heart_disease.ipynb

{
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   "cell_type": "markdown",
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    "# Heart Disease\n",
    "###### Donated on 6/30/1988\n",
    "\n",
    "4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach\n",
    "\n",
    "Dataset Characteristics: Multivariate\n",
    "\n",
    "Subject Area: Health and Medicine\n",
    "\n",
    "Associated Tasks: Classification\n",
    "\n",
    "Feature Type: Categorical, Integer, Real\n",
    "\n",
    "\\#Instances: 303\n",
    "\n",
    "\\# Features: 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Dataset Information\n",
    "##### Additional Information\n",
    "\n",
    "This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them.  In particular, the Cleveland database is the only one that has been used by ML researchers to date.  The \"goal\" field refers to the presence of heart disease in the patient.  It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).  \n",
    "   \n",
    "The names and social security numbers of the patients were recently removed from the database, replaced with dummy values.\n",
    "\n",
    "One file has been \"processed\", that one containing the Cleveland database.  All four unprocessed files also exist in this directory.\n",
    "\n",
    "To see Test Costs (donated by Peter Turney), please see the folder \"Costs\" \n",
    "\n",
    "##### Has Missing Values?\n",
    "\n",
    "Yes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introductory Paper\n",
    "International application of a new probability algorithm for the diagnosis of coronary artery disease.\n",
    "By R. Detrano, A. Jánosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, V. Froelicher. 1989\n",
    "\n",
    "Published in American Journal of Cardiology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 14 attributes on this processed dataset:\n",
    "    age: age in years\n",
    "    sex: sex (1 = male; 0 = female)\n",
    "    cp: chest pain type\n",
    "        - Value 1: typical angina\n",
    "        - Value 2: atypical angina\n",
    "        - Value 3: non-anginal pain\n",
    "        - Value 4: asymptomatic\n",
    "    trestbps: resting blood pressure (in mm Hg on admission to the hospital)\n",
    "    chol: serum cholestoral in mg/dl \n",
    "    fbs: (fasting blood sugar > 120 mg/dl)  (1 = true; 0 = false)\n",
    "    restecg: resting electrocardiographic results\n",
    "        - Value 0: normal\n",
    "        - Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
    "        - Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
    "    thalach: maximum heart rate achieved\n",
    "    exang: exercise induced angina (1 = yes; 0 = no)\n",
    "    oldpeak = ST depression induced by exercise relative to rest\n",
    "    slope: the slope of the peak exercise ST segment\n",
    "        - Value 1: upsloping\n",
    "        - Value 2: flat\n",
    "        - Value 3: downsloping\n",
    "    ca: number of major vessels (0-3) colored by flourosopy\n",
    "    thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "    heart_disease: diagnosis of heart disease (angiographic disease status)\n",
    "        - Value 0: < 50% diameter narrowing\n",
    "        - Value 1: > 50% diameter narrowing \"[This field] refers to the presence of heart disease in the patient. Scorere is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from the absence (value 0).\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Data is from the UCI Machine Learning Repository\n",
    "Creators: Andras Janosi, William Steinbrunn, Matthias Pfisterer, Robert Detrano\n",
    "Licence: This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data: https://archive.ics.uci.edu/dataset/45/heart+disease\n",
    "\n",
    "# import csv file, adding column names\n",
    "column_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'heart_disease']\n",
    "heart = pd.read_csv('processed.cleveland.csv', names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303, 14)\n"
     ]
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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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>heart_disease</th>\n",
       "    </tr>\n",
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       "      <td>37.0</td>\n",
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       "      <td>130.0</td>\n",
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       "      <td>187.0</td>\n",
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       "      <td>3.5</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "\n",
       "   slope   ca thal  heart_disease  \n",
       "0    3.0  0.0  6.0              0  \n",
       "1    2.0  3.0  3.0              2  \n",
       "2    2.0  2.0  7.0              1  \n",
       "3    3.0  0.0  3.0              0  \n",
       "4    1.0  0.0  3.0              0  "
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "# print the first 5 rows of the dataframe\n",
    "print(heart.shape)\n",
    "heart.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
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       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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      "text/plain": [
       "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    NaN      2.3   \n",
       "1  67.0  1.0  4.0     160.0  286.0  NaN      2.0    108.0    1.0      1.5   \n",
       "2  67.0  1.0  4.0     120.0  229.0  NaN      2.0    129.0    1.0      2.6   \n",
       "3  37.0  1.0  3.0     130.0  250.0  NaN      NaN    187.0    NaN      3.5   \n",
       "4  41.0  NaN  2.0     130.0  204.0  NaN      2.0    172.0    NaN      1.4   \n",
       "\n",
       "   slope   ca thal  heart_disease  \n",
       "0    3.0  0.0  6.0            NaN  \n",
       "1    2.0  3.0  3.0            2.0  \n",
       "2    2.0  2.0  7.0            1.0  \n",
       "3    3.0  0.0  3.0            NaN  \n",
       "4    1.0  0.0  3.0            NaN  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace zeros with 'NaN' value\n",
    "heart = heart.replace(0, np.nan)\n",
    "\n",
    "heart.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>206.0</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>mean</th>\n",
       "      <td>54.438944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.158416</td>\n",
       "      <td>131.689769</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.973684</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.544118</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.043165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.038662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.960126</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160602</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.105746</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.013464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age    sex          cp    trestbps        chol   fbs  \\\n",
       "count   303.000000  206.0  303.000000  303.000000  303.000000  45.0   \n",
       "unique         NaN    NaN         NaN         NaN         NaN   NaN   \n",
       "top            NaN    NaN         NaN         NaN         NaN   NaN   \n",
       "freq           NaN    NaN         NaN         NaN         NaN   NaN   \n",
       "mean     54.438944    1.0    3.158416  131.689769  246.693069   1.0   \n",
       "std       9.038662    0.0    0.960126   17.599748   51.776918   0.0   \n",
       "min      29.000000    1.0    1.000000   94.000000  126.000000   1.0   \n",
       "25%      48.000000    1.0    3.000000  120.000000  211.000000   1.0   \n",
       "50%      56.000000    1.0    3.000000  130.000000  241.000000   1.0   \n",
       "75%      61.000000    1.0    4.000000  140.000000  275.000000   1.0   \n",
       "max      77.000000    1.0    4.000000  200.000000  564.000000   1.0   \n",
       "\n",
       "           restecg     thalach  exang     oldpeak       slope   ca thal  \\\n",
       "count   152.000000  303.000000   99.0  204.000000  303.000000  303  303   \n",
       "unique         NaN         NaN    NaN         NaN         NaN    5    4   \n",
       "top            NaN         NaN    NaN         NaN         NaN  0.0  3.0   \n",
       "freq           NaN         NaN    NaN         NaN         NaN  176  166   \n",
       "mean      1.973684  149.607261    1.0    1.544118    1.600660  NaN  NaN   \n",
       "std       0.160602   22.875003    0.0    1.105746    0.616226  NaN  NaN   \n",
       "min       1.000000   71.000000    1.0    0.100000    1.000000  NaN  NaN   \n",
       "25%       2.000000  133.500000    1.0    0.775000    1.000000  NaN  NaN   \n",
       "50%       2.000000  153.000000    1.0    1.400000    2.000000  NaN  NaN   \n",
       "75%       2.000000  166.000000    1.0    2.000000    2.000000  NaN  NaN   \n",
       "max       2.000000  202.000000    1.0    6.200000    3.000000  NaN  NaN   \n",
       "\n",
       "        heart_disease  \n",
       "count      139.000000  \n",
       "unique            NaN  \n",
       "top               NaN  \n",
       "freq              NaN  \n",
       "mean         2.043165  \n",
       "std          1.013464  \n",
       "min          1.000000  \n",
       "25%          1.000000  \n",
       "50%          2.000000  \n",
       "75%          3.000000  \n",
       "max          4.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics including all columns\n",
    "heart.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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    float64\n",
      " 1   sex            206 non-null    float64\n",
      " 2   cp             303 non-null    float64\n",
      " 3   trestbps       303 non-null    float64\n",
      " 4   chol           303 non-null    float64\n",
      " 5   fbs            45 non-null     float64\n",
      " 6   restecg        152 non-null    float64\n",
      " 7   thalach        303 non-null    float64\n",
      " 8   exang          99 non-null     float64\n",
      " 9   oldpeak        204 non-null    float64\n",
      " 10  slope          303 non-null    float64\n",
      " 11  ca             303 non-null    object \n",
      " 12  thal           303 non-null    object \n",
      " 13  heart_disease  139 non-null    float64\n",
      "dtypes: float64(12), object(2)\n",
      "memory usage: 33.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "heart.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0.0' '3.0' '2.0' '1.0' '?']\n",
      "['6.0' '3.0' '7.0' '?']\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "print(heart.ca.unique())\n",
    "print(heart.thal.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace '?' with 'NaN'\n",
    "heart = heart.replace('?', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0.0', '3.0', '2.0', '1.0', nan], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-check replaced missing values\n",
    "heart.ca.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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    float64\n",
      " 1   sex            206 non-null    float64\n",
      " 2   cp             303 non-null    float64\n",
      " 3   trestbps       303 non-null    float64\n",
      " 4   chol           303 non-null    float64\n",
      " 5   fbs            45 non-null     float64\n",
      " 6   restecg        152 non-null    float64\n",
      " 7   thalach        303 non-null    float64\n",
      " 8   exang          99 non-null     float64\n",
      " 9   oldpeak        204 non-null    float64\n",
      " 10  slope          303 non-null    float64\n",
      " 11  ca             299 non-null    object \n",
      " 12  thal           301 non-null    object \n",
      " 13  heart_disease  139 non-null    float64\n",
      "dtypes: float64(12), object(2)\n",
      "memory usage: 33.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# re-check missing values in dataframe\n",
    "heart.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dtype for columns 'ca' to float\n",
    "heart.ca = heart.ca.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-check dtype for columns 'ca'\n",
    "heart.ca.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cp: chest pain type\n",
    "#         - Value 1: typical angina\n",
    "#         - Value 2: atypical angina\n",
    "#         - Value 3: non-anginal pain\n",
    "#         - Value 4: asymptomatic\n",
    "\n",
    "# replace categorical numeric values with string values\n",
    "# heart.cp = heart.cp.replace({1.0: 'typical angina', 2.0: 'atypical angina', 3.0: 'non-anginal pain', 4.0: 'asymptomatic'})\n",
    "# using inplace to override data on df\n",
    "heart.cp.replace({1.0: 'typical angina', 2.0: 'atypical angina', 3.0: 'non-anginal pain', 4.0: 'asymptomatic'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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    float64\n",
      " 1   sex            206 non-null    float64\n",
      " 2   cp             303 non-null    object \n",
      " 3   trestbps       303 non-null    float64\n",
      " 4   chol           303 non-null    float64\n",
      " 5   fbs            45 non-null     float64\n",
      " 6   restecg        152 non-null    float64\n",
      " 7   thalach        303 non-null    float64\n",
      " 8   exang          99 non-null     float64\n",
      " 9   oldpeak        204 non-null    float64\n",
      " 10  slope          303 non-null    float64\n",
      " 11  ca             299 non-null    float64\n",
      " 12  thal           301 non-null    object \n",
      " 13  heart_disease  139 non-null    float64\n",
      "dtypes: float64(12), object(2)\n",
      "memory usage: 33.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# re-check dtypes\n",
    "heart.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>heart_disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>206.0</td>\n",
       "      <td>303</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>301</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.438944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.689769</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.973684</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.544118</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.043165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.038662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160602</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.105746</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.937438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.013464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age    sex            cp    trestbps        chol   fbs  \\\n",
       "count   303.000000  206.0           303  303.000000  303.000000  45.0   \n",
       "unique         NaN    NaN             4         NaN         NaN   NaN   \n",
       "top            NaN    NaN  asymptomatic         NaN         NaN   NaN   \n",
       "freq           NaN    NaN           144         NaN         NaN   NaN   \n",
       "mean     54.438944    1.0           NaN  131.689769  246.693069   1.0   \n",
       "std       9.038662    0.0           NaN   17.599748   51.776918   0.0   \n",
       "min      29.000000    1.0           NaN   94.000000  126.000000   1.0   \n",
       "25%      48.000000    1.0           NaN  120.000000  211.000000   1.0   \n",
       "50%      56.000000    1.0           NaN  130.000000  241.000000   1.0   \n",
       "75%      61.000000    1.0           NaN  140.000000  275.000000   1.0   \n",
       "max      77.000000    1.0           NaN  200.000000  564.000000   1.0   \n",
       "\n",
       "           restecg     thalach  exang     oldpeak       slope          ca  \\\n",
       "count   152.000000  303.000000   99.0  204.000000  303.000000  299.000000   \n",
       "unique         NaN         NaN    NaN         NaN         NaN         NaN   \n",
       "top            NaN         NaN    NaN         NaN         NaN         NaN   \n",
       "freq           NaN         NaN    NaN         NaN         NaN         NaN   \n",
       "mean      1.973684  149.607261    1.0    1.544118    1.600660    0.672241   \n",
       "std       0.160602   22.875003    0.0    1.105746    0.616226    0.937438   \n",
       "min       1.000000   71.000000    1.0    0.100000    1.000000    0.000000   \n",
       "25%       2.000000  133.500000    1.0    0.775000    1.000000    0.000000   \n",
       "50%       2.000000  153.000000    1.0    1.400000    2.000000    0.000000   \n",
       "75%       2.000000  166.000000    1.0    2.000000    2.000000    1.000000   \n",
       "max       2.000000  202.000000    1.0    6.200000    3.000000    3.000000   \n",
       "\n",
       "       thal  heart_disease  \n",
       "count   301     139.000000  \n",
       "unique    3            NaN  \n",
       "top     3.0            NaN  \n",
       "freq    166            NaN  \n",
       "mean    NaN       2.043165  \n",
       "std     NaN       1.013464  \n",
       "min     NaN       1.000000  \n",
       "25%     NaN       1.000000  \n",
       "50%     NaN       2.000000  \n",
       "75%     NaN       3.000000  \n",
       "max     NaN       4.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display summary statistics for all columns\n",
    "heart.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.0\n",
       "1    2.0\n",
       "2    2.0\n",
       "3    3.0\n",
       "4    1.0\n",
       "Name: slope, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display values of 'slope' column\n",
    "heart.slope.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['downsloping' 'flat' 'upsloping']\n",
      "object\n"
     ]
    }
   ],
   "source": [
    "# replace categorical numeric values with string values\n",
    "heart.slope.replace({1.0: 'upsloping', 2.0: 'flat', 3.0: 'downsloping'}, inplace=True)\n",
    "\n",
    "# display comverted values of column 'slope'\n",
    "heart.slope.head()\n",
    "print(heart.slope.unique())\n",
    "print(heart.slope.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category\n",
      "[2 1 0]\n"
     ]
    }
   ],
   "source": [
    "# convert 'slope' column to categorical data type\n",
    "heart.slope = pd.Categorical(heart.slope, ['upsloping', 'flat', 'downsloping'], ordered=True)\n",
    "\n",
    "# re-check dtypes for 'slope' column\n",
    "print(heart.slope.dtype)\n",
    "\n",
    "# encode 'slope' column\n",
    "heart_slope_encode = heart.slope.cat.codes\n",
    "# This line of code is encoding the 'slope' column in the 'heart' DataFrame.\n",
    "# 'slope' is a categorical variable and 'cat.codes' is used to transform the \n",
    "# categories into integers for further analysis or modeling tasks.\n",
    "\n",
    "# display encoded values of 'slope' column\n",
    "print(heart_slope_encode.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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