AIM-PIbd-31-Afanasev-S-S/lab_1/lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало лабораторной\n",
    "\n",
    "Выгрузка данных из csv файла в датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv(\"C:/Users/TIGR228/Desktop/МИИ/Lab1/AIM-PIbd-31-Afanasev-S-S/static/csv/diabetes.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Группируем данные по возрасту и вычисляем среднее значение глюкозы для каждой возрастной группы\n",
    "average_glucose_by_age = df.groupby('Age')['Glucose'].mean()\n",
    "# Постройте гистограмму для среднего значения глюкозы относительно возраста\n",
    "plt.figure(figsize=(10, 6))\n",
    "average_glucose_by_age.plot(kind='bar', edgecolor='black')\n",
    "plt.title('Среднее количество глюкозы относительно возраста')\n",
    "plt.xlabel('Возраст')\n",
    "plt.ylabel('Среднее количество глюкозы')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Постройте диаграмму рассеяния для столбцов \"Age\" и \"Pregnancies\"\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['Age'], df['Pregnancies'], alpha=0.5)\n",
    "plt.title('Количество беременностей относительно возраста')\n",
    "plt.xlabel('Возраст')\n",
    "plt.ylabel('Количество беременностей')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма отображает количество беременностей относительно возраста, что позволяет сделать вывод о том, как частота беременностей изменяется с возрастом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
    "# Группируем данные по возрасту и вычисляем среднее значение инсулина и глюкозы для каждой возрастной группы\n",
    "insulin = subset_df.groupby('Age')['Insulin'].mean()\n",
    "glucose = subset_df.groupby('Age')['Glucose'].mean()\n",
    "\n",
    "# Создаем DataFrame для средних значений\n",
    "average_df = pd.DataFrame({\n",
    "    'Insulin': insulin,\n",
    "    'Glucose': glucose\n",
    "})\n",
    "\n",
    "# Постройте линейный график для средних значений инсулина и глюкозы\n",
    "plt.figure(figsize=(10, 6))\n",
    "average_df.plot.line()\n",
    "plt.title('Среднее значение инсулина и глюкозы 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 глюкозы по возрасту, что позволяет сделать вывод о том, как эти показатели изменяются с возрастом."
   ]
  }
 ],
 "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
}