AIM-PIbd-31-Barsukov-P-O/lab_1/lab1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"../dataset.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма отображает зависимость курения от стоимости страховки, что позволяет сделать вывод о том, что курящие люди платят больше за страховку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='smoker', ylabel='charges'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"smoker\", y=\"charges\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма отображает зависимость количества детей от стоимости страховки, что позволяет сделать вывод о том, что люди с двумя и тремя детьми платят наиболее высокую цену за страховку. Однако в силу других факторов люди с одним ребенком или без детей могут платить даже большую цену"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'charges'}, xlabel='children'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df[\"children\"].min(), df[\"children\"].max())\n",
    "df.boxplot(column=\"charges\", by=\"children\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма отображает зависимость возраста от стоимости страховки, что позволяет сделать вывод о том, что более старые люди платят большую цену за страховку. Желтым цветом составлен тот же самый график, но только на срезе с первой по тридцатую строку, он показывает, что на мельшей выборке можно проследить общую динамику, но сам график становится менее точным"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGwCAYAAABLvHTgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACcxElEQVR4nOzdeXxU9dX48c/MJJnsO0kIhEX2sC+KcUWlREQrSq1Wa6la+2ihVeijlv580Gr74GPrghWlra3YqnVp1SogiCi4EBACYQfZw5YNsm+TzNzfH3funZmsM5OZzExy3q9XXklm7szcSSBz5nzP9xyDoigKQgghhBA9jDHQJyCEEEII4Q8S5AghhBCiR5IgRwghhBA9kgQ5QgghhOiRJMgRQgghRI8kQY4QQggheiQJcoQQQgjRI4UF+gQCyWazcebMGeLi4jAYDIE+HSGEEEK4QVEUqquryczMxGhsP1/Tq4OcM2fOkJWVFejTEEIIIYQXTp48Sf/+/du9vlcHOXFxcYD6Q4qPjw/w2QghhBDCHVVVVWRlZemv4+3p1UGOtkQVHx8vQY4QQggRYjorNZHCYyGEEEL0SBLkCCGEEKJHkiBHCCGEED2SBDlCCCGE6JEkyBFCCCFEjyRBjhBCCCF6JAlyhBBCCNEjdSnIeeqppzAYDDz44IP6ZQ0NDcybN4+UlBRiY2OZM2cOxcXFLrcrLCxk1qxZREdHk5aWxkMPPURzc7PLMRs2bGDSpEmYzWaGDh3KihUrWj3+smXLGDRoEJGRkUydOpVvvvmmK09HCCGEED2I10HO1q1b+dOf/sS4ceNcLl+wYAEfffQR7777Lhs3buTMmTPcfPPN+vVWq5VZs2ZhsVjYtGkTr732GitWrGDx4sX6MceOHWPWrFlcddVVFBQU8OCDD/KTn/yEtWvX6se8/fbbLFy4kMcee4zt27czfvx4cnNzKSkp8fYpCSGEEKInUbxQXV2tDBs2TFm3bp1y5ZVXKg888ICiKIpSUVGhhIeHK++++65+7P79+xVAycvLUxRFUVavXq0YjUalqKhIP+bll19W4uPjlcbGRkVRFOXhhx9WRo8e7fKYt956q5Kbm6t/f9FFFynz5s3Tv7darUpmZqayZMkSt59HZWWlAiiVlZXuP3khhBBCBJS7r99eZXLmzZvHrFmzmD59usvl+fn5NDU1uVw+cuRIBgwYQF5eHgB5eXmMHTuW9PR0/Zjc3FyqqqrYu3evfkzL+87NzdXvw2KxkJ+f73KM0Whk+vTp+jFtaWxspKqqyuVDCCGEED2Tx7Or3nrrLbZv387WrVtbXVdUVERERASJiYkul6enp1NUVKQf4xzgaNdr13V0TFVVFfX19ZSXl2O1Wts85sCBA+2e+5IlS/jNb37j3hMVQgghREjzKJNz8uRJHnjgAd544w0iIyP9dU5+s2jRIiorK/WPkydPBvqUhLcsdYE+AyGEEEHOoyAnPz+fkpISJk2aRFhYGGFhYWzcuJEXXniBsLAw0tPTsVgsVFRUuNyuuLiYjIwMADIyMlrtttK+7+yY+Ph4oqKiSE1NxWQytXmMdh9tMZvN+sRxmTwewr74Azw1AAq3BPpMhBBCBDGPgpxrrrmG3bt3U1BQoH9MmTKFO+64Q/86PDyc9evX67c5ePAghYWF5OTkAJCTk8Pu3btddkGtW7eO+Ph4srOz9WOc70M7RruPiIgIJk+e7HKMzWZj/fr1+jGiBzuxCWxNcHZnoM9ECCFEEPOoJicuLo4xY8a4XBYTE0NKSop++T333MPChQtJTk4mPj6en//85+Tk5HDxxRcDMGPGDLKzs7nzzjt5+umnKSoq4tFHH2XevHmYzWYA7rvvPl588UUefvhh7r77bj777DPeeecdVq1apT/uwoULmTt3LlOmTOGiiy7i+eefp7a2lrvuuqtLPxARAhrtBePN9YE9DyGEEEHN48Ljzjz33HMYjUbmzJlDY2Mjubm5vPTSS/r1JpOJlStXcv/995OTk0NMTAxz587liSee0I8ZPHgwq1atYsGCBSxdupT+/fvzyiuvkJubqx9z6623UlpayuLFiykqKmLChAmsWbOmVTGy6IEaKtXPzY2BPQ8hhBBBzaAoihLokwiUqqoqEhISqKyslPqcUPKHEVBTBJf/Eq5Z3PnxQgghehR3X79ldpUIPZLJEUII4QYJckRosTY5anGaGwJ7LkIIIYKaBDkitDQ4damWIEcIIUQHJMgRoaWhwvG1LFcJIYTogAQ5IrQ0SiZHCCGEeyTIEaFFKzoGyeQIIYTokAQ5IrQ41+Q0STNAIYQQ7ZMgR4QWyeQIIYRwkwQ5IrRITY4QQgg3SZAjQotkcoQQQrhJghwRWqRPjhBCCDdJkCNCi2Ry2nb+GFQUBvoshBAiqPh8CrkQfiU1Oa01NcCfroSwCFh4AEzy31oIIUAyOSLUSCantdoSaKyE2lKoKQ702QghRNCQIEeEFpcgpwEUJXDnEizqyx1fV50J3HkIIUSQkSBHhBbn5SoUsFoCdipBwznIqZYgRwghNBLkiNDinMkBqcsByeQIIUQ7JMgRoUNRXLeQg9TlgAQ5QgjRDglyROiw1IJitX9jUD9JJgfqzju+liBHCCF0EuSI0KHV4xhMYI5Xv5ZMTouanLOBOw8hhAgyEuSI0KHV40QmQHik+rVkcqC+wvF11emAnYYQQgQb6RomQodWjxMZD4pN/VoyOS1qcs6qtUsGQ+DORwghgoRkckTocM7khEkmR+cc5FgbXWt0hBCiF5MgR4QOrSbHHA9hZvVrCXJcgxyQXjlCCGEnQY4IHQ0V6mfnTE6TBDl6kGMMVz/LDishhAAkyBGhRK/JkeUqnaJAvX15qs9I9bMEOUIIAUiQI0JJY1tBTi8vPG6qc4y2SB+tfpYgRwghAAlyRCjRCo+lJsfBeakqdaj6tdTkCCEEIEGOCCVtLlf18kyOFuREJUF8P/VryeQIIQQgQY4IJfoW8nipydE4BzlxfdWvJcgRQghAghwRSlxqcrTlKsnkAC0yOTLaQQghQIIcEUpcanIkkwO0CHLsmZzGSmisCdw5CSFEkJAgR4QO57EOkslRad2No5LAHOcYXCqDOoUQwrMg5+WXX2bcuHHEx8cTHx9PTk4OH3/8sX79tGnTMBgMLh/33Xefy30UFhYya9YsoqOjSUtL46GHHqK5udnlmA0bNjBp0iTMZjNDhw5lxYoVrc5l2bJlDBo0iMjISKZOnco333zjyVMRoajNsQ71gTufYKBlcqKT1c96XY4M6hRCCI+CnP79+/PUU0+Rn5/Ptm3buPrqq7nxxhvZu3evfsy9997L2bNn9Y+nn35av85qtTJr1iwsFgubNm3itddeY8WKFSxevFg/5tixY8yaNYurrrqKgoICHnzwQX7yk5+wdu1a/Zi3336bhQsX8thjj7F9+3bGjx9Pbm4uJSUlXflZiGBmbYamWvVrs/MU8l6eydGXqxLVz/GZ6mepyxFCCM+CnBtuuIHrrruOYcOGMXz4cH73u98RGxvL5s2b9WOio6PJyMjQP+Lj4/XrPvnkE/bt28frr7/OhAkTmDlzJk8++STLli3DYlEbmi1fvpzBgwfzzDPPMGrUKObPn8/3vvc9nnvuOf1+nn32We69917uuususrOzWb58OdHR0fztb3/r6s9DBCut6Bhkd5Uz55occApyJJMjhBBe1+RYrVbeeustamtrycnJ0S9/4403SE1NZcyYMSxatIi6ujr9ury8PMaOHUt6erp+WW5uLlVVVXo2KC8vj+nTp7s8Vm5uLnl5eQBYLBby8/NdjjEajUyfPl0/pj2NjY1UVVW5fIgQoS1VhUeDKVxqcjT1FernlkGO1OQIIQRhnt5g9+7d5OTk0NDQQGxsLO+//z7Z2dkA3H777QwcOJDMzEx27drFI488wsGDB3nvvfcAKCoqcglwAP37oqKiDo+pqqqivr6e8vJyrFZrm8ccOHCgw3NfsmQJv/nNbzx9yiIYOG8fB8nkaFpmcqRXjhBC6DwOckaMGEFBQQGVlZX861//Yu7cuWzcuJHs7Gx++tOf6seNHTu
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg = df.groupby('age')['charges'].mean()\n",
    "avg.plot.line()\n",
    "\n",
    "subset = df.iloc[0:30]\n",
    "avg = subset.groupby('age')['charges'].mean()\n",
    "avg.plot.line()"
   ]
  }
 ],
 "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}