230 lines
78 KiB
Plaintext
230 lines
78 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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"## Начало лабораторной работы\n",
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"\n",
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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": 2,
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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(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
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" 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
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" 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
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" 'Asthma', 'KidneyDisease', 'SkinCancer'],\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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"df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.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": "code",
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"execution_count": 16,
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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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"2\n"
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]
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},
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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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"<Figure size 640x480 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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"import matplotlib.pyplot as plt\n",
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"import pandas as pd \n",
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"df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
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"\n",
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"labels = 'Курящие', 'Некурящие'\n",
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"sizes = [df[df[\"Smoking\"] == \"Yes\"].shape[0],df[df[\"Smoking\"] == \"No\"].shape[0]]\n",
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"\n",
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"print(len([df[df[\"Smoking\"] == \"Yes\"].count(),df[df[\"Smoking\"] == \"No\"].count()]))\n",
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"\n",
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"plt.pie(sizes, labels=labels)\n",
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"plt.show()"
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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": 17,
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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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"<Figure size 640x480 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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"import matplotlib.pyplot as plt\n",
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"import pandas as pd \n",
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"df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
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"\n",
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"labels = 'Пьющие', 'Непьющие'\n",
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"sizes = [df[df[\"AlcoholDrinking\"] == \"Yes\"].shape[0],df[df[\"AlcoholDrinking\"] == \"No\"].shape[0]]\n",
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"\n",
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"plt.pie(sizes, labels=labels)\n",
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"plt.show()"
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]
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},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма означает, что большинство опрашиваемых не употребляют алкоголь"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.hist(df[\"BMI\"], bins=30, edgecolor='black')\n",
|
|||
|
|
"plt.xlabel('BMI')\n",
|
|||
|
|
"plt.ylabel('Частота')\n",
|
|||
|
|
"plt.title('Распределение BMI')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма показывает, что большинство опрашиваемых имеет индекс массы тела в диапазоне от 20 до 40"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Загрузка данных\n",
|
|||
|
|
"df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Словарь для преобразования AgeCategory в числовые значения\n",
|
|||
|
|
"age_mapping = {\n",
|
|||
|
|
" '18-24': 21,\n",
|
|||
|
|
" '25-29': 27,\n",
|
|||
|
|
" '30-34': 32,\n",
|
|||
|
|
" '35-39': 37,\n",
|
|||
|
|
" '40-44': 42,\n",
|
|||
|
|
" '45-49': 47,\n",
|
|||
|
|
" '50-54': 52,\n",
|
|||
|
|
" '55-59': 57,\n",
|
|||
|
|
" '60-64': 62,\n",
|
|||
|
|
" '65-69': 67,\n",
|
|||
|
|
" '70-74': 72,\n",
|
|||
|
|
" '75-79': 77,\n",
|
|||
|
|
" '80 or older': 85\n",
|
|||
|
|
"}\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Преобразование столбца AgeCategory в числовые значения\n",
|
|||
|
|
"df['AgeNumeric'] = df['AgeCategory'].map(age_mapping)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Выбор среза данных с 1-й по 30-ю строку\n",
|
|||
|
|
"df_slice = df.iloc[0:30]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Определение количества уникальных значений возраста\n",
|
|||
|
|
"unique_ages = df_slice['AgeNumeric'].nunique()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Гистограмма для возраста на срезе данных с настройкой bins\n",
|
|||
|
|
"plt.hist(df_slice['AgeNumeric'], bins=unique_ages, edgecolor='black')\n",
|
|||
|
|
"plt.xlabel('Возраст')\n",
|
|||
|
|
"plt.ylabel('Частота')\n",
|
|||
|
|
"plt.title('Распределение по возрасту (строки 1-30)')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма показывает распределение возрастов опрашиваемых людей"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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": "3.12.5"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|