{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа 1\n",
    "\n",
    "Вариант - 11\n",
    "Датасет - цены на бриллианты"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Загрузка и сохранение данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"data/Diamonds Prices2022.csv\")\n",
    "df.to_csv(\"data/Diamonds Prices2022 updated.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получение сведений о датафрейме с данными"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Общая информация о датафрейме"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53943 entries, 0 to 53942\n",
      "Data columns (total 11 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   id       53943 non-null  int64  \n",
      " 1   carat    53943 non-null  float64\n",
      " 2   cut      53943 non-null  object \n",
      " 3   color    53943 non-null  object \n",
      " 4   clarity  53943 non-null  object \n",
      " 5   depth    53943 non-null  float64\n",
      " 6   table    53943 non-null  float64\n",
      " 7   price    53943 non-null  int64  \n",
      " 8   x        53943 non-null  float64\n",
      " 9   y        53943 non-null  float64\n",
      " 10  z        53943 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(3)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Статистическая информация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26972.000000</td>\n",
       "      <td>0.797935</td>\n",
       "      <td>61.749322</td>\n",
       "      <td>57.457251</td>\n",
       "      <td>3932.734294</td>\n",
       "      <td>5.731158</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15572.147122</td>\n",
       "      <td>0.473999</td>\n",
       "      <td>1.432626</td>\n",
       "      <td>2.234549</td>\n",
       "      <td>3989.338447</td>\n",
       "      <td>1.121730</td>\n",
       "      <td>1.142103</td>\n",
       "      <td>0.705679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13486.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26972.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40457.500000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.000000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53943.000000</td>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         carat         depth         table         price  \\\n",
       "count  53943.000000  53943.000000  53943.000000  53943.000000  53943.000000   \n",
       "mean   26972.000000      0.797935     61.749322     57.457251   3932.734294   \n",
       "std    15572.147122      0.473999      1.432626      2.234549   3989.338447   \n",
       "min        1.000000      0.200000     43.000000     43.000000    326.000000   \n",
       "25%    13486.500000      0.400000     61.000000     56.000000    950.000000   \n",
       "50%    26972.000000      0.700000     61.800000     57.000000   2401.000000   \n",
       "75%    40457.500000      1.040000     62.500000     59.000000   5324.000000   \n",
       "max    53943.000000      5.010000     79.000000     95.000000  18823.000000   \n",
       "\n",
       "                  x             y             z  \n",
       "count  53943.000000  53943.000000  53943.000000  \n",
       "mean       5.731158      5.734526      3.538730  \n",
       "std        1.121730      1.142103      0.705679  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        4.710000      4.720000      2.910000  \n",
       "50%        5.700000      5.710000      3.530000  \n",
       "75%        6.540000      6.540000      4.040000  \n",
       "max       10.740000     58.900000     31.800000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получение сведений о колонках датафрейма"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Названия колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price',\n",
       "       'x', 'y', 'z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод отдельных строк и столбцов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Столбец \"carat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53941</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53942</th>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53943 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat\n",
       "0       0.23\n",
       "1       0.21\n",
       "2       0.23\n",
       "3       0.29\n",
       "4       0.31\n",
       "...      ...\n",
       "53938   0.86\n",
       "53939   0.75\n",
       "53940   0.71\n",
       "53941   0.71\n",
       "53942   0.70\n",
       "\n",
       "[53943 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"carat\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Несколько столбцокв"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ideal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>Ideal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>53941</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53941</th>\n",
       "      <td>53942</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53942</th>\n",
       "      <td>53943</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53943 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id        cut\n",
       "0          1      Ideal\n",
       "1          2    Premium\n",
       "2          3       Good\n",
       "3          4    Premium\n",
       "4          5       Good\n",
       "...      ...        ...\n",
       "53938  53939    Premium\n",
       "53939  53940      Ideal\n",
       "53940  53941    Premium\n",
       "53941  53942    Premium\n",
       "53942  53943  Very Good\n",
       "\n",
       "[53943 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"id\", \"cut\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Первая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  carat    cut color clarity  depth  table  price     x     y     z\n",
       "0   1   0.23  Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Вывод по условию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>F</td>\n",
       "      <td>VS1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>402</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.03</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>F</td>\n",
       "      <td>VS1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>402</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.06</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>60.7</td>\n",
       "      <td>59.0</td>\n",
       "      <td>402</td>\n",
       "      <td>3.97</td>\n",
       "      <td>4.01</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>59.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>402</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.06</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>VS1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>402</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>53941</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2756</td>\n",
       "      <td>5.79</td>\n",
       "      <td>5.74</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53941</th>\n",
       "      <td>53942</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2756</td>\n",
       "      <td>5.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53942</th>\n",
       "      <td>53943</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53692 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  carat        cut color clarity  depth  table  price     x     y  \\\n",
       "30        31   0.23  Very Good     F     VS1   60.0   57.0    402  4.00  4.03   \n",
       "31        32   0.23  Very Good     F     VS1   59.8   57.0    402  4.04  4.06   \n",
       "32        33   0.23  Very Good     E     VS1   60.7   59.0    402  3.97  4.01   \n",
       "33        34   0.23  Very Good     E     VS1   59.5   58.0    402  4.01  4.06   \n",
       "34        35   0.23  Very Good     D     VS1   61.9   58.0    402  3.92  3.96   \n",
       "...      ...    ...        ...   ...     ...    ...    ...    ...   ...   ...   \n",
       "53938  53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12   \n",
       "53939  53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87   \n",
       "53940  53941   0.71    Premium     E     SI1   60.5   55.0   2756  5.79  5.74   \n",
       "53941  53942   0.71    Premium     F     SI1   59.8   62.0   2756  5.74  5.73   \n",
       "53942  53943   0.70  Very Good     E     VS2   60.5   59.0   2757  5.71  5.76   \n",
       "\n",
       "          z  \n",
       "30     2.41  \n",
       "31     2.42  \n",
       "32     2.42  \n",
       "33     2.40  \n",
       "34     2.44  \n",
       "...     ...  \n",
       "53938  3.74  \n",
       "53939  3.64  \n",
       "53940  3.49  \n",
       "53941  3.43  \n",
       "53942  3.47  \n",
       "\n",
       "[53692 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"price\"] > 400]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Группировка и агрегация данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Средняя стоимость по типу огранки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
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       "      <th>cut</th>\n",
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       "      <th>Fair</th>\n",
       "      <td>4358.757764</td>\n",
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       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <td>3928.864452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <td>3457.541970</td>\n",
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       "      <th>Premium</th>\n",
       "      <td>4583.992605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <td>3981.658529</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                 price\n",
       "cut                   \n",
       "Fair       4358.757764\n",
       "Good       3928.864452\n",
       "Ideal      3457.541970\n",
       "Premium    4583.992605\n",
       "Very Good  3981.658529"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"cut\"])[[\"price\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Средний вес по типу огранки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "    </tr>\n",
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       "      <th>cut</th>\n",
       "      <th></th>\n",
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       "      <th>Premium</th>\n",
       "      <td>0.891929</td>\n",
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       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <td>0.806373</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat\n",
       "cut                \n",
       "Fair       1.046137\n",
       "Good       0.849185\n",
       "Ideal      0.702837\n",
       "Premium    0.891929\n",
       "Very Good  0.806373"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"cut\")[[\"carat\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сортировка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Сортировка по цене по убыванию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27749</th>\n",
       "      <td>27750</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>18823</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.47</td>\n",
       "      <td>5.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27748</th>\n",
       "      <td>27749</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>18818</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.97</td>\n",
       "      <td>5.04</td>\n",
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       "    <tr>\n",
       "      <th>27747</th>\n",
       "      <td>27748</td>\n",
       "      <td>1.51</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>IF</td>\n",
       "      <td>61.7</td>\n",
       "      <td>55.0</td>\n",
       "      <td>18806</td>\n",
       "      <td>7.37</td>\n",
       "      <td>7.41</td>\n",
       "      <td>4.56</td>\n",
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       "    <tr>\n",
       "      <th>27746</th>\n",
       "      <td>27747</td>\n",
       "      <td>2.07</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>18804</td>\n",
       "      <td>8.20</td>\n",
       "      <td>8.13</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27745</th>\n",
       "      <td>27746</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>18803</td>\n",
       "      <td>7.95</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
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       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53943 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  carat        cut color clarity  depth  table  price     x     y  \\\n",
       "27749  27750   2.29    Premium     I     VS2   60.8   60.0  18823  8.50  8.47   \n",
       "27748  27749   2.00  Very Good     G     SI1   63.5   56.0  18818  7.90  7.97   \n",
       "27747  27748   1.51      Ideal     G      IF   61.7   55.0  18806  7.37  7.41   \n",
       "27746  27747   2.07      Ideal     G     SI2   62.5   55.0  18804  8.20  8.13   \n",
       "27745  27746   2.00  Very Good     H     SI1   62.8   57.0  18803  7.95  8.00   \n",
       "...      ...    ...        ...   ...     ...    ...    ...    ...   ...   ...   \n",
       "4          5   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "3          4   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "2          3   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "1          2   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "0          1   0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "\n",
       "          z  \n",
       "27749  5.16  \n",
       "27748  5.04  \n",
       "27747  4.56  \n",
       "27746  5.11  \n",
       "27745  5.01  \n",
       "...     ...  \n",
       "4      2.75  \n",
       "3      2.63  \n",
       "2      2.31  \n",
       "1      2.31  \n",
       "0      2.43  \n",
       "\n",
       "[53943 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"price\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Сортировка по нескольким столбцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31591</th>\n",
       "      <td>31592</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.8</td>\n",
       "      <td>62.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.77</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31592</th>\n",
       "      <td>31593</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31593</th>\n",
       "      <td>31594</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31594</th>\n",
       "      <td>31595</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.7</td>\n",
       "      <td>62.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.84</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31595</th>\n",
       "      <td>31596</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.7</td>\n",
       "      <td>55.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25998</th>\n",
       "      <td>25999</td>\n",
       "      <td>4.01</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>I1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>15223</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.10</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25999</th>\n",
       "      <td>26000</td>\n",
       "      <td>4.01</td>\n",
       "      <td>Premium</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>62.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15223</td>\n",
       "      <td>10.02</td>\n",
       "      <td>9.94</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27130</th>\n",
       "      <td>27131</td>\n",
       "      <td>4.13</td>\n",
       "      <td>Fair</td>\n",
       "      <td>H</td>\n",
       "      <td>I1</td>\n",
       "      <td>64.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>17329</td>\n",
       "      <td>10.00</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27630</th>\n",
       "      <td>27631</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Fair</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>65.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>18531</td>\n",
       "      <td>10.23</td>\n",
       "      <td>10.16</td>\n",
       "      <td>6.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27415</th>\n",
       "      <td>27416</td>\n",
       "      <td>5.01</td>\n",
       "      <td>Fair</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>65.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18018</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.54</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53943 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  carat      cut color clarity  depth  table  price      x      y  \\\n",
       "31591  31592   0.20  Premium     E     VS2   59.8   62.0    367   3.79   3.77   \n",
       "31592  31593   0.20  Premium     E     VS2   59.0   60.0    367   3.81   3.78   \n",
       "31593  31594   0.20  Premium     E     VS2   61.1   59.0    367   3.81   3.78   \n",
       "31594  31595   0.20  Premium     E     VS2   59.7   62.0    367   3.84   3.80   \n",
       "31595  31596   0.20    Ideal     E     VS2   59.7   55.0    367   3.86   3.84   \n",
       "...      ...    ...      ...   ...     ...    ...    ...    ...    ...    ...   \n",
       "25998  25999   4.01  Premium     I      I1   61.0   61.0  15223  10.14  10.10   \n",
       "25999  26000   4.01  Premium     J      I1   62.5   62.0  15223  10.02   9.94   \n",
       "27130  27131   4.13     Fair     H      I1   64.8   61.0  17329  10.00   9.85   \n",
       "27630  27631   4.50     Fair     J      I1   65.8   58.0  18531  10.23  10.16   \n",
       "27415  27416   5.01     Fair     J      I1   65.5   59.0  18018  10.74  10.54   \n",
       "\n",
       "          z  \n",
       "31591  2.26  \n",
       "31592  2.24  \n",
       "31593  2.32  \n",
       "31594  2.28  \n",
       "31595  2.30  \n",
       "...     ...  \n",
       "25998  6.17  \n",
       "25999  6.24  \n",
       "27130  6.43  \n",
       "27630  6.72  \n",
       "27415  6.98  \n",
       "\n",
       "[53943 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"carat\", \"price\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удаление строк/столбцов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Удаление столбца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>53938</th>\n",
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      "text/plain": [
       "          id  carat        cut color clarity  table  price     x     y     z\n",
       "0          1   0.23      Ideal     E     SI2   55.0    326  3.95  3.98  2.43\n",
       "1          2   0.21    Premium     E     SI1   61.0    326  3.89  3.84  2.31\n",
       "2          3   0.23       Good     E     VS1   65.0    327  4.05  4.07  2.31\n",
       "3          4   0.29    Premium     I     VS2   58.0    334  4.20  4.23  2.63\n",
       "4          5   0.31       Good     J     SI2   58.0    335  4.34  4.35  2.75\n",
       "...      ...    ...        ...   ...     ...    ...    ...   ...   ...   ...\n",
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       "53939  53940   0.75      Ideal     D     SI2   55.0   2757  5.83  5.87  3.64\n",
       "53940  53941   0.71    Premium     E     SI1   55.0   2756  5.79  5.74  3.49\n",
       "53941  53942   0.71    Premium     F     SI1   62.0   2756  5.74  5.73  3.43\n",
       "53942  53943   0.70  Very Good     E     VS2   59.0   2757  5.71  5.76  3.47\n",
       "\n",
       "[53943 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"depth\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удаление строки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>53941</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2756</td>\n",
       "      <td>5.79</td>\n",
       "      <td>5.74</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53941</th>\n",
       "      <td>53942</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2756</td>\n",
       "      <td>5.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53942</th>\n",
       "      <td>53943</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53942 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  carat        cut color clarity  depth  table  price     x     y  \\\n",
       "1          2   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2          3   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3          4   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4          5   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "5          6   0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96   \n",
       "...      ...    ...        ...   ...     ...    ...    ...    ...   ...   ...   \n",
       "53938  53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12   \n",
       "53939  53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87   \n",
       "53940  53941   0.71    Premium     E     SI1   60.5   55.0   2756  5.79  5.74   \n",
       "53941  53942   0.71    Premium     F     SI1   59.8   62.0   2756  5.74  5.73   \n",
       "53942  53943   0.70  Very Good     E     VS2   60.5   59.0   2757  5.71  5.76   \n",
       "\n",
       "          z  \n",
       "1      2.31  \n",
       "2      2.31  \n",
       "3      2.63  \n",
       "4      2.75  \n",
       "5      2.48  \n",
       "...     ...  \n",
       "53938  3.74  \n",
       "53939  3.64  \n",
       "53940  3.49  \n",
       "53941  3.43  \n",
       "53942  3.47  \n",
       "\n",
       "[53942 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(0, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создание новых столбцов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Создание нового столбца \"стоимость 1 карата\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_carat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1417.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1552.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1421.739130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1151.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1080.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>3205.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>3676.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>3881.690141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53941</th>\n",
       "      <td>3881.690141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53942</th>\n",
       "      <td>3938.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53943 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price_carat\n",
       "0      1417.391304\n",
       "1      1552.380952\n",
       "2      1421.739130\n",
       "3      1151.724138\n",
       "4      1080.645161\n",
       "...            ...\n",
       "53938  3205.813953\n",
       "53939  3676.000000\n",
       "53940  3881.690141\n",
       "53941  3881.690141\n",
       "53942  3938.571429\n",
       "\n",
       "[53943 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_carat\"] = df[\"price\"] / df[\"carat\"]\n",
    "df[[\"price_carat\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удаление строк с пустыми значениями"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Удаление строк с NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>price_carat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
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       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1552.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1421.739130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1151.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1080.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "      <td>3205.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3676.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>53941</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2756</td>\n",
       "      <td>5.79</td>\n",
       "      <td>5.74</td>\n",
       "      <td>3.49</td>\n",
       "      <td>3881.690141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53941</th>\n",
       "      <td>53942</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2756</td>\n",
       "      <td>5.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3881.690141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53942</th>\n",
       "      <td>53943</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3938.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53943 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  carat        cut color clarity  depth  table  price     x     y  \\\n",
       "0          1   0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1          2   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2          3   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3          4   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4          5   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "...      ...    ...        ...   ...     ...    ...    ...    ...   ...   ...   \n",
       "53938  53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12   \n",
       "53939  53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87   \n",
       "53940  53941   0.71    Premium     E     SI1   60.5   55.0   2756  5.79  5.74   \n",
       "53941  53942   0.71    Premium     F     SI1   59.8   62.0   2756  5.74  5.73   \n",
       "53942  53943   0.70  Very Good     E     VS2   60.5   59.0   2757  5.71  5.76   \n",
       "\n",
       "          z  price_carat  \n",
       "0      2.43  1417.391304  \n",
       "1      2.31  1552.380952  \n",
       "2      2.31  1421.739130  \n",
       "3      2.63  1151.724138  \n",
       "4      2.75  1080.645161  \n",
       "...     ...          ...  \n",
       "53938  3.74  3205.813953  \n",
       "53939  3.64  3676.000000  \n",
       "53940  3.49  3881.690141  \n",
       "53941  3.43  3881.690141  \n",
       "53942  3.47  3938.571429  \n",
       "\n",
       "[53943 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Заполнить пустые значения для определённого столбца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna({\"price\": df[\"price\"].mean()}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заполнение пустых значений"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Заполнение средним значением"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_mapping = {'Fair': 0, 'Good': 1, 'Very Good': 2, 'Premium': 3, 'Ideal': 4}\n",
    "df['cut'] = df['cut'].map(cut_mapping)\n",
    "\n",
    "color_mapping = {'J': 0, 'I': 1, 'H': 2, 'G': 3, 'F': 4, 'E': 5, 'D': 6} \n",
    "df['color'] = df['color'].map(color_mapping)\n",
    "\n",
    "clarity_mapping = {'I1': 0, 'SI2': 1, 'SI1': 2, 'VS2': 3, 'VS1': 4, 'VVS2': 5, 'VVS1': 6, 'IF': 7} \n",
    "df['clarity'] = df['clarity'].map(clarity_mapping)\n",
    "\n",
    "df.fillna(df.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация данных с Pandas и Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Линейная диаграмма (plot). Вес бриллиантов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
    "df.plot(x=\"id\", y=\"carat\", kind=\"line\")\n",
    "\n",
    "plt.xlabel(\"id\") \n",
    "plt.ylabel(\"weight\")\n",
    "plt.title(\"Weight of diamonds\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Столбчатая диаграмма (bar). Соотношение цены и веса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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(x=\"carat\", y=\"price\", kind=\"bar\")\n",
    "\n",
    "plt.xlabel(\"weight\") \n",
    "plt.ylabel(\"price\")\n",
    "plt.title(\"Price-Weight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Гистограмма (hist). Частота встречаемости по глубине"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"depth\"].plot(kind=\"hist\")\n",
    "\n",
    "plt.xlabel(\"depth\") \n",
    "plt.ylabel(\"Frequency\") \n",
    "plt.title(\"Frequency of depth\") \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Ящик с усами (box). Вес"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"carat\"].plot(kind=\"box\")\n",
    "\n",
    "plt.ylabel(\"weight (carat)\") \n",
    "plt.title(\"Box Plot of diamond weight\") \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Диаграмма с областями (area). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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(x=\"id\", y=\"price\", kind=\"area\")\n",
    "\n",
    "plt.xlabel(\"id\") \n",
    "plt.ylabel(\"price\")\n",
    "plt.title(\"Price by id diamonds\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Диаграмма рассеяния (scatter). Зависимость цены от веса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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(kind=\"scatter\", x=\"carat\", y=\"price\")\n",
    "\n",
    "plt.xlabel(\"weight\") \n",
    "plt.ylabel(\"price\")\n",
    "plt.title(\"The dependence of price on weight\") \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Круговая диаграмма (pie). Прозрачность"
   ]
  },
  {
   "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": [
    "# Определение порога для объединения редких значений\n",
    "threshold = 0.02  # Порог 2%\n",
    "\n",
    "# Подсчёт количества уникальных значений и расчёт частот\n",
    "value_counts = df[\"clarity\"].value_counts()\n",
    "total_count = value_counts.sum()\n",
    "\n",
    "# Условие для агрегации значений ниже порога\n",
    "other_values = value_counts[value_counts / total_count < threshold].sum()\n",
    "main_values = value_counts[value_counts / total_count >= threshold]\n",
    "\n",
    "# Добавление категории \"Other\"\n",
    "main_values[\"Other\"] = other_values\n",
    "\n",
    "# Построение диаграммы\n",
    "main_values.plot(kind=\"pie\", \n",
    "                 autopct='%1.1f%%',  # Проценты\n",
    "                 startangle=90,      # Начальный угол\n",
    "                 counterclock=False, # По часовой стрелке\n",
    "                 cmap=\"Set3\",        # Цветовая схема\n",
    "                 wedgeprops={'edgecolor': 'black'}) # Границы сегментов\n",
    "\n",
    "plt.title(\"Distribution of Daily Customer Count in Stores (Aggregated)\")\n",
    "plt.subplots_adjust(left=0.3, right=0.7, top=0.9, bottom=0.1)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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
}