19                 Denmark    5        23\n",
       "20                 Ecuador    4        29\n",
       "21                   Egypt    3         5\n",
       "22                 England  207      1585\n",
       "23       Equatorial Guinea    1         0\n",
       "24                 Estonia    1         0\n",
       "25                  France   30       208\n",
       "26                   Gabon    1         0\n",
       "27                  Gambia    1         1\n",
       "28                 Germany   12        44\n",
       "29                   Ghana    3        16\n",
       "..                     ...  ...       ...\n",
       "33                   Italy    6        17\n",
       "34                 Jamaica    2         7\n",
       "35                   Japan    3         7\n",
       "36                   Kenya    1         5\n",
       "37               Lithuania    1         0\n",
       "38                    Mali    1         2\n",
       "39                 Morocco    2         0\n",
       "40             Netherlands   18        45\n",
       "41             New Zealand    1         4\n",
       "42                 Nigeria    9        66\n",
       "43        Northern Ireland    7        47\n",
       "44                  Norway    4         9\n",
       "45                  Poland    6         1\n",
       "46                Portugal    5         7\n",
       "47                 Romania    2         0\n",
       "48                Scotland   17       133\n",
       "49                 Senegal    7        68\n",
       "50                  Serbia    5        47\n",
       "51                Slovakia    1         0\n",
       "52            South Africa    1        19\n",
       "53             South Korea    3        28\n",
       "54                   Spain   41       243\n",
       "55                  Sweden    5        38\n",
       "56             Switzerland    6         9\n",
       "57                 Tunisia    2         2\n",
       "58                  Turkey    1         0\n",
       "59           United States    6         2\n",
       "60                 Uruguay    5        15\n",
       "61               Venezuela    1        11\n",
       "62                   Wales   13        70\n",
       "\n",
       "[63 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_goals = pd.merge(left=nat_goals2,right=nat_goals3, left_on='nationality', right_on='nationality')\n",
    "merged_goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Now lets look at nations with over 10 players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_data = merged_goals.loc[merged_goals['id'] >= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ab1af10>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "merged_goals.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~zoe1114/0 or inside your plot.ly account where it is named 'style-bar'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~zoe1114/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "import plotly.plotly as py\n",
    "\n",
    "py.sign_in('zoe1114', 'gqr5grvyef')\n",
    "\n",
    "trace1 = go.Bar(\n",
    "    x=final_data['nationality'],\n",
    "    y=final_data['id'],\n",
    "    name='Players',\n",
    "    marker=dict(\n",
    "        color='rgb(55, 83, 109)'\n",
    "    )\n",
    ")\n",
    "trace2 = go.Bar(\n",
    "    x=final_data['nationality'],\n",
    "    y=final_data['pl_goals'],\n",
    "    name='Goals',\n",
    "    marker=dict(\n",
    "        color='rgb(26, 118, 255)'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(\n",
    "    title='# of Players vs. Goals Made',\n",
    "    xaxis=dict(\n",
    "        tickfont=dict(\n",
    "            size=14,\n",
    "            color='rgb(107, 107, 107)'\n",
    "        )\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='Count',\n",
    "        titlefont=dict(\n",
    "            size=16,\n",
    "            color='rgb(107, 107, 107)'\n",
    "        ),\n",
    "        tickfont=dict(\n",
    "            size=14,\n",
    "            color='rgb(107, 107, 107)'\n",
    "        )\n",
    "    ),\n",
    "    legend=dict(\n",
    "        x=0,\n",
    "        y=1.0,\n",
    "        bgcolor='rgba(255, 255, 255, 0)',\n",
    "        bordercolor='rgba(255, 255, 255, 0)'\n",
    "    ),\n",
    "    barmode='group',\n",
    "    bargap=0.15,\n",
    "    bargroupgap=0.1\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='style-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amount of players for analysis:  536\n"
     ]
    },
    {
     "data": {
      "image/png": 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7P2tsQ3vNQa25C6Hj0rawJzSfPeF5VIV/x6fvwavtRmff7cEoIaNIFixyEQ61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kZCPVknvY17DhaH7dc7gRCE77620yga7YJk1o1+lX2AfSYbgdLOfdemZu+oVHHLU3hO+Tsy1IbR\nVI/RtCrzWtEuftYxdsNEWKwImz3rfgB81z1M+WV3IhNxjOZu+1q+qH0fxf19hBDsZCvj2Yp12Fx3\n8XLcz3ZNX/N5MtyjgMmlKER7GfOmLAyUuDRJYFKt6JhIGo0UzWYKv5nGkJL5qRhfp6LMtnjwiozx\nfiMRYBtL36Kd5nbGjb39N/mindnzaTKClJJVRpJbwquoVXT2sXXlDCoVnZla32s8U3NknbhGsWYw\nogNZzYbJ9a390xGP9Do5r9JdlGOO1VUurPRwRmOgz2dhU/JQMMqZvuIcKxvSK5diRsLIaJjocw+S\n+n4e6WWLkJEQMhZFJmLIRDzzisfASNN81M64j/u/zn2IXpOThC4mSjdYNt0G2/Z7EZ/7DJFHbsd1\n6B9Rq8fgOetKrBtvVdiAhUC0h47UXjTJzuNHo8ReeIzAdedmGD19Nmg37qaRMe7W3MYdJMkvPiBw\n/XmYzfVdE0MHSqSQWK5oPFoxnYdjzZwbWMIhrd9zlnssRzmqUYRgnGqlVfa8xp+1a74kpIm1PXTS\nQWGc1M5KCUuDoGFwUOsCyhWNr1IRfmev4ALP+M74/oJ0nMMdfa/tV+377/DcV5iZVdfJbYtYR7Oz\n0IgzVrVyR/lUXN0m/CpFx5FldTdetWZdbQwVsbhk4aJMEnVlvUFzi0FbQBIMmSSSmWbYA52TBXDA\nvnb22i3fvfLzxog27s+G4yzvx1Pe3Wnj75XuHlS1oWJft527AxG+TPQ1iLe1RTjM46BGK03hSPr7\neZBKkvzqY5LzPkF4ylG85SguD0plLcLhRHG4EE43Zlsz8bnPoo6bhFJRjbFicWYnvROqUvYIvXRA\nqCqeUy7AbFxJ6LbLMUMBXIeciHPv3w9ozMKRMe4dsffuSH33Ff6/HYtweXAfdQbWX+2EWl2H0VxP\n8Lq/kXj/1U4DLU0TaRo5QytGcwP+8/+AsXJJpnp1x31Rx04E0yTy8G2E77m+K8QDxE3J0+EY9wWi\nLEimMZFM1jV2ddrYx21nqkUbEB/bIhTmOKrZXHdxpP8HLg8tZ33dweYWN+to9h4cdQm8EQ8ggEYj\n1UkxbDTTRKTJTN2BbN/uw+oNuCvSSMQ0OMFVy28sXgwynvcY1UJIpnH2ykNIKXklnpkoO8IrhoRH\nfGuzLJ3kvWSIPew+DrRXYBEKjUaK6nYWjF0o6FnOvFLRGIqgsWlK6htNlixN882CNJ9+keTzeSmW\nLTPIwlUYMj77Kjlq3H+OCBkml7YE827jEoJLqjxFNewAFiG4Z0wFWy5pIN7rpgyZklv8YS6s8mb/\n8hAR//ANAJy/PxnnPkcgXJ5MDFpVM+EWpYsWlvzifeJzn8Vz0gVoU6YTa8zQIvsUIRkGRnNDe0il\n57VSK2vx/fNRgrddQeS+G4m//hy+K+9Fmzi1sAEL0RVG6TWpGC2NtP7fHBRfFZX/ei7DyGkfu1Y3\nAedBx7Ub93aDLIB0GnIUI4X+fSWprz+l4obHsMzaokeIyLHvHML3XN+5QpFScnFLkLsD0R7m6ttk\nmm+TYW5tC3NddRl7u/sah7Bp8GkyzNY2D9nKcaZrdq4vm8J+Ld9xRttPvFu9AbOtXh6INnZuU28k\naTZTTNVsfJmKdBr3l+N+Zup2KhSNlJQ42q/ZeZ7xPY7xRLSFS0LL+KpmI8oVncZeIZ6QNPiwPfkp\n28+wWtVpMw0OdVZxqLMrWboknWCvlm/4smYjIDMZqFkeGecgK11NE954N8E/bw3xzXdp4omBe+KD\ngW+ES/6O2LO7LxjtY1i7wybg7jHl1JbIg65QBb/KwW1/PZogWgotdSlJff0J6sRpuI/7K2rNWBSn\nG2GzI3QLQlV7sCU6vNTk5+8hhEBpTyqmFn/fY7dmoBVjWXt8u/3r/gtOoPF3W5D44gOExYrnpPPw\nXf8Iiqec5hP2IvzQrdkrPmXf8xYWW8aw95pk08sWYfqbsW6ydSYB2/1zKYm99Hj7Ls3O/chUqjOp\n2xvxN/8HmoY+c6M++0q83y5o1r6vgCl5KZLbD41LOKsxwKtZ8jmLjQRH+H/g4JYFNGSJQQshqFG6\nvGCAzS0uFqTj1Ldv/1Sshc0sLk5xjeG1RACJpMlI8WC0mTmOagQCBcEE1cqTsZZOAy2l5LlYK+cE\nl7Cpnsll/NZWzuOxFoxuMf6Ho80k2t/H2s95R2sZj8aaMbud9aJ0nENaF+ARXX5gWBqd3+2OgWrk\ntPpNrvxniG33aOLw41r5+LMUkejqMewAhx84smUIRqxxf6GfJOqeLjtbOUpXWKQKweGe7Eu+H1MG\nPw5CgbI/pJf/hOlvxrbVjogC6YwAsVefRppGZ+Vp4u0XMaOZMIxMpQhefy4yEethELUJ0zCaVtF2\n4UmZEn/TxLrpbCpvfQbLepsQuvli4m8837l9J4MlneW8NR209tVFNwhVy3Dx61cgu4mAy2SC6DP3\nEXvhscwf2o2T4imDVAIZ7Vs2D2Ri8abZg/4pjTTpJT8Q+s81ne8BYlLS1E9ILywlFzQHCfeaqKdr\ndg5xVDEvFWWLxq+4PLicdxJBfkjH+CYV5fFoMwe3LqBW0bm5bAqQiWNvoDs4L7CE9xMhbo80cLSz\nhv3tlfyUjvNeIsS5wSVYhWD7dj68SoZf/q9wPc/EWvkqFeGi0HJOaVvEWqqN68oy1a6nuutYmI7z\nl8BiFqRiXBJcxq2Req4smwRAc3us/xhHNR8lw1wUXMYP6Rh3RxrZtXk+ATPN+e0rA6sQNBvpTppl\ndwxkBfz62wlm79bEP28N8+NPhT0LqgpWK7icAq9H4CtXqKpUqKlWqKtVGDtGZdxYlQnjVCaO73pN\nmtD3NW0tjb12W32CaWsCIzIs02QYfBXPz2o/qbx06oQd2MFpY5ym9on7S+Cf/gh3jCmuwFXsxYwn\na5m1eUHb62uvD1Z7hltuGGhTZmDbZjfib72A/+yjcOx3JIk3nif+5gsIlycj/tXunbmOPB3hchO6\n6UJaTtgT6692xHnYH9EnTUOtHQeAUl7ZeSzh9QGic9LohJQYyxehuDx9WC7a1Jno625C/O0XaDl5\nP6ybbYNMJUl+9m5PzZj2sIw6dlJm9fLN51jW3ywThuqWIHUecgKhmy6k9cxDsG61I6q3nOS8T0h+\n9VFXTqGb21hImLchbVKfNplq6ZpMdSG4zDuBPzhr+CQV5oW4n8fammk0U7iEykzdzsnOMWxv81Kn\nZO4BTQiu807m78FlHNq6gGOcNWxucSGAy7wT+WPbIsKmwbXeyZ1evxCCE1211Kg6V4dWsMJIspZm\n4wrvJHa1lXVSHauFxj3lU7k8tIJdmucz2+rh8YoZjFctbG1x429nzUzRbNztm8rVoZXc3TSfSkXn\naGcNh9mrOsNCe9p8fJgM91gFdMf0LCya7pAS7rg3zFU3hPMKhXncgpkzdNZZW2PyRI3JE1W8HgWb\nTWC1Cqw66BaBroGmiXbfQKC0lzkoCp0SF4K8dW0jFmI4NpjIgYIH+remAP/Jwyk/2uvgkhLFvHvj\nBn+YK1r6FnYI4KvJ1VSoxQsLRZ65j9Btl2fK8cdOKug7MpXsoaIoTZPAVWe1N/IQaFOm4zr4BJLf\nfkH0vw9Sdc/czspOgMQHrxO65zrSP32PjEVQvBXo09bFecCxWLfskj4wGlfSfNROOOechuug47uO\nl07TuPeG6OtuQvnl/0H0uh4ylSR02+XE33wBo2klKCpqZS2WDbfEtv2e+P98OLad96P8/Jsw2lpo\nPnoXLBv+ivLzb6TtstOJv/Ycta/+mNmXYRB77gEiz96fkTtIp1DKKtCnzsRx4HH4/3QYKApj3lpO\nq2Gy/dImGvvR51eAF8ZXsr516CX3qxspaSAArZdwWJMRZEF6Bb+2zugjVzxUPP9yjONPa+vBrnXY\nBetM15g5XWfLzSxsuJ7OxAlaobV0IwElmXpGnHFPSsnaP9bnLFpyCcFrE6oYl6PYqNj4PJ5k3+Ut\nWcdz75hydnQWb2kopcxoqeiWIWlpSNPM7EeIzqIgaRiZZGOvfUspM96xYWTcMqGApoJQ+m6XSoKq\n9TDgnX9XFISW3UBKKTNJ0o7wh6KApmP6m2k+emfsO+2H56TzMtu2j1toeud5dBZMdeyr41ygfbwa\nQlFoOXl/jJYGqh96B0NKjq/380Ikv8icCrw4vpJ1f2bGPSFTHNDyD0IyxsuV56N3M/DXh//LCc5d\nsAqtqMa9LWDyq50aCQQzj7Kmwe8PcvCnk914PAJV4RepAUOJjPuIC8v8kEznrUZd26pRpa0+l2C8\nruJRFZqzeIBfJVJFNe5CCLAMPY8gFAWsPcclVLVPTLzzmKqWeQ1ibIWMWQiR0Z/pBdVXRc3Tn/fc\nttt2Wc9DiIxV0fqOt+KmJ7r2LQSXVXlpSPv5LJE7xOdVFMp+hi6mVehoQmVBaiUtZohatav698d0\nAzZR3MnKNCV/vyLYadh33t7KX093M32a9ks16CXHz++u7AeLU/mTYLs4bJ1l2asDFarKhByMnG+z\n8OBHMXxQo6ncOaacjW062UydAuzotFK7Gp2FYkIA49QK5qWWdP4tbMYJmsWXyWhsMnntzcwqaOJ4\nlRuvKmPG2vqoYS8hfp53ZR4sSuU3mPtm4SWXEgLY1ZXdO19QAsbMKIqLGk3libEV3F/nY0+XjUpV\nwSJgukXjqmovV1R5i14nsTogpaRK8XKEYztejH/R+fdGM4BLFJ9F8vrbCZpbM6vXG68qw+0acaZn\n2GHEhWUW5PGGx2vqaou1d8dvHFYuy5JUbSsF130URYdVCGY7rMwuIXV2dSOFwUStko31KdwceZHL\n5KFYhY6KgioU7ou+xWuJeUxUKllPn8C+9i04oPUaznDtyZ2Rucy2rsPhjm34a+B+yhUXhzi2RiC4\nNvQss/TJzLbOYKbeVVj10ONdq4EJ40Z+/9LhgBFn3Ffk4SbPsq2ZpNe6Vh2rgESvlHDQNDNdiX6G\nnt8oft6IySR1qo8Zeh0RM87CdD3r6uPRhUaTEeSy0JN8Xn0VKgq7NF2MS7HxZXIJ90bf4CrvHJ6L\nf8zD0XfZx74521rX5ZzAA6w0/NxW/gesQueR6LukMJmlZ6SUv/i6K29x530R/nqGe/S+LzFG3Noo\nn+pirtj36sD4LMdOSmjprfM6ilGsBsRkkirhoUxxsp11Xb5MLQbAKjS+Si3hH5452IQFXWhsYV2b\nr1PLSJJme+v6VKsedrCuz5OxD9jGmlHyHKtWcIhja6ztidjdbBtxX/TNzuNNndzlR95yZ4SPPite\nv9pRZMeIM+6BPKGOMWvQuOcSCvupnxxBNkgp+Sye5OhVfmYsqmfdRfX8uTFAsB9O9ihG0YGETOFS\nbAgEJzp34d3EdwBY0dCEyg62DTq3rVQ8pDEQCNZtD7WM1yoZo/o6qZIexcE0LVP/IJF8lVrKeLWr\niO2ow52dhUSGASf92c+336cwS6EItpogpcRYuZzU/Hkk531Jct4XJL/6nOSXn5H84jPSixau0fGN\nuLBMLA9v35FFZ311IVeXpoXJFJvZB1ap+nE8xWErW4l0O9eHglF2cVrZqYjUylGMXBiYaGQcjs0t\n0zg7+ABpaWAROjtY12du/Ev+FXmFkIzRbAY5xL41GkqnwVZRGKOWd+7PJaz8NXA/N5YdQ4MZ4Pn4\np5zn/l3n53vvbuPf94RZuCizsl65ymT/37dw2fle9tnj56nMmPzwPdrOPBltrakovkoS776JNn4C\n6cWLkOEwvoeeRpuy5sY34jz3dB5HYE2mcbw5uNDfDZAx05Q2OHxVT8MOmQqv1M+nIG0UwwAxmakI\nUYRgulbHI7H3UFFYmK5HEQp3l5/Ei5V/YxvruphIFCF6MGks3Z4oq9C50HMQj8beY15yCWe796PJ\n7FJl9bgV7rjRx3rrdPmTbQHJH//Uxulnt7GqfjWphRUR8VdfourFN6i47zHK/3kr6rjx+O55lOpX\n30OpGUPi9VfW6PhGnHG35UnSxNeg8XPlWDV8m0wPSAH7rViScJalrIAebc9GMZ+Rm6YAACAASURB\nVIp8EMBKo7Xz/d72zbk9/AoNZoB19LHsYt2QcsVFShr8kF6F0a47mZTZY+UaKi1mmFNcu3OoYzZP\nxj7g2vB/e2yz9loaj91TwaYbWXpICzz6VIxNf9PIo09FSaUkP5eqebOpAeHqarzTUegnfBVYNtuC\n1Lyv1uDoRqBxzxd6aUyvuZh0rsKpJSmD9ABu5o9j2etvFbrapY1iFP1BQ+O5+CfE2733nawbsLNt\nFhcHHyNiJjiu7VZObPs3uzdfyvzUUqQ0qVXKWGR09dztXtUKcG34OVqMEC8mvuC+6Jv8wbljn+N6\nPQpPP1DBfbeV86vNLJ1xeCnhjLMD7H5gM//6T4S2wPDPH2mTphD48ykkv/gMmUph22s/Ul9mKqaF\npqFNW3vNjm+NHr0E8CnZS/0BfhxE8rJY0HLY3ZiUhEyJL1v3gyz4Jpndc1IA5xrMKYzi5wWf4mKa\nNoawGcemWtCEyrme/YFMleqd0bl8nvyJ2dZ1uNVxPF+nllKn+nCLrvj4Uc7tO/8/Ri3nLNdenB18\nAI9wcL/vtB4x+e5QFPjNbBvbbGXlpblxbrkzwhfzUpgmzP82zfxvQ9x8R4SD97Oz6442pkzSKC8T\nw4I6aTQ2YLa2oM+Yifv0v5D84jOiTzyCeOZxPGdfQOTBe5HJBMaKZbjnHL1GxzrihMOOWNnKK9Hs\nYk+TdZV3J2bv01lq/KM1xD9a++qMO4XghfGVTLUUNs9u/FMD9VkmL5uAl8dXFbyfUaxePBOKcZM/\nzCybhQsq3LiH0Ih9pCEel3z5dZLjTmujuaXnvW3RwetVmDxRY589bPxmtpUJ47Q1JuEbvut2jKVL\n8F5wKYmPPiBw9hk9GsjLRAKzqRHh8eI86jjcJ51RyG5HhcMKwWSLCjmkMRanDBYn00xaAwYwW5wc\nMp77qrRRsFEO5qB6Kgg8BXjuppQsTBl8l0jRYJikpcSmCLyKwkRdZW2LhnsNCGFJmalRmJ9MsSJl\nEJUyE2pSBJWqQp2mMnUNjW0oCJsmf2kM8HQ40zxmfjLNq5E4d48pZ0Nb/yyplWmDp0MxXo0m+D6R\nImBKPIpghkVnc7uFre0WNrNZsAzzVVs6LZk3P4U/YBKNSqKxzCselyRTkExKpk/TaG7pGXZMpqCp\n2aSpOclHn2Y+K/MIJk7QqKpUqPQplJUpWCxg0QW6JkgbkkQi01g7Gms/XlQSS0iSGQFS5hzsGFT/\nVNdRXXLVyU8+RF9vFu5T/4y2VoFtJVcjRpxxn2nJXYUqgSdDMc7wuVb7Eq8pR7zfJCMLXGhpe67W\ngZoAbx5v0JSSr5NpzmkM5FU5BDjYY+f0chdjNbXkuilSSuoNk4uagzwXjtNfpHVbu4UTypxsYrfg\nFKVdqptSkpRgINEQWMTAJGnjpmTv5S1824sR1WiYPB6K5TXuppR8HE9xxKpWgr0cA78peT+e5P14\nkn/6oVZVuLjKy28c1jVK982H/70S549/aqMYihttQUnb10MrgvphYWrIzbFlIEDZtTf36UEwXDDi\njPta/XjAL0XjnFjuwr6an4GVeSpn34klObXA/eR6NibpWs6kbdA0Oa8pyPPhONECwnAPB2M8E4qz\nld3ChZUeppRopZOSkmtbw9wbiOAvsJjlzViSN2NJxmkqv7ZbONPnYrxe/PHNjcR5IBhlScogLiVu\nRWGmReOEMidrF6jd/nwk3sewdyAfqwvg9rYIV7SGSBZwWeoNk+Pq/UzTNeZ47czxOocdc+r8S4NF\nMezFwrixQ79nbDvtQtufT8G65VZYNt0CtW5sppvYMLn2I864T9W1rDouHfghabA4lWad1dxcYUme\nZO4n/bQELAQb5zgfv2Hyp4Y2XsyRh8iFmJTMjSaYt6KF62q8bGO3FtWLT0rJxc1B7szTMSsflqcN\nHgnFeCoc49RyF3O8zpyFYoPB71f5e/3F4KtEikdDMQ712Dm3wpN3pQTQmqfTcy7jbkrJs+E4l7eE\nGOhd8UMqzXnNId6JJrmgysNETR0WSUiAjTfUefHVgd2DpUJNtcI1Fw+9E5tMJpGRMPG5LxOf+3Kf\nz62/2hpntzDO6saIM+4eVeEgt4N7g9mNRlxKLm0Ocf9Y32obU9AwsyZBOzAQKmQubJKlyjUlJac2\ntDF3gIa9OxoNkyNX+rm+pqxocsmmlJzXFOT+HL/RQJCU8I/WMHe0RbhnTDmb2Uur3GgC9wdjLEkZ\nPDK2Iu+2+foG2HKETz5PpDi9sW3Ahr07Xoom+Hx5C0+Mreh3Jbu6cPn5Xr6a38zKVWvGfXc6BFPX\n0th9JxuH/s5OhW/woZT0jz+gVNdi3Wp2RoJg8SKchx3ZcyPTRCbiQxv0EDE8fvkiQgCn+1w8HIrm\nXNK+FkvwRjTOdo7VU6r/cTyZl+pTDArjJlk899vbIryex7ALYGu7hfWtOorIcO7fiCYI9QqRpIC/\nNAZwK4IdnLYhp/bfjCZ4LBQdUPFWPkigzZQcutLP6T4Xx5U5sZTYY/20gNWWM0/yN5vnHjcl5zcF\nc963VgH7ue3s57KznlXHqQh+SqV5IZzg5rZwj9+t0TD57fJmbq8tHxZSxTXVKo/8p4KX5sbRNKis\nUBlTo1Bbo1JVqeB0KMMlmtEvwnfcitnWhjZ1GsbypaQXfIfZ2Jj5UJqY0Sjpb77GMns73CcWGnAt\nPkaccQeo1VQ2s1l4N0fBD8DN/gizixxqyIUv+klgDjWcYBX0YQC9G01waRYNecgY9fWsGrfVljOp\nV7w6LiXHrGrljWjPCSkiJf/XFOB/Vj2nCFoh8BsmJzUEciaGu0MH3IqCtb2TvSEz4wuZMmvuISIl\nl7aEaDNMzqpwl9TAjy3gGuRjL1l7fSSl5AZ/mM9z3CtuRXBbbVkfh2SaRWeaT2dXl439lrfQ2i2w\nHTAlpzW08cqEyqI2Yh8s1pqs8cdjXWt6GENG2eXX9njfctQhuM/4S+d7mUxiBgOoFZW9v7pa8fPi\nlQ0AB3nyhxDejyW5uS2yWsbyTT/t9CYOMSFY3evBTUnJ+c3BrNtqwFk+N4/VVfQx7JDxKG+vLeco\nr6PPZ6vSJuc2BYbkcV/vD/XbpEQA+7nsPDGugtcnVPL+xGo+nFjNOxOreHl8JQ/X+dgujzd6c1uE\ni5uDRVsZZMNm9v5zNrn0hIA+E0+rKXkwR5hqiq7ywrjKvCvNtS0a19X0jSPXGyaHrGglNKoYWnTI\nVIrwnf/CWL4M/2knkPzkI6Rh4D/jROLPP0Ny/qj8QEmwl8vOOnnijSZwbWuIT/N498XCsn4qY9cb\nYnK3u+efkJLTGwJZWRpOITi30s0p5U48eVYLTkXhzz4307Ncv/9FErwUHlwsMWiYPBXK/91xmsod\ntWXcUONlU5uFKk3FIgSqEDgVhfG6xtYOK/eNKeeqKg/eHN7x3YEojwSjmCUq0tu+gJBerrFBz3i8\nBG70h2nMYoBrVIWba8oLYizt5LRxZJZJeX4yzXOD/M1GkRuJd97EsukWVP33VVyn/AksFhJvzEWb\nMBHHgYcSvf/uNTq+EWvcLUJwTXUZljwr86SEPza0ES2xpnQ++qEAtiygmCUffN0M9ZvRBM+GY1m3\nu7DSzfFeZ0GhKK+qcHVVdkbBXYHBrXjOaw7mlIaAjDF8fKyP3Vx2lH7GqArBYR4H/67NXuJuAH9u\nDPQbEhsMNDKtE/tDvgm0e1hmQSLFv7OsIu0C7hxTPqAOYudUuKnrdVwJXNgSJDqcuIgjAMkP3sWy\nwYYIqw196jQs688i+sQjuP98Lon338Wor1+j4xuxxh1gXavGTv14WMvSBsfV+4mV0MDvkEdj3aMI\n1rIMLR7aQclbmExxVmOA3gQ8Hfh7hZuDPI6CqXEC2NRuYdssLJz3Y0nmDdBo/pRM82Qo+6QDUKep\nPD+ukgkDCFEJIdjaYeXcCnfWJG9mddZX8mGomKRr6AVcRmeea919gr03mD25fFq5m1kDXNU5hGCn\nLA3ZQ6bk321Rfj5qI8MfRnMT0WeewAy0YaxYjv+0E3DsewAIQfT+u3Ae+vs1Or4RmVDtgEUIbqgp\nY/6yJhancnOOX48m+FtzgEsrvTkpakPBORVu/heOZ+3vuqPDyrgBGDSFvoVMbiFISslfm4I0ZfGM\n93fbOaasMI+9N/Z223mzV+gqDfy9OcBjdRX9etgdeD2a6DPpdMAm4KZq76CLpQ71OLjFH85aCPVa\nNME70QRbF5ExspZFLcgrylct2qG935w2eDzLpLeb08apvoEnHxUhmONxcF8g2uc+eS4c44QyZ16K\n5pqAYUiamk2+/T7N+x8nWLnKIJHMdGwyTUk6nfnXMCBtdP3fNDPvjbTENMnyucx8boBpyB6fpdu/\nrwg49yw3xx8x8GvtOfciQlddQvCS8xAuN57/Ox910hTC11+JcLnRZ21cgqtVOEa0cYeMNsllVV4O\nXdmad7uHgjEsCC6vHnpxQ29YheCmmjIOWtFCdzO5lU3nimrvgKiFqoDeNsyqCG5ri/BhlvzBbLuF\ni6s8g2YFbZEjZPRVPM2KtFFwdehDeTjtZ/ncbDkE41uuKpxX6eHMxkDWz69vDfMru6VozKiNrJaC\nVkBqnl82ZkqSUnJMvb+P7pBdwAWV7hzf7B8zLDqb2XQ+7EXXXJBM82MqzczVXMCXD/42k3MvCfD2\ne0la/eZqX1kYwHU3hQdl3I3FP2K2NuO79S4QCjKZIPHWXGJPPYY0TOIvPY9zzjHFH3SBGPHGHWBb\nh5WLKj1c2hLMWbkKcE8w4+2cV+nGVWSBqi3sFt6cWMXN/jB+Q7KuVeNIrzMvFzobNASpXov4p0Ox\nrMm48vaJbaDH6I6JukqtqvQpwopJyYJkuiDj/lk8yfwcZfiTdZVDPX2TgAPFgR4HV7SEsl6HH1Np\nWgyT6iL10N3aYSloQs5n/6My41B81ssA68DNNeUDCk9lO+7+bnsf426QCVPdMSZ7nqI3pEyTDH6A\nkaxHUexYPL9CaGWEll2JEALnmD+gaIXtKxveejfBaWe30dC4ZnMBtkFqkVg23gzf7ff2+Jt1q9k4\nf38M/pOPxbLxZsUY3qAxomPuHRDAMV4HhxdgRO4LRjm1oa1fAavBYKKucWVVGbfXlnFauYuyQfDb\ns30jm0EDuLa6bMi6MIoQ7JeFVmoC7xXINLo9B+VUBR6t8/Vbxl8IFGCTHKuMVsPsl35ZKGwCNiyC\n53uDP8w5TX3zI5vZLezkHHoIaZbV0odLD/C/SJz6PDpHXZBEGx8GJDbfrphmlHR8EfHW/4FM4hp7\nJvHWFwc9vhfnxjnk2NY1btgBzjixyNx7XUdfZ11C119d3P0OEL8Izx0yybdzKzwoCO4MRPIa7xcj\nCfZe3syVVd6iL2Ez3tzgwwNCUJCy/b4uG7tkSawNBrs7bdzi72ug34n1L2sQMky+zpF83cVpZWwR\nRb/Ws2q8kGUeSQELk2nWzqMYWii2tFkKzjPkQzYhOR04t8JdlPDRWF3FKRQSsu+d/lEsyV79SElI\nKVFUN1bvbAAcVQcgzQSRVbfjnXINibbXMZKrBjW2RUvSnP33QN4QTGWFQmWFgtslsNsEmiaIJyRf\nzksRifb8oqLAujM0yssUGptMFv6UJp1loegrVzj+SCdej0AI0FTB2DqVX28xOLZa9PGHkckkat1Y\n1LHjUGvHICxWkp9/Qnzuy7iOP2lQ+y0WfjHGHTJ6HudWuolJk0eCsbz6HZ/GU+y0rJnrqr3s67YP\nG5W9QmKSVgGnlBfPG5lh0bEI+pTFf5NIEzMl9jyJwxbTpD6H3PHvvc6ijRHyyz2vKFKLxY0GQEsc\nKOZ4HWw0RFpsB8pVhQm6Smui73l/Gk/1a9wB4m2vYnFvjmKpQhphgksuwl51AEJohJZdjnv8Xwc8\nrmDI5He/b6GhqWtcFguMq1PZfhsrO29vY+NZOg579tXcT0vS/HqXps73bpfg5mvK2HG7Lkdm8dI0\nV/0zxIuvxol38z+SKcnOv7EyY+3i/Iaxpx/HaGqEeBwZj2G2+RE2O8LrxX3CKdj32q8oxxksflHG\nHTIMmsurvKxj0Tk3RxVnByRwVmOA58IxbqwpH1QYpdgoJN90ls/N9CKuOFQyhVIrexlIE/gumcpr\nkL6Mp7Ly/NexaPw6C81yKMgXggoUKSxTDO8/GxxCFCX30AFBZmWUjef/QUHhNIGz9mhCK/5BOroA\nRa/AWXs8FvdmRFbehLV8R6xlOwx4XI89HaO+WyhmbJ3K9Zd52XB9Haez/+fL3is+fuIxzh6GHWDS\nBI0bryrjvQ+THH2yv9PTD4cl510W5LG78wu+5YNMJkm8/w6JN19DprqurfBVQCSMPmsjUFXQ1nzS\n+hdn3CHDMT6qzMkYTeWcpkBexcYUMDeaZJ8VLVxU6WG2vTCmRKnQn4LkOE3lMI+jqH27FJEppV+Z\nJZj1YzKd17i/ncOQHFvmRCvydRyrZfgp2a5QukgsDF+JJvjN7RamFVnBcXuHjSuz8Py/TaYIGmbe\nIishBKplLLpjJkJYEEJHKFbSse+J+1/BXvU7EANLUIcjJv/6T9d41pupcf9tPqqrBp/o3mar7PkJ\nVRXM3srKDVeWccwpXfLN736Q5K33Ejm/lwtmOETkP7eTeP0VlLpx6DPWwXnUcWgTJqGOnwCqSvM+\nu+D56/noM9cb9PkUE79I496BXV02NrTp7L28hWX9JJm+T6Y5ZGUr51a4OaHMWZS460AhpexXCnZX\np7UoCcruEIArR+hlWT/hjmzyDg4h2LxI4Yce+1UEFWr2BumySEozhRQvDRQCuLrKW/TJbn2bjk0I\n4r0cgjTwVjTJb925czJSSlLhz3DWdjR5lvi//wOeiRfiW+dBmuftgeaYgdWzVUFjkRIefSrGinbJ\nX1WBu24qH5JhB5g8Mb8J220nG3MOdnDvw11U3CeejQ3YuCc/eI/wTddS89F8lLKyPp/LVAqhW5Dh\n7GJ9awJrPs6whlGrqTw/roJDPPZ+vV0JXNIS4vBVrSzOQe0rJWIyv4myCfhjEWPtHRDklq9t6mdS\nrM/SsKJMFZSppZkc63LQHYvluZcCW9ktjNVLo9o4Mcd+X4n2pzUjMVIN3d4L3BPOJxX5CkUro3z6\nXSSDHxY8jnRa8vTzXcVaO29vZWzd0H1Lt6v/++iiczysvVbXdfjw04HrSVk22hTbLnsQ+PvZpBb+\n0Df5pWkItxujsSH7DtYAfvHGHaBSU/lHdRkXVHpyeqjd8UY0yW7Lm3k1kuisNFwdCPQjkbCny05t\nkbjc3SHoK1HbNab8nnu2G8wpFJyiNLdeVY5JoxDy35rCzkWgPubC+Bz3wyexZD8hPkE68i2pyDxM\nI4w0kyRD76NYajDTIdKxhWi2CQWPIxiSrKzv+hVmrV/8lVsuWCyC3+7alUBeusxg6bKBOWdKRQXl\nN96ObY+9CPz1dNrO/hOJ997GaKhHGgZCCLQp0zCWLyv28AeNUePeDcd6HTw7roIJBRjIgCk5dlUr\nZ+eoiiwF/HlyAwqwR5Goj70hyF1tma8oDOBMX99KS12QV9BtKMiV9C6VOmQxUIjC5GCRS3c+ImWf\nxtvdIQDPxPMRipN46/O0LTqT0NLLCPx4Om0LT0JgYvPtWfA4ojGTYLDreC5HcW6AeH83YDsO2b9n\nsvrZFwankmnfaTcqHngS1x9OJvHmazTvtzutxx5O/LWX0aauTfqnHwe131Jg1Lh3gyIEMyw6z46r\n4Hdue78XJwk8GIqx+7JmPo+XXjo4XyGOVQhmlLClmpbjWewvwXuwx84uvTxTRVCynEWuFcFw9dw3\ntOola0AOUKtlvx5xKQl2u5+S4c+J1N9LKvINZsqPxEAodjTbZOwV++GdeBG+GffinXI1jpojMNJ+\nUpHC9cqTSYh169CyeICecy742wpjQY2tU5k5ves6v//J4J9XYbWiTZ6C5+wLqHrpLRz7HUj81ZeI\nPfYgiXffInj1ZUQfeYDkJx8N+hjFwC86oZoL1ZrKDTVl/Npu4ZymILF+DNgXiRR7Lm/hmmovB7jt\nJevulM9zr1aVgnVeBoNcZ9TfuToVhbvG+Di1oY2nQzHS5O8tOlTkCh8Zw9Rxn+MtLrOpN3JJLsRN\nSdCQmcopIBn8kGjjQ8SaHkLKFJgpJGmQKaRMt783QJoI1YXuXBeLe/OCx2FKMLvNsP99Kc55f/Gg\n5/IaCsSqBpPxYwvbdotNLXyzIDOp/LCwOFLQisuFfc99se+5L5Bh1STefoPoQ/dh22EXLJsWfo2K\njVHjngcHeRysa9U5pynAJ/30zDTJ9Bl9JRLn0ipvSWLf/jwWav8CEsJDQSrHoQt9Nq+p9nK4x8Gi\nVJoNSihclWuyMUral2lwsAvB9iXub1qdI0yVhB6eu6vuBFx1J/TYRpopoo0PkIrOB5lGShPaDTzS\nIB1fhO6YXtA4LDpYbRBvj4bUN5ic9bcAV1/sRR8ABUlVwGqFRHtx0g8/ptl848Li9zXVXdciECzN\n/aC43Nh32xP7boWHrEqF0bBMP1jPqvNoXQVHeh04+vE408ALkQS/W9FSQMJq4GjJ47nvVaJ4O2RY\nQukcxrFQL9wiBJvbLRzscZRUlTBX45Xh6LmP11XcRRao642KPLTYbBLUHZBSElp+DYm211AtY9Ad\n6xBvfhJVH4ORWEK89Tk0+9oFj8PtUqgo7+nwPPlcjDvvjxIvpKFuOywWga+865w+/3IA4ZVuh3EM\nUizs54RR414AbIrg4koP99f5snat741FKYPfrWzhuiI3imjNEXP3KoJpJQzJQG7PvRB20erExzly\nH2tenqovxmlqzjBSsVCex7gvzdPjACSqpZby6XfjHncmzjHHIxQr7gn/R9nUmxCql2TgrYLHUeYV\nbL5JTw87bcAlVwc55f/aCt6Pwy4YV9c1Sbz8WqJgmeBFS7rOd+yYNd8wvNQYDcsUCFUItrRb+HBS\nNRc2B3iyn16gSQnXtXezv6TSU5SkWWuOgqGJulbSqlkJOSmfZUUy7lJKVhkmCxIplqdNgqZJcoAr\nnxbDzNo7FoZnQnWmRSt5MZwvz8ogn+cOkI7OB5kEkVkVCq0cacZQrRNQdB9munCmmKIILvg/N19+\nnWTR4q7jmiY8/1KcrXZu5KRjXey/lx2bLfc10XXBHjvb+PizTJi0uTVT9XriMfnrO5JJydvvdQnN\nbDe7tOGw4YBR4z5AVKkKN9WU8xtHlHOagoT64Z6/EU2w7dIm7qwtZ0endUgPc2uOsMz4HIyIYkFC\nn4YSHRhKNawpJWFT8m4syY3+cEn6nXbAGIZUyA1LUKXbGy5VQSX75Nb9fjKNEPHmp4m3voiRXIFp\nhFBUF81f7YTu2hiLdzbO2uNIR79Dc6yHNJMFx9s7UF2lcvM1ZRx4ZCuhcM/fY/FSg7POD3DVDSHO\n/4ub7bexYbcJdD0zMXR/bI441MmNt0doac2M/6Z/h9lzN3sPj743Xn0zzqqGzPZCwF67lS6MOVww\natwHif1cdtaz6pzXFOSdfoSYDODEBj/7uu1cWOkZVPMMSe6wTF2JQzImPZNv3TFGHdzyttkwuK41\nzCuRBCvSpU93DkfPfcMSKkx2QJCpws7mpXdnXyX8rxJtfADnmOOweLdF0Xwg05ipZtLxxQCoWjnJ\n4AeEll+PvWLPQQmHzVrPwstPVXLepUFefaOvZHRTs8kpfwlQ5g0yrk7F41aw2QRut+C6y8qwWQVW\ni+DsM92cdV5GNtjfJjnj7DbuvKkcj7vvsyUl/PPWrhDp2mtprD11zQt7lRqjxn2QEEIw3aJz95hy\nrmgJ80gomteLj0l4MBijKW1ySZWHcbo2IHaLISWJHN7nuBIwc7rDlOQseMlV7p8LrYbJM6EYV7aE\nCK5Gb3q4JVQ9ihjwtRssqlUlu3HvNmHbK/fFXrlvzw2EBdVah2qt6/yTtXwHbL7dCS65GKEMbuUx\ncbzGXbf4eOzpKHfdH+GbBWl6q1S0BSRtga4Qm80KiQsktvYkxR4727j97gjfL8xs8+6HSQ48spXz\nznIzfZpGWZlCKilZ+JPB368IMu+brn3tsN3ID8nAqHEfMhyKwoWVbvZz2zh8pT+nd92BV6IJFqxo\n5bnxFVQNwOtNytxx77ElDsvk89zHDUATZVnK4JCVLSzKm8grDYab517qCbk7qjUFsvRVyVehmg9m\nug1k/41a8kFV4KB97eyzh51581Oce3GAr7/NXdj097M9Pbxyr0fhkf/42HrXpk5J36/mpzj4mFYc\ndoGmZeL58bgk0W1hrapw/JHF7SMwXDFq3IsAIQQb2iy8NqGSK1tCPBqK5TUmS9MGOy5t5paaMn5d\nIM85JWVOxsqYEhuKRal0n0YdkPE+awo4dlJKbvaHub413K+q5dq6xsY2nfWsOuM0lVpdpUpVsAuB\nTQj09qYhYdNkZdpgQTLN69EEz4XjeRkxw01+YCCT4lCRiw6Zy1kILr0MM9WEoleANJGYCBQy5DoT\nRa+mbNqtQx6XEAKbFTbb2MJLT1by7YI0736Y5OPPk3y3IM3KegNNg2PmODl4f0efnrQ11SrnneXm\nrxd29WUwDPrE8zugKnDZ+R6qK0c+UwZGjXtRUa2p/KOmjC3sFs5vDub1jJoMk2Pr/Tw5toIZlv7Z\nLhnjnn1/tYOMexeK13MoCE619B9aajVM/lDv5908eQmfovBrh4WzfW4mFcAqsguwKypVmsosm4UD\nPQ5OTqQ4ZGVrVrlfGH6eey7Nl1KgPEeOJ9f9lI5+Tzr2LVKmEYoNe8V+WL1bo9omIBQnQnUhiiz8\nJoRg5gydmTN0jjuicM96ziFOVtab3HxHuE9opze22FTngL2L1xBluGPUuJcAB7gdbGyz8Id6f05q\nHmTExw5a2crDdb5+C3tSZIqksqGqxGGZ1yPZDXO+tnaQaeRxXH0r3yWzP3U+VeFsn4sdnLYhV/Su\na9W5utrLUav8WT8fbp57Ls2XUiAXoynbagygfPpdIA2kjCONMGY6gJlqPkKOpwAAIABJREFUIe5/\nhWTwA6QRxFFzBLbyHRlKP+Bi4U+nuCjzCi65JkSuqOgO21r55xVleWmWIw2jRUwlgBAZr/bBOh+/\ntlvy3v7N7Z5tv5xjKXNWvBa7yUN3JKXkkxyFQfn6idanDU6s92c17AqwncPCM2MrOMzrLJpUw04O\nG5NzhDtWv/p+fpRK8jgbvDlqEXJdEyEEQtFQVBeqpRbdMR2rdyucNXMon3YLrrGn0bbwFCKND5Zu\n0AOArglOONrFo3f52GYrCxU+BUUBuw1mTNM49Q8ubr22rEdl6y8Bo557CVGjqdw1ppwDVrTyZR4O\n948pg/9rbOOeMb6c2ihpuWYM1JJUOutxNWBSDgpmyDT57fLmPj1XO7CTw8q/xpQXXUBMEfArm4Wf\nUrE+nw23CtVCKp2LhUIlDqJNj2Kv3B+RpX2emWomHVtIIvQR8eZnkEYQ3T6t2EMdErbawsoWm1pI\npzPJVCHa25lqok+8/peAUeNeYrgUhUfH+jh+lZ8388Sd34om+W84zt45utKnWTPdhL5JZJ9SrEIw\nLkdo4frWcE7Dvo3dws21ZSVThrTkEg4bZmEZ+2qUbXAUcCzTiBBe/g9Cy65GKJkkf0ffVISCUN2o\nei2qbQKusaeguzdFs44r9dAHDFUVlDgF9bPBqHFfDXArCldVe9l1WTP+HEnWNHBaQxtb2y1UZAlT\nGFKuEWXDz3OoYfrU7DztrxNJ/tUWybm/P/ncOEoolpUrvDXcEqqrM/Tbn+AdgKI6qd6o8LZ5oxj+\n+GUFodYgxuuZGHy+hzoJvBDJzkwxWDOFOB/miLcf5HH0CSEZUnJtazjnFDTTouWN0w8VhpR8lGO8\nw62IaXUOx/5LjEmMYuR47oaULE0ZtJkmbkVhoq6iD7Obel2rzjYOKy9HcheAzI0mONzblwpmktsg\ntBhmXmnXwaIhbTA/S67AIuD3nr6UsrApmZ8jjANwlNdZ0uTvR/FUzj6z5jDTc48MsoBoMBhoCCwa\nk9xyR5hHn46hqbDeOjqbb2Jhneka4+pUKisUHHZRUrG6YkFKCIZM6hsMli43eOvdBHPfSmC1Cs79\nk5sdtxu5GjMjxrif3RTggWCs8xGepKlcU+NlK/vwKTXWhOBQtyOvcf8iRxjElDJnUvD0Bj/31VUU\nYYQ98UAwmjWZuoFVpypLSCYmJU15NOdnO0orlPV4KJrzs+HmuefT5i82rAOc9594Nsq1N3dpsSxe\navDfl7pWlEJkJHynraUzeYLK2DqV2hqVqgpBebmK163gcoLdrmC1gkUXqGomqVmq+cAwoKHRYNkK\ng5+WpvlxUZpvFqSY902K5pbsP/75lwVHjftwx/PhOPcHezIkFqcNDl7RyrYOK+dXephWwj6VA8GW\n9vwGbjAP/dxokh2XNnFtdRkbFCnsETVNnskha7xjjqpaQ5JXpreihJmu75Mpng/nlmEebjH3+hwJ\n51IgV3PzXLj7gdyTJNAp1vXRp0k++nQoI+sy9h2Gv+OlCFCUjCKkqmXojg67wG4XpFKSdDoj45tI\nSuJxSCTkgNdmDU3DjUNVXAwPizdEfJEjzpomE+b4ekULd43xsYG19PrZ/aG/nqPWQbIovkmmOanB\nz3/HVQ5JhrcDi1IGy9N9/XYV2N2VndGjiEzbvVzFMfVpg7VKMMk2pA0OWtGatyJ4uHnuK7Jc21Jh\noG1Kf1y8+sbW4Qvk9glk578tRT72PnuMXK8dfiEJ1QbDZP8VLTyTx7NbXfg+T8UqZPTiB4sfUwZ/\nawoUhfZ3TyBKLMtu9nLZWCtHoZBViJyl7gCPBPvyz4eKmCk5od5PQz8rnuHmuS9ZjeJpygA99/Vn\njmw53MoKhasu9HLxOZ41PZSSYkR47tP7KYOHTDz49IY2VqYNjitz5uRDlxKmlDwRym/gNhxif9H/\nReIcEEuyzRAaL3+bSPFIsO/S3CUEZ/jcORNpLkUwSddoMLKvpP4diDDHa2dckfTnFyRSnNkY4PMC\nmnwUi+derDzo4pSBKTOrnVJjoMc4/ggnJ37ZhtstqPApVFWqlHsFFovANDMFQoYpO/+fefV+X8A2\nUmIY9Plb5/+N9m36OU7neSqZoiWrRWCzCZwOgcej4CsX1FSpTJmosdEsnU02tPwieqiOCOO+s9OK\nVYiceucdSAGXtoRYlkpzaZW33xBJseE3Jc/0Y9y3HYJRhoxu/JyVrXw8qTpr0rM/SCm5wR/Omkjd\n1G7JWd4PmQKiQzz2nPTJhJRc3hLmhpqhXXtDSp4OxzmlofDem8WKrhaL5RKWkmbDoHo1CIgN9Erv\nuZudPXfLHnobyZBSguyYVdq7NunW0mWBS4wRYdw9qsINNV5Orm/rV1IW4N5gjKVpgwsqPQV5/cXA\n/ESKE+r9tOTRe/epgp2dQ48DJoHLWkJcV1M24O++E0vyUhauvVsRXFrl6dco7+e280Ikzks5GEFP\nhWP4TZPLqzxMHIQH/1okzi3+SE4+ey4UKwiyqj/pwV6wCYjnmA9+Sq0e4z6K/DDDfhLz3yW94nsU\nmwNhcyF0K8LmwrrJzlnlGH4OGBHGHWA3p43DPA7uC0YLepDfiCZ5a2kzh3kcHOxxMElXKVMHGp3M\nD0km2fdaNMElzUHa8nh9dgH31PrydqvPBUuWJOajoRi7OK3s4rQVzEf+IZni9ytb6W02BfBnn5vJ\nBRhjTQjOr/AwL9GSU4LgjWiCbZY0cXK5iz1dNsbratbWgxKImSYNaZNvkylu9Uf4dJB9VgfKc9fI\nruWTS44hF8oUhfoc+YAlqTRb9MOeGkXpEbz/QtSaibj3PR1Wo6BbqTFijLsmBBdWeZioq1zYEiro\nOyZwXzDKI6Eo5YrC+lad3Vw2fmO3UjuEZgpxU/JuLMH9wSifxFO0Gv2bllPKXWw8SBrjH8tcvBtL\n8HE3jrwETm8M8ORYrV85YYCmtMHRq/x9DDvALKuetWgpFyZZNJ4fV8m+K1pYnCNxmAKu84e5rS1C\nmSqYrGvMtGj4VBVBhi+/IJlmcSpNwJT9NiLvDwPV5anJ0Xf00wGuGKZbdOpj2Vcx/SmBjmI1QQis\nM39ddMNuRgK0/etUyo6/FsVd/DqU/jBijDuALgTHlTmZZtG4qjXMVwV6eUmZYdQ0RBO8Gs08iGWK\nYLyuUqmqVKoKLkXgFAKrIlARqCKT7EnKTLK2zTBpMkyWpw2WpNI5l+K9YQHO9Lk5udw16NDeFnYL\nJ5Q5OXRVK591M/BBUzJnlZ9zKtzs5bJlrQ5NmJL/ReJc2hLM6mnPsuo8UufDNoCsnCBjHB+s8/H3\n5iBzI4mcq6molETTkpXpZN6GHv2hTBGcWu7CBC7JMrkPNOa+g8PKvVmSyt8m07QZJmUFrrBmOyy8\nmcO4N6xGrvtwR4Y7b/LpF0mWLjdIpSROh2DcWI11Z2hUVSr9rkAbmgz+dG6ADz5JIk3J1Ck6v93V\nxlGHOnC5cv9eZqgVxVU+6LEnF32JZfL6fSYHmYyRXvHDGlsNjCjjDqAIwfZOG9s5bdzRFuH61hAB\nc+AFDm2mpC2RppRCu+WK4Nba8iExWwDqNAWPqvBQnY+9lrWwINU15pVpg5Mb2ri4WeH4MiezHVZq\nVAW/KXknmuC2tgjLcniQtZrCXWPKcQ+SnjlJ1/hPbTn3BqNc1hwiXAJlRouA3zis3FpTjk0RObXn\nB8qWOaHMySOhKIleXzOBj2JJdnYVlhs5wG3n8pZQ1sltdVapDmckEpJHn4py0VWhzn6o3aGqsO9v\n7fzlVDdj63Ib+c+/TPH624lOzvy8bzIVqjf/O8xLT1YycXx2c2dGAginN+tnMhGl7dZTQdXxHv8P\nFKuD1qsPx7X3aVjW3gyA2FuPopZVk165kMCdf0GfujHeoy5HxsLYtz14SBPHUDDijHsHFOA4r5O9\nXDbeiCa4sy3C/H445qsLdiE4pszBEV5nUdqtedvj1W5F4bFxPi5qDvF4L1ZOg2FycUsICgxZ7ey0\ncmWVt6AeqfmgCMGRXic7OGw8Horx77Zw3txDoXAIwcEeOwe47axn1TsTvc5ckr8D3P84XeUYr5Nb\nsihcvhVLsJPTWlAuo1xV2MSm81EWWYmElEgpfxYaLaXCDz+mOeOcNj77Mvcq2zDg8WdivPJ6nH1+\na+ecM924s3jiG26gM32axnff93zOgyHJYce18tAdPsaP62vyZCKC4vCQrv+J+Ccvkl46H+vGO2Hf\ncm+ir92Pc8+TUCvHEbznb3iPvIz0qkWoFXXddmASfeMhzJCfivOfAkUl/uFzaBPXRfVWkW5ZgVYx\ndvAXaZAYOdmDLBACajWVgz0OXh5fyaN1PvZx2Zmiq6zuMg1BxsM+2G3n1QmVnFPhKVofze563ZWq\nyg01ZVxT7WWSPrDCcwFM1FQuqvRw9xjfkA17d4zXVc7wuXhrYhVn+dxsaNVzdgjKNbZaVWFLm865\nFW7en1TNJVVeZtksBdEqB9pmTxOCv1V6+Ee1l/9n77zjrCivPv59ptxettO7qCBVBEEUC0aNscQW\nY6yviRqTWGKJsUZNjC0xajT2EmtEsRdsqKgoikJQeu8sW+/efqc87x9395btLLsLEn58+OzM3Gee\neebemfOc55zfOWeUU6NIUdABnW3LsqgJwQ0lAXo2s/op1ZQfLM2uMzB3fooTz6xqVbDnIlQn+fdz\nMS64tJZNW5pO1z3LVN6dXsIJx7ho/HWvWmNx/qU1RKLNrJaMBNW3/ZzIWw+gDxyB/4ybAIFMxkgu\n+AR90GjUYCn6oJGEX7krnd/ek6/pJ+d/iO/436EW9kQJlGBuXE7NXecSef0+Ut/Nau9X0qkQcicr\nYtAKOmWgtpTEpGSjafGfujjT6mIt5ljvLIxyalxS6Gey24FPER1KgfDfRIofb2g+AHvNkJ5NgrKk\nlESkZEYkwT+qI6xpw3m3h65xWZGPqV4nPtG1Gf9kva8iIiXfJVLMjCWZlzRYbVjUWTaC9Cqkj66y\nl0NlvMvBQW4nhaqCRxFoQrQ4aS1JGkxdX9nkYXEJWDWkVwfGmtaw4zkFygtUZZuD4DaZFvdUR3g7\nkiAiJUd7ndxaGiTQBdk8GyNi281WA1OB9Xts+3fSGfj2vyl+dk41sebCoNuBEcM0XnuuBHczwUiG\nIXlvZpKLr6ohnsPqFQKuvdzPb37ly2u/9bIDCPziBlz7HZV3PLV8LuaWNXgOOhkAaaSovPFYZDJG\n6W0zEVpaRQw9cTWK24/v1KsRQiClpO7RK3DtfyyKvxhtwD6I1msYdMnLtsuaZVqCIgQ+IdjLofCn\nEp1riv0sTposSKZYmDRZmjJZbpgdtof6FMFQPc1Q2deps5/bwRBd7dKcNlozz4YQAr8QnBLwcILf\nzbKUyYqUyRbTJiZtFAQBVdBLUxmqawzs4jE2HptTgBPBIfX+kU7ru/5/Y5HR0SpWQqRL4rm28/3r\nrancXhbk9rLmbbtdCZ+i8HrfYt6JJJgejvNVIlVPWfV3+1gAKqttLrmqNk+wKwqMHaWz91CNokIF\nKaE2ZLN0hcX3iw3ijSaB7xebnPObah6+p5BgIF9w6rrg6COcFBcXc/VNIZYuT5tppIQ77g0zZbKT\nEcNy1+4C4WzKBjM3LMMxfFJOM4Fa3Btz0wpQc0SnbeGccEyeQiRtK92nw9WWYO8y/M8J98bQhWCU\nS8/Lphits4l5JHPjBl9tSbI4YlKlWST9kCKdetepCIKKQg9VYYCusbdDZ4xLZ49OFJLJuCQWsSko\naf3haOtymhAMd+rtokT+0KEJgS5o1hH6vwxdCI7zuzmuhTKO3YVozObYUytZuz69knS5YOrBLm69\nIUhJcfPPeVW1zS1/q+P1dxJ5E8KnX6R49Kkol/+u6SQlhGDifg7+/UAhP/lZJVXV6fNSKXjo8Qj/\nvDPr5BRufzoytRHscDVqUdZWnlzwMYq/CKHmv0d2LIw+YHjOEYnQ6uMXmum3u7BL29w7inuuClFg\nKSz6U4LAQ5LfbvJy4ZcefvWimy/6l/HVwB582r+MN/uW8FivIm4oCfCzgJs9HZ2bdfLLDxJc+KPK\nHfl8/OBQpin0bcZX8MOMMdz18PCT0YxgH9hf5cUni3nwroIWBTtAcZHCXX8t4JVni5skNXvoiSir\n17ZMlOjfV+O9l0vp1yf7BLz7UZK6uuxLpQ8ejR2ubnKutEzsWCi9bdtEXv8nQnei9hgAqSxhwVy3\nCGN5Tu5jKVGCpQDYsboWx9bV2C3cm0E0JPn0rQRev8LFtwU56CcuTjzPh8sjWLOkG1O1qoKjfu5G\nUVufMH44bpOuh19RuLdHAaOdOl4hUIAiRXBRoa/Nc3eja7F0hcF9D6eLgAzsrzL9qWLGjXGgtvF8\nN2DkcJ2Xny7igAnZqN5IVPLHG0MYrdjdevVU+cMl/swKNxKR/P2+LGvMe/hZpFZ8gzQNrMoNxD97\niZq7f0Vq8RfUPX4VMhElOf8DlGAJwuXFc9ApGGu+R6YSRD96FjtSQ3j63zA2LMWOR4hM/zuOEQeB\nppOc88YOe0F3C/cW8OH0GOde7UfNSYY9YaqTf9/ZNpVw8zqTXx9eQSy8fSq3LSX7H9G2Pbo9j05N\n8ms+r5jCnMqfELfWtdnekgk2xv7D/Jpf8enWyXy0ZSSfbZ3Et9Vnsi76GKaMtNlHd8KWBt/XXson\n5fsSMO7ltb4FfDuojEWDejBnYBmXFe0W7mFjCZ9XHMyXlUdTk/q6W69tmpLr/lyXcXBedamfXj23\nfT3l8Sg8fn9hnqY/b4HBuvWtEwZOOMbFYVOy8STPvhjPcOr1/sPRh4yl4veTqLnnfKSUBC+4m6I/\nPINz7I+ouPpwUsu/oeBXf0NxeXGOPZzkd59QceXBaXv76MMo+O191D11PVU3Hotj74k49pqAWtyH\n5NKvkWbHg/O2B//zNvfGsCxJKik54CgXujNfoyjrq7Jobtu0rUitpK7azpsYOgJpQ++Bbb8A6RCt\n1q+1MPR7YtYqAL6uOpkJxa/hUhszJSSh1DzWRh+hIvkBlszneBtWFTFrLZXJD1ladxPFzin085xN\niXMqYgfn5JhdcWjm/lZG7iJsLGJ04WOI7sip+wNBzFpJ1FwOwDdVP2NU4YOUuY7slmtv2mKzYFH2\n3Rm/b8dy6ggBAb/CNZf5ufy6EFKmtfd5C1IMGdSyOFMUwcUX+Jg1O4lhpKs4rVlrss8wHYTAc+DJ\neA48ucl5nkNOw3PIaQDY8TDoLoTmwH/ylfhPvhKA0JrvUfxFFF8zLe9ctaCM0r+806H77Azs1twb\nwTQgHpUceqKriaPS6RbYlmTtMoNrf1HF3y6ppXKTxeolBhtWmlx5UhULv0pRVW5x5uU+nG7BBVMr\nCFWltQrbltRUWGzdYHH1z6t4+KY6ErG0dh+qsrnqlCouOaaS6Q9HSMbTeau9/rZ/ovZUGfLrIzLb\nCWsDG2P/yfvclgZro48yp+oYtiReywh2VXhxKr1xqwPxqINwKr1QhRuJSWVyJvNq/o8VkTuwZOcX\n4tgWqEq+Zl6T+nIHjWTnRW6ctk2S5XV/7bZrf/BxgnA4e/2yNkgCbWH//Ry4XNkXdOaslusSN2Bg\nf5WigvR1LTudrqA12NFapJU1w6YWfo6xegGppV+RSyG3Y3XIaGhbb6HLsVu4N4JlSLwBQUFxU405\nXGszcG+d96fFufzuAi65I8iSeSl+86NKvvkkyZX3FvDwzXVUl9sUlqqEqi2qt1iZggKJqOTaX1Tz\nwUtxrry3gJN/7eWBG+owDcm7/4lx8oU+7pxeTGGxylN/CyPtNEWszTG3wzDT13N63n7cWpPZtmWS\nRaE/sKzu5swxVXjZw3cV+xe/yQGlHzK59BMml87igNKZTCh+nd7un5F+fGxWR+5lcejqtgfahQhq\no/L2HWrZDhrJToxGtt+YtbrbLv3GjPw00smWajG2Ez3LVHKzdS9Y2PaK2u9X8HrTE4KUadt7a0j+\n9yPq/n0dVvUWjHWLCP37Ohx7jqf2vt+Q/OpNZCpB8vvPSC2bi7Fx+XbdT1dgt1mmEWwbdKfASEl0\nR77qvmWtxb5TnPTfU6WoLC38+w7RsC0YNEyjqExh5EQHT90Zpmd/lQtuCuD2KWh6uh+3T6Gkt0pd\njUVhaTpHRs/+KtP+FWHDSpMTz/ei6YJ99td59u4wQ4brpJLgaiMhY3s43IWO/fP2NZGmgklpsyb6\nIJviL9JAGCxyHMjewb/g0/Zs0o8uguhKkH2Cd+HXhrM8/FdsUmyKT8Op9mSI7woU0f2PlU8fBjmL\nh4A+ootCQ37IyH9QNKX7OPcLF+cL32UrTMaO6ni6Y4czX/HZtKVt/5ZDTxfabkBbr437gBNQe+1B\nbObT2JEaii59FH3IGFz7H0v8k+dJPnU9Wr+98f74POIfP4dj7/13uHkyF7uFeyNYFiybb/Dyw1FO\n/V3+Un/NEpOFX6c44tTsS9Gjv4oQ0GeQhqYLzr3aT0GJwonneREKePwCvf4Zbqjsvt+h2bwkU0/0\ncMfFNfQaoGXiIjRdgEybccK1Fi5P6z9Te/g7ish/kXz6EAAqkx+xInx75niR4yD2LXoKRbSezEwI\nhf7e8xBCZ0ndtQCsjTxImfNIgo6x7RhR50JX8guTFDsOpeU41v9V5AtArzqo267ceAU67ZX4dgn3\nZELmldhT2+GbNYy0YxfS877T0fbz4Rg0Mp3xMQdqsATfcRdl9mV9TcCd7XnbeaaZnQSmIenRT+XN\nf0eZPSNBPGoTCdm8+VQUocCy/xoEczz1ui4QAvyF6WOKKlCU9F8hBB6vyKwAZH19yEF7Z9eTRkoy\nYE+d0y/z8cJ9Ue67JsQTt4YJhyS2hAVftO1pT3WAauXT0kEXqyP3Zo6VOA9nXNGzbQr2Bggh6O0+\nGZ+2DwA2KZaFb6kP8+pe2DJfMww6xnT7GHZ2NP5dAvrobrv2wZPzn6lnX4xx/yORjLDdVqzdYJHM\niVS78Fxvm+dUVtnUhNLfgaJAUWHniD+hKOlUBDtZnqDdwr0RbAvOusLHDY8V8uV7Ca7+eTW3XlhL\nn4EaR/7cg6rmF+WN1juJGptw7Hovp8MtUOq1CsuEWJ3Mmxy++TiJv1Dhradi7Huwg/OuD3DkqR6k\nlEgbytugeAFt1o5tDh5tMBuiz1JrpClxTqWUfYJ/R2yjSUVT/OwTvBNRvwisSc2mKtn9iZJkToFF\ngY5HHdjtY9jZIWX+sxR0jOu2a190vg9XDqvXNOHWf4S5/e4wldXblrPTtiWvvBEnUe9DLSoQnPLT\n1m2Xti159e041TXpd0XVoG+fXTu0bbdZphnUVtqMP8zFZXc1XTbuNUZn6waLfnukv7q1S9NCpfGk\nXVdrU1CsYluQiEncXkEkZLNmqUEsLAkUCSxT8vIjUZIJyUMzSwlktH8wkuml5taNbWvBHdHcDbuG\npeEb09fDyciCB3CqpdvcD6S15ALHeGpSXwCwMfY8xY4p3Wp/lDmau1cb/IOte9mVkDkGvO6eAIft\npXHBOT7ueTAbH2HbcP+jUV57O8HpP/PwyzM9+LytPzNSSp58LsZDT2Zpuqee6KFP79bPe/K5GHfe\nk41RGTvaQc+yXVu33S3cG0FR4P1pcSb/2IWnGRri2Vf5mfVGnBPP9xINSz57q2kxaZdX4bXHYpx1\npZ9+e2g8f0+EvkM0PpweJxGTvDctxnHneHnnuRiDhmnEIpL7rwkx5kAnqxcbzHg+hpEEj19h+X9T\nbQa4JTog3NdHH8/QHYudB1Hg2G+b+8hFqfNHGeEeNr7HJoVK5yUEawt2juBy79bam4Uts9+RKjzo\nSvcVkVBVwVWX+vH5BH//ZzijdQNs2GRx+91hnng2ypGHuZi8v4OB/TV691LxeQW6DtGoZOkKk1ff\nivPsizGMHCvcBf/nbTbtRyye5r9PeznGy28maKht7nYJbrshiLKLx0DsFu6N4AumM9Jt3WgzcO+m\nwn3wcJ0NK01+eVAF/gKFq+4rYMm3+fbeffbTufXxKKf/3se51/j5y3m1LP7G4OoHCvn772tRFMEv\n9i3nnD/6OfvKAFvWmxgpePjPIQSCk37tZfE3BoOHa9RW21htENmbS1HdGI1rUa2LPQ6AwMGIgnua\nOFy3FUWOyZntmLUGS0ZRRfcJ91zN3aX2bqXl/y5yNffuFu4N+O2vfJzyUw+nnF3J8pX55pitFTZP\nvxDj6RealjdsDXPnpThsipO6iGTTZovvFpl88nmCDz5OkmrGZXX8T1zsNXTXF33/c/ncdzasWmRQ\nW2mz75TWnZit5XN/plchh7WRNteWBh9sGdDkeH/vr9g7cHMzZ2wbbJni4/LRmDIdzDEieC+9PU0j\n/roKq8L3siJyGwB7+K5isP+Sbrv2DwVrIg+xLHwTAF5tKAeUfLTDqHuxmM0ns1M891KMj2Yl8/xY\nXYmpBzu557aCTnOmdhK6ZAmxU93h/wI2rTExjew8teCLFA7n9v224XYUG7HspuXiAHq5TtyuazdA\nEQ6KHAdm9mtSczql3/YiVyt1qEXdeu0fCmyZtYU4lZ47lJPt8Sj8+HAXTz9YxEdvlHDWaR72GKxm\ngow6G2634MRjXTz0j51OsHcZdv21yU6GT99MUL3V4ozf+1n+XZpP/4/Xi7erz3A71B5D1jQ5FtT3\nJaCPbKZ1xxDQR7I1+RYAEXNRp/XbHuRSITUR6NZr/1Bgk/UPOXeiCN6hQ3RuvSFANCqpqrH5el6K\nF1+J82k7aMDtwaTxOtddGWDkcB1tO/M9/ZCwW7h3M079nY+l81O8/UwM05Dc+nwRxT22j9kRaofm\nnrS3NjqisKf/T53KKvHrwzLbcWtDp/XbHuRSIVXRNuf5fxG5+X8cSseYUV0FIQQ+n8DnUxjQT+Pk\n4zxEYzaLlpgsXGKwbIVJ+VaLrZU2FVU2kYgkmZQkkunYEU0Dj1sQDCiUlSoM6KcyYpjOQQc4Gban\nvrNR0LsFu4X7DsBeYxzsNWb7HJi5qGlHScCEtTlv36326/RAH13TobjnAAAgAElEQVTJrkAMu3uL\nFORq7u0NwvpfQ26WT4eyfavF7oDXozB+X0eHM0h2BElDcsYfqzl4Pye/+XmWhWPbktc/SXDbY2F6\nl6o8emMhRcGd27yzc49uNzJoLbS5fcJ9Y96+Xx+G6OT6RLqSaw7pvqImkK+5by/zZ1eFaWd53jta\nuEsJ736Y4NI/1vKHG7J1TnckbFvyp/vr6FOm8vD0CF8uyJqFvvreYM1Gk5mPlHLEJCcb28gouTNg\nt+b+A4Eq0qXimnukqtphc28s3Av0CZ3vUMuxDqmi7eIYEknC3MjW5Ay2xF8jYi5BFR7KXEfT13Ma\nfm2fdpuNcjnc2zJppaxqyhOvsyH+HDFzJYpw4lL7UeyYQonzUAL6SDTFR1uEBluaVCTfZ0noWgL6\naEYW3Ium+HM+T1GV/IQ1kQcIGfNwqb3p4T6WHq5j8Gl7dnhCsqVJbepr1sUeoTo5G4lFQB9DH8+p\nlDgOy3MuGzKblnZH0CAhLdTXbTC5+Y4w77yf9QH85+UYrz9fzJiRO25irgrZbKm0ePzmIh6ZHuX9\nL5IcMDq9Cvzs2yS//pkXj1vw659ln+0TLqnkwp/7OGJS99F+24vdwv0HAqcQOIUg1gx1davZHuG+\nKW+/0Ll/Cy07joSdNf3ka/HNY2v8bRaGrqynT6bvy5JRNsT+zab4NEqdP2JEwd3t4stLcp1v7TOw\nmjLGt9W/oM5ckDlmyTiGXUvY+I610Ydwq/3YJ/gPCp0TWu2rKvkx/605D7CpSG4laZfnCfdVkbtZ\nHfknsn56jlmrWR25l7XRRyjQ92NM4aN57dsDKSUrw3ewNvoINlkmTE3qc2pSs3EoJYwquJ8iZ5rF\nZFjZOqG62DHC/f2PE1xyVS2huvzn2DThrfcSO1S4P/5KlF8c7UFR4LQfuznn+iwJYVOFhUNv+lw9\nelMh7u1ku3UVdmmzTEJaPBxZztSK9xmw+RXGlL/FlaFvqbLaTuy/s8ElBK4WvELlZjvyz9jlmW2B\nhk/bu9PG1oCouSyz7dH2aLGdlBbro0/yXe3FmLKW5kIYbBmnPPE6cyqPpibZduENKbdtWW/aYb6t\nyhfsTfrEJGat5uvqn7IkdH2rBUlSdgXZrIsyEzQmpWRd9AlWRe7OCHaHUkJfz5n085yLUymhOvUp\nn1dMoTo5u93jl0g2xJ5mTfShPMGe2yJlVzCv5pesiTyIJRN5z4CuFjRzTtfBsiSPPh3lgktr8gR7\njzKFSRMcHH+0i5OOdXfrmHJRWWvx3NtxxtYn9Qv4lLy6rJGYRFXBsiWWJXn94zhfLkhRFFTwuBW+\nX27w2MtRlq/NmgfXbjJ5YFqEN2fFsdpBeuhs7LLC3ZaSh6LLuDG8gCLFycelP+KN4kNYZYY5tuoj\nwnbbyf13JngUgbuFcOktlk28DdNMysoGQLnVAaii81+kkDE/s93a5BEy5rO07mbs+gTsCi5KnFMZ\nX/wKU3ss4+Cy+Qz1/xFNBImYS/i25mzCxiJaC7iz2bbfsyLxQSZpWhoCr7YX44peYGrPlUwumUUv\n90kopDXJdbHH+Lb6dCzZfPSkrjTi1tcP1ZJRVkfuyxwO6vsxpWwuw4O3Myz4Fw4s+5yAPoqkXc63\n1ae3eZ8NCBvfsbjujxlfgyo8DPVfw6E9ljClbC6DfRejCh+WDLMsfDOLQ1fnC/du1tzXbbC44+4w\nyfp5qKRY4cG7Cpj7URnTnyrmgbsK2XtPvfVOuhCz5qYYv4+D0qKsSc/rFtTWWdRFbeJJSSgsufOJ\nMFfeFWLkUJ3HXoly/MVVlFdZnH1dNcOHaKzeZPHlgiQpU/LrP9cycZQDw4CTL6sisZ0FSrYVu6xw\n/zC5hTvDizjC2YsnCicxSPPRT/NyX8EEwtLk7siSHT3EbYJPEfhaEO4SmBlrmRMspZ3HlHCrfTt7\neEB+4FKRY2KzbZLWVhbUnJ/hXDuUEvYrnsbYoqcodOyPqvhwqmUM8l3MpNL38esjsGSYuVUnEzGX\ntnhtKdsv3E07zNLwzWRXDIIBnvPYv/gNip0HoQo3Xn0PRhTcy8iCf+VkvPyKleG7mu0zvx6tzPS9\nLPxnkvXmqoA2ijGFj+XZ1wUaewfS5e5skiypuz6j4beGNZEHMtsOpZT9iqYzyPc7dCWAS+3NEN9V\n7F/8Fl41vYLaFH8h7/zG+e+7Go89HSUSTX8nbhe8+lwxxx3tRlV3DpPGzK8SXHZWvp+od6nKlwtS\n/OYvtdSGbRavNkgZEI5JBvRSOWx/J98sNvj36zFuuDDAxFEODhjt4PI7Q7w9K8EhE5yM2UvnuENc\nVIVs5i7s3kLZu6xwfya2Co/QuD4wEp+S1Qh6qW4OcZTxWmI9diMNyZI2Y8vf4u/hjgXgfJOqYkT5\nGyw0ardr7M3BIQSD9ZZdJE+Eok3upwESKy+3TFc401J2FYl6brtAJ6g3n4hsdfS+jG3epfRjUslM\nChz7NcsGcqt92a/oBVxqHwxZy6LQH1o0jbRfc5dsir9AKkeLLdDHMcR/Zb3jNAuBoMz9Y3q5T8pc\nZU30AUKpb5v06lSaFhsPG4vZEHsaAIdSxpiiJ5rNvFng2JeeruOBdO3XkPFNq3cQMZZSnngrsz/E\ndxlBR35udiEEPn0oewVupLEPQsGBIrpXS37z3azz9Po/BBg8YOdx96UMyeZKm+FD8r+TXiUqFbWS\nZ24tYnBfLeNcPfZgF4oiOO0oD5oKNXU2xx/iRgiB2ykor7b4ZlGKs47xIIRAVQX7DNGJxnZr7p2C\n74wQvVQ3vdSm5oeT3APYZMX5zsiP2lSFgiltvkpVduiaKoKQbbDM7BqO90R3y86meQmDDe2wvQMo\nXZDQK2xkJ0SX2htVaXoNicmm2EuZ/T0D1+JUS1rtV1cKGej9NQAhYy7roo812y7f5t7ySySlTXni\n7bz+9y1+Hk1pPvBJIBgW/CsKDc+RZF30qSamk8YTpk2K9bEnM/tD/VfjVHq2OK4h/svqVwiS72sv\nbZLoLXtnkuXhv2bSLRTqE+nrObPFfoudh1DqPDzvmCraqNvYBYjFs/dzzJE7zrbeHD76KsHQ/tnJ\nJhq3WbrGIBKzWbDMQEry7O/7N3L69izJmnLmLTEYOVTn7OM89MwpAq5pgu42u+8802cno8pOUKYG\ncTVDi9tTT7MSFpt1jHbk20oNJDUdtMc3LMbDdtdwdg/xtByck5CSrxMG/ZvV7rNmAuiaLEV1xn8z\n2261f8ZWnYuloZvqHahQ6jyCHq5j2tV3D9exLKu7BZsEm2Iv0t97bhMBlau5tyQYIc2QqTO+z+z3\ncZ+O1kZEqyrcBBwjqU19BUDUWkraeZp9thrXjV0bfYgt8TcBKHMdRW/PKa3GKjiUMlxqb+LWOuLW\nWsLG982mhkhZWwnlfNd9vWe1SmkVQmGo/2oqkx9n7PNKF/hb2sKPDnHxypvpVdfc+SmOmrrzUAef\nfTvOwhUGC1caCNKRrgGfQmWNxdrNFsvWerCsbM2GwkD+9+1zZ3/XPQdqPHJjEes2m9z7bIRkCmwJ\nM+ck+fHk7g2u22WFu0CQlDZJbNyNBHyZmn6wvm/GfGJKm0q7aY52W0oq7SRJaeEWKoWKA7XRS5UW\noZJamW9bi9smVTKFE4UixYnaDOslbBvU2ilcQm2xzVCHRh9NYWMz1EcJvFQX40S/u4kISdtwu1Zt\nqE1lTQkFjvFN+OlJqyKjySo42cN/Vbt59k61jB6uo9mceJmotYKwuZQCPb9Oa3tt7hWJd7FkQ8EI\nhT6eU9t1nlcdQi1p4W7YNUjsVvn0m+MvA2mb+iDvxYg2Fsma8OFS+xC31gHp77M54R41V5OyK+v7\n1psUPm8OLrVP/cSxFtgxmvvvf+vj/Y8SRKKS8y6q4dXnihk7St/hOdVjCcmilQY3/DrA8YfmT3pv\nfhLnkekx9hygYUsyq7XGlMjcVZzfo/DeFwnWbjK55Iy0EmnbkqVrjDbrMnQ2dlmzTG/VzWYrToXV\nVFBr9bc9M1ne5DMDSbWdL5wlkn9Fl3FwxXtMqpjBYZUfcGHtV9Q20w6gLkfzj9gGZ9XMZvLWGRxa\n+T5X1X1LopFmv8GMclzVx0yqmMGhFe9zYe0cqu3m6ZqHeVrWeD6Pp9hkNDXN2DJFrnC3MahKftqk\n7FpHYUuDmlSWrtjD9ZMmbWqNbzKOQpfaF7faf5uu0c/7f/VbkjWRfzYdQ67NXbbMHNqceCWzXeSY\njEcb3K7rO3Mcpo3rtbaGAsd4/PqINtsJoebVNK1Nfd1su83xl2igXBY5JrZq6mmAIpw4lKz5qyuY\nUm1h0ACVP18bQFHAsuGM86u5/LoQFZU7NtKzotoiZcCPD2z6XimKYNVGk5o6SThqt5iWOBTJvluL\nVxtccnsti1aZxBI2kZjN3c9E+GBOcrdw7ywc4CilThrcEv6ORDNCTAECSnbhErFNNlgxTGxS2HyS\nKGdmYgvlVoJyK8Ej0eWMcxSzoOwYniw8gLmpKs6r+ZKobSKlpKK+nQRWmxFmJcuZk6rk09RWvkxV\ncmtwLLNLj2KDGeOsmtnE6gW8IW2uqZvPMrOOczxD+LzsSH7i6sOZ1bPZ2szE9BNfy8LdBBYmmwoe\nW6byTBWb49P5pvpUlodv7RQBvz76ZCaPe1Afl5dArAFVyY8z2z3cx7Zo424JHm1QJttjReJDzEYp\njHNt7q2xTepynKE93Me2u2J9vk29fW+pQGNY8LYmJpuWEMxZjdQZTfn3loxTnngjsz/AeyGiHRmx\nBFqetr4jhLuqCE490cMDdxXQv69KqE7ywstxJkyt4Ko/1TJrdpKqbayl2hmoDdsM6K02G6B00DgH\nj95YSNAvSCQlKRNcjQKW+vVU8XsFj0yP8PBLUa69t467riigV4nKgWdXsM8J5Tz1RowDRju6PXnZ\nLmuWucI/nFcT63kjsZFedW5uDOazCc70DOYkd1p7XGyEOLlqFjFpZl7b02s+QwK3B8cSty0itsn9\nBeMJKg6KVCfX+kdyUehr3kps5ItUBa/G12PXnz0juYl3k5soVpzspQWYoBdzmnsgQgiu8A/nuKqP\n+UdkCdcGRhCyU3ybqqaf6uXawEjcQuVIV2/ujSzhxfhafuvbK2/cB7odXF7o4x81ERorEh4hGN+M\n09WWcWjSGtbH/s0Q/xWo25FjxrBDrI42aNKCgd4Lmm2XK9x7Z9gn7YeCE4dSimnVITGIWasIKFmz\nRa423Zpw15QCDCs9EQW0fdp9fT0njXBrNv1c+PUR+LSh7b5GIEfDj1krMWUMLUco1xkLMGU6P4wm\nghQ7p7SzZ5FnQtoRwr0Bxxzp4ohDXVx8VS1vzEiQTEqefiHOsy/GcegwZbKT88/xMXE/B0o3qJ4j\nh+q8+Pfm8+z4PUrGefqfO4tx6vD7M/MZVR8+UoqmgmGlA7XOPs6D0yE46kAnl5zuw7IlqpIuFdjd\nmvsuK9x7qm6mFU3hvJoveTy2kq12gj8FRtGjnj1zazCrJbmFymmegXxjVDMnVclZnsEc6Cilh+pm\nlF7Ajytn8ivfHgSVrOAcoRegI1hjRRik+TjPO5T7omke9p3BfemhuChTXPyk6iPuKRif0bCG6UEg\nTdW8NjCChLSplSlGa4U46xdSThTcQmOt1bTAhiIElxX5OCngZnHSpNKySMp0BOsBbgeFatM3wpIJ\nZDOmCktGqTPmU9gCJ709KE+8ScpOh7U7lBIKmrEBx6z1GVuyW+2HVxuyzddRhJYOz6+X2wlrc55N\nOjdxWGvRquOKnmde9dlEreVo28D1Fnm5X9pXNqjIcUC7+4e0uUqgZ+4lnFpAoTP724RS8zLbfn3Y\nNuQGsvOKmewIh2oDliwzWbXW5JCDnNSGbD77Ml0j2LYhkYT3ZiZ5b2aSokKFYXtp7DFYY8hAjb69\nVXr2UCgIKgQDAqdTQdfSLBQh0oJT1tvFLQtMS2IYkEhIIlHJ+g0Wi5cZ1IUlRx3uYvSINO1RUQSu\ndmQ8cDnS729jvoKz/riqQi5VQREClzP/WHdjlxXuAPs6iphRchj/VzObVxMbWGDU8kjhRPbSAnkF\ndQdqPq4LjOTv4UXMSVVyuLMnh7uyNtaUtJnsyOcnO4SCQ6gY0uYP/rQG2CDcT/UMREXwVmIjJpK9\ntazWt8lKMwYaKJqqEDhQcKJmHgMbiY3E2YJGLYRgoK4xsBXeey4s4sgWBNLyulvZt+jZJhzvtiCl\nJGquYEndDaSFnWCw75JmedwNLBMAr7bnNl2nAQItj9WSsioajScrvCzZ1JzVAI82kP1L3qQ8MQOn\n0mMbrp/7krZPBWuPszMXitBxKj1J2OsBCJtL8oR7OIfl41EHtbtficxb2ewozf2tdxOcf2lNuzTY\n6hqbz79M8fmXnR/48+7MBDNf37ny2XcFdlmbewNKVRfPFR3E1b59WG9FOaHqE56IrWz19VQazbav\nFh/MOD1/6Sbr/zUnMhu+1MVGevm/oJ5Pb0vJo9HlqAhuCowCICB0+mteFpshUvW91dgpyq0Ee2jb\nJnBbgi0TtKRt1hpfM6/mHAw71OznLSFpb2ZezTn1Jh8ochxIP89ZzbaNGIsz2+11YDaFQOQE3ljk\nBzPZOYnDDFlNa9AUP308p6Ap7WeNxMxVme32mmUKHM0HcrUGl5p1kMas1Xmf5aYP2JbcQBITi2za\nhB0h3C1L8uhT0W43TTSHlat3fHrh7sAurbk3IKDo/M63F0P1ALeGv+f6uv+y3AxzU2AUzmZSyjZe\nSIWkwe2RRbyd2EhNI4aM3cyL3qDlbbLSL9S1dfPZbMUJSYNX4+u5wDuUSfUrAa+icZV/Hy6tncsF\nNXO4I7gvRYqD+wrGM1zvnBBxWyYz05CCE6daRtxan/m8JjWbuVU/Y4j/ckqch7SSflaSsqspj7/N\nsvBfsHLsv0MD1yBacBzmVmVyiI7WNxV5dEJbNmYq5WruLSf4yoUl41QlZ7E18Q41qTkkrI15/bSI\ndkgojzqwQ5HADrWUBgtT0tqS91nSzgbXebRt0NylkZfLfUeYZSwLYvFuqoLdBlyunSPlQVfjf0K4\nQ9qUcaSrNwc6Sjm1+lOeiq1irRnhmaIDm+WUN2BuqoqfVn2CT2hc4R/G0c6+eBWVf0WX8c/I0lat\nr0lps7+jhLPdg/hT+DuGqD5eKT6EYVogj+VwlLM3TxVN5pSqWRxX9RHTiw9mkrPzlo25VEin2ovJ\npZ8wr+bsPCdn2PyO+TXnoAg3/T3/R5nrSLzanqjCg2GHqDPmsTn+CuWJtxqVtPMwvuRl/FpThkwD\nDDurSavboC3nQzaypef/ZrlUSDPDY28ZUXM5X1edXJ/NcdvH0hYKOujH0EV2Qk/ZVXmfGTn7LrVP\nu/u0MTBlNmq6PSmUOxuaBg7HziFUD5uy8wRQdSV2SeG+ygxToDgoUvIjwgTgU3ReLJrCGTWf83mq\ngm+NasY78k0uuQL76tA8ChUH7xQfSl8ta/M92d2fhyPLm83gZyNREAQVnQ1WjGPcffmpJ8vrTkiL\nq2vncYizB1usOKP1QiY6S/my7CgejCxnasUHnOEZxKW+vfPy4nQUaW00O05F6IwpfIzy+FusitxD\nzFqZHbuMsyb6L9ZE/9Vmvx51EMOCt+NTWzcR5LNXOsbMkdh5ppfGpoU8m7vdsnCX0mJ5+FbWR5/I\nM+2owkdQH4tP3wun0hNNeLBkkpi1mvLEm3kTVHoV1LqA74jTGEBXgtn7kPkO9dwViXMbaqCmrK0Y\ndjZgryvST7QFRRGcd5aXLeVhNm2xkBIUJf1f00DXRNpBqgucjvRE4NAFTmf9f0da43Y6svtOp0i3\ncwgcOui6yDhZVbW+f0FGD1AUQc8yhYnj/zcqde2Swv2kqlnYwCOF+7OfXpznPAXwKBoTHCXMSVUS\nzq29Wf8UJHOE0QozTA/VRamafSHqbINHoitIYmPlvOQNlZIMaeMUKiP0Aj5KlhORJgX1pg5T2jwX\nW82L8bVMdJTwdmIT/4ou44nCSYzRi7g5OJpj3X05q/pz5hvVTCua0mT824rmPAOqcNPbczI93Mey\nIfY0G2PPE7fW50RvtgxdKaLIcQDDgrfhaJzqthnkFqy222kyaQyJkVeX1dHE5JG9x1xBlouUVcOC\n2guoTn2WOeZRh9DPexZ93L9okXu/d+Bmvqo6PifFQjvMMtrANts0h/zvqpEJUCZz2rV/BVSZ/Jjc\n72dHlSE87mg3xx29c+WV2ZWxSwr3RwsncUb1Z5xe/Tknu/tzsrs/e+tBXEKlykrwTmITT8dWMUD1\nMVbPCqeGtASVOcU8JjvL+CRZzoU1X3GEqxeVdpLp8XUsrU8OlmtzH6z5WW6GqbVT9FDdHOvqy53h\nRdxUt4DrAyOJSZPbwgt5I76Rg509ONbdlx+5evHLmi/5WfWnTHGUcbl/OENUH8WKk6VGXeckDZCN\nd2XGL6AKJ/29v6Sv+xck7a2EzYVUJWcRMuYRN9fUmzgETqUnQce+9HQdS9AxDpfSu10BNAC6yGqj\nhl3TSsuWYUsj79ymZomsPd6QzV/j+9BFOYJdMMB7PoN9l7SZ/lYRDvp6TmdRqP3CvaM1SrWc8oSN\n+foyT0C3X/veEHs2b7+za+fuxs6JXVK4j6unQL4cX8+XqUouqJlDhZ3EwiYoHAzSfJzrGcIF3qF5\nZo+JjhIKhSPPBn9XcBz3RBbzYXILH4Q24xAKe2sBbgqM4rnYmjzmxOmeQXl2eL+i83DhRG6qW8D4\nre9gSsmemp+r/MM5zzsUXSh4hMazhZP5W2QRbyY2cnTlTPyKziitkHMDo7qAziTT4fk5jmSBQFU8\neJSBeLSBeekDGgRKW7lRWoNb65fZTuQ4V7cFKbsixz4umrBuFOHMmDGS1ta8z6S0WRG5ncrkzMyx\nIsdk9vBf1W77c24itPawZTqav0URLSeXUnFmTEk2SVTa1oKj5oo8sxs0v5LbjV0Pu6RwBxig+fi9\nv2UnX3MYovlZ2PPYvGNlqotbgmO5pZn2m604nhyGyPneoZzvzY9IHO8o5s2SQ1u9rkfRuCEwihvq\n6ZFdCSlNbJlqNx1ue4R6AwI5YfVhc3HeyqG92BJ/LZvm1jGxiTlIE34M0nbxZE4t1/T+VjbG/pN3\nbJDvom1yLMbqk26l0bZwV+hYBsDWTCaq4sWy08I9ZVe1q+hKVfLTJsdyzTu7setil+e5dyVuCIzi\nCv/wHT2MtpEjR21SmLJp5GtXosAxLlPNKGquItmoWHdbMOw61kYfzewP8l3UpE1uYqykVZ4XtJOy\nq5qYgwoc47ZhBJKKxHs5+21rvh39jgUtO9BzTVFRc2WL7XKxNTGjyTG7hVKBu7FrYbdw/x+DJaN5\nlLrugEMpyklxYLEu+mS76oQ2oDL5YSYPvFsdQIE+vkmbXMEnsUjaWY64lGYT+3V7g7YsmWRp3Y2E\nzYU5/bc99pTdsYIvrfkxPGrWFBVKtV6tKd1mfp7zuAHtuXcp4YOPE4w7uJw77wmzcInBxs0WtSGb\nRFJu0++3M8Ew5O4gpt3YhZDzHloyRq0xF5++V8vtuwA9XMdkBE154g0G+n6Do51FmnO19h6uo5u1\nZ/u0oeQmcI6Za3CraVu/pvhRhTePCbQ2+jB7Bm5o1TwksVlWdxPrY/9u9EHbgi1hbWyzTfNo2dlZ\n6jqcLfUpi6tSsxjCZS22Ne0wC2p/S8OP71b7Z/L7JNvB7U8kZH1KXpt/PBDhgScieNxp2qGuC1xO\nwYC+KoMGqAwcoDGwv0b/vip9eqk7ZZCQYUje+yjJvQ+GWbPO4s0Xihk6ZMcV5O4O7NLCPfHJDKyt\nm/Ge8n/IVIrotMeIz3gZc+0KhKqhDx+D7+zf4dz/4B06ztAd15CY+RbFj7+J1rtf2ydsIxonmFoR\nvoMix4F4tAGdfq2WUOo6guXhWzBlmLi1nvXRJxjs/32rwtWWKZbV3UKdkU6Y5VEHM8h3UbPabcCR\nX7wjai6n2HkQkC77F9BHU5P6PPP5htjTOJQS+nh+3oTZYtghKpMzWRt5iDqzaerd9mjuuRHAnYVS\n5xF4taFEzeWEUt9QHn+LHu6mufNtabAy8g/i1hogHRg1pvBxvqo8DosYiXaM7bJra6mozJqfEom0\nwM/VFJavbKoBCwF+n6CoUKGwIJ3kqyCYTvgV8Cv4fQKvV+D1CDwegdul4HaD2yUyvHWnI81j1zSB\npoGmpv8KAUIRNNT3sO10kjDDhETCJpGAcMQmVGdTVW1TXmGzdp3F0hUm879LEarLjv2qG+t4+emO\nMZp+KNilhXvNH36JY/QEPMecStVvTsFcuwL/+VfiPvbn2HU1RJ+4h+rLzqb4kVdxDB+z4wZq29ih\naoSza8pwpTVdhQZbccquYGndnxhT+Pg2ZBbcPrjUngwP3s6C2t8BNisjf8OhFNLXc1aTqk2QFuxr\now+zLpbW2gU6w4O3tUhbLHZMJhtp0JAPXQICVbgYXfgQsysOI2WnmTSWjLE8fAsrw3+n1DUVn7YP\nNgnCqYVUpWblReE2RfPCXRW+zOqg45p7y9AUL/sE/87XVSchMfhv7QWMU16g0DGh3qdhYck4i0NX\nszmRrgSl4GTfomfw68NxawOImIuJmauxZapV5+1Hn3bM6Sol1IUldWGLNet2bCGO1rB6za5vmtm1\nbe5S4hh/IOFH78KurqTkyXfw/vxXKF4fWq9+BP54O+6jTiR83y1Io/Ozz7WG2GvPEXvlaQCEy43a\newCKP9jGWR2DUylDaeSoq0x+nBd12R3o6f4pfT2nZ/aX1t3MvOpz2JqYgWHXYEuTuLWBDbHnmFt1\nCsvDt5IWpAp7Bq5rNaRfEU78Ocm0alPf5FlPHEoR+xX9B3+j0nU2CcoTb7EycgerI/dSmfqwWcHe\nKy8HffMO1YCeZTsl7a3NttleBPTRDPFfQdpLbjOv5izmVp3Mf2t/yTfVv+DzikMzgl2gMbLgfoKO\nfYF0ucL06FNsTbzb6nWuuMjfJePfWXDaKd1farC7sUtr7svyT7gAABtXSURBVADCFyD+1jSKH38T\ntTg/ZFsoKp4TzqDm8nOQsSgi2H2Re5FnHsCu3IrnhDORlolSVAxK1wSXuLVB+PXhhIxsPnBdCaIp\ngVbO6hrsHfgzoLAp9gI2CSpTH1KZ+rDF9poIMsR/GQO857XZd4nz8Izjs7n8Mj59byYWv8Pq6D/Z\nFJtGzFpDa7RGh1JKUB/LEP8V+PXhbI5PBxrMMk3PG+A9n9rUHCRWuyJ9m0P+JNzU/KQIncG+i9BF\nAauj95CwNlFrfE3j+cinDWdEwV15E06Jc2o9NdJmXfQJylw/brFK1C/P9PDmuwm++qZ7lZ7uwKQJ\nDq743a49ecGurrkD0acfwHPiWU0EewP0IXtjh2u7XXMPXvlXtP71mf0sE8Xrp6tKz6jCybiiaQz0\n/gZdKUJXihkevK1V2l1XQREOhgX+woSS1wjqrdMRi52HMKn0ffp7ftmuvgf7L6LUeSSaKGCgr/mK\nUEIoDPJdzMSSGYwvfpm+7tNxq/1QcKIpBfi0YfT1nMW4ov9wQOlHjCl8jIA+op7v3yBsm58QSp2H\n09/7S1ThoafrhHaNuTFKXFMZFrgNh1JGX/dpLbbr6zmDSSUfMDJ4P0WOKWgiiIKTgDaK0QWPMKH4\nNfxa/iqln+dsRhU8hEvpjVtrO/HYQ3cXMLD/rhPN6vMKbrk+wNMPFnZLlacdDfEDojRt80A3T+iN\nUlhC6UuftmjykMk4Ww4eStkbc1FL2y423BUI3foHrJpKCm9/rN0h/bsCpLSImisIm4tIWpuxZBJV\nuHCqvQjoo/B2OPd712BW+QQS9gZAcHjPVa1GkzZASsns1GLmGasYrPXkCOdYtGZ8DO3BjMQ3HOXa\nFn7+9qM2ZPPa23G2VthYFlj1TkyrvqycZaWrKDVsW7nbzezbtsRs2LbS25nz7fx+c69nN+7Lzr++\npoLXKwgGFIoKFUqKFcpKFHqUpRk8ewzRGDFMx+PeKd+vLhnULm+WcR/381Zt2VbFVoTXD1pai5VG\nKu2WV1RkXQ3StlGCRQg1+0La8RgyEgZFQXi9KK7m7XfSspCJODKZAMsEVUMJFqY1dGkj6s0w0jQR\n6o7/KcKWTdSWaECBpqB10URjSkmNaVOkKfj0vTqVlmlKSdSSBLXOV83GFD3KwtrLcWt9M0FZrUEi\n+Sy1iFfiX3KV/0S22CFeiX/BKZ4DO3T960LPdKpwt+NRos8/RPydl3BOmIL/t9ehePILxBQEFc4+\nbduKme9MWGmWc2zlXRzrGssV4WXsnejNqe6JHODcE/d2JlCLy1STPg6tuIV3S67C0c6i6F2JHT+C\nLoZz/EGtfp78YiZqr34onvQDXH3JGSiFxXiOO42a6y6EVArPT0/Df/Gf0gLeMqm+6DSMhd+C7kDr\nNxj/+ZfjOvioTJ8ylSL+/qsk3n8dc/1q7OoKZDyGcHso+sczoGmEH7gd/6//gGPkuLSKIrY1IL/z\nIIGXquL8fUuYjSkLjyI4LOjkb/2CeJupybq9WJEwOW5ZFa/uWcxwd+eahpYnTM5ZVcOcfco6tV9I\nO0wnlb7f7vabrRqejH7IfYW/xi0c9FAL2WJVE7ET+JRtT7s72blt6TRagx2PUnf3n/CeeDa+sy/G\nrthC+MHb8F/wRxRv51QA2xkwO7UckFwXOB6PcBKWCVaYW7g2NI2LfUcyUOt43YSno59xvu+wvGNu\n4UDbSazdO8couhBKQcspae1wiNirz+I+6gSEM/2ypeZ+RvLzD4i9+gylL31G4MpbiL3yLObqZUgp\nqb3jGsxVSym48V56vDUPx36TqbnyXBKfZsPTa/5wLqG/XolSWELBTffSY8Z3uKYeg0wmQVWxaypJ\nff0pxpI0h1paJiDSK4YdgBUJgz9tDLGfV2fuiDI+GlZKzJI8Vdk1YeqKAENK/Ern368lIWbvHKbG\nuanlXOk/MU+721vry+uJOdvUzw2hZ/jOWMMEvWP1Z5tDas4sPEefgr7XSISqofbsi/uok0h993Wn\nXWNnwP76ECY5huJX3KhCoUDxsJ9jMLcEfsbL8bnb1XekmRw9Q7WeSODkqnu2q+/OwC4v3FuCuX4N\nVb86DqWgGO9p+UwMGYvinHQYaqAA99EnoZT1Qmg61paNxN94Ad/Zv8N95Ako/gC+s34LQOTpbHEL\nY8USih+YTvD6v+MYMQ5z1RISH72N++iT0ffJBtvoQ9OFtbEs0tkad4xQenBrlL4OjX8MKKCHrtLb\noXJDnwD3lUcImdufQTBlS366rAqr4f7q/xR0genElJLhrp1jQbrVCtFHzVcueqqFzE0t36Z+Ku0w\nQ7U+uJXOYXNJyyI67RH04fvmHdf6DCTx3qudco2dBVvtOnqoTc2yLqGzydo+KrBJUx5/gfCgIHii\nsHmHfndi53gLugBWRTq3SPQ/j6AUloCUyFQSu64Wa8MajGULUXv0xn/e5U3s3cIXwHX4MWlTiVAp\ne/FTEILE7JlgmbgOPjLTVi0qQSnrhbU2W0C59IWPM0tba9N6aq78Ja7DjiF49R2IHDe9tkc9L9s0\nYAfa3N+rTfDnfkH0nJVDT13Bryp8G0txaGD7Kvc4FMHiuIFNOsyogUjo6gLNPSlh8E4i3IOKh0ei\n73GZ/6eZY06hM99YTcxOclv4JYoUH321UvbW+nBj3fO8VPxHAL431lJrRznQOZxSNYgTjXIrxN2R\n1ylTghzhHEuJ2jEqq7FoHo59JyP0fJOY8Pgw168CKYl/9BZW+UbcR56IWlSKtCzsmioSM9/AdfBR\nWJXlCIeL5Nez8J50DjIRI/ry07gPPgp14FCSs2ZgrFiMY/QEHOMmI6NhjIXfovbuT3zGyzgPPgqt\nV19irz6L74zfdBlTLCwTjNGbRmJLII5BhVXHM7HPGecYxCi9H7OTyznYOYxnY58jEPzCO4kPEgvp\nr5Yw1pHfjyktZieXMye1gsOc+zDK0Q9dqAgh8AsXCZni+dgXOIXOia7xuDqhqtq2YJfV3O1QOgtg\ncvZHJGa+ReKTGSRnz8T47hvsqq0ogQLsmiqqLjiBmht+hx3LZvFzTjo037HUIPRMAzQNte+gvM+0\n3v2RZk5Fp3rBLhNxam+5HBEIErzmjjynLIDiS7+c0rJANldqu3sQk1DaSIt2KwKXIlifymoncVuy\nNmlSZVhNxiqlpNKwWJc0idlNA/RTOauSBuHekDdfSkm5YbEmaVLTaKVQaVhELBtTStbXf96Y4RWx\nbNYkTUKmTcS2KVR3DkbEeMeePBSdwfzUqrzjbuHk0dh7HO4azaX+4znetT+3h6dj5WiCm60arg79\nm7XmVhYYa3g+9gn/jLxBL6WQqc7R/DU8jXgHU/eaKxbhnDClyXGZjIOiYKxYTOKTd/Ac/TNS87/E\nqthCxZlTMRZ+i4xFCN11PfF3phN77RlcB0zFWDSfxOcf4Jo8lcRXn2BtWEPstWfxHH86iQ/fIDb9\nCayqrRjLFxH62zU4xk7EXLGI+AevYyxdkCfYa645H7uu+UpaHUFCGgzXm9I+EzJFQLh5MvYphzqH\n00MJMi0+h++MDbwan0tCGvzINYJLap/m6dhnzExmE8ddUvs0K81yVplbuTz0LMe7x7HY3MgasxIJ\nWNLm/cT3vBmfz+zUcg5xDucv4VfZatU1GUdXYpcV7voeaedT4W0PU/rSZ5S99Bml0z+j9KVPKZ02\ni9LnP6LkqRkU/Pl+Ul9+TPiB2zLnOkY2z0hwTjyE4kdeQ2j5mqFMpdIFMHKP2Tahu67Hqiin6N7n\nm7AQ8mCZaZbODjLLHOR3MKM2kXdMqXfwrkumBY4ELlpTy5TFFfxoaSU3bgiRzLFtzwqn+NHSSqYs\nruCcldXErPzvw5BZLmvD34aHb0YoyWGLKzhoUQVHLKng6Rxb/2krq3m+KsadmyO8F0ry5411rEhm\nQ8dNKTljZQ0HLargJ8sqeasmgb8LnMAdwQCtlAcLf8tvax/klfgXmUnpwYILqbWjHORMm+V0oTLV\nOZohaq/MuYe7RlNtR7gj8jK1doSA6uUE9yRO9RxED7WAn7jHc1e4YyYUq3Ir6sChTY7H330Z5/6H\nYG1eT8EN96AEC3HufyiRR+5E1tXiPOAw3EedhF2bzirqnHgIau/+JL+djblmOdoew9H6DiL6wqP4\nL7watbQngcv/QuzNFwAwFs/HMW4yjv0OxH3UScTfeB7HuMl5YzBWLIJOTIkRlyl6NUpZEZMpLg09\nw/6OPeihBBit92cvvRd7aD34MPk95XaI3/p+xECtlJ+4xrLSLKcsZ5V0rGsslXaYeeZaphVfxGCt\njKnOEbwYm4OJhUSy1Q7xbPxz7gz+gt5qAed4p7DAWNdp99Ue7BxvQRdCuL1pjVkIhFAQqoZwulCC\nBWh9B+I+4qf4f3ct8TenZc5Ryno135ltY61fTeXZP2bzxL5sntCbzRN6YyyalybbNkBKEu+8RGrO\nLAr/+iBqSevMDWlZkErRASp/p+Cynn5eqonzRThfE5zgdTDKk15Kvl0b5+NwkvsGFvDAwEKeqIgx\nL5YO/JIS7tgc5vCAi3kjejDaq3Pe6hqStuT1mji3barDBo5eUsmZK6vz7tKWkkcqovTQVWYPL+P+\ngYXcuKGORbH0SqjGtPk0nOL7eIqzSz0cV+jm3FU1mYnlycoY5YbFtyPKOKbAxX+q4/h2EuEuEBzi\nGMHNgdP5V+RtbgtPp8oK41Pc7KX1yUuatq8+hCFaz7xzS9Ug9xf8mrH6YI5xjWefnIpWe2l9eSP+\nVYfGJY0kqrdphKb3pHPwn/t77Fg4Y6oUuo6xehna4L0QugO1Z1/8v7oCY+l/cU48FDQdY+G36HuO\nQCgKWr9BpL77Cq3/kPrzHZm+kvPn4D3lXIQQ6cR9Yyai9e6fNwbh8XZajqXFxkY+SHzPfHMt1XaE\nqJ3k8+Qy7o+8x9megxDA0a4xmdiSMiVIpR3h177DUesnmBF6X6rtKIPULKtmoJauHTBQLaF3fS3f\ngOLiK2Mlifo6At+k1jBK70+BkqZJFwgPSdlavqLOx87xFnQl2sFAsSu3ohRliz0Id/O89dCd11F7\n/W9RikooenA6Ze8vovihl9NpA3KEe+q/X/P/7d15kJT1ncfx9+/pu3u6ey4ucbjEoBLwiChG8NjI\nKhjlNBDDoW4ilG7tqmvW1FpJdKUEyxgXdaNJiKglISQatZBojKLRLGpWjSiCyLgzck7P9Jx9dz/9\n/PaPp+mZRkkswjD41PdVRdUw3U8/v+7p/vTv+Z1dy28mesuK8hXEX6WtAa25Twi4+eHwCNc2d/JJ\nn1rxioYIl1X7sbTmiY4Mpwc9zIj6OSPowQS6i3Z5NydzxAoWdzVEqHEbzK8N8noyz/uZAvGCxZMd\nGc4OeVnZEOXBUdVowFv6u1jA++kCPxwe5nivwaSQh/FBN3sKvU0UL/fkuH9kNW6lON7rYl/BYlum\nQN7SPN2R4cKIj3q3weJ6ezhrqB/a8g+XUoqv+U/lt3X/wXZzN3M7VtBjpYkale8xj3Ixok+A5D8j\nCPou8lZvhGk5xEbgf7NMvgA6e+iNygsf9C5TYe5uInDhpbiGVu765D/vEpTbYwej2433tLMB7P4t\nAF/ffprSssPTZlVcwXrGjEMdNAdF+fzgOTJt0+8VdvNKfjuLOx7i9NitjI/dwi/Sr3Bj1XTO9X2J\n9wu7SZQ2bNdovt/9BJf7zyDYZ3SThcbEYqSr/lOPP9Soxih9QXtwk7JytFsJAN7INzI7cGb5vvck\nN1J9iA3Y+8ux0fPUj7R16JXptNZkN20ktX410dvu673hM45JrfsZmQ3riNxwG8H5/1RuP3edPhn3\nyDGYe3cdOCE9P1lBcM5CvGd9vqWElduLlUwMWLgrpVhQF+R4r4uFH3dwRW2Q64dUldvEC5ZmZ9bk\n2kEhXEqh0RhAoHT7L9vTTAp5MEr/H+qxJ0A1Zk2uGRzima4MI3wuvhLyopQ9M7Wm1MavgFk1fs6p\n8pVrUEM8LvqOZpwY9FDrtl/vapc9fagpV2Skz01LociycBVKKepLjxk6Rtrc+wobAR6pvYHfpjdz\nRcddfC88r+L2bYXdhI3erQ93mr1bBVrYm2P07ZXx/h0fXdewBgqf7MR7ij1yK/HIKnQqgVE7GOV2\nYzZ9RGL1j9D5HK4hwwnMXoz5kzsrHsM78Sz7h9LfzKixv5iUP0gxto/kI6tQPj860YO5p9k+5qDm\nTqN+CMrVG+RWT1f5CvtImB+cTFbnWRycSkrn+GPuQ36U3MjSzodZEZ1Pu5XkD9mt7C12Um0Eub7q\nIjqsyh20dplxgspb0SwTK/bgVgbuPuW0sAgY3nK7eszq4gTXkPLt11VNY4jRPwsDHopjwz29odTO\n9+FWXIOGYVSFweMFNDqTwYrHSP1mDZln1hL4+oKKDiad/vT47tyf7b0ofef9Y2/HqNbk/vIGZtPO\n8gzX9HNPYsVjRH/2VMXxOpvB6u7EqPt0E40RidofAGtgNy6eEvbx8smD+E5TJ9c2dXL3iCi1boMi\n0FKwmBqxL5ctDf88JMT4oP32eTddYEa0t6YWMOyVWBLF3jBS9F5EaeCUgH2sASysC7G0qZOmvN1R\n25QzmVPTG3Rj+4x+CRoKl4J20yJjaTqLmomliVCZ0jdC8BipuX+z/W5uj1zJie7jUEphoJgXPJeI\nEeS57NvMDkxGoShok19lXmWMeyj/4JuIQvHrjL2xiUaT1jlQkLZ6+0WMvyMAvV8+g9STj+I56VSU\nYRC+6l/tc2lN7vVN9sS8b99s/y6fI7XuITioqevgWd8HPhPZ/3kRIxgifPUN9vGFPNmXn7WPqa5c\nP92oimAleq8+8tvfPezndCg+5SkPh7w0cBoX+yfw3e51TG37T8a6h3BHZF55Qtl7+V10HhTum3Lb\nGOGqo8NKMcxVjdaap7NvMy8wiS4rXVpUGlI6R70Rptm0N0JRqIoO8qFGNd/v+Q0rowuO+HM8FMc2\ny/gmTcE1dDg9K/+d+OKLiS+ZTvtVM4gvmUF88SXEl0wn89yTRH+wishNt1eMZDlQ0+grOHcJKEX7\nsrkk19xH+ndP0HnrMjq/e419h1KHavqpx3GfeAq6UMCM7SOzaSNdy28iftUM4kum2zNbS3TebuN2\njRqL1dlmt70PMLdSPDiqhgsiXq5s7CiPTslYmuM89tvFayhuGRamw7TDtKjB0ydQ86XO07416IPX\nUUyVgv/tdIF7YwnuaIjwwrh6/jCunqlVvooNMer6jOTxGHZLdU5rTK3JWZrBpXLtKY3sCRwj4X6W\n90vMbF/OmvSLtBW7MXWRZjPGW/lGilisTb1CWue4N/EM03yn8WFhDws67mZO+51szm3nwCu2uxjH\n0po/5rcekXK5GsZgVIUr3osAxdheeu65taJZMrvpWXJvvnrQA7jIvbP5Mx+7uLcZbWm0aWI276Tj\npkXlz9OBSXsHKLeH3Gv20sNa634ZY+9Vbl7M9r5ubuXirugCfly9kEYzxp9LI5lai90s7nyIOxJP\n0Wjae3rtMuM0mjEu8n+ZB5IvYGnN87n3+HX6DSxgh7m/HOB7ih2c6R1NsbQc9DneE3mhdF6N5oHk\nC2SOcpu7Y2vurqHD8U6+gOCl30CnkxQ+2kqxtQW0xqitxzNuAr6zz0d5KyeGuEefSOb3TxG++l8q\nfu+fchE1K39Ocu1DJB+9H53LYtQNxnfOhVQtvp74Invse+Brl5Fcs4qWKaNQ/gAqHMWIVKPCETyD\nh6GqohgeHyocwdz1f3jGnoz//ItJrf0pVut+jJEnHLXXCODVnhx7C0Vm1wTK4879hmJebZAHW1P8\nsj3NNYNCVBmKnqJmUClnu4qamz7pYsO4eiYEPbQVeq86WgpFClozrlTjVtijWg5UcxTQVhryeF1z\nF0sHhzjea9+33bTYli2g6a25+/r0m7iwa0Va219EXnVgCV7Floz94ckfIzNUbwzP5BuBKbyU38ID\nqY24UEz0jObfwrMwMFifeZXlPev5qvdkvh6YxKzAZNakX2KsayjjPSPZnN+OQrEsdAl5CkzzVe42\ntaxq+mGVSylF6FvXkfjv5WRf+z2uoQ1Y7THMxm1EbrwDzAKpX/2cYmcc8+PtRH+wiszTj5ePN0Lh\niqAOzlpU/jlwyVySj91P6+WnY6WSuI4bgWvECSiPl/xbf4KF1/UWxOVGF/Kk1q/GSnTjOWki5t7m\nw3pOhzLMqOZ7yfXMDHylXEP3KjczfKcSC3ezKvk8TcVW3sw3Mj8wGb/ysCrxPGd4R9NstvHj6LcI\nGT6Wdj7MV9tu50zPKKb6TsKNwQhXHb/LbiGtc7yc285tkTm8ltuBicUEdwOfFOM8lvoT7xaaabUS\n3BO98og+t79Ja/1F+XfMsopF3bro4oEuxmFZH0/pYe/s07N3xPWbiZzOFou6q1DUj7Wl9Oi/7NMP\ntyZ1wbL0rB1xvaY1qS1L63zR0rN2tOm1bSmttdZb03k95YOYtixLW5al18dTevqHreVzLGps1wt2\nxnXRsrTWWu/I5PXYd/dr09L68o/i+uytMb0lldP/m8jpC7a16mHv7NMbOtJaa63PfL9F37m3u6LM\n499r0fftT+hus6jP/SCm30jktGVZ+qqP2/WNzZ16XTx1lF49cUQVi7pt6cwBO317MaGfzbwzEKfu\nl8x0bM39aFKGwaDHnh/oYhyWebUBRvjcPNuV4ebd3ezKmfgNxalBDysbqplZ48etFCsaovxXS4It\n6QJZSzOrJsDcWrt2PT7g4ZpBIb7T3EWVoXApxeMn9E67v7IuyAOxZLmhpc7tYojHYHfeZM3oam7f\nm+CKxg78SnF+2Mv+QrHcmXtexFf++YAxPhdeA8KG4vbhEVa3pXg0DpNCXi6rDnBPS4IFdc7faeeL\nrvDRVnuIZam/ykolUN7+2Wry88pYR7fppD85ej13IcSxK371dIJzFhO4aCZ4vWRf2oDV2U5o/rcH\npDwdVpJ16de5vmra0T51v3QSObZDVQhxbKu593Gstv203/BN2pfNxkolCc5ZMmDlMTB4K980YOc/\n0qTmLoQQQEGbrE69wtWh8/Gro7rIV7/U3CXchRBiYEmzjBBCiM9Hwl0IIRxIwl0IIRxIwl0IIRxI\nwl0IIRxIwl0IIRzoi7T8wLGx1J8QQnwBSM1dCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJd\nCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEc\nSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJd\nCCEcSMJdCCEcSMJdCCEcSMJdCCEcSMJdCCEc6P8By/IaOuJ9ejwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a76d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import pandas as pd\n",
    "\n",
    "nd = pd.read_csv('/Users/zoeolson1/player_data3.csv')\n",
    "nd.head(2)\n",
    "nd['nationality'] = str(nd['nationality'])\n",
    "\n",
    "\n",
    "words =' '.join(nd['nationality'])\n",
    "print \"amount of players for analysis: \", (len(words.split(\",\")))\n",
    "\n",
    "cloud = WordCloud(font_path='System/Library/Fonts/Noteworthy.ttc', stopwords=STOPWORDS,\n",
    "                      background_color='white',\n",
    "                      width=500, height=500).generate(words)\n",
    "\n",
    "plt.imshow(cloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~zoe1114/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "\n",
    "py.sign_in('zoe1114', 'gqr5grvyef')\n",
    "\n",
    "nd = pd.read_csv('/Users/zoeolson1/player_data3.csv', parse_dates = True)\n",
    "nd['dob'] = pd.to_datetime(nd['dob'])\n",
    "nd['year2'] = nd['dob'].dt.year\n",
    "\n",
    "layout = go.Layout(title='Year Born vs. Number of Appearances')\n",
    "\n",
    "# Create traces\n",
    "trace0 = go.Scatter(\n",
    "    x = nd['year2'],\n",
    "    y = nd['appearances'],\n",
    "    mode = 'lines+markers',\n",
    "    name = 'lines+markers'\n",
    ")\n",
    "#trace1 = go.Scatter(\n",
    " #   x = nd['dob'],\n",
    "#    y = nd['wins'],\n",
    "   # mode = 'lines+markers',\n",
    "#    name = 'lines+markers'\n",
    "#)\n",
    "#trace2 = go.Scatter(\n",
    "#    x = random_x,\n",
    "#    y = random_y2,\n",
    "#    mode = 'markers',\n",
    "#    name = 'markers'\n",
    "#)\n",
    "data = [trace0]\n",
    "\n",
    "# Plot and embed in ipython notebook!\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='line-mode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~zoe1114/4.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv('/Users/zoeolson1/player_data3.csv')\n",
    "\n",
    "dt = pd.DataFrame({'count' : df.groupby(['nationality', 'Latitude', 'Longitude'])['id'].count()}).reset_index()\n",
    "\n",
    "dt['text'] = dt['nationality'] + '<br>Count ' + (dt['count']).astype(str)\n",
    "limits = [(0,1),(2,6),(7,12),(13,17),(18,24),(25,30)]\n",
    "colors = [\"rgb(0,116,217)\",\"rgb(255,65,54)\",\"rgb(133,20,75)\",\"rgb(255,133,27)\",\"rgb(0,255, 255)\", \"rgb(255,255,51)\"]\n",
    "cities = []\n",
    "                                    \n",
    "                                      \n",
    "for i in range(len(limits)):\n",
    "    lim = limits[i]\n",
    "    df_sub = dt[lim[0]:lim[1]]\n",
    "    city = dict(\n",
    "        type = 'scattergeo',\n",
    "        locationmode = 'Africa',\n",
    "        lon = df_sub['Latitude'],\n",
    "        lat = df_sub['Longitude'],\n",
    "        text = df_sub['text'],\n",
    "        marker = dict(\n",
    "            size = df_sub['count']*20,\n",
    "            color = colors[i],\n",
    "            line = dict(width=0.5, color='rgb(40,40,40)'),\n",
    "            sizemode = 'area'\n",
    "        ),\n",
    "        name = '{0} - {1}'.format(lim[0],lim[1]) )\n",
    "    cities.append(city)\n",
    "                                      \n",
    "layout = dict(\n",
    "        title = '2014 English Primer League Player Orgins<br>(Click legend to toggle traces)',\n",
    "        showlegend = True,\n",
    "        geo = dict(\n",
    "            scope='Europe',\n",
    "            projection=dict( type='Mercator' ),\n",
    "            showland = True,\n",
    "            landcolor = 'rgb(217, 217, 217)',\n",
    "            subunitwidth=1,\n",
    "            countrywidth=1,\n",
    "            subunitcolor=\"rgb(255, 255, 255)\",\n",
    "            countrycolor=\"rgb(255, 255, 255)\"\n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = dict( data=cities, layout=layout )\n",
    "py.iplot( fig, validate=False, filename='d3-bubble-map-populations' )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please view using NBVIEWER through this link: http://nbviewer.jupyter.org/github/kylemh/FPL-DataVisualization/blob/master/jupyter_viz.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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