Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da3ca6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "741c1f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(np.arange(0,40).reshape(10,4),index=['Row1','Row2','Row3','Row4','Row5','Row6','Row7','Row8','Row9','Row10'],columns=['Column1','Column2','Column3','Column4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa55b4bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column1</th>\n",
       "      <th>Column2</th>\n",
       "      <th>Column3</th>\n",
       "      <th>Column4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column1  Column2  Column3  Column4\n",
       "Row1        0        1        2        3\n",
       "Row2        4        5        6        7\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15\n",
       "Row5       16       17       18       19"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "afe2ba57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Column1</th>\n",
       "      <th>Column2</th>\n",
       "      <th>Column3</th>\n",
       "      <th>Column4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row6</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row7</th>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row8</th>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row9</th>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row10</th>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Column1  Column2  Column3  Column4\n",
       "Row6        20       21       22       23\n",
       "Row7        24       25       26       27\n",
       "Row8        28       29       30       31\n",
       "Row9        32       33       34       35\n",
       "Row10       36       37       38       39"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dcfa395b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data_file_pd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b27dc55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column1    0\n",
       "Column2    1\n",
       "Column3    2\n",
       "Column4    3\n",
       "Name: Row1, dtype: int32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Accessing the elements\n",
    "\n",
    "df.loc['Row1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65db818e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column4</th>\n",
       "      <th>Column4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>Row4</th>\n",
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       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row6</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row7</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row8</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row9</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row10</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Column4  Column4\n",
       "Row1         3        3\n",
       "Row2         7        7\n",
       "Row3        11       11\n",
       "Row4        15       15\n",
       "Row5        19       19\n",
       "Row6        23       23\n",
       "Row7        27       27\n",
       "Row8        31       31\n",
       "Row9        35       35\n",
       "Row10       39       39"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Column4', 'Column4']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f8f4fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check the type\n",
    "## 1. loc (It is used to access the row index)\n",
    "## 2. iloc(index location is used to access the row as well as column index)\n",
    "\n",
    "type(df.loc['Row1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb70c005",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Row4</th>\n",
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       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>Row6</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
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       "      <th>Row7</th>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row8</th>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row9</th>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row10</th>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Column1  Column2  Column3  Column4\n",
       "Row1         0        1        2        3\n",
       "Row2         4        5        6        7\n",
       "Row3         8        9       10       11\n",
       "Row4        12       13       14       15\n",
       "Row5        16       17       18       19\n",
       "Row6        20       21       22       23\n",
       "Row7        24       25       26       27\n",
       "Row8        28       29       30       31\n",
       "Row9        32       33       34       35\n",
       "Row10       36       37       38       39"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07cf6a34",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column2</th>\n",
       "      <th>Column3</th>\n",
       "      <th>Column4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>Row3</th>\n",
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       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row6</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row7</th>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row8</th>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row9</th>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row10</th>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Column2  Column3  Column4\n",
       "Row1         1        2        3\n",
       "Row2         5        6        7\n",
       "Row3         9       10       11\n",
       "Row4        13       14       15\n",
       "Row5        17       18       19\n",
       "Row6        21       22       23\n",
       "Row7        25       26       27\n",
       "Row8        29       30       31\n",
       "Row9        33       34       35\n",
       "Row10       37       38       39"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Take the elements from the Column2\n",
    "df.iloc[:,1:]   ## row +1:n Column +1:n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b16e7be6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>18</td>\n",
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      ],
      "text/plain": [
       "      Column3\n",
       "Row3       10\n",
       "Row4       14\n",
       "Row5       18"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:5,2:3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c3daefe",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Column3</th>\n",
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       "      <th>Row4</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row6</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row7</th>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row8</th>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row9</th>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column2  Column3\n",
       "Row4       13       14\n",
       "Row5       17       18\n",
       "Row6       21       22\n",
       "Row7       25       26\n",
       "Row8       29       30\n",
       "Row9       33       34"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:9,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "40b1465c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 5,  6,  7],\n",
       "       [ 9, 10, 11],\n",
       "       [13, 14, 15],\n",
       "       [17, 18, 19],\n",
       "       [21, 22, 23],\n",
       "       [25, 26, 27],\n",
       "       [29, 30, 31],\n",
       "       [33, 34, 35],\n",
       "       [37, 38, 39]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert Dataframes into array\n",
    "df.iloc[:,1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8995c85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,:].values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d80227de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "4     1\n",
       "8     1\n",
       "12    1\n",
       "16    1\n",
       "20    1\n",
       "24    1\n",
       "28    1\n",
       "32    1\n",
       "36    1\n",
       "Name: Column1, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#used in checking the unique values in a column\n",
    "df['Column1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0d98596c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column1    0\n",
       "Column2    0\n",
       "Column3    0\n",
       "Column4    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "71e1d229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4,  8, 12, 16, 20, 24, 28, 32, 36])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Column1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7cbe7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Accessing the different files in pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "99d23498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Chest pain type</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Slope of ST</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>Thallium</th>\n",
       "      <th>Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>322</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>128</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  Chest pain type   BP  Cholesterol  FBS over 120  EKG results  \\\n",
       "0     70    1                4  130          322             0            2   \n",
       "1     67    0                3  115          564             0            2   \n",
       "2     57    1                2  124          261             0            0   \n",
       "3     64    1                4  128          263             0            0   \n",
       "4     74    0                2  120          269             0            2   \n",
       "..   ...  ...              ...  ...          ...           ...          ...   \n",
       "265   52    1                3  172          199             1            0   \n",
       "266   44    1                2  120          263             0            0   \n",
       "267   56    0                2  140          294             0            2   \n",
       "268   57    1                4  140          192             0            0   \n",
       "269   67    1                4  160          286             0            2   \n",
       "\n",
       "     Max HR  Exercise angina  ST depression  Slope of ST  \\\n",
       "0       109                0            2.4            2   \n",
       "1       160                0            1.6            2   \n",
       "2       141                0            0.3            1   \n",
       "3       105                1            0.2            2   \n",
       "4       121                1            0.2            1   \n",
       "..      ...              ...            ...          ...   \n",
       "265     162                0            0.5            1   \n",
       "266     173                0            0.0            1   \n",
       "267     153                0            1.3            2   \n",
       "268     148                0            0.4            2   \n",
       "269     108                1            1.5            2   \n",
       "\n",
       "     Number of vessels fluro  Thallium Heart Disease  \n",
       "0                          3         3      Presence  \n",
       "1                          0         7       Absence  \n",
       "2                          0         7      Presence  \n",
       "3                          1         7       Absence  \n",
       "4                          1         3       Absence  \n",
       "..                       ...       ...           ...  \n",
       "265                        0         7       Absence  \n",
       "266                        0         7       Absence  \n",
       "267                        0         3       Absence  \n",
       "268                        0         6       Absence  \n",
       "269                        3         3      Presence  \n",
       "\n",
       "[270 rows x 14 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('Heart_Disease_Prediction-Copy1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "56a8b3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## comma seperated value\n",
    "df=pd.read_csv('Heart_Disease_Prediction-Copy1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6b6c2bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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>Chest pain type</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Slope of ST</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>Thallium</th>\n",
       "      <th>Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>322</td>\n",
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       "      <td>109</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Presence</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
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       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>128</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  Chest pain type   BP  Cholesterol  FBS over 120  EKG results  \\\n",
       "0   70    1                4  130          322             0            2   \n",
       "1   67    0                3  115          564             0            2   \n",
       "2   57    1                2  124          261             0            0   \n",
       "3   64    1                4  128          263             0            0   \n",
       "4   74    0                2  120          269             0            2   \n",
       "\n",
       "   Max HR  Exercise angina  ST depression  Slope of ST  \\\n",
       "0     109                0            2.4            2   \n",
       "1     160                0            1.6            2   \n",
       "2     141                0            0.3            1   \n",
       "3     105                1            0.2            2   \n",
       "4     121                1            0.2            1   \n",
       "\n",
       "   Number of vessels fluro  Thallium Heart Disease  \n",
       "0                        3         3      Presence  \n",
       "1                        0         7       Absence  \n",
       "2                        0         7      Presence  \n",
       "3                        1         7       Absence  \n",
       "4                        1         3       Absence  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a5863640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 270 entries, 0 to 269\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Age                      270 non-null    int64  \n",
      " 1   Sex                      270 non-null    int64  \n",
      " 2   Chest pain type          270 non-null    int64  \n",
      " 3   BP                       270 non-null    int64  \n",
      " 4   Cholesterol              270 non-null    int64  \n",
      " 5   FBS over 120             270 non-null    int64  \n",
      " 6   EKG results              270 non-null    int64  \n",
      " 7   Max HR                   270 non-null    int64  \n",
      " 8   Exercise angina          270 non-null    int64  \n",
      " 9   ST depression            270 non-null    float64\n",
      " 10  Slope of ST              270 non-null    int64  \n",
      " 11  Number of vessels fluro  270 non-null    int64  \n",
      " 12  Thallium                 270 non-null    int64  \n",
      " 13  Heart Disease            270 non-null    object \n",
      "dtypes: float64(1), int64(12), object(1)\n",
      "memory usage: 29.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1b2276e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120    34\n",
       "130    31\n",
       "140    30\n",
       "110    17\n",
       "150    17\n",
       "160    11\n",
       "125    10\n",
       "138     9\n",
       "112     9\n",
       "128     9\n",
       "118     7\n",
       "132     6\n",
       "135     6\n",
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       "124     5\n",
       "145     5\n",
       "134     4\n",
       "100     4\n",
       "152     4\n",
       "180     3\n",
       "136     3\n",
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       "105     3\n",
       "126     3\n",
       "122     3\n",
       "142     3\n",
       "94      2\n",
       "102     2\n",
       "178     2\n",
       "170     2\n",
       "104     1\n",
       "200     1\n",
       "155     1\n",
       "101     1\n",
       "129     1\n",
       "123     1\n",
       "192     1\n",
       "117     1\n",
       "158     1\n",
       "146     1\n",
       "156     1\n",
       "165     1\n",
       "174     1\n",
       "144     1\n",
       "106     1\n",
       "148     1\n",
       "172     1\n",
       "Name: BP, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the unique category counts\n",
    "df['BP'].value_counts()   ## inside df put the name of rows and column you want to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5df61637",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "      <th>BP</th>\n",
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       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
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       "      <th>Slope of ST</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Age, Sex, Chest pain type, BP, Cholesterol, FBS over 120, EKG results, Max HR, Exercise angina, ST depression, Slope of ST, Number of vessels fluro, Thallium, Heart Disease]\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
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   "source": [
    "df[df['Sex']>4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "803c04b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_12472\\1134722465.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()\n"
     ]
    },
    {
     "data": {
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       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Chest pain type</th>\n",
       "      <th>BP</th>\n",
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       "      <th>Thallium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.094401</td>\n",
       "      <td>0.096920</td>\n",
       "      <td>0.273053</td>\n",
       "      <td>0.220056</td>\n",
       "      <td>0.123458</td>\n",
       "      <td>0.128171</td>\n",
       "      <td>-0.402215</td>\n",
       "      <td>0.098297</td>\n",
       "      <td>0.194234</td>\n",
       "      <td>0.159774</td>\n",
       "      <td>0.356081</td>\n",
       "      <td>0.106100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>-0.094401</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034636</td>\n",
       "      <td>-0.062693</td>\n",
       "      <td>-0.201647</td>\n",
       "      <td>0.042140</td>\n",
       "      <td>0.039253</td>\n",
       "      <td>-0.076101</td>\n",
       "      <td>0.180022</td>\n",
       "      <td>0.097412</td>\n",
       "      <td>0.050545</td>\n",
       "      <td>0.086830</td>\n",
       "      <td>0.391046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chest pain type</th>\n",
       "      <td>0.096920</td>\n",
       "      <td>0.034636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043196</td>\n",
       "      <td>0.090465</td>\n",
       "      <td>-0.098537</td>\n",
       "      <td>0.074325</td>\n",
       "      <td>-0.317682</td>\n",
       "      <td>0.353160</td>\n",
       "      <td>0.167244</td>\n",
       "      <td>0.136900</td>\n",
       "      <td>0.225890</td>\n",
       "      <td>0.262659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BP</th>\n",
       "      <td>0.273053</td>\n",
       "      <td>-0.062693</td>\n",
       "      <td>-0.043196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173019</td>\n",
       "      <td>0.155681</td>\n",
       "      <td>0.116157</td>\n",
       "      <td>-0.039136</td>\n",
       "      <td>0.082793</td>\n",
       "      <td>0.222800</td>\n",
       "      <td>0.142472</td>\n",
       "      <td>0.085697</td>\n",
       "      <td>0.132045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cholesterol</th>\n",
       "      <td>0.220056</td>\n",
       "      <td>-0.201647</td>\n",
       "      <td>0.090465</td>\n",
       "      <td>0.173019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025186</td>\n",
       "      <td>0.167652</td>\n",
       "      <td>-0.018739</td>\n",
       "      <td>0.078243</td>\n",
       "      <td>0.027709</td>\n",
       "      <td>-0.005755</td>\n",
       "      <td>0.126541</td>\n",
       "      <td>0.028836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBS over 120</th>\n",
       "      <td>0.123458</td>\n",
       "      <td>0.042140</td>\n",
       "      <td>-0.098537</td>\n",
       "      <td>0.155681</td>\n",
       "      <td>0.025186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053499</td>\n",
       "      <td>0.022494</td>\n",
       "      <td>-0.004107</td>\n",
       "      <td>-0.025538</td>\n",
       "      <td>0.044076</td>\n",
       "      <td>0.123774</td>\n",
       "      <td>0.049237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EKG results</th>\n",
       "      <td>0.128171</td>\n",
       "      <td>0.039253</td>\n",
       "      <td>0.074325</td>\n",
       "      <td>0.116157</td>\n",
       "      <td>0.167652</td>\n",
       "      <td>0.053499</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.074628</td>\n",
       "      <td>0.095098</td>\n",
       "      <td>0.120034</td>\n",
       "      <td>0.160614</td>\n",
       "      <td>0.114368</td>\n",
       "      <td>0.007337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max HR</th>\n",
       "      <td>-0.402215</td>\n",
       "      <td>-0.076101</td>\n",
       "      <td>-0.317682</td>\n",
       "      <td>-0.039136</td>\n",
       "      <td>-0.018739</td>\n",
       "      <td>0.022494</td>\n",
       "      <td>-0.074628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.380719</td>\n",
       "      <td>-0.349045</td>\n",
       "      <td>-0.386847</td>\n",
       "      <td>-0.265333</td>\n",
       "      <td>-0.253397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exercise angina</th>\n",
       "      <td>0.098297</td>\n",
       "      <td>0.180022</td>\n",
       "      <td>0.353160</td>\n",
       "      <td>0.082793</td>\n",
       "      <td>0.078243</td>\n",
       "      <td>-0.004107</td>\n",
       "      <td>0.095098</td>\n",
       "      <td>-0.380719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274672</td>\n",
       "      <td>0.255908</td>\n",
       "      <td>0.153347</td>\n",
       "      <td>0.321449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST depression</th>\n",
       "      <td>0.194234</td>\n",
       "      <td>0.097412</td>\n",
       "      <td>0.167244</td>\n",
       "      <td>0.222800</td>\n",
       "      <td>0.027709</td>\n",
       "      <td>-0.025538</td>\n",
       "      <td>0.120034</td>\n",
       "      <td>-0.349045</td>\n",
       "      <td>0.274672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.609712</td>\n",
       "      <td>0.255005</td>\n",
       "      <td>0.324333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope of ST</th>\n",
       "      <td>0.159774</td>\n",
       "      <td>0.050545</td>\n",
       "      <td>0.136900</td>\n",
       "      <td>0.142472</td>\n",
       "      <td>-0.005755</td>\n",
       "      <td>0.044076</td>\n",
       "      <td>0.160614</td>\n",
       "      <td>-0.386847</td>\n",
       "      <td>0.255908</td>\n",
       "      <td>0.609712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109498</td>\n",
       "      <td>0.283678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <td>0.356081</td>\n",
       "      <td>0.086830</td>\n",
       "      <td>0.225890</td>\n",
       "      <td>0.085697</td>\n",
       "      <td>0.126541</td>\n",
       "      <td>0.123774</td>\n",
       "      <td>0.114368</td>\n",
       "      <td>-0.265333</td>\n",
       "      <td>0.153347</td>\n",
       "      <td>0.255005</td>\n",
       "      <td>0.109498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thallium</th>\n",
       "      <td>0.106100</td>\n",
       "      <td>0.391046</td>\n",
       "      <td>0.262659</td>\n",
       "      <td>0.132045</td>\n",
       "      <td>0.028836</td>\n",
       "      <td>0.049237</td>\n",
       "      <td>0.007337</td>\n",
       "      <td>-0.253397</td>\n",
       "      <td>0.321449</td>\n",
       "      <td>0.324333</td>\n",
       "      <td>0.283678</td>\n",
       "      <td>0.255648</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Age       Sex  Chest pain type        BP  \\\n",
       "Age                      1.000000 -0.094401         0.096920  0.273053   \n",
       "Sex                     -0.094401  1.000000         0.034636 -0.062693   \n",
       "Chest pain type          0.096920  0.034636         1.000000 -0.043196   \n",
       "BP                       0.273053 -0.062693        -0.043196  1.000000   \n",
       "Cholesterol              0.220056 -0.201647         0.090465  0.173019   \n",
       "FBS over 120             0.123458  0.042140        -0.098537  0.155681   \n",
       "EKG results              0.128171  0.039253         0.074325  0.116157   \n",
       "Max HR                  -0.402215 -0.076101        -0.317682 -0.039136   \n",
       "Exercise angina          0.098297  0.180022         0.353160  0.082793   \n",
       "ST depression            0.194234  0.097412         0.167244  0.222800   \n",
       "Slope of ST              0.159774  0.050545         0.136900  0.142472   \n",
       "Number of vessels fluro  0.356081  0.086830         0.225890  0.085697   \n",
       "Thallium                 0.106100  0.391046         0.262659  0.132045   \n",
       "\n",
       "                         Cholesterol  FBS over 120  EKG results    Max HR  \\\n",
       "Age                         0.220056      0.123458     0.128171 -0.402215   \n",
       "Sex                        -0.201647      0.042140     0.039253 -0.076101   \n",
       "Chest pain type             0.090465     -0.098537     0.074325 -0.317682   \n",
       "BP                          0.173019      0.155681     0.116157 -0.039136   \n",
       "Cholesterol                 1.000000      0.025186     0.167652 -0.018739   \n",
       "FBS over 120                0.025186      1.000000     0.053499  0.022494   \n",
       "EKG results                 0.167652      0.053499     1.000000 -0.074628   \n",
       "Max HR                     -0.018739      0.022494    -0.074628  1.000000   \n",
       "Exercise angina             0.078243     -0.004107     0.095098 -0.380719   \n",
       "ST depression               0.027709     -0.025538     0.120034 -0.349045   \n",
       "Slope of ST                -0.005755      0.044076     0.160614 -0.386847   \n",
       "Number of vessels fluro     0.126541      0.123774     0.114368 -0.265333   \n",
       "Thallium                    0.028836      0.049237     0.007337 -0.253397   \n",
       "\n",
       "                         Exercise angina  ST depression  Slope of ST  \\\n",
       "Age                             0.098297       0.194234     0.159774   \n",
       "Sex                             0.180022       0.097412     0.050545   \n",
       "Chest pain type                 0.353160       0.167244     0.136900   \n",
       "BP                              0.082793       0.222800     0.142472   \n",
       "Cholesterol                     0.078243       0.027709    -0.005755   \n",
       "FBS over 120                   -0.004107      -0.025538     0.044076   \n",
       "EKG results                     0.095098       0.120034     0.160614   \n",
       "Max HR                         -0.380719      -0.349045    -0.386847   \n",
       "Exercise angina                 1.000000       0.274672     0.255908   \n",
       "ST depression                   0.274672       1.000000     0.609712   \n",
       "Slope of ST                     0.255908       0.609712     1.000000   \n",
       "Number of vessels fluro         0.153347       0.255005     0.109498   \n",
       "Thallium                        0.321449       0.324333     0.283678   \n",
       "\n",
       "                         Number of vessels fluro  Thallium  \n",
       "Age                                     0.356081  0.106100  \n",
       "Sex                                     0.086830  0.391046  \n",
       "Chest pain type                         0.225890  0.262659  \n",
       "BP                                      0.085697  0.132045  \n",
       "Cholesterol                             0.126541  0.028836  \n",
       "FBS over 120                            0.123774  0.049237  \n",
       "EKG results                             0.114368  0.007337  \n",
       "Max HR                                 -0.265333 -0.253397  \n",
       "Exercise angina                         0.153347  0.321449  \n",
       "ST depression                           0.255005  0.324333  \n",
       "Slope of ST                             0.109498  0.283678  \n",
       "Number of vessels fluro                 1.000000  0.255648  \n",
       "Thallium                                0.255648  1.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e77bad35",
   "metadata": {},
   "outputs": [],
   "source": []
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