update

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, \n",
+    "        9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, \n",
+    "        8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, \n",
+    "        19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, \n",
+    "        15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, \n",
+    "        20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, \n",
+    "        15.7, 10.2, 8.9, 21.0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.113000000000001"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.min(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.4"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.5"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.median(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.312369534258399"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18.596531"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.var(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>14.113000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.334094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>11.850000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>14.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>16.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>23.400000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                0\n",
+       "count  100.000000\n",
+       "mean    14.113000\n",
+       "std      4.334094\n",
+       "min      2.800000\n",
+       "25%     11.850000\n",
+       "50%     14.500000\n",
+       "75%     16.800000\n",
+       "max     23.400000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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c0JeWqL8gV4Iuqwu+atWcpaB/5SvA7bdTP4MMZpGLzKFPT2sdxl5n6Kr2cd8IegRvIKaGZ9JxlU+wE/RcZ+iHDmn/4/Llwoy+a26O/oDUafCyDF2Ifq4EXVZ10C5Df/55uj1yRP664NnYSDm1zKGLTtGZGfksUdGmeFzNlWiUoUvQIopNhAxe8x4cpNtcjsdWydmJQ6+u1uqMqOYt4pbaWqCgYE1G36UXceOsSZlDF+83dooCuRP09evLLQX9hRfo9tgx+euCQ20t/ekduixysRJ0wFnfwZkztHyeU8gcuqp93DeCXliYmXvxK7zm7YWgq+Q8M6NNopEJydgYjddWKWx69PbS/7vmGlrsIRPoBd3o0K0EXe/QhfDkStCrq62v/kSpArPy4VNTQGkpba/aWu1EFovJO0XtBN3JFdkXvgC89a3OfyOZoKvax30j6KK+ctjgNW8xBG4u83jXMVRynpnRJqGYOfT6+twK+k03kehcuJDZPzNz6GLYYnk5PbYSdMFbRdQkE/T5+fGVseRGLCxo2bmVQxft1zt0feRSVkYn8elpTdD1qxUB7nifPUvRjNN+TZmgq9rHfSPo+YAf/hD48Y+9bkXuwLk3Dl0lZmYobxX3jRAOXfVK8ACJ7sAAsHs3CXqmGbqdQy8oSC6hayXouXLo5eWLK2004sUXaXx+XR3w3HOQLlV38WKqoC8uUvuFM2dMq+ciBF3PWd8mJ7xFf6ZZR60RMkFXBd8IeoOYt+whHngA+Mxncvs/veQ9NaUd/LkU9GxwXlgAPvhBbX1QgZkZbbV3M0HPlUN/5hm6FYIej2fWc6af8i8c+vKyNsoFSC5UJd7vZYbe0ECXDbL4S8Qtr3sdtUeYCz30Dr2mhh7rKy0K6AW9pkYr7SDg5gQunPmLL9q/F5B3iqo6rn0j6Fu2bPG6CTh/PvdOVSXvgweBj33M/HX9AZTLyCUbnA8fBj73ORpDr4de0I0iMj9PeW6uMvTeXnKPHR0kOHNzjleElEI47uJi7f7cHF1pyQRdVFoUHcBA7gV961YKs2WCLjpE3/hGupXFLrLIRV8LXUAv6Mb8XN8mO0FfXgbOnaP7TgSdc7lDV3Vc+0bQxZqEXmFhgdxerlexUcn73/4N+Nu/NeekF/RcnsiywVm4T71rFZfiwhwZHboYslhfn5vI5emnqTO0ujq1ImA6mJqiglQbN2oOXRSkEvm5UdDXrKGaKgK5FvSRkecAmDv00lLgVYlVF8wEvaaG7tfW0m8ouBsdusjQZYLuNEO/cEErqewkchHvNQq6quPaN4LuNbxaxUYlxOgCs1EGXjn0bMBqFZuqqtQa2YC2jRsaSBgZU7e9OSeHLmaAV1YCy8tMOnvVKaamKD4xdg4C5g7dmCV7laGbCfrVV9P22rRJPtLF6NABzTmn49DteOs7Qp04dBlnlfCNoJeWlnr6/8+fp9tcO3SVvIWQm40DHhoi91NZmdsTWTY4yxy61bJkQLJDZ0xt5cEXXwQmJmiEC5A8+SVdCHGrqUkevgeYZ+j6/BzIvaDX1lLMJDMVL7ygzQBtbU116HNz1E4zQdc79Kqq7EQuokO0udmZoItx7cYMXdVx7RtB97pIlcjNcu3QVfK2E/TBQWDLFvUlVY3IBmeZoAueZoKud+iAWt7GGamyOituMTmpTbBxGrlkMtrDLWSC3tn5MgCp++DycrKg79pFs0YXF7X3GEfpiOhFRCFmnaLZEPRXvIIEXTbcUg8zhx764ly9oiSdRxCCnmuHrpK3OIisIpctWyjHzGXkkg3OTiIXo4gIkV23jm5VFmwSgikO9GwIut6h+yVyGRighR6M22JkhN4v6py3tpLbFR2lQGrpArvIZWyM9mOrDN1J5FJYSFdW8ThdZVnBTNBVHde+EfQ5j0NcEbnk2qGr5G3l0JeXKXLxwqFng7Nd5CIuwfUYGyNREJfHKnmLA10ISbYEva5Oi1w4dy/oKjuDZeLGGDXQuA8K4dZHLkBy7GJ06FaRiz42zNShNzTQcaH/X2YwE3RVx7VvBN1reBW5qIRVp+joKO2Mzc25F/RsQBzsbjJ0MUtUIBeCrsKh19bSJJyZGbmgz89rKzMZM/SiIvrLlUMvLKQ4yCjoYgy6EPRrr6VbfceonaDrHbroowAyF/TGRi0qsxvpEnWKmsDrxZKFQ19aSs7xVEMlbyuHLka4eBG5ZIOzEHJZ5GLVKZorQc925DI/T7XDReQCkEuXZegARQecpzp0QB1vmbjt2bMHVVVyh75qlbYUYHk57Ysyhy74VldTZ7YwX4IzkCzumQj68DANC920iR7bOXSzTtHQLxI9KJsmlkXE49YdHGInEe/NFVTxXl7WDiKZoIsaLrmIXJaWkhcuzgZnIeizs9r2Mgq6kbfRoate9ADIXuRirDoonpM5dEDbvl4L+uDgIKqr5Q59y5bkMfLGkS5Gh15QQOK+vExiXqBTNztBd5qhC4e+Zg39pulGLqqOa98I+qgYgqAAnNOl3Ze/bP4erwRdFW/9JBZZ5CL2t1xELu9/Px0gr3418OCDwPHjk/YfssHUlOaKhLjbOfTz54H167XHfopc9KVw9Q5dCHpZWfL/EVFBpoJ+6ZK8xooMMnEbHR1FVVXqPnjqlBa3CLS2Uu0bMVlnaooEXy/Wgrs+Pwey49BnZujE09hIVwKbN6cfuag6rn0j6Cpx5QpdSp08KX99YYF6s8XB7rc8WQb9AWQWuTQ0UNyiOnI5e5YOuKEh4H3vAz74wesz+r6lJRIz2UrwRUV0cFVV0XYXl8SxGLn5XAm6MXIRApRthz47S9tPOF2joBszdMA574UF+o0ffNBZG+Nx+v2NlWONkQvnFLmI7Sdw3XX0P8ViF6IwF2PaewR3M0EvKCDzYIQTQRdDFjdupNtNm9J36KrgG0HfuXOnsu8Wl/uyim+ANj5561a6zaWgq+KtF3Qzhy568lU79HicOr1OnAD++38HpqYym3Tx0kskCtu302O9Q6+s1KrviecArY8k1w5dXOoXFABlZctZEXSjQzeO9gCy49CHh+m3PXzYWRtlNU127tyZIugTE7RdjA79jjto2z3yCD2WjdIRj/WOHNA6RevqkqMYgVWrtDaaQQi6qNa5eXP6Gbqq49o3gr7k9LouDdgJujjYhcDlMnJRxduJQxd8VTt0UY2OMRoDHosVOL6Ml0GIm5mgA8nrTAJyQc9Fhq4XuIoKnlVBFxm6TNCtMvTiYmeCLmI5p1UHZYK+tLSUIujGES4C69YBv/VbwPe/T4+tBN3MocviFsDZzGCjoG/aRBOV9P0/Rpg5dFXHtW8E/cSJE8q+W2wQs4NJ5OdC4HLp0FXxFoJeWpoq6IuLFIM0N9Nj1Q5dX15UVP7LdMYkAOzYQbdWCwd75dDF9+qd2+rV8bR560vhlpXR9zpx6LL4wemCyULQz5511kaZoJ84cSJF0I1j0PV4y1socjl1Krkwl4CZQ7cTdMD+BC7quOgFHbDmbyboqo5r3wi6Stg5dKOg53q2qAoIQW9qSo1chocph85l5CJ2eCHomSxK7cahexm5rF6dfPlfWrpkKejPPqvFf0aISovl5eQ2xeQisVqRgH7YorHSooBT3npBd1Il0myhBzHKRXzHiRPULmEo9Hjzm+n2Bz+QO3S7TtFMBH1khL5fdDCLsehWVyhRhm6CRnFaVAAnkUtBgTYmNpcOXRVvIZibNqU6dP0YdCB3kQuQHUEXAr5xIx1IMkE3FsM6f57aoHd8TqOHdCATt5qaQlNBn54GOjuBD3xA/roQN9FBKCoumjl0sRKQDG4FPRajfgs7yDg3NjaiqorEXByHR47Q1ZUxdwZof+3ooNjFTYaeLUHXH47CoVuNdBEZuoy3CvhG0DeKrmUFcOLQGxo0p5NLQVfFW+/QjYIuLiGFAykpIQFQNaFKlaDX1dGfk8hldJS2sX7EhNPoAaCOvJ//3Hkb5+dTD/K6umJTQf/Wt2g//elPtWF7ehjFTTh0o6CXlGiuXJafi/c42cdFDg84i11kgr5x48aVk6vY5keOAC97mfn3vOUtwK9/Tfut0wy9sJA63q2+1463mFQksGEDfa8Th248Oak6rn0j6CqLVDnJ0NevV7uArhmywfu554D9+5Ofu3SJdsaGBrqvv2QWBYdEkSrVvFVELoxRpFBX57xTVB+3ANoB7iRO+Pzngde8xrqDTA+ZuMXjF6T7IOc0R6KkhDj09KS+R9RxERAO3Ri56Ef4ZCrog4OAGKyRrqD39vauCPr0NPEbHLQXdAGnDh0Ajh+nOQ9mcOvQi4rocTqRS+iLc6mEWFTAKnLZsEFtaVGVeOAB4A//MPm56WkSz+pqysv1kcrEBOWx4qAQpZtVxS4qHHpNDcVktbXOM3SZoHMud8RGnDlDMxTdLBwsTpQCZWXyDP2ZZ4BDh4BPfpJ+p0cfTX2PKJ0rYObQgewI+twc/WZ799Jjp4s9yLJkvaCLWi3XXWf+PS0t2onEqUN3AitBX1ig0hDGpGTTJuttbubQVcE3gl6RzhZyCH3kInNjXjr0bPAeH6eDT8/t0iVN0MVjgQsXgKuu0uIH1etrqhB04VZF5MK5e0FPZ+Fgp4Iui1yqqwukgv7lL1NH3H33Afv2AT/+cep7ZJGLLEMHNO6ZZOiC55495FTTdegVFRVJgi7GtFs5dEBz6dkUdKuITRw/xqTEbix6PE7myDj2XZWe+UbQOzo6lH23EPSlpdQNKmaJ5tqhx2LA//yfwLp1mfOenKQ268VCCLr+YBKYmEjuPBKCrtKhu4lczp8H/vqvzaecG5clE/yXljQxW7VKizCuXKH3yBw64Gx7C0HT58pWkInb9u0NuHw5mdelS8B3vgO8/e20re66iyI0YykQo6DX1tI2vXw5OXIB7B26k85g8f+vvppca7qC3tHRsbLNp6cpPxdLzlnhnnuA669PFf7Nm2nxiZtvtm+PEVa8jWPQBTZt0kaFyaA3K3qo0jPfCLrKxZL1uafZKjYbNuRmJXiALpXvuAP41KeAz30ue4Wqxse154wOXS/owqELiMhF9RA+gH7jVauWLQX9y18GPv5xGsYng8yhm60EPz2tbeN0BZ1z9w5dFrlMTg4BSI7+vvUtEuX77qPHr30t3epjl3ic9mG949aP1lERuehHQjU1pS/oBw4cSHHo112X3Dktw7ZtwG9+kyqwZWXAk0/SSBi3sIpczAR9/XoyfWKxDSPMYqbQLxIdV5hz6AXdmKOLMei5ilxGR4FbbwUOHqTH09MZLAMPEhsh6GJFHiDVoesF1MyhqxB0kVHrXUxZ2aLpsngA0NVFt2aCru8grKsjlyREW7ZwsHHpOQGnvKemtPc4deiyyGX1atqx9Kbin/+ZnKgQqO3bScz0gm6sOmi8n46g25WJHhqi9jc0kKCnm6HH4/GkffDwYfu4RRWMgv6jHwHvfS9FXGL2qjFysbuiNBN0VXrmG0FXCStBFxNOchG5zM1RJ9OpU7QTrV4NzM4WZfSdly9rO6lR0Kuq5JGL0aGrjFxEh6Ne0MvLl0wPkPl5GrIGkEOTQd9BKG6F0MoEXTapCHBXI1vAjUM3HuhlZXTdLgR9bo5OWm95S7Jjfe1rgV/+UuvMlwm6E4dulaGLNpphcJDijYICLXZwsr6mTNxEe44do/3SqkNUJYwZ+oMPAl/6EvC61wEf+Qi13XgSTFfQVcFW0BljX2WMjTPGjuqee4AxNsIYezbxd5faZgKdnZ3KvtupQxeV4lQ59IEBqvj4hS9Q5FJVBVRXZzZeVb9ij8yhG3dIcfmod+gqIxfZsK76+hLTA+Tpp7WOJplDv3KFBFHv0AG5oItl6MwE3alDF3FDa6u7DN0YuXR0tADQBF18r5jgJXDXXdQmcaVi59DdZuhOeOtr/TQ10X6jj/RkkIlbZ2cnioooKhHDMb106HrOo6NU0vmnPwXe8x7gz/88NQqyE3R9/5AeqvTMiUP/OoA7Jc9/lnN+feJPMpAquxgYGFD23VYZ+rlz5EL0CwerrsAnFlmorARGRiyyBweQCTrn2rBFo0MX4pCryEVWjW7VqsumB0h3Nx1Ub30rDeUzukKRZRoFXThnWYZ+/rxWGEwPNwsHAzSTc3TU2e8ki1xeeomBzDFsAAAgAElEQVQUXOyDos3GDsJ9+0gAH36YHutroQtkmqGLNprBKOiAs1KyRs7iuK6qAp5/np7btcv6e1TBGLmMjtJv/9u/TU79k59M/YwThy7rFFWlZ7aCzjnfD2DK7n2qMW53+s8As7Pajy6LXBoatNl1Kut7GMWtqgqYmspseqZe0MVPODtLGalM0MWkIlmnqIrIRSboq1dbC/p11wGvfCVtq9Onk18XfN1GLuvW0RWYHk4jNrESvBhZkX6efAGAJujie4yCXlwM3H038I1v0HvcRi5im6cr6NPT9D+Ngm7XMSrjLI5r0abNmzWRzDX0gr60RMeLsV/FiHQjF1V6lkmG/n7G2OFEJFNj//b8xeys5oplkYuxYFOuSqpWVQGXL0uqJ7mAELhVqzSHLna+6mqsXO6K5y6QpuTMocsiF7MM/coV4KmnyKFen1gDw5ij66f962/tBN0YtwDOO8GHh+nzol6+k9jFbGIRkOzQCwpSR1YAwCc+QVdaDzyQehIDkgXdGLncfTfwD/9gn6GL7T09TScrMbnRWOvHSdVBwDpPFsLoVdwCJB/bFy6QqKsSdFVIt8ftSwA+BYAnbv83gD+SvZExdi+AewFgw4YN6EoEf1u3bkVlZSUOHToEAKirq0Nrayv2J+aoFxUVobOzE/39/Ziensbi4iJisRjGxsZwNrHnbN++HcXFxTiamF62bt067NixAz2JMK64uBh79uxBX18fYrEYLl8uxMc+dgs+8YlhrFlD1q6lpQWXLq1FWdkcgCqcOjWKeLxmZVjRCy/ciJaWcvT29mJubg6c78bs7Go8//zJlWWkdu7ciaWlpZWSmI2Njdi4cePK9N6Kigp0dHTgwIEDK73bnZ2dGBgYWDlT79q1K5HXr8XRo/1obKxGWdlmjI4WoqurC1VVVWhvb0dPTw8WE8MP9u7di2PHjmEycUS3tbVhZmYGpxO2tbm5GcPD9QBK0dg4m3Cz5Xj88WcA3IiRkeMAdqKsbAHPPz+Brq4BnDlzE4AyDA09g66uWWzduhXz81UA1uDQoQEcORK33E4AjbF1up3Oni0FsBurV2NlO5WWbsWlSxwnT57CSGK8WEtLC/r7izE3V4u1a4+iqGgViop24OGHX8RVVw2itLQUu3fvxq9/PQBgBwYH+xCPX4fx8UEA1+DUqQUAqzA7O4quLrq2X1xsw8zMGgwMxLBmzRX09Q0mbafTp8sB3IiTJ8+iq+uFle0Uj8dxMrG8VVNTE4aGNqO6ehbnzx8HsAdnzsB2O12+XIULF86jq+sUmpubUVtbuzLK5bnnhgFsxNNPj6Kubg2eeqoX+/btw6FDh3AxkSm1t7fj93+f42tfq8KePZMoKqrDwsJL6OrSjqfy8l2YnWU4evTXmJlZTNpON94IzM7Kt9PJk+cBXIfjx0+jpWUTvvWtQ+jtvQEf+MBL6O1dg8cfPwVgGyYnD2JubicmJ4dRXLwVTz55Dr/3e0UoLCzE8ePHAQANDQ3YsmULenp+jeXlfZiYOAugaeV4WlxcRDweR2HhIoByVFWdwfh4aVaOJ+N2qq+vR19fHwBIj6fVq/dhfh544oluvPACbfvCwgl0dR1bOZ5qa2vR398PAKipqcHOnW0AgGefHUR394sp2+ny5U4UFCygq4vaLnRvcXERXV1dtronjifH4Jzb/gFoBnDU7WvGvxtuuIGni+Hh4bQ/K/Dss5wDnH/pS8nPX3MN5699Lb3293+f/FpjI+fvepf2+NprOX/rWzNuihQ/+AG1ob+fHt99N+fNzQsZfedf/RV95+/8DufbttFzTz1Fzz36KD1uaeH8bW+j+w8+SK+dO6d9x8WL9NxnP5tRU6Q4coS++9/+TXvu/vsvccY4X1pKfu/f/i29d2KCHl93Hed33ZX8nq98hd4zOKg9V1FBzwGcL+h+zk98gp5raEjexgIDA/T6N79pzWHHDtonFhY4Lyzk/KMftWPNeVkZ5x/6UPJzJ0+OJO2Dt93G+S23mH/HxATnVVXUxvr61Nebmui10VH79ujxs5/R53p66PEjj2i/37PP0n6g3w6c02/wu79r/p2xGH3m059Ofl4c129+M73+ne+4a2s28alPURuuXOH8pz9N/g2sINuWAjfeyPmdd6Y+71bPAPRxBxqbVuTCGNNfoL4ZwFGz92YLJ80W/HQBUeLTeHk0O0sRA2OpkYtYt1Ag1xn6pUs2Y8FsMDlJ39PYKI9cxP8xZuheRi5XroyD89Rt0d1NI0lE266/PnWki6yDUNwvKUnOyUX8Mjoqj1ycZOhiUtHGjfTdTU3Ohi7KIpezZwdQUJAcuVjNmFy7FvizP6P7sjzcrDa4HYzbW8S9hYXAZz5DkUtFRfJvvGlTegs9iONaZOheDVkEkoepms1NkKG62n3kkg09k8HJsMWHABwA0MIYG2aMvRvAPzDGjjDGDgO4DcAHlbQuyxAjIGSCXlFBf3oRicdprG8ua2SL/wFkL0Ovq6M+gpkZ6ti0EvQLF+h5scaivj1OOkU5p+JJX/6ys/bJOkXLyylL1m+nhQWaAbhvn/bc9ddTH4e+f2lyktquFzEhPGY1soH0M/SXXqJ9RHQMNjfbZ+iLi5TPGg90UQlxZoZG75w9az8F/o//mLatcYQOoIl8qcslWo28xe/7X/8rlSHo6aH8XD+Ez262qN1CD3V11E6xypQX0PNWLeiq4GSUy9s55+s556s45xs551/hnP8+5/w6zvnLOOdv4JyfV93QJnHEZAArh15eTgeTXtDFCcDo0FV1ihrFrbISiMcLM6pDrhd0gA5OId5C0PU75MRE8ggXwNl6iwJjYzSe/sknnbVPJuibNlHD9Nupv5+2jVHQgWSXLiYVyVaCT1fQrXgLERMzCDdvtnfoZuLW1NSUNHt1YUGrSW+Gigqqw/6FL6S+VlND+7VsUWQryBx6RQVNruGctoVxNaGmJupcFtvTCCvOAPChDxEP40ijXEJ/RTY6SvuHsUNZBrqSlr9mJujZ0DMZfDNTtF4oUgaQOXRRkKu8PNWhi8t3Lx06kPn6mnpBHxuzd+iyVV3crmJjLB5lBtmKLk1NqYIuSl+Icq2AXNCNdcGBzB263aIHgCbozc1U98NM2ABtOxsjl/r6+hVBNxuyKMPLXiaPKjZsSD05O4FM0Ovr6eRy9930nHGy06ZNJPZiIp4RZoIujusNG2gcv5cwRi5O3Dlg7dDNJhZlQ89k8I2gi97pTCAcun6au5hUJARdL57iBKAX9Fw6dNm0fLcwE3TGtFjCzqEDxNtJ5CLiBjczJoFkh37u3HMAknm/+CK1V3+Q1daSkBgdulNBF78vIBd0ETs5WThY79D1xbpkEEJpPND7+vrSEnQzPPAA8JOfuP+cUdDHxrRI50//lG6vuSb5M3Zj0c0EPRvHdbagF3Qx/8QJxJqoMphNLFLF2zeCng3IIhdRD8PKoeeqU9S40xtX1UkHQuDEATk+rtVxEbGEmAK/vGzu0EtL3Tn0kRFnJz55hk4Zk347jYyQizPC2DFqXOgBcBa5yA5eJ1HT8DBFGuKEIKIIqxOaVZ4sBF3ENnaRixXWrk0VXieQOXSx/1x/Pc3Qfde7kj9jN1s014slpwNjhp4Nh553GXo+4MgR4PDhzNfgk0UueodulqHnKnIxc+jpRi6Li8RV5tD1s/HEIr2xmLVDdyPonDubMSkT9IYG6sXTb6dz58wF/cSJ5EJVbiOX6mrzjkM73mfPanV+AE2AnQi6MXKpqqpKcuj6mby5hJWgAxTxGNuerkOv8oKgCYwZukpBV8XbF4L+f/8v8MlPbsv4e2QO3Ri5OHHoKmeKFhRo4pBp5KIfwldSQt8nE3RxX2S/Zhm6k8hlcFBrv5McXXagd3ZSIGx06LIZk7t305XFgw/SYzeRi3gsi1sE3C4cvHEjbUOrjlGzyKW9vT1J0DNx55lAL2zLy3SSl42i0aOigvYjtxl6e3t7Zo3NIkTbLl2iPzeCPjubWm54eZk6tmWCroq3LwSdiihlNh4bsHfoZhm6XvxUO3S9U800cjFOg6+vN3foAPACTYaUOnSnkcvQEIks4EzQZQ69v78HhYXadhKdbTKH/prXUHnZv/gL4D//k9roNHIRvK0E3W7h4OFhzZ0KHhs2pBe59PT0JEUumeTnmUBsi/l5MgXLy/aCDmjRnQxWnPMFom3iZOxG0IHU41RWGlpAFW/fCPrCQoHlyAEn0HeKivU1haCXlaU69IsXaeX4Qt1QcNUOXb/DZxq5CEEXjttM0MV9UegqXYe+tETO8pZbqEPRSceoTNCXlhaTLmOnpui3kTl0xoCvf52yYjECw6lDLymhbZuuQ+ecIgbjogfNzc4WDjbGFouLiysVIF980TtBF30H8bg2Gc3JoIzy8uTKpXqYCfpiJmNyswyxPdIVdGPsYtVvoIq3LwRdjMYwzhx0CyHoS0vajifL0IXYT00l5+eAdoDLFpPOFEaHnmnkYnTo69ZpnaIqHPrICLmSq6+muCDdyAVIziXFZbzMoQO03R5+WPvtnAo6Y5QHW5XKsBL0S5do/5EtHGx1MjOLXEQbRU16ryIXQOMtJhU5cejpCHo+QbRNbDurE70e6Qi6KvhC0I0rtKeLixe1iQLixzdGLsvL2gFnnPYP0MYRy6ZlG0aHLk5k6Qq6qJzoNnJJdxy6vgpfc3P6kcvevXuTBN1sPUc9tm0DHnqItte11ya/1thIbWprS/1cfz/wQYt5zlYRmxiaaJwj0txMr5mZMLMDfe/evUknHa8cOpCeoBuvcPWw4pwvyDRycSPoqniHRtAXFki8xbAyM0HX/x8zhw6oiV2MDr2oCCgpWc44ctEL+uQkXanIIhcrh+4kchEC3txMApquoB87dsyVQxe48046iRkFvayM4qRXv9q+PUZYRWzGMegCGzbQVaCoi2OEWeRy7NgxXwt6Og792LFj6Tcyy9A7dMacT8qSrcsLyCfNCajiHRpBF3GLuIy1EnThMswcOqCuUFXqOpMLGUUuq1drVyUiBxWLWwiIHXJwkLJvYzQBOItcxIGwaRMJ+sSEfUwWj9OJSz89fXJyMmk6tXDoTi6B7VaLdwurKxPjtH8B8fuZiZtZ5DI5OZn02+dL5FJQYL4Yhh7pCPqkfgUWjyFOsOfOkZg7LUNg59BlnaKqeIdO0K0cuvg/QoS8dugALXogE/SlJeBXvwI++lHgiSfk3yeG8AmR03ds6QVd8I7HaUeWiaLTyKWxkQ5aMTXcrq6JjLNon96hr13rTf5qxXt4mH4r44lGnEDTyZPFtigqcn7JrwJ6Qb/qquSBAWZIJ3LJJ4i2ce7ut48ydJfIRqeolUMvKiJR0f8fzsmhmwl6rhz62rWrU05kH/84icjevcDf/Z15Bmwck62/bNYLekGBJiSy/BxwHrkIIRe3drGLTNDb2tpSMnS7uEUVzDJ0zoEf/pBqqOgrUwL2+6tZ5NLW1rayHZqanImoKghB10/7t0M6Dr1N1rHhEfRtUy3oqnj7QtCz4dDFmHIh6ML1ikqLQHKGHotRp1YuIxeZuJWWLiY59OVlEvGtW4Hvfhf4m7+hqdiySM4o6HqHbpyoJh6b5YYicrEa3aMXdHEllI6gz8zMrNTHEGPQrTpEVcIsQ3/sMeDwYSpfa4SdQzeLXGZmZla2g5f5OaCdyIyzRK3gxKEbT34zmY50yCKSZys7/1xxMf25ydBV8Q6NoFtFLkZBj8Xk0/4BtZGLzKEzFksSdLHW4TvfCbztbcC7301O7lvfSv0+K0E3LsQrHls5dM6ty6OOjGiCvm4ddUbaCbqM8+nTp1FdTTwvX/bWoZtFLv/rf9FV0n/5L6mvpRu5nD59emVf91rQ9ZGLG4e+sCAfASa2szHOO21c5dtDFBRoou427pIV6LLK0FXxDoygv/AC8Hu/l7oKvIAQaDE1Wybo+gxdVgsdyL1DLy9fTOJtLLxfXw/cfjvw7W+Te9fDKOgVFVrNEqOg2zl0u6jp7FkSfHHCZMzZ0EWrDF1wGBvz1qEbOff3A48/Tu5c5r6cRi5WGbqXHaKAdmXiVtAB+Yks10Wq0oVoo9Mx6AKyei5Rhm6C4mKgqIhbCvpDDwHf+x7VVD4qWRBPOPSamuSC9GYOXVYLHci9Q6+vL0s688tWUnnHO6jz8amntOc4TxV0xrSD061DFycCM0E3rgQv7tvNFpUJenNz80p7BgaIi5cZunFbf+YzJLz33Sf/jJPIZfXqVLfa3NyMmhrgwx8G3v72zNqdKUpKyNTMzDibJQpYn8jMBL3ZuFKGxxBtTMehuxF0Vbx9IeiM0UFi1Sl68CCdVRmjVW2efjr59ZdeooOotNRe0GdmzB26yk5RWTH8tWuLbQX9TW8iXvrYZWaG+gCMsybFwZmuQzfrGJUJuhOHLjvQa2trV9qXWDze88hF9B2cOUPG4d57U39DAScOXXaQ19bWgjHqI9m5M/O2Z4KSEm24qEqHXutkPGQOkStBV8XbF4IOAMXFcUuH3tcH3HorDeVbswZ41auSK7+JuiyMJf/4ekEvKaE4xsqhqx6HntpBOIKFBW3nkAl6ZSWJ+ve+p2XcxklFAkLQzQpVZeLQV61Kjka2bKHfWZwcZZA59P7+/hWxfI7WuvA0clle1mZ9fvGLtA/df7/5Z8rK6NYqQzeOcAGId76gpIT6MAD3gu7GoecTZ0DbLtkQdKtOUVW8fSPopaVLpoI+Pk5jgjs6aPTHv/wL7VT6RUFeeokEHUj+8S9f1nZEsUivVYauehy6ceOLBZOFSx8d1Ra01uMd76CT0M9+Ro/NBL2hgfgbh8QJAU03Qx8aoo48/fc6Gbpol6F77dCNJ/Df/AZob0+d7q9HYSH9XlaRS77nyfoTjptRLkAwMnS3gi5bV9SqU1QVfCPoVqU5Dx6k2xtuoNttidLp+kkt+jHlZg4d0IZeTU3R+HTjIrG5duhr19I/1Au6bGd79avJXX/1q/TYTNA/9CGqTmiEnUN3ErkY15l0IuiyA72mpibJoRcWOheVbMN4Aj9zJnWBZBnshvDJxK3GeDnoIdIR9HQil3ziDGhDEM3iNDO4jVxU8faNoNfXl9sK+stfTrfr1tEOqe+QM3PoMkEXGbpx9XjAnUP/9rep8qDTsr8yh75rF41fE9zNBH3VKuB976PJLn19qYW5BFpaKJ4xQl/vRQYnkYtR6ISgW3WMmk0sEieYiQnqG3G7cn22oL8yWV52Luh2k2xkkUu+TrJx69DdRC75xBmgNjY0uC8hUV1Nx6iIqYBoYpEl5ucvmAp6Xx8JlRABxmjYl96hWwm6yDyBZIcuO4m66RT93vdoGKWbBZON4jY4eAiA5tCtFq/9kz8hh/3hD5s7dDO8853AI4+YH7xmDn1hAfj0pyn22mZYVGrNGvqt3Qp6d3c3Kiq0g8qr/BxI3t5jY9ReJ0MKKyrcRy7d3d3pNzTLELzLy1OvUs2QjkPPJ84AaYixNo8TCEev1yirDF0Vb4flZ7xHaemi6UrqBw/SNHg9mpuThUQWuXCe6tBFhn7lirwgkdPIZXmZOmgB4NQpYMcO6/eLSTvGjV9aSr1xeod+++3y76iqAv7yL2l89Pw8CaLTK7s1a4DXv978ddmJ7MABGu1x9Cjw5jfTfdn3mq23CMhPYpxzFBRouaRX+TmQvL1F9USnDt1t5MJVFNlPE2J7u4m67ARdFmPkE2cA+PznU+dzOIF++r8wjmazYwF1vH3j0MvL5Z2iokNU5OcC+lVjOE916IuLJPKcm2foMjEUB6Jd5HL0qDZS5tQpW3qmy1XpO0Xn54mHVYfNe95DDvLJJ4mv04pxdjBGLuPjwG23UXt++EPg+9+X/15WThWQn8RYwpqLgyQfHHo8ru1PThx6OpELc3udrxDpCHo6kUs+cQboSt9YftkJZPVchFmRUVTF2zeC3tLSKBV0Y4eowObNlCPPztJIloWFZIcOaMMazTJ0mUAVFNAZ186hiyuqoiKtzrgVzPK222+/CQAJulgOzErQi4uBT36S7juNW5zAGLmcPElt/ud/Bt74RvPPWQkbII9c9u3bB0DbTl46dP2Vibjicxq5mDl0s8hF8M4HpCPopaUkXm4il3zinAnMBN1sZI8q3r4R9FjsPK5cSe1g7OujnUh0iAqIy+IzZ7RZonqHDlAeDcgduqwWuoCTdUW7u+nA37XLmUOXLfQAAENDRwDQSUY2Bl2Gd7yDllbLZj0QY+QiJp3Y5Y3pCPqhQ9RvkI+CXleXOmRUBjuHLjvQBe98QDqCbjUB0A+cM4FbQVfF2zcZOkB7ycxMsvM8eJDyaWP1QOGixPhoIFXQZQ69spI2yuXL5vmzXW1wzoH9+2lF+tlZ4MgRe3ZmDn1+fhKMkUMXgm5XZ6KwEPjlL5N73DOFMXIR/Rl2cUh5uSb+Msh2+ouJSQD5ELnoM3SnI1wA66jJLHK5aDUDK8cQ7XM67V/A7ERmJm75xDkTyARdFicKqOLtG4deVkbqZIxdDh5MjVsAuUN3Grlcvkz3zRy61TqTAI2dnpigEgTbttGQPjtxNXPoola5XtCdTHqoq8vu2G1j5DIyQqODxEnSDOk4dIF8cujxOJkDp0WzrDpF/TSxyO0+5FbQgwKxr+rLdMg6/FXDN4Le2ko2W3+QjI1pM0SNaGigH3NoyDxyMRN0ASuHbhW5iPxcCPrCgrZcmRnMHHp7ezsqK7XIxc1ah9mEWCZOH7k0NtqP101H0Nvb2wHkh0PXn8jcOHTBWzaYwUzcBO98QLqCbtZ34AfOmUC2rqjVSUwVb98IOkDWXO/QzTpEARKfTZvoIDTWNhc/vp2gp+vQu7tJhLZupYlFgH2ObubQp6amUFVFZ/7z52mcuWwYlGowlryu6PCwM6G1EvSlJfoz7vRTieFB115Lv6ExTsslRNvOniVRd+rQKyqIm+zEbxa5CN75gM2baV/ctcvd59w69HzinAlKS8n0OBV0Vbx9I+jT0xTE6gVd1Pl42cvknxFj0d04dH3RqnQcOudAVxe5c8a0yTZ2I13MHPrQ0NCKoJvNEs0V9MvQjYw4m4BhJejmHcFDAID3v59G03g5sk0I74kTdOvGoQNy7maRi+CdD9i6lbb1dde5+5wsajKbYwHkF+dMYCz6B1hn6Kp4+0bQS0tTM/SxMTozmtVd2LyZBF04dPE+IdrpOnSrTtGBAWqXGJUkFk1O16GL9orIxUtBFw59edn5snBiFRtZ+QMrzgAdJF5N+RdIV9CDUKgqnd9e1hlsNWMySDAKepShW+Caa6hnzCjo9fXmDq65md5z/jztaCKqKCwkkTQbtihg5tCtIhd9fg7QQbF1q72gmzn0rVu35p1Dv3CBDlKngg6YCxsg55wvEIL+/PN066ZTFEh1q4uLdEKURS75xDtdyK7IrGqaBIGzgEzQzU5iqnj7RtDr6+kIMXaKWnXaiIPv8OHU0RjV1drszGx2ij7+OA0r1E/137bNPnIxc6uVlZV5Jejz887HoAPWgm7FOV8g2ibq6TutwmfG22yBaCC/eKcLWaeo9ZJ7/ucs4EbQVfH2jaAPDh4GIHfoZhCXx4cPp4qzODBFZ5+A+J3Lysw3hplDX1igeuR33ZV81XD11eTQrco3mO30hw4dWrmaiMfdr3WYTYjIxekYdMA6ejAT9HyabMKYtk3crPNpNg3eStzyiXe6cOvQg8BZwI2gq+LtG0FftWoZRUXuBF0cgLGY3KEDJNx68RUHolVRKzOH/uST5KRf+9rk57dto6hCRDwyWOXJVVXaijleO/S5Oc2hZytyyXXO6BYiHnGzDKQZb8FZFrkEAeXlNI9DX+DKi8WSvYCbTlFV8I2gr11bt9I5CNAOMzFhLegbNmjFqawEXQ8h6FZL/pl1ij76KOX0xmqITka6mO30dXV1ScP2vBZ0EbkUFjpri5PIRcY5nyDENx2H7iZyyTfe6aCigq5E9WWWrQQ9CJwF3HSKquLtG0FvbW1dKZwFUCXDpSXrDL2oSFsqzCxyMdZ6duLQzSKXH/+Yyvga4zEnY9HNHHpra2vS9+XDKJfhYWqHcRk7GdLJ0FtbWzNraJYhhCgdh+4mcsk33ulAtr2Dzlmgupqu0MXViVXkooq3raAzxr7KGBtnjB3VPVfLGHuMMXYycat8Han9+/cnOXRRedCu1oRwVWYOXbbEXFGRvUM3Ri5DQzQu3hi3iDYUFqbn0Pfv359XDl1ELk4XAUgnctm/f3/6jVSAdBy6XeQiO9DzjXc6kJ3Igs5ZoKqKrk4EdytBV8XbiUP/OoA7Dc99GMAvOOfbAfwi8Vg5xOITgHNBF67KqUNnjFy6W4f+6KN0e9ddqe9ftYrakY5DB7SZkqtX29dOUQl95OJ0On46kUu+IZ0M3S5yCWqGLuMdpgwd0CYy5mWGzjnfD8A4T/WNAL6RuP8NAJJVKrOLoqKiJIc+Pk632XboAC2p9p73mH9nSQltLP2olUcfpWjFbGUiMdLFDGY7veANpLfWYTZRWkoO3em0fyC9yKUoW6tyZAnpOPTVq+mqzE3kkm+804HbyCUInAVaWuhWDGCxytBV8U43Q6/nnJ8HgMSt8jXZOzs704pchKtyI+j33gvcdJP5dxoXip6bo3K1xuGKemzbZj100UzcOjs7Vxy6l3ELQLwvXqScMBuRixXnfEJxMTlPqxjOCFEb3E3kkm+804FsuGbQOQvs3k0cu7spR19cNHfoqngrPz0yxu4FcC8AbNiwAV1dXQBoplRlZeXKeMy6ujq0trauZEtFRUXo7OxEf38/pqencfnyZZSU3IGLF4Gurifx9NNbUFS0CYuLk+jqonh/3bp12LFjB3p6egAAxcXFaG7eAwAYGTmCrq5J7N69G8PDwxgZWQRwLQoK5jA+PoPjicIwDQ0N2LJlCw4cOAAAKC0txe7du9Hb24u5RNd9YWEngCI8/vivUFGxhHPnrsfc3Bps3AkNWb8AABZMSURBVHgIXV0X0djYiI0bN6K3txcAUFFRgauv7sClS8Ajj/SgunoRnZ2dGBgYwHjiUuPixZsAlOHJJ7vAGNDU1IT6+nrs378fFy/WAdiN9euBnp4eLCbGMO7duxfHjh3DZGJF6La2NszMzOD06dMAgObmZtTW1qK/vx8AUFNTg7a2NnR3d4NzDsYY9u3bh0OHDq3UZ25vb8fU1NRKrQn9dpqY2IKFBbKply4dR1fXeMp2AoCOjg6MjY3h7NmzibLBt2JiYhZdXc8kbaf+/hMAWvHcc8+ivf169PX1IRaL4fLly7jtttsS24nGSLa0tKCwsNDVdtqzZw8GBwcxmqg7vHPnTiwtLeFEYh6/bDt1dHTgwIEDiCdUqLOzE8vL87jqKqC7uw+7du1CPB7HyZMnAWjbqa+vDwBQVVWF9vZ29PT0YPXqG3Hy5BSWl+tXttMzz9QBuA4vvXQeXV0nkrZTT08PysrKMt5OTo4n43YCgO3bt6O4uBhHj5ofT3v27FnZTgB0xxNtp3i8FcBV+PWvj6CoaBINDQ2Ixa4GsAqHDz+DpaXlpO10+fJlvOpVr8rKdtIfT262UzaPp9279+EnP7mMO+7oA7AXnM9jaGg0ZTsdOHAAZWVljreTY3DObf8ANAM4qnt8AsD6xP31AE44+Z4bbriBp4snnniCv/e9nNfW0uM/+iPON2yw/9ziIudf/CLn8/PJzz/8MOcAfY9bPPggfXZ0lB5/4AOcl5ZyPjdn/pl/+Rf6zMmT8tf/4i84X7069fknnniCj47SZ++7z31bs4kHHqB2AJw/8YTzz5WUcP6nf5r6/De/Sd81MJD8/BNuvjwH6O3lvLvb/ee2b+f87ruTn/ve94jzkSOp78833ung+eeJ3ze/qT0ntvOJE6nvDwJnPT7+cc4LCjg/c4Y4/+M/yt/nljeAPu5AY9ONXB4BcE/i/j0AHk7ze1zBGLk4WU2lsBB473tTL32sIhc76FexAajOR2urdUeX1QK6gHWPeHU11YTxsi44kMzPaeQCmFdctCvOlS+46SYajuoWskJVQe8gDHOnKEA1nJaXgSeeoMd51ynKGHsIwAEALYyxYcbYuwH8PYA7GGMnAdyReKwUHR0dqKzUKveNj7tfHkuPTATdmKEPDgJbtlh/xmxcsoDZyj0dHR0oKQF+8hM6MXkJfYkENycXs+XYzIYturrEzGPISslajXIJAm+3naJB4KzHnj00qu3nP6fHZmZFFW/bDJ1z/naTl16V5bZYYmxsDJWVdPqfmSGHfu216X9fNgRdlJI9cwZ4k804H6uaJoC5Qx8bG0NFRQVe/Wr37cw2BO+ammRxt4OdQzfyFpz9jvJyrXSzgJW4BYG323HoQeCsR1kZXdE99hg9NnPoqnj7Zqbo2bNnV0RRCHomDr2ujs6k6SznJjZSPE71Wa5csXfodpGLmUM/a7d2XQ4hBN1N3AK4j1zyiXMmcFt5MAi8V62i7enUoQeBsxH79lFZEsBc0FXx9o2gA9qU+pER2kkyEfSqKuCZZ4B77rF/rxF6hz44SPczFXQ/LHogXLnbLN9qWTIg/zP0dCHjHfSJRUAq7zBl6IC2FgKQe86+GdW/ffv2lYNBTNDJRNABoK0tvc/pO0UTI62UZejbt29Pr5EKIEQoHUGXVZoUDt24Rmo+cc4EVp2i+b6tM4HxyiQep3Iasto/QeGsxyteQVyXlszNiirevnHoxcXFKw5d1ETJVNDThb5TVDh0u1mE6WboxXlka1RELqtXp07GyifOmUDWKSpmD8omoAWJt9Ghm68tEAzOelRUAKLPM9e8fSPoR48eTRF0q0qLKqGPXIaGaNEJu0vo0lI6iN06dDHJIx+gInLJd86ZoLyc9hGaXEWYnzffV4LE2+jQzfQrKJyNELFLrnn7RtABzeVmK3JJF/pOUSdDFgEaR15W5u8MvamJLp3dRlVWDj3fOWcCszHZQeYMpEZNYeBsxOtfT8dKrueO+CZDX7du3YpDP3WK3O7atd60xdgp6rQsg9l4bIDETTaEcp1XlyESbN5MI4zcdujZRS5G5BPnTKAfky3q8ViJW5B4i1pLQDg4G9HZSTWPzIb3quLtG4e+Y8eOFUGfmiIx96pQm9g5YzHg7FlnDh2QD2MTMNvpd5iVb/QI6YzOKC8n8RaLcguYCXq+cU4XModuFbkEibfTyCUonGWwmquhirdvBL2np2dl8QnAu/wc0A7IkydpYlE2BN1M3ERhJD/DarGHoHIGzCfZmIlbkHg7jVyCwtktVPH2jaADFLMIl+5Vfg5ogv7883TrdOED2agHgSDnjGaCHvQM3WwafJA5A+46RSNkF74RdDHMR1zGeinoYty0EHQ3Dt0qQ5e51SAM6zIbshlkzoD7yCVIvMM8bNEJQj9scc8eqmueDw6dMTooR0ZoAoFYiNoO6WTogref4TZyCQJnwH3kEiTeoogeEA7ObqGKt28EXRSmzwdBBzSXJYbyOYFV5GLmVgVvP8Nt5BIEzoD7YYtB4a3f3pzTSDCz1baCwtktVPH2zbBFsUKKEHSvRzuJg9Jp3AJYRy5mB3rM7AzgI1gJuqzgXBA4A3KHbhW5BIW3/kQ2PQ2cO0fT4WUICme3UMXbNw5dIN8cutuV4N069CDAbeQSFIS5UxQg3k8+SffNBD1CduEbQd+9ezeA/OgUBdJ36Map4AAtJru8LD/QBW8/w23kEgTOAM0MBpwLelB46yuLPvkkPb7uOvl7g8LZLVTx9o2gDw8PA8g/h+5G0K2cKiB3q4K3n2El6EHlDFCHeWmp88glKLz12/upp4Cbb5ZXWgSCw9ktVPH2jaCLVcXFFOp0FqbIJtIRdLOa6GYr9wAabz/DbeQSBM4CbobwBYW32N7nzwOHDwO33GL+3qBwdgtVvH3TKSrwrncBW7d6v0BAupELYL6KTVDz5LBOLALCOclG7Oe//CVFiVF+njv4RtBbWloAADt20J/XKCmhA9NsOJYM6Th0wdvPKCwkbjLespNYEDgL6KfBc24duQSFtziB//znVGX05pvN3xsUzm6hirdvIpdCsxDOI1RUkDsvcPEL2mXoMkHPN97pQlZx0UzQg8IZSI5cFhdJ1M0celB4C+MyNESdoSImlSEonN1CFW/fCPrx48e9bkISPvUp4Gtfc/cZO4cuE7d8450uzJZjCzJnIDlysVtbMyi89WWg7eKWoHB2C1W8fRO55BtaW91/xi5DD3K2anTonIcjQ6+ooHLPQDgWiAa01bk4t+4QjZB9+MahN7gJq/MUVp2DgNytBoE3kCroi4t0G2TOQDJvuxN3UHgzpu3rdg49KJzdQhVv3zj0LW6Gk+Qp0nHoQeANyGtkA3JBDwpnwF3kEjTelZX2M6mDxNkNVPH2jUM/cOCA103IGOlk6EHgDaQKutXInqBwBpL7DuwilyDxbmgAbruN3LoVgsTZDVTx9o1DDwJKSmhUjJtRLkGBmaAHdey9gHDonIdjOwv8+MfyNXIjqIVvBL3UaoE+n0Bki24cehB4A+4il6BwBsihLy8TXztBDxJvp6vdB4mzG6ji7ZvIJShFfGQVF60O9KDwdhO5BIUzkNwRPjFB980ilyDxdoowcgai4lzo7e31uglZgUzQrRx6UHi7iVyCwhnQBP23fgt4wxvoKs2ssFyQeDtFGDkD6nj7RtDn5ua8bkJWIJsxaeXQg8Q7HteGK1pFLkHhDFCZisJCYM0a4NOfBk6cAHbulL83SLydIoycAXW8fZOhBwVuHXpQoI8eqqvDwRkAOjvp5BXSGe4RcgzfOPSgLCbrNkMPCm/jpCqrDD0onAWcinnQeDtBGDkD0SLRGBwc9LoJWYGspomVWw0SbyBV0IPM2S3CyDuMnAF1vH0j6KOjo143ISuQDVu0ypODxBtInQYfZM5uEUbeYeQMqOPtG0EPCswy9KIid6V4/QY3kUuECBHSg28kZKfZ0ACfwSxDNxO2oPA2E3SZQw8KZ7cII+8wcgbU8faNoC8tLXndhKygooLEbGFBe85soQcgOLzdRC5B4ewWYeQdRs6AOt6+EfQTJ0543YSsQFZC18qhB5W3VeQSFM5uEUbeYeQMqOOd0Th0xtgQgBkASwAWOecd2WhUkKGvuLhmDd23cuhBgZvIJUKECOkhGxOLbuOcX8jC91ii0Wm1nzyHcfgeYO3Qg8LbTeQSFM5uEUbeYeQMqOPtm8hl48aNXjchKxDCpu8YtXLoQePtxKEHhbNbhJF3GDkD6nhnKugcwM8ZYwcZY/dmo0FmCEoRH9kiF1YOPSi8i4pIvJ1k6EHh7BZh5B1GzoA63plGLrdwzs8xxtYBeIwx9jznfL/+DQmhvxcANmzYgK6uLgDA1q1bUVlZiUOHDgEA6urq0Nraiv376eNFRUXo7OxEf38/pqenEYvFEIvFMDY2hrNnzwIAtm/fjuLiYhw9ehQAsG7dOuzYsQM9PT0AgOLiYuzZswd9fX2IJRR09+7dGB4exsjICACgpaUFhYWFK6twNzQ0YMuWLSsripSWlmL37t3o7e1dKaizZ88eDA4OrkwO2LlzJ5aWllY6OhobG7Fx48aVjVZRUYGOjg4cOHAAJ06sBnADpqeXcPz4CYyPj2Ns7GUoKKjEyMg4Tp48CQBoampCfX09YrEYurq6UFVVhfb2dvT09GAxUeFq7969OHbsGCYnJwEAbW1tmJmZwenTpwEAzc3NqK2tRX9/PwCgpqYGbW1t6O7uBuccjDHs27cPhw4dwsWLFwEA7e3tmJqawtDQUFrbCQA6Ojqk26m4uBonTozj+PFLmJu7BgUFDL/6VXfKdorFYpibm/N0O8UTmVBnZycGBgYwPj4OANi1axfi8XjKdurr6wOAjLaT2NZeb6dcHk+xWAzxeNxX2ykbx5PY1k63k2NwzrPyB+ABAH9q9Z4bbriBp4tnnnkm7c/mE44d4xzg/Lvf1Z7bu5fzffvk7w8Kb845b2ri/A//kO7/2Z9xXlIif1+QOLtBGHmHkTPn7nkD6OMOdDjtyIUxVs4YqxT3AbwawNF0v88Ors5SeQy3GXpQeAO0aLBY5OHKFfOYKUic3SCMvMPIGVDHO5MMvR5AD2PsEICnAfyYc/7T7DQrFUFZTNZthh4U3gBw++3Az39Oom51EgsSZzcII+8wcgbycJFozvlpAG1ZbIslRFbmd8iGLVqJW1B4A8C99wKf/zzw9a/TSSwMnN0gjLzDyBlQx9s3wxaDgtWrqT62U4ceJLS2ArfcAvzTP4WHc4QIuYRvBL2zs9PrJmQFjKUW6LJy6EHhLXDffcCpU8Avfxkezk4RRt5h5Ayo4+0bQR8YGPC6CVmDcZELK7caJN4A8Lu/C9TUAOfPmwt60Dg7RRh5h5EzoI63bwRdjC8NAtw49CDxBoDSUuAP/oDum53EgsbZKcLIO4ycAXW8fSPoQYJx1aKw5cn3JuYUR4W5IkTILrJRnCsn2LVrl9dNyBrcOPQg8RbYuRO4806gtlb+ehA5O0EYeYeRM6COt28EPUjDmyoqAHHFtbwMLC6aO/Qg8dbjkUdotI8MQeVshzDyDiNnIBq2uFKTIQjQO3SrMrJAsHjrsWqV+RqqQeVshzDyDiNnQB1v3wh6kKDP0KPFkiNEiJAt+EbQm5qavG5C1qAftmjn0IPE2ynCyBkIJ+8wcgbU8faNoNfX13vdhKxBH7nYOfQg8XaKMHIGwsk7jJwBdbx9I+iijnEQUF4OLCyQmNs59CDxdoowcgbCyTuMnAF1vH0j6EGCvkBXlKFHiBAhW/CNoFdVVXndhKxBX0LXzqEHibdThJEzEE7eYeQMqOPtm3Ho7e3tXjchaxCC/rnPUQVCwNyhB4m3U4SRMxBO3mHkDKjj7RuHLtY1DAJuuQXYuxf47GeBd7+bnjNz6EHi7RRh5AyEk3cYOQPqePvGoYuFXIOAjRuB7m6aLfqf/wn85jfAzTfL3xsk3k4RRs5AOHmHkTOgjrdvBD2IWLdOc+gRIkSIkCkYLSidG3R0dPB0h+ssLy+jwGyueIARRt5h5AyEk3cYOQPueTPGDnLObVeW9s0veezYMa+b4AnCyDuMnIFw8g4jZ0Adb98I+uTkpNdN8ARh5B1GzkA4eYeRM6COt28EPUKECBEiWMM3gt7W1uZ1EzxBGHmHkTMQTt5h5Ayo4+0bQZ+ZmfG6CZ4gjLzDyBkIJ+8wcgbU8faNoJ8+fdrrJniCMPIOI2cgnLzDyBlQx9s3gh4hQoQIEayR03HojLEJAGfS/PhaABey2By/IIy8w8gZCCfvMHIG3PPezDm/yu5NORX0TMAY63MysD5oCCPvMHIGwsk7jJwBdbyjyCVChAgRAoJI0CNEiBAhIPCToP+T1w3wCGHkHUbOQDh5h5EzoIi3bzL0CBEiRIhgDT859AgRIkSIYAFfCDpj7E7G2AnG2CnG2Ie9bo8KMMaaGGNPMMaeY4wdY4zdn3i+ljH2GGPsZOK2xuu2ZhuMsULG2G8YYz9KPA4D5zWMsX9njD2f2OZ7gs6bMfbBxL59lDH2EGOsJIicGWNfZYyNM8aO6p4z5ckY+0hC204wxn47k/+d94LOGCsE8EUArwGwE8DbGWM7vW2VEiwC+BDn/FoANwN4X4LnhwH8gnO+HcAvEo+DhvsBPKd7HAbO/wfATznn1wBoA/EPLG/GWCOADwDo4JzvAlAI4G4Ek/PXAdxpeE7KM3GM3w2gNfGZBxOalxbyXtAB3ATgFOf8NOf8CoDvAHijx23KOjjn5znn/Yn7M6ADvBHE9RuJt30DwJu8aaEaMMY2AngtgP+nezronKsA7AXwFQDgnF/hnL+EgPMGrZBWyhgrAlAG4BwCyJlzvh/AlOFpM55vBPAdznmccz4I4BRI89KCHwS9EcBZ3ePhxHOBBWOsGcDLAfQCqOecnwdI9AGs865lSvA5AH8OYFn3XNA5bwUwAeBriajp/zHGyhFg3pzzEQCfAfAigPMALnHOf44AczbAjGdW9c0Pgs4kzwV2aA5jrALAfwD4Y875tNftUQnG2OsAjHPOD3rdlhyjCEA7gC9xzl8OYBbBiBpMkciM3whgC4ANAMoZY+/0tlV5gazqmx8EfRhAk+7xRtClWuDAGFsFEvNvcc6/n3h6jDG2PvH6egDjXrVPAW4B8AbG2BAoSnslY+ybCDZngPbpYc55b+Lxv4MEPsi8bwcwyDmf4JwvAPg+gFcg2Jz1MOOZVX3zg6A/A2A7Y2wLY2w1qAPhEY/blHUwxhgoU32Oc/6PupceAXBP4v49AB7OddtUgXP+Ec75Rs55M2i7/pJz/k4EmDMAcM5HAZxljLUknnoVgOMINu8XAdzMGCtL7OuvAvUTBZmzHmY8HwFwN2OsmDG2BcB2AE+n/V8453n/B+AuAAMAXgDwMa/bo4hjJ+hS6zCAZxN/dwGoA/WKn0zc1nrdVkX8bwXwo8T9wHMGcD2AvsT2/iGAmqDzBvBXAJ4HcBTAvwIoDiJnAA+B+gkWQA783VY8AXwsoW0nALwmk/8dzRSNECFChIDAD5FLhAgRIkRwgEjQI0SIECEgiAQ9QoQIEQKCSNAjRIgQISCIBD1ChAgRAoJI0CNEiBAhIIgEPUKECBECgkjQI0SIECEg+P8JWOCupinZqgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data.values, c='blue')\n",
+    "plt.grid(linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 25)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data.values, color='blue', edgecolor='black')\n",
+    "plt.grid(linestyle='--')\n",
+    "plt.ylim((0, 25))"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +332,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-