sem08_pandas/sem08_pandas_intro_2012-209.ipynb

{
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    "id": "3Mk-Yh-B60hr",
    "toc": true
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Введение-в-pandas\" data-toc-modified-id=\"Введение-в-pandas-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Введение в pandas</a></span><ul class=\"toc-item\"><li><span><a href=\"#Загружаем-и-смотрим-датасет\" data-toc-modified-id=\"Загружаем-и-смотрим-датасет-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Загружаем и смотрим датасет</a></span></li><li><span><a href=\"#Считаем-различные-характеристики\" data-toc-modified-id=\"Считаем-различные-характеристики-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Считаем различные характеристики</a></span><ul class=\"toc-item\"><li><span><a href=\"#Агрегирующие-функции\" data-toc-modified-id=\"Агрегирующие-функции-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Агрегирующие функции</a></span></li><li><span><a href=\"#Группировки\" data-toc-modified-id=\"Группировки-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Группировки</a></span></li><li><span><a href=\"#Уникальные-значения\" data-toc-modified-id=\"Уникальные-значения-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>Уникальные значения</a></span></li></ul></li><li><span><a href=\"#Гистограмма-распределения\" data-toc-modified-id=\"Гистограмма-распределения-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Гистограмма распределения</a></span><ul class=\"toc-item\"><li><span><a href=\"#Среднее,-медиана,-мода\" data-toc-modified-id=\"Среднее,-медиана,-мода-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Среднее, медиана, мода</a></span></li></ul></li><li><span><a href=\"#Создаем-и-удаляем-колонки\" data-toc-modified-id=\"Создаем-и-удаляем-колонки-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Создаем и удаляем колонки</a></span></li><li><span><a href=\"#Индексация-и-срезы\" data-toc-modified-id=\"Индексация-и-срезы-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Индексация и срезы</a></span><ul class=\"toc-item\"><li><span><a href=\"#Индексация\" data-toc-modified-id=\"Индексация-1.5.1\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>Индексация</a></span></li><li><span><a href=\"#Условные-срезы\" data-toc-modified-id=\"Условные-срезы-1.5.2\"><span class=\"toc-item-num\">1.5.2&nbsp;&nbsp;</span>Условные срезы</a></span></li><li><span><a href=\"#Условные-операторы\" data-toc-modified-id=\"Условные-операторы-1.5.3\"><span class=\"toc-item-num\">1.5.3&nbsp;&nbsp;</span>Условные операторы</a></span></li><li><span><a href=\"#Работа-со-строками\" data-toc-modified-id=\"Работа-со-строками-1.5.4\"><span class=\"toc-item-num\">1.5.4&nbsp;&nbsp;</span>Работа со строками</a></span></li></ul></li></ul></li><li><span><a href=\"#Ревью\" data-toc-modified-id=\"Ревью-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Ревью</a></span></li><li><span><a href=\"#Практика\" data-toc-modified-id=\"Практика-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Практика</a></span></li><li><span><a href=\"#Практика\" data-toc-modified-id=\"Практика-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Практика</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RiX4BSlA60hs"
   },
   "source": [
    "# Введение в pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WtUhQqnm60ht"
   },
   "source": [
    "- Pandas — библиотека языка Python для удобных обработки и анализа данных.\n",
    "- Официальная документация [тут](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)\n",
    "---\n",
    "Небольшой список литературы:\n",
    "1. [Шпаргалка по pandas](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf)\n",
    "2. [Введение в pandas](https://khashtamov.com/ru/pandas-introduction/)\n",
    "3. [Туториалы в официальной документации (на английском)](https://pandas.pydata.org/docs/getting_started/10min.html)\n",
    "---\n",
    "Необязательное ДЗ\n",
    "- проработать [статью](https://habr.com/ru/company/ods/blog/322626/) про то как в pandas можно делать разные штуки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/1LYA/Downloads'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir('new folder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('new folder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir('super new folder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/1LYA/Downloads'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/Users/1LYA/Downloads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('/Users/1LYA/Downloads/new folder/super new folder/untitled.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/Users/1LYA/Downloads/new folder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/1LYA/Downloads/new folder'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('./super new folder/untitled.txt')\n",
    "s = f.read()\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6]]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [list(map(int,x.split())) for x in s.split('\\n')]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/1LYA/Downloads/new folder'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/1LYA/Downloads/new folder/super new folder\n"
     ]
    }
   ],
   "source": [
    "cd 'super new folder'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/1LYA/Downloads/new folder/super new folder'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ZYX4eDk60hu"
   },
   "source": [
    "## Загружаем и смотрим датасет"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fLnoJjg960hv"
   },
   "source": [
    "Импортируем библиотеку `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "Zhy04XbE60hw"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xWFrQv-V60h0"
   },
   "outputs": [],
   "source": [
    "# ! pip install pandas # установка модуля, если его нет и выше ошибка\n",
    "# ! pip install --upgrade pandas # обновление модуля"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pIHCC4YN60h3"
   },
   "source": [
    "- Будем анализировать классический датасет о бриллиантах. \n",
    "- Больше информации о нем [тут](https://www.kaggle.com/shivam2503/diamonds).\n",
    "- Скачать можно [тут](https://drive.google.com/file/d/1PwB6fLZiAOwpDu7VzyI1suOdRgs5YvdA/view)\n",
    "\n",
    "Описание колонок датасета:\n",
    "- `price` – цена бриллианта (в долларах)\n",
    "\n",
    "- `carat` – масса бриллианта (в каратах)\n",
    "\n",
    "- `cut` – качество огранки бриллианта (Fair, Good, Very Good, Premium, Ideal)\n",
    "\n",
    "- `color` – цвет бриллианта, from J (худший) to D (лучший)\n",
    "\n",
    "- `clarity` – чистота бриллианта (I1 (худший), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (лучший))\n",
    "\n",
    "- `x`, `y`, `z` – длина, ширина, высота (в мм)\n",
    "\n",
    "- `depth` – процент глубины равный 2 * z / (x + y)\n",
    "\n",
    "- `table` – ширина площадки сверху бриллианта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tpZLcfGE60h4"
   },
   "source": [
    "Загружаем датасет из `csv`-файла в переменную `df` специального типа `pd.DataFrame`. Внимательно указываем путь к файлу.\n",
    "\n",
    "Если не хотите указывать путь к файлу, переместите датасет в ту же папку, где и ноутбук!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/1LYA/Downloads/new folder/super new folder'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/1LYA/Downloads/Анализ данных на python 2021 - Оценки.tsv', decimal=',', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>Адрес студенческой почты</th>\n",
       "      <th>группа</th>\n",
       "      <th>Login</th>\n",
       "      <th>num</th>\n",
       "      <th>hw01_a</th>\n",
       "      <th>hw01_b</th>\n",
       "      <th>hw01_c</th>\n",
       "      <th>hw01_d</th>\n",
       "      <th>hw01_e</th>\n",
       "      <th>...</th>\n",
       "      <th>cw01</th>\n",
       "      <th>cw02_A</th>\n",
       "      <th>cw02_B</th>\n",
       "      <th>cw02_C</th>\n",
       "      <th>cw02</th>\n",
       "      <th>cw03</th>\n",
       "      <th>КР</th>\n",
       "      <th>ЭКЗ</th>\n",
       "      <th>Сумма</th>\n",
       "      <th>Итог</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Алгазинов Александр Иванович</td>\n",
       "      <td>aialgazinov@edu.hse.ru</td>\n",
       "      <td>БЭК201</td>\n",
       "      <td>dap_econ_2021_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,935</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Арайсси Габриел -</td>\n",
       "      <td>garayssi@edu.hse.ru</td>\n",
       "      <td>БЭК201</td>\n",
       "      <td>dap_econ_2021_2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,09</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Батавиль Абдулрахман -</td>\n",
       "      <td>abatavil@edu.hse.ru</td>\n",
       "      <td>БЭК201</td>\n",
       "      <td>dap_econ_2021_3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Адрес студенческой почты  группа  \\\n",
       "0  Алгазинов Александр Иванович   aialgazinov@edu.hse.ru  БЭК201   \n",
       "1             Арайсси Габриел -      garayssi@edu.hse.ru  БЭК201   \n",
       "2        Батавиль Абдулрахман -      abatavil@edu.hse.ru  БЭК201   \n",
       "\n",
       "             Login  num  hw01_a  hw01_b  hw01_c  hw01_d  hw01_e  ...  cw01  \\\n",
       "0  dap_econ_2021_1  1.0     5.0     5.0     5.0     5.0    10.0  ...  75.0   \n",
       "1  dap_econ_2021_2  2.0     5.0     5.0     5.0     4.0    10.0  ...  55.0   \n",
       "2  dap_econ_2021_3  3.0     NaN     NaN     NaN     NaN     NaN  ...   0.0   \n",
       "\n",
       "   cw02_A  cw02_B  cw02_C   cw02  cw03  КР  ЭКЗ  Сумма  Итог  \n",
       "0    34.0    33.0    33.0  100.0   NaN NaN  NaN  2,935   3.0  \n",
       "1     0.0     NaN     NaN    0.0   NaN NaN  NaN   1,09   1.0  \n",
       "2     NaN     NaN     NaN    0.0   NaN NaN  NaN      0   0.0  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('/Users/1LYA/Downloads/Анализ данных на python 2021 - Оценки copy.csv', decimal=',', sep='\\t')\n",
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 450 entries, 0 to 449\n",
      "Data columns (total 43 columns):\n",
      " #   Column                                                Non-Null Count  Dtype  \n",
      "---  ------                                                --------------  -----  \n",
      " 0                                                         339 non-null    object \n",
      " 1   Адрес студенческой почты                              338 non-null    object \n",
      " 2   группа                                                338 non-null    object \n",
      " 3   Login                                                 449 non-null    object \n",
      " 4   num                                                   449 non-null    float64\n",
      " 5   hw01_a                                                283 non-null    float64\n",
      " 6   hw01_b                                                280 non-null    float64\n",
      " 7   hw01_c                                                259 non-null    float64\n",
      " 8   hw01_d                                                249 non-null    float64\n",
      " 9   hw01_e                                                227 non-null    float64\n",
      " 10  hw01_f                                                238 non-null    float64\n",
      " 11  hw01_g                                                217 non-null    float64\n",
      " 12  hw01_h                                                208 non-null    float64\n",
      " 13  hw01_i                                                220 non-null    float64\n",
      " 14  hw01_j                                                219 non-null    float64\n",
      " 15  hw01                                                  338 non-null    float64\n",
      " 16  MVP для приложения \"Пенькофф Инвестиции\" (20 баллов)  144 non-null    float64\n",
      " 17  Stonks (10 баллов)                                    245 non-null    float64\n",
      " 18  Аэрохоккей (20 баллов)                                223 non-null    float64\n",
      " 19  Иначе говоря (10 баллов)                              228 non-null    float64\n",
      " 20  Каверы (10 баллов)                                    187 non-null    float64\n",
      " 21  Лоликек (15 баллов)                                   231 non-null    float64\n",
      " 22  Сжатие последовательности (15 баллов)                 211 non-null    float64\n",
      " 23  Экзамен по машинному обучению 2 (10 баллов)           221 non-null    float64\n",
      " 24  hw02                                                  337 non-null    float64\n",
      " 25  hw03                                                  0 non-null      float64\n",
      " 26  hw04                                                  0 non-null      float64\n",
      " 27  hw05                                                  0 non-null      float64\n",
      " 28  cw01_A                                                238 non-null    float64\n",
      " 29  cw01_B                                                258 non-null    float64\n",
      " 30  cw01_C                                                163 non-null    float64\n",
      " 31  cw01_D                                                255 non-null    float64\n",
      " 32  cw01_E                                                125 non-null    float64\n",
      " 33  cw01                                                  338 non-null    float64\n",
      " 34  cw02_A                                                275 non-null    float64\n",
      " 35  cw02_B                                                209 non-null    float64\n",
      " 36  cw02_C                                                246 non-null    float64\n",
      " 37  cw02                                                  337 non-null    float64\n",
      " 38  cw03                                                  0 non-null      float64\n",
      " 39  КР                                                    0 non-null      float64\n",
      " 40  ЭКЗ                                                   0 non-null      float64\n",
      " 41  Сумма                                                 338 non-null    object \n",
      " 42  Итог                                                  335 non-null    float64\n",
      "dtypes: float64(38), object(5)\n",
      "memory usage: 151.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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></th>\n",
       "      <th>Адрес студенческой почты</th>\n",
       "      <th>группа</th>\n",
       "      <th>Login</th>\n",
       "      <th>num</th>\n",
       "      <th>hw01_a</th>\n",
       "      <th>hw01_b</th>\n",
       "      <th>hw01_c</th>\n",
       "      <th>hw01_d</th>\n",
       "      <th>hw01_e</th>\n",
       "      <th>...</th>\n",
       "      <th>cw01</th>\n",
       "      <th>cw02_A</th>\n",
       "      <th>cw02_B</th>\n",
       "      <th>cw02_C</th>\n",
       "      <th>cw02</th>\n",
       "      <th>cw03</th>\n",
       "      <th>КР</th>\n",
       "      <th>ЭКЗ</th>\n",
       "      <th>Сумма</th>\n",
       "      <th>Итог</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dap_econ_2021_447</td>\n",
       "      <td>447.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dap_econ_2021_448</td>\n",
       "      <td>448.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dap_econ_2021_449</td>\n",
       "      <td>449.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Марудас Павлос</td>\n",
       "      <td>pmaroudas@edu.hse.ru</td>\n",
       "      <td>БЭК2011</td>\n",
       "      <td>dap_econ_2021_450</td>\n",
       "      <td>450.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#REF!</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Адрес студенческой почты   группа              Login  \\\n",
       "445             NaN                      NaN      NaN  dap_econ_2021_446   \n",
       "446             NaN                      NaN      NaN  dap_econ_2021_447   \n",
       "447             NaN                      NaN      NaN  dap_econ_2021_448   \n",
       "448             NaN                      NaN      NaN  dap_econ_2021_449   \n",
       "449  Марудас Павлос     pmaroudas@edu.hse.ru  БЭК2011  dap_econ_2021_450   \n",
       "\n",
       "       num  hw01_a  hw01_b  hw01_c  hw01_d  hw01_e  ...  cw01  cw02_A  cw02_B  \\\n",
       "445  446.0     NaN     NaN     NaN     NaN     NaN  ...   NaN     NaN     NaN   \n",
       "446  447.0     NaN     NaN     NaN     NaN     NaN  ...   NaN     NaN     NaN   \n",
       "447  448.0     NaN     NaN     NaN     NaN     NaN  ...   NaN     NaN     NaN   \n",
       "448  449.0     NaN     NaN     NaN     NaN     NaN  ...   NaN     NaN     NaN   \n",
       "449  450.0     NaN     NaN     NaN     NaN     NaN  ...   0.0     NaN     NaN   \n",
       "\n",
       "     cw02_C  cw02  cw03  КР  ЭКЗ  Сумма  Итог  \n",
       "445     NaN   NaN   NaN NaN  NaN    NaN   NaN  \n",
       "446     NaN   NaN   NaN NaN  NaN    NaN   NaN  \n",
       "447     NaN   NaN   NaN NaN  NaN    NaN   NaN  \n",
       "448     NaN   NaN   NaN NaN  NaN    NaN   NaN  \n",
       "449     NaN   NaN   NaN NaN  NaN  #REF!   NaN  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "xAZWeHWk60h5"
   },
   "outputs": [],
   "source": [
    "# df = pd.read_csv('diamonds.csv', index_col=0)\n",
    "\n",
    "## если нужно указать путь к файлу\n",
    "df = pd.read_csv('~/Downloads/diamonds.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "pRiY9qir60h8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/1LYA/Downloads/new folder/super new folder'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## если вы потерялись, где вы, проверьте!\n",
    "import os \n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "hA-ey00260h_",
    "outputId": "b79297bc-648c-42bd-cdb5-75eed16b4274"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>5</th>\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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\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>53940</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "1       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "4       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "5       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53936   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53937   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53938   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4feYnVGt60iE",
    "outputId": "15e8c555-a1be-4810-d31d-111b89659659"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SMM1eCCb60iI"
   },
   "source": [
    "у функции `read_csv`, есть ряд важных параметров, которые иногда могут пригодиться\n",
    "- `sep` — разделитель данных, по умолчанию `','`\n",
    "- `decimal` — разделитель числа на целую и дробную часть, по умолчанию `'.'`\n",
    "- `names` — список с названиями колонок\n",
    "- `skiprows` — если файл содержит системную информацию, можно просто её пропустить\n",
    "- `index_col` - номер колонки с индексом"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z0YX_gLM60iJ"
   },
   "source": [
    "Посмотрим на первые 10 строк датасета с помощью метода `.head()` и последние 5 строк с помощью метода `.tail()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "XrTqXjQf60iK",
    "outputId": "d1f3b78c-0c61-411f-8319-1786bd3b5368"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <th>y</th>\n",
       "      <th>z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>5</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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",
       "      <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>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "5Wkd8BHJ60iQ",
    "outputId": "554af535-461f-444b-c777-208419da6ba3"
   },
   "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",
       "      <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>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\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>53940</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "53936   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53937   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53938   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hesPGLf560iT"
   },
   "source": [
    "Посмотрим на размер нашего датасета c помощью атрибута `shape`, который выводит `кортеж` из двух чисел\n",
    "- первое число – количество строк (наблюдений)\n",
    "- второе число – количество столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TO2l073E60iU",
    "outputId": "b4b086a7-4c6b-4f0d-d14d-04cc6d780715"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6uj06yTe60iX",
    "outputId": "eff8340d-9928-476c-ed53-fe088dac76f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53940"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0] # кол-во строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "CWssMCSm60ia",
    "outputId": "bbcc4721-eaea-4f35-e426-da3dbe7e517e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[1] # кол-во колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y',\n",
       "       'z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([    1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "               10,\n",
       "            ...\n",
       "            53931, 53932, 53933, 53934, 53935, 53936, 53937, 53938, 53939,\n",
       "            53940],\n",
       "           dtype='int64', length=53940)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0OUf5cy860ie"
   },
   "source": [
    "Давайте посмотрим на информацию о датасете. \n",
    "\n",
    "У метода `.info()` можно передать дополнительные параметры, среди которых:\n",
    "\n",
    "- `verbose`: печатать ли информацию о `DataFrame` полностью (если таблица очень большая, то некоторая информация может потеряться)\n",
    "- `memory_usage`: печатать ли потребление памяти (по умолчанию используется `True`, но можно поставить либо `False`, что уберёт потребление памяти, либо `'deep'` , что подсчитает потребление памяти более точно)\n",
    "- `null_counts`: подсчитывать ли количество пустых элементов (по умолчанию `True`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "LiktcjXH60if",
    "outputId": "71e3e7a6-3daf-4939-af17-aa9b27b42e50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 53940 entries, 1 to 53940\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   carat    53940 non-null  float64\n",
      " 1   cut      53940 non-null  object \n",
      " 2   color    53940 non-null  object \n",
      " 3   clarity  53940 non-null  object \n",
      " 4   depth    53940 non-null  float64\n",
      " 5   table    53940 non-null  float64\n",
      " 6   price    53940 non-null  int64  \n",
      " 7   x        53940 non-null  float64\n",
      " 8   y        53940 non-null  float64\n",
      " 9   z        53940 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A4OChr8X60ij"
   },
   "source": [
    "Можно вывести только тип данных в каждой колонке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "oFDhtHZS60ik",
    "outputId": "153570e8-c31b-4870-fcc4-260dec6d8a5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat      float64\n",
       "cut         object\n",
       "color       object\n",
       "clarity     object\n",
       "depth      float64\n",
       "table      float64\n",
       "price        int64\n",
       "x          float64\n",
       "y          float64\n",
       "z          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lc5KwCvd60im"
   },
   "source": [
    "## Считаем различные характеристики"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yfBPzFQc60in"
   },
   "source": [
    "### Агрегирующие функции"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aQ2aCc_360in"
   },
   "source": [
    "Метод `.describe()` показывает основные статистические характеристики данных по каждому числовому признаку (типы `int64` и `float64`):  \n",
    "- `count` – число непропущенных значений\n",
    "- `mean`, `std` – среднее, стандартное отклонение\n",
    "- `min`, `max` – минимум, максимум\n",
    "- `25%`, `50%`, `75%` – квартили уровней 0.25, 0.50, 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>5</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uFyG5gBA60io",
    "outputId": "72b45755-5e28-43d8-ead7-58d1eaf9e4e7",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>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>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>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>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>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</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>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": [
       "              carat         depth         table         price             x  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
       "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
       "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
       "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
       "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
       "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
       "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
       "\n",
       "                  y             z  \n",
       "count  53940.000000  53940.000000  \n",
       "mean       5.734526      3.538734  \n",
       "std        1.142135      0.705699  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.720000      2.910000  \n",
       "50%        5.710000      3.530000  \n",
       "75%        6.540000      4.040000  \n",
       "max       58.900000     31.800000  "
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LL4yog_x60is"
   },
   "source": [
    "Чтобы посмотреть статистику по нечисловым признакам (например, по строчным (`object`) или булевым (`bool`) данным), нужно явно указать интересующие нас типы в параметре `include` метода `describe`, тогда\n",
    "- `unique` – число уникальных категорий в колонке\n",
    "- `top` – самая часто встречаемая категория\n",
    "- `freq` – количество вхождений самой часто встречаемой категории в датасете"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "JsXD8wCx60is",
    "outputId": "80f2e072-bb63-4453-9c2c-d25c8c51de7d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940</td>\n",
       "      <td>53940</td>\n",
       "      <td>53940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>21551</td>\n",
       "      <td>11292</td>\n",
       "      <td>13065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cut  color clarity\n",
       "count   53940  53940   53940\n",
       "unique      5      7       8\n",
       "top     Ideal      G     SI1\n",
       "freq    21551  11292   13065"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = ['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>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>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>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</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>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": [
       "              carat         depth         table         price             x  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
       "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
       "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
       "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
       "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
       "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
       "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
       "\n",
       "                  y             z  \n",
       "count  53940.000000  53940.000000  \n",
       "mean       5.734526      3.538734  \n",
       "std        1.142135      0.705699  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.720000      2.910000  \n",
       "50%        5.710000      3.530000  \n",
       "75%        6.540000      4.040000  \n",
       "max       58.900000     31.800000  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(exclude = ['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OgKE77H960iv"
   },
   "source": [
    "Из таблицы мы можем \"достать\" отдельный столбец, который будет уже другого специального типа `pd.Series`\n",
    "\n",
    "Это можно сделать несколькими способами, например:\n",
    "```python\n",
    "df['col']\n",
    "```\n",
    "\n",
    "Если название колонки на латинице без пробелов, можно писать:\n",
    " ```python\n",
    "df.col\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "5Gwf-2Nb60iv",
    "outputId": "720f319f-9174-4083-e1a5-5e873d5856d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1            Ideal\n",
       "2          Premium\n",
       "3             Good\n",
       "4          Premium\n",
       "5             Good\n",
       "           ...    \n",
       "53936        Ideal\n",
       "53937         Good\n",
       "53938    Very Good\n",
       "53939      Premium\n",
       "53940        Ideal\n",
       "Name: cut, Length: 53940, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "7QxWANYa60iy",
    "outputId": "cc56c3f3-a1b7-482c-98e7-33f25aa14711"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1            Ideal\n",
       "2          Premium\n",
       "3             Good\n",
       "4          Premium\n",
       "5             Good\n",
       "           ...    \n",
       "53936        Ideal\n",
       "53937         Good\n",
       "53938    Very Good\n",
       "53939      Premium\n",
       "53940        Ideal\n",
       "Name: cut, Length: 53940, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "SPgqRZS760i1",
    "outputId": "79fab416-c3fd-424b-8cab-d199290de18c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['cut'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lRvHC0ME60i4",
    "outputId": "210e581a-b0f0-4dd2-cf98-4fbfb67b5ea7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     53940\n",
       "unique        5\n",
       "top       Ideal\n",
       "freq      21551\n",
       "Name: cut, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lm-n1ORk60i7"
   },
   "source": [
    "У отдельного столбца также можно посчитать все характеристики, которые выводятся у метода `describe`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "id": "YdS8p3z760i8",
    "outputId": "9175cfdc-710e-45c3-95e8-5e949cebf133"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7979397478680014"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['carat'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "id": "iWxPNJE560i_",
    "outputId": "134885b4-6055-43a0-df32-a69088be311e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['carat'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.23, 0.21, 0.23, ..., 0.7 , 0.86, 0.75])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['carat'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "XOShFQ8U60jC",
    "outputId": "518e82a5-8286-48d8-b1f0-c6d6ea8c409d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Ideal\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].mode() # наиболее часто встречаемое значение в колонке"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8rGmc4Ib60jG"
   },
   "source": [
    "### Группировки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "raVxwmf760jH"
   },
   "source": [
    "![](https://i.imgur.com/Ij090Kn.jpg)\n",
    "\n",
    "Таблицы можно также группировать для того, чтобы посмотреть разные характеристики датасета.\n",
    "\n",
    "Например, посмотрим на среднюю цену бриллианта в зависимости от цвета.\n",
    "\n",
    "```python\n",
    "df.groupby('col to group')['col of interest'].mean().sort_values()\n",
    "```\n",
    "- `mean` – агрегирующая функция, можно использовать другие\n",
    "- `sort_values` – функция сортировки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "E    3076.752475\n",
       "D    3169.954096\n",
       "F    3724.886397\n",
       "G    3999.135671\n",
       "H    4486.669196\n",
       "I    5091.874954\n",
       "J    5323.818020\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('color')['price'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "E    1739.0\n",
       "D    1838.0\n",
       "G    2242.0\n",
       "F    2343.5\n",
       "H    3460.0\n",
       "I    3730.0\n",
       "J    4234.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('color')['price'].median().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3076.752475</td>\n",
       "      <td>1739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3169.954096</td>\n",
       "      <td>1838.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3724.886397</td>\n",
       "      <td>2343.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>3999.135671</td>\n",
       "      <td>2242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>4486.669196</td>\n",
       "      <td>3460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>5091.874954</td>\n",
       "      <td>3730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>5323.818020</td>\n",
       "      <td>4234.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean  median\n",
       "color                     \n",
       "E      3076.752475  1739.0\n",
       "D      3169.954096  1838.0\n",
       "F      3724.886397  2343.5\n",
       "G      3999.135671  2242.0\n",
       "H      4486.669196  3460.0\n",
       "I      5091.874954  3730.0\n",
       "J      5323.818020  4234.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('color')['price'].agg(['mean','median']).sort_values(by='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3169.954096</td>\n",
       "      <td>1838.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3076.752475</td>\n",
       "      <td>1739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3724.886397</td>\n",
       "      <td>2343.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>3999.135671</td>\n",
       "      <td>2242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>4486.669196</td>\n",
       "      <td>3460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>5091.874954</td>\n",
       "      <td>3730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>5323.818020</td>\n",
       "      <td>4234.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean  median\n",
       "color                     \n",
       "D      3169.954096  1838.0\n",
       "E      3076.752475  1739.0\n",
       "F      3724.886397  2343.5\n",
       "G      3999.135671  2242.0\n",
       "H      4486.669196  3460.0\n",
       "I      5091.874954  3730.0\n",
       "J      5323.818020  4234.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg = df.groupby('color')['price'].agg(['mean','median']).sort_index()\n",
    "df_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['D', 'E', 'F', 'G', 'H', 'I', 'J'], dtype='object', name='color')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mean', 'median'], dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">price</th>\n",
       "      <th colspan=\"2\" halign=\"left\">carat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3169.954096</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>0.657795</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3076.752475</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>0.657867</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3724.886397</td>\n",
       "      <td>2343.5</td>\n",
       "      <td>0.736538</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>3999.135671</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>0.771190</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>4486.669196</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>0.911799</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>5091.874954</td>\n",
       "      <td>3730.0</td>\n",
       "      <td>1.026927</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>5323.818020</td>\n",
       "      <td>4234.0</td>\n",
       "      <td>1.162137</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price             carat       \n",
       "              mean  median      mean median\n",
       "color                                      \n",
       "D      3169.954096  1838.0  0.657795   0.53\n",
       "E      3076.752475  1739.0  0.657867   0.53\n",
       "F      3724.886397  2343.5  0.736538   0.70\n",
       "G      3999.135671  2242.0  0.771190   0.70\n",
       "H      4486.669196  3460.0  0.911799   0.90\n",
       "I      5091.874954  3730.0  1.026927   1.00\n",
       "J      5323.818020  4234.0  1.162137   1.11"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('color')[['price','carat']].agg(['mean','median']).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.657795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.657867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.736538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.771190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>0.911799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.026927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>1.162137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean\n",
       "color          \n",
       "D      0.657795\n",
       "E      0.657867\n",
       "F      0.736538\n",
       "G      0.771190\n",
       "H      0.911799\n",
       "I      1.026927\n",
       "J      1.162137"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('color')['carat'].agg(['mean']).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1182994a8>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('color')['carat'].agg(['mean']).sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119218e80>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='carat', y='price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>carat</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028224</td>\n",
       "      <td>0.181618</td>\n",
       "      <td>0.921591</td>\n",
       "      <td>0.975094</td>\n",
       "      <td>0.951722</td>\n",
       "      <td>0.953387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <td>0.028224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.295779</td>\n",
       "      <td>-0.010647</td>\n",
       "      <td>-0.025289</td>\n",
       "      <td>-0.029341</td>\n",
       "      <td>0.094924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table</th>\n",
       "      <td>0.181618</td>\n",
       "      <td>-0.295779</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127134</td>\n",
       "      <td>0.195344</td>\n",
       "      <td>0.183760</td>\n",
       "      <td>0.150929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.921591</td>\n",
       "      <td>-0.010647</td>\n",
       "      <td>0.127134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.884435</td>\n",
       "      <td>0.865421</td>\n",
       "      <td>0.861249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.975094</td>\n",
       "      <td>-0.025289</td>\n",
       "      <td>0.195344</td>\n",
       "      <td>0.884435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974701</td>\n",
       "      <td>0.970772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.951722</td>\n",
       "      <td>-0.029341</td>\n",
       "      <td>0.183760</td>\n",
       "      <td>0.865421</td>\n",
       "      <td>0.974701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.952006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.953387</td>\n",
       "      <td>0.094924</td>\n",
       "      <td>0.150929</td>\n",
       "      <td>0.861249</td>\n",
       "      <td>0.970772</td>\n",
       "      <td>0.952006</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          carat     depth     table     price         x         y         z\n",
       "carat  1.000000  0.028224  0.181618  0.921591  0.975094  0.951722  0.953387\n",
       "depth  0.028224  1.000000 -0.295779 -0.010647 -0.025289 -0.029341  0.094924\n",
       "table  0.181618 -0.295779  1.000000  0.127134  0.195344  0.183760  0.150929\n",
       "price  0.921591 -0.010647  0.127134  1.000000  0.884435  0.865421  0.861249\n",
       "x      0.975094 -0.025289  0.195344  0.884435  1.000000  0.974701  0.970772\n",
       "y      0.951722 -0.029341  0.183760  0.865421  0.974701  1.000000  0.952006\n",
       "z      0.953387  0.094924  0.150929  0.861249  0.970772  0.952006  1.000000"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4AVAi5we60jH",
    "outputId": "b4bdf734-01ce-47bd-fea8-868f2c393d58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "D    3169.954096\n",
       "E    3076.752475\n",
       "F    3724.886397\n",
       "G    3999.135671\n",
       "H    4486.669196\n",
       "I    5091.874954\n",
       "J    5323.818020\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# применяем метод groupby\n",
    "#df.groupby(by='color')\n",
    "\n",
    "# выбираем интересующий столбец\n",
    "#df.groupby(by='color')['price']\n",
    "\n",
    "# выбираем, что хотим посчитать\n",
    "#df.groupby(by='color')['price'].mean()\n",
    "\n",
    "\n",
    "df.groupby(by='color')['price'].mean()\n",
    "# на выходе pd.series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cDM2kFxC60jK",
    "outputId": "254808b1-fca5-4487-cf6c-da2c85662b6f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
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      ],
      "text/plain": [
       "             price\n",
       "color             \n",
       "D      3169.954096\n",
       "E      3076.752475\n",
       "F      3724.886397\n",
       "G      3999.135671\n",
       "H      4486.669196\n",
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       "J      5323.818020"
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     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('color')[['price']].mean()\n",
    "\n",
    "# на выходе pd.dataframe – посмотрите на разницу!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bm_UD6bk60jN",
    "outputId": "ebf4983e-75d4-445c-a322-7cf5bfa4b6bc"
   },
   "outputs": [
    {
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       "      <th>carat</th>\n",
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       "      <th>color</th>\n",
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       "      <td>0.657867</td>\n",
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       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3724.886397</td>\n",
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       "      <th>G</th>\n",
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       "      <td>1.026927</td>\n",
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       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>5323.818020</td>\n",
       "      <td>1.162137</td>\n",
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      "text/plain": [
       "             price     carat\n",
       "color                       \n",
       "D      3169.954096  0.657795\n",
       "E      3076.752475  0.657867\n",
       "F      3724.886397  0.736538\n",
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       "J      5323.818020  1.162137"
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     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ['price','carat'] - список из названий колонок\n",
    "gr = df.groupby('color')[['price','carat']].mean()\n",
    "gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9oT7J_2w60jQ",
    "outputId": "60b03753-60b0-4977-a89f-48fa3ff61628"
   },
   "outputs": [
    {
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       "      <th>E</th>\n",
       "      <td>3076.752475</td>\n",
       "      <td>0.657867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
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       "      <td>0.736538</td>\n",
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       "      <th>J</th>\n",
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      "text/plain": [
       "             price     carat\n",
       "color                       \n",
       "D      3169.954096  0.657795\n",
       "E      3076.752475  0.657867\n",
       "F      3724.886397  0.736538\n",
       "G      3999.135671  0.771190\n",
       "H      4486.669196  0.911799\n",
       "I      5091.874954  1.026927\n",
       "J      5323.818020  1.162137"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# можно список отдельно задать\n",
    "cols_to_use = ['price','carat']\n",
    "gr = df.groupby('color')[cols_to_use].mean()\n",
    "gr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7LCy0oc360jT"
   },
   "source": [
    "В pandas строено много способов построения простых графиков. Подробнее о них [тут](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html). О более сложных позднее.\n",
    "\n",
    "Воспользуемся методом `.plot()` и построим столбчатую диаграмму (`kind='bar'`) средней цены бриллианта в зависимости от цвета."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S51f4b6g60jU",
    "outputId": "2d012efd-a5f4-4f88-b2d7-f5c79fa2ce6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f4c3dd8>"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# самый простой график\n",
    "df.groupby('color')['price'].mean().sort_values().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sMNzhVIq60jW",
    "outputId": "4304a710-dc75-4392-be10-e6df7f66ceab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x101b8a550>"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# столбчатый график\n",
    "df.groupby('color')['price'].mean().sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GdjlFjIt60jZ"
   },
   "source": [
    "Можно посмотреть сразу несколько характеристик с помощью метода `.agg(['some_char_1','some_char_2'])`у нескольких колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h-AW39sc60ja",
    "outputId": "8289cfce-8450-4a10-ad83-941f461d281c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">price</th>\n",
       "      <th colspan=\"2\" halign=\"left\">carat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3169.954096</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>0.657795</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3076.752475</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>0.657867</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3724.886397</td>\n",
       "      <td>2343.5</td>\n",
       "      <td>0.736538</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>3999.135671</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>0.771190</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>4486.669196</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>0.911799</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>5091.874954</td>\n",
       "      <td>3730.0</td>\n",
       "      <td>1.026927</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>5323.818020</td>\n",
       "      <td>4234.0</td>\n",
       "      <td>1.162137</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price             carat       \n",
       "              mean  median      mean median\n",
       "color                                      \n",
       "D      3169.954096  1838.0  0.657795   0.53\n",
       "E      3076.752475  1739.0  0.657867   0.53\n",
       "F      3724.886397  2343.5  0.736538   0.70\n",
       "G      3999.135671  2242.0  0.771190   0.70\n",
       "H      4486.669196  3460.0  0.911799   0.90\n",
       "I      5091.874954  3730.0  1.026927   1.00\n",
       "J      5323.818020  4234.0  1.162137   1.11"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('color')[['price', 'carat']].agg(['mean','median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4D-Qt_sG60jf",
    "outputId": "08af7610-76cf-4743-e886-ce76bb45d33b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1117be550>"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('color')['price'].agg(['mean','median']).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M6tK5bnU60ji"
   },
   "source": [
    "![title](https://i.imgur.com/6PannTm.jpg)\n",
    "\n",
    "Можно строить сложные сводные таблицы. \n",
    "\n",
    "Например, можно посмотреть на среднюю цену бриллианта в зависимости от цвета (color) и качества огранки (cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-i9eJLld60ji",
    "outputId": "a780b825-6a39-41ba-890c-b8ed93b86018"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>color</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>J</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cut</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fair</th>\n",
       "      <td>4291.061350</td>\n",
       "      <td>3682.312500</td>\n",
       "      <td>3827.003205</td>\n",
       "      <td>4239.254777</td>\n",
       "      <td>5135.683168</td>\n",
       "      <td>4685.445714</td>\n",
       "      <td>4975.655462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <td>3405.382175</td>\n",
       "      <td>3423.644159</td>\n",
       "      <td>3495.750275</td>\n",
       "      <td>4123.482204</td>\n",
       "      <td>4276.254986</td>\n",
       "      <td>5078.532567</td>\n",
       "      <td>4574.172638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <td>2629.094566</td>\n",
       "      <td>2597.550090</td>\n",
       "      <td>3374.939362</td>\n",
       "      <td>3720.706388</td>\n",
       "      <td>3889.334831</td>\n",
       "      <td>4451.970377</td>\n",
       "      <td>4918.186384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>3631.292576</td>\n",
       "      <td>3538.914420</td>\n",
       "      <td>4324.890176</td>\n",
       "      <td>4500.742134</td>\n",
       "      <td>5216.706780</td>\n",
       "      <td>5946.180672</td>\n",
       "      <td>6294.591584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <td>3470.467284</td>\n",
       "      <td>3214.652083</td>\n",
       "      <td>3778.820240</td>\n",
       "      <td>3872.753806</td>\n",
       "      <td>4535.390351</td>\n",
       "      <td>5255.879568</td>\n",
       "      <td>5103.513274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color                D            E            F            G            H  \\\n",
       "cut                                                                          \n",
       "Fair       4291.061350  3682.312500  3827.003205  4239.254777  5135.683168   \n",
       "Good       3405.382175  3423.644159  3495.750275  4123.482204  4276.254986   \n",
       "Ideal      2629.094566  2597.550090  3374.939362  3720.706388  3889.334831   \n",
       "Premium    3631.292576  3538.914420  4324.890176  4500.742134  5216.706780   \n",
       "Very Good  3470.467284  3214.652083  3778.820240  3872.753806  4535.390351   \n",
       "\n",
       "color                I            J  \n",
       "cut                                  \n",
       "Fair       4685.445714  4975.655462  \n",
       "Good       5078.532567  4574.172638  \n",
       "Ideal      4451.970377  4918.186384  \n",
       "Premium    5946.180672  6294.591584  \n",
       "Very Good  5255.879568  5103.513274  "
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns='color' - что будет колонками в сводной таблице\n",
    "# index='cut' - что будет строками в сводной таблице\n",
    "# values='price' - какую колонку будем использовать для подсчета чего-нибудь\n",
    "# aggfunc='mean' - что будем считать\n",
    "\n",
    "df.pivot_table(columns='color', index='cut', values='price', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y_nS4kgd60jl",
    "outputId": "6785550c-df81-4825-aec4-04fc7d161395"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1117900b8>"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(columns='color', index='cut', values='price', aggfunc='mean').sort_values(by='J').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QhRKuyga60jo"
   },
   "source": [
    "### Уникальные значения"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AdbzQWxb60jo"
   },
   "source": [
    "Посмотрим, как часто бриллианты различного качества (колонка cut) встречаются в датасете, с помощью метода `.value_counts()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "id": "q0ENggMg60jp",
    "outputId": "61000bd8-b3bb-48a0-8d47-04a34fc7936e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1            Ideal\n",
       "2          Premium\n",
       "3             Good\n",
       "4          Premium\n",
       "5             Good\n",
       "           ...    \n",
       "53936        Ideal\n",
       "53937         Good\n",
       "53938    Very Good\n",
       "53939      Premium\n",
       "53940        Ideal\n",
       "Name: cut, Length: 53940, dtype: object"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "id": "YmjdOCsx60js",
    "outputId": "390192d5-ba54-415a-cc23-f76ccdf302e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ideal        21551\n",
       "Premium      13791\n",
       "Very Good    12082\n",
       "Good          4906\n",
       "Fair          1610\n",
       "Name: cut, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "id": "TLcxQZop60ju"
   },
   "outputs": [],
   "source": [
    "# посмотрим аргументы\n",
    "?pd.value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "id": "IW0MskXK60jx",
    "outputId": "a4f499c8-2ff1-41f9-f3fe-4126198366b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ideal        0.399537\n",
       "Premium      0.255673\n",
       "Very Good    0.223990\n",
       "Good         0.090953\n",
       "Fair         0.029848\n",
       "Name: cut, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mZXJodzb60j2"
   },
   "source": [
    "Просто посмотреть уникальные категории в колонке можно с помощью метода `.unique()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3gQyyTZ-60j2",
    "outputId": "e5df1f1c-777a-4b72-8b5d-6027b78ed3ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ideal', 'Premium', 'Good', 'Very Good', 'Fair'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DKRPwV6V60j6"
   },
   "source": [
    "А просто количество уникальных категорий (или значений) можно посмотреть с помощью метода `.nunique()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2Wt5SS5T60j6",
    "outputId": "b04a53c3-2468-4377-9b84-1070a3ca814d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N6KD9pBm60j9",
    "outputId": "93f7d61c-cbcb-4df5-92f7-73f19279ad02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111c3f7b8>"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jnt30l7ZlH9vQ/Z5FPC7TVuDft3mSf9/mSeO3/AUAA+ZgUcWbe0onvbO7bPLh/YPHFR8p8I9FXLn8d7ozkReJSBvQ+Ynfc4wxDT289lljTJ8OZ9JVFCd1lASAMWaPiKTv/MRRu9CiGLCry4c/e9jlmh3vdXd/0j3rQGHb8k8/Z86QJNzcJyDFh7aPKz60fdyYd5a/t/yId/COvUMnvrm7LLi/0X+s71BhWaURzzGI5MKzTAkVBXDIGJPQ8P7dlYSIeIwxPR7mpG2PQkRKjTF7YqHK0rjtzvQ8xQCtLvS99NSgooRnsbr/LPeZTUXtKy94qv0USdHfua9l/4iKHWtHVOxY+96yNlfBgUb/sW/sKg3u2Tt0ghwcVDGsze07FpFsu0yeaFG8j4iUAI8ApTi3I1xnjHkk9r0mY0yJiMwGvg/swZkaoMfRtNL1j/UmYJWI/Db29eeAH6Zp251pUQzAEeHw/MryIkT6dBPQo6e6Zhzw8fylj7dPlTSdI3K3NxeX7amvKttT/96ydnG1NhWPfnV3WfCfe4ZOam0qGT2kxVsyHpGMmjm8i0Qf8S8SkQ2xzzfj/BubY4zZJyLDgedE5FHzwZOSHwFONMZs7m3l6bqP4jci8gJwdmzRZ4wxL6dj213otHoDsKhixPPNIv065/C3qa5TDvlYe8Uj7UEBKzdiuUy7Z0jTWxOGNL01IfDmE+8tP1Q4fOvu0knv7C6dfHDfkMCgZp9/jBH3KBsZu/F2gq9736GHOOePfiQiZwLtOEPqVQDvdnnf6nglAWnc/Y8Vg41y6Kxfu3EKVhQVbnqusHBAE+c+e4Lr5IM+Nn3zofaAQMZcvSg6vHPM6G07x4zedvQ5t2Zv8e69/glbdpcF9+31H+c5XDi8st3lCfR1byoJ3urn+y7AGR3rZGNMi4g00P3dyQcSWZmN8wQ2vW47QDY6KHLgqxUj/Mk4ObjhONdJ3/2S1IXvbWt1QVky8qVCQcuBsvKdG8rKd254b1mby3to35DA5t2lwV17hk7gQPHI0jZ34bEpvlW9vzO6+YF/xkriLOCYgYSIWxSdLrt4gDqgxhhzcCAb7WYb84CDxpjfJHO93aiP/xLV1WWV5WtbnV3YpKgfK8FvXux+7YZ72lpchopkrTfV3O0tRaV7Xz2hdO+r7y0zSHtT8ag39pROfnd36aSWpsFjS5q9JQHElYxxJBoX3nn2nn6+9z7gMRGJAi8wwJ/9uDdcdZwhjX1+H7DWGHNzp+/3elklo4T95SR+ckgBTwwqWvff5cOnpuIW61G7zJabFre53e2MSfa6bTvsK922p3TSW7tLJx9qHDLed8RXOtq43GP7uJpnF9559oyUBOyjvhbFPOAk4CHef1klCNQCs3EevrrNGPPL2OWX64G9QFXsfVHgCpyHXc4xxrwuImGgyRjzExF5GrjaGPNC7GztC8aYgIjMBc4BinEmGv4Jzhn0C4EjwL8bY+Jf1Qj7d5HBu7yZZL/IvpnHjGlqE0nZib3hjWbbT3/ZdtjblvsjpLe4ixr3Dj1+8+6y4L5G/3Gug0Xl5e0u7/hebly7a+GdZ1+W1pA9SPgchYh4gE8Cj8cWvXdZRUQuBRqNMdNFxAesFJGO08ofximS3cAbwGJjzEdjY2ZeDlzZh7wnAlNxTsq8BnzDGDNVRG4BLgJuTWAdLwJJ243OZV8eWbGpTeSMVG5jp19GLlzg3vHzO9r+4WvNjFmxUsXbdsg/Yld0yohdR2+gbBfPkX2Dx9XtLgvu2jN0YtuB4lFlrZ6iACKDcX5WM0IiRdH5+uwK4FfA6bz/ssrHgZNE5LOxr/04/+s3A2uMMdsAROR1oKNAosBZfcz7lDFmP7BfRBqBxzqt66QE17EeLYq4HikpXlPvK0hpSXTYWyIj5i9ye39xe9tLg5r5UDq2mSlcptU3dN8bwaH73gAiQOxW9UGVW/aXjF179I4CuxIpig/cGho7XO18WUWAy40xS7u8bjbOYUGH9k5ft/ew/VaO3u7b9XJOX9fVnfUJvi5v7XW59nxneNm4dG6zqUiGzrvc7fnF7W0bhhwioVuRc5WAFB98d2zxwXczZra2ZN0LvxSYH7vJAxGZKCLF/VxXA3By7PPP9vK6/tKiiOOikRV17SJpvxpxuEBKFix0T9pVgs4hAv8I1tfttx2iQ7KKYjHOzVTrRORF4Jf0/x6Nn+CUznpSM6fByziD+6puPDi45LnNBV5rA6U0e6Vo0QL3lHeHsspWhgzxvO0AnVkb4cqqsP8R4D9tx8g0O92uHWePHS3GudpklavdtNX+um1V4J+k5TxJBrooWF/3v7ZDdMiFx3D74/H4L8k/XxpZ+XomlARAu0vc13zZPaNuDMtsZ7HkSdsBOtOiUAD8yj/k2be9nnQPeNw7EfnuhZ5Za4+Tp21HSbO6YH3dNtshOsvPogg3bgZejfu6PPGu2/3uraX+jL0seePn3bOXnZhXZZFRexOQr0Xh0L2KmPNHVWxFxG87R29u+w/37D9Pk2Xm6PwwuUyLIoMsjf+S3Pfzof4VOzyeabZzJOKej7ln/fYM10oDbbazpNAR4CnbIbrK56J4ivffwJV33vJ4tt41dEhW3dz0u5muM379MddqAy22s6TI45l0/0SH/C2KcONBYHnc1+UoA+aCURU7Ys8UZJXHp7lOu+1Trg0m/XPDpMODtgN0J3+LwpEx16nT7cdlQ1fscbttjISeFMurXNP/37muepMhc3MmyUGOPr+UUfK9KH6H8wh8Xnnd62m4d8jgrDgv0ZsXJrqmfP881xaTO3+HjwXr6xIami7d8rsowo2HcEYCyhvt0H7hyMr9uTLT+IsB14euvci9vV1I76xzqZGRhx2Q70XhuNt2gHS6fnjZ8v1uV5XtHMn02miZ9PUvu5vahIy6SamPdgB/sR2iJ1oU4caNwNq4r8sBdQXe1/9QUnya7Ryp8Fa5jL/yUndbqytrZ6y/O1hfl7FX4bQoHIttB0i1VmidO7KiGWcEspy0vUzGLJrvLmz28JrtLH3UCtxhO0RvtCgc9+Occc5Z3xwxbOVBlytoO0eq7R4iFfMXussOeamznaUP/hisr+vvsPxpoUUBEG7cB/yf7RipssFXUP948SBrY0yk2/5BUjZvkXt0UyGbbGdJ0C9sB4hHi+KoH5ODtwY3Q/N/VZa7exnpOScdKpQh8xa5j99bnPHnn6LB+rqMf5Rei6JDuLEe5xAkp1xVMfzZIy7XBNs5bGj2yqAFC9wn7hiSWaNFdXGj7QCJ0KJ4v+txTizlhOcLfS8tKyoa0Hyh2a7VI77L57tP3jqMlfFfnXavAA/YDpEILYrOwo2vA0tsx0iGwyKH5leWD7IwqW7GaXeJ578vcZ/22khW2M7SxQ+C9XXttkMkQovig76PMx9JVltUMWJ1i0jOz76VKCPi+tZcz8yNAcmU8wEvkcChrohUiMj9IvKGiKwVkVUiMmegGxeRp0Uk4dv4tSi6CjduwZnkKGstKyrc+HyhL68POXryw/Pcs56dnBGjZV0Xb29CnAl0/ggsN8Yca4w5GfgipH+uVi2K7v2QLH2E+aDIgSsrRpQion+3Pbh1jnv2E1Ot7lmsCtbX/TGB150NNBtj7uxYYIzZYoz5uYgUisivRSQqIutF5CyAXpYXiciDIlInIg/jzP2bMP1h6k648W3gdtsx+uOSyvJ1rSJpneUrGy3+N/esP5wuK4wzy1w6tQELEnzth4B1PXxvIWCMMVXAecASESnsZfl84KAxJgh8l6OTbCVEi6Jn1wNv2w7RF48XD1q7SQ85EvbgLPfMe89yrTLpvdJ1e7C+bkP8l32QiNwmIhtFZA1wBnAvgDGmHtgCTOxl+Zmdlm+Cvt2MpkXRE+duzStsx0jUPpc0hkYMG2k7R7Z57FTXjLv+zbXWpGdYxHeBb/fh9S8BH+n4whizEPgXYESSc8WlRdGbcOPv6ZhiOsN9ubIi2iYyynaObPS3qa5Tbj3H9aJJ/fM+Xw/W1zX24fV/BwpFZH6nZR3jiKwALgBnrl9gHM59GT0tXw6cH1t+InBSX4JrUcS3gAyfq/ThkuLVr/gK8nXqvaRYFXSd/KPPu14z0Jd/yH2xLFhfd29f3mCc+T7PAWaJyGYRWY1zn883cM6huUQkivOc0lxjzJFelt8BlIhIHfA9+ji0Qn7OPdpXYf9lwJ1xX2fBXpdrz6xxo1vaRcptZ8kFk94yddff21bugmFJXO0+YEqwvm5zEteZVrpHkYhw4y+Bv9qO0Z0LR1bUa0kkzytjJRi62L23XdiexNVens0lAVoUffEVnP8ZMsZ9Q0pWNRR4c3LEKpsaKuW4qy5xH251kYwxIh4K1tf9JgnrsUqLIlHhxreAuWTIlHY73K4dN5aVTrSdI1e9M0yO+eo8t7vFzUD2BLYC85KVySYtir4INz4M/Mh2DIALRla+YUSSeRytutjpl5ELFrhLjnj4Rz/e3g5cFKyv25PsXDZoUfTdd4A/2wxwt3/Iym1ezyk2M+SLxhIZMX+Ru/xgAS/18a3fCtbXZdwcov2lVz36I+z3A2uAtA8Is83t3vbxsaMGZfrs47mmsNk0/eL2tleHHCKR2dXuC9bXfSnlodJI9yj6I9zYCMzBwnR254+qfFtLIv0OF0jJ/EXu4K4S1sR56Rrgv9KRKZ20KPor3PgSUEMaT27+tNS/YqfHnfVTAWarFo8ULlrgnrKtlFU9vOQd4JxgfV1WPnncGy2KgQg3/gHnkfSU2+LxvLXYPyRrJxXOFW1u8X7tUvdHN1fwTJdvHQLmBOvr3rGRK9W0KAYq3Pht4LZUbsKA+dKoil2IlKRyOyox7S5xf+Ni94yXx9IxpkUL8Nlgfd1qm7lSSYsiOS4H7krVym8oK12x1+2ekqr1q34QkfCXPLPWTJCngJpgfZ3VK2Gpplc9kiXsF5wh9C5O5mpf83o3zxldWYlIn0YkUmlhgEujNdGcn5JS9yiSJdxocM529+kJwd60QduFIyuatCQy1sJ8KAnQokiucGM7zm3eDyZldcPLVjS5XVXJWJdKKgNcEa2JZvTEwsmkRZFs4cY24ELgdwNZzcsF3tf+WFKsD3xlnmbgwmhN9Ge2g6STFkUqhBtbcQY27dfVkFZonTuyogURX3KDqQFqBD4ZrYneZztIuunJzFQL+xcAPwU8ib7l6hHDnl5aUjw7ZZlUf2wF/j1aE43aDmKD7lGkWrjxduCTwN5EXr7OV1C3tHjQjNSGUn0UBU7L15IALYr0CDc+CZwKvNrby5qh+dLKcg8i3vQEUwn4GzAzWhNNxiA2WUuLIl3Cja8Ap+CMrNytKytGrDricqX9iVTVrXacuV0+Ea2JpmrA3ayh5yjSLez3ALcAizovfq7Q9+IlleVBnX08I7wDXBCtiT5tO0im0KKwJeyvBv4HKD8scuj0Y8a8q7OPZ4Q/A3OjNdEdtoNkEi0Km8L+cmDxlyvLh6wpKpxlO06eawG+CdwcrYnqP4outCgywEn3nDjXiNwCDLWdJU+tx3lm4wXbQTKVnszMAJvmvngPcALwqOUo+aZjftnpWhK90z2KDFO1pOozwI3A8baz5LgHgKuiNdF3bQfJBloUGahqSZUXWIgz83WZ5Ti5ph7nqc8eL1OrD9KiyGBVS6qGAtfhXErV5z4GZhvwY+D2aE202XaYbKNFkQWqllSNB24APg+I5TjZ5h2cQ7m7ojXRnBv0Nl20KLJI1ZKqIPA1nMfYCy3HyXRbgVpgcbQmesR2mGynRZGFqpZUjQDmAwuACstxMs0/gFuBX+khRvJoUWSxqiVVPuACnL2MEy3HsekQzkBBi6M10eW2w+QiLYocUbWk6lTgi8DngFGW46TLemAxcJ8+uJVaWhQ5pmpJlQs4E6c0zgWG202UdPVABLg/WhNdZztMvtCiyGFVS6o8wL8CnwZmA5OtBuqfw8DTOOUQidZEN9uNk5+0KPJI1ZKqCpzCmBX7NWgzTw8OA5twJvtdCvwtWhM9aDeS0qLIY1VLqsqBM3BOhAZx9jgmAemaR+QwsBFY2+njpWhNtDVN21cJ0qJQ71O1pEqAY3BKIxj7vBTnVvKOXzs+L+jy9lacx7U7Pg4B/wTejX28DWwBGjp+1VLIDsjIEsYAAALVSURBVFoUqt+qllQNin3aEq2JtlgNo1JKi0IpFZeOR6GUikuLQikVlxaFSisReUpEPtFl2ZUikvQJf0XkKhGpF5GoiGwUkZslCXOmiMhcEflFMjJmCy0KlW4P4Nw12tkXY8vjkgSnMxCRecDHgVONMVXAdJwrMOm69JtTtChUuv0OqBaRAgARCeA8m7JCRD4uIqtEZJ2I/FZESmKvaRCRG0VkHRCK/UrsexM6f93JtcB8Y8xeAGNMszGm1hizL/a+82J7Gi+KyI2d1tfT8otF5B8ishrIuykftShUWhljdgOrceZjBWdv4iFgGM5oXv9qjPkI8AJwVae37jLGfMQY80OgUUSmxJZfDPy68zZEZAhQYozp9nZvERmFM5jN2cAUYLqInNPL8pE4s4bNwLlB7YSB/BlkIy0KZUPnw4+Ow45Tcf4BrhSRDUANzs1eHf6v0+eLgYtjhyFfAO7vbWMi8gkR2RDbMzkd5zDkaWPMDmNMK3AfzoN0PS0/pdPy5i5Z8oIWhbLhEeBfROQjwCBjzFqcIf7+aoyZEvs4wRjzlU7vOdDp89/j7JF8ClhrjNnVeeWxw4smic28ZoxZaoyZArzIB+8mVQnQolBpZ4xpAp7CmVKx4yTmc8AMETkeQESKRWRiD+8/jPPA2B10Oezo5AbgDhEZGlufcHT4wNXALBEZHtsrOQ9Y1svy52PLh8Wumnyu/7/77OSxHUDlrQeAh4kdghhjdojIXOABEekYcfw6nKHtunMfMAd4oofv3wEUA8+LyBGgCVgJrDfGNIpICKesBIgYYx4B6GV5GFgF7AU29PP3nLX0Fm6VlUTkasBvjPm27Sz5QPcoVNYRkYeB43CuTqg00D0KpVRcejJTKRWXFoVSKi4tCqVUXFoUSqm4tCiUUnFpUSil4tKiUErFpUWhlIpLi0IpFZcWhVIqLi0KpVRcWhRKqbi0KJRScWlRKKXi+v+zthZBxchuuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['cut'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oohk75LX60kD"
   },
   "source": [
    "## Гистограмма распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dXljIojR60kE",
    "outputId": "44791a6b-e7b1-40dc-976b-26bbfc0a0912"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    53940.000000\n",
       "mean      3932.799722\n",
       "std       3989.439738\n",
       "min        326.000000\n",
       "25%        950.000000\n",
       "50%       2401.000000\n",
       "75%       5324.250000\n",
       "max      18823.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "id": "pgF9Wc7z60kG",
    "outputId": "369579e9-6a9f-4fbd-a0d8-3fdfc8d3eb9b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bed3d30>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['price'].plot(kind='hist', bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bec3550>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['carat'].plot(kind='hist', bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fw_rxPVr60kJ"
   },
   "source": [
    "### Среднее, медиана, мода\n",
    "\n",
    "<img src='https://i.pinimg.com/originals/5e/70/90/5e70902088cb6036fa8954327c426aa4.png' width=300>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WZ5qpXJZ60kJ"
   },
   "source": [
    "## Создаем и удаляем колонки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_hIUbefW60kK",
    "outputId": "35aa4f51-e9a3-4bd2-9c75-018e34b804ba"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>5</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kPbkHgtp60kM"
   },
   "source": [
    "Добавим в таблицу новую колонку объем бриллианта (volume), используя длину, высоту и глубину брилланта"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "id": "Njz6ul7060kN",
    "outputId": "d1849f7b-8e39-4800-deff-b8ebc0690b8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         38.202030\n",
       "2         34.505856\n",
       "3         38.076885\n",
       "4         46.724580\n",
       "5         51.917250\n",
       "            ...    \n",
       "53936    115.920000\n",
       "53937    118.110175\n",
       "53938    114.449728\n",
       "53939    140.766120\n",
       "53940    124.568444\n",
       "Length: 53940, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x'] * df['y'] * df['z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "id": "ZRUo6-xd60kb"
   },
   "outputs": [],
   "source": [
    "df['volume'] = df['x'] * df['y'] * df['z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "id": "RzYDezlo60kg",
    "outputId": "56d79219-cff5-47f3-e700-428e95bf035a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>34.505856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>38.076885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>46.724580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>51.917250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z  \\\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43   \n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31   \n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31   \n",
       "4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63   \n",
       "5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75   \n",
       "\n",
       "      volume  \n",
       "1  38.202030  \n",
       "2  34.505856  \n",
       "3  38.076885  \n",
       "4  46.724580  \n",
       "5  51.917250  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['x','y','z'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>34.505856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     volume\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  38.202030\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  34.505856"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c107a20>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='volume', y='price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WhBKt_lQ60ki"
   },
   "source": [
    "А колонки с длинной, высотой и глубиной удалим с помощью метода `.drop()`\n",
    "\n",
    "- укажем колонки, которые нужно удалить – список `drop_cols`\n",
    "- `axis=0` – означает, что удаляем строки\n",
    "- `axis=1` – означает, что удаляем колонки, а не строки\n",
    "- `inplace=True` – означает, что удаляем колонки в текущем датасете, а не выводим новый"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "354.4266119"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['volume'].quantile(0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KQK2wgOn60kj",
    "outputId": "ab994d19-e233-4129-8d3d-e39c4ef8c1cb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>34.505856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cut color clarity  depth  table  price     x     y     z     volume\n",
       "1    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43  38.202030\n",
       "2  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31  34.505856"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# без inplace удалим carat\n",
    "df.drop('carat', axis=1).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W5M0RqTg60ko",
    "outputId": "55706a60-7afa-4522-a032-37f4d8fc731f",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>34.505856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z  \\\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43   \n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31   \n",
       "\n",
       "      volume  \n",
       "1  38.202030  \n",
       "2  34.505856  "
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# carat остались!\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dZCExEN160kr"
   },
   "outputs": [],
   "source": [
    "drop_cols = ['x', 'y', 'z']\n",
    "df.drop(drop_cols, axis=1, inplace=True)\n",
    "\n",
    "# или то же самое без inplace – лучше первый способ\n",
    "### df = df.drop(drop_cols, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ADlkd8ic60kt",
    "outputId": "3516254e-29f2-41fc-cde3-d8d7fe6205ff",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>34.505856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     volume\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  38.202030\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  34.505856"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ce4ghMKV60kx"
   },
   "source": [
    "Переведем также караты в миллиграммы (умножив массу в каратах на 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uugSAmTQ60kx",
    "outputId": "29073f88-a3c0-4cdd-efe6-3872e426c223"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         46.0\n",
       "2         42.0\n",
       "3         46.0\n",
       "4         58.0\n",
       "5         62.0\n",
       "         ...  \n",
       "53936    144.0\n",
       "53937    144.0\n",
       "53938    140.0\n",
       "53939    172.0\n",
       "53940    150.0\n",
       "Name: carat, Length: 53940, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# просто выводится на экран\n",
    "df['carat']*200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "id": "7kVcqP3a60k2",
    "outputId": "adaf9ed1-5aa5-44c3-ec42-8dfe7f2399ad"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.0</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>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</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>34.505856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.0</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>38.076885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</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>46.724580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62.0</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>51.917250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     volume\n",
       "1   46.0    Ideal     E     SI2   61.5   55.0    326  38.202030\n",
       "2   42.0  Premium     E     SI1   59.8   61.0    326  34.505856\n",
       "3   46.0     Good     E     VS1   56.9   65.0    327  38.076885\n",
       "4   58.0  Premium     I     VS2   62.4   58.0    334  46.724580\n",
       "5   62.0     Good     J     SI2   63.3   58.0    335  51.917250"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# записываем в колонку\n",
    "df['carat'] = df['carat']*200\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zz1PUKzx60k4"
   },
   "source": [
    "Переименуем колонку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "id": "7KH47FjT60k5"
   },
   "outputs": [],
   "source": [
    "# передадим словарь, где укажем что на что менять\n",
    "# {'carat':'weight'} - словарь с одной парой key-value\n",
    "\n",
    "df.rename(columns={'carat':'weight'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "rename_dict = {\n",
    "    'old_name_1':'new_name_1',\n",
    "    'old_name_2':'new_name_2',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'new_name_1': 'old_name_1', 'new_name_2': 'old_name_2'}"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{v:k for k,v in rename_dict.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C9Ih-fPj60k9"
   },
   "source": [
    "Посмотрим теперь на датасет "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "id": "MfkTi2xI60k-",
    "outputId": "21d2a246-c31d-41cd-c3a2-ada78140b916"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>weight</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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.0</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>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</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>34.505856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.0</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>38.076885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</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>46.724580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62.0</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>51.917250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight      cut color clarity  depth  table  price     volume\n",
       "1    46.0    Ideal     E     SI2   61.5   55.0    326  38.202030\n",
       "2    42.0  Premium     E     SI1   59.8   61.0    326  34.505856\n",
       "3    46.0     Good     E     VS1   56.9   65.0    327  38.076885\n",
       "4    58.0  Premium     I     VS2   62.4   58.0    334  46.724580\n",
       "5    62.0     Good     J     SI2   63.3   58.0    335  51.917250"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# название поменялось\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "30GmPR4W60lA"
   },
   "source": [
    "Для построения точечного графика зависимости цены от массы используем метод `.plot()`, указываем его тип `kind=` и остальные необходимые аргументы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "id": "KhqAjoQS60lB",
    "outputId": "9b17cedc-d0b4-4a94-e174-124d38e3110c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c6a1160>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s – размер точек\n",
    "# x,y – что по каким осям\n",
    "df.plot(kind='scatter', x='weight', y='price', s=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "id": "vUkFvGq-60lD",
    "outputId": "309d88fb-c6d6-45c7-e8d5-f99f209c0567"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c8a54a8>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# логарифмируем x и y\n",
    "df.plot(kind='scatter', x='weight', y='price', s=0.2, loglog=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w4qM8aq360lF"
   },
   "source": [
    "## Индексация и срезы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VUlyjhzO60lF"
   },
   "source": [
    "### Индексация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YyvCZdaw60lF"
   },
   "source": [
    "Разберемся с тем, как доставать определенные строки и столбцы из датасета\n",
    "\n",
    "- для индексации по номеру строки и столбца используется атрибут `iloc[:, :]`\n",
    "    - сначала указываются индексы строк, затем – столбцов\n",
    "- для индексации по имени строки и столбца используется атрибут `loc[:, :]`\n",
    "    - сначала указываются имена строк, затем – столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DU6HUt3E60lG",
    "outputId": "11c271aa-3426-43ab-d2f6-b67c41afef6e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>weight</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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.0</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>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</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>34.505856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.0</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>38.076885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</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>46.724580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62.0</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>51.917250</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>115.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>118.110175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>140.0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>114.449728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>172.0</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>140.766120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>150.0</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>124.568444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight        cut color clarity  depth  table  price      volume\n",
       "1        46.0      Ideal     E     SI2   61.5   55.0    326   38.202030\n",
       "2        42.0    Premium     E     SI1   59.8   61.0    326   34.505856\n",
       "3        46.0       Good     E     VS1   56.9   65.0    327   38.076885\n",
       "4        58.0    Premium     I     VS2   62.4   58.0    334   46.724580\n",
       "5        62.0       Good     J     SI2   63.3   58.0    335   51.917250\n",
       "...       ...        ...   ...     ...    ...    ...    ...         ...\n",
       "53936   144.0      Ideal     D     SI1   60.8   57.0   2757  115.920000\n",
       "53937   144.0       Good     D     SI1   63.1   55.0   2757  118.110175\n",
       "53938   140.0  Very Good     D     SI1   62.8   60.0   2757  114.449728\n",
       "53939   172.0    Premium     H     SI2   61.0   58.0   2757  140.766120\n",
       "53940   150.0      Ideal     D     SI2   62.2   55.0   2757  124.568444\n",
       "\n",
       "[53940 rows x 8 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# индексы от 1 до 53940\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "id": "y05B_qSl60lI",
    "outputId": "d10e4703-df70-4491-a6cc-bbd279af450f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.32</td>\n",
       "      <td>Good</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>403</td>\n",
       "      <td>52.155950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>60.7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>404</td>\n",
       "      <td>49.954344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    carat        cut color clarity  depth  table  price     volume\n",
       "45   0.32       Good     H     SI2   63.1   56.0    403  52.155950\n",
       "50   0.29  Very Good     H     SI2   60.7   60.0    404  49.954344"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ровно 45 и 50 индексы\n",
    "df.loc[[45,50]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "id": "VLqr1pq560lJ",
    "outputId": "c30c503f-0d3c-4f2c-cec6-aef82cd66236"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>volume</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>52.155950</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>49.954344</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       volume  price\n",
       "45  52.155950    403\n",
       "50  49.954344    404"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# можно взять часть колонок\n",
    "df.loc[[45,50], ['volume','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "id": "dNA_BpNP60lL",
    "outputId": "f15a1ea5-7e9e-49ef-eedc-4375935bb7c4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>403</td>\n",
       "      <td>47.86536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>61.0</td>\n",
       "      <td>404</td>\n",
       "      <td>39.69147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    carat        cut color clarity  depth  table  price    volume\n",
       "46   0.29    Premium     F     SI1   62.4   58.0    403  47.86536\n",
       "51   0.24  Very Good     F     SI1   60.9   61.0    404  39.69147"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# индексы по счёту 45 и 50 по счёту от 0\n",
    "df.iloc[[45,50]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "id": "qTuzZYdd60lN",
    "outputId": "dea5d041-1b1b-4ede-f6a0-804ac219cebe"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>carat</th>\n",
       "      <th>depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.29</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.24</td>\n",
       "      <td>60.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    carat  depth\n",
       "46   0.29   62.4\n",
       "51   0.24   60.9"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# добавим колонки\n",
    "df.iloc[[45,50], [0,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "id": "sqhHqkD660lR",
    "outputId": "f0f8cf4d-d084-4114-acab-0060bc6f4099"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    carat        cut color clarity\n",
       "46   0.29    Premium     F     SI1\n",
       "51   0.24  Very Good     F     SI1"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# можно также использовать срезы\n",
    "df.iloc[[45,50], 0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3hisuJ2560lT"
   },
   "source": [
    "### Условные срезы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sY0-jQTG60lT"
   },
   "source": [
    "Вместо срезов (например, 45:50), можно использовать условия.\n",
    "\n",
    "Рассмотрим пример. Если рассмотреть  описательные статистики у колонки volume, то у нее встречаются нулевые значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "id": "VWt_auP760lU",
    "outputId": "16494e74-01db-4cf1-cd60-8ead90b5c631"
   },
   "outputs": [
    {
     "data": {
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       "\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>weight</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>159.587950</td>\n",
       "      <td>129.849403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>94.802249</td>\n",
       "      <td>78.245262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>65.136830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>114.808572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>208.000000</td>\n",
       "      <td>170.842451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1002.000000</td>\n",
       "      <td>3840.598060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             weight        volume\n",
       "count  53940.000000  53940.000000\n",
       "mean     159.587950    129.849403\n",
       "std       94.802249     78.245262\n",
       "min       40.000000      0.000000\n",
       "25%       80.000000     65.136830\n",
       "50%      140.000000    114.808572\n",
       "75%      208.000000    170.842451\n",
       "max     1002.000000   3840.598060"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['weight','volume']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k30s9GCc60lW"
   },
   "source": [
    "При этом если рассмотреть массу и объем бриллиантов с нулевым значением объема, то масса у таких бриллиантов в большинстве оказывается значительно выше среднего, поэтому вряд ли их настоящий объем равен нулю. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "id": "xm2RPR4g60lX",
    "outputId": "98fd6486-54c3-43ed-8a8e-992f7f8545e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "5        False\n",
       "         ...  \n",
       "53936    False\n",
       "53937    False\n",
       "53938    False\n",
       "53939    False\n",
       "53940    False\n",
       "Name: volume, Length: 53940, dtype: bool"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# сравнение каждой строки с нулем\n",
    "df['volume'] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>64.0</td>\n",
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       "      <th>11183</th>\n",
       "      <td>214.0</td>\n",
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       "      <td>61.6</td>\n",
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       "      <th>13602</th>\n",
       "      <td>230.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.2</td>\n",
       "      <td>56.0</td>\n",
       "      <td>5564</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>15952</th>\n",
       "      <td>228.0</td>\n",
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       "      <td>57.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>6381</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>24395</th>\n",
       "      <td>436.0</td>\n",
       "      <td>Premium</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>59.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12631</td>\n",
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       "      <td>VS2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>12800</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>26124</th>\n",
       "      <td>450.0</td>\n",
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       "      <td>61.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15397</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>26244</th>\n",
       "      <td>240.0</td>\n",
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       "      <td>62.1</td>\n",
       "      <td>59.0</td>\n",
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       "      <th>27113</th>\n",
       "      <td>440.0</td>\n",
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       "      <td>61.2</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>27430</th>\n",
       "      <td>450.0</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18034</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>27504</th>\n",
       "      <td>404.0</td>\n",
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       "      <td>VS2</td>\n",
       "      <td>62.7</td>\n",
       "      <td>53.0</td>\n",
       "      <td>18207</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27740</th>\n",
       "      <td>560.0</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>63.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>18788</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>49557</th>\n",
       "      <td>142.0</td>\n",
       "      <td>Good</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>49558</th>\n",
       "      <td>142.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI2</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>51507</th>\n",
       "      <td>224.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>G</td>\n",
       "      <td>I1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2383</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "       weight        cut color clarity  depth  table  price  volume\n",
       "2208    200.0    Premium     G     SI2   59.1   59.0   3142     0.0\n",
       "2315    202.0    Premium     H      I1   58.1   59.0   3167     0.0\n",
       "4792    220.0    Premium     G     SI2   63.0   59.0   3696     0.0\n",
       "5472    202.0    Premium     F     SI2   59.2   58.0   3837     0.0\n",
       "10168   300.0       Good     G      I1   64.0   61.0   4731     0.0\n",
       "11183   214.0      Ideal     F     SI2   61.6   56.0   4954     0.0\n",
       "11964   200.0  Very Good     H     VS2   63.3   53.0   5139     0.0\n",
       "13602   230.0      Ideal     G     VS2   59.2   56.0   5564     0.0\n",
       "15952   228.0       Fair     G     VS1   57.5   67.0   6381     0.0\n",
       "24395   436.0    Premium     H     SI2   59.4   61.0  12631     0.0\n",
       "24521   312.0      Ideal     G     VS2   62.2   54.0  12800     0.0\n",
       "26124   450.0    Premium     I     SI1   61.3   58.0  15397     0.0\n",
       "26244   240.0    Premium     D    VVS1   62.1   59.0  15686     0.0\n",
       "27113   440.0    Premium     H     SI1   61.2   59.0  17265     0.0\n",
       "27430   450.0    Premium     H     SI2   62.8   59.0  18034     0.0\n",
       "27504   404.0    Premium     H     VS2   62.7   53.0  18207     0.0\n",
       "27740   560.0       Good     G     SI2   63.8   58.0  18788     0.0\n",
       "49557   142.0       Good     F     SI2   64.1   60.0   2130     0.0\n",
       "49558   142.0       Good     F     SI2   64.1   60.0   2130     0.0\n",
       "51507   224.0    Premium     G      I1   60.4   59.0   2383     0.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['volume'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>61.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>11183</th>\n",
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       "      <td>61.6</td>\n",
       "      <td>56.0</td>\n",
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       "      <th>13602</th>\n",
       "      <td>230.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.2</td>\n",
       "      <td>56.0</td>\n",
       "      <td>5564</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>228.0</td>\n",
       "      <td>Fair</td>\n",
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       "      <td>VS1</td>\n",
       "      <td>57.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>6381</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>24395</th>\n",
       "      <td>436.0</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>59.4</td>\n",
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       "      <td>12631</td>\n",
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       "      <td>312.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>12800</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>26124</th>\n",
       "      <td>450.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15397</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>26244</th>\n",
       "      <td>240.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>62.1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>15686</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27113</th>\n",
       "      <td>440.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17265</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27430</th>\n",
       "      <td>450.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18034</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27504</th>\n",
       "      <td>404.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.7</td>\n",
       "      <td>53.0</td>\n",
       "      <td>18207</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27740</th>\n",
       "      <td>560.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>G</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>18788</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49557</th>\n",
       "      <td>142.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI2</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49558</th>\n",
       "      <td>142.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI2</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51507</th>\n",
       "      <td>224.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>G</td>\n",
       "      <td>I1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2383</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight        cut color clarity  depth  table  price  volume\n",
       "2208    200.0    Premium     G     SI2   59.1   59.0   3142     0.0\n",
       "2315    202.0    Premium     H      I1   58.1   59.0   3167     0.0\n",
       "4792    220.0    Premium     G     SI2   63.0   59.0   3696     0.0\n",
       "5472    202.0    Premium     F     SI2   59.2   58.0   3837     0.0\n",
       "10168   300.0       Good     G      I1   64.0   61.0   4731     0.0\n",
       "11183   214.0      Ideal     F     SI2   61.6   56.0   4954     0.0\n",
       "11964   200.0  Very Good     H     VS2   63.3   53.0   5139     0.0\n",
       "13602   230.0      Ideal     G     VS2   59.2   56.0   5564     0.0\n",
       "15952   228.0       Fair     G     VS1   57.5   67.0   6381     0.0\n",
       "24395   436.0    Premium     H     SI2   59.4   61.0  12631     0.0\n",
       "24521   312.0      Ideal     G     VS2   62.2   54.0  12800     0.0\n",
       "26124   450.0    Premium     I     SI1   61.3   58.0  15397     0.0\n",
       "26244   240.0    Premium     D    VVS1   62.1   59.0  15686     0.0\n",
       "27113   440.0    Premium     H     SI1   61.2   59.0  17265     0.0\n",
       "27430   450.0    Premium     H     SI2   62.8   59.0  18034     0.0\n",
       "27504   404.0    Premium     H     VS2   62.7   53.0  18207     0.0\n",
       "27740   560.0       Good     G     SI2   63.8   58.0  18788     0.0\n",
       "49557   142.0       Good     F     SI2   64.1   60.0   2130     0.0\n",
       "49558   142.0       Good     F     SI2   64.1   60.0   2130     0.0\n",
       "51507   224.0    Premium     G      I1   60.4   59.0   2383     0.0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('volume == 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 2208,  2315,  4792,  5472, 10168, 11183, 11964, 13602, 15952,\n",
       "            24395, 24521, 26124, 26244, 27113, 27430, 27504, 27740, 49557,\n",
       "            49558, 51507],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('volume == 0').index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 8)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53920, 8)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.query('volume == 0').index, axis=0, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53914, 8)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['volume'] < df['volume'].quantile(0.9999)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f9ace48>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='volume', y='price', s=0.4, c='red', alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121e6b080>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='volume', y='price', s=0.4, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "sns.scatterplot(data=df, x='volume', y='price', alpha=0.2, hue='color', linewidth=0)\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jPQu95Js60lZ",
    "outputId": "2aeed27f-4894-42e4-cd90-8bc136b348de"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>weight</th>\n",
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       "      <th>color</th>\n",
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       "      <td>214.0</td>\n",
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       "      <td>62.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>12800</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>26124</th>\n",
       "      <td>450.0</td>\n",
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       "      <td>SI1</td>\n",
       "      <td>61.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15397</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>26244</th>\n",
       "      <td>240.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>62.1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>15686</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27113</th>\n",
       "      <td>440.0</td>\n",
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       "      <td>450.0</td>\n",
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       "      <td>59.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27504</th>\n",
       "      <td>404.0</td>\n",
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       "      <td>VS2</td>\n",
       "      <td>62.7</td>\n",
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       "      <td>18207</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>560.0</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>63.8</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>142.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI2</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51507</th>\n",
       "      <td>224.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>G</td>\n",
       "      <td>I1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2383</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight        cut color clarity  depth  table  price  volume\n",
       "2208    200.0    Premium     G     SI2   59.1   59.0   3142     0.0\n",
       "2315    202.0    Premium     H      I1   58.1   59.0   3167     0.0\n",
       "4792    220.0    Premium     G     SI2   63.0   59.0   3696     0.0\n",
       "5472    202.0    Premium     F     SI2   59.2   58.0   3837     0.0\n",
       "10168   300.0       Good     G      I1   64.0   61.0   4731     0.0\n",
       "11183   214.0      Ideal     F     SI2   61.6   56.0   4954     0.0\n",
       "11964   200.0  Very Good     H     VS2   63.3   53.0   5139     0.0\n",
       "13602   230.0      Ideal     G     VS2   59.2   56.0   5564     0.0\n",
       "15952   228.0       Fair     G     VS1   57.5   67.0   6381     0.0\n",
       "24395   436.0    Premium     H     SI2   59.4   61.0  12631     0.0\n",
       "24521   312.0      Ideal     G     VS2   62.2   54.0  12800     0.0\n",
       "26124   450.0    Premium     I     SI1   61.3   58.0  15397     0.0\n",
       "26244   240.0    Premium     D    VVS1   62.1   59.0  15686     0.0\n",
       "27113   440.0    Premium     H     SI1   61.2   59.0  17265     0.0\n",
       "27430   450.0    Premium     H     SI2   62.8   59.0  18034     0.0\n",
       "27504   404.0    Premium     H     VS2   62.7   53.0  18207     0.0\n",
       "27740   560.0       Good     G     SI2   63.8   58.0  18788     0.0\n",
       "49557   142.0       Good     F     SI2   64.1   60.0   2130     0.0\n",
       "49558   142.0       Good     F     SI2   64.1   60.0   2130     0.0\n",
       "51507   224.0    Premium     G      I1   60.4   59.0   2383     0.0"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# оставляем только те строки, где нулевой объем\n",
    "df.loc[df['volume'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HGxHEcdI60lb",
    "outputId": "57c4f5f0-fe08-4fb7-fd3e-f188067e5d97"
   },
   "outputs": [
    {
     "data": {
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       "      <td>436.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>24521</th>\n",
       "      <td>312.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26124</th>\n",
       "      <td>450.0</td>\n",
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       "    <tr>\n",
       "      <th>27504</th>\n",
       "      <td>404.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18207</td>\n",
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       "    <tr>\n",
       "      <th>27740</th>\n",
       "      <td>560.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>49557</th>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>49558</th>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2130</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "       weight  volume  price\n",
       "2208    200.0     0.0   3142\n",
       "2315    202.0     0.0   3167\n",
       "4792    220.0     0.0   3696\n",
       "5472    202.0     0.0   3837\n",
       "10168   300.0     0.0   4731\n",
       "11183   214.0     0.0   4954\n",
       "11964   200.0     0.0   5139\n",
       "13602   230.0     0.0   5564\n",
       "15952   228.0     0.0   6381\n",
       "24395   436.0     0.0  12631\n",
       "24521   312.0     0.0  12800\n",
       "26124   450.0     0.0  15397\n",
       "26244   240.0     0.0  15686\n",
       "27113   440.0     0.0  17265\n",
       "27430   450.0     0.0  18034\n",
       "27504   404.0     0.0  18207\n",
       "27740   560.0     0.0  18788\n",
       "49557   142.0     0.0   2130\n",
       "49558   142.0     0.0   2130\n",
       "51507   224.0     0.0   2383"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим на часть колонок\n",
    "df.loc[df['volume']==0, ['weight', 'volume', 'price']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DzVzbdFQ60le"
   },
   "source": [
    "Оставим в датасете только строчки с ненулевым объемом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "455i5e1x60le",
    "outputId": "3720aac1-929a-41c1-b1b7-72c200ba849c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 8)"
      ]
     },
     "execution_count": 70,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# было\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eVLayVAu60lg"
   },
   "outputs": [],
   "source": [
    "df = df[df['volume']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N16Ctj1g60lj",
    "outputId": "7d0d3af8-1da8-4a0b-ccd2-086806b29472"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53920, 8)"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# стало\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gtlgxYlv60ll"
   },
   "source": [
    "### Условные операторы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vwfx8Tzs60lm"
   },
   "source": [
    "Также можно делать срезы из таблицы, используя несколько условий с помощью операторов \n",
    "    - `|` (или)\n",
    "    - `&` (и)\n",
    "\n",
    "```python\n",
    "df[(`condition_1`) | (`condition_2`)]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GZLmTxlD60ln",
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    {
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       "      <th>91</th>\n",
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       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>116.396280</td>\n",
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       "      <th>92</th>\n",
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       "      <th>94</th>\n",
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       "      <td>62.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2759</td>\n",
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       "      <th>95</th>\n",
       "      <td>156.0</td>\n",
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       "      <td>63.8</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>63.1</td>\n",
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       "      <td>2757</td>\n",
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       "      <td>140.0</td>\n",
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       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>114.449728</td>\n",
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       "      <th>53939</th>\n",
       "      <td>172.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
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       "      <td>150.0</td>\n",
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       "      <td>2757</td>\n",
       "      <td>124.568444</td>\n",
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       "       weight        cut color clarity  depth  table  price      volume\n",
       "91      140.0      Ideal     E     SI1   62.5   57.0   2757  116.396280\n",
       "92      172.0       Fair     E     SI2   55.1   69.0   2757  143.716320\n",
       "93      140.0      Ideal     G     VS2   61.6   56.0   2757  113.116500\n",
       "94      142.0  Very Good     E     VS2   62.4   57.0   2759  115.865184\n",
       "95      156.0  Very Good     G     SI2   63.8   56.0   2759  126.437220\n",
       "...       ...        ...   ...     ...    ...    ...    ...         ...\n",
       "53936   144.0      Ideal     D     SI1   60.8   57.0   2757  115.920000\n",
       "53937   144.0       Good     D     SI1   63.1   55.0   2757  118.110175\n",
       "53938   140.0  Very Good     D     SI1   62.8   60.0   2757  114.449728\n",
       "53939   172.0    Premium     H     SI2   61.0   58.0   2757  140.766120\n",
       "53940   150.0      Ideal     D     SI2   62.2   55.0   2757  124.568444\n",
       "\n",
       "[21024 rows x 8 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
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    }
   ],
   "source": [
    "df[(df['volume'] >= 100) & (df['volume'] <= 200)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MxOLtxk660lp",
    "outputId": "896fd4b7-5849-4081-dd7f-f8a02927ff57"
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    {
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       "      <td>63.8</td>\n",
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       "      <td>114.449728</td>\n",
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       "      <td>172.0</td>\n",
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       "      <td>150.0</td>\n",
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       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>124.568444</td>\n",
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       "<p>21024 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "       weight        cut color clarity  depth  table  price      volume\n",
       "91      140.0      Ideal     E     SI1   62.5   57.0   2757  116.396280\n",
       "92      172.0       Fair     E     SI2   55.1   69.0   2757  143.716320\n",
       "93      140.0      Ideal     G     VS2   61.6   56.0   2757  113.116500\n",
       "94      142.0  Very Good     E     VS2   62.4   57.0   2759  115.865184\n",
       "95      156.0  Very Good     G     SI2   63.8   56.0   2759  126.437220\n",
       "...       ...        ...   ...     ...    ...    ...    ...         ...\n",
       "53936   144.0      Ideal     D     SI1   60.8   57.0   2757  115.920000\n",
       "53937   144.0       Good     D     SI1   63.1   55.0   2757  118.110175\n",
       "53938   140.0  Very Good     D     SI1   62.8   60.0   2757  114.449728\n",
       "53939   172.0    Premium     H     SI2   61.0   58.0   2757  140.766120\n",
       "53940   150.0      Ideal     D     SI2   62.2   55.0   2757  124.568444\n",
       "\n",
       "[21024 rows x 8 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
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   ],
   "source": [
    "# то же самое по шагам и без лишних скобок\n",
    "over100 = df['volume'] >= 100\n",
    "under200 = df['volume'] <= 200\n",
    "df[over100 & under200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QKdFTV6f60lt",
    "outputId": "2f764081-d807-433e-a27a-da80600c02e5"
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   "outputs": [
    {
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       "      <td>21024.000000</td>\n",
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       "      <td>21024.000000</td>\n",
       "      <td>21024.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>185.104357</td>\n",
       "      <td>61.828501</td>\n",
       "      <td>57.801532</td>\n",
       "      <td>4447.483019</td>\n",
       "      <td>150.260471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.920223</td>\n",
       "      <td>1.615258</td>\n",
       "      <td>2.297051</td>\n",
       "      <td>2072.115478</td>\n",
       "      <td>27.350647</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>468.000000</td>\n",
       "      <td>100.001446</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>148.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>2863.000000</td>\n",
       "      <td>120.880244</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>61.900000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>4118.000000</td>\n",
       "      <td>156.358128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>208.000000</td>\n",
       "      <td>62.700000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5395.000000</td>\n",
       "      <td>170.158740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>412.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>18542.000000</td>\n",
       "      <td>199.999475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             weight         depth         table         price        volume\n",
       "count  21024.000000  21024.000000  21024.000000  21024.000000  21024.000000\n",
       "mean     185.104357     61.828501     57.801532   4447.483019    150.260471\n",
       "std       33.920223      1.615258      2.297051   2072.115478     27.350647\n",
       "min       70.000000     43.000000     43.000000    468.000000    100.001446\n",
       "25%      148.000000     61.000000     56.000000   2863.000000    120.880244\n",
       "50%      200.000000     61.900000     58.000000   4118.000000    156.358128\n",
       "75%      208.000000     62.700000     59.000000   5395.000000    170.158740\n",
       "max      412.000000     79.000000     73.000000  18542.000000    199.999475"
      ]
     },
     "execution_count": 75,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[over100 & under200].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ayWqdsDf60lv"
   },
   "source": [
    "Посмотрим на все идеальные и премиум бриллианты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nDrRG5M260lw",
    "outputId": "519e77a3-5b52-4833-8a30-8e798230f4bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ideal', 'Premium', 'Good', 'Very Good', 'Fair'], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CSg71b6860ly",
    "outputId": "f2732a93-7842-4dfd-aaf4-02fa05ac8391"
   },
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>46.0</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>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</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>34.505856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</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>46.724580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>VS1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>340</td>\n",
       "      <td>37.704420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>342</td>\n",
       "      <td>34.715136</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53932</th>\n",
       "      <td>142.0</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>112.813386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.7</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>116.721246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>115.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>172.0</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>140.766120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>150.0</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>124.568444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35328 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight      cut color clarity  depth  table  price      volume\n",
       "1        46.0    Ideal     E     SI2   61.5   55.0    326   38.202030\n",
       "2        42.0  Premium     E     SI1   59.8   61.0    326   34.505856\n",
       "4        58.0  Premium     I     VS2   62.4   58.0    334   46.724580\n",
       "12       46.0    Ideal     J     VS1   62.8   56.0    340   37.704420\n",
       "13       44.0  Premium     F     SI1   60.4   61.0    342   34.715136\n",
       "...       ...      ...   ...     ...    ...    ...    ...         ...\n",
       "53932   142.0  Premium     F     SI1   59.8   62.0   2756  112.813386\n",
       "53935   144.0  Premium     D     SI1   62.7   59.0   2757  116.721246\n",
       "53936   144.0    Ideal     D     SI1   60.8   57.0   2757  115.920000\n",
       "53939   172.0  Premium     H     SI2   61.0   58.0   2757  140.766120\n",
       "53940   150.0    Ideal     D     SI2   62.2   55.0   2757  124.568444\n",
       "\n",
       "[35328 rows x 8 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition_1 = df['cut']=='Ideal'\n",
    "condition_2 = df['cut']=='Premium'\n",
    "df[condition_1 | condition_2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y2XsG4_r60l0"
   },
   "source": [
    "Посмотрим на все идеальные бриллианты массой больше 500 миллиграмм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "L1HZ-8xC60l1",
    "outputId": "7940cbe9-cf86-43bd-dfd2-bbeed748f8ff"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>weight</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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23581</th>\n",
       "      <td>544.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>I1</td>\n",
       "      <td>59.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11594</td>\n",
       "      <td>456.285445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24298</th>\n",
       "      <td>644.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>I1</td>\n",
       "      <td>62.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>12545</td>\n",
       "      <td>529.223136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24329</th>\n",
       "      <td>700.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>I1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>12587</td>\n",
       "      <td>558.037305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24435</th>\n",
       "      <td>508.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>12687</td>\n",
       "      <td>419.423234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24785</th>\n",
       "      <td>550.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>I1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>13156</td>\n",
       "      <td>445.673808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25580</th>\n",
       "      <td>508.0</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>14421</td>\n",
       "      <td>414.404375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26091</th>\n",
       "      <td>502.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.9</td>\n",
       "      <td>56.0</td>\n",
       "      <td>15324</td>\n",
       "      <td>404.407395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26456</th>\n",
       "      <td>506.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>53.0</td>\n",
       "      <td>15993</td>\n",
       "      <td>413.613508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26468</th>\n",
       "      <td>602.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.7</td>\n",
       "      <td>58.0</td>\n",
       "      <td>16037</td>\n",
       "      <td>484.219000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26658</th>\n",
       "      <td>518.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>VS1</td>\n",
       "      <td>61.7</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16465</td>\n",
       "      <td>420.494313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26745</th>\n",
       "      <td>602.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>65.4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16538</td>\n",
       "      <td>470.444902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26819</th>\n",
       "      <td>506.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16709</td>\n",
       "      <td>401.884868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26909</th>\n",
       "      <td>526.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16914</td>\n",
       "      <td>431.257200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27024</th>\n",
       "      <td>502.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>17162</td>\n",
       "      <td>407.496936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27082</th>\n",
       "      <td>522.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>64.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>17209</td>\n",
       "      <td>409.521735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27095</th>\n",
       "      <td>504.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>17231</td>\n",
       "      <td>408.788028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27136</th>\n",
       "      <td>508.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.7</td>\n",
       "      <td>56.0</td>\n",
       "      <td>17339</td>\n",
       "      <td>414.209250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27164</th>\n",
       "      <td>528.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>17407</td>\n",
       "      <td>432.825408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27285</th>\n",
       "      <td>512.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>17753</td>\n",
       "      <td>410.669676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27303</th>\n",
       "      <td>544.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>17801</td>\n",
       "      <td>445.367676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27431</th>\n",
       "      <td>502.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI2</td>\n",
       "      <td>58.4</td>\n",
       "      <td>62.0</td>\n",
       "      <td>18037</td>\n",
       "      <td>401.531595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27562</th>\n",
       "      <td>520.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>18369</td>\n",
       "      <td>423.694044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27728</th>\n",
       "      <td>522.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>18756</td>\n",
       "      <td>421.842330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight    cut color clarity  depth  table  price      volume\n",
       "23581   544.0  Ideal     H      I1   59.6   55.0  11594  456.285445\n",
       "24298   644.0  Ideal     I      I1   62.6   55.0  12545  529.223136\n",
       "24329   700.0  Ideal     H      I1   62.8   57.0  12587  558.037305\n",
       "24435   508.0  Ideal     I     SI2   61.4   54.0  12687  419.423234\n",
       "24785   550.0  Ideal     D      I1   60.9   57.0  13156  445.673808\n",
       "25580   508.0  Ideal     G     SI2   62.5   55.0  14421  414.404375\n",
       "26091   502.0  Ideal     H     SI1   62.9   56.0  15324  404.407395\n",
       "26456   506.0  Ideal     J     SI2   61.5   53.0  15993  413.613508\n",
       "26468   602.0  Ideal     J     SI2   61.7   58.0  16037  484.219000\n",
       "26658   518.0  Ideal     J     VS1   61.7   56.0  16465  420.494313\n",
       "26745   602.0  Ideal     J      I1   65.4   60.0  16538  470.444902\n",
       "26819   506.0  Ideal     I     SI1   62.4   57.0  16709  401.884868\n",
       "26909   526.0  Ideal     J     SI2   62.1   57.0  16914  431.257200\n",
       "27024   502.0  Ideal     H     SI2   62.0   57.0  17162  407.496936\n",
       "27082   522.0  Ideal     J     SI2   64.6   55.0  17209  409.521735\n",
       "27095   504.0  Ideal     I     SI1   61.5   56.0  17231  408.788028\n",
       "27136   508.0  Ideal     G     SI2   62.7   56.0  17339  414.209250\n",
       "27164   528.0  Ideal     H     SI2   62.3   55.0  17407  432.825408\n",
       "27285   512.0  Ideal     J     SI1   61.4   58.0  17753  410.669676\n",
       "27303   544.0  Ideal     J     SI2   62.1   56.0  17801  445.367676\n",
       "27431   502.0  Ideal     I     SI2   58.4   62.0  18037  401.531595\n",
       "27562   520.0  Ideal     J     SI2   62.5   56.0  18369  423.694044\n",
       "27728   522.0  Ideal     I     SI2   62.1   56.0  18756  421.842330"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['cut']=='Ideal') & (df['weight']>500)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pJafFoZB60l7"
   },
   "source": [
    "### Работа со строками"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lP-g5Ktm60l7"
   },
   "source": [
    "Для работы со строкововыми колонками можно также воспользоваться методами \n",
    "\n",
    "- `.isin()` – для того, чтобы оставить лишь те строки, категории которой лежат в некотором списке\n",
    "\n",
    "```python \n",
    "df['col'].isin(['category1', 'category2'])\n",
    "```\n",
    "\n",
    "- `.str` – для того, чтобы использовать методы, которые есть у строк\n",
    "- `.str.contains()` – для того, чтобы оставить строки, содержание которых имеет определенный текст\n",
    "\n",
    "```python \n",
    "df['col'].str.contains(['some_text'])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jSR4AM4H60l7",
    "outputId": "480335ee-aa61-43d0-d319-3d16164305c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         True\n",
       "2         True\n",
       "3        False\n",
       "4         True\n",
       "5        False\n",
       "         ...  \n",
       "53936     True\n",
       "53937    False\n",
       "53938    False\n",
       "53939     True\n",
       "53940     True\n",
       "Name: cut, Length: 53920, dtype: bool"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# снова строки с логическими переменными\n",
    "cut_interested = ['Ideal', 'Premium']\n",
    "df['cut'].isin(cut_interested)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uzwIIOJT60l9",
    "outputId": "2f5a65f2-ec06-4a77-deda-5bec9897b90f"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.0</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>38.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</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>34.505856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</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>46.724580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>VS1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>340</td>\n",
       "      <td>37.704420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>342</td>\n",
       "      <td>34.715136</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53932</th>\n",
       "      <td>142.0</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>112.813386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.7</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>116.721246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>115.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>172.0</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>140.766120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53940</th>\n",
       "      <td>150.0</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>124.568444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35328 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight      cut color clarity  depth  table  price      volume\n",
       "1        46.0    Ideal     E     SI2   61.5   55.0    326   38.202030\n",
       "2        42.0  Premium     E     SI1   59.8   61.0    326   34.505856\n",
       "4        58.0  Premium     I     VS2   62.4   58.0    334   46.724580\n",
       "12       46.0    Ideal     J     VS1   62.8   56.0    340   37.704420\n",
       "13       44.0  Premium     F     SI1   60.4   61.0    342   34.715136\n",
       "...       ...      ...   ...     ...    ...    ...    ...         ...\n",
       "53932   142.0  Premium     F     SI1   59.8   62.0   2756  112.813386\n",
       "53935   144.0  Premium     D     SI1   62.7   59.0   2757  116.721246\n",
       "53936   144.0    Ideal     D     SI1   60.8   57.0   2757  115.920000\n",
       "53939   172.0  Premium     H     SI2   61.0   58.0   2757  140.766120\n",
       "53940   150.0    Ideal     D     SI2   62.2   55.0   2757  124.568444\n",
       "\n",
       "[35328 rows x 8 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# делаем условный срез\n",
    "df[df['cut'].isin(['Ideal', 'Premium'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pJnglmBg60l_",
    "outputId": "de8037ee-b002-4aca-c5ef-8088337f5b8b"
   },
   "outputs": [
    {
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       "      <th>weight</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
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       "      <td>46.0</td>\n",
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       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>Good</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48.0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>J</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>38.693952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48.0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>62.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>38.830870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>337</td>\n",
       "      <td>42.321081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>53928</th>\n",
       "      <td>158.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>58.1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2756</td>\n",
       "      <td>131.503212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53933</th>\n",
       "      <td>140.0</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>114.126912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53934</th>\n",
       "      <td>140.0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>113.588332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>118.110175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>140.0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>114.449728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16983 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight        cut color clarity  depth  table  price      volume\n",
       "3        46.0       Good     E     VS1   56.9   65.0    327   38.076885\n",
       "5        62.0       Good     J     SI2   63.3   58.0    335   51.917250\n",
       "6        48.0  Very Good     J    VVS2   62.8   57.0    336   38.693952\n",
       "7        48.0  Very Good     I    VVS1   62.3   57.0    336   38.830870\n",
       "8        52.0  Very Good     H     SI1   61.9   55.0    337   42.321081\n",
       "...       ...        ...   ...     ...    ...    ...    ...         ...\n",
       "53928   158.0       Good     F     SI1   58.1   59.0   2756  131.503212\n",
       "53933   140.0  Very Good     E     VS2   60.5   59.0   2757  114.126912\n",
       "53934   140.0  Very Good     E     VS2   61.2   59.0   2757  113.588332\n",
       "53937   144.0       Good     D     SI1   63.1   55.0   2757  118.110175\n",
       "53938   140.0  Very Good     D     SI1   62.8   60.0   2757  114.449728\n",
       "\n",
       "[16983 rows x 8 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['cut'].str.contains('Good')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xGV7V1Qv60mT"
   },
   "source": [
    "# Ревью\n",
    "\n",
    "Сегодня мы научились: \n",
    "- загружать таблички \n",
    "    - `df = pd.read_csv('some_data.csv')`\n",
    "- смотреть на них \n",
    "    - `df.head()`,`df.tail()`, \n",
    "    - `df.shape`, `df.info()`, `df.dtypes()`\n",
    "- считать различные характеристики\n",
    "    - `df.describe()` \n",
    "    - `df['col'].mean()`, `df['col'].median()`\n",
    "- делать группировки\n",
    "    - `df.groupby('col1')['col2'].agg(['mean','median'])`\n",
    "    - `df.pivot_table(columns='col1', index='col2', values='col3', aggfunc='mean')`\n",
    "- считать уникальные значения\n",
    "    - `df['col'].value_counts(normalize=True)`, `df['col'].unique()` `df['col'].nunique()`\n",
    "- даже строить немного графики\n",
    "    - `df['col'].value_counts(normalize=True).plot(kind='bar')`\n",
    "- удалять и переименовывать столбцы\n",
    "    - `df.drop(['col1', 'col2'], axis=1, inplace=True)`\n",
    "    - `df.rename(columns={'old_col_name':'new_col_name'}, inplace=True)`\n",
    "- фильтровать таблички, используя сложные условия\n",
    "    - `df.loc[df['col']==some_value, ['col1', 'col2']]`\n",
    "    - `df[(df['col1']==some_value) | (df['col2']!=some_value)]`\n",
    "    - `df[(df['col1']>some_value) & (df['col2']<some_value)]`\n",
    "    - `df['col'].isin(['category1', 'category2'])`\n",
    "    - `df['col'].str.contains(['some_text'])`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Yw_-d1f60mB"
   },
   "source": [
    "# Практика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "class triangle:\n",
    "    def __init__(self,a,b,c):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        self.c = c\n",
    "        \n",
    "    def calculate_perimeter(self):\n",
    "        return self.a + self.b + self.c\n",
    "    \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = triangle(a=3,b=4,c=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.a # атрибуты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.calculate_perimeter() # методы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZMYvOujC60mB"
   },
   "source": [
    "У бриллиантов какого качества (колонка cut) самая большое значение медианной цены?\n",
    "\n",
    "`Fair`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>38.076885</td>\n",
       "      <td>good</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</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>46.724580</td>\n",
       "      <td>premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62.0</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>51.917250</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight      cut color clarity  depth  table  price     volume  cut_new\n",
       "1    46.0    ideal     E     SI2   61.5   55.0    326  38.202030    ideal\n",
       "2    42.0  premium     E     SI1   59.8   61.0    326  34.505856  premium\n",
       "3    46.0     good     E     VS1   56.9   65.0    327  38.076885     good\n",
       "4    58.0  premium     I     VS2   62.4   58.0    334  46.724580  premium\n",
       "5    62.0     good     J     SI2   63.3   58.0    335  51.917250     good"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['weight', 'cut', 'color', 'clarity', 'depth', 'table', 'price',\n",
       "       'volume', 'cut_new'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([    1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "               10,\n",
       "            ...\n",
       "            53931, 53932, 53933, 53934, 53935, 53936, 53937, 53938, 53939,\n",
       "            53940],\n",
       "           dtype='int64', length=53920)"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cut\n",
       "fair         3282.0\n",
       "good         3050.5\n",
       "ideal        1809.5\n",
       "premium      3182.0\n",
       "very good    2647.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg = df.groupby('cut')['price'].median()\n",
    "agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'very good'"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg.index[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fair'"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg[agg == agg.max()].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cut\n",
       "fair    3282.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cut')['price'].median().sort_values(ascending=False).iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fair'"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cut')['price'].median().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cut')['price'].median().argmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8S3uCfRY60mF"
   },
   "source": [
    "Какой самый распространенный цвет брилланта (color)?\n",
    "\n",
    "`G`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        E\n",
       "2        E\n",
       "3        E\n",
       "4        I\n",
       "5        J\n",
       "        ..\n",
       "53936    D\n",
       "53937    D\n",
       "53938    D\n",
       "53939    H\n",
       "53940    D\n",
       "Name: color, Length: 53920, dtype: object"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CLOaryj060mI"
   },
   "source": [
    "Какой самый распространенный цвет брилланта (color) среди тех, у которых значение цены (price) выше медианной?\n",
    "\n",
    "`G`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'G'"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'G'"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'].describe()['top']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DfLf1CfN60mJ"
   },
   "source": [
    "Какое самое распространенное качество брилланта (cut) среди тех, у которых значение цены (price) выше медианной, а масса (weight) выше среднего?\n",
    "\n",
    "`Premium`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'premium'"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = df['weight'] > df['weight'].mean()\n",
    "c2 = df['price'] > df['price'].median()\n",
    "\n",
    "df[c1 & c2]['cut'].mode()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ff7XGraJ60mO"
   },
   "source": [
    "Чему равна средняя цена брилланта (price) среди бриллиантов Premium и Ideal качества (cut)? \n",
    "(Предложите, как минимум, два варианта решения)\n",
    "\n",
    "`3894.8`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3894.877207880435"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = df['cut'] == 'premium'\n",
    "c2 = df['cut'] == 'ideal'\n",
    "\n",
    "df[c1 | c2]['price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3894.877207880435"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['cut'].isin(['premium', 'ideal'])]['price'].mean()"
   ]
  }
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