EDA20.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "data = pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
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       "  </thead>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\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",
       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
       "\n",
       "   caa  thall  output  \n",
       "0    0      1       1  \n",
       "1    0      2       1  \n",
       "2    0      2       1  \n",
       "3    0      2       1  \n",
       "4    0      2       1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns top 5 rows\n",
    "# cp: chestpain\n",
    "# trtbps: resting blood pressure\n",
    "# chol: Cholestprl\n",
    "# fbs: Fasting blood sugar\n",
    "# restecg: ECG\n",
    "# thall: maximum heart rate achived\n",
    "# Oldpeak: exercise related to rest\n",
    "# caa(coronary artery anomaly): supply oxigenized blood, to heart\n",
    "# Output:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No: of rows:  303\n",
      "No: of columns:  14\n"
     ]
    }
   ],
   "source": [
    "# data.shape\n",
    "rows, col = data.shape\n",
    "\n",
    "print('No: of rows: ', rows)\n",
    "print('No: of columns: ', col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4242"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\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",
       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
       "5   57    1   0     140   192    0        1       148     0      0.4    1   \n",
       "6   56    0   1     140   294    0        0       153     0      1.3    1   \n",
       "7   44    1   1     120   263    0        1       173     0      0.0    2   \n",
       "\n",
       "   caa  thall  output  \n",
       "0    0      1       1  \n",
       "1    0      2       1  \n",
       "2    0      2       1  \n",
       "3    0      2       1  \n",
       "4    0      2       1  \n",
       "5    0      1       1  \n",
       "6    0      2       1  \n",
       "7    0      3       1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "      <td>236</td>\n",
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       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "298   57    0   0     140   241    0        1       123     1      0.2    1   \n",
       "299   45    1   3     110   264    0        1       132     0      1.2    1   \n",
       "300   68    1   0     144   193    1        1       141     0      3.4    1   \n",
       "301   57    1   0     130   131    0        1       115     1      1.2    1   \n",
       "302   57    0   1     130   236    0        0       174     0      0.0    1   \n",
       "\n",
       "     caa  thall  output  \n",
       "298    0      3       0  \n",
       "299    0      3       0  \n",
       "300    2      3       0  \n",
       "301    1      3       0  \n",
       "302    1      2       0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "  <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>"
      ],
      "text/plain": [
       "              age         sex          cp      trtbps        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    thalachh        exng     oldpeak         slp         caa  \\\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",
       "            thall      output  \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  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()     # Statistical calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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   trtbps    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   thalachh  303 non-null    int64  \n",
      " 8   exng      303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slp       303 non-null    int64  \n",
      " 11  caa       303 non-null    int64  \n",
      " 12  thall     303 non-null    int64  \n",
      " 13  output    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77     1\n",
       "76     1\n",
       "74     1\n",
       "29     1\n",
       "37     2\n",
       "34     2\n",
       "38     3\n",
       "40     3\n",
       "69     3\n",
       "71     3\n",
       "70     4\n",
       "68     4\n",
       "39     4\n",
       "35     4\n",
       "47     5\n",
       "49     5\n",
       "50     7\n",
       "66     7\n",
       "48     7\n",
       "46     7\n",
       "53     8\n",
       "65     8\n",
       "45     8\n",
       "42     8\n",
       "43     8\n",
       "55     8\n",
       "61     8\n",
       "67     9\n",
       "63     9\n",
       "41    10\n",
       "64    10\n",
       "44    11\n",
       "60    11\n",
       "56    11\n",
       "62    11\n",
       "51    12\n",
       "52    13\n",
       "59    14\n",
       "54    16\n",
       "57    17\n",
       "58    19\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duplicate values in a column\n",
    "\n",
    "data['age'].value_counts(sort=True, ascending=True, dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          41\n",
       "sex           2\n",
       "cp            4\n",
       "trtbps       49\n",
       "chol        152\n",
       "fbs           2\n",
       "restecg       3\n",
       "thalachh     91\n",
       "exng          2\n",
       "oldpeak      40\n",
       "slp           3\n",
       "caa           5\n",
       "thall         4\n",
       "output        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([63, 37, 41, 56, 57, 44, 52, 54, 48, 49, 64, 58, 50, 66, 43, 69, 59,\n",
       "       42, 61, 40, 71, 51, 65, 53, 46, 45, 39, 47, 62, 34, 35, 29, 55, 60,\n",
       "       67, 68, 74, 76, 70, 38, 77], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].unique()\n",
    "# pd.unique(data['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>236</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>506</td>\n",
       "      <td>855</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "      <td>465</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>357</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>396</td>\n",
       "      <td>581</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>528</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>490</td>\n",
       "      <td>959</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>653</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>402</td>\n",
       "      <td>589</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>473</td>\n",
       "      <td>2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>1190</td>\n",
       "      <td>2207</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1647</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>1974</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1282</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1011</td>\n",
       "      <td>2054</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1239</td>\n",
       "      <td>3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>1328</td>\n",
       "      <td>2435</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1857</td>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>979</td>\n",
       "      <td>1979</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1247</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>896</td>\n",
       "      <td>1612</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1051</td>\n",
       "      <td>3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>598</td>\n",
       "      <td>1232</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>748</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>870</td>\n",
       "      <td>1756</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1164</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>632</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>761</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>913</td>\n",
       "      <td>1589</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1059</td>\n",
       "      <td>1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>1494</td>\n",
       "      <td>3104</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1782</td>\n",
       "      <td>7</td>\n",
       "      <td>15.2</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>1699</td>\n",
       "      <td>2967</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>2174</td>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1061</td>\n",
       "      <td>1868</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1104</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>2066</td>\n",
       "      <td>4053</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2364</td>\n",
       "      <td>5</td>\n",
       "      <td>14.9</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1137</td>\n",
       "      <td>2245</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1117</td>\n",
       "      <td>5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1481</td>\n",
       "      <td>2853</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1602</td>\n",
       "      <td>6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>2313</td>\n",
       "      <td>4055</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2445</td>\n",
       "      <td>8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2431</td>\n",
       "      <td>4836</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2790</td>\n",
       "      <td>5</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>2089</td>\n",
       "      <td>3358</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2066</td>\n",
       "      <td>5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1449</td>\n",
       "      <td>2701</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1628</td>\n",
       "      <td>5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1105</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1161</td>\n",
       "      <td>5</td>\n",
       "      <td>13.7</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1480</td>\n",
       "      <td>2823</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1470</td>\n",
       "      <td>2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1202</td>\n",
       "      <td>2324</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1383</td>\n",
       "      <td>4</td>\n",
       "      <td>15.3</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1388</td>\n",
       "      <td>2744</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1330</td>\n",
       "      <td>5</td>\n",
       "      <td>10.8</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1108</td>\n",
       "      <td>2232</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1169</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1026</td>\n",
       "      <td>1720</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>972</td>\n",
       "      <td>3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1150</td>\n",
       "      <td>2581</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1220</td>\n",
       "      <td>3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>562</td>\n",
       "      <td>955</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>557</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>440</td>\n",
       "      <td>727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>428</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>591</td>\n",
       "      <td>1010</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>489</td>\n",
       "      <td>2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>382</td>\n",
       "      <td>716</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>417</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>304</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  caa  \\\n",
       "age                                                                           \n",
       "29     1   1     130   204    0        0       202     0      0.0    2    0   \n",
       "34     1   4     236   392    0        1       366     0      0.7    4    0   \n",
       "35     3   1     506   855    0        3       642     2      3.0    7    0   \n",
       "37     1   4     250   465    0        2       357     0      3.5    2    0   \n",
       "38     3   7     396   581    0        3       528     1      3.8    5    8   \n",
       "39     2   6     490   959    0        3       653     0      1.2    6    0   \n",
       "40     3   3     402   589    0        2       473     2      3.4    5    0   \n",
       "41     6  12    1190  2207    0        6      1647     1      3.4   18    1   \n",
       "42     6  10    1016  1974    1        6      1282     1      4.0   11    2   \n",
       "43     6   4    1011  2054    2        5      1239     3     10.4   11    5   \n",
       "44     9  13    1328  2435    0        7      1857     2      4.1   18    3   \n",
       "45     5   6     979  1979    0        2      1247     3      5.0   10    3   \n",
       "46     4   6     896  1612    1        4      1051     3      7.6    9    2   \n",
       "47     5   6     598  1232    0        3       748     1      1.1    9    1   \n",
       "48     6   6     870  1756    2        3      1164     1      1.9   10    4   \n",
       "49     3   6     632  1143    0        4       761     0      3.4    8    6   \n",
       "50     4   7     913  1589    0        4      1059     1      6.8   10    1   \n",
       "51     8  17    1494  3104    1        6      1782     7     15.2   20    6   \n",
       "52    12  15    1699  2967    5       11      2174     2      4.9   22   12   \n",
       "53     5   6    1061  1868    3        1      1104     3      6.7   10    5   \n",
       "54    11  17    2066  4053    2        7      2364     5     14.9   23   10   \n",
       "55     4   3    1137  2245    0        8      1117     5     15.6    9    3   \n",
       "56     8   9    1481  2853    4        2      1602     6     15.4   10    7   \n",
       "57    13   9    2313  4055    2       11      2445     8     12.2   24   12   \n",
       "58    13  16    2431  4836    4        7      2790     5     26.4   26   23   \n",
       "59    13  17    2089  3358    3        7      2066     5     15.1   18    7   \n",
       "60     7   9    1449  2701    2        5      1628     5     18.5   16   13   \n",
       "61     6   5    1105  1950    1        4      1161     5     13.7   11    6   \n",
       "62     4   6    1480  2823    2        6      1470     2     20.5   11   15   \n",
       "63     4   6    1202  2324    2        3      1383     4     15.3   12   13   \n",
       "64     7  12    1388  2744    0        6      1330     5     10.8   12    6   \n",
       "65     4   9    1108  2232    2        2      1169     0      8.6   13    8   \n",
       "66     4   6    1026  1720    1        3       972     3      6.4    8    7   \n",
       "67     6   6    1150  2581    1        4      1220     3      8.9   11   12   \n",
       "68     3   6     562   955    2        2       557     1      7.5    5    3   \n",
       "69     2   8     440   727    1        1       428     0      3.9    4    6   \n",
       "70     4   3     591  1010    0        2       489     2      7.9    4    4   \n",
       "71     0   3     382   716    1        2       417     0      2.0    5    3   \n",
       "74     0   1     120   269    0        0       121     1      0.2    2    1   \n",
       "76     0   2     140   197    0        2       116     0      1.1    1    0   \n",
       "77     1   0     125   304    0        0       162     1      0.0    2    3   \n",
       "\n",
       "     thall  output  \n",
       "age                 \n",
       "29       2       1  \n",
       "34       4       2  \n",
       "35      10       2  \n",
       "37       4       2  \n",
       "38       7       2  \n",
       "39       9       3  \n",
       "40       9       1  \n",
       "41      20       9  \n",
       "42      16       7  \n",
       "43      20       5  \n",
       "44      22       8  \n",
       "45      18       6  \n",
       "46      17       4  \n",
       "47      10       3  \n",
       "48      17       4  \n",
       "49      11       3  \n",
       "50      17       4  \n",
       "51      28       9  \n",
       "52      28       9  \n",
       "53      17       6  \n",
       "54      39      10  \n",
       "55      20       3  \n",
       "56      25       5  \n",
       "57      40       7  \n",
       "58      46       7  \n",
       "59      32       5  \n",
       "60      29       3  \n",
       "61      20       1  \n",
       "62      27       4  \n",
       "63      21       3  \n",
       "64      23       6  \n",
       "65      18       4  \n",
       "66      13       4  \n",
       "67      22       3  \n",
       "68      11       2  \n",
       "69       7       2  \n",
       "70      10       1  \n",
       "71       6       3  \n",
       "74       2       1  \n",
       "76       2       1  \n",
       "77       2       0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "data.groupby('age').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_10552\\628779518.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.set_index except for the argument 'keys' will be keyword-only\n",
      "  data.set_index('sex' , 1)\n"
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       "     age  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  caa  \\\n",
       "sex                                                                           \n",
       "1     63   3     145   233    1        0       150     0      2.3    0    0   \n",
       "1     37   2     130   250    0        1       187     0      3.5    0    0   \n",
       "0     41   1     130   204    0        0       172     0      1.4    2    0   \n",
       "1     56   1     120   236    0        1       178     0      0.8    2    0   \n",
       "0     57   0     120   354    0        1       163     1      0.6    2    0   \n",
       "..   ...  ..     ...   ...  ...      ...       ...   ...      ...  ...  ...   \n",
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       "0     57   1     130   236    0        0       174     0      0.0    1    1   \n",
       "\n",
       "     thall  output  \n",
       "sex                 \n",
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       "[303 rows x 13 columns]"
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     },
     "execution_count": 56,
     "metadata": {},
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    "data.set_index('sex' , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.drop('city', axis='columns')\n",
    "# data.drop('chol', axis=1, inplace=True)     # inplace=Ture: data is removed in current dataset, data is permanently removed\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
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     "execution_count": 61,
     "metadata": {},
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   "source": [
    "pd.get_dummies(data['slp'])"
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  },
  {
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   "execution_count": 149,
   "metadata": {},
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       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalachh</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exng</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slp</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caa</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thall</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>output</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp    trtbps      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",
       "trtbps    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",
       "thalachh -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exng      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",
       "slp      -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "caa       0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thall     0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "output   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg  thalachh      exng   oldpeak       slp       caa  \\\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",
       "trtbps   -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",
       "thalachh  0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exng     -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",
       "slp       0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "caa      -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thall    -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "output    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "             thall    output  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trtbps    0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalachh -0.096439  0.421741  \n",
       "exng      0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slp      -0.104764  0.345877  \n",
       "caa       0.151832 -0.391724  \n",
       "thall     1.000000 -0.344029  \n",
       "output   -0.344029  1.000000  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Corr=data.corr()\n",
    "Corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x21e1ae14a90>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2520x2520 with 210 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist('cp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.pie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.barplot(x='cp', y='output', data=data)\n",
    "sns.barplot('cp', 'output', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='age', x='exng', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cp', ylabel='count'>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='cp', hue='sex', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='age', y='trtbps', hue='sex', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcPklEQVR4nO3deXxU5b3H8c9zziTDPmxhES2DuAUYS4UqiBAVe70YrbvX3lZHrYCguC9Te7G5XTRtrRttta3Vxtpaq7QqjnWtsghURYEDZBTRIKsJkIRACElmzv3jDL2UBswkZ+Y5M/N7v155JSRzzvNj+fI8c85znkfZto0QwnsM3QUIIdom4RTCoyScQniUhFMIj5JwCuFREk4hPErCKYRHSTiF8CgJpxAeJeEUwqN8ugsQor2WLVs2wOfzPQqMIvs6lgSwqrW19eoxY8ZUt+cACafIGj6f79FBgwYVFxUV1RqGkVWTwhOJhKqpqRmxdevWR4Gvt+eYbPvfR+S3UUVFRTuzLZgAhmHYRUVF9Ti9fvuOSWM9QrjNyMZg7pOsvd2Zk3AK4VHynlNkrWAkOsbN81WVly77otdcfPHFwTfeeCPQr1+/1rVr164GWLx4cdcZM2YM3bt3r+Hz+ew5c+asP+200xo7W4/0nEKk4Kqrrtr2wgsvrN3/e7fddtvh3/3udzfHYrE1s2fP3nzHHXcc4UZbEk4hUjBlypRdRUVFrft/TylFfX29CVBXV2cOHDiw2Y22ZFgrRCc99NBDG0pLS4+ePXv2EYlEgkWLFsXcOK/0nMI1SqnHlFLVSqlV+31vtFJqqVJquVLqPaXUiTprTIeHHnqo6J577tmwdevWlXffffeGK664IujGeSWcwk2/A/7zgO/9BPhf27ZHA3clf51T5s6d2+/yyy+vA7jqqqtqV65c2d2N80o4hWts214A7Djw20Cv5NcBYHNGi8qAoqKilpdeeqknwLx583oOHTq0yY3zyntOkW43Aq8ope7F6QxOduvE7bn14bZzzjln2NKlS3vW1tb6Bg4ceHwkEtn88MMPr7/55puPuOWWW5Tf70888sgj691oS8Ip0m0GcJNt23OVUpcAvwXO0FxTh82bN+/Ttr6/evXqSrfbkmGtSLcw8Jfk188AOXdBKF0knCLdNgMlya9PB9Ye4rViPzKsFa5RSj0FnAr0V0ptBL4HTAUeVEr5gCZgmr4Ks4uE08OCkagCgsCxwGFA/zY++gDmQU7RgnP1dFsbHxuAWFV5qWtXT23b/sZBfuTqHNh8IeH0iGAkOhznH/FxQHHy4xiga5rbrQc+BCqBWPLzO1XlpVvS2a74YhJODYKRqIHz0O0kYGLyY7CmcgI4F2n+5UJNMBJdBywEFgALq8pLP9ZQW16TcGZIMBIdjLM8RSlOGHtrLeiLDU9+XAEQjES3APOBF4FoVXlpnbbK9ikLuDtcLqtv133TIUOGhLp37x43DAOfz2evWrXK9dsoIOFMq2AkOhS4BLgQp2dSeivqlMHApcmPlmAkOh94Fni2qrx0u9bKNJg/f/5HgwcPbv3iV3achNNlwUi0J/At4HLgJLI7kAdTgDOR4Azg58FI9A2cebV/qSovdeVxKSHhdE0wEh0FzMQJZk/N5WSSDzgz+fF5MBL9LfCrqvLSz/SWlV6TJ08+WinFlVdeWXPrrbduS0cbEs5OCEaiBThD1pk47yPz3UDgTuCOYCQaBX4JvFpVXpq1i3K1ZdGiRbFhw4a1bNq0yXf66acfM3LkyKYpU6bscrsdmSHUAcFI1B+MRK8DqoCnkGAeyMS5+PUysDoYif5X8p5tThg2bFgLwJAhQ1pLS0vrlixZ4sojYgeScKYgGIkWBCPR6ThT0ObgTAwQh1YM/AlYEYxEL8j2kO7cudOora019n395ptv9jr++OP3pKMtGda2QzAS9eFc4JmNM2NHpC4EzAU+CEaid1WVl77Y6TO289aHmzZu3Og7//zzjwKIx+Pqwgsv3H7RRRftTEdbEs4vEIxEvwb8HGe2jui8rwDzgpHoYmBmVXnpCt0FpWLEiBHNH3744ZpMtCXhPIhgJHoYcD/OfUrhvpOBZcFIdA5wV1V5aYPugrxG3nMeIBiJmsFI9AacOaYSzPQycVZKqAxGohdrrsVzJJz7CUaiY4F3gQf4/3VvRPoNAf4cjET/FoxEh+kuxisknDgT0YOR6HeAJTjviYQe/wksD0ai/627EC/I+3AmJ6S/CtyNvAf3gl7AH4KR6O+CkWgP3cXolNfhDEaipcBKYLLuWsS/CeNcMDpBdyG65GVPkZx291PgBt21iEM6BlgSjEQjVeWl9x/4w1BFyNVHxqyw1aFdxkpLS49ct25dF4CGhgazZ8+e8Vgs1unbLXnXcwYj0T7AK0gws0UhcF8wEn0cDzzh09YuY9Fo9JNYLLYmFoutOeuss2rPPvvsWjfayqtwJpcCWQKcprsWkbIrapviA1rjiYOtl5QRbe0ytk8ikWDevHl9w+Hwgaved0jehDMYiU4AluIsliWyUEucLh9X7ypuaon7ddfSlldeeaVH//79W0Kh0F43zpcX4Uxemn8DZ7U6kcWa4wn/uppdxzU0tXjuSu6TTz7Z98ILL3Sl14Q8uCAUjERvxJmGJ3JEPGH7qrY3emquc0tLCy+//HKfd955x7V5tzndcwYj0ZuQYOYk27a1Xxza3/PPP9/ryCOPbBo+fHiLW+fM2Z4zGcz7dNch0ucPkxeiUPYRfbuu692tsD4Tbba1y9hNN9207amnnup78cUXuzakhRwNpwQzf9jYasOOPcOBjAT0YLuMzZ07t8rttnJuWCvBzD/7Alrb2BzQXYubciqcwUg0jAQzL9nYamPtnuFevIrbUTkTzmAkWgL8WncdQh/bttVnOxqHe/U+aKpyIpzBSPQYnA1aC3XXIvSKJ2xf1bbdR+meSeSGrA9nMBLtB0SBvrprEd7QHE90qdreODzhsdstqcrqcAYjUT/wHHCU5lKExzQ2t/bcsKNxqO46OiPbb6X8AjhFdxFCj4Izxh/y543QrxL6tfd8xbFK2WXMDcFI9FvAt3XXIfJTJnYZy8phbfIC0MO66xAinbIvnGWBwo/93/rtNPPFrFqMWOSWyZMnHz1y5Mjie++9N21POmXjsPYHPpU45c6CP3K+ufDti5rLvrybrjlz41l4n+wy1payQAlw675fFhsbJnzgn77tqyqWljfkQrRFdhk7UFmgC/AYB9RcqFqDfy78/vAf+R6dD3ZO7QMpvEd2GWtbBDiyrR8oReE3fX8v+Zr5/ntn7/3R0Gr6FGW4NqFBy+tLOnTc4X26fdK3e2GHFuGSXcYOVBY4Erjji142QNWNXeK/ruaGluuWvZgY7+qyiSJ3bK1vOiLQtaDeNFQi1WMzuctYtgxrHwS6tOeFprKL5hTMOaGioHy+j1bXnkoXuaM1kSjYUr/H8xsfez+cZYFzgLNTOUQpVIm5smS5f9ra4WrT+jRVJrJY7e6WAY3NrV1113Eo3g6ncxHowY4e3kM1jXi98LZ+M83n33axKqGJjY3t0jU/G1ttqt2T0bm3iURCAe0eSns7nHAb0Kkt4ZSix+0FT094pfD2t7uzRzZozWLr61pobdzpWkD3tMS712Vo9YREIqFqamoCwKr2HqPc+o26riwQANYDrv3hNdvm+m8137n7Hbt4hFvnFJnTy28w66Q+DO1dgHJpZwafQXPfruYWV052aAlgVWtr69Vjxoypbs8BXg7n/wA/cPu0tk3L0/FT3460Ti0BldXP+wnXXFBVXvpX3UUcyJvhLAv0AKpI4XGfVFXbgWXn7P3REZ/Td0C62hBZYyUwuqq81FNh8Op7zpmkMZgAA1T9mCX+Wepc4+330tmOyArHAxfpLuJA3us5ywLdgE+BjPRoto39dmLUgitbbh/fgk/WIMpfa4BRXuo9vdhzTiNDwQTnnugp5qqS5f6p645WG6sy1a7wnBHA13QXsT9vhbMsYLLfUyeZ1F3tLX618Pb+s8y/yD3R/DVTdwH781Y4YQowRFfjStHjloJnJ7xWeKvcE81PZwcj0SN0F7GP18J5le4CAI42Nk9Y7p9WO85YvVp3LSKjTGC67iL28c4FobLAAGAjUKC7lH1sm5Zn45MW39Y6fZLcE80bnwNfqiovbdZdiJd6zsvwUDABlKLgYt+Ckvf8Mz4YxI7PddcjMmIgcKHuIsDlcCqlqpRSllJquVIq1fuHnhjStqW/2nnCYv8s83xj4bu6axEZcZnuAsDlYa1SqgoYa9v2tpQOLAuMAzr2WHsG2Tb20sSIhZe3RMbJPdGc1gwMqCovzciGvAfjlWHt5boLaA+lUOPNNZNW+Kd+cqz6rM1NVEVOKATO0V2E2+G0gVeVUsuUUtPadURZQAHnulxHWnVTe497uTAy4Ebfs4t01yLSRvt0PreHtUNs296klBoAvAbMsm17wSEPKguMBbL2vdy6xODF5zb/YNQuuvXSXYtwVRNQVFVe6vp6tO3las9p2/am5Odq4K/Aie04TPvwoTOGG1tO/sA/vX6CsardD9GKrNAFOEtnAa6FUynVXSnVc9/XwH/Qvqe+tf4BuKFAxY94suDu4+4r+OV8RSLlFd2EZ52ns3E3e86BwCKl1ArgHSBq2/bLhzyiLNAPOMHFGrRRCt8F5qKS9/wzVgxm+1bd9QhXlOhsXO8MobLAJcDT+gpIj4Sttt/eOm3ds/GS9gzrhbcdVVVeuk5Hw7pvpZyhuf20MJTd796CX534dOH3FxTSsld3PaJTJulqWHc4x2luP61OMmKTlvunVh2nPvtEdy2iwybqalhfOJ01aYu1tZ8h3VTzsX8rjAy62fdnuSeanfIwnM66LdmxV0snKUW3633PnfL3wpuX9GS31ilhImVHBSPRwToa1hnOnLhKm4ojja3jP/BPbzjFsCzdtYiUjNXRqIQzw3wqcfjvC+4pfqDg53JPNHtoefulM5xf0di2VkrhO89cXLLMP2PlYWzLxGrjonPyKJxlAR8Q0tK2h/RVDaMX+W/wX2K++Y7uWsQhHaejUV0953DAr6ltTzGU3fcnBb858ZnC/13gp7lJdz2iTXnUc8Lhmtr1rK8aH05a7p/2WbFar2U2ijikgI4rtrrCqW35Sy/rqpqPeanwO4Nv8z29UHct4t9kfGgr4fQYpeh2re/5ifMLb1waYFed7nrEP2X836yE06OGGtXjlvmv2X2qsXyl7loEAEWZblDC6WE+lRjyeMFPRj5UMEfuierXP9MNSjg9TinMr5tLSt73X7NyCDVyT1SfvAnnYZrazVp91K7RC/03dPmG+cY/dNeSp/JmWNtDU7tZzVD0uafgtyfNLfye3BPNvLzpOfPiaZR0GWOsnbTCP3XDSPXpx7prySNp3Wm9LbrC6ak9UbJRF9Vy9IuF3x0S8f3x0EuPCrdkfEab9JxZTCm6XuN7cdKCwhvknmj6mZluMPPhdFZ41708Sk75klEzbpn/msbTjA9W6K4lh2U8nJlffa8sUAjIolft0KBUQ61p7txuGg01prmn2mc21Zi+1hrTiG83TVVrmma9YRTsNlSXJqW6tSjVY9ZzxqrxsZYTANlP1EW2MraMqlw9KpNt6hhe5tWQNgGJesOo32EaO7eZ5q5kyJprTLO1xjTt7aap6kzDbDCMwkZldG0yVPdW6GlDb5xFunum0t6D59uDq99SC89bYo9XefZnnVZ2Ymemm5S/vHZqhuZa06zbYRoN20yzMRmylmTI2GGaqt4wChoMw7/HUF2blerRCr2AXijVB+iTqVqfOtWcuG5w4oNb/pIYpqB3ptrNca2ZblDPotJlgTia3nfuVmp3rWnUb3d6scYa02yqcUKW2Gaa1JqGUW8YBbsMY99QsWccAijVTUe9nXF4jf3pjx+Pq4I4Qd215IAPi2OVGX0yRVfP2QAEOnqwDfZOw9i5wzTq9w0Va0xzb7XPGSruME1qDcO30zQKGpXq2qSMbq2KXgknZN2B7u79VrxrY5EaNn2WWXf/r+MfBBrzd1kYl2R81URd4dwJBFqhtc406nYYZsM2n7m72glZc7XPjG8zTXu7aag6w/Qlh4pd9g0VbSdkAToR8Hyxq6vqPX2WGfrhE/GFR23RtwZrDvg80w1qCefYoYd/vFepPijVA2daVManRuWThKF8d17hm3jVK/EFZ75vT1AabgvkgOpMN6jlfd9ew0gkgyky6LEzzUm/ONt433ZGLiI1Ge85dU0GqNHUbt5bEDK+GrnSrG412Ki7liyTHz0nIM8lavTpIHXUjOvMLrv8yCoL7Zc34ZQV5jSr7676T7vePPazIt7WXUuWyJth7Uea2hX7afUp/61X+ybMH6XeskHjLspZYXOmG9QVzrWa2hVt+MU55qmPnmn8w4ZG3bV4VDOQ8WdndYXzM2Tyu6e8doIxbvZl5mdxJdcD2lBZHKvM+PQ9LeG0wlYCkN2ePeajw9Vx1840jT2FrNFdi8do2bJR53OV8r7Tg3b0UgOnXm8O29yHJbpr8ZC8C+cyjW2LQ2guUF1vnG6OW3qsekt3LR6Rd+GU/5m9TCl13wXmqU+cbiy2Id9X+su7cP4DkFXMPe7Fk4yTf3ip8XFC5e2srtriWKWW2VTawmmFrQZgta72RftZw4xR119jNu/15eV1Am2TNHQvtLVUc/uinap7qyFTbzAP29aLfNuF+3VdDesOp7zvzCJNharHtTPNscuH5dWFotd0Naw7nH/X3L5Ika2Ucfel5qnPnKIW2tCiu54021wcq9R2z1drOK2wtR5NV8JE5zwz0Zz444uMNQnYobuWNNI2pAX9PSfAPN0FiI55/2jjyzdPMxuazZx9ykjbkBYknKKTNvdTQ6ddb/av7Z6Tk0ryvud8Bw0Psgr3NHZRgRnXmaNjh5NLmyq9Xxyr3KqzAO3hTE6Cj+quQ3ROwlDmXZf5Js07US2wNSzAnAZ/0l2A9nAmPau7AOGO3082Jz1wnrHC1rDOq4tsJJz/9AoanjQX6bGk2Bhz27fN7a0G63XX0kFvF8cqN7TnhUqpKqWUpZRarpR6z80iPBFOK2zFgSd01yHc89kAdeT0WWbPhq4s111LB6T6b/E027ZH27Y91s0iPBHOpMd0FyDc1dBN9Z02yxz5ySAW6q4lBY14YEgLHgqnFbbWQlb9JYp2iJuqIHKlb+Jro9V8OzueQnq2OFbZkMLrbeBVpdQypdQ0NwvxTDiTpPfMUb+ZYpY8fJbxng27dNfyBX6T4utPsW37BGAKcK1SapJbhXgtnH8GtusuQqTHW182TrwzbG6OKzbpruUgFhfHKhelcoBt25uSn6uBvwInulWMp8Jpha1GYI7uOkT6rDtMHTPjOrNwt9+Tc6p/nMqLlVLdlbP7OMrZWvI/gFVuFeOpcCbNwftDH9EJdT1U0dTrzWM29vPUavNrSH0q6UBgkVJqBc5Mt6ht2y+7VZCena2/QKgi9DPgZt11iPS7/vn4WxPW2CUKlOZSriyOVf5Ocw3/wos9J8B9OKtsixz30LnmqY9/zVhqwx6NZWwA/qCx/TZ5MpxW2NoE/F53HSIzXh5rjC/7pvlpXKFrovn9xbFKzz047slwJv0Q2bIhb1R+SY24boZpNxVQmeGmtwC/znCb7eLZcFphqwp4QHMZIoO2B9Tgq28wh37eO6MLv32nOFa5O4PttZtnw5l0N/KsZ15pLlDdZl1jnvTu0RlZROwdPDyn25NXa/cXqghNBx7RXYfIvPMWJ97+xvzEWAX+NJzeBk4ujlV6dnlWr/ecAI/i4o1dkT2eO9mYcPclxocJ2JaG0//Ry8GELAhn8nGym3TXIfRYMdw4/obpZtNen6sbLu8G7nDxfGnh+XACWGHrdeB3uusQenzeVx0+7Xpz0PaevOvSKcuLY5Vend/7T1kRzqQbwbMTpkWa7fGrnjOvNcdYQ9X8Tp7qfVKcQ6uL5y8I7S9UEZoCvKS7DqHXpfPjC89fbI9TUJDioXuAMcWxykzfS+2QbOo5scLW34DHddch9PpTiTnx3guMVTbUpnjo7dkSTMiycCbdBGjZL1F4x7vHGl+55WqzvsXk03Ye8grwi3TW5LasGtbuE6oInYqzGrepuRShWfc9dv0Dv46vCzRywiFeth0IFccqt2SqLjdkY8+JFbbeAu7SXIbwgN1dVWD6LPP4jw475Grz07MtmJCl4Uy6B1kpXgAJQ/n+J+yb9Lcxar4N8QN+PKc4VjlXS2GdlJXD2n1CFaHewLvAUZpLER5xyurEe7NeSByjoBfO/q9nFscqs3J7iKwOJ0CoIjQC+AfQQ3ctwhuCW+11338yvrVLC+cWxyqzdsG4rA8nQKgiVAo8B/g0lyK8oR442Qpb2naldkM2v+f8JytsRYGrddchPKEFuDDbgwk5Ek4AK2xVkAWTmUVa2cBUK2y9obsQN+RMOAGssPUTnMXBRH6akfxPOifkVDiTbgWe1F2EyLjrrbD1K91FuCnnwmmFLRu4EgloPrnVCls5t1NAzoUTwApbrcDlyPIm+eBOK2z9THcR6ZATt1IOJVQR+inOUFfkFhu4xQpb9+suJF1yPpwAoYrQbOD7uusQrtkLXGaFrWd0F5JOeRFOgFBF6FqcdXBlokJ2qwXOtcJWzm+0nDfhBAhVhCbj7AHaV3ctokPWA1OssJU1D0x3Rk5eEDqY5M3prwKrddciUrYYGJ8vwYQ8CyeAFbY+AcYDz+uuRbTbfUCJFbay7pnMzsirYe3+QhUhBczGeWhbVlTwpjrgSitsPae5Di3yNpz7hCpC43C2G5RnQr3lfeDi5EgnL+XdsPZAVthaCowGfqO5FOGIAz/FeeQrb4MJ0nP+i1BF6Os4e7MU6a4lT60ErrLC1jLdhXhB3vec+7PC1gvASJy1ceV/rcxpxnnvP1aC+f+k5zyIUEVoPPBzOOSSi6LzFuM8g5n1D0e7TcJ5CKGKkAFMA36ETFxw28fAd6yw9azuQrxKwtkOoYpQP5xh1zSgi+Zyst02nHnOj1hhq0V3MV4m4UxBqCJ0GM5SKBLS1DUCDwLlVtjaqbuYbCDh7IBQRWgwTkinIyH9ItXAHOCXVtjaobuYbCLh7IRkSGfirPw3SHM5XvMR8DPgCStsNekuJhtJOF0QqggVAOcBM4DT9FajVRxng6lHgBessJXQXE9Wk3C6LFQROg64Bvgm0F9zOZmyGqgA/mCFrc26i8kVEs40CVWEfMDpwH/h9Kq5ditmC/AMzrBVJg6kgYQzA5JBPQUnpFOAY7QW1DEJnMnoLwHzgGXJlQ5Fmkg4NQhVhAYBk4CS5OeRgNJa1L9rASycXdzeBF63wlbWbgqUjSScHpCc5HAyMAonqCOB48jcbZq9wDrgPZwwvguskKusekk4PSo5dfBInKAeAQzEuV2z/+c+QEHy48AHxm1gN7ATZ9etfZ8/Bz4BPk1+/gTYLENU75FwpkAp9RhwNlBt2/ao5PeeBo5NvqQ3UGfb9uhM15Zc2cGX/DCBRrmVkd0knClQSk0CdgFP7AvnAT//GVBv27askSs6TdZwTYFt2wuUUsG2fqaUUsAlOLdPhOg0edjaPROBz23bXqu7EJEbJJzu+QbwlO4iRO6QYa0LlFI+4AJgjO5aRO6QntMdZwAx27Y36i5E5A4JZwqUUk8BS4BjlVIblVLfTv7oUmRIK1wmt1KE8CjpOYXwKAmnEB4l4RTCoyScQniUhFMIj5JwCuFREk4hPErCKYRHSTiF8CgJpxAeJeEUwqMknEJ4lIRTCI+ScArhURJOITxKwimER0k4hfAoCacQHiXhFMKjJJxCeNT/AcSvoT5hSVQOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "di=data.groupby('cp').sum()\n",
    "plt.pie(di['fbs'], labels=di['fbs'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='cp'>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='age', y='cp', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d406864f2ee78ecc67afccccd0a48c216f41ca3a0f9ca739a0b9d8b15ae422ba"
  },
  "kernelspec": {
   "display_name": "Python 3.10.1 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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  "orig_nbformat": 4
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