{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 2: Example for pandas using the heart.csv data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the csv Data \n",
    "df = pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
      "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n",
      "None\n",
      "age           int64\n",
      "sex           int64\n",
      "cp            int64\n",
      "trestbps      int64\n",
      "chol          int64\n",
      "fbs           int64\n",
      "restecg       int64\n",
      "thalach       int64\n",
      "exang         int64\n",
      "oldpeak     float64\n",
      "slope         int64\n",
      "ca            int64\n",
      "thal          int64\n",
      "target        int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# What is the number of columns and rows\n",
    "print(df.columns)\n",
    "print (df.info())\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
      "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
      "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
      "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
      "\n",
      "   ca  thal  target  \n",
      "0   0     1       1  \n",
      "1   0     2       1  \n",
      "2   0     2       1  \n"
     ]
    }
   ],
   "source": [
    "# get first 3 lines\n",
    "print(df.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              age         sex          cp    trestbps        chol         fbs  \\\n",
      "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
      "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
      "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
      "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
      "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
      "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
      "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
      "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
      "\n",
      "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
      "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
      "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
      "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
      "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
      "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
      "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
      "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
      "\n",
      "             thal      target  \n",
      "count  303.000000  303.000000  \n",
      "mean     2.313531    0.544554  \n",
      "std      0.612277    0.498835  \n",
      "min      0.000000    0.000000  \n",
      "25%      2.000000    0.000000  \n",
      "50%      2.000000    1.000000  \n",
      "75%      3.000000    1.000000  \n",
      "max      3.000000    1.000000  \n"
     ]
    }
   ],
   "source": [
    "#display statistics summary\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               age       sex        cp  trestbps      chol       fbs  \\\n",
      "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
      "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
      "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
      "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
      "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
      "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
      "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
      "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
      "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
      "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
      "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
      "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
      "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
      "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
      "\n",
      "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
      "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
      "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
      "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
      "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
      "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
      "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
      "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
      "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
      "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
      "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
      "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
      "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
      "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
      "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
      "\n",
      "              thal    target  \n",
      "age       0.068001 -0.225439  \n",
      "sex       0.210041 -0.280937  \n",
      "cp       -0.161736  0.433798  \n",
      "trestbps  0.062210 -0.144931  \n",
      "chol      0.098803 -0.085239  \n",
      "fbs      -0.032019 -0.028046  \n",
      "restecg  -0.011981  0.137230  \n",
      "thalach  -0.096439  0.421741  \n",
      "exang     0.206754 -0.436757  \n",
      "oldpeak   0.210244 -0.430696  \n",
      "slope    -0.104764  0.345877  \n",
      "ca        0.151832 -0.391724  \n",
      "thal      1.000000 -0.344029  \n",
      "target   -0.344029  1.000000  \n"
     ]
    }
   ],
   "source": [
    "#display correlation\n",
    "print (df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              age       sex        cp    trestbps        chol       fbs  \\\n",
      "target                                                                    \n",
      "0       56.601449  0.826087  0.478261  134.398551  251.086957  0.159420   \n",
      "1       52.496970  0.563636  1.375758  129.303030  242.230303  0.139394   \n",
      "\n",
      "         restecg     thalach     exang   oldpeak     slope        ca      thal  \n",
      "target                                                                          \n",
      "0       0.449275  139.101449  0.550725  1.585507  1.166667  1.166667  2.543478  \n",
      "1       0.593939  158.466667  0.139394  0.583030  1.593939  0.363636  2.121212  \n"
     ]
    }
   ],
   "source": [
    "# Print mean values for each column with and without disease\n",
    "print(df.groupby('target').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
      "167   62    0   0       140   268    0        0      160      0      3.6   \n",
      "181   65    0   0       150   225    0        0      114      0      1.0   \n",
      "182   61    0   0       130   330    0        0      169      0      0.0   \n",
      "190   51    0   0       130   305    0        1      142      1      1.2   \n",
      "204   62    0   0       160   164    0        0      145      0      6.2   \n",
      "\n",
      "     slope  ca  thal  target  \n",
      "167      0   2     2       0  \n",
      "181      1   3     3       0  \n",
      "182      2   0     2       0  \n",
      "190      1   0     3       0  \n",
      "204      0   3     3       0  \n"
     ]
    }
   ],
   "source": [
    "# get table with selection on more than 1 column\n",
    "df1 = df[(df[\"sex\"] == 0) & (df[\"target\"] == 0) ]\n",
    "print (df1.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      63\n",
      "1      37\n",
      "3      56\n",
      "5      57\n",
      "7      44\n",
      "       ..\n",
      "295    63\n",
      "297    59\n",
      "299    45\n",
      "300    68\n",
      "301    57\n",
      "Name: age, Length: 207, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc9f07ea340>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# age dirtibution group into male and female (1 = male; 0 = female)\n",
    "# male\n",
    "plt.title('age distribution according to Sex') \n",
    "df[df[\"sex\"] == 1]['age'].plot.hist()\n",
    "print(df[df[\"sex\"] > 0]['age'])\n",
    "# female\n",
    "df[df[\"sex\"] == 0]['age'].plot.hist()\n",
    "plt.xlabel('age [years]')\n",
    "plt.legend([\"male\", \"female\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'max heart rate')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yew7za3vVV199hRkzZmD37t1o2bIlbG1t0aNHD9y6dQtA2bwnDB48GN999x3u3buHXr16wd7eHj4+PlrXLT58+BC//PJLrm3Wq1dPa5sVAa+ZqSA2b94MNzc3bNu2Tet/Jq9egJjj22+/xd69e/Huu+9ixYoV6Nu3L3x8fMq0xpw3+1dr0rc/yJzz+TNnzpSuSXqVh4cHgJfj3qJFC6xatUprfn7XWpTm3UZ5rWvz5s0YMmQIFi5cqNX+6NEj2NjYFLrO4ux7XsLCwmBsbIz//e9/Wh/uu3fv1upXpUoVAC8viHV2ds5zXf/uk5+cD+C4uLhc8x48eAADAwNUqlRJq/3VcctZR3x8PKpWrSq1Z2ZmFjkIFIWdnV2eFxXHx8e/9nqB/Mfg1etTSipnPT/99BNcXFzy7ZeUlIT//e9/mDt3Lj7++GOpPee6vry87t/Fv5/DVxVlfC0sLDBv3jzMmzcPDx8+lI7SdO3aFX/++WeZvScMHz4cw4cPR1paGo4fP465c+eiS5cuuHnzJlxcXFC5cmXUr18f//3vf/PcppOTU6H7Vl4wzFQQCoUCJiYmWm8K8fHxue5mAoArV65g4sSJGDJkCNatW4emTZuib9++uHDhQq43/tKU88VVly9fRvv27aX2PXv2lNk2S8LDwwPu7u64dOlSrlDwKoVCIf0PNsfly5dx6tSpfD+kX/Xq/4BfR1717N27F3///Tdq1qxZ6DaLs+/5bd/IyEjrNN7z58/x/fffa/Vr164dDA0NsWrVKvj6+ua5rqZNm0KlUmH16tXo169fnh94Hh4eqFq1KrZs2YKpU6dKfdLS0rBjxw7pDqeC5FyQ+cMPP2hdQL59+/ZSvWPEz88P27dvx/79+7VOG4eFhRVp+fyeM19fX5iZmWHz5s3SHU/AyxB49OhR9O7duxSqB9q3bw8jIyPcuXOnwNNCCoUCQohcr8Nvv/0WWVlZRd5ecf4uPDw8oFarsXXrVgQGBkqvg3v37uHkyZPF+tB3cHDAsGHDcOnSJYSEhODZs2dl/p5gYWGBjh07Ij09HT169MAff/wBFxcXdOnSBfv27cNbb71Vpu/NcsAwU0F06dIFO3fuREBAAHr37o379+9j/vz5UKvV0qFS4OWbfJ8+feDm5oaVK1fCxMQE27dvh7e3N4YPH57rf9ClydHREW3atEFwcDAqVaoEFxcXHDlyBDt37iyzbZbUmjVr0LFjR7Rv3x7Dhg1D1apV8fjxY1y/fh3nz5/Hjz/+CODluM+fPx9z586Fn58fbty4gc8++wxubm5F/iB86623YGZmhh9++AF16tSBpaUlnJycSvS/ri5dumDDhg2oXbs26tevj3PnzmHp0qW5TtUUtM2i7nteOnfujOXLl2PAgAH44IMPkJiYiM8//zzXm7urqytmzZqF+fPn4/nz5+jfvz9UKhWuXbuGR48eYd68ebC0tMSyZcswatQotGnTBqNHj4aDgwNu376NS5cuYcWKFTAwMMCSJUswcOBAdOnSBWPGjIFGo8HSpUvx9OlT6Vb+gtSpUweDBg1CSEgIjI2N0aZNG1y9elW646y0DB06FF988QUGDRqEBQsWoGbNmti/fz8OHjwIAIXecePp6QkAWLt2LaysrGBqago3NzfY2dlhzpw5mDVrFoYMGYL+/fsjMTER8+bNg6mpKebOnVsq9bu6uuKzzz7D7NmzcffuXXTo0AGVKlXCw4cPcebMGenohrW1NZo3b46lS5eicuXKcHV1RWRkJNavX1+ko4M5vLy8AABffvklhg4dCmNjY3h4eMDKyipXXwMDA8yfPx+jRo1Cz549MXr0aDx9+hRBQUFFOs3k4+ODLl26oH79+qhUqRKuX7+O77//XisMl/Z7wujRo2FmZob33nsParUa8fHxCA4Ohkqlkr464bPPPkN4eDiaNm2KiRMnwsPDAy9evEBMTAz27duH1atXF+nOx3JB11cgU9Hk3Gnxzz//aLUPHTpUWFhY5Orv5+cn6tWrp9W2aNEi4erqKpRKpahTp45Yt26dtN4cgwYNEubm5rmugs+5U+KLL76Q2pDP3UxRUVElrj0uLk707t1b2NraCpVKJQYNGiTOnj2b591MRd1vIYRwcXERnTt3ztX+bzl3FC1dujTXvFf3VQghLl26JPr06SPs7e2FsbGxcHR0FK1atRKrV6+W+mg0GjF16lRRtWpVYWpqKry9vcXu3bvF0KFDte6gKGjbQgixdetWUbt2bWFsbJxnLf+W3/MgxMs7W0aOHCns7e2Fubm5aNasmThx4oTw8/MTfn5+Rd5mUfY9P999953w8PAQSqVS1KhRQwQHB4v169fneWfKpk2bROPGjYWpqamwtLQUDRs2zHVXz759+4Sfn5+wsLAQ5ubmom7dumLx4sVafXbv3i18fHyEqampsLCwEK1btxa//fabVp/8XqdCvHwep0yZIuzt7YWpqalo0qSJOHXqlHBxcSnS3Ux5vVZf/dsT4uUdQf7+/sLS0lJYWVmJXr16iX379gkA4ueffy5gVF8KCQkRbm5uwtDQMNffzLfffivq168vTExMhEqlEt27dy/S3S75vZ7yu/tw9+7domXLlsLa2loolUrh4uIievfuLQ4fPiz1+euvv0SvXr1EpUqVhJWVlejQoYO4evVqrvEs6LUshBAzZ84UTk5OwsDAIM9aXvXtt98Kd3d3YWJiImrVqiW+++67XH+LQuT+e//4449Fo0aNRKVKlaTX7eTJk8WjR4+0livN94SNGzeKli1bCgcHB2FiYiKcnJxEnz59xOXLl7W2+c8//4iJEycKNzc3YWxsLGxtbcU777wjZs+eLVJTUwscj/JEIUQe31RFRER6Iee7W2JjYyvO/7KJiomnmYiI9ETON2/Xrl0bGRkZOHr0KL766isMGjSIQYaoAAwzRER6wtzcHF988QViYmKg0WhQvXp1zJgxA5988omuSyPSazzNRERERLLGL80jIiIiWWOYISIiIlljmCEiIiJZK/cXAGdnZ+PBgwewsrIq1a+KJyIiorIjhEBKSgqcnJwK/dLIch9mHjx4UOSvjSciIiL9cv/+/UK/mqDch5mcr7a+f/9+qX71OBEREZWd5ORkODs75/kTFa8q92Em59SStbU1wwwREZHMFOUSEV4ATERERLLGMENERESyxjBDREREslbur5khIiJ5ysrKQkZGhq7LoDJibGwMQ0PDUlkXwwwREekVIQTi4+Px9OlTXZdCZczGxgaOjo6v/T1wDDNERKRXcoKMvb09zM3N+YWn5ZAQAs+ePUNCQgIAQK1Wv9b6GGaIiEhvZGVlSUHGzs5O1+VQGTIzMwMAJCQkwN7e/rVOOfECYCIi0hs518iYm5vruBJ6E3Ke59e9NophhoiI9A5PLVUMpfU8M8wQERGRrDHMEBERlZEWLVpg0qRJAABXV1eEhITotJ7yihcAExGR3nP9eO8b3V7Mos6lvs6oqChYWFiU+nqJYYaIiOiNqFKliq5LKLd4momIiKgUpKWlYciQIbC0tIRarcayZcu05r96mikoKAjVq1eHUqmEk5MTJk6cKM1LT0/H9OnTUbVqVVhYWMDHxwfHjh2T5icmJqJ///6oVq0azM3N4eXlha1bt2pt76effoKXlxfMzMxgZ2eHNm3aIC0tTZofGhqKOnXqwNTUFLVr18bKlStLd0DeIB6ZISIiKgXTpk1DREQEdu3aBUdHR8yaNQvnzp3D22+/navvTz/9hC+++AJhYWGoV68e4uPjcenSJWn+8OHDERMTg7CwMDg5OWHXrl3o0KEDrly5And3d7x48QLvvPMOZsyYAWtra+zduxeDBw9GjRo14OPjg7i4OPTv3x9LlixBz549kZKSghMnTkAIAQBYt24d5s6dixUrVqBhw4a4cOECRo8eDQsLCwwdOvRNDVmpYZghIqrA3vS1KIX5M6i1rksokdTUVKxfvx6bNm1C27ZtAQAbN25EtWrV8uwfGxsLR0dHtGnTBsbGxqhevTreffddAMCdO3ewdetW/PXXX3BycgIATJ06FQcOHEBoaCgWLlyIqlWrYurUqdL6JkyYgAMHDuDHH3+UwkxmZib8/f3h4uICAPDy8pL6z58/H8uWLYO/vz8AwM3NDdeuXcOaNWsYZoiIiCqiO3fuID09Hb6+vlKbra0tPDw88uz/n//8ByEhIahRowY6dOiATp06oWvXrjAyMsL58+chhECtWrW0ltFoNNK3ImdlZWHRokXYtm0b/v77b2g0Gmg0GukC4wYNGqB169bw8vJC+/bt0a5dO/Tu3RuVKlXCP//8g/v372PkyJEYPXq0tP7MzEyoVKrSHpo3gmGGiIjoNeWcvikqZ2dn3LhxA+Hh4Th8+DACAgKwdOlSREZGIjs7G4aGhjh37lyur/i3tLQEACxbtgxffPEFQkJC4OXlBQsLC0yaNAnp6ekAAENDQ4SHh+PkyZM4dOgQvv76a8yePRu///679K2769atg4+Pj9b6S+tXrN80hhkiIqLXVLNmTRgbG+P06dOoXr06AODJkye4efMm/Pz88lzGzMwM3bp1Q7du3TBu3DjUrl0bV65cQcOGDZGVlYWEhAS8//77eS574sQJdO/eHYMGDQIAZGdn49atW6hTp47UR6FQ4L333sN7772HTz/9FC4uLti1axcCAwNRtWpV3L17FwMHDizlkdANvbmbKTg4GAqFQvpyIeBl0g0KCoKTkxPMzMzQokUL/PHHH7orkoiIKA+WlpYYOXIkpk2bhiNHjuDq1asYNmwYDAzy/pjdsGED1q9fj6tXr+Lu3bv4/vvvYWZmBhcXF9SqVQsDBw7EkCFDsHPnTkRHRyMqKgqLFy/Gvn37ALwMTzlHXq5fv44xY8YgPj5eWv/vv/+OhQsX4uzZs4iNjcXOnTvxzz//SGEnKCgIwcHB+PLLL3Hz5k1cuXIFoaGhWL58edkPVhnQiyMzUVFRWLt2LerXr6/VvmTJEixfvhwbNmxArVq1sGDBArRt2xY3btyAlZWVjqolIiLKbenSpUhNTUW3bt1gZWWFKVOmICkpKc++NjY2WLRoEQIDA5GVlQUvLy/88ssv0jUxoaGhWLBgAaZMmYK///4bdnZ28PX1RadOnQAAc+bMQXR0NNq3bw9zc3N88MEH6NGjh7Q9a2trHD9+HCEhIUhOToaLiwuWLVuGjh07AgBGjRoFc3NzLF26FNOnT4eFhQW8vLy0DijIiUIU90RfKUtNTYW3tzdWrlyJBQsW4O2330ZISAiEEHBycsKkSZMwY8YMAC8vfnJwcMDixYsxZsyYIq0/OTkZKpUKSUlJsLa2LstdISKSHX28myk6Ohpubm4wNTXVdTlUxl68eJHv812cz2+dn2YaN24cOnfujDZt2mi1R0dHIz4+Hu3atZPalEol/Pz8cPLkyXzXp9FokJycrPUgIiKi8kunp5nCwsJw/vx5REVF5ZqXc+7PwcFBq93BwQH37t3Ld53BwcGYN29e6RZKREREektnR2bu37+Pjz76CJs3by7wUKJCodCaFkLkavu3mTNnIikpSXrcv3+/1GomIiIi/aOzIzPnzp1DQkIC3nnnHaktKysLx48fx4oVK3Djxg0AL4/QqNVqqU9CQkKuozX/plQqoVQqy65wIiIi0is6OzLTunVrXLlyBRcvXpQejRo1wsCBA3Hx4kXUqFEDjo6OCA8Pl5ZJT09HZGQkmjZtqquyiYiISM/o7MiMlZUVPD09tdosLCxgZ2cntU+aNAkLFy6Eu7s73N3dsXDhQpibm2PAgAG6KJmIiIj0kF58z0x+pk+fjufPnyMgIABPnjyBj48PDh06xO+YISIiIolehZljx45pTSsUCgQFBSEoKEgn9RAREZH+0/n3zBARERG9DoYZIiIimYiJiYFCocDFixcBvDyjoVAo8PTpU53WpWt6dZqJiIgoT0GqN7y9vH9TSd80bdoUcXFxUKne8PjoGYYZIiIimTIxMYGjo6Ouy9A5nmYiIiJ6TS1atMDEiRMxffp02NrawtHRMdfNK7GxsejevTssLS1hbW2NPn364OHDhwWu98yZM2jYsCFMTU3RqFEjXLhwQWv+q6eZ7t27h65du6JSpUqwsLBAvXr1sG/fPqn/tWvX0KlTJ1haWsLBwQGDBw/Go0ePpPkHDhxAs2bNYGNjAzs7O3Tp0gV37tyR5qenp2P8+PFQq9UwNTWFq6srgoODpflJSUn44IMPYG9vD2tra7Rq1QqXLl0q7nAWG8MMERFRKdi4cSMsLCzw+++/Y8mSJfjss8+kL34VQqBHjx54/PgxIiMjER4ejjt37qBv3775ri8tLQ1dunSBh4cHzp07h6CgIEydOrXAGsaNGweNRoPjx4/jypUrWLx4MSwtLQEAcXFx8PPzw9tvv42zZ8/iwIEDePjwIfr06aO1zcDAQERFReHIkSMwMDBAz549kZ2dDQD46quvsGfPHmzfvh03btzA5s2b4erqKu1j586dER8fj3379uHcuXPw9vZG69at8fjx49cZ2kLxNBMREVEpqF+/PubOnQsAcHd3x4oVK3DkyBG0bdsWhw8fxuXLlxEdHQ1nZ2cAwPfff4969eohKioKjRs3zrW+H374AVlZWfjuu+9gbm6OevXq4a+//sKHH36Ybw2xsbHo1asXvLy8AAA1atSQ5q1atQre3t5YuHCh1Pbdd9/B2dkZN2/eRK1atdCrVy+t9a1fvx729va4du0aPD09ERsbC3d3dzRr1gwKhQIuLi5S34iICFy5cgUJCQnSzwp9/vnn2L17N3766Sd88MEHxR3SIuORGSIiolJQv359rWm1Wo2EhAQAwPXr1+Hs7CwFGQCoW7cubGxscP369TzXd/36dTRo0ADm5uZSm6+vb4E1TJw4EQsWLMB7772HuXPn4vLly9K8c+fOISIiApaWltKjdu3aACCdSrpz5w4GDBiAGjVqwNraGm5ubgBehiQAGDZsGC5evAgPDw9MnDgRhw4d0lp/amoq7OzstLYRHR2tdaqqLPDIDBERUSkwNjbWmlYoFNLpGSEEFApFrmXya8+ZV1yjRo1C+/btsXfvXhw6dAjBwcFYtmwZJkyYgOzsbHTt2hWLFy/OtVzODzp37doVzs7OWLduHZycnJCdnQ1PT0+kp6cDALy9vREdHY39+/fj8OHD6NOnD9q0aYOffvoJ2dnZUKvVub4AFwBsbGyKvS/FwTBDRERUxurWrYvY2Fjcv39fOjpz7do1JCUloU6dOvku8/333+P58+cwMzMDAJw+fbrQbTk7O2Ps2LEYO3YsZs6ciXXr1mHChAnw9vbGjh074OrqCiOj3B//iYmJuH79OtasWYP3338fAPDrr7/m6mdtbY2+ffuib9++6N27Nzp06IDHjx/D29sb8fHxMDIykq6jeVN4momIiKiMtWnTBvXr18fAgQNx/vx5nDlzBkOGDIGfnx8aNWqU5zIDBgyAgYEBRo4ciWvXrmHfvn34/PPPC9zOpEmTcPDgQURHR+P8+fM4evSoFJbGjRuHx48fo3///jhz5gzu3r2LQ4cOYcSIEcjKykKlSpVgZ2eHtWvX4vbt2zh69CgCAwO11v/FF18gLCwMf/75J27evIkff/wRjo6OsLGxQZs2beDr64sePXrg4MGDiImJwcmTJ/HJJ5/g7NmzpTOQ+WCYISIiKmMKhQK7d+9GpUqV0Lx5c7Rp0wY1atTAtm3b8l3G0tISv/zyC65du4aGDRti9uzZeZ4i+resrCyMGzcOderUQYcOHeDh4YGVK1cCAJycnPDbb78hKysL7du3h6enJz766COoVCoYGBjAwMAAYWFhOHfuHDw9PTF58mQsXbo0V02LFy9Go0aN0LhxY8TExGDfvn0wMDCAQqHAvn370Lx5c4wYMQK1atVCv379EBMTAwcHh9cfxAIoRElOyslIcnIyVCoVkpKSYG1tretyiIj0iuvHe3VdgpY/g1ojOjoabm5uMDU11XU5VMZevHiR7/NdnM9vHpkhIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIivVPO702h/6+0nmeGGSIi0hs536L77NkzHVdCb0LO8/zqtycXF78BmIiI9IahoSFsbGyk3zQyNzfP9+v+Sb6EEHj27BkSEhJgY2MDQ0PD11ofwwwREekVR0dHAJACDZVfNjY20vP9OhhmiIhIrygUCqjVatjb2yMjI0PX5VAZMTY2fu0jMjkYZoiISC8ZGhqW2ocdlW+8AJiIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGRNp2Fm1apVqF+/PqytrWFtbQ1fX1/s379fmj9s2DAoFAqtR5MmTXRYMREREekbnf6cQbVq1bBo0SLUrFkTALBx40Z0794dFy5cQL169QAAHTp0QGhoqLSMiYmJTmolIiIi/aTTMNO1a1et6f/+979YtWoVTp8+LYUZpVJZKr+oSUREROWT3lwzk5WVhbCwMKSlpcHX11dqP3bsGOzt7VGrVi2MHj260J+E12g0SE5O1noQERFR+aXzMHPlyhVYWlpCqVRi7Nix2LVrF+rWrQsA6NixI3744QccPXoUy5YtQ1RUFFq1agWNRpPv+oKDg6FSqaSHs7Pzm9oVIiIi0gGFEELosoD09HTExsbi6dOn2LFjB7799ltERkZKgebf4uLi4OLigrCwMPj7++e5Po1GoxV2kpOT4ezsjKSkJFhbW5fZfhARyZHrx3t1XYKWmEWddV0C6Ynk5GSoVKoifX7r9JoZ4OUFvTkXADdq1AhRUVH48ssvsWbNmlx91Wo1XFxccOvWrXzXp1QqoVQqy6xeIiIi0i86P830KiFEvqeREhMTcf/+fajV6jdcFREREekrnR6ZmTVrFjp27AhnZ2ekpKQgLCwMx44dw4EDB5CamoqgoCD06tULarUaMTExmDVrFipXroyePXvqsmwiIiLSIzoNMw8fPsTgwYMRFxcHlUqF+vXr48CBA2jbti2eP3+OK1euYNOmTXj69CnUajVatmyJbdu2wcrKSpdlExERkR7RaZhZv359vvPMzMxw8ODBN1gNERERyZHeXTNDREREVBwMM0RERCRrOr81m4iootC373TRR/o4RvzuG/3HIzNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGs6DTOrVq1C/fr1YW1tDWtra/j6+mL//v3SfCEEgoKC4OTkBDMzM7Ro0QJ//PGHDismIiIifaPTMFOtWjUsWrQIZ8+exdmzZ9GqVSt0795dCixLlizB8uXLsWLFCkRFRcHR0RFt27ZFSkqKLssmIiIiPaLTMNO1a1d06tQJtWrVQq1atfDf//4XlpaWOH36NIQQCAkJwezZs+Hv7w9PT09s3LgRz549w5YtW3RZNhEREekRvblmJisrC2FhYUhLS4Ovry+io6MRHx+Pdu3aSX2USiX8/Pxw8uTJfNej0WiQnJys9SAiIqLyS+dh5sqVK7C0tIRSqcTYsWOxa9cu1K1bF/Hx8QAABwcHrf4ODg7SvLwEBwdDpVJJD2dn5zKtn4iIiHRL52HGw8MDFy9exOnTp/Hhhx9i6NChuHbtmjRfoVBo9RdC5Gr7t5kzZyIpKUl63L9/v8xqJyIiIt0z0nUBJiYmqFmzJgCgUaNGiIqKwpdffokZM2YAAOLj46FWq6X+CQkJuY7W/JtSqYRSqSzboomIiEhv6PzIzKuEENBoNHBzc4OjoyPCw8Oleenp6YiMjETTpk11WCERERHpE50emZk1axY6duwIZ2dnpKSkICwsDMeOHcOBAwegUCgwadIkLFy4EO7u7nB3d8fChQthbm6OAQMG6LJsIiIi0iM6DTMPHz7E4MGDERcXB5VKhfr16+PAgQNo27YtAGD69Ol4/vw5AgIC8OTJE/j4+ODQoUOwsrLSZdlERESkRxRCCKHrIspScnIyVCoVkpKSYG1tretyiKgCc/14r65LoBKIWdRZ1yVUSMX5/Na7a2aIiIiIioNhhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZE2nv81ERJRLkErXFRRdUJKuKyAi8MgMERERyRzDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJmk7DTHBwMBo3bgwrKyvY29ujR48euHHjhlafYcOGQaFQaD2aNGmio4qJiIhI35QozERHR5fKxiMjIzFu3DicPn0a4eHhyMzMRLt27ZCWlqbVr0OHDoiLi5Me+/btK5XtExERkfwZlWShmjVronnz5hg5ciR69+4NU1PTEm38wIEDWtOhoaGwt7fHuXPn0Lx5c6ldqVTC0dGxRNsgIiKi8q1ER2YuXbqEhg0bYsqUKXB0dMSYMWNw5syZ1y4mKSkJAGBra6vVfuzYMdjb26NWrVoYPXo0EhIS8l2HRqNBcnKy1oOIiIjKL4UQQpR04czMTPzyyy/YsGED9u/fD3d3d4wcORKDBw9GlSpVirUuIQS6d++OJ0+e4MSJE1L7tm3bYGlpCRcXF0RHR2POnDnIzMzEuXPnoFQqc60nKCgI8+bNy9WelJQEa2vr4u8kUXkQpNJ1BeVTUFKxurt+vLeMCqGyFLOos65LqJCSk5OhUqmK9Pn9WmEmh0ajwcqVKzFz5kykp6fD2NgYffv2xeLFi6FWq4u0jnHjxmHv3r349ddfUa1atXz7xcXFwcXFBWFhYfD398+zFo1GI00nJyfD2dmZYYYqNoaZssEwUyEwzOhGccLMa93NdPbsWQQEBECtVmP58uWYOnUq7ty5g6NHj+Lvv/9G9+7di7SeCRMmYM+ePYiIiCgwyACAWq2Gi4sLbt26led8pVIJa2trrQcRERGVXyW6AHj58uUIDQ3FjRs30KlTJ2zatAmdOnWCgcHLbOTm5oY1a9agdu3aBa5HCIEJEyZg165dOHbsGNzc3ArddmJiIu7fv1/kIz5ERERUvpUozKxatQojRozA8OHD873LqHr16li/fn2B6xk3bhy2bNmCn3/+GVZWVoiPjwcAqFQqmJmZITU1FUFBQejVqxfUajViYmIwa9YsVK5cGT179ixJ6URERFTOlCjM5HeK599MTEwwdOjQAvusWrUKANCiRQut9tDQUAwbNgyGhoa4cuUKNm3ahKdPn0KtVqNly5bYtm0brKysSlI6ERERlTMlCjOhoaGwtLTEf/7zH632H3/8Ec+ePSs0xOQo7NpjMzMzHDx4sCQlEhERUQVRoguAFy1ahMqVK+dqt7e3x8KFC1+7KCIiIqKiKlGYuXfvXp4X67q4uCA2Nva1iyIiIiIqqhKdZrK3t8fly5fh6uqq1X7p0iXY2dmVRl1ERFRKYkwH6LqEInN9sUXXJZAMlejITL9+/TBx4kREREQgKysLWVlZOHr0KD766CP069evtGskIiIiyleJjswsWLAA9+7dQ+vWrWFk9HIV2dnZGDJkCK+ZISIiojeqRGHGxMQE27Ztw/z583Hp0iWYmZnBy8sLLi4upV0fERERUYFKFGZy1KpVC7Vq1SqtWoiIiIiKrURhJisrCxs2bMCRI0eQkJCA7OxsrflHjx4tleKIiIiIClOiMPPRRx9hw4YN6Ny5Mzw9PaFQKEq7LiIiIqIiKVGYCQsLw/bt29GpU6fSroeIiIioWEp0a7aJiQlq1qxZ2rUQERERFVuJwsyUKVPw5ZdfFvrbSkRERERlrUSnmX799VdERERg//79qFevHoyNjbXm79y5s1SKIyIiIipMicKMjY0NevbsWdq1EBERERVbicJMaGhoaddBREREVCIlumYGADIzM3H48GGsWbMGKSkpAIAHDx4gNTW11IojIiIiKkyJjszcu3cPHTp0QGxsLDQaDdq2bQsrKyssWbIEL168wOrVq0u7TiIiIqI8lejIzEcffYRGjRrhyZMnMDMzk9p79uyJI0eOlFpxRERERIUp8d1Mv/32G0xMTLTaXVxc8Pfff5dKYURERERFUaIwk52djaysrFztf/31F6ysrF67KCIiWQhSFat7jGkZ1UFUwZXoNFPbtm0REhIiTSsUCqSmpmLu3Ln8iQMiIiJ6o0p0ZOaLL75Ay5YtUbduXbx48QIDBgzArVu3ULlyZWzdurW0ayQiIiLKV4nCjJOTEy5evIitW7fi/PnzyM7OxsiRIzFw4ECtC4KJiIiIylqJwgwAmJmZYcSIERgxYkRp1kNERERULCUKM5s2bSpw/pAhQ0pUDBEREVFxlSjMfPTRR1rTGRkZePbsGUxMTGBubs4wQ0RERG9Mie5mevLkidYjNTUVN27cQLNmzXgBMBEREb1RJf5tple5u7tj0aJFuY7aEBEREZWlUgszAGBoaIgHDx6U5iqJiIiIClSia2b27NmjNS2EQFxcHFasWIH33nuvVAojIiIiKooShZkePXpoTSsUClSpUgWtWrXCsmXLSqMuIiIioiIp8W8zEREREemDUr1mpriCg4PRuHFjWFlZwd7eHj169MCNGze0+gghEBQUBCcnJ5iZmaFFixb4448/dFQxERER6ZsSHZkJDAwsct/ly5fnOy8yMhLjxo1D48aNkZmZidmzZ6Ndu3a4du0aLCwsAABLlizB8uXLsWHDBtSqVQsLFixA27ZtcePGDf5CNxEREZUszFy4cAHnz59HZmYmPDw8AAA3b96EoaEhvL29pX4KhaLA9Rw4cEBrOjQ0FPb29jh37hyaN28OIQRCQkIwe/Zs+Pv7AwA2btwIBwcHbNmyBWPGjClJ+URERFSOlCjMdO3aFVZWVti4cSMqVaoE4OUX6Q0fPhzvv/8+pkyZUqJikpKSAAC2trYAgOjoaMTHx6Ndu3ZSH6VSCT8/P5w8eTLPMKPRaKDRaKTp5OTkEtVCRERE8lCia2aWLVuG4OBgKcgAQKVKlbBgwYIS380khEBgYCCaNWsGT09PAEB8fDwAwMHBQauvg4ODNO9VwcHBUKlU0sPZ2blE9RAREZE8lCjMJCcn4+HDh7naExISkJKSUqJCxo8fj8uXL+f5cwivnq4SQuR7CmvmzJlISkqSHvfv3y9RPURERCQPJTrN1LNnTwwfPhzLli1DkyZNAACnT5/GtGnTpGtbimPChAnYs2cPjh8/jmrVqkntjo6OAF4eoVGr1VJ7QkJCrqM1OZRKJZRKZbFrICIiInkq0ZGZ1atXo3Pnzhg0aBBcXFzg4uKCgQMHomPHjli5cmWR1yOEwPjx47Fz504cPXoUbm5uWvPd3Nzg6OiI8PBwqS09PR2RkZFo2rRpSUonIiKicqZER2bMzc2xcuVKLF26FHfu3IEQAjVr1pRupy6qcePGYcuWLfj5559hZWUlXQejUqlgZmYGhUKBSZMmYeHChXB3d4e7uzsWLlwIc3NzDBgwoCSlExERUTlTojCTIy4uDnFxcWjevDnMzMwKvJYlL6tWrQIAtGjRQqs9NDQUw4YNAwBMnz4dz58/R0BAAJ48eQIfHx8cOnSI3zFDREREAEoYZhITE9GnTx9ERERAoVDg1q1bqFGjBkaNGgUbG5si39EkhCi0j0KhQFBQEIKCgkpSKhEREZVzJbpmZvLkyTA2NkZsbCzMzc2l9r59++b6IjwiIiKislSiIzOHDh3CwYMHte48AgB3d3fcu3evVAojIiIiKooSHZlJS0vTOiKT49GjR7wtmoiIiN6oEoWZ5s2bY9OmTdK0QqFAdnY2li5dipYtW5ZacURERESFKdFppqVLl6JFixY4e/Ys0tPTMX36dPzxxx94/Pgxfvvtt9KukYiIiChfJToyU7duXVy+fBnvvvsu2rZti7S0NPj7++PChQt46623SrtGIiIionwV+8hMRkYG2rVrhzVr1mDevHllURMRERFRkRX7yIyxsTGuXr1arC/HIyIiIiorJTrNNGTIEKxfv760ayEiIiIqthJdAJyeno5vv/0W4eHhaNSoUa7fZFq+fHmpFEdERERUmGKFmbt378LV1RVXr16Ft7c3AODmzZtafXj6iYiIiN6kYoUZd3d3xMXFISIiAsDLny/46quv4ODgUCbFERERERWmWNfMvPrDkPv370daWlqpFkRERERUHCW6ADhHUX71moiIiKgsFSvMKBSKXNfE8BoZIiIi0qViXTMjhMCwYcOkH5N88eIFxo4dm+tupp07d5ZehUREREQFKFaYGTp0qNb0oEGDSrUYIiIiouIqVpgJDQ0tqzqIiIiISuS1LgAmIiIi0jWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKStWJ9AzARvSJIpesKiIgqPB6ZISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlnTaZg5fvw4unbtCicnJygUCuzevVtr/rBhw6BQKLQeTZo00U2xREREpJd0GmbS0tLQoEEDrFixIt8+HTp0QFxcnPTYt2/fG6yQiIiI9J1Ob83u2LEjOnbsWGAfpVIJR0fHN1QRERERyY3eXzNz7Ngx2Nvbo1atWhg9ejQSEhIK7K/RaJCcnKz1ICIiovJLr8NMx44d8cMPP+Do0aNYtmwZoqKi0KpVK2g0mnyXCQ4Ohkqlkh7Ozs5vsGIiIiJ60/T6G4D79u0r/dvT0xONGjWCi4sL9u7dC39//zyXmTlzJgIDA6Xp5ORkBhoiIqJyTK/DzKvUajVcXFxw69atfPsolUoolco3WBURERHpkl6fZnpVYmIi7t+/D7VaretSiIiISE/o9MhMamoqbt++LU1HR0fj4sWLsLW1ha2tLYKCgtCrVy+o1WrExMRg1qxZqFy5Mnr27KnDqomIiEif6DTMnD17Fi1btpSmc651GTp0KFatWoUrV65g06ZNePr0KdRqNVq2bIlt27bByspKVyUTERGRntFpmGnRogWEEPnOP3jw4BushoiIiORIVtfMEBEREb2KYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGTNSNcFEBER6TPXj/fqugQtMYs667oEvcMjM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGs6DTPHjx9H165d4eTkBIVCgd27d2vNF0IgKCgITk5OMDMzQ4sWLfDHH3/oplgiIiLSSzoNM2lpaWjQoAFWrFiR5/wlS5Zg+fLlWLFiBaKiouDo6Ii2bdsiJSXlDVdKRERE+spIlxvv2LEjOnbsmOc8IQRCQkIwe/Zs+Pv7AwA2btwIBwcHbNmyBWPGjHmTpRIREZGe0ttrZqKjoxEfH4927dpJbUqlEn5+fjh58mS+y2k0GiQnJ2s9iIiIqPzS6ZGZgsTHxwMAHBwctNodHBxw7969fJcLDg7GvHnzyrQ2KmNBKl1XQEREMqK3R2ZyKBQKrWkhRK62f5s5cyaSkpKkx/3798u6RCIiItIhvT0y4+joCODlERq1Wi21JyQk5Dpa829KpRJKpbLM6yMiIiL9oLdHZtzc3ODo6Ijw8HCpLT09HZGRkWjatKkOKyMiIiJ9otMjM6mpqbh9+7Y0HR0djYsXL8LW1hbVq1fHpEmTsHDhQri7u8Pd3R0LFy6Eubk5BgwYoMOqiYiISJ/oNMycPXsWLVu2lKYDAwMBAEOHDsWGDRswffp0PH/+HAEBAXjy5Al8fHxw6NAhWFlZ6apkIiIi0jMKIYTQdRFlKTk5GSqVCklJSbC2ttZ1OVQUvJuJqMJyfbFF1yXovZhFnXVdwhtRnM9vvb1mhoiIiKgoGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWdPrbTERERP8WYyqfHxLmTy/oDx6ZISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWdPrMBMUFASFQqH1cHR01HVZREREpEeMdF1AYerVq4fDhw9L04aGhjqshoiIiPSN3ocZIyMjHo0hIiKifOn1aSYAuHXrFpycnODm5oZ+/frh7t27BfbXaDRITk7WehAREVH5pddhxsfHB5s2bcLBgwexbt06xMfHo2nTpkhMTMx3meDgYKhUKunh7Oz8BismIiKiN00hhBC6LqKo0tLS8NZbb2H69OkIDAzMs49Go4FGo5Gmk5OT4ezsjKSkJFhbW7+pUul1BKl0XQERUaFcX2zRyXZjFnXWyXbftOTkZKhUqiJ9fuv9NTP/ZmFhAS8vL9y6dSvfPkqlEkql8g1WRURERLqk16eZXqXRaHD9+nWo1Wpdl0JERER6Qq/DzNSpUxEZGYno6Gj8/vvv6N27N5KTkzF06FBdl0ZERER6Qq9PM/3111/o378/Hj16hCpVqqBJkyY4ffo0XFxcdF0aERER6Qm9DjNhYWG6LoGIiIj0nF6fZiIiIiIqDMMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJml7/nAGVoiCVrisgIqJS4PrxXl2XoCVmUWddl8AjM0RERCRvDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrRrouQPaCVLqugIiIdCDGdICuSygS1xdbdF1CmeORGSIiIpI1hhkiIiKSNYYZIiIikjVZhJmVK1fCzc0NpqameOedd3DixAldl0RERER6Qu/DzLZt2zBp0iTMnj0bFy5cwPvvv4+OHTsiNjZW16URERGRHtD7MLN8+XKMHDkSo0aNQp06dRASEgJnZ2esWrVK16URERGRHtDrMJOeno5z586hXbt2Wu3t2rXDyZMndVQVERER6RO9/p6ZR48eISsrCw4ODlrtDg4OiI+Pz3MZjUYDjUYjTSclJQEAkpOTy6ZIjSib9RIREZWCbM2zMl1/WX2+5qxXiMI/Z/U6zORQKBRa00KIXG05goODMW/evFztzs7OZVIbERGRfutTpmtXhZTp6pGSkgKVquAvqNXrMFO5cmUYGhrmOgqTkJCQ62hNjpkzZyIwMFCazs7OxuPHj2FnZ5dvAJKj5ORkODs74/79+7C2ttZ1OTrH8ciNY5IbxyQ3jkluHJPcdDEmQgikpKTAycmp0L56HWZMTEzwzjvvIDw8HD179pTaw8PD0b179zyXUSqVUCqVWm02NjZlWaZOWVtb84/tXzgeuXFMcuOY5MYxyY1jktubHpPCjsjk0OswAwCBgYEYPHgwGjVqBF9fX6xduxaxsbEYO3asrksjIiIiPaD3YaZv375ITEzEZ599hri4OHh6emLfvn1wcXHRdWlERESkB/Q+zABAQEAAAgICdF2GXlEqlZg7d26uU2oVFccjN45JbhyT3DgmuXFMctP3MVGIotzzRERERKSn9PpL84iIiIgKwzBDREREssYwQ0RERLLGMENERESyxjCjxzIzM/HJJ5/Azc0NZmZmqFGjBj777DNkZ2dLfYQQCAoKgpOTE8zMzNCiRQv88ccfOqy6dB0/fhxdu3aFk5MTFAoFdu/erTW/KPuv0WgwYcIEVK5cGRYWFujWrRv++uuvN7gXpaeg8cjIyMCMGTPg5eUFCwsLODk5YciQIXjw4IHWOsrTeACFv0b+bcyYMVAoFAgJCdFqr4hjcv36dXTr1g0qlQpWVlZo0qQJYmNjpfkVbUxSU1Mxfvx4VKtWDWZmZqhTpw5WrVql1ac8jUlwcDAaN24MKysr2Nvbo0ePHrhx44ZWHzm9vzLM6LHFixdj9erVWLFiBa5fv44lS5Zg6dKl+Prrr6U+S5YswfLly7FixQpERUXB0dERbdu2RUpKig4rLz1paWlo0KABVqxYkef8ouz/pEmTsGvXLoSFheHXX39FamoqunTpgqysrDe1G6WmoPF49uwZzp8/jzlz5uD8+fPYuXMnbt68iW7dumn1K0/jART+Gsmxe/du/P7773l+NXpFG5M7d+6gWbNmqF27No4dO4ZLly5hzpw5MDU1lfpUtDGZPHkyDhw4gM2bN+P69euYPHkyJkyYgJ9//lnqU57GJDIyEuPGjcPp06cRHh6OzMxMtGvXDmlpaVIfWb2/CtJbnTt3FiNGjNBq8/f3F4MGDRJCCJGdnS0cHR3FokWLpPkvXrwQKpVKrF69+o3W+iYAELt27ZKmi7L/T58+FcbGxiIsLEzq8/fffwsDAwNx4MCBN1Z7WXh1PPJy5swZAUDcu3dPCFG+x0OI/Mfkr7/+ElWrVhVXr14VLi4u4osvvpDmVcQx6du3r/Q+kpeKOCb16tUTn332mVabt7e3+OSTT4QQ5X9MEhISBAARGRkphJDf+yuPzOixZs2a4ciRI7h58yYA4NKlS/j111/RqVMnAEB0dDTi4+PRrl07aRmlUgk/Pz+cPHlSJzW/SUXZ/3PnziEjI0Orj5OTEzw9PSvEGCUlJUGhUEi/T1YRxyM7OxuDBw/GtGnTUK9evVzzK9qYZGdnY+/evahVqxbat28Pe3t7+Pj4aJ12qWhjArx8v92zZw/+/vtvCCEQERGBmzdvon379gDK/5gkJSUBAGxtbQHI7/2VYUaPzZgxA/3790ft2rVhbGyMhg0bYtKkSejfvz8ASL8m/uoviDs4OOT6pfHyqCj7Hx8fDxMTE1SqVCnfPuXVixcv8PHHH2PAgAHSD8NVxPFYvHgxjIyMMHHixDznV7QxSUhIQGpqKhYtWoQOHTrg0KFD6NmzJ/z9/REZGQmg4o0JAHz11VeoW7cuqlWrBhMTE3To0AErV65Es2bNAJTvMRFCIDAwEM2aNYOnpycA+b2/yuLnDCqqbdu2YfPmzdiyZQvq1auHixcvYtKkSXBycsLQoUOlfgqFQms5IUSutvKsJPtf3scoIyMD/fr1Q3Z2NlauXFlo//I6HufOncOXX36J8+fPF3v/yuuY5NxA0L17d0yePBkA8Pbbb+PkyZNYvXo1/Pz88l22vI4J8DLMnD59Gnv27IGLiwuOHz+OgIAAqNVqtGnTJt/lysOYjB8/HpcvX8avv/6aa55c3l95ZEaPTZs2DR9//DH69esHLy8vDB48GJMnT0ZwcDAAwNHREQByJeCEhIRcabo8Ksr+Ozo6Ij09HU+ePMm3T3mTkZGBPn36IDo6GuHh4dJRGaDijceJEyeQkJCA6tWrw8jICEZGRrh37x6mTJkCV1dXABVvTCpXrgwjIyPUrVtXq71OnTrS3UwVbUyeP3+OWbNmYfny5ejatSvq16+P8ePHo2/fvvj8888BlN8xmTBhAvbs2YOIiAhUq1ZNapfb+yvDjB579uwZDAy0nyJDQ0Ppf1Zubm5wdHREeHi4ND89PR2RkZFo2rTpG61VF4qy/++88w6MjY21+sTFxeHq1avlcoxygsytW7dw+PBh2NnZac2vaOMxePBgXL58GRcvXpQeTk5OmDZtGg4ePAig4o2JiYkJGjdunOs23Js3b8LFxQVAxRuTjIwMZGRkFPh+W97GRAiB8ePHY+fOnTh69Cjc3Ny05svu/fWNXm5MxTJ06FBRtWpV8b///U9ER0eLnTt3isqVK4vp06dLfRYtWiRUKpXYuXOnuHLliujfv79Qq9UiOTlZh5WXnpSUFHHhwgVx4cIFAUAsX75cXLhwQbo7pyj7P3bsWFGtWjVx+PBhcf78edGqVSvRoEEDkZmZqavdKrGCxiMjI0N069ZNVKtWTVy8eFHExcVJD41GI62jPI2HEIW/Rl716t1MQlS8Mdm5c6cwNjYWa9euFbdu3RJff/21MDQ0FCdOnJDWUdHGxM/PT9SrV09ERESIu3fvitDQUGFqaipWrlwpraM8jcmHH34oVCqVOHbsmNZ7xbNnz6Q+cnp/ZZjRY8nJyeKjjz4S1atXF6ampqJGjRpi9uzZWh9M2dnZYu7cucLR0VEolUrRvHlzceXKFR1WXboiIiIEgFyPoUOHCiGKtv/Pnz8X48ePF7a2tsLMzEx06dJFxMbG6mBvXl9B4xEdHZ3nPAAiIiJCWkd5Gg8hCn+NvCqvMFMRx2T9+vWiZs2awtTUVDRo0EDs3r1bax0VbUzi4uLEsGHDhJOTkzA1NRUeHh5i2bJlIjs7W1pHeRqT/N4rQkNDpT5yen9VCCFEWR31ISIiIiprvGaGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoh0SqFQYPfu3boug4hkjGGGiCqsDRs2wMbGpszWHxMTA4VCgYsXL5bZNoiIYYaIKqiMjAydLEtEpY9hhqgCatGiBSZMmIBJkyahUqVKcHBwwNq1a5GWlobhw4fDysoKb731Fvbv3y8tk5WVhZEjR8LNzQ1mZmbw8PDAl19+Kc1/8eIF6tWrhw8++EBqi46Ohkqlwrp16wqs59GjR+jZsyfMzc3h7u6OPXv2aM2/du0aOnXqBEtLSzg4OGDw4MF49OiRNP/AgQNo1qwZbGxsYGdnhy5duuDOnTvS/JwjJNu3b0eLFi1gamqKzZs3Y/jw4UhKSoJCoYBCoUBQUFCe9QUFBeHtt9/Gd999hxo1akCpVEIIUeh2c36JuGHDhlAoFGjRooU0LzQ0FHXq1IGpqSlq166NlStXFjhGRFSAN/5rUESkc35+fsLKykrMnz9f3Lx5U8yfP18YGBiIjh07irVr14qbN2+KDz/8UNjZ2Ym0tDQhhBDp6eni008/FWfOnBF3794VmzdvFubm5mLbtm3Sei9cuCBMTEzErl27RGZmpnjvvfdE9+7dC6wFgKhWrZrYsmWLuHXrlpg4caKwtLQUiYmJQgghHjx4ICpXrixmzpwprl+/Ls6fPy/atm0rWrZsKa3jp59+Ejt27BA3b94UFy5cEF27dhVeXl4iKytLCCGkH+F0dXUVO3bsEHfv3hX37t0TISEhwtraWvrF4JSUlDxrnDt3rrCwsBDt27cX58+fF5cuXRLZ2dmFbvfMmTMCgDh8+LCIi4uT9mnt2rVCrVZLtezYsUPY2tqKDRs2lOwJJargGGaIKiA/Pz/RrFkzaTozM1NYWFiIwYMHS21xcXECgDh16lS+6wkICBC9evXSaluyZImoXLmymDBhgnB0dBT//PNPgbUAEJ988ok0nZqaKhQKhdi/f78QQog5c+aIdu3aaS1z//59AUDcuHEjz3UmJCQIANIv/OaEmZCQEK1+oaGhQqVSFVifEC/DjLGxsUhISCiwX37bvXDhglY/Z2dnsWXLFq22+fPnC19f30JrIaLceJqJqIKqX7++9G9DQ0PY2dnBy8tLanNwcAAAJCQkSG2rV69Go0aNUKVKFVhaWmLdunWIjY3VWu+UKVPg4eGBr7/+GqGhoahcuXKxarGwsICVlZW03XPnziEiIgKWlpbSo3bt2gAgndK5c+cOBgwYgBo1asDa2lo6vfNqbY0aNSp8YPLh4uKCKlWqaLUVdbv/9s8//+D+/fsYOXKk1j4tWLBA6xQVERWdka4LICLdMDY21ppWKBRabQqFAgCQnZ0NANi+fTsmT56MZcuWwdfXF1ZWVli6dCl+//13rfUkJCTgxo0bMDQ0xK1bt9ChQ4cS1ZKz3ezsbHTt2hWLFy/OtZxarQYAdO3aFc7Ozli3bh2cnJyQnZ0NT09PpKena/W3sLAotJb85LVsUbf7bzn7tW7dOvj4+GjNMzQ0LHF9RBUZwwwRFcmJEyfQtGlTBAQESG15HUkYMWIEPD09MXr0aIwcORKtW7dG3bp1S7xdb29v7NixA66urjAyyv2WlZiYiOvXr2PNmjV4//33AQC//vprkdZtYmKCrKysEtVVlO2amJgAgNY2HBwcULVqVdy9excDBw4s0baJSBvDDBEVSc2aNbFp0yYcPHgQbm5u+P777xEVFSWdWgGAb775BqdOncLly5fh7OyM/fv3Y+DAgfj999+lD/biGjduHNatW4f+/ftj2rRpqFy5Mm7fvo2wsDCsW7cOlSpVgp2dHdauXQu1Wo3Y2Fh8/PHHRVq3q6srUlNTceTIETRo0ADm5uYwNzcv0rJF2a69vT3MzMxw4MABVKtWDaamplCpVAgKCsLEiRNhbW2Njh07QqPR4OzZs3jy5AkCAwOLPUZEFR2vmSGiIhk7diz8/f3Rt29f+Pj4IDExUesozZ9//olp06Zh5cqVcHZ2BvAy3Dx9+hRz5swp8XadnJzw22+/ISsrC+3bt4enpyc++ugjqFQqGBgYwMDAAGFhYTh37hw8PT0xefJkLF26tEjrbtq0KcaOHYu+ffuiSpUqWLJkSZHrKsp2jYyM8NVXX2HNmjVwcnJC9+7dAQCjRo3Ct99+iw0bNsDLywt+fn7YsGGDVjAkoqJTCCGErosgIiIiKikemSEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIln7f1yJlvYL2cq5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# Plot maximum heart rate\n",
    "# Heart disease (0 = no, 1 = yes)\n",
    "plt.title('maximum heart rate according to heart disease') \n",
    "df[df[\"target\"] == 1]['thalach'].plot.hist()\n",
    "# no disease\n",
    "df[df[\"target\"] == 0]['thalach'].plot.hist()\n",
    "plt.legend([\"disease\", \"no disease\"])\n",
    "plt.xlabel('max heart rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fca5819bd30>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9oaGheZYfEBCQp5+bm9tlQ9yRI0fUsmVLHTt2TP/+97+1atUqbdq0yX4vT870OfcXFXZJ6kr6nDlzRsaYAtdLUp51y933n76PcuTeZm5ubpL+f51PnTqlcuXKqUKFCg79ci4f/12X27fZ2dk6c+bMZedTuXJlPf744/rggw/066+/6tNPP9WFCxf0zDPPSPrzWJWkUaNG5TlWo6OjJcnheLXZbOrbt68uXLig4OBg7j35h7gHpYy64YYbZLPZtGrVKvsflb/Kadu5c6e2b9+uOXPm6NFHH7WP379/f4Hzvh7Poli0aJEkXfa5J/369VO/fv2UlpamlStXavz48erYsaP27dunypUr64YbblCDBg308ssv5zt9zh/8BQsWyMXFRf/9738d/me7cOHCPNO0bNlSLVu2VFZWljZv3qxp06Zp+PDhCgoK0sMPP6wbbrhBAQEB+vbbb/Ndpo+PT4Hrk/OBkJSUlGdcfm25eXl52e89+O233+xnUzp16qRffvnlstNLV79/C6r15ptvtr92d3fP99k2J0+edLhP5GrkbKsTJ07k+cA/fvz4355vbgsXLlRaWpq+/PJLh7MPuZ/dUbFiRUnKc4Pu1fYpX768nJycdOLEiTzjjh8/Lkl51i33Prvc+6ionpkUEBCgS5cu6fTp0w4h5Ureq5ebr6QCt4GTk9NVneXL0a1bN8XGxtrvCcrZjmPGjNH999+f7zR/vV/nxIkTGjp0qBo1aqRdu3Zp1KhRevPNN6+6DvyJMyhlVMeOHWWM0bFjx/I945JzWjLnD1vuEPPOO+9c1fJy/8/qn4iPj9f777+v8PDwQm+E+ysvLy+1a9dOY8eOVWZmpnbt2iXpz+2wc+dOVa9ePd/tkBNQbDabypUrJ2dnZ/s809PT9dFHHxW4TGdnZzVv3tz+P+mtW7fal3nq1CllZWXlu8zCblCsVauWQkJCNH/+fIfLXIcPH9batWuvaFvkCAoKUt++fdWjRw/t3bvXfkq8KPeVJH3yyScOr9euXavDhw87hMsqVaro559/dui3b9++PJcqrqa2e+65R5L08ccfO7Rv2rRJe/bsUatWra54HQqT3zFijNF7773n0C88PFx+fn6aOXNmgZcoa9asqerVq2vWrFn5Bjbpz/dy8+bN9eWXXzpsh+zsbH388ce66aabVLNmzUJrvv322+Xu7l7gvikqERERkqRPP/3UoX3BggVXNH1B+7tWrVq68cYbNW/ePIdtmZaWpi+++ML+zZ6C5BdsJOncuXNKTEy0H/e1atVSjRo1tH379nyP1WbNmtn/Q5GVlaUePXrIZrNpyZIlio2N1bRp0/Tll19e0boiL86glFF33HGHHnvsMfXr10+bN2/WXXfdJS8vL504cUKrV69W/fr19fjjj6t27dqqXr26nnvuORljVKFCBX3zzTcOl0muRE7gmTx5stq1aydnZ2c1aNBArq6uBU6TnZ1tv5SUkZGhI0eOaMmSJfrss89Up04dh6/55WfQoEHy8PDQHXfcoZCQECUlJSk2NlZ+fn72ry+++OKLio+PV3h4uIYNG6ZatWrpwoULOnTokBYvXqyZM2fqpptuUocOHTR16lT17NlTjz32mE6dOqXXXnstT3CbOXOmli1bpg4dOqhSpUq6cOGC/RsNrVu3liQ9/PDD+uSTT9S+fXs99dRTuu222+Ti4qKjR49q+fLl6tKli+67775818nJyUkvvfSSBg4cqPvuu0+DBg3SH3/8oZiYmCs6Nd+8eXN17NhRDRo0UPny5bVnzx599NFHDn/Q/86+KszmzZs1cOBAPfTQQ0pMTNTYsWN144032k+RS39ejnvkkUcUHR2tBx54QIcPH1ZcXJz9jEKO6tWry8PDQ5988onq1Kkjb29vhYaG5nupr1atWnrsscc0bdo0OTk5qV27dvZv8YSFhWnEiBF/a31ya9OmjVxdXdWjRw+NHj1aFy5c0Ntvv53nEoO3t7emTJmigQMHqnXr1ho0aJCCgoK0f/9+bd++XdOnT5f051eWO3XqpNtvv10jRoxQpUqVdOTIEX333Xf2QBEbG6s2bdro7rvv1qhRo+Tq6qoZM2Zo586dmj9//mXPcpUvX16jRo3SxIkTHfbNlb6PrtS9996rO+64Q08//bRSU1PVtGlTrVu3zv61+Mt966yw/R0XF6devXqpY8eOGjx4sDIyMvTqq6/qjz/+0CuvvFLofF9++WWtWbNG3bt3tz9mISEhQdOnT9epU6f06quv2vu+8847ateundq2bau+ffvqxhtv1OnTp7Vnzx5t3bpV//nPfyRJ48eP16pVq7R06VIFBwfr6aef1ooVKzRgwAA1btzY4XkruELFdnsuilTO3fqbNm3Kd3yHDh3yfMPDGGNmzZplmjdvbry8vIyHh4epXr266dOnj9m8ebO9z+7du02bNm2Mj4+PKV++vHnooYfMkSNH8tyFn3Nn/++//55nORkZGWbgwIGmYsWKxmazGUl57pb/q0cffdThG0geHh6mUqVKplOnTmbWrFkmIyMjzzS5v/Exd+5cc/fdd5ugoCDj6upqQkNDTbdu3fLcVf/777+bYcOGmapVqxoXFxdToUIF07RpUzN27Fhz7tw5h21Vq1Yt4+bmZqpVq2ZiY2PNBx984LAu69atM/fdd5+pXLmycXNzMwEBASYiIsIsWrTIYZkXL140r732mmnYsKFxd3c33t7epnbt2mbw4MHm119/LXC75Hj//fdNjRo1jKurq6lZs6aZNWuWefTRRy/7LZ7nnnvONGvWzJQvX96+HiNGjDAnT5609ylsX0kyQ4cOzbem3MvKeU8uXbrU9O7d2/j7+xsPDw/Tvn37POuYnZ1t4uLiTLVq1Yy7u7tp1qyZWbZsWZ59asyf34KqXbu2cXFxcVhm7m/xGPPntz0mT55satasaVxcXMwNN9xgHnnkEZOYmOjQLyIiwtxyyy151im/bZqfb775xr4vb7zxRvPMM8+YJUuWGElm+fLlDn0XL15sIiIijJeXl/H09DR169Y1kydPduizbt06065dO+Pn52fc3NxM9erVzYgRIxz6rFq1ytxzzz32Y/f2228333zzjUOfwv4uZGdnm9jYWBMWFmZcXV1NgwYNzDfffJNnmxf2LZ7cx3rO8v56bJ8+fdr069fP+Pv7G09PT9OmTRuzfv16I8n8+9//vuy2LWh/G2PMwoULTfPmzY27u7vx8vIyrVq1MmvWrLnsPNevX2+GDh1qGjZsaCpUqGCcnZ1NxYoVzb333msWL16cp//27dtNt27dTGBgoHFxcTHBwcHmnnvuMTNnzjTGGLN06VLj5OSU51tJp06dMpUqVTK33nprvn+zUDibMVfwdQgAAIpIzjNM1qxZc0XfPkPZREABAFwz8+fP17Fjx1S/fn05OTlp/fr1evXVV9W4cWOHr/EDuXEPCgDgmvHx8dGCBQs0ceJEpaWlKSQkRH379tXEiROLuzRYHGdQAACA5fA1YwAAYDkEFAAAYDkEFAAAYDkl8ibZ7OxsHT9+XD4+PtflseoAAOCfM8bo7NmzCg0NveyD+kpkQDl+/LjCwsKKuwwAAPA3JCYmFvqjmFIJDSg5v32QmJgoX1/fYq4GAABcidTUVIWFhRX6o6g5SmRAybms4+vrS0ABAKCEuZLbM7hJFgAAWA4BBQAAWA4BBQAAWE6JvAflSmVlZenixYvFXQauIRcXFzk7Oxd3GQCAIlYqA4oxRklJSfrjjz+KuxRcB/7+/goODuaZOABQipTKgJITTgIDA+Xp6ckHVylljNH58+eVnJwsSQoJCSnmigAARaXUBZSsrCx7OAkICCjucnCNeXh4SJKSk5MVGBjI5R4AKCVK3U2yOfeceHp6FnMluF5y9jX3GwFA6VHqAkoOLuuUHexrACh9Sm1AAQAAJRcBpQw6dOiQbDabtm3bJkn68ccfZbPZ+NYTAMAySt1NsoW63pcCjLm+y/ubwsPDdeLECfn5+RV3KQAASCprAQX5cnV1VXBwcHGXAQCAHZd4LCQyMlLDhg3T6NGjVaFCBQUHBysmJsahz5EjR9SlSxd5e3vL19dX3bp102+//VbofDdu3KjGjRvL3d1dzZo1008//eQwPvclnsOHD6tTp04qX768vLy8dMstt2jx4sX2/rt371b79u3l7e2toKAg9e7dWydPnrSP//bbb3XnnXfK399fAQEB6tixow4cOGAfn5mZqSeeeEIhISFyd3dXlSpVFBsbax+fkpKixx57TIGBgfL19dU999yj7du3X+3mBACUYAQUi5k7d668vLy0YcMGxcXF6cUXX1R8fLykPx9M1rVrV50+fVorVqxQfHy8Dhw4oO7duxc4v7S0NHXs2FG1atXSli1bFBMTo1GjRhVaw9ChQ5WRkaGVK1dqx44dmjx5sry9vSVJJ06cUEREhBo1aqTNmzfr22+/1W+//aZu3bo5LHPkyJHatGmTfvjhBzk5Oem+++5Tdna2JOnNN9/UokWL9Nlnn2nv3r36+OOPVaVKFfs6dujQQUlJSVq8eLG2bNmiJk2aqFWrVjp9+vQ/2bQAgJLElEApKSlGkklJSckzLj093ezevdukp6fnnfDPu0Ku33CVIiIizJ133unQduutt5pnn33WGGPM0qVLjbOzszly5Ih9/K5du4wks3Hjxnzn+c4775gKFSqYtLQ0e9vbb79tJJmffvrJGGPM8uXLjSRz5swZY4wx9evXNzExMfnO74UXXjBRUVEObYmJiUaS2bt3b77TJCcnG0lmx44dxhhjnnzySXPPPfeY7OzsPH1/+OEH4+vray5cuODQXr16dfPOO+/kO/9C9zlQklzvv1FWGVBmFPb5nRtnUCymQYMGDq9DQkLsj3Lfs2ePwsLCFBYWZh9ft25d+fv7a8+ePfnOb8+ePWrYsKHDg+tatGhRaA3Dhg3TxIkTdccdd2j8+PH6+eef7eO2bNmi5cuXy9vb2z7Url1bkuyXcQ4cOKCePXuqWrVq8vX1VdWqVSX9eXlKkvr27att27apVq1aGjZsmJYuXeow/3PnzikgIMBhGQkJCQ6XiQAApRs3yVqMi4uLw2ubzWa/NGKMyfehZAW154y7WgMHDlTbtm31v//9T0uXLlVsbKymTJmiJ598UtnZ2erUqZMmT56cZ7qc38Lp1KmTwsLC9N577yk0NFTZ2dmqV6+eMjMzJUlNmjRRQkKClixZou+//17dunVT69at9fnnnys7O1shISH68ccf88zf39//qtcFAFAyEVBKkLp16+rIkSNKTEy0n0XZvXu3UlJSVKdOnQKn+eijj5Senm7/3Zr169dfdllhYWEaMmSIhgwZojFjxui9997Tk08+qSZNmuiLL75QlSpVVK5c3rfPqVOntGfPHr3zzjtq2bKlJGn16tV5+vn6+qp79+7q3r27HnzwQd177706ffq0mjRpoqSkJJUrV85+XwoAoOzhEk8J0rp1azVo0EC9evXS1q1btXHjRvXp00cRERFq1qxZvtP07NlTTk5OGjBggHbv3q3FixfrtddeK3Q5w4cP13fffaeEhARt3bpVy5YtswegoUOH6vTp0+rRo4c2btyogwcPaunSperfv7+ysrJUvnx5BQQE6N1339X+/fu1bNkyjRw50mH+r7/+uhYsWKBffvlF+/bt03/+8x8FBwfL399frVu3VosWLdS1a1d99913OnTokNauXavnn39emzdvLpoNCQCwPAJKCWKz2bRw4UKVL19ed911l1q3bq1q1arp008/LXAab29vffPNN9q9e7caN26ssWPH5nt55q+ysrI0dOhQ1alTR/fee69q1aqlGTNmSJJCQ0O1Zs0aZWVlqW3btqpXr56eeuop+fn5ycnJSU5OTlqwYIG2bNmievXqacSIEXr11Vfz1DR58mQ1a9ZMt956qw4dOqTFixfLyclJNptNixcv1l133aX+/furZs2aevjhh3Xo0CEFBQX9840IACgRbObv3KRQzFJTU+Xn56eUlBT5+vo6jLtw4YISEhJUtWpVubu7F1OFuJ7Y5yg1yuoPX5a8jyH8TYV9fufGGRQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBSLi4yM1PDhwyVJVapU0RtvvFGs9QAAcD2UqV8zvt5PkS7qpzdv2rRJXl5eRTtTAAAsqEwFlJKuYsWKxV0CAADXBZd4LCQtLU19+vSRt7e3QkJCNGXKFIfxuS/xxMTEqFKlSnJzc1NoaKiGDRtmH5eZmanRo0frxhtvlJeXl5o3b64ff/zRPv7UqVPq0aOHbrrpJnl6eqp+/fqaP3++w/I+//xz1a9fXx4eHgoICFDr1q2VlpZmHz979mzVqVNH7u7uql27tv0XjwEA+Kc4g2IhzzzzjJYvX66vvvpKwcHB+te//qUtW7aoUaNGefp+/vnnev3117VgwQLdcsstSkpK0vbt2+3j+/Xrp0OHDmnBggUKDQ3VV199pXvvvVc7duxQjRo1dOHCBTVt2lTPPvusfH199b///U+9e/dWtWrV1Lx5c504cUI9evRQXFyc7rvvPp09e1arVq1Szo9fv/feexo/frymT5+uxo0b66efftKgQYPk5eWlRx999HptMgBAaWVKoJSUFCPJpKSk5BmXnp5udu/ebdLT0/OM+/OukOs3XI2zZ88aV1dXs2DBAnvbqVOnjIeHh3nqqaeMMcZUrlzZvP7668YYY6ZMmWJq1qxpMjMz88xr//79xmazmWPHjjm0t2rVyowZM6bAGtq3b2+efvppY4wxW7ZsMZLMoUOH8u0bFhZm5s2b59D20ksvmRYtWlx2XYtaYfscKFGu9x8pqwwoMwr7/M6NMygWceDAAWVmZqpFixb2tgoVKqhWrVr59n/ooYf0xhtvqFq1arr33nvVvn17derUSeXKldPWrVtljFHNmjUdpsnIyFBAQIAkKSsrS6+88oo+/fRTHTt2TBkZGcrIyLDfhNuwYUO1atVK9evXV9u2bRUVFaUHH3xQ5cuX1++//67ExEQNGDBAgwYNss//0qVL8vPzK+pNAwAogwgoFmGu8is/YWFh2rt3r+Lj4/X9998rOjpar776qlasWKHs7Gw5Oztry5YtcnZ2dpjO29tbkjRlyhS9/vrreuONN1S/fn15eXlp+PDhyszMlCQ5OzsrPj5ea9eu1dKlSzVt2jSNHTtWGzZskKenp6Q/L/M0b97cYf65lwcAwN9BQLGIm2++WS4uLlq/fr0qVaokSTpz5oz27duniIiIfKfx8PBQ586d1blzZw0dOlS1a9fWjh071LhxY2VlZSk5OVktW7bMd9pVq1apS5cueuSRRyRJ2dnZ+vXXX1WnTh17H5vNpjvuuEN33HGHxo0bp8qVK+urr77SyJEjdeONN+rgwYPq1atXEW8JAAAIKJbh7e2tAQMG6JlnnlFAQICCgoI0duxYOTnl/0WrOXPmKCsrS82bN5enp6c++ugjeXh4qHLlygoICFCvXr3Up08fTZkyRY0bN9bJkye1bNky1a9fX+3bt9fNN9+sL774QmvXrlX58uU1depUJSUl2QPKhg0b9MMPPygqKkqBgYHasGGDfv/9d/v4mJgYDRs2TL6+vmrXrp0yMjK0efNmnTlzRiNHjrxu2w0AUDoRUCzk1Vdf1blz59S5c2f5+Pjo6aefVkpKSr59/f399corr2jkyJHKyspS/fr19c0339jvMZk9e7YmTpyop59+WseOHVNAQIBatGih9u3bS5JeeOEFJSQkqG3btvL09NRjjz2mrl272pfn6+urlStX6o033lBqaqoqV66sKVOmqF27dpKkgQMHytPTU6+++qpGjx4tLy8v1a9f3/7UWwAA/gmbudqbHywgNTVVfn5+SklJka+vr8O4CxcuKCEhQVWrVpW7u3sxVYjriX2OUuN6P+7aKkrexxD+psI+v3PjQW0AAMByCCgAAMByCCgAAMByCCgAAMBySm1AKYH3/uJvYl8DQOlT6gKKi4uLJOn8+fPFXAmul5x9nbPvAQAlX6l7Doqzs7P8/f2VnJwsSfL09JStrH51r5Qzxuj8+fNKTk6Wv78/j9kHgFKk1AUUSQoODpYke0hB6ebv72/f5wCA0qFUBhSbzaaQkBAFBgbq4sWLxV0OriEXFxfOnABAKVQqA0oOZ2dnPrwAACiBSt1NsgAAoOS76oCycuVKderUSaGhobLZbFq4cKHDeGOMYmJiFBoaKg8PD0VGRmrXrl0OfTIyMvTkk0/qhhtukJeXlzp37qyjR4/+oxUBAAClx1UHlLS0NDVs2FDTp0/Pd3xcXJymTp2q6dOna9OmTQoODlabNm109uxZe5/hw4frq6++0oIFC7R69WqdO3dOHTt2VFZW1t9fEwAAUGr8o18zttls+uqrr9S1a1dJf549CQ0N1fDhw/Xss89K+vNsSVBQkCZPnqzBgwcrJSVFFStW1EcffaTu3btLko4fP66wsDAtXrxYbdu2vexyr+bXEAGgxCirj0TgYYtlRrH9mnFCQoKSkpIUFRVlb3Nzc1NERITWrl0rSdqyZYsuXrzo0Cc0NFT16tWz98ktIyNDqampDgMAACi9ijSgJCUlSZKCgoIc2oOCguzjkpKS5OrqqvLlyxfYJ7fY2Fj5+fnZh7CwsKIsGwAAWMw1+RZP7ie3GmMu+zTXwvqMGTNGKSkp9iExMbHIagUAANZTpAEl52meuc+EJCcn28+qBAcHKzMzU2fOnCmwT25ubm7y9fV1GAAAQOlVpAGlatWqCg4OVnx8vL0tMzNTK1asUHh4uCSpadOmcnFxcehz4sQJ7dy5094HAACUbVf9JNlz585p//799tcJCQnatm2bKlSooEqVKmn48OGaNGmSatSooRo1amjSpEny9PRUz549JUl+fn4aMGCAnn76aQUEBKhChQoaNWqU6tevr9atWxfdmgEAgBLrqgPK5s2bdffdd9tfjxw5UpL06KOPas6cORo9erTS09MVHR2tM2fOqHnz5lq6dKl8fHzs07z++usqV66cunXrpvT0dLVq1Upz5szhsfQAAEDSP3wOSnHhOSgASiWeg4JSrtiegwIAAFAUCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByyhV3AQCAss1mK+4KiocxxV2BtXEGBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWE6RB5RLly7p+eefV9WqVeXh4aFq1arpxRdfVHZ2tr2PMUYxMTEKDQ2Vh4eHIiMjtWvXrqIuBQAAlFBFHlAmT56smTNnavr06dqzZ4/i4uL06quvatq0afY+cXFxmjp1qqZPn65NmzYpODhYbdq00dmzZ4u6HAAAUAIVeUBZt26dunTpog4dOqhKlSp68MEHFRUVpc2bN0v68+zJG2+8obFjx+r+++9XvXr1NHfuXJ0/f17z5s0r6nIAAEAJVOQB5c4779QPP/ygffv2SZK2b9+u1atXq3379pKkhIQEJSUlKSoqyj6Nm5ubIiIitHbt2qIuBwAAlEDlinqGzz77rFJSUlS7dm05OzsrKytLL7/8snr06CFJSkpKkiQFBQU5TBcUFKTDhw/nO8+MjAxlZGTYX6emphZ12QAAwEKK/AzKp59+qo8//ljz5s3T1q1bNXfuXL322muaO3euQz+bzebw2hiTpy1HbGys/Pz87ENYWFhRlw0AACykyAPKM888o+eee04PP/yw6tevr969e2vEiBGKjY2VJAUHB0v6/zMpOZKTk/OcVckxZswYpaSk2IfExMSiLhsAAFhIkQeU8+fPy8nJcbbOzs72rxlXrVpVwcHBio+Pt4/PzMzUihUrFB4enu883dzc5Ovr6zAAAIDSq8jvQenUqZNefvllVapUSbfccot++uknTZ06Vf3795f056Wd4cOHa9KkSapRo4Zq1KihSZMmydPTUz179izqcgAAQAlU5AFl2rRpeuGFFxQdHa3k5GSFhoZq8ODBGjdunL3P6NGjlZ6erujoaJ05c0bNmzfX0qVL5ePjU9TlAACAEshmjDHFXcTVSk1NlZ+fn1JSUrjcA6D0KOCLAqWdTSXuY6hIlLxP33/uaj6/+S0eAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOeWKuwDgSthsxV1B8TCmuCsAgOLBGRQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA51ySgHDt2TI888ogCAgLk6empRo0aacuWLfbxxhjFxMQoNDRUHh4eioyM1K5du65FKQAAoAQq8oBy5swZ3XHHHXJxcdGSJUu0e/duTZkyRf7+/vY+cXFxmjp1qqZPn65NmzYpODhYbdq00dmzZ4u6HAAAUALZjDGmKGf43HPPac2aNVq1alW+440xCg0N1fDhw/Xss89KkjIyMhQUFKTJkydr8ODBl11Gamqq/Pz8lJKSIl9f36IsHxZlsxV3BcWjaI9OWF4ZfaPbVDbf6GXx+L6az+8iP4OyaNEiNWvWTA899JACAwPVuHFjvffee/bxCQkJSkpKUlRUlL3Nzc1NERERWrt2bVGXAwAASqAiDygHDx7U22+/rRo1aui7777TkCFDNGzYMH344YeSpKSkJElSUFCQw3RBQUH2cbllZGQoNTXVYQAAAKVXuaKeYXZ2tpo1a6ZJkyZJkho3bqxdu3bp7bffVp8+fez9bLlOZRpj8rTliI2N1YQJE4q6VAAAYFFFfgYlJCREdevWdWirU6eOjhw5IkkKDg6WpDxnS5KTk/OcVckxZswYpaSk2IfExMSiLhsAAFhIkQeUO+64Q3v37nVo27dvnypXrixJqlq1qoKDgxUfH28fn5mZqRUrVig8PDzfebq5ucnX19dhAAAApVeRX+IZMWKEwsPDNWnSJHXr1k0bN27Uu+++q3fffVfSn5d2hg8frkmTJqlGjRqqUaOGJk2aJE9PT/Xs2bOoywEAACVQkQeUW2+9VV999ZXGjBmjF198UVWrVtUbb7yhXr162fuMHj1a6enpio6O1pkzZ9S8eXMtXbpUPj4+RV0OAAAogYr8OSjXA89BKXvK6OMhyuRzEsq0MvpG5zkoZUexPgcFAADgnyKgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAy7nmASU2NlY2m03Dhw+3txljFBMTo9DQUHl4eCgyMlK7du261qUAAIAS4poGlE2bNundd99VgwYNHNrj4uI0depUTZ8+XZs2bVJwcLDatGmjs2fPXstyAABACXHNAsq5c+fUq1cvvffeeypfvry93RijN954Q2PHjtX999+vevXqae7cuTp//rzmzZt3rcoBAAAlyDULKEOHDlWHDh3UunVrh/aEhAQlJSUpKirK3ubm5qaIiAitXbs233llZGQoNTXVYQAAAKVXuWsx0wULFmjr1q3atGlTnnFJSUmSpKCgIIf2oKAgHT58ON/5xcbGasKECUVfKAAAsKQiP4OSmJiop556Sh9//LHc3d0L7Gez2RxeG2PytOUYM2aMUlJS7ENiYmKR1gwAAKylyM+gbNmyRcnJyWratKm9LSsrSytXrtT06dO1d+9eSX+eSQkJCbH3SU5OznNWJYebm5vc3NyKulQAAGBRRX4GpVWrVtqxY4e2bdtmH5o1a6ZevXpp27ZtqlatmoKDgxUfH2+fJjMzUytWrFB4eHhRlwMAAEqgIj+D4uPjo3r16jm0eXl5KSAgwN4+fPhwTZo0STVq1FCNGjU0adIkeXp6qmfPnkVdDgAAKIGuyU2ylzN69Gilp6crOjpaZ86cUfPmzbV06VL5+PgURzkAAMBibMYYU9xFXK3U1FT5+fkpJSVFvr6+xV0OroMC7p8u9Ure0Yl/pIy+0W0qm2/0snh8X83nN7/FAwAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALKdccReAq2SzFXcFxcQUdwEAgOuIMygAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByijygxMbG6tZbb5WPj48CAwPVtWtX7d2716GPMUYxMTEKDQ2Vh4eHIiMjtWvXrqIuBQAAlFBFHlBWrFihoUOHav369YqPj9elS5cUFRWltLQ0e5+4uDhNnTpV06dP16ZNmxQcHKw2bdro7NmzRV0OAAAogWzGmGv6iM7ff/9dgYGBWrFihe666y4ZYxQaGqrhw4fr2WeflSRlZGQoKChIkydP1uDBgy87z9TUVPn5+SklJUW+vr7XsnzrKaNPkrWV0SfJXtujE5bD8V2mlMXj+2o+v6/5PSgpKSmSpAoVKkiSEhISlJSUpKioKHsfNzc3RUREaO3atfnOIyMjQ6mpqQ4DAAAova5pQDHGaOTIkbrzzjtVr149SVJSUpIkKSgoyKFvUFCQfVxusbGx8vPzsw9hYWHXsmwAAFDMrmlAeeKJJ/Tzzz9r/vz5ecbZcp3KNMbkacsxZswYpaSk2IfExMRrUi8AALCGa/Zrxk8++aQWLVqklStX6qabbrK3BwcHS/rzTEpISIi9PTk5Oc9ZlRxubm5yc3O7VqUCAACLKfIzKMYYPfHEE/ryyy+1bNkyVa1a1WF81apVFRwcrPj4eHtbZmamVqxYofDw8KIuBwAAlEBFfgZl6NChmjdvnr7++mv5+PjY7yvx8/OTh4eHbDabhg8frkmTJqlGjRqqUaOGJk2aJE9PT/Xs2bOoywEAACVQkQeUt99+W5IUGRnp0D579mz17dtXkjR69Gilp6crOjpaZ86cUfPmzbV06VL5+PgUdTkAAKAEuubPQbkWeA5K2cNzElAmcHyXKWXx+LbUc1AAAACuFgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYTrEGlBkzZqhq1apyd3dX06ZNtWrVquIsBwAAWESxBZRPP/1Uw4cP19ixY/XTTz+pZcuWateunY4cOVJcJQEAAIuwGWNMcSy4efPmatKkid5++217W506ddS1a1fFxsYWOm1qaqr8/PyUkpIiX1/fa12qtdhsxV1BsbCpWN6mxa54jk4UG47vMqUsHt9X8/ldLGdQMjMztWXLFkVFRTm0R0VFae3atcVREgAAsJByxbHQkydPKisrS0FBQQ7tQUFBSkpKytM/IyNDGRkZ9tcpKSmS/kxiKCvK5r7mLY6yoWy+0cvi8Z3zuX0lF2+KJaDksOU6nWmMydMmSbGxsZowYUKe9rCwsGtWG6zGr7gLKBZ+ZXO1UeaUzTd6WT6+z549K7/LbIBiCSg33HCDnJ2d85wtSU5OznNWRZLGjBmjkSNH2l9nZ2fr9OnTCggIyDfQoHRJTU1VWFiYEhMTy949R0Apx/FdthhjdPbsWYWGhl62b7EEFFdXVzVt2lTx8fG677777O3x8fHq0qVLnv5ubm5yc3NzaPP397/WZcJifH19+QMGlFIc32XH5c6c5Ci2SzwjR45U79691axZM7Vo0ULvvvuujhw5oiFDhhRXSQAAwCKKLaB0795dp06d0osvvqgTJ06oXr16Wrx4sSpXrlxcJQEAAIso1ptko6OjFR0dXZwloARwc3PT+PHj81zmA1DycXyjIMX2oDYAAICC8GOBAADAcggoAADAcggoAADAcggoAADAcggoAADAcor1a8ZAfo4ePaq3335ba9euVVJSkmw2m4KCghQeHq4hQ4bwG0wAUAbwNWNYyurVq9WuXTuFhYUpKipKQUFBMsYoOTlZ8fHxSkxM1JIlS3THHXcUd6kAroHExESNHz9es2bNKu5SUMwIKLCUW2+9VXfeeadef/31fMePGDFCq1ev1qZNm65zZQCuh+3bt6tJkybKysoq7lJQzAgosBQPDw9t27ZNtWrVynf8L7/8osaNGys9Pf06VwagKCxatKjQ8QcPHtTTTz9NQAH3oMBaQkJCtHbt2gIDyrp16xQSEnKdqwJQVLp27SqbzabC/m9ss9muY0WwKgIKLGXUqFEaMmSItmzZojZt2igoKEg2m01JSUmKj4/X+++/rzfeeKO4ywTwN4WEhOitt95S165d8x2/bds2NW3a9PoWBUsioMBSoqOjFRAQoNdff13vvPOO/TSvs7OzmjZtqg8//FDdunUr5ioB/F1NmzbV1q1bCwwolzu7grKDe1BgWRcvXtTJkyclSTfccINcXFyKuSIA/9SqVauUlpame++9N9/xaWlp2rx5syIiIq5zZbAaAgoAALAcniQLAAAsh4ACAAAsh4ACAAAsh4ACWNQHH3ygqKio4i7DkubMmSN/f//iLsOSfvzxR9lsNv3xxx9XPM2tt96qL7/88toVBfwNBBSgEMnJyRo8eLAqVaokNzc3BQcHq23btlq3bt01XW5GRobGjRunF154waH9iy++UN26deXm5qa6devqq6++uqZ1HDp0SDabLc/wyCOPXNPlFpennnpKTZs2lZubmxo1anRdlhkZGSmbzaZXXnklz7j27dvLZrMpJibmmtbwwgsv6LnnnlN2dvY1XQ5wNQgoQCEeeOABbd++XXPnztW+ffu0aNEiRUZG6vTp09d0uV988YW8vb3VsmVLe9u6devUvXt39e7dW9u3b1fv3r3VrVs3bdiw4ZrWIknff/+9Tpw4YR/eeuuta77M4mCMUf/+/dW9e/frutywsDDNnj3boe348eNatmzZdXlycocOHZSSkqLvvvvumi8LuFIEFKAAf/zxh1avXq3Jkyfr7rvvVuXKlXXbbbdpzJgx6tChg71fSkqKHnvsMQUGBsrX11f33HOPtm/fLkn6/fffFRwcrEmTJtn7b9iwQa6urlq6dGmBy16wYIE6d+7s0PbGG2+oTZs2GjNmjGrXrq0xY8aoVatW1+XJugEBAQoODrYPfn5+kgpfd0mKiYlRo0aNNGvWLFWqVEne3t56/PHHlZWVpbi4OAUHByswMFAvv/yyw/KmTp2q+vXry8vLS2FhYYqOjta5c+cKrfGbb75R06ZN5e7urmrVqmnChAm6dOnSVa3nm2++qaFDh6patWpXNd0/1bFjR506dUpr1qyxt82ZM0dRUVEKDAx06Pvxxx+rWbNm8vHxUXBwsHr27Knk5ORC57927Vrddddd8vDwUFhYmIYNG6a0tDT7eGdnZ7Vv317z588v2hUD/gECClAAb29veXt7a+HChcrIyMi3jzFGHTp0UFJSkhYvXqwtW7aoSZMmatWqlU6fPq2KFStq1qxZiomJ0ebNm3Xu3Dk98sgjio6OLvT+klWrVqlZs2YObevWrcszTdu2bbV27doC53PkyBH7ehQ0DBky5Cq2ypWve44DBw5oyZIl+vbbbzV//nzNmjVLHTp00NGjR7VixQpNnjxZzz//vNavX2+fxsnJSW+++aZ27typuXPnatmyZRo9enSBtXz33Xd65JFHNGzYMO3evVvvvPOO5syZkyf4XAvt2rW77Da+HFdXV/Xq1cvhLMqcOXPUv3//PH0zMzP10ksvafv27Vq4cKESEhLUt2/fAue9Y8cOtW3bVvfff79+/vlnffrpp1q9erWeeOIJh3633XabVq1adeUrDlxrBkCBPv/8c1O+fHnj7u5uwsPDzZgxY8z27dvt43/44Qfj6+trLly44DBd9erVzTvvvGN/HR0dbWrWrGl69epl6tWrZ9LT0wtc5pkzZ4wks3LlSod2FxcX88knnzi0ffLJJ8bV1bXAeV28eNH8+uuvhQ6//fZbgdMnJCQYScbDw8N4eXnZh61bt17Ruo8fP954enqa1NRU+/i2bduaKlWqmKysLHtbrVq1TGxsbIF1fPbZZyYgIMD+evbs2cbPz8/+umXLlmbSpEkO03z00UcmJCSkwHkWZvz48aZhw4ZX1Pfo0aOX3caFiYiIME899ZTZvn278fHxMefOnTMrVqwwgYGBJjMz0zRs2NCMHz++wOk3btxoJJmzZ88aY4xZvny5kWTOnDljjDGmd+/e5rHHHnOYZtWqVcbJycnhffj1118bJycnh/0CFCd+iwcoxAMPPKAOHTpo1apVWrdunb799lvFxcXp/fffV9++fbVlyxadO3dOAQEBDtOlp6frwIED9tevvfaa6tWrp88++0ybN2+Wu7t7gctMT0+XpHz75P6VV2NMob/8Wq5cOd18881XtK6F+fTTT1WnTh3767CwML355ptXtO5VqlSRj4+P/XVQUJCcnZ3l5OTk0PbXyxTLly/XpEmTtHv3bqWmpurSpUu6cOGC0tLS5OXllae+LVu2aNOmTQ5nTLKysnThwgWdP39enp6e/2wDFOLGG28skvk0aNBANWrU0Oeff67ly5erd+/e+f68w08//aSYmBht27ZNp0+ftt/YeuTIEdWtWzdP/y1btmj//v365JNP7G3GGGVnZyshIcG+Xz08PJSdna2MjAx5eHgUyToB/wQBBbgMd3d3tWnTRm3atNG4ceM0cOBAjR8/Xn379lV2drZCQkL0448/5pnur1+DPXjwoI4fP67s7GwdPnxYDRo0KHB5AQEBstlsOnPmjEN7cHCwkpKSHNqSk5MVFBRU4LwK+tD6q0ceeUQzZ84stE9YWFieoHOl6577Q9Zms+XblvNBe/jwYbVv315DhgzRSy+9pAoVKmj16tUaMGCALl68mG992dnZmjBhgu6///484woLg0WhXbt2l700crn7Z3L0799fb731lnbv3q2NGzfmGZ+WlqaoqChFRUXp448/VsWKFXXkyBG1bdtWmZmZ+c4zOztbgwcP1rBhw/KMq1Spkv3fp0+flqenJ+EElkFAAa5S3bp1tXDhQklSkyZNlJSUpHLlyqlKlSr59s/MzFSvXr3UvXt31a5dWwMGDNCOHTsKDBaurq6qW7eudu/e7XDPSYsWLRQfH68RI0bY25YuXarw8PACaw0NDdW2bdsKXR9fX99CxxfkStb979i8ebMuXbqkKVOm2M+yfPbZZ5etZe/evUVytuhqvf/++/azXv9Uz549NWrUKDVs2DDfYPnLL7/o5MmTeuWVVxQWFibpz+1VmCZNmmjXrl2X3TY7d+5UkyZN/n7xQBEjoAAFOHXqlB566CH1799fDRo0kI+PjzZv3qy4uDh16dJFktS6dWu1aNFCXbt21eTJk1WrVi0dP35cixcvVteuXdWsWTONHTtWKSkpevPNN+Xt7a0lS5ZowIAB+u9//1vgstu2bavVq1dr+PDh9rannnpKd911lyZPnqwuXbro66+/1vfff6/Vq1cXOJ+iusSTnytZ97+jevXqunTpkqZNm6ZOnTppzZo1lz3DM27cOHXs2FFhYWF66KGH5OTkpJ9//lk7duzQxIkTr3jZ+/fv17lz55SUlKT09HR7uKtbt65cXV3znaaoLvFIUvny5XXixIkCf7m7UqVKcnV11bRp0zRkyBDt3LlTL730UqHzfPbZZ3X77bdr6NChGjRokLy8vLRnzx7Fx8dr2rRp9n6rVq3iwYCwluK+CQawqgsXLpjnnnvONGnSxPj5+RlPT09Tq1Yt8/zzz5vz58/b+6Wmpponn3zShIaGGhcXFxMWFmZ69epljhw5YpYvX27KlStnVq1aZe9/+PBh4+fnZ2bMmFHgsvfs2WM8PDzMH3/84dD+n//8x9SqVcu4uLiY2rVrmy+++KLoV/wvcm6S/emnn/IdX9i6G5P/zaaPPvqo6dKli0Nbzo2iOaZOnWpCQkKMh4eHadu2rfnwww8dbvzMfZOsMcZ8++23Jjw83Hh4eBhfX19z2223mXfffddhuREREYWub0REhJGUZ0hISCh0un8i97rnlvsm2Xnz5pkqVaoYNzc306JFC7No0SKHfZT7Jllj/ryRtk2bNsbb29t4eXmZBg0amJdfftk+/ujRo8bFxcUkJiYW8doBf5/NGGOKLR0BKFC3bt3UuHFjjRkzprhLKRUiIyMVGRl5zZ/KWhI988wzSklJ0bvvvlvcpQB2PAcFsKhXX331ip6hgcs7e/asDhw4oFGjRhV3KZYUGBh42UtFwPXGGRQAAGA5nEEBAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACW83+CZ5HHqp2ebQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot sex and target in one histogramm via crosstab\n",
    "pd.crosstab(df.sex,df.target).plot(kind=\"bar\",color=['red','blue' ])\n",
    "plt.title('Heart Disease distribution according to Sex')\n",
    "plt.xlabel('Sex (0 = Female, 1 = Male)')\n",
    "plt.legend([\"no disease\", \"disease\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Disease or Not')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot target and cp in one histogramm via crosstab\n",
    "pd.crosstab(df.cp,df.target).plot(kind=\"bar\",figsize=(15,6),color=['#11A5AA','#AA1190' ])\n",
    "plt.title('Heart Disease Distribution According To Chest Pain Type')\n",
    "plt.xlabel('Chest Pain Type')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.ylabel('Frequency of Disease or Not')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot correlations for target\n",
    "plt.figure()\n",
    "plt.scatter(x=df.age[df.target==1], y=df.thalach[(df.target==1)], c=\"red\")\n",
    "plt.scatter(x=df.age[df.target==0], y=df.thalach[(df.target==0)])\n",
    "plt.title('Age-max Heart Rate Plot')\n",
    "plt.xlabel('age[years]')\n",
    "plt.ylabel('max. heart rate')\n",
    "plt.legend([\"Disease\", \"No Disease\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter(x=df.age[df.target==1], y=df.chol[(df.target==1)], c=\"red\")\n",
    "plt.scatter(x=df.age[df.target==0], y=df.chol[(df.target==0)])\n",
    "plt.title('Age-Cholesterol Plot')\n",
    "plt.xlabel('age[years]')\n",
    "plt.ylabel('Cholesterol')\n",
    "plt.legend([\"Disease\", \"No Disease\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}