TSK_AIBA/LR1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа №1\n",
    "## Вариант №7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('data.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 369 entries, 0 to 368\n",
      "Data columns (total 14 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   stock index          369 non-null    object \n",
      " 1   country              369 non-null    object \n",
      " 2   year                 369 non-null    float64\n",
      " 3   index price          317 non-null    float64\n",
      " 4   log_indexprice       369 non-null    float64\n",
      " 5   inflationrate        326 non-null    float64\n",
      " 6   oil prices           369 non-null    float64\n",
      " 7   exchange_rate        367 non-null    float64\n",
      " 8   gdppercent           350 non-null    float64\n",
      " 9   percapitaincome      368 non-null    float64\n",
      " 10  unemploymentrate     348 non-null    float64\n",
      " 11  manufacturingoutput  278 non-null    float64\n",
      " 12  tradebalance         365 non-null    float64\n",
      " 13  USTreasury           369 non-null    float64\n",
      "dtypes: float64(12), object(2)\n",
      "memory usage: 40.5+ KB\n",
      "None\n",
      "              year   index price  log_indexprice  inflationrate  oil prices  \\\n",
      "count   369.000000    317.000000      369.000000     326.000000  369.000000   \n",
      "mean   2000.000000   7898.648297        3.610542       0.041748   39.743171   \n",
      "std      11.848225   7811.336862        0.482481       0.039579   25.452654   \n",
      "min    1980.000000    168.610000        2.230000      -0.040000   11.350000   \n",
      "25%    1990.000000   2407.100000        3.320000       0.020000   19.410000   \n",
      "50%    2000.000000   5160.100000        3.600000       0.030000   28.520000   \n",
      "75%    2010.000000  10279.500000        3.980000       0.057500   57.880000   \n",
      "max    2020.000000  47751.330000        4.680000       0.240000   98.560000   \n",
      "\n",
      "       exchange_rate  gdppercent  percapitaincome  unemploymentrate  \\\n",
      "count     367.000000  350.000000       368.000000        348.000000   \n",
      "mean       27.897548    0.037114     20719.964674          0.068908   \n",
      "std        49.620521    0.037850     17435.037783          0.043207   \n",
      "min         0.900000   -0.110000        27.000000          0.020000   \n",
      "25%         1.330000    0.020000      2090.250000          0.040000   \n",
      "50%         5.440000    0.030000     19969.500000          0.060000   \n",
      "75%        15.055000    0.060000     36384.000000          0.090000   \n",
      "max       249.050000    0.150000     65280.000000          0.260000   \n",
      "\n",
      "       manufacturingoutput  tradebalance  USTreasury  \n",
      "count           278.000000    365.000000  369.000000  \n",
      "mean            328.084820    -15.996384    0.059024  \n",
      "std             622.395923    154.557170    0.033086  \n",
      "min               0.590000   -770.930000    0.010000  \n",
      "25%              80.380000    -25.370000    0.030000  \n",
      "50%             188.160000     -0.140000    0.050000  \n",
      "75%             271.977500     19.080000    0.080000  \n",
      "max            3868.460000    366.140000    0.140000  \n"
     ]
    }
   ],
   "source": [
    "print(df.info()) \n",
    "print(df.describe())  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n",
      "       'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n",
      "       'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n",
      "       'tradebalance', 'USTreasury'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stock index                              NASDAQ\n",
      "country                United States of America\n",
      "year                                     1980.0\n",
      "index price                              168.61\n",
      "log_indexprice                             2.23\n",
      "inflationrate                              0.14\n",
      "oil prices                                21.59\n",
      "exchange_rate                               1.0\n",
      "gdppercent                                 0.09\n",
      "percapitaincome                         12575.0\n",
      "unemploymentrate                           0.07\n",
      "manufacturingoutput                         NaN\n",
      "tradebalance                             -13.06\n",
      "USTreasury                                 0.11\n",
      "Name: 0, dtype: object\n",
      "0      United States of America\n",
      "1      United States of America\n",
      "2      United States of America\n",
      "3      United States of America\n",
      "4      United States of America\n",
      "                 ...           \n",
      "364                       Spain\n",
      "365                       Spain\n",
      "366                       Spain\n",
      "367                       Spain\n",
      "368                       Spain\n",
      "Name: country, Length: 369, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[0]) \n",
    "print(df['country']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           index price\n",
      "country                               \n",
      "China                      2329.655417\n",
      "France                     3166.182683\n",
      "Germany                    5030.647561\n",
      "Hong Kong                 14790.129091\n",
      "India                     14204.314516\n",
      "Japan                     16142.580000\n",
      "Spain                      7735.470294\n",
      "United Kingdom             5323.457097\n",
      "United States of America   2282.994878\n"
     ]
    }
   ],
   "source": [
    "grouped = df.groupby('country').agg({'index price': 'mean'})\n",
    "print(grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    stock index country    year  index price  log_indexprice  inflationrate  \\\n",
      "307      CAC 40  France  2000.0      5926.42            3.77           0.02   \n",
      "304      CAC 40  France  1997.0      2998.90            3.48           0.01   \n",
      "303      CAC 40  France  1996.0      2315.70            3.36           0.02   \n",
      "302      CAC 40  France  1995.0      1872.00            3.27           0.02   \n",
      "301      CAC 40  France  1994.0      1881.20            3.27           0.02   \n",
      "..          ...     ...     ...          ...             ...            ...   \n",
      "228      SZCOMP   China  2003.0      1467.88            3.17           0.01   \n",
      "227      SZCOMP   China  2002.0      1561.31            3.19          -0.01   \n",
      "226      SZCOMP   China  2001.0      1940.96            3.29           0.01   \n",
      "235      SZCOMP   China  2010.0      2795.88            3.45           0.03   \n",
      "244      SZCOMP   China  2019.0      2928.94            3.47           0.03   \n",
      "\n",
      "     oil prices  exchange_rate  gdppercent  percapitaincome  unemploymentrate  \\\n",
      "307       28.44           0.92        0.04          22420.0              0.10   \n",
      "304       18.33           5.84        0.02           3694.0              0.12   \n",
      "303       25.23           5.12        0.01           4097.0              0.12   \n",
      "302       19.03           4.99        0.02           4099.0              0.12   \n",
      "301       17.16           5.55        0.02           3582.0              0.12   \n",
      "..          ...            ...         ...              ...               ...   \n",
      "228       32.13           8.28        0.10           1289.0              0.04   \n",
      "227       29.46           8.28        0.09           1149.0              0.03   \n",
      "226       19.39           8.28        0.08           1053.0              0.03   \n",
      "235       89.15           6.77        0.11           4550.0              0.04   \n",
      "244       59.88           6.91        0.06          10217.0              0.05   \n",
      "\n",
      "     manufacturingoutput  tradebalance  USTreasury  \n",
      "307               197.71         18.15        0.06  \n",
      "304                32.50          6.67        0.06  \n",
      "303                35.44          4.36        0.06  \n",
      "302                36.32          3.90        0.07  \n",
      "301                31.23          3.18        0.07  \n",
      "..                   ...           ...         ...  \n",
      "228                  NaN         35.82        0.04  \n",
      "227                  NaN         37.38        0.05  \n",
      "226                  NaN         28.09        0.05  \n",
      "235              1924.32        222.40        0.03  \n",
      "244              3823.41        164.99        0.02  \n",
      "\n",
      "[369 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "sorted_df = df.sort_values(by='stock index', ascending=True) \n",
    "print(sorted_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dropped = df.drop(columns=['USTreasury'])  \n",
    "df_dropped = df_dropped.drop(index=[0, 1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['real_index_price'] = df['index price'] / (1 + df['inflationrate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cleaned = df.dropna()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filled = df.fillna(value=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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",
    "for country in df['country'].unique():\n",
    "    plt.plot(df[df['country'] == country]['year'], df[df['country'] == country]['index price'], label=country)\n",
    "plt.title('Индексные цены по годам')\n",
    "plt.xlabel('Год')\n",
    "plt.ylabel('Индексная цена')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAJmCAYAAABBkbStAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAbE9JREFUeJzt3QeUU+X2//9N7x1p0i10QUGK0ntTml5RFBQEG00UEAsIeAVRUBCVi0qzIghcBKVIV1CR3hEERaWotAtKn//6PL/vyT8ThiGDM0xOeL/Wyprk5ExykswkO/vZz35SxMTExBgAAADilTL+qwEAACAETQAAAGEgaAIAAAgDQRMAAEAYCJoAAADCQNAEAAAQBoImAACAMBA0AQAAhIGgCQAAIAwETQAAAGEgaEJE27lzpz300ENWvHhxS58+vWXNmtVuvfVWGzlypP3999/JfXgAgCtI6uQ+AOBCZs+ebXfeeaelS5fO2rdvb2XLlrVTp07ZV199Zb1797ZNmzbZ2LFjk/swAQBXiBQs2ItItGvXLrvhhhusYMGCtnDhQsufP3+s63fs2OGCqh49eiTbMQIAriwMzyEiDRs2zI4dO2bvvvvueQGTXHvttbECphQpUljXrl3tgw8+sBIlSrihvIoVK9rSpUvP+91ff/3VOnbsaHnz5nVZrDJlyti4cePiPI7nn3/e3XboqXbt2rH202VlwkK98sorbv/du3cHthUtWtTuv//+WPtNmTLF7afrgp07d85ee+01d4x6TDpmDVceOnTILkb3EXp7e/bssQwZMsR5TM2bNz/vNvScat9QixcvjvN5iWvfcJ5v7/b00/Pbb7+546pUqZL7W/CcOHHCvS7XX3+9e07099G6dWs3lCt6XLotPfeh9BoFv3be/U6dOjVBz+M/fV0yZ8583nYdQ+hzICdPnrQBAwa4v3k9f4UKFbI+ffq47cG8/4FQel2Djz++5yf07/5i9Fxe6O8g9G9M3nzzTfec6XEUKFDAHnvsMTt8+LCFQ39HnTp1cr+n3y9WrJg98sgjLvs8YcKEeI9DJ+0T/Pz/+OOP1qhRI8uUKZO7zUGDBlloDkHP0S233GK5cuVy/zd6T4nrbyXc5z6ht6mT/s5ClSxZ8oL3iaTF8Bwi0meffebqmPTmEq4lS5bY5MmTrXv37u5NVW/QjRs3tu+++y4Q0Ozfv9+qVq0aeMO56qqr7IsvvnBvxkePHrWePXvGedtvvfVW4IOuX79+lpjOnDljzzzzTJzX6YNYb/YPPPCAe1zKwI0ePdrWrFljX3/9taVJkyZB99W/f38XdCQWHdPNN9/szk+aNMnmz58f6/pLfb6PHDliTZo0cY/v888/Dzz3Z8+edR9ECxYssLZt27rA+X//+5+7340bN9o111xjl0Nivy4XouDs9ttvd0PSXbp0sVKlStmGDRvs1Vdfte3bt9uMGTMsuSkbPGTIkFjb9Jp99NFH5wViAwcOtPr167tgZ9u2be7/auXKlRd9zhRAV65c2QVYeh4UNCiIUrDx119/Wc2aNe29994L7P/vf//b/Qz+vwp+L9Hfkd4b9LepL2hz5sxxgan+FxU8eVQ7qee/Xbt2Ljj7+OOPXcnArFmzrFmzZpf0fCXkNhWQjx8/Ptb/yfLly+2nn366pPtGItDwHBBJjhw5oq97MS1atAj7d7S/Tt9//31g208//RSTPn36mFatWgW2derUKSZ//vwxf/zxR6zfb9u2bUy2bNli/vrrr1jbn376aXe7wfuXKVMmplatWrH202VtD/Xyyy+739+1a1dgW5EiRWI6dOgQuPzmm2/GpEuXLqZOnTruOs+yZcvc737wwQexbnPOnDlxbg+l+wi+vY0bN8akTJkypkmTJnEeU7Nmzc67jccee8ztG2revHlu+9SpU+PdN9zne9GiRe539fPEiRMxtWvXjsmTJ0/Mjh07Yv3euHHj3H4jRow475jOnTvnfupxaR8996FCXzvvfqdMmRIT7vOYGK9LpkyZztuuY/CeA897773nXjPdZ7AxY8a4fb/++uvANl3WaxBKr2vw8cf3/HgGDBgQ5+seKty/+wMHDsSkTZs2pmHDhjFnz54N7Dd69Gi3n17X+LRv3949DytXrrzg6x56XKH/o8HPv+6zW7dusW5Dz5OO8ffffw9sD30/OHXqVEzZsmVj6tatG2t7uM99Qm/zjjvuiEmdOnWs9zX9T91zzz0XvE8kLYbnEHGUgZAsWbIk6PeqVavmUt2ewoULW4sWLWzu3Lnum6Xehz799FO77bbb3Pk//vgjcFKaXtmN1atXx7pNLyujb3wXo/sIvk2d9C04Prpe32yVhdHxhg7ZZcuWzRo0aBDrNvUYlXlZtGhRgp4fZchuuukm9602LqdPnz7v+C+UlQrnebmU51uZFRX9f/PNNy5bEZo50u3lzp3bunXrdt79hQ4n6bkNfTx6jeKibJWuD2eoKLFfl4vdl7JLyqwE31fdunXd9aH3pdcl9DHrdY2L9/xoSDGpS1u//PJLl1VRxiRlyv//Y6dz585uRqzqEy9EfxPKqOnvSEO1ocIZRoxL8NCWlwnVMepYPRo+8+h50t9sjRo1zvu7Tchzn5Db1LCvsk/KNnmv2SeffOIynEgeDM8h4uhN1PsgS4jrrrvuvG2qe9Ebze+//+7erPWhqBl3F5p1d+DAgViX9canYYOMGTNe9P63bt3qhp8SYsSIEe7N9umnn7ZevXrFuu6HH35wb6h58uQJ61jjo+EdDXlqWOvnn3+Oc5958+aFffx6XkTBw4XoOU/o863hFAVM+hCLK+BU3ZJq1lKnvvhbl4ZbdIrrgyiUaq48Cnz0Aa0hsLj2TczX5WJ0X1u2bLng6xJ6X6oB1ClUkSJF4n1+FPwqEFP9TFz/R/+UN5yk1y5Y2rRp3TB8fMNN+jvSF6m4agYvld4LdL+h7xUSXIelIbMXXnjB1q5dG6uGLK5ALdznPiG3KQqQdBo+fLgLonPkyBEImnH5ETQhIoMmFWaqRiUx6Rur3HvvvdahQ4c499GMvWB6A1UGKJxvsyr4fPvtt2Nt05vchQIGBR4vv/yyywDlzJkzzuPVB7OK2+OSkACtb9++LrujN1uvIDZUlSpV3Jt5MNXp/Pe//z1vX++DJbTINfT4E/p8f/vtt+74dL+qXdEHi+rTLoV+PzSrpszGhWq99G1fmYFVq1a57J8CPmW7kvJ1uRjdV7ly5VxwHRcVhQdTZjW0OPjZZ5+1ffv2XfD5UfZNgZlqjlq2bOlaecBs2bJlrvZI9VKqj9SEA32BUtbnww8/PG//cJ77hN6mKNOk4FLZNu2n/6XgbB0uL4ImRCQV+yrYWLFihRt2C/dbeSgVyypL5H2QachPHxIqRr0YFYWuW7fOFYyGQ7NwQm9XH/oXogBFx3OhtgkamtJQgZp5Bqf0E0pvtnoe40r/B9OwV+jxX6jQ+Pvvv7d8+fK5IuAL0XOekOdbVCisD4UKFSq4oRg9R4MHD471nCiwUnBzsWJrZUxC71evUVwUmHj7qgBd2biJEye6v4Gkel3CofvS32C9evXCCtz1eoQ+ZmWP4gqagp8fBdTK7CnTd6FM5D/hZVtU/B2c4dFwmIro4/v70N+Rvkgl5pcoBaOaPedll7z3iuAvAhoKVgZOw/vBgbs3VHYpz31Cb1OUVb3vvvtccbsC2gvN9MXlQbiKiKQp1fqAe/DBB90MrLiGaTQLJVhoYKDp9cqSNGzY0FKlSuVObdq0cW9ccb0BaxggdLhKwzD6BpnYlKnRzCF9u7/QB++//vUvF3AEBw0efZiHU3+j39fQ3z333OMCkcTw559/uloafWOOT0Kfb1G2R8qXL29PPvmkvfTSS7F+V7enDJ0yUaESsy5HH6r6Nh9XoJIYr0u4dF+aJRaawRR1xD9+/Hii3ZeXGdTrltgUTChbMmrUqFivk4az9D8W30w0vQ7KgGl4WcF6Yr3uwX9Dug1dViCuANV7HvT6B9fB6f/2n8xYvNTb1PCxZk0qQxU6rIjLi0wTIpK+YStdfdddd7lC2OCO4Jpyq2Gv0F5Hul7fmINbDnjZC8/
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('country')['index price'].mean().plot(kind='bar')\n",
    "plt.title('Средние индексные цены по странам')\n",
    "plt.ylabel('Средняя индексная цена')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['index price'].hist(bins=30)\n",
    "plt.title('Гистограмма индексных цен')\n",
    "plt.xlabel('Индексная цена')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='index price')\n",
    "plt.title('Ящик с усами для индексных цен')\n",
    "plt.ylabel('Индексная цена')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.area(x='year', y='percapitaincome', alpha=0.5)\n",
    "plt.title('Диаграмма с областями для ВВП на душу населения')\n",
    "plt.xlabel('Год')\n",
    "plt.ylabel('ВВП на душу населения')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['oil prices'], df['index price'])\n",
    "plt.title('Диаграмма рассеяния: Цены на нефть vs Индексная цена')\n",
    "plt.xlabel('Цены на нефть')\n",
    "plt.ylabel('Индексная цена')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['country'].value_counts().plot.pie(autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Распределение стран по индексной цене')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  }
 ],
 "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
}