Chapter07/Activity7.01/Activity7.01.ipynb

{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\n","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('https://raw.githubusercontent.com/PacktWorkshops/The-Data-Analysis-Workshop/master/Chapter07/Dataset/heart.csv')","execution_count":2,"outputs":[]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"df.describe()","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"              age         sex          cp    trestbps        chol         fbs  \\\ncount  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \nmean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \nstd      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \nmin     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \nmax     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n\n          restecg     thalach       exang     oldpeak       slope          ca  \\\ncount  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \nmean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \nstd      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \nmin      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \nmax      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n\n             thal      target  \ncount  303.000000  303.000000  \nmean     2.313531    0.544554  \nstd      0.612277    0.498835  \nmin      0.000000    0.000000  \n25%      2.000000    0.000000  \n50%      2.000000    1.000000  \n75%      3.000000    1.000000  \nmax      3.000000    1.000000  ","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>age</th>\n      <th>sex</th>\n      <th>cp</th>\n      <th>trestbps</th>\n      <th>chol</th>\n      <th>fbs</th>\n      <th>restecg</th>\n      <th>thalach</th>\n      <th>exang</th>\n      <th>oldpeak</th>\n      <th>slope</th>\n      <th>ca</th>\n      <th>thal</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>54.366337</td>\n      <td>0.683168</td>\n      <td>0.966997</td>\n      <td>131.623762</td>\n      <td>246.264026</td>\n      <td>0.148515</td>\n      <td>0.528053</td>\n      <td>149.646865</td>\n      <td>0.326733</td>\n      <td>1.039604</td>\n      <td>1.399340</td>\n      <td>0.729373</td>\n      <td>2.313531</td>\n      <td>0.544554</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>9.082101</td>\n      <td>0.466011</td>\n      <td>1.032052</td>\n      <td>17.538143</td>\n      <td>51.830751</td>\n      <td>0.356198</td>\n      <td>0.525860</td>\n      <td>22.905161</td>\n      <td>0.469794</td>\n      <td>1.161075</td>\n      <td>0.616226</td>\n      <td>1.022606</td>\n      <td>0.612277</td>\n      <td>0.498835</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>29.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>94.000000</td>\n      <td>126.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>71.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>47.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>120.000000</td>\n      <td>211.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>133.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>55.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>130.000000</td>\n      <td>240.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>153.000000</td>\n      <td>0.000000</td>\n      <td>0.800000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>61.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>140.000000</td>\n      <td>274.500000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>166.000000</td>\n      <td>1.000000</td>\n      <td>1.600000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>77.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>200.000000</td>\n      <td>564.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>202.000000</td>\n      <td>1.000000</td>\n      <td>6.200000</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.rename(index = str, columns = {\n    'cp' : 'chest_pain',\n    'trestbps' : 'rest_bp', \n    'fbs' : 'fast_bld_sugar', \n    'restecg' : 'rest_ecg', \n    'thalach' : 'max_hr', \n    'exang' : 'ex_angina', \n    'oldpeak' : 'st_depr', \n    'ca' : 'colored_vessels',\n    'thal' : 'thalassemia', \n}, inplace = True)\n","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.info()","execution_count":5,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 303 entries, 0 to 302\nData 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   chest_pain       303 non-null    int64  \n 3   rest_bp          303 non-null    int64  \n 4   chol             303 non-null    int64  \n 5   fast_bld_sugar   303 non-null    int64  \n 6   rest_ecg         303 non-null    int64  \n 7   max_hr           303 non-null    int64  \n 8   ex_angina        303 non-null    int64  \n 9   st_depr          303 non-null    float64\n 10  slope            303 non-null    int64  \n 11  colored_vessels  303 non-null    int64  \n 12  thalassemia      303 non-null    int64  \n 13  target           303 non-null    int64  \ndtypes: float64(1), int64(13)\nmemory usage: 35.5+ KB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.set(palette = 'pastel', rc = {\"figure.figsize\": (12,8), \"axes.titlesize\" : 18, \"axes.labelsize\" : 16, \n            \"xtick.labelsize\" : 16, \"ytick.labelsize\" : 16 })\n\nchol = sns.boxplot(df['chol'])\nplt.show()","execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAqwAAAHsCAYAAAAXcv/BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAWfUlEQVR4nO3de2yWZ93A8V/L4RXK6R0DKsdl4y2jdQWdwNwIccw43dBEEyQetgw0Ak7EhUNCNEEzT2QqZh5iISOTOKdiIjqiJMp8xR1gM2qGdJtsyrGK3RytlFNHn/cPwvOutDg2Wp4f9PNJ+gf3c/V6rgeu++Hbm7ulrFAoFAIAAJIqL/UCAADgPxGsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqvV9twEsvtURbm598xSlDhw6IF188XOplcBGwVzhX9grnyl65dJWXl8V//3fFWR9/1WBtaysIVtqxHzhX9grnyl7hXNkrPZNbAgAASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACk1rvUC6Dn+N///XU0Nh4s9TLO6siRloiI6N+/osQref2GDRsRb3/7O0q9DADoUoKVC6ax8WD84+DB6Dd4WKmX0qkj/z4cERGt5f1KvJLX52hTY6mXAADdQrByQfUbPCz+Z/rsUi+jU7se2RARkXZ9r+b0+gHgUuMeVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQWspgra/fEfX1O0q9DIAUvCcCPV3vUi+gMzt3PhUREdXV15R4JQCl5z0R6OlSXmEFAIDTBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWALpNS8vh2LDhgWhpOdxl87yWObvq+aEnyHy+CFYAus22bY/GgQP7Yvv2R7tsntcyZ1c9P/QEmc8XwQpAt2hpORz19TsiIqK+fsfrvmrzynl27nwq6uufOqc5u+r5oSfIfr70LvUCOnPkSEu0tLTEhg0PlHopnKFv395x4sTLr+tzGxv/GWV9+3fxijit9fiRaPz3kTTnzfnsFdprbPxnVFRUlHoZr9m2bY9GoVCIiIi2tkJs3/5ozJx583nNc/LkyYgoO6c5u+r5oSfIfr64wgpAt3j22Z3R1nYyIiLa2k7GM8/sPO95Tjn9l+p/nrOrnh96guznS8orrP37V0T//hUxe/aHS70UzjBs2MBobPz36/rcDRseiKZjbV28Ik7r81/9Y/DgAWnOm/PZK7SX5ar5azVhQk3s3PlUtLWdjPLyXnH11TXnPc8pZRFReNU5u+r5oSfIfr64wgpAt7juuhuirOzUP9+Xl5fFtGk3nPc8vXr1il69ys9pzq56fugJsp8vghWAblFRMSCqq6+JiIjq6muiomLAec9TU1Mb1dW15zRnVz0/9ATZz5eUtwQAcGm47rob4l//euG8r9acOc+5ztlVzw89QebzRbAC0G0qKrrmvuoz5znXObvq+aEnyHy+uCUAAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNR6l3oBnampqS31EgDS8J4I9HQpg7W6+ppSLwEgDe+JQE/nlgAAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkFrvUi+AnuVoU2PsemRDqZfRqSNNjRERadf3ao42NcbgN4wo9TIAoMsJVi6YYcNyx1SftgEREdH/DRfnPzwMfsOI9L/HAPB6CFYumLe//R2lXgIAcBG6OC8lAQDQYwhWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUer/agPLysguxDi4i9gTnyl7hXNkrnCt75dL0an+uZYVCoXCB1gIAAK+ZWwIAAEhNsAIAkJpgBQAgNcEKAEBqghUAgNQEKwAAqQlWAABSE6wAAKQmWAEASE2wAgCQmmDtoTZv3hyLFi2KG2+8MWpra+Pmm2+Or33ta3H48OF245qamuIzn/lMTJs2LSZPnhx33HFHPPvssx3mO378eKxatSqmT58etbW1MWfOnHjyyScv1MuhG/3ud7+L22+/PW644YZ405veFDNmzIjFixfHc889126cvUJnPvrRj8aECRNi9erV7Y7bLz3b9u3bY8KECR0+3vrWt7YbZ59wWlmhUCiUehFceB/4wAfijW98Y9x0001RWVkZ9fX18a1vfSuuvPLK+OEPfxjl5eVRKBTiwx/+cOzfvz+WL18egwYNijVr1sSuXbviZz/7WVRWVhbnW7JkSfz2t7+N5cuXx5gxY+KBBx6IrVu3xo9+9KOYOHFiCV8p52vTpk2xc+fOmDRpUlx22WXR0NAQa9eujb///e/x0EMPxahRo+wVOrVp06b4yle+Eo2NjbFgwYK46667IiLsF2L79u1x++23x2c/+9m45pprisd79epV/LV9QjsFeqQXX3yxw7Gf/vSnhaqqqsJjjz1WKBQKhV/96leFqqqqwuOPP14c09zcXJgyZUrh7rvvLh57+umnC1VVVYWf/OQnxWOtra2Fd77znYX58+d346ugVJ5//vlCVVVV4b777isUCvYKHTU1NRWuv/76wkMPPVSoqqoqfP3rXy8+Zr+wbdu2QlVVVeHRRx896xj7hFdyS0APddlll3U4dvqr2oMHD0ZExMMPPxzDhw+P6667rjhm4MCBceONN8aWLVuKx7Zs2RJ9+vSJW265pXisd+/eceutt8YjjzwSJ06c6K6XQYkMGTIkIk79OUfYK3R0zz33xPjx42PWrFkdHrNfOBf2Ca8kWCl64oknIiLiqquuioiI5557LqqqqjqMGz9+fDQ0NERLS0tx3KhRo6Jfv34dxrW2tsaePXu6eeVcCCdPnowTJ07E7t27Y+XKlTFs2LC49dZbI8Jeob3f//73sXHjxli5cmWnj9svnLZ06dKYOHFiTJs2LZYsWRINDQ3Fx+wTXql3qRdADgcPHox77703rr/++uKV1qamphg1alSHsaevrjU3N0dFRUU0NTXF4MGDzzquqampG1fOhTJ79uzYuXNnRESMGzcuvve978XQoUMjwl7h/7W2tsbKlStj3rx5ceWVV3Y6xn5h4MCBMW/evJgyZUoMGDAg6uvro66uLp544onYuHFjDB061D6hHcFKtLS0xMKFC6NXr17x5S9/uXi8UChEWVlZh/GFM75P71zHcXG755574vDhw7Fv375Yt25dzJ07N37wgx/E6NGj7RWK1q5dG8eOHYuFCxeedYz9QnV1dVRXVxd/PXXq1JgyZUrMnj071q9fH3fddZd9QjtuCejhjh8/HgsXLoz9+/fHfffd1+67LgcPHtzpV6anjw0aNKg47tChQ2cd19lXvlx8rrrqqpg0aVLMmjUr7r///jhy5EisWbMmIuwVTmloaIjvfve7sXjx4jhx4kQ0NzdHc3NzRETx1ydPnrRf6FRNTU1cccUV8ec//zkivK/QnmDtwVpbW2PRokWxY8eOWLNmTUyYMKHd4+PHj49du3Z1+Lznn38+Ro4cGRUVFcVxBw4ciKNHj3YY16dPnxg3blz3vQhKYtCgQTF27NjYu3dvRNgrnLJv3744fvx4LFu2LKZMmVL8iIhYt25dTJkyJf7yl7/YL5zVK6+K2ie8kmDtodra2mLp0qXx+OOPx3e+852YPHlyhzE33XRTHDx4sPjNWBERhw8fjt/85jcxc+bMduNaW1tj8+bNxWMvv/xy/OIXv4jp06dH3759u/fFcMG98MIL8be//S3Gjh0bEfYKp0ycODHWr1/f4SMi4r3vfW+sX78+xo4da7/QqR07dsTu3btj0qRJEeF9hfbcw9pDff7zn4/NmzfHggULol+/fvGnP/2p+FhlZWVUVlbGzJkz481vfnMsW7as3Q9tLhQK8bGPfaw4fuLEiXHLLbfEl770pXj55Zdj9OjR8eCDD8b+/fvjq1/9aileHl3ozjvvjOrq6pgwYUIMGDAgdu/eHffff3/06tUr5s6dGxFhrxARp668T5s2rdPHRo4cWXzMfmHJkiUxevToqKmpiYEDB8bTTz8ddXV1MWLEiPjIRz4SEfYJ7fmfrnqomTNnxoEDBzp97JOf/GQsWrQoIiIOHToUq1atii1btsTx48dj8uTJsWLFirj66qvbfc6xY8di9erVsWnTpmhubo6rr746li5deta/vLh4rFmzJjZv3hx79+6N1tbWqKysjGnTpsXHP/7xGD16dHGcvcLZTJgwod3/dBVhv/R0dXV1sWnTpmhoaIhjx47F5ZdfHjNmzIhFixbF8OHDi+PsE04TrAAApOYeVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghXgdbjtttvigx/8YJfOd9ttt3XZfACXEsEKAEBqghUAgNQEK8BZPPPMM3HnnXfGtGnTora2Nm6++eaoq6trN+axxx6L973vfTFp0qSYNWtW/PrXv+4wz9atW2POnDlRW1sb1157bXziE5+Iv/71rxfqZQBc9AQrQCeeeuqpmDNnTuzduzdWrFgRdXV1MXfu3PjHP/5RHLNv37744he/GHfccUd885vfjGHDhsWnPvWp2LNnT3HM1q1bY/78+dG/f/9YvXp1fO5zn4tdu3bFhz70oTh48GApXhrARad3qRcAkNGqVatiyJAh8eMf/zj69esXERFve9vb2o156aWX4vvf/35cccUVERFRU1MT06dPj1/+8pexYMGCiIj4xje+EWPGjIm1a9dG796n3nInT54c73rXu2LdunWxYsWKC/eiAC5SrrACnOHo0aPxhz/8Id7znvcUY7Uz48aNK8ZqRMTQoUNj6NCh0dDQEBERR44cifr6+nj3u99djNWIiDFjxsRb3vKWePLJJ7vtNQBcSgQrwBmam5ujra0tKisr/+O4wYMHdzjWt2/fOHHiRHGeQqEQw4cP7zDu8ssvj0OHDnXNggEucYIV4AyDBg2K8vLy877HdNCgQVFWVhaNjY0dHnvhhRdiyJAh5zU/QE8hWAHO0K9fv7j22mvj5z//eRw7dux1z9O/f/+oqamJzZs3x8mTJ4vHDxw4EH/84x9j6tSpXbFcgEueYAXoxPLly+PQoUMxZ86c2LhxY2zbti02bNgQd99992uaZ/HixbFnz56YP39+PPzww7Fp06aYN29eDBgwIObOndtNqwe4tPgpAQCdqK2tjQcffDDuvffe+MIXvhAnTpyIkSNHxvvf//7XNM+MGTOirq4uvv3tb8enP/3p6NOnT0ydOjWWLVsWI0aM6KbVA1xaygqFQqHUiwAAgLNxSwAAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBQAgNcEKAEBqghUAgNT+DyllO3p22uxSAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Activity 7.01","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sd = sns.boxplot(df['st_depr'])\nplt.show()","execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAqsAAAHtCAYAAAA+8jcdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAaDUlEQVR4nO3de2yW9d348U+hMuWkFDrL2SG/VkCGLOM4Nx0efh7QZMl0UadE8XGAMoOHZFMz1MW46XTxEA8wkanMA9k0HkZN5jIdbGrc5qYCE50dIAqlah0gUuj9/OGP/qzAMx9lvT5yv15JE+/rvvj2c/cb4c3VqzcVpVKpFAAAkFCnogcAAIBdEasAAKQlVgEASEusAgCQllgFACAtsQoAQFqV/+6Et9/eGK2tHf/uVr17d4+mpg0d/nlpzz7kYB9ysA852Ic87EUOe8I+dOpUEb16ddvpc/82VltbS4XE6vbPTfHsQw72IQf7kIN9yMNe5LAn74PbAAAASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkVVn0ADvzu9/9Jpqbm2LLlq1Fj/KZsmnTxoiI6Nq1225bs0uXyrLZh+rq/ePww48segwA4ENSxmpj49p4c+3a2Gff6qJH+UzZ9K8NERHR0mmf3bfo5i27b63E3mtuLHoEAGAnUsZqRMQ++1bH/zn0pKLH+ExZsXhhRISv2yew/WsHAOTinlUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkFZl0QPszKZNG6OlpVT0GAAks3TpC9Gz5z4xYMDQokcBOkjKWN24cWNsbRWrALT30kt/iy5dKsUqlBG3AQAAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQpA2WpsXBu33PLTaGxcu1vW27hxQyxcuCA2btywW9ajeOW0p1lfq1gFoGwtWvRwbNnyftTXP7xb1nv66SXx+uur4plnluyW9SheOe1p1tcqVgEoS42Na+Ott5oiIqKpqelTX13duHFDLF36QkRELF36QrqrU/zvldOeZn6tlUUPABm0vL8pGv+1KRYuXFD0KDvVpUtlbNmytegxyp59KF5j47ro2bPHbllr0aL2V1Pr6x+O00//r0+83tNPL4lSqRQREa2tpXjmmSUxadL//VQzUqxy2tPMr9WVVQDK0varqts1NTXt4syP5+9/fylaW7dFRERr67ZYvvylT7UexSunPc38Wl1ZhYjY63NdY999u8dJJ51W9Cg7VV3dIxob/1X0GGXPPhRv4cIF0aXL7vmjq6qqd7tg7d2796dar65uRLz00t+itXVbdOrUOQ46aMSnHZGCldOeZn6trqwCUJaOPfbEdo+POebEXZz58Ywf/5WoqKiIiIhOnSpi3LivfKr1KF457Wnm1ypWAShL1dX7R1XVB1dTe/fuHdXV+3+q9bp16x7Dh4+MiIjhw0dGt27dP/WMFKuc9jTzaxWrAJStY489Mbp0+dynvqq63fjxX4n+/QemuirFp1NOe5r1tbpnFYCyVV29f8yYMWu3rdetW9573/lkymlPs75WV1YBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASKuy6AF2plu3brGppVT0GAAkM2LEF6Nnz32KHgPoQCljtWvXbtGyubXoMQBIZvjwkVFd3SMaG/9V9ChAB3EbAAAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIq7LoAXblvebGWLF4YdFjfKZsam6MiPB1+wTea26Mfffev+gxAICPSBmr1dX7R5culbFly9aiR/lM2au1e0REdN17910wL5d92Hfv/aO6WqwCQDYpY/Xww4+M6uoe0dj4r6JHKXv2AQAokntWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANISqwAApCVWAQBIS6wCAJCWWAUAIC2xCgBAWmIVAIC0xCoAAGmJVQAA0hKrAACkJVYBAEhLrAIAkJZYBQAgLbEKAEBaYhUAgLTEKgAAaYlVAADSEqsAAKRV+e9O6NSpoiPmSPe5+f/sQw72IQf7kIN9yMNe5PBZ34f/af6KUqlU6sBZAADgY3MbAAAAaYlVAADSEqsAAKQlVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqAABpiVUAANKqLHqAD3vjjTfi6quvjiVLlkSpVIqJEyfGJZdcEv369St6tLLy5ptvxty5c+PFF1+M5cuXx+bNm+OJJ56IAQMGFD1a2aivr4/HHnssXnzxxWhqaoq+ffvG0UcfHd/5zneie/fuRY9XNn7/+9/H3Llz49VXX43m5uaoqqqK0aNHx8yZM2Po0KFFj1fWpk6dGosXL45p06bFrFmzih6nbDzzzDNxxhln7HC8R48e8dxzzxUwUXl78sknY86cObF06dKoqKiIAw44IC6++OKYMGFC0aPtVmli9b333ospU6ZEly5d4sc//nFERNxwww1xxhlnxMMPPxxdu3YteMLy8c9//jMWLVoUI0aMiC9/+cuxePHiokcqO/PmzYu+ffvGrFmzoqamJpYuXRo333xzPPPMM3HfffdFp06+KdIRmpubY8SIEXHqqadGVVVVrFmzJubOnRsnn3xyPPLII9G/f/+iRyxLjz76aPz9738veoyydtlll8XIkSPbHnfu3LnAacrTfffdFz/84Q/jtNNOixkzZkRra2ssW7YsNm/eXPRou18pifnz55cOOuigUkNDQ9uxlStXloYNG1aaN29egZOVn23btrX99wMPPFCqra0trVq1qsCJyk9TU9MOxx588MFSbW1t6Q9/+EMBE7Hdq6++WqqtrS3dcccdRY9Slpqbm0sTJ04sPfLII6Xa2trS9ddfX/RIZeXpp58u1dbWlpYsWVL0KGVt1apVpZEjR5buvPPOokfpEGkuz/z2t7+NUaNGxeDBg9uODRw4ML70pS/FE088UeBk5cdVu+JVVVXtcGz7VYy1a9d29Dh8yH777RcREZWVab4xVVauvfbaGDp0aEyePLnoUaAwv/zlL6NTp05xyimnFD1Kh0hTJa+88krU1tbucHzo0KHxyiuvFDAR5PLss89GRMSBBx5Y8CTlZ9u2bbFly5ZoaGiI2bNnR3V1dRx//PFFj1V2nnvuuXjooYdi9uzZRY9S9i666KIYNmxYjBs3Li688MJYs2ZN0SOVlT/96U8xZMiQeOyxx+LII4+M4cOHx1FHHRULFiwoerT/iDSXBpqbm6Nnz547HN93333j3XffLWAiyGPt2rVx4403xsSJE9vdJ0bHOOmkk+Kll16KiIjBgwfHz3/+8+jdu3fBU5WXlpaWmD17dpx11lkxZMiQoscpWz169IizzjorxowZE927d4+lS5fG7bffHs8++2w89NBD/r/oIOvWrYt169bFNddcExdccEEMHDgw6uvr48orr4ytW7fGlClTih5xt0oTqxERFRUVRY8A6WzcuDGmT58enTt3jquvvrroccrStddeGxs2bIhVq1bFvHnz4swzz4xf/OIX3iGjA82dOzc2b94c06dPL3qUsjZ8+PAYPnx42+OxY8fGmDFj4qSTToq77rrLOzN0kFKpFBs3bowf/ehHcfTRR0dExIQJE+L111+POXPmxBlnnLFHNVWa2wB69uwZzc3NOxzf1RVXKAfvv/9+TJ8+PVavXh133HFH1NTUFD1SWTrwwANj1KhRMXny5Jg/f35s2rQp5syZU/RYZWPNmjVx2223xfnnnx9btmyJd999t+07btsfb9u2reApy9eIESPigAMOiBdffLHoUcrG9nvnJ06c2O74oYceGuvXr49169YVMdZ/TJpYHTp0aKxYsWKH46+++qr3M6QstbS0xMyZM+OFF16IOXPmRF1dXdEjER/8xXrQoEGxcuXKokcpG6tWrYr3338/Lr744hgzZkzbR8QHb/M2ZsyYePnllwuesryVSqWiRygru+qi7fuwp/2gdJpXM2nSpPjrX/8aq1ataju2evXq+POf/xyTJk0qcDLoeK2trXHRRRfFH//4x7jlllvikEMOKXok/p/169fHa6+9FoMGDSp6lLIxbNiwuOuuu3b4iIg48cQT46677rIfBXrhhReioaEhRo0aVfQoZeOoo46KiNjhfdAXL14cNTU1UV1dXcRY/zFp7lk9+eSTY8GCBTFjxow4//zzo6KiIm644YaoqamJb33rW0WPV3bq6+sjItq+rfPUU09FVVVVVFVVxdixY4scrSxcccUVUV9fH9OmTYt99tknnn/++bbnampq3A7QQc4999wYPnx41NXVRffu3aOhoSHmz58fnTt3jjPPPLPo8cpGz549Y9y4cTt9rl+/frt8jt3vwgsvjAEDBsSIESOiR48esWzZsrj99ttj//33j29/+9tFj1c2DjvssBg3blzMnj073n777Rg4cGA8/vjjsXjx4j3yZxsqSomu3a9Zs6bdP7c6YcKEuOSSS/wQQwF29S3nsWPHxt13393B05SfSZMmxeuvv77T584777yYOXNmB09UnubMmRP19fWxcuXKaGlpiZqamhg3blycc845fl9KoK6uzj+32sFuv/32ePTRR2PNmjWxefPm6NOnT3zta1+LmTNnxuc///mixysrGzZsiOuuuy4ef/zxePfdd+MLX/hCnHPOOXHCCScUPdpulypWAQDgw9LcswoAAB8lVgEASEusAgCQllgFACAtsQoAQFpiFQCAtMQqUNaWLVsWN910U7zzzju7Zb3Vq1dHXV1d/OpXv9ot6wGUO7EKlLVly5bFzTffHM3NzUWPAsBOiFWAPcCWLVuKHgHgP0KsAnu81157Lc4999yYMGFCjBw5Mg4//PD47ne/GwsXLozvf//7ERFx9NFHR11dXdTV1cXq1as/1rrvvfdeXH755TFu3LgYPXp0TJs2Ld58882dnvvss8/GlClTYvTo0XHIIYfE1KlT4+WXX253zumnnx6nnHJK/OY3v4nJkyfHwQcfHMccc0z8+te/bnfeTTfdFHV1dfHyyy/H1KlTY/To0XH++ed/gq8MQH6VRQ8A8J82bdq06NGjR1x++eXRq1evWLt2bTz55JPx9a9/PaZPnx633npr3HDDDVFTUxMR8bH/jfMf/OAHsWjRojj33HNj5MiRsWTJkrjooot2OO93v/tdzJgxIw477LC49tprIyLiZz/7WZx22mnx8MMPR9++fdvOXblyZVx11VVx3nnnRe/evePee++NCy64IKqqqmL8+PHt1p0xY0Z885vfjLPPPjs6dXLtAdgziVVgj/bWW29FQ0ND3HLLLXHEEUe0HT/hhBMiImLQoEERETFs2LAYPHjwx173H//4Rzz66KMxa9asOOeccyIi4tBDD41NmzbFfffd1+7cq666KsaMGRO33npr27Hx48fHEUccEfPmzYtLL7207fj69evj/vvvj0MOOSQiIr761a/G8ccfHzfeeOMOsXr66afHlClTPvbMAJ9F/ioO7NF69eoVAwcOjOuuuy4eeOCBaGho2C3r/u1vf4vW1tY49thj2x0//vjj2z1uaGiIlStXxgknnBBbt25t+9h7771j9OjR8dxzz7U7v2/fvm2hGhHRuXPnOOaYY9o+34cdddRRu+W1AGTmyiqwR6uoqIg777wzbrrpprjuuuvinXfeiQEDBsTUqVPj1FNP/cTrrlu3LiIievfu3e74Rx83NTVFRMSll17a7grqdv369fsff31ERJ8+faKlpSXeeuut6NOnT9vx6urqTzY8wGeIWAX2eAMHDoxrrrkmSqVSLF++PO6555644ooron///p94ze33tTY1NUXXrl3bjm+P0+3222+/iIi48MILY8KECTuss9dee7V7/NFfH/HBrQF77bVXVFVVtTteUVHxyYYH+AxxGwBQNioqKmLYsGFt7wCwYsWK6NKlS0REbN68+X+11he/+MXo1KlTLFq0qN3xxx57rN3jIUOGRP/+/WPFihUxcuTIHT4OOuigdue/8cYb8fzzz7c93rZtW9TX17d9PoBy48oqsEdbvnx5XHXVVXHcccfF4MGDY9u2bfHggw9GZWVljB8/PiorP/htcMGCBfGNb3wjKisro66uri1id2XIkCExefLkuPHGG6O1tbXt3QCeeuqpdudVVFTE7NmzY8aMGdHS0hLHHnts9OrVK9avXx9/+ctfol+/fnHmmWe2nd+nT5+YNWtWzJw5M6qqquLee++NhoaGuPzyy3f71wbgs0CsAnu06urq6NevX8yfPz/efPPN+NznPhe1tbVx2223xcEHHxwRETNnzoz7778/Fi5cGK2trfHEE0/EgAED/u3aV155ZXTt2jXmzZsXLS0tMW7cuPjJT36yw72whx12WNxzzz1x2223xWWXXRabN2+O6urqGDVqVBx33HHtzh00aFCcffbZ8dOf/jQaGhqif//+cf311+/wTgAA5aKiVCqVih4CgA/eimrr1q1x7733Fj0KQBpugAIAIC23AQB8RGtr6w7vafphFRUV0blz5w6cCKB8uQ0A4CO+973vxYMPPrjL58eOHRt33313B04EUL7EKsBHrF69Ot5+++1dPt+tW7cYMmRIB04EUL7EKgAAafkBKwAA0hKrAACkJVYBAEhLrAIAkNZ/A+Ab6arUhd6yAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"cv = sns.boxplot(df['colored_vessels'])\nplt.show()","execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"t = sns.boxplot(df['thalassemia'])\nplt.show()","execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 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UlVVtd6xhx9+uKisrCxeeOGFeq+zTxrO5q5JUdgnje3tt98uKisri1tvvbXeczLtlUZ7hvWZZ56JAQMGRI8ePeqOde/ePfbcc8+YNm3aRq+dNm1aNGvWLA477LC6Y2VlZXH44YfH888/HzU1NQ0297ZsS9aEhrWxuxAbY680nM1dExpO+/bt1zvWv3//iIiYP39+vdfZJw1nc9eExte2bduI+OS//fpk2iuN9n/gt956KyorK9c73qtXr3jrrbc2eW3Xrl3X+5JAr169ora2Nt57772tOutXxZasyafuu+++6NevXwwYMCB+8pOfxMsvv7y1x+QLsFfyslcax0svvRQREbvssku959gnjevzrMmn7JOGtWbNmqipqYl33303Lr300qioqIjDDz+83vMz7ZVG+6arxYsXR+vWrdc73qZNm6iurt7ktW3atFnv+Kd/O1i8ePHWGfIrZkvWJCLie9/7Xhx44IHRsWPHmDNnTtx6661x4oknxm233Rb77LNPQ4zMJtgrOdkrjWP+/Plx7bXXxpAhQ+ru6m2IfdJ4Pu+aRNgnjeGYY46J119/PSIievToEXfccUd06NCh3vMz7ZVG/SkBTZo02azriqLY4LVF43y/2DZtc9ckImLs2LF1v99rr73i4IMPju9+97sxYcKEuPfee7fGeHxB9kpO9krDW7ZsWYwcOTKaNm0aY8aM2ei59knj+CJrEmGfNIaxY8fG0qVLY/bs2XHbbbfF8OHD45577qn3m0cz7ZVGeySgdevWGyzx+u7y/a82bdrEokWLNnjtp+/ni9uSNdmQli1bxgEHHBAzZszYGuOxGeyVLwd7ZetatWpVjBw5Mj744IO49dZbo1OnThs93z5peF90TTbEPtn6dtlllxgwYEAcccQRcfvtt8fy5cvj5ptvrvf8THul0YK1V69e8eabb653/O23397kzwDr1atXzJkzJ1asWLHetc2aNVvnm4b4/LZkTepT39/GaBz2ypeHvbJ11NbWxhlnnBEzZsyIm2++ud6f5fm/7JOGtTlrUh/7pOG0bt06dtppp3j//ffrPSfTXmm0YD3ooIPi1VdfjdmzZ9cd++CDD+KVV16Jgw46aKPXHnzwwVFbWxtTpkypO7Z69ep44oknYr/99ovy8vIGm3tbtiVrsiFLly6NZ599NgYMGLA1x+QLsFe+HOyVrWPt2rVx7rnnxl//+te4/vrrY4899vhc19knDWdz12RD7JOG9fHHH8esWbNip512qvecTHul6ejRo0c3xgtVVlbG448/Hk899VR07NgxZs2aFZdcckk0b948rrjiirpPes6cOTF48OAoiiIGDRoUEREVFRXxzjvvxN133x3t2rWL6urqGDduXLz22msxduzY6NixY2N8CtucLVmTW2+9NR566KFYtmxZLFq0KP7+97/HxRdfHB9++GFcddVVn+tfymLjpkyZEm+99Va88sorMXPmzOjZs2fMmTMnFi5cGF27drVXSmBz1sReaTijR4+ORx55JH7+85/HrrvuGvPmzav7FfHJl5Ttk8a1uWtinzSs0047Ld59992orq6Ojz76KJ5//vm45JJLYuXKlXHllVdGu3bt0u+VRvumqxYtWsQdd9wRY8aMiVGjRkVRFLHvvvvGhRdeGDvuuGPdeUVRxJo1a9Z7oHfMmDFx9dVXx4QJE6K6ujr69OkTt9xyS+y2226N9Slsc7ZkTXr27BlPP/10PP3007F06dJo2bJlDBw4MK644orYfffdS/HpbHPOOuusdd6+7LLLIiJi0KBBceedd9orJbA5a2KvNJzp06dHRMSNN94YN9544zrvO/300+OMM86wTxrZ5q6JfdKwBgwYEFOmTInJkydHbW1tdOrUKfbZZ584+eST677hKvteabR/mhUAADaHf7oFAIDUBCsAAKkJVgAAUhOsAACkJlgBAEhNsAIAkJpgBVKaOnVqTJ48eZ1jL774YvTu3TteeOGFrfIaH3zwQfTu3TseeuihrfLxMjrhhBPihBNOKPUYAFuk0f7hAIAvYurUqfHCCy/E8OHDSz3Kl9qll15a6hEAtphgBdiG9erVq9QjAGwxjwQA6Zx//vnx8MMPx/z586N3797Ru3fvOOigg+rev2LFirj88stjn332icGDB8e5554b1dXV63yMu+66K4YNGxaDBg2KvfbaK4499tj485//vMnXfu211+LMM8+M/fffP3bfffc49NBDY/z48bFy5cp1zps+fXr86Ec/im9+85sxcODAOPTQQ+O6666re/+sWbPitNNOi3333Tf69+8f3/rWt+LMM8+M1atX152zcOHCuPTSS2Po0KHRr1+/+Pa3vx3333//Oq/z0EMPRe/eveOVV16Js846KwYOHBhDhgyJm266KSIinnvuufj+978fe+yxRxx99NExc+bMda7/7CMBq1atiiuvvDKOOOKIGDhwYPzf//1fnHLKKfH2229v8s8GoFTcYQXSOfXUU2PhwoUxY8aMuOGGGyIiory8PJYsWRIREVdccUUceOCBMW7cuJg1a1aMHTs2mjZtGr/61a/qPsacOXPihz/8YXTr1i1Wr14df/rTn2LEiBFx8803xwEHHFDva8+dOzf69OkTRx11VOy4447x5ptvxvXXXx+zZ8+Oq6++OiIiZs+eHSNHjoxDDz00Tj311GjWrFm89957MXv27LqPc8opp0SrVq1i9OjR0a5du5g/f348++yzsXbt2oiIWLp0afz4xz+OVatWxRlnnBHdunWL6dOnx+jRo6Ompma9507PP//8OPLII2PYsGExZcqUGD9+fFRXV8dzzz0Xp5xySrRo0SLGjh0bp512Wjz99NNRXl6+wc+vpqYmli1bFiNHjoyKiopYvHhx3HPPPTFs2LB48skno6KiYjNWDKCBFQAJnXfeecXQoUPXOfa3v/2tqKysLEaNGrXO8csuu6zo169fsXbt2g1+rDVr1hS1tbXF8OHDi1NOOaXu+OzZs4vKysriwQcf3OB1a9euLWpra4tHHnmk6N27d7Fw4cKiKIriySefLCorK4slS5Zs8LqqqqqisrKymDp1ar2f33XXXVf069evmDVr1jrHL7roomLQoEFFbW1tURRF8eCDDxaVlZXFxIkT686pra0tBg8eXHzjG98o3n///brjU6dOLSorK4sXX3yx7tjxxx9fHH/88fXOsXr16mL58uXFHnvsUUyePLne8wBKyR1W4Evns3dIKysro6amJj7++OO6O4QzZ86MiRMnxowZM2LhwoVRFEVERPTs2XOjH3vp0qVxww03xFNPPRXz5s2L2trauve999570a5du+jbt280a9Yszj777Dj66KNj7733jg4dOtSd165du+jevXuMGzcuqqqqYtCgQfH1r399ndeZPn16DBgwoO4O8Kf222+/eOCBB+Ktt96KPn361B3ff//9635fVlYWPXr0iCVLlkT37t3rju+8884R8cld4o154oknYvLkyTFr1qy6u9YREe+8885GrwMoFcEKfOm0bdt2nbc//fL3qlWrIuKTYDvppJOiV69ecfHFF0eXLl2iadOmcc0112wyyi644IJ44YUX4swzz4y+ffvGDjvsEK+99lpcfvnldR+/R48eccstt8SkSZNi1KhRUVNTE/37949f/vKXMWjQoGjSpElMnjw5Jk6cGOPGjYtFixZFt27d4qc//Wkcd9xxEfHJ86vvvfde7LbbbhucY9GiReu83aZNm3XebtasWbRu3Xq9YxGffNm/Ps8880ycffbZcdRRR8Xpp58e7dq1iyZNmsTJJ5+80esASkmwAtuc6dOnx5IlS2LChAnRqVOnuuOf/capz1q1alVMmzYtTj/99DjxxBPrjv/73/9e79zBgwfH4MGDo6amJv7xj3/EtddeGyNGjIhp06ZF+/bto3v37vHrX/86iqKIN954I+6666647LLLomvXrnHAAQdE27Zto3379nHRRRdtcJZN3QneXI8//nj06NEjrrrqqrpjtbW1sXjx4gZ5PYCtwU8JAFIqLy+vu6P5Ra1YsSIiPvnS+admzZoVr7zyykavq6mpiTVr1qxzXUTEww8/vNE599133/jZz34Wy5cvjw8++GCd9zdp0iT69u0bF1xwQUREvPnmmxERMXTo0Jg1a1Z06dIl+vfvv96vli1bfv5P+AtYuXJlNG3adJ1jf/jDH2LNmjUN8noAW4M7rEBKu+yySyxatCjuueee6NevXzRv3vxzXztkyJAoKyuL8847L4YPHx4LFiyIiRMnRufOneueZd2QVq1axR577BGTJ0+Ojh07Rrt27eLBBx+M+fPnr3PevffeGy+//HLsv//+0blz5/jPf/4TN910U3Ts2DEqKyvjjTfeiCuuuCIOO+yw6NGjR6xZsyYefvjhKCsri8GDB0dExEknnRRPPPFEHHfccXHSSSdFz549Y8WKFfHOO+/Eyy+/XPfTEba2oUOHxtSpU+PKK6+MAw88MGbOnBl33nnneo8XAGQiWIGUjjnmmHj11Vfj6quvjurq6ujatWuMGTPmc1276667xtixY+Paa6+NkSNHxk477RTnnHNOTJ8+PV566aWNXjtu3LgYPXp0XHbZZbH99tvHd77znbjoootixIgRdef06dMnnnvuuRg/fnxUVVVF27ZtY88994zf/OY3sf3220dFRUV06dIlbr/99pg3b140b948Kisr48Ybb4x+/fpFxCdxfN9998Vvf/vbmDRpUnz00UfRqlWr6NmzZxxyyCGb/we3Cccee2zMnTs3Hnzwwbj//vujf//+ceONN8bpp5/eYK8JsKWaFBu73QAAACXmGVYAAFITrAAApCZYAQBITbACAJCaYAUAIDXBCgBAaoIVAIDUBCsAAKn9Pw3xFLPnXt/tAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{"collapsed":true,"trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 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