2024-09-20 21:07:57 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Начало лабораторной работы №1\n",
"\n",
2024-09-21 01:11:22 +04:00
"*Н а б о р
"\n",
2024-09-20 21:07:57 +04:00
"Выгрузка данных из CSV файла в датафрейм"
]
},
{
"cell_type": "code",
2024-09-21 01:11:22 +04:00
"execution_count": 22,
2024-09-20 21:07:57 +04:00
"metadata": {},
"outputs": [
{
2024-09-21 01:11:22 +04:00
"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>HeartDisease</th>\n",
" <th>BMI</th>\n",
" <th>Smoking</th>\n",
" <th>AlcoholDrinking</th>\n",
" <th>Stroke</th>\n",
" <th>PhysicalHealth</th>\n",
" <th>MentalHealth</th>\n",
" <th>DiffWalking</th>\n",
" <th>Sex</th>\n",
" <th>AgeCategory</th>\n",
" <th>Race</th>\n",
" <th>Diabetic</th>\n",
" <th>PhysicalActivity</th>\n",
" <th>GenHealth</th>\n",
" <th>SleepTime</th>\n",
" <th>Asthma</th>\n",
" <th>KidneyDisease</th>\n",
" <th>SkinCancer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>No</td>\n",
" <td>16.60</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>3.0</td>\n",
" <td>30.0</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>55-59</td>\n",
" <td>White</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Very good</td>\n",
" <td>5.0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>No</td>\n",
" <td>20.34</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>80 or older</td>\n",
" <td>White</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Very good</td>\n",
" <td>7.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No</td>\n",
" <td>26.58</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>20.0</td>\n",
" <td>30.0</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>65-69</td>\n",
" <td>White</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Fair</td>\n",
" <td>8.0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>No</td>\n",
" <td>24.21</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>75-79</td>\n",
" <td>White</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Good</td>\n",
" <td>6.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>No</td>\n",
" <td>23.71</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>Yes</td>\n",
" <td>Female</td>\n",
" <td>40-44</td>\n",
" <td>White</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Very good</td>\n",
" <td>8.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
"0 No 16.60 Yes No No 3.0 \n",
"1 No 20.34 No No Yes 0.0 \n",
"2 No 26.58 Yes No No 20.0 \n",
"3 No 24.21 No No No 0.0 \n",
"4 No 23.71 No No No 28.0 \n",
"\n",
" MentalHealth DiffWalking Sex AgeCategory Race Diabetic \\\n",
"0 30.0 No Female 55-59 White Yes \n",
"1 0.0 No Female 80 or older White No \n",
"2 30.0 No Male 65-69 White Yes \n",
"3 0.0 No Female 75-79 White No \n",
"4 0.0 Yes Female 40-44 White No \n",
"\n",
" PhysicalActivity GenHealth SleepTime Asthma KidneyDisease SkinCancer \n",
"0 Yes Very good 5.0 Yes No Yes \n",
"1 Yes Very good 7.0 No No No \n",
"2 Yes Fair 8.0 Yes No No \n",
"3 No Good 6.0 No No Yes \n",
"4 Yes Very good 8.0 No No No "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
2024-09-20 21:07:57 +04:00
}
],
"source": [
"import pandas as pd\n",
2024-09-21 01:11:22 +04:00
"import matplotlib.pyplot as plt\n",
2024-09-20 21:07:57 +04:00
"\n",
"df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
2024-09-21 01:11:22 +04:00
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Построение диаграмм\n",
"\n",
"## Распределение индекса массы тела"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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.hist(df.head(15000)['BMI'], bins=30, color='skyblue', edgecolor='black')\n",
"plt.title('Распределение индекса массы тела')\n",
"plt.xlabel('Индекс массы тела')\n",
"plt.ylabel('Количество записей')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Данная диаграмма отображает распределение индекса массы тела среди всех наблюдаемых. Это позволяет сделать вывод о с
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Соотношение людей с
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"heart_disease_sex = df.head(15000).groupby(['Sex', 'HeartDisease']).size().unstack()\n",
"\n",
"heart_disease_sex.plot(kind='bar', stacked=True, figsize=(10, 6), color=['lightcoral', 'cornflowerblue'])\n",
"plt.title('Соотношение болезней сердца по полу')\n",
"plt.xlabel('Пол')\n",
"plt.ylabel('Количество')\n",
"plt.legend(title='Heart Disease', labels=['Нет', 'Да'])\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Данная столбчатая диаграмма показывает соотношение людей с о
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Влияние физического и психического здоровья на наличие болезней сердца"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_slice = df.iloc[0:2000]\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"plt.scatter(df_slice[df_slice['HeartDisease'] == 'Yes']['PhysicalHealth'],\n",
" df_slice[df_slice['HeartDisease'] == 'Yes']['MentalHealth'],\n",
" color='red', label='С
"plt.scatter(df_slice[df_slice['HeartDisease'] == 'No']['PhysicalHealth'],\n",
" df_slice[df_slice['HeartDisease'] == 'No']['MentalHealth'],\n",
" color='green', label='Без заболеваний', alpha=0.5)\n",
"\n",
"plt.title('Зависимость физического и психического здоровья от наличия сердечных заболеваний')\n",
"plt.xlabel('Физическое здоровье (дни с
"plt.ylabel('Психическое здоровье (дни с
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Эта диаграмма рассеяния показывает взаимосвязь между физическим и психическим здоровьем людей, страдающих и не страдающих сердечными заболеваниями. Это позволяет сделать вывод о с
2024-09-20 21:07:57 +04:00
]
}
],
"metadata": {
"kernelspec": {
"display_name": "aimenv",
"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
}
