PROG_DA_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DzbPuJ2_2eSJ"
   },
   "source": [
    "# Анализ данных в Python \n",
    "\n",
    "# Введение в инструменты. `pandas` и `numpy`\n",
    "\n",
    "Автор: Перевышина Татьяна, НИУ ВШЭ\n",
    "\n",
    "На основе блокнота Рогович Татьяны\n",
    "\n",
    "Дополнения: Седашов Данила"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gDRv8ZTivrKC"
   },
   "source": [
    "## **Датафреймы. Библиотека `Pandas`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x7t7DpJrx10o"
   },
   "source": [
    "Pandas — библиотека для работы с табличными данными в питоне.\n",
    "\n",
    "Numpy — библиотека для выполнения арифметических операций в питоне. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "UevbXu6ExYPl"
   },
   "outputs": [],
   "source": [
    "import pandas as pd # импортировали библиотеку pandas и назвали ее pd\n",
    "import numpy as np # импортируем библиотеку numpy и называем её np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2eVrTC80zekH"
   },
   "source": [
    "### Создание собственного датафрейма"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HYLQTeEHy4Lj"
   },
   "source": [
    "1. Создаем пустой датафрейм с помощью функции `DataFrame()` из библиотеки `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "id": "rFY5xBnEysZb",
    "outputId": "5ad1c08c-fc49-4956-db9e-4895d3a9adf3"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # выводим датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n27jKoMtzqwE"
   },
   "source": [
    "2. Добавялем в, пока что пустой, датафрейм `df` столбец **a** с данными [10, 20, 30]\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "датафрейм[столбец] = [строка 1, строка 2, ...]\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "FvWtQARbxYPl",
    "outputId": "dc8c6979-b25c-4ed3-99e9-e11e21662003"
   },
   "outputs": [],
   "source": [
    "# создаем колонки \"а\" и помещаем в нее столбец с данными - [10, 20, 30]\n",
    "df[\"c\"] = [10, 20, 30]\n",
    "df[\"a\"] = ['one', 12, 13]\n",
    "df[\"a\"] = [1, 12, 13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    c   a\n",
       "0  10   1\n",
       "1  20  12\n",
       "2  30  13"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0nYOA5_NxYPm"
   },
   "source": [
    "В датафрейме автоматически создалась нумерация строк - по умолчанию она с 0.\n",
    "\n",
    "Заметим, что синтаксис обращения к столбцам похож на python-словари (dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qmve0tQl0gXJ"
   },
   "source": [
    "3. Можно добавлять еще данные "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "source": [
    "df[\"b\"] = ['one', 1, 'three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qqXXCehuIubQ",
    "outputId": "b7678418-151d-4779-f129-eae48216f818"
   },
   "outputs": [
    {
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      "text/plain": [
       "    c   a      b\n",
       "0  10   1    one\n",
       "1  20  12      1\n",
       "2  30  13  three"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "df"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. А можно перезаписывать старые"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"b\"] = ['new_one', 2, 'new_three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>new_one</td>\n",
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       "      <td>20</td>\n",
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      "text/plain": [
       "    c   a          b\n",
       "0  10   1    new_one\n",
       "1  20  12          2\n",
       "2  30  13  new_three"
      ]
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c9PJlWTL1OXQ"
   },
   "source": [
    "### Работа с готовыми таблицами"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OHTeC-rx12b1"
   },
   "source": [
    "#### Считывание таблицы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z8GwLPVBxYPm"
   },
   "source": [
    "Конечно, чаще всего приходится работать с уже готовыми наборами данных. Такие данные обычно хранятся в формтае xls(x) - для работы в Excel, или (чаще) в формате csv - comma-separated value. Попробуем импортировать csv файл с данными о пассажирах Титаника: они лежат в файле 'titanic.csv' (попробуйте открыть его в текстовом редакторе и посмотрите, как он устроен внутри)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "K2lYGK5zxYPn"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/rogovich/Data/master/data/titanic/train.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# также можно загружать DataFrame из файла с компьютера\n",
    "# пусть файл лежит у нас на компьютере с названием data.csv\n",
    "# data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ziFA0l2bxYPn"
   },
   "source": [
    "Функция `read_csv` читает данные из файла формата *csv* данные и преобразует в pandas.DataFrame. \n",
    "\n",
    "Аналогичная функция `read_excel` может читать данные в офрмате *xls(x)*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yYF2xAuR1-Fs"
   },
   "source": [
    "#### Просмотр датафрейма"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0X4TymMq2PyS"
   },
   "source": [
    "Посмотреть весь датафрейм"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Md8kRBV43B6x"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "название датафрейма\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aXnBoAN02SmO",
    "outputId": "0767f91a-05f9-44ef-d19b-ef3da9bb6418"
   },
   "outputs": [
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
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       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qG3FgenZ2Xg2"
   },
   "source": [
    "Посмотреть первые строки датафрейма с помощью функции `head()`. По умолчанию 5\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "название датафрейма.head()\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
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    },
    "id": "dM6YoxtuxYPn",
    "outputId": "d8578228-6613-4018-dafc-13b50ff4a2d1"
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   "outputs": [
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       "      <td>8.0500</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
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     "execution_count": 24,
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   ],
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    "data.head()"
   ]
  },
  {
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   "execution_count": 25,
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
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       "      <td>8</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
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       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
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       "      <th>9</th>\n",
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       "      <td>C</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10) # можно передать аргументом количество строк, которые хотите увидеть"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eZK9xIb23fzQ"
   },
   "source": [
    "Посмотреть последние строки датафрейма с помощью функции `tail()`. По умолчанию 5\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "название датафрейма.tail()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "on6gy26q3lgH",
    "outputId": "229d1227-e31d-41bf-be3d-c0c9f574b5a0"
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   "outputs": [
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "Aq4hSrnsxYPo",
    "outputId": "8ad3ab05-08b5-44fc-cec1-fcc5b2565028"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.050</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
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       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "884          885         0       3                    Sutehall, Mr. Henry Jr   \n",
       "885          886         0       3      Rice, Mrs. William (Margaret Norton)   \n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch           Ticket    Fare Cabin Embarked  \n",
       "884    male  25.0      0      0  SOTON/OQ 392076   7.050   NaN        S  \n",
       "885  female  39.0      0      5           382652  29.125   NaN        Q  \n",
       "886    male  27.0      0      0           211536  13.000   NaN        S  \n",
       "887  female  19.0      0      0           112053  30.000   B42        S  \n",
       "888  female   NaN      1      2       W./C. 6607  23.450   NaN        S  \n",
       "889    male  26.0      0      0           111369  30.000  C148        C  \n",
       "890    male  32.0      0      0           370376   7.750   NaN        Q  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(7) # можно передать аргументом количество строк, которые хотите увидеть"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогично можно посмотреть на случайные строки датафрейма с помощью функции `sample()`. По умолчанию 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                              Name     Sex  \\\n",
       "875          876         1       3  Najib, Miss. Adele Kiamie \"Jane\"  female   \n",
       "\n",
       "      Age  SibSp  Parch Ticket   Fare Cabin Embarked  \n",
       "875  15.0      0      0   2667  7.225   NaN        C  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>642</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sagesser, Mlle. Emma</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17477</td>\n",
       "      <td>69.3000</td>\n",
       "      <td>B35</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>685</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Brown, Mr. Thomas William Solomon</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29750</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "6              7         0       1            McCarthy, Mr. Timothy J    male   \n",
       "641          642         1       1               Sagesser, Mlle. Emma  female   \n",
       "684          685         0       2  Brown, Mr. Thomas William Solomon    male   \n",
       "\n",
       "      Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
       "6    54.0      0      0     17463  51.8625   E46        S  \n",
       "641  24.0      0      0  PC 17477  69.3000   B35        C  \n",
       "684  60.0      1      1     29750  39.0000   NaN        S  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XNu5MKrcxYPo"
   },
   "source": [
    "### Базовая информация про датафрейм"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bNePIln6OmE9"
   },
   "source": [
    "```\n",
    "датафрейм.info()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zgGyAc9fOe-P"
   },
   "source": [
    "#### Основная информация `.info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "abZBXRxSOkQg",
    "outputId": "a844bf8e-12f1-4adb-b818-c87eccce5b88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5WjRDlBcO_k_"
   },
   "source": [
    "#### Формат данных `.dtypes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rEVqcv_GPJ-E",
    "outputId": "1f43acc5-6e1d-4a60-b3b4-845c27de5d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yBbF8Zn_39eO"
   },
   "source": [
    "#### Размер датафрейма `.shape`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0qOeqXqj4IgR"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "датафрейм.shape\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QZTJclzXxYPo",
    "outputId": "dc2eb805-c2ac-4ef2-ad30-01cc7a5894a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #(строк, столбцов)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# количество строк \n",
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# количество колонок \n",
    "data.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FU7q7A054Vsx"
   },
   "source": [
    "#### Название колонок `.columns`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xSGwAPts4iwh"
   },
   "source": [
    "```\n",
    "название датафрейма.columns\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zy6BtlPnxYPo",
    "outputId": "00720f07-c1c9-4ced-d3a2-56157cf3d5fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns # список столбцов "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mmScvFD045h2"
   },
   "source": [
    "### Обращение к элементам датафрейма"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yr81fkRMxYPp"
   },
   "source": [
    "Описание признаков:\n",
    "\n",
    "+ `PassengerId` — id пассажира\n",
    "+ `Survived` — бинарная переменная: выжил пассажирил (1) или нет (0)\n",
    "+ `Pclass` — класс пассажира\n",
    "+ `Name` — имя пассажира\n",
    "+ `Sex` — пол пассажира\n",
    "+ `Age` — возраст пассажира\n",
    "+ `SibSp` — количество родственников (братьев, сестер, супругов) пассажира на борту\n",
    "+ `Parch` — количество родственников (родителей / детей) пассажира на борту\n",
    "+ `Ticket` — номер билета\n",
    "+ `Fare` — тариф (стоимость билета)\n",
    "+ `Cabin` — номер кабины\n",
    "+ `Embarked` — порт, в котором пассажир сел на борт (C - Cherbourg, S - Southampton, Q = Queenstown)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40i-mE6Y5MQT"
   },
   "source": [
    "#### Обращение к столбцу"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_uRWguS55PyW"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "датафрейм[столбец]\n",
    "```\n",
    "или\n",
    "\n",
    "```\n",
    "датафрейм.столбец\n",
    "```\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "FBBWXZtnGaj5"
   },
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wu6MkICI5Yf5",
    "outputId": "5c853337-b6a3-4372-dce1-1b17c3ad5b7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "886    27.0\n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Age\"] # обращаемся к колонке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NG6nAt20xYPp",
    "outputId": "3bdd1607-29ac-41cb-8860-fa3c487a4f0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZKnZosfQ5tJ2",
    "outputId": "79ba7c39-669b-430f-9737-3df83573c092"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "886    27.0\n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ekok-6ZuxYPp",
    "outputId": "64cfa69b-1520-45f4-ecc4-3c39f539d656"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "886    27.0\n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173    21.0\n",
       "527     NaN\n",
       "163    17.0\n",
       "435    14.0\n",
       "858    24.0\n",
       "137    37.0\n",
       "97     23.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age.sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5FRGNmf0xYPp"
   },
   "source": [
    "#### Обращение нескольким столбцам"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q5xETWKN6NLI"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "датафрейм[[столбец 1, столбец 2, ...]]\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "WtU2sOG6xYPp",
    "outputId": "5458119d-6c84-43df-c303-d147028ec59a"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>male</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>female</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>female</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>male</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>male</td>\n",
       "      <td>211536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>female</td>\n",
       "      <td>112053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>W./C. 6607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>male</td>\n",
       "      <td>111369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>male</td>\n",
       "      <td>370376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age     Sex            Ticket\n",
       "0    22.0    male         A/5 21171\n",
       "1    38.0  female          PC 17599\n",
       "2    26.0  female  STON/O2. 3101282\n",
       "3    35.0  female            113803\n",
       "4    35.0    male            373450\n",
       "..    ...     ...               ...\n",
       "886  27.0    male            211536\n",
       "887  19.0  female            112053\n",
       "888   NaN  female        W./C. 6607\n",
       "889  26.0    male            111369\n",
       "890  32.0    male            370376\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Age', 'Sex', 'Ticket']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vAK0yk1ZxYPq"
   },
   "source": [
    "#### Обращение к строке"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d7ZipQNK_rmb"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "датафрейм.loc[индекс]\n",
    "```\n",
    "или\n",
    "```\n",
    "датафрейм.iloc[индекс]\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "L2EtI25WDc-e",
    "outputId": "82cb301f-bb0b-4dde-e046-327c56bdfcfa"
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GoDKTa6exYPq",
    "outputId": "290386b8-e4ed-457c-9694-130d60531f6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                          1\n",
       "Survived                             0\n",
       "Pclass                               3\n",
       "Name           Braund, Mr. Owen Harris\n",
       "Sex                               male\n",
       "Age                               22.0\n",
       "SibSp                                1\n",
       "Parch                                0\n",
       "Ticket                       A/5 21171\n",
       "Fare                              7.25\n",
       "Cabin                              NaN\n",
       "Embarked                             S\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9uZ8Ane8xYPq",
    "outputId": "0c6e70dc-f837-43a9-cc91-f98d9cf9aefb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                          1\n",
       "Survived                             0\n",
       "Pclass                               3\n",
       "Name           Braund, Mr. Owen Harris\n",
       "Sex                               male\n",
       "Age                               22.0\n",
       "SibSp                                1\n",
       "Parch                                0\n",
       "Ticket                       A/5 21171\n",
       "Fare                              7.25\n",
       "Cabin                              NaN\n",
       "Embarked                             S\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bZQlSFbbzOy_"
   },
   "source": [
    "`loc` возвращает данные на основе индекса, а `iloc` - основываясь исключительно на позиции строки в датафрейме, начиная с 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PassengerId  Survived  Pclass  \\\n",
       "new_index                                  \n",
       "5                    1         0       3   \n",
       "6                    2         1       1   \n",
       "7                    3         1       3   \n",
       "8                    4         1       1   \n",
       "9                    5         0       3   \n",
       "\n",
       "                                                        Name     Sex   Age  \\\n",
       "new_index                                                                    \n",
       "5                                    Braund, Mr. Owen Harris    male  22.0   \n",
       "6          Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "7                                     Heikkinen, Miss. Laina  female  26.0   \n",
       "8               Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "9                                   Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "           SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "new_index                                                          \n",
       "5              1      0         A/5 21171   7.2500   NaN        S  \n",
       "6              1      0          PC 17599  71.2833   C85        C  \n",
       "7              0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "8              1      0            113803  53.1000  C123        S  \n",
       "9              0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# создадим датафрейм-копию и сделаем там новый индекс,\n",
    "# который начинается с 5\n",
    "\n",
    "data_copy = data.copy()\n",
    "data_copy['new_index'] = list(range(5, 5 + 891))\n",
    "data_copy.set_index('new_index', inplace=True)\n",
    "data_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                          1\n",
       "Survived                             0\n",
       "Pclass                               3\n",
       "Name           Braund, Mr. Owen Harris\n",
       "Sex                               male\n",
       "Age                               22.0\n",
       "SibSp                                1\n",
       "Parch                                0\n",
       "Ticket                       A/5 21171\n",
       "Fare                              7.25\n",
       "Cabin                              NaN\n",
       "Embarked                             S\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# работает\n",
    "data_copy.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/8p/kp5vz65x3glcls8rq8n579qs2t_rqg/T/ipykernel_10098/354107042.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# падает с ошибкой\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata_copy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1162\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1164\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1111\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m         \u001b[0;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1113\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3774\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Expected label or tuple of labels, got {key}\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3775\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3776\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3777\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3778\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/projects/teaching/venv/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "# падает с ошибкой\n",
    "data_copy.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Фильтрация данных**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Очень часто на практике возникает необходимость рассматривать не весь датафрейм, а какое-то подмножество его строк. \n",
    "\n",
    "Фильтрация в pandas устроена похожим с numpy образом. Возьмём какую-нибудь колонку (например, Age) и проверим какое-нибудь условие для неё"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2      False\n",
       "3       True\n",
       "4       True\n",
       "       ...  \n",
       "886     True\n",
       "887    False\n",
       "888    False\n",
       "889    False\n",
       "890     True\n",
       "Name: Age, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# берём какую-нибудь колонку и проверяем условие для неё\n",
    "data['Age'] >= 27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# как видно, для каждой строки мы получили True, если строка подходит под условие, \n",
    "# и False, если не подходит"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Фильтрация по столбцу"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь мы можем выбрать строки из таблицы согласно этому списку True и False:\n",
    "\n",
    "```датафрейм[датафрейм['признак'] ==/!=/</<=/>/>= условие]```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Одно условие"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### `==`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "..           ...       ...     ...   \n",
       "880          881         1       2   \n",
       "882          883         0       3   \n",
       "885          886         0       3   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "8        2            347742  11.1333   NaN        S  \n",
       "9        0            237736  30.0708   NaN        C  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "880      1            230433  26.0000   NaN        S  \n",
       "882      0              7552  10.5167   NaN        S  \n",
       "885      5            382652  29.1250   NaN        Q  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[314 rows x 12 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Sex'] == 'female']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### `<`/>/`<=`/`>=` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>851</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Master. Sigvard Harald Elias</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Miss. Nourelain</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>854</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lines, Miss. Mary Conover</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17592</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>D28</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                     Name  \\\n",
       "7              8         0       3           Palsson, Master. Gosta Leonard   \n",
       "9             10         1       2      Nasser, Mrs. Nicholas (Adele Achem)   \n",
       "10            11         1       3          Sandstrom, Miss. Marguerite Rut   \n",
       "14            15         0       3     Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "16            17         0       3                     Rice, Master. Eugene   \n",
       "..           ...       ...     ...                                      ...   \n",
       "850          851         0       3  Andersson, Master. Sigvard Harald Elias   \n",
       "852          853         0       3                  Boulos, Miss. Nourelain   \n",
       "853          854         1       1                Lines, Miss. Mary Conover   \n",
       "869          870         1       3          Johnson, Master. Harold Theodor   \n",
       "875          876         1       3         Najib, Miss. Adele Kiamie \"Jane\"   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
       "7      male   2.0      3      1    349909  21.0750   NaN        S  \n",
       "9    female  14.0      1      0    237736  30.0708   NaN        C  \n",
       "10   female   4.0      1      1   PP 9549  16.7000    G6        S  \n",
       "14   female  14.0      0      0    350406   7.8542   NaN        S  \n",
       "16     male   2.0      4      1    382652  29.1250   NaN        Q  \n",
       "..      ...   ...    ...    ...       ...      ...   ...      ...  \n",
       "850    male   4.0      4      2    347082  31.2750   NaN        S  \n",
       "852  female   9.0      1      1      2678  15.2458   NaN        C  \n",
       "853  female  16.0      0      1  PC 17592  39.4000   D28        S  \n",
       "869    male   4.0      1      1    347742  11.1333   NaN        S  \n",
       "875  female  15.0      0      0      2667   7.2250   NaN        C  \n",
       "\n",
       "[113 rows x 12 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Age'] < 18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Несколько условий"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пассажиры первого или второго классов:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ `|` - или\n",
    "+ `&` - и"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <th>Name</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>26.5500</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 12 columns</p>\n",
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      ],
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       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "3              4         1       1   \n",
       "9             10         1       2   \n",
       "11            12         1       1   \n",
       "15            16         1       2   \n",
       "..           ...       ...     ...   \n",
       "871          872         1       1   \n",
       "874          875         1       2   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "887          888         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "\n",
       "     Parch     Ticket     Fare Cabin Embarked  \n",
       "1        0   PC 17599  71.2833   C85        C  \n",
       "3        0     113803  53.1000  C123        S  \n",
       "9        0     237736  30.0708   NaN        C  \n",
       "11       0     113783  26.5500  C103        S  \n",
       "15       0     248706  16.0000   NaN        S  \n",
       "..     ...        ...      ...   ...      ...  \n",
       "871      1      11751  52.5542   D35        S  \n",
       "874      0  P/PP 3381  24.0000   NaN        C  \n",
       "879      1      11767  83.1583   C50        C  \n",
       "880      1     230433  26.0000   NaN        S  \n",
       "887      0     112053  30.0000   B42        S  \n",
       "\n",
       "[170 rows x 12 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\n",
    "    ((data['Pclass'] == 1) | (data['Pclass'] == 2)) \n",
    "    &\n",
    "    (data['Sex']=='female')\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Девушки в возрасте от 18:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <th>8</th>\n",
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       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
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       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
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       "      <th>...</th>\n",
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       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
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       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
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       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <td>888</td>\n",
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       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
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       "      <td>S</td>\n",
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       "</table>\n",
       "<p>193 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "8              9         1       3   \n",
       "11            12         1       1   \n",
       "..           ...       ...     ...   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "882          883         0       3   \n",
       "885          886         0       3   \n",
       "887          888         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "8        2            347742  11.1333   NaN        S  \n",
       "11       0            113783  26.5500  C103        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "879      1             11767  83.1583   C50        C  \n",
       "880      1            230433  26.0000   NaN        S  \n",
       "882      0              7552  10.5167   NaN        S  \n",
       "885      5            382652  29.1250   NaN        Q  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "\n",
       "[193 rows x 12 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['Sex'] == 'female') & (data['Age'] > 18)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Девушки в возрасте от 18 до 25, путешествующие в одиночку (без каких-либо родственников):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\n",
    "    (data['Sex'] == 'female')\n",
    "    & (data['Age'] > 18)\n",
    "    & (data['Age'] < 25)\n",
    "    & (data['SibSp'] == 0) & (data['Parch'] == 0)\n",
    "       ].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Девушки в возрасте от 18 лет, севшие или в порту Cherbourg(С), или Southampton (S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "8              9         1       3   \n",
       "11            12         1       1   \n",
       "..           ...       ...     ...   \n",
       "874          875         1       2   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "882          883         0       3   \n",
       "887          888         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "8        2            347742  11.1333   NaN        S  \n",
       "11       0            113783  26.5500  C103        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "874      0         P/PP 3381  24.0000   NaN        C  \n",
       "879      1             11767  83.1583   C50        C  \n",
       "880      1            230433  26.0000   NaN        S  \n",
       "882      0              7552  10.5167   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "\n",
       "[183 rows x 12 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(\n",
    "    (data['Embarked'] =='C') | (data['Embarked'] =='S'))\n",
    "    & (data['Sex'] == 'female') & \n",
    "     (data['Age'] > 18)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подсчет количества наблюдений после фильтрации `.shape`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['Sex'] == 'female') & (data['Age'] > 18) & \n",
    "     (data.Age < 25) & (data.SibSp == 0) &(data.Parch == 0)].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Сортировка данных** `.sort_values()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2629</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "803          804         1       3           Thomas, Master. Assad Alexander   \n",
       "755          756         1       2                 Hamalainen, Master. Viljo   \n",
       "644          645         1       3                    Baclini, Miss. Eugenie   \n",
       "469          470         1       3             Baclini, Miss. Helene Barbara   \n",
       "78            79         1       2             Caldwell, Master. Alden Gates   \n",
       "..           ...       ...     ...                                       ...   \n",
       "859          860         0       3                          Razi, Mr. Raihed   \n",
       "863          864         0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n",
       "868          869         0       3               van Melkebeke, Mr. Philemon   \n",
       "878          879         0       3                        Laleff, Mr. Kristo   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "803    male  0.42      0      1        2625   8.5167   NaN        C  \n",
       "755    male  0.67      1      1      250649  14.5000   NaN        S  \n",
       "644  female  0.75      2      1        2666  19.2583   NaN        C  \n",
       "469  female  0.75      2      1        2666  19.2583   NaN        C  \n",
       "78     male  0.83      0      2      248738  29.0000   NaN        S  \n",
       "..      ...   ...    ...    ...         ...      ...   ...      ...  \n",
       "859    male   NaN      0      0        2629   7.2292   NaN        C  \n",
       "863  female   NaN      8      2    CA. 2343  69.5500   NaN        S  \n",
       "868    male   NaN      0      0      345777   9.5000   NaN        S  \n",
       "878    male   NaN      0      0      349217   7.8958   NaN        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['Age']) # сортируем по возрасту, по умолчанию сортирвка по возрастанию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "<p>891 rows × 12 columns</p>\n",
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       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "630          631         1       1      Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                       Svensson, Mr. Johan   \n",
       "493          494         0       1                   Artagaveytia, Mr. Ramon   \n",
       "96            97         0       1                 Goldschmidt, Mr. George B   \n",
       "116          117         0       3                      Connors, Mr. Patrick   \n",
       "..           ...       ...     ...                                       ...   \n",
       "859          860         0       3                          Razi, Mr. Raihed   \n",
       "863          864         0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n",
       "868          869         0       3               van Melkebeke, Mr. Philemon   \n",
       "878          879         0       3                        Laleff, Mr. Kristo   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "630    male  80.0      0      0       27042  30.0000   A23        S  \n",
       "851    male  74.0      0      0      347060   7.7750   NaN        S  \n",
       "493    male  71.0      0      0    PC 17609  49.5042   NaN        C  \n",
       "96     male  71.0      0      0    PC 17754  34.6542    A5        C  \n",
       "116    male  70.5      0      0      370369   7.7500   NaN        Q  \n",
       "..      ...   ...    ...    ...         ...      ...   ...      ...  \n",
       "859    male   NaN      0      0        2629   7.2292   NaN        C  \n",
       "863  female   NaN      8      2    CA. 2343  69.5500   NaN        S  \n",
       "868    male   NaN      0      0      345777   9.5000   NaN        S  \n",
       "878    male   NaN      0      0      349217   7.8958   NaN        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
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   "source": [
    "data.sort_values(by=['Age'], ascending=False) # сортируем по возрасту, теперь по убыванию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                   Svensson, Mr. Johan   \n",
       "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
       "96            97         0       1             Goldschmidt, Mr. George B   \n",
       "116          117         0       3                  Connors, Mr. Patrick   \n",
       "\n",
       "      Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
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       "116  male  70.5      0      0    370369   7.7500   NaN        Q  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.sort_values(by=['Age', 'Fare'], ascending=False).head() # сортируем сперва (по убыванию),\n",
    "                                                             # потом по стоимости билета  (по убыванию)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
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       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                   Svensson, Mr. Johan   \n",
       "96            97         0       1             Goldschmidt, Mr. George B   \n",
       "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
       "116          117         0       3                  Connors, Mr. Patrick   \n",
       "\n",
       "      Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
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       "851  male  74.0      0      0    347060   7.7750   NaN        S  \n",
       "96   male  71.0      0      0  PC 17754  34.6542    A5        C  \n",
       "493  male  71.0      0      0  PC 17609  49.5042   NaN        C  \n",
       "116  male  70.5      0      0    370369   7.7500   NaN        Q  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['Age', 'Fare'], ascending=[False, True]).head() # сортируем сперва возраст (по убыванию),\n",
    "                                                                     # потом по стоимости билета  (по возрастанию)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                Name  \\\n",
       "258          259         1       1                    Ward, Miss. Anna   \n",
       "737          738         1       1              Lesurer, Mr. Gustave J   \n",
       "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
       "27            28         0       1      Fortune, Mr. Charles Alexander   \n",
       "88            89         1       1          Fortune, Miss. Mabel Helen   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket      Fare        Cabin Embarked  \n",
       "258  female  35.0      0      0  PC 17755  512.3292          NaN        C  \n",
       "737    male  35.0      0      0  PC 17755  512.3292         B101        C  \n",
       "679    male  36.0      0      1  PC 17755  512.3292  B51 B53 B55        C  \n",
       "27     male  19.0      3      2     19950  263.0000  C23 C25 C27        S  \n",
       "88   female  23.0      3      2     19950  263.0000  C23 C25 C27        S  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['Fare', 'Age', 'PassengerId'], ascending=[False, True, True]).head() # сортируем сперва возраст (по убыванию),\n",
    "                                                                     # потом по стоимости билета  (по возрастанию)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заметим при этом, что наш исходный датафрейм никак не меняется!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На самом деле, `sort_values()` по умолчанию создаёт **копию** датафрейма, сортирует её и возвращает, никоим образом не меняя исходный датафрейм. Если необходимо отсортировать исходный датафрейм, можно либо явно указать, что сортировать надо на месте, при помощи аргумента `inplace`: \n",
    "\n",
    "```data.sort_values(..., inplace=True)```\n",
    "\n",
    "либо присвоить переменной `data` то, что возвращает сортировка:\n",
    "\n",
    "```data = data.sort_values(...)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>631</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                   Svensson, Mr. Johan   \n",
       "96            97         0       1             Goldschmidt, Mr. George B   \n",
       "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
       "116          117         0       3                  Connors, Mr. Patrick   \n",
       "\n",
       "      Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
       "630  male  80.0      0      0     27042  30.0000   A23        S  \n",
       "851  male  74.0      0      0    347060   7.7750   NaN        S  \n",
       "96   male  71.0      0      0  PC 17754  34.6542    A5        C  \n",
       "493  male  71.0      0      0  PC 17609  49.5042   NaN        C  \n",
       "116  male  70.5      0      0    370369   7.7500   NaN        Q  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['Age', 'Fare'], ascending=[False, True], inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qUnd0PU8ALCh"
   },
   "source": [
    "## **Типы данных**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ohKwKmzaCQjN"
   },
   "source": [
    "![типыданных.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAC4cAAAeuCAYAAAD6wqc+AAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEbAAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAAFKAAAAAQAAAUoAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAAC4egAwAEAAAAAQAAB64AAAAAs9RIbQAAAAlwSFlzAAAywAAAMsABKGRa2wAAQABJREFUeAHs281NW2EUBNDPxGZHKgGxdglux6kjKHRAD3TAirRBG5Ygz1KWszI/fsM7lqxIV7I9PsMiIpMxPAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEC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AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBD4t8AfJsPNIeR5brUAAAAASUVORK5CYII=)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GdJnNVR1DN89"
   },
   "source": [
    "**Числовые**\n",
    "*   *Непрерывные*: рост людей, заработная плата\n",
    "*   *Дискретные*: количество детей, количество команд в турнире, количество лет образования\n",
    "\n",
    "**Категориальные**\n",
    "*   *Порядковые*: оценки студентов (A, B, C...), уровень образования, уровень зарплаты\n",
    "*   *Номинальные*: пол, цвет глаз, семейное положение"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Новые столбцы и удаление старых**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Иногда нужно создать новый признак из уже существующих, например, нам интересно, сколько всего родственников путешествовало с каждым пассажиром - просто сложим столбцы `SibSp` и `Parch` и поместим сумму в новый столбец `FamilySize`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>FamilySize</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>314</th>\n",
       "      <td>315</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Hart, Mr. Benjamin</td>\n",
       "      <td>male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F.C.C. 13529</td>\n",
       "      <td>26.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>723</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gillespie, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12233</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SC/Paris 2123</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>388</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Buss, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27849</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>380</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Karl Gideon</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347069</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Crosby, Capt. Edward Gifford</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>WE/P 5735</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>B22</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>349</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Coutts, Master. William Loch \"William\"</td>\n",
       "      <td>male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 37671</td>\n",
       "      <td>15.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bishop, Mr. Dickinson H</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11967</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Coxon, Mr. Daniel</td>\n",
       "      <td>male</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364500</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenfield, Mr. William Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>D10 D12</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "314          315         0       2                        Hart, Mr. Benjamin   \n",
       "722          723         0       2              Gillespie, Mr. William Henry   \n",
       "43            44         1       2  Laroche, Miss. Simonne Marie Anne Andree   \n",
       "387          388         1       2                          Buss, Miss. Kate   \n",
       "379          380         0       3               Gustafsson, Mr. Karl Gideon   \n",
       "745          746         0       1              Crosby, Capt. Edward Gifford   \n",
       "348          349         1       3    Coutts, Master. William Loch \"William\"   \n",
       "484          485         1       1                   Bishop, Mr. Dickinson H   \n",
       "94            95         0       3                         Coxon, Mr. Daniel   \n",
       "97            98         1       1           Greenfield, Mr. William Bertram   \n",
       "\n",
       "        Sex   Age  SibSp  Parch         Ticket     Fare    Cabin Embarked  \\\n",
       "314    male  43.0      1      1   F.C.C. 13529  26.2500      NaN        S   \n",
       "722    male  34.0      0      0          12233  13.0000      NaN        S   \n",
       "43   female   3.0      1      2  SC/Paris 2123  41.5792      NaN        C   \n",
       "387  female  36.0      0      0          27849  13.0000      NaN        S   \n",
       "379    male  19.0      0      0         347069   7.7750      NaN        S   \n",
       "745    male  70.0      1      1      WE/P 5735  71.0000      B22        S   \n",
       "348    male   3.0      1      1     C.A. 37671  15.9000      NaN        S   \n",
       "484    male  25.0      1      0          11967  91.0792      B49        C   \n",
       "94     male  59.0      0      0         364500   7.2500      NaN        S   \n",
       "97     male  23.0      0      1       PC 17759  63.3583  D10 D12        C   \n",
       "\n",
       "     FamilySize  \n",
       "314           2  \n",
       "722           0  \n",
       "43            3  \n",
       "387           0  \n",
       "379           0  \n",
       "745           2  \n",
       "348           2  \n",
       "484           1  \n",
       "94            0  \n",
       "97            1  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['FamilySize'] = data['SibSp'] + data['Parch']\n",
    "data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# также мы можем делить, умножать столбцы друг на друга и возводить их в степень\n",
    "# создадим два признака\n",
    "# класс пассажира, умноженный на цену за билет \n",
    "data['Pclass_mult_Fare'] = data['Pclass'] * data['Fare']\n",
    "\n",
    "# а также квадрат возраста пассажира \n",
    "data['Age_squared'] = data['Age'] ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
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       "      <td>Svensson, Mr. Johan</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>5476.00</td>\n",
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       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>5041.00</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>23.2500</td>\n",
       "      <td>4970.25</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                   Svensson, Mr. Johan   \n",
       "96            97         0       1             Goldschmidt, Mr. George B   \n",
       "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
       "116          117         0       3                  Connors, Mr. Patrick   \n",
       "\n",
       "      Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  FamilySize  \\\n",
       "630  male  80.0      0      0     27042  30.0000   A23        S           0   \n",
       "851  male  74.0      0      0    347060   7.7750   NaN        S           0   \n",
       "96   male  71.0      0      0  PC 17754  34.6542    A5        C           0   \n",
       "493  male  71.0      0      0  PC 17609  49.5042   NaN        C           0   \n",
       "116  male  70.5      0      0    370369   7.7500   NaN        Q           0   \n",
       "\n",
       "     Pclass_mult_Fare  Age_squared  \n",
       "630           30.0000      6400.00  \n",
       "851           23.3250      5476.00  \n",
       "96            34.6542      5041.00  \n",
       "493           49.5042      5041.00  \n",
       "116           23.2500      4970.25  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверим \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Функции"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А теперь давайте создадим признак, который бы нам показывал, что пассажир ехал в одиночку. \n",
    "\n",
    "Для этого опишем функцию, которая принимает количество родственников на борту, и возвращает True, если пассажир ехал один, и False иначе.\n",
    "\n",
    "Применим ее к столбцу `FamilySize` с помощью метода `.apply()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def was_alone(fam_size):\n",
    "    return fam_size == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3411</td>\n",
       "      <td>8.7125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>26.1375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>784</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Mr. Andrew G</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>70.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>718</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Troutt, Miss. Edwina Celia \"Winnie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34218</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>E101</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>729.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>21.6750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                 Name  \\\n",
       "509          510         1       3                       Lang, Mr. Fang   \n",
       "494          495         0       3           Stanley, Mr. Edward Roland   \n",
       "68            69         1       3      Andersson, Miss. Erna Alexandra   \n",
       "584          585         0       3                  Paulner, Mr. Uscher   \n",
       "783          784         0       3               Johnston, Mr. Andrew G   \n",
       "717          718         1       2  Troutt, Miss. Edwina Celia \"Winnie\"   \n",
       "26            27         0       3              Emir, Mr. Farred Chehab   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \\\n",
       "509    male  26.0      0      0        1601  56.4958   NaN        S   \n",
       "494    male  21.0      0      0   A/4 45380   8.0500   NaN        S   \n",
       "68   female  17.0      4      2     3101281   7.9250   NaN        S   \n",
       "584    male   NaN      0      0        3411   8.7125   NaN        C   \n",
       "783    male   NaN      1      2  W./C. 6607  23.4500   NaN        S   \n",
       "717  female  27.0      0      0       34218  10.5000  E101        S   \n",
       "26     male   NaN      0      0        2631   7.2250   NaN        C   \n",
       "\n",
       "     FamilySize  Pclass_mult_Fare  Age_squared  Alone  \n",
       "509           0          169.4874        676.0   True  \n",
       "494           0           24.1500        441.0   True  \n",
       "68            6           23.7750        289.0  False  \n",
       "584           0           26.1375          NaN   True  \n",
       "783           3           70.3500          NaN  False  \n",
       "717           0           21.0000        729.0   True  \n",
       "26            0           21.6750          NaN   True  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Alone'] = data['FamilySize'].apply(was_alone)\n",
    "data.sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анонимные функции (`lambda` функции)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Это же можно реализовать короче с помощью анонимных функций.\n",
    "\n",
    "Это особый вид функций, которые объявляются с помощью ключевого слова `lambda` вместо `def`:\n",
    "Лямбда-функции принимают любое количество аргументов, но не могут содержать несколько выражений и всегда возвращают только одно значение.\n",
    "\n",
    "В программировании на **Python** можно обойтись без анонимных функций, которые по сути являются обычными, но без имени и с ограничением в одно выражение. Однако их использование в нужных местах упрощает написание и восприятие кода. Пишется так:\n",
    "\n",
    "    lambda arguments: expression\n",
    "\n",
    "arguments - аргументы, expression - выражение, возвращающее значение."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Pclass_mult_Fare</th>\n",
       "      <th>Age_squared</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Alone_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>676</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Edvardsson, Mr. Gustaf Hjalmar</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>324.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>492</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Windelov, Mr. Einar</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>21.7500</td>\n",
       "      <td>441.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sutton, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36963</td>\n",
       "      <td>32.3208</td>\n",
       "      <td>D50</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>32.3208</td>\n",
       "      <td>3721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>754</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Jonkoff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349204</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>23.6874</td>\n",
       "      <td>529.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>516</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Walker, Mr. William Anderson</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36967</td>\n",
       "      <td>34.0208</td>\n",
       "      <td>D46</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0208</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>350</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dimic, Mr. Jovan</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315088</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>25.9875</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>328</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Ball, Mrs. (Ada E Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28551</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                            Name     Sex  \\\n",
       "675          676         0       3  Edvardsson, Mr. Gustaf Hjalmar    male   \n",
       "491          492         0       3             Windelov, Mr. Einar    male   \n",
       "625          626         0       1           Sutton, Mr. Frederick    male   \n",
       "753          754         0       3              Jonkoff, Mr. Lalio    male   \n",
       "515          516         0       1    Walker, Mr. William Anderson    male   \n",
       "349          350         0       3                Dimic, Mr. Jovan    male   \n",
       "327          328         1       2         Ball, Mrs. (Ada E Hall)  female   \n",
       "\n",
       "      Age  SibSp  Parch            Ticket     Fare Cabin Embarked  FamilySize  \\\n",
       "675  18.0      0      0            349912   7.7750   NaN        S           0   \n",
       "491  21.0      0      0  SOTON/OQ 3101317   7.2500   NaN        S           0   \n",
       "625  61.0      0      0             36963  32.3208   D50        S           0   \n",
       "753  23.0      0      0            349204   7.8958   NaN        S           0   \n",
       "515  47.0      0      0             36967  34.0208   D46        S           0   \n",
       "349  42.0      0      0            315088   8.6625   NaN        S           0   \n",
       "327  36.0      0      0             28551  13.0000     D        S           0   \n",
       "\n",
       "     Pclass_mult_Fare  Age_squared  Alone  Alone_1  \n",
       "675           23.3250        324.0   True     True  \n",
       "491           21.7500        441.0   True     True  \n",
       "625           32.3208       3721.0   True     True  \n",
       "753           23.6874        529.0   True     True  \n",
       "515           34.0208       2209.0   True     True  \n",
       "349           25.9875       1764.0   True     True  \n",
       "327           26.0000       1296.0   True     True  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Alone_1'] = data['FamilySize'].apply(lambda x: x == 0)\n",
    "data.sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Удаление повторений** `.drop_duplicates()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>Продукты</th>\n",
       "      <th>Цвет</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Красный</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Бананы</td>\n",
       "      <td>Желтый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Морковка</td>\n",
       "      <td>Оранжевый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Огурец</td>\n",
       "      <td>Зеленый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Яблоки</td>\n",
       "      <td>Зеленый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Томат</td>\n",
       "      <td>Красный</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Яблоки</td>\n",
       "      <td>Зеленый</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Продукты       Цвет\n",
       "0    Яблоки    Красный\n",
       "1    Бананы     Желтый\n",
       "2  Морковка  Оранжевый\n",
       "3    Огурец    Зеленый\n",
       "4    Яблоки    Зеленый\n",
       "5     Томат    Красный\n",
       "6    Яблоки    Зеленый"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame()\n",
    "df1[\"Продукты\"] = [\"Яблоки\", \"Бананы\", \"Морковка\", \"Огурец\", \"Яблоки\", \"Томат\", \"Яблоки\"]\n",
    "df1[\"Цвет\"] = [\"Красный\", \"Желтый\", \"Оранжевый\", \"Зеленый\", \"Зеленый\", \"Красный\", \"Зеленый\"]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Цвет</th>\n",
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       "      <td>Красный</td>\n",
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       "      <th>1</th>\n",
       "      <td>Бананы</td>\n",
       "      <td>Желтый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Морковка</td>\n",
       "      <td>Оранжевый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Огурец</td>\n",
       "      <td>Зеленый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Томат</td>\n",
       "      <td>Красный</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Продукты       Цвет\n",
       "0    Яблоки    Красный\n",
       "1    Бананы     Желтый\n",
       "2  Морковка  Оранжевый\n",
       "3    Огурец    Зеленый\n",
       "5     Томат    Красный"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop_duplicates(['Продукты'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Яблоки</td>\n",
       "      <td>Красный</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Бананы</td>\n",
       "      <td>Желтый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Морковка</td>\n",
       "      <td>Оранжевый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Огурец</td>\n",
       "      <td>Зеленый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Яблоки</td>\n",
       "      <td>Зеленый</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Томат</td>\n",
       "      <td>Красный</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Продукты       Цвет\n",
       "0    Яблоки    Красный\n",
       "1    Бананы     Желтый\n",
       "2  Морковка  Оранжевый\n",
       "3    Огурец    Зеленый\n",
       "4    Яблоки    Зеленый\n",
       "5     Томат    Красный"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop_duplicates(['Продукты', 'Цвет'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Сохранение датафрейма "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# читать: read_csv()\n",
    "\n",
    "# записывать: to_csv()\n",
    "\n",
    "df1.to_csv('products.csv')"
   ]
  }
 ],
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