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simgeekiz/türkiye_yunanistan_madalyasayılarıkarsilastırma.ipynb

Created October 8, 2014 12:07
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türkiye_yunanistan_madalyasayılarıkarsilastırma.ipynb
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from pandas import DataFrame, read_csv, concat\n",
"from numpy import random\n",
"import numpy.numarray as na\n",
"from pylab import *\n",
"from __future__ import division\n",
"%matplotlib inline\n",
"# General syntax to import a library but no functions: \n",
"##import (library) as (give the library a nickname/alias)\n",
"import pandas as pd\n",
"\n",
"# Enable inline plotting\n",
"%matplotlib inline\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dftur=read_csv('../dosya/olimpiyat/total_countries_TUR__medals.csv')\n",
"dfgre=read_csv('../dosya/greece/totalgre.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dftur=dftur[['Bronze','Games','Gender','Gold','Silver','Sport']]\n",
"dfgre=dfgre[['Bronze','Games','Gender','Gold','Silver','Sport']]\n",
"dftur\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Bronze</th>\n",
" <th>Games</th>\n",
" <th>Gender</th>\n",
" <th>Gold</th>\n",
" <th>Silver</th>\n",
" <th>Sport</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 1</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Fencing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Fencing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Fencing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Sailing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Cycling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Fencing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Cycling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Fencing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Equestrianism</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Equestrianism</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Equestrianism</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Equestrianism</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Cycling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Fencing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Fencing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Cycling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Sailing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Sailing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Basketball</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>NaN</td>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td> 1</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Wrestling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td> 1</td>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> Athletics</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>800 rows \u00d7 6 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" Bronze Games Gender Gold Silver Sport\n",
"0 NaN 1936 Male 1 NaN Wrestling\n",
"1 1 1936 Male NaN NaN Wrestling\n",
"2 NaN 1936 Male NaN NaN Fencing\n",
"3 NaN 1936 Male NaN NaN Football\n",
"4 NaN 1936 Male NaN NaN Football\n",
"5 NaN 1936 Male NaN NaN Basketball\n",
"6 NaN 1936 Male NaN NaN Football\n",
"7 NaN 1936 Male NaN NaN Football\n",
"8 NaN 1936 Male NaN NaN Football\n",
"9 NaN 1936 Male NaN NaN Wrestling\n",
"10 NaN 1936 Male NaN NaN Football\n",
"11 NaN 1936 Male NaN NaN Basketball\n",
"12 NaN 1936 Female NaN NaN Fencing\n",
"13 NaN 1936 Male NaN NaN Wrestling\n",
"14 NaN 1936 Male NaN NaN Fencing\n",
"15 NaN 1936 Male NaN NaN Sailing\n",
"16 NaN 1936 Male NaN NaN Wrestling\n",
"17 NaN 1936 Male NaN NaN Cycling\n",
"18 NaN 1936 Male NaN NaN Wrestling\n",
"19 NaN 1936 Female NaN NaN Fencing\n",
"20 NaN 1936 Male NaN NaN Cycling\n",
"21 NaN 1936 Male NaN NaN Fencing\n",
"22 NaN 1936 Male NaN NaN Wrestling\n",
"23 NaN 1936 Male NaN NaN Wrestling\n",
"24 NaN 1936 Male NaN NaN Football\n",
"25 NaN 1936 Male NaN NaN Wrestling\n",
"26 NaN 1936 Male NaN NaN Equestrianism\n",
"27 NaN 1936 Male NaN NaN Basketball\n",
"28 NaN 1936 Male NaN NaN Equestrianism\n",
"29 NaN 1936 Male NaN NaN Basketball\n",
"30 NaN 1936 Male NaN NaN Equestrianism\n",
"31 NaN 1936 Male NaN NaN Basketball\n",
"32 NaN 1936 Male NaN NaN Football\n",
"33 NaN 1936 Male NaN NaN Basketball\n",
"34 NaN 1936 Male NaN NaN Equestrianism\n",
"35 NaN 1936 Male NaN NaN Basketball\n",
"36 NaN 1936 Male NaN NaN Football\n",
"37 NaN 1936 Male NaN NaN Football\n",
"38 NaN 1936 Male NaN NaN Wrestling\n",
"39 NaN 1936 Male NaN NaN Cycling\n",
"40 NaN 1936 Male NaN NaN Fencing\n",
"41 NaN 1936 Male NaN NaN Fencing\n",
"42 NaN 1936 Male NaN NaN Cycling\n",
"43 NaN 1936 Male NaN NaN Sailing\n",
"44 NaN 1936 Male NaN NaN Football\n",
"45 NaN 1936 Male NaN NaN Sailing\n",
"46 NaN 1936 Male NaN NaN Basketball\n",
"47 NaN 1936 Male NaN NaN Wrestling\n",
"48 NaN 1948 Male 1 NaN Wrestling\n",
"49 NaN 1948 Male 1 NaN Wrestling\n",
"50 NaN 1948 Male 1 NaN Wrestling\n",
"51 NaN 1948 Male 1 NaN Wrestling\n",
"52 NaN 1948 Male 1 NaN Wrestling\n",
"53 NaN 1948 Male 1 NaN Wrestling\n",
"54 NaN 1948 Male NaN 1 Wrestling\n",
"55 NaN 1948 Male NaN 1 Wrestling\n",
"56 NaN 1948 Male NaN 1 Wrestling\n",
"57 NaN 1948 Male NaN 1 Wrestling\n",
"58 1 1948 Male NaN NaN Wrestling\n",
"59 1 1948 Male NaN NaN Athletics\n",
" ... ... ... ... ... ...\n",
"\n",
"[800 rows x 6 columns]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = dftur.groupby(['Games','Gender'], as_index=False).sum()\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Games</th>\n",
" <th>Gender</th>\n",
" <th>Bronze</th>\n",
" <th>Gold</th>\n",
" <th>Silver</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 1936</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 1936</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 1948</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 1948</td>\n",
" <td> Male</td>\n",
" <td> 2</td>\n",
" <td> 6</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 1952</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 1956</td>\n",
" <td> Male</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 1960</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 1960</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td> 7</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 1964</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 1968</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 1972</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1972</td>\n",
" <td> Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 1984</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 1984</td>\n",
" <td> Male</td>\n",
" <td> 6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 1992</td>\n",
" <td> Female</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 1992</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 1996</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 1996</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 2000</td>\n",
" <td> Female</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 2000</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 2004</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 2004</td>\n",
" <td> Male</td>\n",
" <td> 5</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 2008</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 2008</td>\n",
" <td> Male</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 2012</td>\n",
" <td> Female</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 2012</td>\n",
" <td> Male</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>26 rows \u00d7 5 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
" Games Gender Bronze Gold Silver\n",
"0 1936 Female NaN NaN NaN\n",
"1 1936 Male 1 1 NaN\n",
"2 1948 Female NaN NaN NaN\n",
"3 1948 Male 2 6 4\n",
"4 1952 Male 1 2 NaN\n",
"5 1956 Male 2 3 2\n",
"6 1960 Female NaN NaN NaN\n",
"7 1960 Male NaN 7 2\n",
"8 1964 Male 1 2 3\n",
"9 1968 Male NaN 2 NaN\n",
"10 1972 Female NaN NaN NaN\n",
"11 1972 Male NaN NaN 1\n",
"12 1984 Female NaN NaN NaN\n",
"13 1984 Male 6 NaN NaN\n",
"14 1992 Female 1 NaN NaN\n",
"15 1992 Male 1 2 2\n",
"16 1996 Female NaN NaN NaN\n",
"17 1996 Male 1 4 1\n",
"18 2000 Female 1 NaN NaN\n",
"19 2000 Male 1 3 NaN\n",
"20 2004 Female NaN 1 NaN\n",
"21 2004 Male 5 2 3\n",
"22 2008 Female NaN NaN 4\n",
"23 2008 Male 3 1 NaN\n",
"24 2012 Female NaN 1 2\n",
"25 2012 Male 1 1 NaN\n",
"\n",
"[26 rows x 5 columns]"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfturmen = df[df['Gender']=='Male']\n",
"dfturwomen = df[df['Gender'] == 'Female']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfturmen=dfturmen[['Games','Gold','Silver','Bronze']]\n",
"dfturwomen=dfturwomen[['Games','Gold','Silver','Bronze']]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_axis= dfturmen['Games']\n",
"dfturwomen = dfturwomen.set_index('Games')\n",
"dfturwomen= dfturwomen.reindex(x_axis)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rcParams['figure.figsize'] = 18,10"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2 = dfgre.groupby(['Games','Gender'], as_index=False).sum()\n",
"dfgremen = df2[df2['Gender']=='Male']\n",
"dfgrewomen = df2[df2['Gender'] == 'Female']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfgremen=dfgremen[['Games','Gold','Silver','Bronze']]\n",
"dfgrewomen=dfgrewomen[['Games','Gold','Silver','Bronze']]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_axisgre= dfgremen['Games']\n",
"dfgrewomen = dfgrewomen.set_index('Games')\n",
"dfgrewomen = dfgrewomen.reindex(x_axisgre)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfturwomen=dfturwomen.fillna(0)\n",
"dfturmen=dfturmen.fillna(0)\n",
"dfgrewomen=dfgrewomen.fillna(0)\n",
"dfgremen=dfgremen.fillna(0)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"width = 0.5\n",
"xlocationstur = na.array(range(len(x_axis)))*(2*width)+0.5\n",
"xlocationsgre = na.array(range(len(x_axisgre)))*(2*width)+0.5\n",
"\n",
"\n",
"n_group = len(dfgremen['Games'])\n",
"indexgre = np.arange(n_group) \n",
"\n",
"n_group2 = len(dfturmen['Games'])\n",
"indextur = np.arange(n_group2) \n",
"\n",
"\n",
"plt.figure(1)\n",
"ax = plt.subplot(2,1,1)\n",
"ax2 = plt.subplot(2,1,2)\n",
"\n",
"ax.set_xticks(indextur+(3*width/2))\n",
"ax.set_xticklabels(list(dfturmen['Games']))\n",
"plt.xticks(indexgre+(3*width/2), (list(dfgremen['Games'])))\n",
"\n",
"#turkiye\n",
"menGolds = dfturmen['Gold']\n",
"menSilver = dfturmen['Silver']\n",
"menBronze = dfturmen['Bronze']\n",
"#yunanistan\n",
"menGolds2 = dfgremen['Gold']\n",
"menSilver2 = dfgremen['Silver']\n",
"menBronze2 = dfgremen['Bronze']\n",
"\n",
"\n",
"#fig, ax = plt.subplots()\n",
"Goldt = ax.bar(xlocationstur, menGolds, width/3, color='#D4AF37', label='Male Gold')\n",
"Silvert = ax.bar(xlocationstur+(width/3), menSilver, width/3, color='#A0A0A0', label='Male Silver')\n",
"Bronzet = ax.bar(xlocationstur+(2*width/3), menBronze, width/3, color='#5B391E', label='Male Bronze')\n",
"\n",
"Goldg = ax2.bar(xlocationsgre, menGolds2, width/3, color='#D4AF37', label='Male Gold')\n",
"Silverg = ax2.bar(xlocationsgre+(width/3), menSilver2, width/3, color='#A0A0A0', label='Male Silver')\n",
"Bronzeg = ax2.bar(xlocationsgre+(2*width/3), menBronze2, width/3, color='#5B391E', label='Male Bronze')\n",
"\n",
"#turkiye\n",
"womenGolds = dfturwomen['Gold']\n",
"womenSilver = dfturwomen['Silver']\n",
"womenBronze = dfturwomen['Bronze']\n",
"#yunanistan\n",
"womenGolds2 = dfgrewomen['Gold']\n",
"womenSilver2 = dfgrewomen['Silver']\n",
"womenBronze2 = dfgrewomen['Bronze']\n",
"\n",
"Goldt2 = ax.bar(xlocationstur, womenGolds, width/3, color='#FFDF00',bottom=menGolds, label='Female Gold')\n",
"Silvert2 = ax.bar(xlocationstur+(width/3), womenSilver, width/3, color='#EEE6EE',bottom=menSilver, label='Female Silver')\n",
"Bronzet2 = ax.bar(xlocationstur+(2*width/3), womenBronze, width/3, color='#cd7f32',bottom=menBronze, label='Female Bronze')\n",
"\n",
"Goldg2 = ax2.bar(xlocationsgre, womenGolds2, width/3, color='#FFDF00',bottom=menGolds2, label='Female Gold')\n",
"Silverg2 = ax2.bar(xlocationsgre+(width/3), womenSilver2, width/3, color='#EEE6EE',bottom=menSilver2, label='Female Silver')\n",
"Bronzeg2 = ax2.bar(xlocationsgre+(2*width/3), womenBronze2, width/3, color='#cd7f32',bottom=menBronze2, label='Female Bronze')\n",
"\n",
"#ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )\n",
"\n",
"ax.set_xticks(indextur+(3*width/2))\n",
"ax.set_xticklabels(list(dfturmen['Games']))\n",
"plt.xticks(indexgre+(3*width/2), (list(dfgremen['Games'])))\n",
"\n",
"ax2.set_xticks(indexgre+(3*width/2))\n",
"ax2.set_xticklabels(list(dfgremen['Games']))\n",
"\n",
"#xlocationsgre = na.array(range(len(x_axisgre)))*(2*width)+0.5\n",
"\n",
"ax.legend(loc='upper right')\n",
"ax2.legend(loc='upper right')\n",
"ax.set_title('Turkiye')\n",
"ax2.set_title('Yunanistan')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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gEIgqXWJiYpv+BuquSkpKKCkp+dJ07Q0SnA0cAw4CycA1wOKmiRoG\nCSRF70h1De+9NDrq9Bddvz6OpVF3UV1dzbJly6JOX1BQELeySJK6Lv9Oaa68vJK6rdGnD5yL1+QY\nuOGrOVGle/atHXEuSdfQ9If7xYub3cID7Q8SZALPEBqukAD8CnitnXlJkiRJkqTTQHuDBJuAy2JZ\nEEmSJEmS1Lk6MnGhJEmSJEnqRgwSSJIkSZIkwCCBJEmSJEkKM0ggSZIkSZIAgwSSJEmSJCnMIIEk\nSZIkSQIMEkiSJEmSpDCDBJIkSZIkCTBIIEmSJEmSwgwSSJIkSZIkwCCBJEmSJEkKM0ggSZIkSZIA\ngwSSJEmSJCnMIIEkSZIkSQIMEkiSJEmSpDCDBJIkSZIkCTBIIEmSJEmSwgwSSJIkSZIkwCCBJEmS\nJEkKM0ggSZIkSZIAgwSSJEmSJCnMIIEkSZIkSQIMEkiSJEmSpDCDBJIkSZIkCTBIIEmSJEmSwgwS\nSJIkSZIkwCCBJEmSJEkK60iQIBv4D2Az8B5wa0xKJEmSJEmSOkWvDnz2KDAP2AD0A/4MvAp8EINy\nSZIkSZKkU6wjPQn2EAoQAFQRCg5kdbhEkiRJkiSpU8RqToJcYCSwPkb5SZIkSZKkUywWQYJ+wL8C\ntxHqUSBJkiRJkrqgjsxJANAb+DdgOfB/mm5ctGhR5P24ceMYN25cB3enLxMIBKJKl5iYSHV1dZxL\nE53kPkkcqa6JKm2fpEQOHzk9yn26ifb/vq1Op2OlraKtk6SkJI4cORLn0sRHW86fnsI2RWq/eLYp\nZyQEOF5bF1Xas9JSKT9YEZdy6PQWPCuNgxWHok5/uhwrvXtB4Nzo0/c6I35l0ektPT2V8vLKqNP3\n7tWLo8eORZU2GAxy4MCBVreXlJRQUlLypfl0JEgQAJ4C3gf+d0sJGgYJdGrc8NWcqNI9+9aOOJck\nekeqa3jvpdFRpb3oeke0tGbZsmVRpSsoKIj6OIHT61hpq7bUSVfl+dOcdSK1X1vPn2jbWQi1tX++\n+7Ko0v7t4neizlfdy8GKQ1EfJ3D6HCtHjxH1uQNef3qy8vJK6rZGnz5w7jEO7G79xr+h9Kz0k25v\n+sP94sWLW0zXkeEGVwAzga8B74Zf13YgP0mSJEmS1Ik60pPg98Ru4kNJkiRJktTJvMmXJEmSJEmA\nQQJJkiRJkhRmkECSJEmSJAEGCSRJkiRJUphBAkmSJEmSBBgkkCRJkiRJYQYJJEmSJEkSYJBAkiRJ\nkiSFGSSQJEmSJEmAQQJJkiRJkhRmkECSJEmSJAEGCSRJkiRJUphBAkmSJEmSBBgkkCRJkiRJYQYJ\nJEmSJEkSYJBAkiRJkiSFGSSQJEmSJEmAQQJJkiRJkhRmkECSJEmSJAEGCSRJkiRJUphBAkmSJEmS\nBBgkkCRJkiRJYQYJJEmSJEkSYJBAkiRJkiSFGSSQJEmSJEmAQQJJkiRJkhRmkECSJEmSJAEGCSRJ\nkiRJUlhHggRLgb3AphiVRZIkSZIkdaKOBAmeBq6NVUEkSZIkSVLn6kiQ4HdAeawKIkmSJEmSOpdz\nEkiSJEmSJAB6xTPzQCAQddo+SYkcPlIdVdqkpCRqamqizjsxMZHq6ujyVvfSlmPF40RqLtp23POn\ne+nTp0/U/59JSUkcOXIkziVSd3BGAvzt4neiStsrIfq/IU83bfn7t616QlvbKyEQ9XFSn16nr+Q+\nSRypjv6+rSfo3QsC58Yv/1i0QXENErz30uio0150/fqo09bU1HDDV3OiTv/sWzuiTqvupS3HiseJ\n1JznT89UXV3NsmXLokpbUFAQ17Ko+zhe2zPalGjPHQidP/5N29ix2jrrpBs5Ul0T9T1hW+4Hu7Kj\nx+J3nwzRt7PQ+vnjcANJkiRJkgR0LEiwEvgjcAHwKfC9mJRIkiRJkiR1io4MN8iPWSkkSZIkSVKn\nc7iBJEmSJEkCDBJIkiRJkqQwgwSSJEmSJAkwSCBJkiRJksIMEkiSJEmSJMAggSRJkiRJCjNIIEmS\nJEmSAIMEkiRJkiQpzCCBJEmSJEkCDBJIkiRJkqQwgwSSJEmSJAkwSCBJkiRJksIMEkiSJEmSJMAg\ngSRJkiRJCjNIIEmSJEmSAIMEkiRJkiQpzCCBJEmSJEkCDBJIkiRJkqQwgwSSJEmSJAkwSCBJkiRJ\nksIMEkiSJEmSJMAggSRJkiRJCjNIIEmSJEmSAIMEkiRJkiQpzCCBJEmSJEkCDBJIkiRJkqQwgwSS\nJEmSJAnoWJDgWuBD4CPgB7EpTnMlJSXxyrrL+uCDDzq7CKcdj5PmPE5aZr00Z500Z5vSnMdJyzxW\nmvNYac46ac5zp2UeK839/o+/7+winHbiXSftDRKcATxMKFCQB+QDF8aqUA3ZgDT34YcfdnYRTjse\nJ815nLTMemnOOmnONqU5j5OWeaw057HSnHXSnOdOyzxWmjNI0NzpGiT4CvAxsA04CjwHXBejMkmS\nJEmSpE7Q3iDBIODTBss7w+skSZIkSVIXFWjn5yYTGmrwj+HlmcBo4H80SLMBuLT9RZMkSZIkSXGy\nERjRdGWvdma2C8husJxNqDdBQ812JkmSJEmSup9eQBmQCyQS6jUQl4kLJUmSJEnS6e+bwBZCExgu\n6OSySJIkSZIkSZIkSZKkrmopsBfY1GDdpcBbwH8BLwMp4fVfAd4Nv/4LmNbgM4nA44R6M3wATIpr\nqeOrLXVSLweoAm5vsO574Tw2AmuA/nEq76nQljrJBQ5z4lh5JLy+L1BM6Ph4D7g/3oWOs1jUCXSv\ncwfafv5cEt72Xnh7Ynj934bz+Aj4eXyLHHcdrZOkJvm93CSvrihWx0l3amehbfVyAyfalHeB44Tq\nKZme29b2AVaG178P3NFCfj3t/EkEng6v3wBcFV7fk4+T1uqkflt3uSZnA/8BbCb0f3xreH068Crw\nF2AdcFaDzywgdN39EBjfYH13uibHsl7qdfV2JZZ10l2uy22tk/Rw+krglw3y6bJt7VhgJI0P7P8M\nr4fQf/Q94ffJnHhM40BgH3BGeHlxg3TQdQ8IaFud1PtX4HlOBAkSgf2EDhiAB4C741HYU6QtdZJL\nyw1lMicuxL2BNwk9laOrikWdQPc6d6Bt9dKL0EXk4vBykBNtTCmhwCTAv9NzjpWT1QmE/mB9ltAf\nt11ZLOqku7Wz0L7rD8BFhP5Yg57d1hYQChJAqB62Egri1+uJ58/3gafC7zOAtwk9TasnHyct1Um9\n7nRNHsiJicv7EQp8XAj8BPh/w+t/APw4/D6PUNCkN6G/Wz7mxJPXutM1ORb10t2uy7E6VrrTdbmt\nddIXuAL4f2geJOiybW0ujRvVgw3eZxOKoDR1LqHJEuvtIFQJ3UUu0dfJREIHzN2cCBIkEDphcgid\nNI8C/z1OZT1VcomuTpqma83/BubEomCdKJeO10l3O3cg+nr5FvCrFj6fSSjiWm868FgMy9cZculY\nnUDoIvU7QheprvyLRb1cOlYn3bGdhfZdk+8DlrSSX09qa/+e0C96ZwBnE/qjrv5Xnp56/jxM6NHY\n9X4L/LcW8utJx0lLdTIq/L47XpPr/R/gG4R++R0QXjcwvAyhX4Z/0CD9b4AxdM9rckPtrRfofu1K\nvfbUyWi673UZvrxO6hXQOEjQVLva2oQvT3JKbAauC7+fQuPHK34lvH0zMD+8rv4CfC/wZ+DXwDnx\nL+Yp1Vqd9CMUTVrUJH0tcBuhbiW7CDUeS+NeylPrZMfJuYS6v5YAV7bw2bOA/xt4LY7l6wxtrZOe\ncO5A6/VyAVBH6OLyZ+B/htcPovFjXHeF13Unba0TCN0EPgj89RSV8VRra530hHYWTt6u1JvKiV/Q\nG+ppbe1a4BDwGbAN+F+cuFHsqefPRuDbhAIn5xLqNj64yWd72nHSUp1k072vybmEelqsJ3SDsze8\nfi8nbniyaHzt3Uno2tt0fXe6JufSvnrJCr/vju1KLu2rk8F03+tyLl9eJ/XqTpJPu9va0yVIMBv4\nJ0Ldr/oBNQ22lQLDgcsIjUlKJdQVdDDwB0IN7VuETpjupLU6WQQ8RKhxCDRInwr8gtB4uCxC0cXu\n9tSJ1upkN6GL7UhCgaQVNB5b3IvQH7M/J/RHXHfSljrpR884d6D1eulFKGAyI/zv9cDVnLyB7S7a\nWicjgKHAKhq3Nd1JW+ukJ7SzcPJrMoR+vfkroTH4DfXEtnYmoV+BMwnd/P1z+N+efP4sJfQH/NuE\n/l75I6H5K+r1xOOktTrprtfkfsC/Ebp5q2yyrY6ecc1tSUfqJUD3bFc6Uid1dM/rcqzOny7Z1ubS\neheZCwhFTVryGqFGNEBo0r562YQiSF1ZLievkz+F379JaMzjVqCc0DicfyLU4+K3DT7zd4QmrejK\ncmnfcfIfhIJK9ZYS6mrTHeTSsTrpjucORF8v04BlDbbdReiP+oE07tqYT9fv2phLx+rkZkJR+a3A\np0A18Hocynkq5dKxOumO7Sy0vV15iJYn6OtJbW39NfkRGncjf4rQL8g9+fxp6g/A3zRY7knHyZfV\nSXe8Jvcm1MNmboN1HxK6zkIooFbfXfoOGrcl9V3Iu+M1ORb10t3alVjUyWi613W5LXVS77u0PNyg\nS7a1uTRuVDPC/yYARYTGVtSn6xV+P4TQuK3U8PJK4Gvh9wWEJvHrynKJrk4aupsTQzAyCEWpzw4v\nLyHU7bEryyW6OjmbExNaDiVUDw278P0r3SfimkvH66S7nTsQfb2cRahLZzKhtuVV4JvhbesJXWwC\ndP1JkiA2dVJvCN1j7GMuHauTs+l+7Sy07fqTQKgOcpvk0VPb2ls50bX1TELdzS9qkldPO3+SCdUF\nwDWEhrzV66nHycnqpDtdkwOEvvdDTdb/hBPjye+g+WR0iZyYe6z+2OhO1+RY1ku9rt6uxKpOutP9\nT1vrpF4BzYMEXbKtXUmoK3QNoSjYbEIX2S3h130N0s4kFFF9l9Cwg4YNRA7wBqFxXq/SfLxbV9KW\nOmmoYZAA4EZOPAJkFaEZubuqttTJJE4cJ38G/iG8vn6s0mZOPLJr9ikoe7zEok6ge5070Pbz5wZC\ndbOJxg1t/eOWPibUda0ri1Wd1Mula8+iDLGrk+7UzkLb62Ucoa7SDfXktjYJWE7omNhM48cS18ul\nZ50/uYR+6Xqf0OO66sfl9+TjJJeW6wS61zX5SkL/xxs48X98LaGZ539Ly4+1u5PQdfdDQhOB1utO\n1+RY1ku9XLp2uxLLOuku1+X21Mk2Qr3KKwm1Q39D92trJUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSvtQC4InOLoQkSZIkST3RcmBpk3VXAfuAAae+OFErAeZ0diEkSZIkSepO0oHPgG+E\nl/sAfwFu7LQSRec/MEggSZIkSVLMfQf4BOgL3A/8O1ALDG2QZhmwJPx+HLATmA/sBXYDBQ3S/gPw\nLlAB7ADubrAtN5z3jcB24HPgzgbbFwG/Cr/vQ6inwz6gHCgFzgF+BBwDDgOVwC/C6X8e3l8F8Db8\n/+3de3xV5Z3o/88OEC6ShB2MkgAxYi1jKFUsrY6VIbWVsa1nZPAHEgSN8vt5/I0cFTzTiuUSxNHa\nsWNtPWorAqYIWme0qAwFj07QtpYcKzJ4oxq5CBFGSEiCSgIk54+1s8nVsJOdC+Tzfr32i73Xetbz\nfPOw91p7ffeznsXFjer9DfA4UAG8BXythf6QJEmSJKlH+1fgOYKT9mE0TRIsA+6MPM8BDhOcePcC\nvgt8CqRE1o8HRkWejwb2AFdEXmdF6v4l0Bf4KnAIGBlZvxAoiDz/75GY+gEhYAyQFFn3H8D1jf6G\nq4EwkECQwPgYSIysyydIKlwWqetu4LWWu0OSJMVTQlcHIEmSYvIPwLcIEgG7WigTqvf8cKTsUWAt\ncJBjJ/obgLcjz7cATxIkDupbBFQB/wlsBs6t10ZdO9XAYOBsoJZgdEJlC/EAPEEw4qAG+BeCJMTI\neutfBX4XqWtFvTYlSVIHM0kgSdKJ5b8IhvW/3VrBiP0EJ+N1PgMGRp5fQPBL/38BBwhGBAxutP2e\nFrat79fAOoIkw27gXqB3vfW1jcr/T+CdSJtlBCMbTq23fm+jNvvhdxZJkjqFB1xJkk5snxHMUVAn\nnaYn5S1ZCfyW4LKFQcAjtO27wRGC0QqjgIuAyzk2oWLjWMYB/whMjrQZJpiboPFoA0mS1AVMEkiS\ndGJ7k+Aa/14E1/H/TQzbDiT4Jb8a+AYwjeNPMNSXQzCnQS+CywwOE1zeAMGogLPqlU0iSCrsI5iH\nYAGQ3IY2JUlSBzBJIEnSie0W4L8RnOxPA55ttP6LTvr/gWAEQAUwH3gqhm1r660fAjxNMCLgHaCQ\nY3c+eIDgrgylwM8I5hr4HcHtG7cTTFK4s4V6jycOSZLUiZYS/AKwpd6ybxDc2mgT8H+Ar3dBXJIk\nSZIkqZONI7iNUf0kQSHwt5Hn3yWY8EiSJEmSJJ3gWrvc4FWC4Yv1fcyx+ysPIpjFWJIkSZIk9QBZ\nNBxJcAbwEcH1g7uA4V0QkyRJkiRJirPerRdp4jHgZoKJkSYTzFtwaeNCZ511Vm1xcXH7opMkSZIk\nSR1hM3Be44VtubvBNzg2c/K/Rl43UVxcTG1trY92PhYuXNjlMZwMD/vRfuxOD/vRfuxOD/vRfuxO\nD/vRfuzsB0BpSWmzjx/M+UGL64Auj/1Eefh+7L79CJzb3Ll8W5IEHwDjI88vIbiFkSRJkiRJOsG1\ndrnBKoKEwKkE8xAsAG4A/hfQl+Dexjd0ZICSJEmSJKlztJYkyG1h+QXxDkTNy8nJ6eoQTgr2Y3zY\nj/FhP8aH/Rgf9mN82I/xYT/Gh/0YHxdfdHFXh3BS8P0YH53Zj6EOrLs2cp2DJEmSJHU7oVAoOsdA\nLFIzUvFcRye6UCgEzeQE2nJ3A0mSJElSN5WamkpZWVlXh6FuIhwOU1p6/MkwkwSSJEmSdBIpKytz\npIOiIiMGjltb7m4gSZIkSZJOQiYJJEmSJEkSYJJAkiRJkiRFmCSQJEmSJJ1Qtm/fTkJCAjU1NV3S\nfkJCAh9++GGz65YvX864ceM6OaL4MUkgSZIkSeo0WVlZ9O3bl/379zdYPmbMGBISEti5c2eHx/D+\n++8zdepUTjvtNFJSUvjyl7/MzTffzO7duzu87e7OJIEkSZIkneRSwymEQqEOe6SGU447llAoxIgR\nI2o6BlcAACAASURBVFi1alV02ZYtW/j8889jnom/LT744AMuuOAChg0bxptvvkl5eTl/+MMfOOus\ns/j973/f4e13d94CUZIkSZJOcmUHKnjr2Qs6rP6v/P3GmMpPnz6dgoICZs2aBcDjjz/ONddcw7x5\n86Jl1qxZw7x58/jwww9JSUlh5syZLFy4sNn6ysvLmTNnDmvXriUhIYHrrruORYsWkZDQ9Hfx/Px8\nxo0bx3333RddlpaWxi233NKg3KOPPspPfvITSktLufjii3nkkUdIT09vUt/+/fu57rrr2LBhA3/1\nV3/FhAkTYuqL7saRBJIkSZKkTnXhhRdSUVHBe++9x9GjR3nqqaeYPn16gzIDBw5kxYoVlJeXs2bN\nGh5++GFWr17dbH15eXkkJiZSXFzMpk2bWL9+PUuWLGm27EsvvcSVV175hfG9/PLL3HHHHTz99NN8\n/PHHnHHGGUydOrXZsjfddBMDBgxgz549LF26lGXLlnXKiIiOYpJAkiRJktTpZsyYQUFBAS+++CLZ\n2dkMHTq0wfrx48czatQoAEaPHs3UqVPZsGFDk3r27t3L2rVruf/+++nfvz9paWnceuutPPnkk822\nu2/fPoYMGRJ9/eCDDxIOh0lKSuKGG24A4IknnmDmzJmcd955JCYmcs899/Daa681mS/h6NGjPPPM\nM9x5553079+fUaNGce2111JbW9uuvulKXm4gSZIkSepUoVCIGTNmMG7cOLZt28Y111zT5MR648aN\n3H777bz99ttUV1dTVVXFlClTmtS1Y8cODh8+3OBSgJqaGjIzM5tte/DgwZSUlERfz5o1i1mzZjF/\n/vzoxIUff/wxY8eOjZY55ZRTGDx4MLt3725Q7yeffMKRI0cYPnx4dFlL7Z4oHEkgSZIkSep0mZmZ\njBgxgrVr1zJp0qQm66dNm8bEiRPZtWsXBw4c4MYbb2z2lofDhw+P3i2hrKyMsrIyysvL2bJlS7Pt\nfvvb3+aZZ55psry2tjaaqMjIyGD79u3RdZ9++in79+9vMtohLS2N3r17Nxhh0Bl3Z+hIJgkkSZIk\nSV3iscce4+WXX6Z///5N1h08eJBwOExiYiJFRUWsXLmy2Wv909PTmTBhAnPmzKGyspKamhqKi4t5\n5ZVXmm0zPz+fV199ldtuuy06omDfvn28++670fpzc3NZtmwZmzdvpqqqijvuuIMLL7ywySiBXr16\nMWnSJPLz8/n888955513ePzxx52TQJIkSZKkWI0YMYLzzz8/+rr+yfVDDz3EggULSE5OZvHixVx1\n1VUNtq1ftqCggOrqarKzs0lNTWXy5Mns2bOn2TbPPvtsNm7cyK5duzj33HNJTk7m4osvZtiwYSxe\nvBgIRhssXryYK6+8koyMDLZt29ZgjoP6bT/44IMcPHiQIUOGcP3113P99de3r1O6WEemN2pP5Mka\nJEmSJJ3cQqEQpSWlMW+XmpHarSemC4VCTeJLDadQdqCiw9oMD0qmtKy8w+pX2zX3fqhbTjM5AScu\nlCRJkqSTnCfwOl5ebiBJkiRJkoDWkwRLgb1A42kh/wfwLvAWcG8HxCVJkiRJkjpZa5cbLAN+ARTU\nW/Yt4O+ArwKHgbSOCU2SJEmSJHWm1kYSvAqUNVr2/wP3ECQIAD6Jd1CSJEmSJKnztWVOgrOBvwH+\nBBQCY+MZkCRJkiRJ6hptubtBbyAMXAh8HfgNMKK5gvn5+dHnOTk55OTktKE5SZIkSZLUHoWFhRQW\nFrZarsk9EZuRBTwPjI68Xgv8GNgQef0BcAGwv9F2td353qGSJEmSerZQKERpSWnM26VmpDZ73/nu\nIhQKdev41Llaej+EQiFoJifQlssNfgtcEnn+ZSCRpgkCSZIkSZI6xPbt20lISKCmpibudSclJbF9\n+3YA8vLymD9/ftzb6M5aSxKsAv5IkAz4CLiO4LaIIwhui7gKuKYjA5QkSZIknTyysrLo27cv+/c3\n/K15zJgxJCQksHPnzg5t/8CBA1x//fWkp6eTnJzMyJEjuffee6PrKysrycrKAoJf2yO/uPcYrc1J\nkNvC8hnxDkSSJEmS1DHC4TAHDhzosPoHDRpEWVnjG+M1LxQKMWLECFatWsWsWbMA2LJlC59//nmn\nnJDPnj2bzz//nPfee4+UlBS2bt3KW2+91WL5eF+6UVdfd00+tGXiQkmSJEnSCeTAgQMsX768w+rP\ny8uLqfz06dMpKCiIJgkef/xxrrnmGubNmxcts2bNGubNm8eHH35ISkoKM2fOZOHChc3WV15ezpw5\nc1i7di0JCQlcd911LFq0iISEpoPnX3/9de666y5SUlIAGDlyJCNHjoyuT0hI4IMPPmDEiGB+/rqT\n+XPOOYf77ruP73//+wAcOXKE9PR0XnzxRc477zz+9Kc/MWfOHN59913OOOMMHnjgAcaPHw8EE/lf\nfPHF/Md//AebNm3irbfeitbf3bRlTgJJkiRJktrswgsvpKKigvfee4+jR4/y1FNPMX369AZlBg4c\nyIoVKygvL2fNmjU8/PDDrF69utn68vLySExMpLi4mE2bNrF+/XqWLFnSYts/+tGPWL58Oe+///5x\nxzxt2jRWrVoVfb1u3TpOO+00zjvvPHbv3s3ll1/OggULKCsr47777uPKK69scEnFihUrWLJkCQcP\nHiQzM/O42+1sJgkkSZIkSZ1uxowZFBQU8OKLL5Kdnc3QoUMbrB8/fjyjRo0CYPTo0UydOpUNGzY0\nqWfv3r2sXbuW+++/n/79+5OWlsatt97Kk08+2Wy7v/jFL7j66qt58MEHGTVqFGeffTa/+93vWoyz\n7vKA3NxcnnvuOQ4dOgTAypUryc0NrtBfsWIF3/ve97jssssA+M53vsPYsWNZs2YNEIxGyMvL45xz\nziEhIYHevbvvoH6TBJIkSZKkThUKhZgxYwZPPPFE9FKDxtf+b9y4kW9961ucdtppDBo0iF/+8pdN\nJjsE2LFjB4cPHyY9PZ1wOEw4HObGG2/kk08+abbtfv36MXfuXF5//XX279/PlClTmDx5cqtzNnzp\nS1/inHPO4bnnnuOzzz7j+eefZ9q0adEYnn766Wj74XCYP/zhD+zZsye6/fDhw2Ptpi5hkkCSJEmS\n1OkyMzMZMWIEa9euZdKkSU3WT5s2jYkTJ7Jr1y4OHDjAjTfe2OwtD4cPHx69W0JZWRllZWWUl5ez\nZcuWVmNISkpi7ty5fPrpp2zbtq3V8rm5uaxatYrVq1eTnZ0dnVcgMzOTGTNmRNsvKyujsrKSH/zg\nB9Ftu+tEhY2ZJJAkSZIkdYnHHnuMl19+mf79+zdZd/DgQcLhMImJiRQVFbFy5cpmT7TT09OZMGEC\nc+bMobKykpqaGoqLi3nllVeabXPx4sW8/vrrVFdXc+jQIR544AHC4XCDyQvrNB7dMHXqVNatW8cj\njzzC1VdfHV0+ffp0nn/+edavX8/Ro0c5dOgQhYWF7N69u8W6uiuTBJIkSZKkLjFixAjOP//86Ov6\nSYCHHnqIBQsWkJyczOLFi7nqqqsabFu/bEFBAdXV1WRnZ5OamsrkyZMbDPWvr+7uB2lpaQwdOpSX\nXnqJNWvWMGDAgCb1hkKhBq+HDBnCRRddxGuvvdYgnmHDhrF69WruvvtuTjvtNDIzM/npT3/aIDFw\noowk6Mgoa0+UTIkkSZKknicUClFaUhrzdqkZqd36V+FQKNQkvnA43Oo19+0xaNAgysrKOqx+tV1z\n74e65TSTE+i+UypKkiRJkuLCE3gdLy83kCRJkiRJgEkCSZIkSZIUYZJAkiRJkiQBJgkkSZIkSVKE\nSQJJkiRJkgSYJJAkSZIkSREmCSRJkiRJEmCSQJIkSZJ0gtm+fTsJCQnU1NR0dSgnHZMEkiRJkqRO\nk5WVRd++fdm/f3+D5WPGjCEhIYGdO3d2aPv5+fn06dOHpKQkkpKSyM7O5plnnunQNk8kJgkkSZIk\n6SSXkpJMKBTqsEdKSvJxxxIKhRgxYgSrVq2KLtuyZQuff/45oVCoI/78Ju3n5uZSWVlJZWUlP/vZ\nz5g+fTqffPJJs+WPHDnS4TF1J60lCZYCe4Etzay7DagBUuMdlCRJkiQpfioqKrn6rzM77FFRURlT\nPNOnT6egoCD6+vHHH+eaa66htrY2umzNmjWMGTOGlJQUMjMzWbRoUYv1lZeXM3PmTDIyMhg2bBjz\n589v8VKE2traBu1MmDCBpKQkiouLASgsLGTYsGH85Cc/IT09nZkzZ1JdXc2tt97K0KFDGTp0KLNn\nz6a6urpB+X/5l3/h9NNPJyMjg+XLlwNQUlISHbGQlJTEgAEDSEg4dhq+dOlSsrOzSU1N5bLLLuvw\nURTHo7UkwTLgsmaWDwcuBXbEPSJJkiRJ0kntwgsvpKKigvfee4+jR4/y1FNPMX369AZlBg4cyIoV\nKygvL2fNmjU8/PDDrF69utn68vLySExMpLi4mE2bNrF+/XqWLFnSahy1tbW88MILHD58mOzs7Ojy\nvXv3UlZWxs6dO/nlL3/JXXfdRVFREZs3b2bz5s0UFRVx1113NShfUVFBSUkJjz32GDfddBPl5eVk\nZGRERyxUVlYyadIkcnNzAVi9ejX33HMPzz77LPv27WPcuHHRdV2ptSTBq0BZM8v/BfhB/MORJEmS\nJPUEM2bMoKCggBdffJHs7GyGDh3aYP348eMZNWoUAKNHj2bq1Kls2LChST179+5l7dq13H///fTv\n35+0tDRuvfVWnnzyyRbb/s1vfkM4HCYpKYmJEydyxx13kJx87JKJhIQEFi1aRJ8+fejXrx8rV65k\nwYIFnHrqqZx66qksXLiQX//619Hyffr0YcGCBfTq1Yvvfve7DBw4kK1btzZo895772Xr1q0sXboU\ngEceeYS5c+cycuRIEhISmDt3Lm+++SYfffRR7J0ZR73bsM0VwC7gP+MciyRJkiSpBwiFQsyYMYNx\n48axbdu2JpcaAGzcuJHbb7+dt99+m+rqaqqqqpgyZUqTunbs2MHhw4dJT0+PLqupqSEzM7PF9q+6\n6qro5Q47duzg8ssvJzk5mRtuuAGAtLQ0EhMTo+VLSko444wzoq8zMzMpKSmJvh48eHCDywgGDBjA\nwYMHo6/Xrl3Lz3/+c4qKiujbt2+03VtuuYXbbrutQWy7d+9m+PDhLcbe0WKduHAAcAewsN6yjp9Z\nQpIkSZJ0UsnMzGTEiBGsXbuWSZMmNVk/bdo0Jk6cyK5duzhw4AA33nhjs/MMDB8+PHq3hLKyMsrK\nyigvL2fLluam1gsSFPUTEmeccQaXXXYZzz//fIMy9WVkZLB9+/bo6507d5KRkXFcf+fWrVvJy8vj\n6aefbjBaIjMzk1/96lfRmMvKyvj000+58MILj6vejhLrSIKzgCxgc+T1MODPwDeA/2pcOD8/P/o8\nJyeHnJycNoQoSZIkSToZPfbYYxw4cID+/fs3uYvAwYMHCYfDJCYmUlRUxMqVK/nbv/3bJnWkp6cz\nYcIE5syZw+LFiznllFPYtm0bu3fv5m/+5m+alG88YmHXrl2sW7eO73//+y3GmZuby1133cXXv/51\nAO68805mzJjR6t9XUVHBFVdcwT/90z9x0UUXNVh34403Mn/+fM4991yys7MpLy9n/fr1TJ48udV6\n26KwsJDCwsJWy8WaJNgCnF7v9Tbga0Bpc4XrJwkkSZIkSapvxIgRDV7X/wX/oYce4rbbbmPWrFmM\nHz+eq666igMHDjRbtqCggNtvv53s7GwqKysZMWIEt99+e7NthkIhnnrqKX77298CROclWLhwYYMy\n9c2bN4+Kigq++tWvAjBlyhTmzZvXYvk6b7zxBn/5y1+YPXs2s2fPjpatqKhg4sSJHDx4kKlTp7Jj\nxw5SUlKYMGFChyUJGv9w39LdIlq7VGAVMB4YTDBSYAHBHQ/qfAiMpfkkQW3jDI0kSZIkdRehUIjS\nkmZ/7/xCqRmpTX6N7k4aD6cHSElJjvk2hbFITk6ivLyiw+pX2zX3fqhbTjM5gY6cT8AkgSRJkqRu\nqyclCdRzxZokiHXiQkmSJEmSdJIySSBJkiRJkgCTBJIkSZIkKcIkgSRJkiRJAkwSSJIkSZKkCJME\nkiRJkiQJMEkgSZIkSZIiTBJIkiRJknqM7du3k5CQQE1NTZe0n5CQwIcfftjsuuXLlzNu3LhOjqgh\nkwSSJEmSpE6TlZXFgAEDSEpKIikpieTkZPbs2dPVYR23999/n6lTp3LaaaeRkpLCl7/8ZW6++WZ2\n797d1aHFhUkCSZIkSTrJpaYmEwqFOuyRmpp83LGEQiFeeOEFKisrqayspKKigiFDhnTgXx8/H3zw\nARdccAHDhg3jzTffpLy8nD/84Q+cddZZ/P73v+/q8OLCJIEkSZIkneTKyiqp3UaHPcrKKtsdY3l5\nOTNnziQjI4Nhw4Yxf/786CUBy5cv55vf/CZz5swhHA7zpS99iT/+8Y8sW7aMzMxMTj/9dAoKCqJ1\nrVmzhjFjxpCSkkJmZiaLFi1qU7uN5efnM27cOO677z4yMjIASEtL45ZbbuGqq66Klnv00Uc5++yz\nGTx4MFdccQUff/xxs/Xt37+fv/u7vyMlJYULLriA4uLimPst3kwSSJIkSZI6VW1tbZNleXl5JCYm\nUlxczKZNm1i/fj1LliyJri8qKuLcc8+ltLSU3NxcpkyZwhtvvEFxcTErVqxg1qxZfPbZZwAMHDiQ\nFStWUF5ezpo1a3j44YdZvXp1s7G01m59L730EldeeeUX/m0vv/wyd9xxB08//TQff/wxZ5xxBlOn\nTm227E033cSAAQPYs2cPS5cuZdmyZYRCoS+sv6OZJJAkSZIkdZra2lomTpxIOBwmHA4zadIk9u7d\ny9q1a7n//vvp378/aWlp3HrrrTz55JPR7c4880yuvfZaQqEQU6ZMoaSkhAULFtCnTx8uvfRSEhMT\n+eCDDwAYP348o0aNAmD06NFMnTqVDRs2NInleNqtb9++fQ0ujXjwwQcJh8MkJSVxww03APDEE08w\nc+ZMzjvvPBITE7nnnnt47bXX2LlzZ4O6jh49yjPPPMOdd95J//79GTVqFNdee22zCZTO1LtLW5ck\nSZIk9SihUIjVq1dzySWXRJcVFRVx+PBh0tPTo8tqamrIzMyMvj799NOjz/v37w8EQ/3rLzt48CAA\nGzdu5Pbbb+ftt9+murqaqqoqpkyZ0iSWHTt2tNpufYMHD6akpCT6etasWcyaNYv58+dHJy78+OOP\nGTt2bLTMKaecwuDBg9m9e3eDej/55BOOHDnC8OHDo8taarczOZJAkiRJktSlhg8fTt++fdm/fz9l\nZWWUlZVRXl7Oli1b2lTftGnTmDhxIrt27eLAgQPceOONzc4zEGu73/72t3nmmWeaLK+trY2OAMjI\nyGD79u3RdZ9++in79+9n6NChDbZJS0ujd+/eDUYYNB5t0BVMEkiSJEmSulR6ejoTJkxgzpw5VFZW\nUlNTQ3FxMa+88kqb6jt48CDhcJjExESKiopYuXJls9f6x9pufn4+r776Krfddlt0RMG+fft49913\no/Xn5uaybNkyNm/eTFVVFXfccQcXXnhhk1ECvXr1YtKkSeTn5/P555/zzjvv8PjjjzsngSRJkiRJ\nBQUFVFdXk52dTWpqKpMnT2bPnj0A0Vst1vdFJ9MPPfQQCxYsIDk5mcWLFze480Djbb+o3cbOPvts\nNm7cyK5duzj33HNJTk7m4osvZtiwYSxevBgIRhssXryYK6+8koyMDLZt29ZgjoP6bT/44IMcPHiQ\nIUOGcP3113P99dcfZ291nI5MUdR29YQLkiRJktSSUChEaUlpzNulZqR2+eRyXyQUCjWJLzU1OS63\nKWxJOJxEaWlFh9Wvtmvu/VC3nGZyAk5cKEmSJEknOU/gdbyO53KDpcBeoP7MDf8MvAtsBp4BUuIf\nmiRJkiRJ6kzHkyRYBlzWaNl6YBRwLvAXYG6c45IkSZIkSZ3seJIErwJljZa9CNTdP2IjMCyeQUmS\nJEmSpM4Xj7sbXA/8exzqkSRJkiRJXai9SYIfAdXAyjjEIkmSJEmSulB77m6QB3wP+HZLBfLz86PP\nc3JyyMnJaUdzkiRJkiSpLQoLCyksLGy1XJN7IrYgC3geGB15fRnwU2A8sK+FbWq7871DJUmSJPVs\noVCI0pLSmLdLzUht9r7z3UUoFOrW8alztfR+CIVC0ExO4HguN1gF/BEYCXxEMAfBL4CBBBMYbgIe\nanPEkiRJkiR1ku3bt5OQkEBNTU3rhWOUlJTE9u3bAcjLy2P+/Plxb6OjHU+SIBfIABKB4cBS4Gzg\nDGBM5PEPHRWgJEmSJOnkkZWVxYABA0hKSiIpKYnk5GT27NnT1WEdlwMHDnD99deTnp5OcnIyI0eO\n5N57742ur6ysJCsrCwh+qY/8Wn9Cac+cBJIkSZKkE0BqaiplZY3vbB8/4XCY0tLju3QjFArxwgsv\ncMkll3RYPB1l9uzZfP7557z33nukpKSwdetW3nrrrRbLx/uyj7r6OjL5EI9bIEqSJEmSurGysjJK\nS0o77BGPBER5eTkzZ84kIyODYcOGMX/+/OglAcuXL+eb3/wmc+bMIRwO86UvfYk//vGPLFu2jMzM\nTE4//XQKCgqida1Zs4YxY8aQkpJCZmYmixYtalO7jb3++uvk5uaSkpICwMiRI7nyyiuj6xMSEvjw\nww+jr+tO5s855xzWrFkTXX7kyBHS0tJ48803AfjTn/7ERRddRDgc5rzzzmPDhg3Rsjk5OcybN49v\nfvObnHLKKWzbtu24+7QtTBJIkiRJkjpVc7+w5+XlkZiYSHFxMZs2bWL9+vUsWbIkur6oqIhzzz2X\n0tJScnNzmTJlCm+88QbFxcWsWLGCWbNm8dlnnwEwcOBAVqxYQXl5OWvWrOHhhx9m9erVzcbSWrv1\nXXjhhfzoRz9i+fLlvP/++8f9906bNo1Vq1ZFX69bt47TTjuN8847j927d3P55ZezYMECysrKuO++\n+7jyyivZv39/tPyKFStYsmQJBw8eJDMz87jbbQuTBJIkSZKkTlNbW8vEiRMJh8OEw2EmTZrE3r17\nWbt2Lffffz/9+/cnLS2NW2+9lSeffDK63Zlnnsm1115LKBRiypQplJSUsGDBAvr06cOll15KYmIi\nH3zwAQDjx49n1KhRAIwePZqpU6c2+HW+zvG0W98vfvELrr76ah588EFGjRrF2Wefze9+97sv/FsB\ncnNzee655zh06BAAK1euJDc3FwgSAN/73ve47LLLAPjOd77D2LFjoyMPQqEQeXl5nHPOOSQkJNC7\nd8fOGuCcBJIkSZKkThMKhVi9enWDOQmKioo4fPgw6enp0WU1NTUNfjU//fTTo8/79+8PQFpaWoNl\nBw8eBGDjxo3cfvvtvP3221RXV1NVVcWUKVOaxLJjx45W262vX79+zJ07l7lz51JZWcmPf/xjJk+e\nzEcffcSgQYNa/Ju/9KUvcc455/Dcc89x+eWX8/zzz7N48eJoDE8//TTPP/98tPyRI0ca9M/w4cNb\nrDveTBJIkiRJkrrU8OHD6du3L/v37ychof0D3qdNm8bNN9/MunXrSExMZPbs2ezbty+u7SYlJTF3\n7lzuuecetm3bxpgxY76wfG5uLqtWreLo0aNkZ2czYsQIADIzM5kxYwa/+tWvWty2M++S4OUGkiRJ\nkqQulZ6ezoQJE5gzZw6VlZXU1NRQXFzMK6+80qb6Dh48SDgcJjExkaKiIlauXNnsiXas7S5evJjX\nX3+d6upqDh06xAMPPEA4HGbkyJFNyjaed2Hq1KmsW7eORx55hKuvvjq6fPr06Tz//POsX7+eo0eP\ncujQIQoLC9m9e3eLdXUkkwSSJEmSpC5XUFBAdXU12dnZpKamMnnyZPbs2QMEv6Q3Psn/ol/XH3ro\nIRYsWEBycjKLFy/mqquuanHbL2q3sYSEBK677jrS0tIYOnQoL730EmvWrGHAgAFN6m0c85AhQ7jo\noot47bXXGsQzbNgwVq9ezd13381pp51GZmYmP/3pTxskBjpzJEFHtlTbmdkOSZIkSYpFKBSitKQ0\n5u1SM1I79ZfdWIVCoSbxpaamxuU2hS0Jh8OUlsbel+p4zb0f6pbTTE7AOQkkSZIk6STnCbyOl5cb\nSJIkSZIkwCSBJEmSJEmKMEkgSZIkSZIAkwSSJEmSJCnCJIEkSZIkSQJMEkiSJEmSpAiTBJIkSZIk\nCTBJIEmSJEnqQbZv305CQgI1NTVdHUq3ZJJAkiRJktRpsrKyGDBgAElJSSQlJZGcnMyePXu6Oqzj\nkp+fT58+faKxZ2dn88wzz3R1WHHVWpJgKbAX2FJvWSrwIvAXYD0wqGNCkyRJkiTFQ3hQCqFQqMMe\n4UEpxx1LKBTihRdeoLKyksrKSioqKhgyZEgH/vXxEwqFyM3Njcb+s5/9jOnTp/PJJ580W/7IkSOd\nHGH79W5l/TLgF0BBvWW3EyQJfgL8MPL69g6JTpIkSZLUbgfKK/jzwvM7rP6vLXqj3XWUl5czZ84c\n1q5dS0JCAtdddx2LFi0iISGB5cuX8+ijj3LBBRewbNkyBg8eTEFBAVu3bmXhwoVUVVXxz//8z1xz\nzTUArFmzhnnz5vHhhx+SkpLCzJkzWbhwYcztNlZbW0ttbW309YQJE0hKSqK4uJi0tDQKCwuZPn06\nN998M/fffz8TJkzg0Ucf5Qc/+AFPP/00AFOmTOHee+8lMTExWn7OnDnce++99OrVi7vvvpu8vDxK\nSkoYOXJktK2jR49y6NCh6GUSS5cu5b777mPPnj184xvf4Fe/+hWZmZnt/n9obSTBq0BZo2V/Bzwe\nef44MLHdUUiSJEmSeoz6J9p18vLySExMpLi4mE2bNrF+/XqWLFkSXV9UVMS5555LaWkpubm5TJky\nhTfeeIPi4mJWrFjBrFmz+OyzzwAYOHAgK1asoLy8nDVr1vDwww+zevXqZmNprd0v+hteeOEFZciM\nFQAAFj9JREFUDh8+THZ2dnT53r17KSsrY+fOnfzyl7/krrvuoqioiM2bN7N582aKioq46667GpSv\nqKigpKSExx57jJtuuony8nIyMjKiIxYqKyuZNGkSubm5AKxevZp77rmHZ599ln379jFu3LjouvZq\ny5wEpxNcgkDk39PjEokkSZIk6aRXW1vLxIkTCYfDhMNhJk2axN69e1m7di33338//fv3Jy0tjVtv\nvZUnn3wyut2ZZ57JtddeSygUYsqUKZSUlLBgwQL69OnDpZdeSmJiIh988AEA48ePZ9SoUQCMHj2a\nqVOnsmHDhiaxHE+7jf3mN78hHA6TlJTExIkTueOOO0hOTo6uT0hIYNGiRfTp04d+/fqxcuVKFixY\nwKmnnsqpp57KwoUL+fWvfx0t36dPHxYsWECvXr347ne/y8CBA9m6dWuDNu+99162bt3K0qVLAXjk\nkUeYO3cuI0eOJCEhgblz5/Lmm2/y0UcfteF/pKHWLjdoTW3kIUmSJElSq0KhEKtXr+aSSy6JLisq\nKuLw4cOkp6dHl9XU1DQYPn/66cd+n+7fvz8AaWlpDZYdPHgQgI0bN3L77bfz9ttvU11dTVVVFVOm\nTGkSy44dO1ptt7GrrrqKgoKC6PaXX345ycnJ3HDDDdGYEhMTo+VLSko444wzoq8zMzMpKSmJvh48\neHCDSxsGDBgQ/TsA1q5dy89//nOKioro27dvtN1bbrmF2267rUFsu3fvZvjw4S3GfjzakiTYCwwB\n9gDpwH+1VDA/Pz/6PCcnh5ycnDY0J0mSJEk6mQ0fPpy+ffuyf//+ZucCiNW0adO4+eabWbduHYmJ\nicyePZt9+/a1u91QKNTgUokzzjiDyy67jOeffz6aJAiFQg22ycjIYPv27ZxzzjkA7Ny5k4yMjOP6\nO7Zu3UpeXh7PPvssQ4cOjS7PzMxk/vz5MV1iUFhYSGFhYavl2tL7zwHXRp5fC/y2pYL5+fnRhwkC\nSZIkSVJz0tPTmTBhAnPmzKGyspKamhqKi4t55ZVX2lTfwYMHCYfDJCYmUlRUxMqVK5ucvLel3cZz\nKezatYt169bxla98pcVYcnNzueuuu9i3bx/79u3jzjvvZMaMGa3+DRUVFVxxxRX80z/9ExdddFGD\ndTfeeCN3330377zzDhBMvlg3MWJLcnJyGpyjt6S1JMEq4I/ASOAj4Drgx8ClBLdAvCTyWpIkSZKk\nNisoKKC6uprs7GxSU1OZPHkye/bsAYjearG+5k766zz00EMsWLCA5ORkFi9ezFVXXdXitl/UbmOh\nUIinnnqKpKQkkpKS+MY3vsHFF1/c4M4JjeOaN28eY8eO5atf/Spf/epXGTt2LPPmzWv173jjjTf4\ny1/+wuzZs6Pt1c19MHHiRH74wx8ydepUUlJSGD16NOvWrWuxP2LRcq+2X21zM1ZKkiRJUncQCoUo\nLSmNebvUjNRmZ+fvLhoPiQcID0rhQHlFh7U5KCWZsgPlHVa/2q6590PdcprJCbR34kJJkiRJUjfn\nCbyOV/tnhJAkSZIkSScFkwSSJEmSJAkwSSBJkiRJkiJMEkiSJEmSJMAkgSRJkiRJijBJIEmSJEmS\nAG+BKEmSJEknlXA4TCgU6uow1E2Ew+GYypskkCRJkqSTSGlpaVeHoBOYlxtIkiRJkiTAJIEkSZIk\nSYowSSBJkiRJkgCTBJIkSZIkKcIkgSRJkiRJAkwSSJIkSZKkCJMEkiRJkiQJMEkgSZIkSZIiTBJI\nkiRJkiTAJIEkSZIkSYpoT5JgLvA2sAVYCfSNS0SSJEmSJKlLtDVJkAX8f8D5wGigFzA1TjFJkiRJ\nkqQu0LuN21UAh4EBwNHIv7vjFZQkSZIkSep8bR1JUAr8FNgJlAAHgP8dr6AkSZIkSVLna2uS4Czg\nVoLLDjKAgcDVcYpJkiRJkiR1gbZebjAW+COwP/L6GeAi4In6hfLz86PPc3JyyMnJaWNzkiRJktor\nPCiFA+UVMW83KCWZsgPlHRBR/KSmJlNWVtnVYUjdVmFhIYWFha2WC7Wx/nMJEgJfBw4By4Ei4H/V\nK1NbW1vbxuolSZIkxVsoFOLPC8+PebuvLXqD7v7dPhQKUbstxm3OhNKS0pjbSs1I7fb9IbUmFApB\nMzmBtl5usBkoAF4H/jOy7FdtrEuSJEmSJHUDbb3cAOAnkYckSZIkSToJtHUkgSRJkiRJOsmYJJAk\nSZIkSYBJAkmSJEmSFGGSQJIkSZIkASYJJEmSJElShEkCSZIkSZIEmCSQJEmSJEkRJgkkSZIkSRJg\nkkCSJEmSJEWYJJAkSZIkSYBJAkmSJEmSFGGSQJIkSZIkASYJJEmSJElShEkCSZIkSZIEmCSQJEmS\nJEkRJgkkSZIkSRJgkkCSJEmSJEWYJJAkSZIkSYBJAkmSJEmSFGGSQJIkSZIkAe1LEgwC/hV4F3gH\nuDAuEUmSJEmSpC7Rux3bPgD8O/D/ROo5JS4RSZIkSZKkLtHWJEEKMA64NvL6CFAel4gkSZIkSVKX\naOvlBmcCnwDLgDeAR4EB8QpKkiRJkiR1vraOJOgNnA/MAv4P8DPgdmBB/UL5+fnR5zk5OeTk5LSx\nOUmSJEl1UlNTKSsr6+owJJ1ACgsLKSwsbLVcqI31DwFeIxhRAHAxQZLg8nplamtra9tYvSRJkqSW\nhEIhSktKY94uNSOVPy88P+btvrboDbr7d/tQKETtthi3OZM292N37w+pNaFQCJrJCbT1coM9wEfA\nlyOvvwO83ca6JEmSJElSN9Ceuxv8D+AJIBEoBq6LS0SSJEmSJKlLtCdJsBn4erwCkSRJkiRJXaut\nlxtIkiRJkqSTjEkCSZIkSZIEmCSQJEmSJEkRJgkkSZIkSRJgkkCSJEmSJEWYJJAkSZIkSYBJAkmS\nJEmSFGGSQJIkSZIkASYJJEmSJElShEkCSZIkSYpBrwQIhUIxP8KDUro6dKlVvbs6AEmSJEk6kRyt\ngT8vPD/m7b626I0OiEaKL0cSSJIkSZIkwCSBJEmSJEmKMEkgSZIkSZIAkwSSJEmSJCnCJIEkSZIk\nSQJMEkiSJEmSpAiTBJIkSZIkCTBJIEmSJEmSItqbJOgFbAKej0MskiRJkiSpC7U3SXAL8A5QG4dY\nJEmSJElSF2pPkmAY8D1gCRCKTziSJEmSJKmrtCdJcD/wj0BNnGKRJEmSJEldqHcbt7sc+C+C+Qhy\nWiqUn58ffZ6Tk0NOTotFJUlA3759qa6ujnm7xMREqqqqOiAiSWpZ/359OVQV2z6rd+/eHDlyJOa2\nwuEwpaWlMW93ImhLP6qpPr0hdGbntNUrAb626I2Yt+ud4ADs+sKDUjhQXhHzdoNSkik7UN4BEZ3c\nCgsLKSwsbLVcW9+ldwMzgCNAPyAZ+DfgmnplamtrnapAkmIRCoW4+q8zY97uidd24j5XUmcLhUK8\n9ewFMW3zlb/fSGlJ7Cf7qRmpJ+1+rrP78c8Lz495u68teqPb939n96PH6/YLhUIn7fvxRBAKhaCZ\nnEBbLze4AxgOnAlMBV6mYYJAkiRJkiSdYNp7d4M6pnEkSZIkSTrBtXVOgvo2RB6SJEmSJOkEFq+R\nBJIkSZIk6QRnkkCSJEmSJAEmCSRJkiRJUoRJAkmSJEmSBJgkkCRJkiRJESYJJEmSJEkSYJJAkiRJ\nkiRFmCSQJEmSJEmASQJJkiRJkhRhkkCSJEmSJAHQu6sDONn079eXQ1XVMW3Tr28inx+q6qCIJEmS\nuo/evXuTmpHapu10TFv7UeoI/fr1o6rK85muEh6UwoHyirjV5942zg5VVfPWsxfEtM1X/n5jB0Uj\nSZLUvRw5coTly5fHvF1eXl7cYzmR2Y/qTqqqqnw/dqED5RX8eeH5MW/3tUVvNLvcyw0kSZIkSRJg\nkkCSJEmSJEWYJJAkSZIkSYBJAkmSJEmSFGGSQJIkSZIkASYJJEmSJElShEkCSZIkSZIEtC9JMBz4\nD+Bt4C3g5rhEJEmSJEmSukTvdmx7GJgNvAkMBP4MvAi8G4e4JEmSJElSJ2vPSII9BAkCgIMEyYGM\ndkckSZIkSZK6RLzmJMgCxgAb41SfJEmSJEnqZPFIEgwE/hW4hWBEgSRJkiRJOgG1Z04CgD7AvwEr\ngN82Xpmfnx99npOTQ05OTjubk9TR+vXrR1VVVczb9e3bl0OHDnVARJIUf6mpyZSVVca0TTicRGlp\nRQdFJKkr9O7dm9SM1Ni369Xe0yip872+vZI/b2/92Need3cIeAx4B/hZcwXqJwkknRiqqqpYvnx5\nzNvl5eXFPRZJ6ihlZZXUbottm9CZsSUVJHV/R44c8XuPeoyxWUmMzUqKvv7Vhj3NlmvP5QbfBKYD\n3wI2RR6XtaM+SZIkSZLUhdozkuD3xG/iQ0mSJEmS1MU8yZckSZIkSYBJAkmSJEmSFGGSQJIkSZIk\nASYJJEmSJElShEkCSZIkSZIEmCSQJEmSJEkRJgkkSZIkSRJgkkCSJEmSJEWYJJAkSZIkSYBJAkmS\nJEmSFNG7Kxrt27cv1dXVMW+XmJhIVVVVB0Sknsz3o1rTv19fDlXF9h7p1zeRzw/5/mivfv36dern\nzM91Qyfz/rFPbwidGfs20omuVwJ8bdEbMW/XOyHUAdFIiofeCaE2fa5brC9uNcWgurqaq/86M+bt\nnnhtZwdEo57O96Nac6iqmreevSCmbb7y9xs7KJqepaqqiuXLl8e8XV5enp/rODiZ94+Hj+DnWj3S\n0RpO2s+11FMdqamN6+fayw0kSZIkSRJgkkCSJEmSJEWYJJAkSZIkSYBJAkmSJEmSFGGSQJIkSZIk\nASYJJEmSJElShEkCSZIkSZIEtC9JcBnwHvA+8MP4hCNJkiRJkrpKW5MEvYAHCRIF2UAucE68gtIx\nhYWFXR3CScF+jI933323q0M4Kfh+jA/fj/Hh+zE+7Mf48HMdH/ZjfNiP8WE/xkdnHmfamiT4BvAB\nsB04DDwJXBGnmFSPXzriw36Mj/fee6+rQzgp+H6MD9+P8eH7MT7sx/jwcx0f9mN82I/xYT/Gx4mQ\nJBgKfFTv9a7IMkmSJEmSdIJqa5KgNq5RSJIkSZKkLhdq43YXAvkEcxIAzAVqgHvrlfkAOKvNkUmS\nJEmSpI6yGTgvXpX1BoqBLCAReBMnLpQkSZIkqcf6LrCVYMTA3C6ORZIkSZIkSZIkSZIkdbalwF5g\nS71l5wKvAf8JPAckRZZnAZ8DmyKPh+ptkwj8imAUwrvApI4MuhuKpR8BvhpZ91ZkfWJk+dcidbwP\nPNCxIXdL8erH6yJ1bAbWAoM7NOruJ5Z+vJpjn+lNwFGCfu0PrCH4PL8F3NMZgXczsfRjP2BVZPk7\nwO31tvFzHZ9+rPNco7p6inj1o/vH4+/HRGBZZPmbwPjIcveP8enHunU9+fvjcOA/gLcJ3ks3R5an\nAi8CfwHWA4PqbTOX4HjyHjCh3vKefKyJZz/W6YnHmnj2Y08+1sTaj6mR8pXAL+rV06XHmnHAGBp+\nCP5PZDkE/8F3Rp5n0fKHZVG9ctCz3ggQWz/2JvjAjI68DnPsjhRFwDciz/+dY5NI9hTx6MdEYD/B\nBw6CiTcXdlzI3VIs/VjfVwh29BDsmOq+yPUBXsH3I7Tcj3kEJ2UQ9N02IDPy2s91fPoRghOIJwhO\nNnqaePSj+8fY+vEm4LHI8zTgdYLJod0/tr8f6/T0749DODa52ECCZMk5wE+AH0SW/xD4ceR5NkGi\npQ/B9/IPODZheU8+1sSjH+vfHa6nHmvi9X7s6ceaWPtxAPBN4L/TNEnQpceaLBru5A/Uez6cIAvS\nXLn6dhL8IT1ZFsfXj98Dft3M9ukEmaI6U4FH4hjfiSKL9vVjAsFOKpNgR/Uw8P/GPcruL4vj68f6\n7gYWt1Dfz4CZcYnsxJLF8fXj3xL86tALOJXggDAIP9d1smhfP0JwoH2V4EDb037dqZNF+/rR/WMg\ni+PrxweB6fXW/W/g683U5/4xEEs/jo089/tjQ78FvkPwq+zpkWVDIq8h+NX2h/XK/47g7mQeaxpq\naz+Cx5r62tKPF+CxprHW+rFOHg2TBI21+1iT0HqRL/Q2cEXk+WSCHX2dMwmGJBc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"text": [
"<matplotlib.figure.Figure at 0x7f559edd2810>"
]
}
],
"prompt_number": 13
}
],
"metadata": {}
}
]
}
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