AIM-PIbd-31-Sagirov-M-M/Lab_1/lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lab 1 Sagirov M.M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Загрузка датасета по варианту 5       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv(\"..//datasets//Lab_1//Medical_insurance.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Первая диаграмма: Распределение по возрасту"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist(df['age'],bins=20, color='lightblue', edgecolor='black')\n",
    "plt.title('Распределение по годам рождения клиентов страхования')\n",
    "plt.xlabel('Возраст')\n",
    "plt.ylabel('Количество клиентов')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из данной диаграммы видно, что в нашем датасете с перевесом преобладают данные о людях в возрасте до 20-ти лет "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вторая диаграмма: Столбчатая диаграмма сумма расходов по регионам за первые 30 строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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[0:30]\n",
    "region_charges = df_slice.groupby('region')['charges'].sum()\n",
    "plt.figure(figsize=(10, 6))\n",
    "region_charges.plot(kind='bar', color=['red','green','lightblue','purple'], edgecolor='black')\n",
    "plt.title('Суммарные расходы по регионам')\n",
    "plt.xlabel('Регионы')\n",
    "plt.ylabel('Суммарные расходы')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма позволяет сделать вывод о том, что лидером по расходам является Юго-восток, а меньше всего тратят на Северо-западе"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Третья диаграмма: Круговая диаграмма курящих по полу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smokers_df = df[df['smoker'] == 'yes']\n",
    "smokers_by_sex = smokers_df['sex'].value_counts()\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.pie(smokers_by_sex, labels=smokers_by_sex.index, autopct='%1.1f%%', colors=['lightcoral', 'lightskyblue'])\n",
    "plt.title('Процент курящих по полу')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма позволяет сделать вывод о том, что среди мужчин процент курящих больше, чем среди женщин."
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}