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projekt.ipynb
2024-04-04 22:23:05 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"id": "73edef6d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <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",
" <th>OccuPational Hazards</th>\n",
" <th>Genetic Risk</th>\n",
" <th>chronic Lung Disease</th>\n",
" <th>Balanced Diet</th>\n",
" <th>...</th>\n",
" <th>Fatigue</th>\n",
" <th>Weight Loss</th>\n",
" <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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <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",
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" <td>4</td>\n",
" <td>Low</td>\n",
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" <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",
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" <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",
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" <td>6</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",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
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" <td>7</td>\n",
" <td>5</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <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",
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" <td>2</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" </tbody>\n",
"</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": 5,
"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": 6,
"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": 7,
"id": "af7da17c",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>Gender</th>\n",
" <td>1000.0</td>\n",
" <td>1.402</td>\n",
" <td>0.490547</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Air Pollution</th>\n",
" <td>1000.0</td>\n",
" <td>3.840</td>\n",
" <td>2.030400</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>Alcohol use</th>\n",
" <td>1000.0</td>\n",
" <td>4.563</td>\n",
" <td>2.620477</td>\n",
" <td>1.0</td>\n",
" <td>2.00</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
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" </tr>\n",
" <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",
" </tr>\n",
" <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",
" </tr>\n",
" <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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dane.describe().T"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dane.columns"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e6cad188",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Gender\n",
"1 598\n",
"2 402\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dane2 = dane.groupby('Gender').size()\n",
"dane2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "966e57b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Gender'>"
]
},
"execution_count": 10,
"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": 11,
"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": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dane3 = [dane.groupby('Smoking').size()]\n",
"dane3 "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d85261ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='count'>"
]
},
"execution_count": 12,
"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": 13,
"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": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dane4 = [dane.groupby('Passive Smoker').size()]\n",
"dane4"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c78bbd4c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='count'>"
]
},
"execution_count": 14,
"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": 15,
"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": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dane.groupby(['Smoking','Gender']).size()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "af3dd196",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Smoking,Gender'>"
]
},
"execution_count": 16,
"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": 21,
"id": "15eebd5b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Air Pollution\n",
"8 19\n",
"5 20\n",
"7 30\n",
"4 90\n",
"1 141\n",
"3 173\n",
"2 201\n",
"6 326\n",
"Name: count, dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dane7 = dane['Air Pollution'].value_counts()\n",
"dane7.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "98359ff2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Air Pollution'>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGrCAYAAADqwWxuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAm6UlEQVR4nO3df1SVdYLH8c/l1xWDewkCrmwIpmNA/shV06uNOUkiMmYbncQ1tY5HTx50tygr2lLTZjCnpmZG0zOzNlYr/minbKXRRmnEncJfOIymxqjpwCxcdHUBtREUnv1jjvd08ycI3i/wfp3znON9vt97n+9zKHn73F82y7IsAQAAGCTA3wsAAAD4LgIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYJ8vcCWqKpqUmVlZUKDw+XzWbz93IAAMB1sCxLp0+fVlxcnAICrn6NpF0GSmVlpeLj4/29DAAA0AIVFRW6/fbbrzqnXQZKeHi4pL+foMPh8PNqAADA9airq1N8fLz39/jVtMtAufi0jsPhIFAAAGhnruflGbxIFgAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcYL8vQB/SXzhE78d+9iiDL8dGwCA9oArKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIzTrEBZtmyZ+vXrJ4fDIYfDIbfbrY0bN3rHz507p+zsbEVFRSksLEyZmZmqrq72eYzy8nJlZGSoa9euiomJ0Zw5c3ThwoXWORsAANAhNCtQbr/9di1atEglJSXavXu37r//fo0fP1779++XJD399NPasGGDPvjgAxUVFamyslIPP/yw9/6NjY3KyMhQQ0ODvvjiC7377rtauXKl5s6d27pnBQAA2jWbZVnWjTxAZGSkfvKTn+iRRx5RdHS08vPz9cgjj0iSvvrqKyUnJ6u4uFhDhw7Vxo0b9cMf/lCVlZWKjY2VJC1fvlzPP/+8Tpw4oZCQkOs6Zl1dnZxOp2pra+VwOFq07sQXPmnR/VrDsUUZfjs2AAD+0pzf3y1+DUpjY6PWrFmjs2fPyu12q6SkROfPn1dqaqp3TlJSkrp3767i4mJJUnFxsfr27euNE0lKS0tTXV2d9yrM5dTX16uurs5nAwAAHVezA2Xfvn0KCwuT3W7Xk08+qY8++kgpKSnyeDwKCQlRRESEz/zY2Fh5PB5Jksfj8YmTi+MXx64kLy9PTqfTu8XHxzd32QAAoB1pdqDceeedKi0t1Y4dOzRz5kxNnTpVBw4caIu1eeXm5qq2tta7VVRUtOnxAACAfwU19w4hISHq1auXJGngwIHatWuXfvazn2nChAlqaGhQTU2Nz1WU6upquVwuSZLL5dLOnTt9Hu/iu3wuzrkcu90uu93e3KUCAIB26oY/B6WpqUn19fUaOHCggoODVVhY6B0rKytTeXm53G63JMntdmvfvn06fvy4d87mzZvlcDiUkpJyo0sBAAAdRLOuoOTm5io9PV3du3fX6dOnlZ+fr61bt+rTTz+V0+nUtGnTlJOTo8jISDkcDs2ePVtut1tDhw6VJI0ePVopKSmaPHmyFi9eLI/Ho5deeknZ2dlcIQEAAF7NCpTjx49rypQpqqqqktPpVL9+/fTpp5/qgQcekCS9+eabCggIUGZmpurr65WWlqa3337be//AwEAVFBRo5syZcrvduuWWWzR16lQtWLCgdc8KAAC0azf8OSj+wOegAADQ/tyUz0EBAABoKwQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIzTrEDJy8vT4MGDFR4erpiYGD300EMqKyvzmTNy5EjZbDaf7cknn/SZU15eroyMDHXt2lUxMTGaM2eOLly4cONnAwAAOoSg5kwuKipSdna2Bg8erAsXLujFF1/U6NGjdeDAAd1yyy3eedOnT9eCBQu8t7t27er9c2NjozIyMuRyufTFF1+oqqpKU6ZMUXBwsH784x+3wikBAID2rlmBsmnTJp/bK1euVExMjEpKSjRixAjv/q5du8rlcl32MX73u9/pwIED2rJli2JjY3X33Xdr4cKFev755zV//nyFhIS04DQAAEBHckOvQamtrZUkRUZG+uxftWqVbrvtNvXp00e5ubn65ptvvGPFxcXq27evYmNjvfvS0tJUV1en/fv3X/Y49fX1qqur89kAAEDH1awrKN/W1NSkp556SsOHD1efPn28+//5n/9ZCQkJiouL0969e/X888+rrKxMH374oSTJ4/H4xIkk722Px3PZY+Xl5emVV15p6VIBAEA70+JAyc7O1pdffqk//OEPPvtnzJjh/XPfvn3VrVs3jRo1SkeOHFHPnj1bdKzc3Fzl5OR4b9fV1Sk+Pr5lCwcAAMZr0VM8s2bNUkFBgX7/+9/r9ttvv+rcIUOGSJIOHz4sSXK5XKqurvaZc/H2lV63Yrfb5XA4fDYAANBxNStQLMvSrFmz9NFHH+mzzz5Tjx49rnmf0tJSSVK3bt0kSW63W/v27dPx48e9czZv3iyHw6GUlJTmLAcAAHRQzXqKJzs7W/n5+fr4448VHh7ufc2I0+lUaGiojhw5ovz8fI0dO1ZRUVHau3evnn76aY0YMUL9+vWTJI0ePVopKSmaPHmyFi9eLI/Ho5deeknZ2dmy2+2tf4YAAKDdadYVlGXLlqm2tlYjR45Ut27dvNvatWslSSEhIdqyZYtGjx6tpKQkPfPMM8rMzNSGDRu8jxEYGKiCggIFBgbK7Xbrscce05QpU3w+NwUAAHRuzbqCYlnWVcfj4+NVVFR0zcdJSEjQb3/72+YcGgAAdCJ8Fw8AADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4zQrUPLy8jR48GCFh4crJiZGDz30kMrKynzmnDt3TtnZ2YqKilJYWJgyMzNVXV3tM6e8vFwZGRnq2rWrYmJiNGfOHF24cOHGzwYAAHQIzQqUoqIiZWdna/v27dq8ebPOnz+v0aNH6+zZs945Tz/9tDZs2KAPPvhARUVFqqys1MMPP+wdb2xsVEZGhhoaGvTFF1/o3Xff1cqVKzV37tzWOysAANCu2SzLslp65xMnTigmJkZFRUUaMWKEamtrFR0drfz8fD3yyCOSpK+++krJyckqLi7W0KFDtXHjRv3whz9UZWWlYmNjJUnLly/X888/rxMnTigkJOSax62rq5PT6VRtba0cDkeL1p74wictul9rOLYow2/HBgDAX5rz+zvoRg5UW1srSYqMjJQklZSU6Pz580pNTfXOSUpKUvfu3b2BUlxcrL59+3r
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dane7 = dane['Air Pollution'].value_counts().plot(kind = 'bar')\n",
"dane7"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85924b6b",
"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
}
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