{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7ce53ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "73edef6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Air Pollution</th>\n",
       "      <th>Alcohol use</th>\n",
       "      <th>Dust Allergy</th>\n",
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       "      <th>Genetic Risk</th>\n",
       "      <th>chronic Lung Disease</th>\n",
       "      <th>Balanced Diet</th>\n",
       "      <th>...</th>\n",
       "      <th>Fatigue</th>\n",
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       "      <th>Shortness of Breath</th>\n",
       "      <th>Wheezing</th>\n",
       "      <th>Swallowing Difficulty</th>\n",
       "      <th>Clubbing of Finger Nails</th>\n",
       "      <th>Frequent Cold</th>\n",
       "      <th>Dry Cough</th>\n",
       "      <th>Snoring</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P10</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P100</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P1000</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>High</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P101</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "      <td>4</td>\n",
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       "      <td>High</td>\n",
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       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Patient Id  Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "index                                                                     \n",
       "0             P1   33       1              2            4             5   \n",
       "1            P10   17       1              3            1             5   \n",
       "2           P100   35       1              4            5             6   \n",
       "3          P1000   37       1              7            7             7   \n",
       "4           P101   46       1              6            8             7   \n",
       "\n",
       "       OccuPational Hazards  Genetic Risk  chronic Lung Disease  \\\n",
       "index                                                             \n",
       "0                         4             3                     2   \n",
       "1                         3             4                     2   \n",
       "2                         5             5                     4   \n",
       "3                         7             6                     7   \n",
       "4                         7             7                     6   \n",
       "\n",
       "       Balanced Diet  ...  Fatigue  Weight Loss  Shortness of Breath  \\\n",
       "index                 ...                                              \n",
       "0                  2  ...        3            4                    2   \n",
       "1                  2  ...        1            3                    7   \n",
       "2                  6  ...        8            7                    9   \n",
       "3                  7  ...        4            2                    3   \n",
       "4                  7  ...        3            2                    4   \n",
       "\n",
       "       Wheezing  Swallowing Difficulty  Clubbing of Finger Nails  \\\n",
       "index                                                              \n",
       "0             2                      3                         1   \n",
       "1             8                      6                         2   \n",
       "2             2                      1                         4   \n",
       "3             1                      4                         5   \n",
       "4             1                      4                         2   \n",
       "\n",
       "       Frequent Cold  Dry Cough  Snoring   Level  \n",
       "index                                             \n",
       "0                  2          3        4     Low  \n",
       "1                  1          7        2  Medium  \n",
       "2                  6          7        2    High  \n",
       "3                  6          7        5    High  \n",
       "4                  4          2        3    High  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane = pd.read_csv(r'C:\\Users\\HP\\Desktop\\podyplomówka\\cancer_patient_data_sets.csv', index_col = 0)\n",
    "dane.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1831fdd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1000 entries, 0 to 999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Patient Id                1000 non-null   object\n",
      " 1   Age                       1000 non-null   int64 \n",
      " 2   Gender                    1000 non-null   int64 \n",
      " 3   Air Pollution             1000 non-null   int64 \n",
      " 4   Alcohol use               1000 non-null   int64 \n",
      " 5   Dust Allergy              1000 non-null   int64 \n",
      " 6   OccuPational Hazards      1000 non-null   int64 \n",
      " 7   Genetic Risk              1000 non-null   int64 \n",
      " 8   chronic Lung Disease      1000 non-null   int64 \n",
      " 9   Balanced Diet             1000 non-null   int64 \n",
      " 10  Obesity                   1000 non-null   int64 \n",
      " 11  Smoking                   1000 non-null   int64 \n",
      " 12  Passive Smoker            1000 non-null   int64 \n",
      " 13  Chest Pain                1000 non-null   int64 \n",
      " 14  Coughing of Blood         1000 non-null   int64 \n",
      " 15  Fatigue                   1000 non-null   int64 \n",
      " 16  Weight Loss               1000 non-null   int64 \n",
      " 17  Shortness of Breath       1000 non-null   int64 \n",
      " 18  Wheezing                  1000 non-null   int64 \n",
      " 19  Swallowing Difficulty     1000 non-null   int64 \n",
      " 20  Clubbing of Finger Nails  1000 non-null   int64 \n",
      " 21  Frequent Cold             1000 non-null   int64 \n",
      " 22  Dry Cough                 1000 non-null   int64 \n",
      " 23  Snoring                   1000 non-null   int64 \n",
      " 24  Level                     1000 non-null   object\n",
      "dtypes: int64(23), object(2)\n",
      "memory usage: 203.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dane.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "af7da17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th>Age</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>37.174</td>\n",
       "      <td>12.005493</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.75</td>\n",
       "      <td>36.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>73.0</td>\n",
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       "      <th>Gender</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.402</td>\n",
       "      <td>0.490547</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>Air Pollution</th>\n",
       "      <td>1000.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>Alcohol use</th>\n",
       "      <td>1000.0</td>\n",
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       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Dust Allergy</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>5.165</td>\n",
       "      <td>1.980833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>OccuPational Hazards</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.840</td>\n",
       "      <td>2.107805</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Genetic Risk</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.580</td>\n",
       "      <td>2.126999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronic Lung Disease</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.380</td>\n",
       "      <td>1.848518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Diet</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.491</td>\n",
       "      <td>2.135528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obesity</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.465</td>\n",
       "      <td>2.124921</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smoking</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.948</td>\n",
       "      <td>2.495902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passive Smoker</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.195</td>\n",
       "      <td>2.311778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chest Pain</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.438</td>\n",
       "      <td>2.280209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coughing of Blood</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.859</td>\n",
       "      <td>2.427965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fatigue</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.856</td>\n",
       "      <td>2.244616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight Loss</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.855</td>\n",
       "      <td>2.206546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shortness of Breath</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.240</td>\n",
       "      <td>2.285087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wheezing</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.777</td>\n",
       "      <td>2.041921</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swallowing Difficulty</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.746</td>\n",
       "      <td>2.270383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clubbing of Finger Nails</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.923</td>\n",
       "      <td>2.388048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frequent Cold</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.536</td>\n",
       "      <td>1.832502</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dry Cough</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.853</td>\n",
       "      <td>2.039007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snoring</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.926</td>\n",
       "      <td>1.474686</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count    mean        std   min    25%   50%   75%  \\\n",
       "Age                       1000.0  37.174  12.005493  14.0  27.75  36.0  45.0   \n",
       "Gender                    1000.0   1.402   0.490547   1.0   1.00   1.0   2.0   \n",
       "Air Pollution             1000.0   3.840   2.030400   1.0   2.00   3.0   6.0   \n",
       "Alcohol use               1000.0   4.563   2.620477   1.0   2.00   5.0   7.0   \n",
       "Dust Allergy              1000.0   5.165   1.980833   1.0   4.00   6.0   7.0   \n",
       "OccuPational Hazards      1000.0   4.840   2.107805   1.0   3.00   5.0   7.0   \n",
       "Genetic Risk              1000.0   4.580   2.126999   1.0   2.00   5.0   7.0   \n",
       "chronic Lung Disease      1000.0   4.380   1.848518   1.0   3.00   4.0   6.0   \n",
       "Balanced Diet             1000.0   4.491   2.135528   1.0   2.00   4.0   7.0   \n",
       "Obesity                   1000.0   4.465   2.124921   1.0   3.00   4.0   7.0   \n",
       "Smoking                   1000.0   3.948   2.495902   1.0   2.00   3.0   7.0   \n",
       "Passive Smoker            1000.0   4.195   2.311778   1.0   2.00   4.0   7.0   \n",
       "Chest Pain                1000.0   4.438   2.280209   1.0   2.00   4.0   7.0   \n",
       "Coughing of Blood         1000.0   4.859   2.427965   1.0   3.00   4.0   7.0   \n",
       "Fatigue                   1000.0   3.856   2.244616   1.0   2.00   3.0   5.0   \n",
       "Weight Loss               1000.0   3.855   2.206546   1.0   2.00   3.0   6.0   \n",
       "Shortness of Breath       1000.0   4.240   2.285087   1.0   2.00   4.0   6.0   \n",
       "Wheezing                  1000.0   3.777   2.041921   1.0   2.00   4.0   5.0   \n",
       "Swallowing Difficulty     1000.0   3.746   2.270383   1.0   2.00   4.0   5.0   \n",
       "Clubbing of Finger Nails  1000.0   3.923   2.388048   1.0   2.00   4.0   5.0   \n",
       "Frequent Cold             1000.0   3.536   1.832502   1.0   2.00   3.0   5.0   \n",
       "Dry Cough                 1000.0   3.853   2.039007   1.0   2.00   4.0   6.0   \n",
       "Snoring                   1000.0   2.926   1.474686   1.0   2.00   3.0   4.0   \n",
       "\n",
       "                           max  \n",
       "Age                       73.0  \n",
       "Gender                     2.0  \n",
       "Air Pollution              8.0  \n",
       "Alcohol use                8.0  \n",
       "Dust Allergy               8.0  \n",
       "OccuPational Hazards       8.0  \n",
       "Genetic Risk               7.0  \n",
       "chronic Lung Disease       7.0  \n",
       "Balanced Diet              7.0  \n",
       "Obesity                    7.0  \n",
       "Smoking                    8.0  \n",
       "Passive Smoker             8.0  \n",
       "Chest Pain                 9.0  \n",
       "Coughing of Blood          9.0  \n",
       "Fatigue                    9.0  \n",
       "Weight Loss                8.0  \n",
       "Shortness of Breath        9.0  \n",
       "Wheezing                   8.0  \n",
       "Swallowing Difficulty      8.0  \n",
       "Clubbing of Finger Nails   9.0  \n",
       "Frequent Cold              7.0  \n",
       "Dry Cough                  7.0  \n",
       "Snoring                    7.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a043ec73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Patient Id', 'Age', 'Gender', 'Air Pollution', 'Alcohol use',\n",
       "       'Dust Allergy', 'OccuPational Hazards', 'Genetic Risk',\n",
       "       'chronic Lung Disease', 'Balanced Diet', 'Obesity', 'Smoking',\n",
       "       'Passive Smoker', 'Chest Pain', 'Coughing of Blood', 'Fatigue',\n",
       "       'Weight Loss', 'Shortness of Breath', 'Wheezing',\n",
       "       'Swallowing Difficulty', 'Clubbing of Finger Nails', 'Frequent Cold',\n",
       "       'Dry Cough', 'Snoring', 'Level'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e6cad188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "1    598\n",
       "2    402\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane2 = dane.groupby('Gender').size()\n",
    "dane2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "966e57b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dane['Gender'].value_counts().plot(kind = 'bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8d81604c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Smoking\n",
       " 1    181\n",
       " 2    222\n",
       " 3    172\n",
       " 4     59\n",
       " 5     10\n",
       " 6     60\n",
       " 7    207\n",
       " 8     89\n",
       " dtype: int64]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane3 = [dane.groupby('Smoking').size()]\n",
    "dane3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d85261ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dane['Smoking'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "86122d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Passive Smoker\n",
       " 1     60\n",
       " 2    284\n",
       " 3    140\n",
       " 4    161\n",
       " 5     30\n",
       " 6     30\n",
       " 7    187\n",
       " 8    108\n",
       " dtype: int64]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane4 = [dane.groupby('Passive Smoker').size()]\n",
    "dane4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c78bbd4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dane['Passive Smoker'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6385071c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Smoking  Gender\n",
       "1        1         102\n",
       "         2          79\n",
       "2        1         102\n",
       "         2         120\n",
       "3        1          79\n",
       "         2          93\n",
       "4        1          49\n",
       "         2          10\n",
       "5        1          10\n",
       "6        1          28\n",
       "         2          32\n",
       "7        1         167\n",
       "         2          40\n",
       "8        1          61\n",
       "         2          28\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane.groupby(['Smoking','Gender']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "af3dd196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Smoking,Gender'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dane6 = dane.groupby(['Smoking','Gender'])\n",
    "dane6[['Smoking', 'Gender']].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15eebd5b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}