344 lines
166 KiB
Plaintext
344 lines
166 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Лабораторная 1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Выгрузка данных из csv файла в датафрейм"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
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" 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"df = pd.read_csv(\".//static//scv//diabetes.csv\")\n",
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"print(df.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Посмотрим краткое содержание датасета. Видим, что датасет состоит из 768 строк и 9 столбцов"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 768 entries, 0 to 767\n",
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"Data columns (total 9 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Pregnancies 768 non-null int64 \n",
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" 1 Glucose 768 non-null int64 \n",
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" 2 BloodPressure 768 non-null int64 \n",
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" 3 SkinThickness 768 non-null int64 \n",
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" 4 Insulin 768 non-null int64 \n",
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" 5 BMI 768 non-null float64\n",
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" 6 DiabetesPedigreeFunction 768 non-null float64\n",
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" 7 Age 768 non-null int64 \n",
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" 8 Outcome 768 non-null int64 \n",
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"dtypes: float64(2), int64(7)\n",
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"memory usage: 54.1 KB\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Pregnancies</th>\n",
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" <th>Glucose</th>\n",
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" <th>BloodPressure</th>\n",
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" <th>SkinThickness</th>\n",
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" <th>Insulin</th>\n",
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" <th>BMI</th>\n",
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" <th>DiabetesPedigreeFunction</th>\n",
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" <th>Age</th>\n",
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" <th>Outcome</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>6</td>\n",
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" <td>148</td>\n",
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" <td>72</td>\n",
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" <td>35</td>\n",
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" <td>0</td>\n",
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" <td>33.6</td>\n",
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" <td>0.627</td>\n",
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" <td>50</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>85</td>\n",
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" <td>66</td>\n",
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" <td>29</td>\n",
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" <td>0</td>\n",
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" <td>26.6</td>\n",
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" <td>0.351</td>\n",
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" <td>31</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>8</td>\n",
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" <td>183</td>\n",
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" <td>64</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>23.3</td>\n",
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" <td>0.672</td>\n",
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" <td>32</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
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" <td>89</td>\n",
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" <td>66</td>\n",
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" <td>23</td>\n",
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" <td>94</td>\n",
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" <td>28.1</td>\n",
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" <td>0.167</td>\n",
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" <td>21</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0</td>\n",
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" <td>137</td>\n",
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" <td>40</td>\n",
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" <td>35</td>\n",
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" <td>168</td>\n",
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" <td>43.1</td>\n",
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" <td>2.288</td>\n",
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" <td>33</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
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"0 6 148 72 35 0 33.6 \n",
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"1 1 85 66 29 0 26.6 \n",
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"2 8 183 64 0 0 23.3 \n",
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"3 1 89 66 23 94 28.1 \n",
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"4 0 137 40 35 168 43.1 \n",
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"\n",
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" DiabetesPedigreeFunction Age Outcome \n",
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"0 0.627 50 1 \n",
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"1 0.351 31 0 \n",
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"2 0.672 32 1 \n",
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"3 0.167 21 0 \n",
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"4 2.288 33 1 "
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]
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},
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|
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.info()\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Группируем данные по возрасту и вычисляем среднее значение глюкозы для каждой возрастной группы"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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|
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"<Figure size 1400x800 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"glucose_by_age = df.groupby(['Age'])['BloodPressure'].mean()\n",
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"\n",
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"glucose_by_age.plot(kind='bar', figsize=(14, 8), width=0.6)\n",
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"plt.title('Уровень глюкозы с возрастом')\n",
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"plt.xlabel('Возраст')\n",
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"plt.ylabel('Уровень глюкозы')\n",
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|
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"plt.xticks(rotation=0)\n",
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|
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"plt.show()"
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]
|
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},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает среднее количество глюкозы для каждой возрастной группы, что позволяет сделать вывод о том, как уровень глюкозы изменяется с возрастом."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"plt.scatter(df['Age'], df['BloodPressure'], alpha=0.5)\n",
|
|||
|
|
"plt.title('Уровень давления относительно возраста')\n",
|
|||
|
|
"plt.xlabel('Возраст')\n",
|
|||
|
|
"plt.ylabel('Уровень давления')\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает уровень давления относительно возраста, что позволяет сделать вывод о том, как уровень давления изменяется с возрастом."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 0 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"subset_df = df.iloc[0:30]\n",
|
|||
|
|
"insulin = subset_df.groupby('Age')['Insulin'].mean()\n",
|
|||
|
|
"bmi = subset_df.groupby('Age')['BMI'].mean()\n",
|
|||
|
|
"\n",
|
|||
|
|
"average_df = pd.DataFrame({\n",
|
|||
|
|
" 'Insulin': insulin,\n",
|
|||
|
|
" 'BMI': bmi\n",
|
|||
|
|
"})\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"average_df.plot.line()\n",
|
|||
|
|
"plt.title('Среднее значение инсулина и индекса тела по возрасту')\n",
|
|||
|
|
"plt.xlabel('Возраст')\n",
|
|||
|
|
"plt.ylabel('Среднее значение')\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данный график отображает среднее значение инсулина и индекса тела по возрасту, что позволяет сделать вывод о том, как эти показатели изменяются с возрастом."
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"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": "3.12.6"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|