{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a href=\"https://mipt-stats.gitlab.io/courses/ad_fivt.html\">Введение в анализ данных</a>\n",
    "\n",
    "## Титаник\n",
    "\n",
    "Был крупнейшим пассажирским судном в 1912—1913 годах. Во время первого рейса затонул в северной Атлантике, столкнувшись с айсбергом в ночь с 14 на 15 апреля 1912 года, при этом погибло 1502 из 2224 пассажиров и членов экипажа.\n",
    "\n",
    "Данные о пассажирах были собраны в таблицы, которые можно скачать с <a href=\"https://www.kaggle.com/c/titanic/data\" target=\"_blank\">Kaggle</a>. Эти данные часто используются в качестве обучающих в анализе данных."
   ]
  },
  {
   "attachments": {
    "kartinki-titanik-34.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![kartinki-titanik-34.jpg](attachment:kartinki-titanik-34.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Этот обучающий ноутбук на примерах показывает основную функциональность библиотек `pandas` и `seaborn`.**\n",
    "\n",
    "Подробные обучающие ноутбуки можно посмотреть по ссылкам:\n",
    "* <a href=\"https://mipt-stats.gitlab.io/courses/python/08_pandas1.html\" target=\"_blank\">Библиотека pandas — типы данных Series и DataFrame, запись и чтение файлов, работа с датами.</a>\n",
    "* <a href=\"https://mipt-stats.gitlab.io/courses/python/10_pandas2.html\" target=\"_blank\">Библиотека pandas — объединение таблиц, группировки, таблицы сопряженности и сводные таблицы.</a>\n",
    "* <a href=\"https://mipt-stats.gitlab.io/courses/python/09_seaborn.html\" target=\"_blank\">Библиотека seaborn.</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:16:17.765129Z",
     "start_time": "2022-02-12T11:16:17.118199Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(palette='Set2', font_scale=1.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Чтение данных\n",
    "\n",
    "<i><a href=\"https://mipt-stats.gitlab.io/courses/python/08_pandas1.html#3.-Чтение-и-запись-данных\" target=\"_blank\">Подробнее про чтение и запись данных.</a></i>\n",
    "\n",
    "Прочитаем данные с помощью библиотеки `pandas`. Библиотека pandas предназначена для статистической обработки данных, по функциональности близкий к SQL и R. Включает в себя функциональность работы с базами данных и таблицами Excel.\n",
    "\n",
    "При чтении укажем, что первую колонку нужно использовать в качестве индекса (т.е. ключа) в таблице: `index_col=0`.\n",
    "\n",
    "Мы видим таблицу со следующими колонками:\n",
    "* `Pclass` — класс пассажира (`1` — высший, `2` — средний, `3` — низший);\n",
    "* `Name` — имя;\n",
    "* `Sex` — пол;\n",
    "* `Age` — возраст;\n",
    "* `SibSp` — количество братьев, сестер, сводных братьев, сводных сестер, супругов на борту титаника;\n",
    "* `Parch` — количество родителей, детей (в том числе приемных) на борту титаника;\n",
    "* `Ticket` — номер билета;\n",
    "* `Fare` — плата за проезд;\n",
    "* `Cabin` — каюта;\n",
    "* `Embarked` — порт посадки (`C` — Шербур; `Q` — Квинстаун; `S` — Саутгемптон)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:16:21.466897Z",
     "start_time": "2022-02-12T11:16:21.398832Z"
    }
   },
   "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>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>PassengerId</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>1</th>\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>2</th>\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>3</th>\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>4</th>\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>5</th>\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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\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>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\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>889</th>\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>890</th>\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>891</th>\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 × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "...               ...     ...   \n",
       "887                 0       2   \n",
       "888                 1       1   \n",
       "889                 0       3   \n",
       "890                 1       1   \n",
       "891                 0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "...                                                        ...     ...   ...   \n",
       "887                                      Montvila, Rev. Juozas    male  27.0   \n",
       "888                               Graham, Miss. Margaret Edith  female  19.0   \n",
       "889                   Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN   \n",
       "890                                      Behr, Mr. Karl Howell    male  26.0   \n",
       "891                                        Dooley, Mr. Patrick    male  32.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  \n",
       "...            ...    ...               ...      ...   ...      ...  \n",
       "887              0      0            211536  13.0000   NaN        S  \n",
       "888              0      0            112053  30.0000   B42        S  \n",
       "889              1      2        W./C. 6607  23.4500   NaN        S  \n",
       "890              0      0            111369  30.0000  C148        C  \n",
       "891              0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train.csv', index_col=0)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно вывести только строго указанное количество строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:18:44.963057Z",
     "start_time": "2022-02-12T11:18:44.915292Z"
    }
   },
   "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>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>PassengerId</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>1</th>\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>2</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "\n",
       "             SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                   \n",
       "1                1      0  A/5 21171   7.2500   NaN        S  \n",
       "2                1      0   PC 17599  71.2833   C85        C  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Соответствующая переменная имеет тип данных `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T12:06:49.900538Z",
     "start_time": "2022-02-12T12:06:49.892119Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Способы извлечения данных\n",
    "\n",
    "Данные из отдельной колонки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:17:18.484984Z",
     "start_time": "2022-02-12T11:17:18.460683Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1                                Braund, Mr. Owen Harris\n",
       "2      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "3                                 Heikkinen, Miss. Laina\n",
       "4           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "5                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "887                                Montvila, Rev. Juozas\n",
       "888                         Graham, Miss. Margaret Edith\n",
       "889             Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "890                                Behr, Mr. Karl Howell\n",
       "891                                  Dooley, Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если имя колонки \"достаточно хорошее\", то можно и так"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:17:30.885498Z",
     "start_time": "2022-02-12T11:17:30.871867Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1                                Braund, Mr. Owen Harris\n",
       "2      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "3                                 Heikkinen, Miss. Laina\n",
       "4           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "5                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "887                                Montvila, Rev. Juozas\n",
       "888                         Graham, Miss. Margaret Edith\n",
       "889             Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "890                                Behr, Mr. Karl Howell\n",
       "891                                  Dooley, Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные по столбцу имеют тип данных `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:22:03.403727Z",
     "start_time": "2022-02-12T11:22:03.393293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.Name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно вытащить сразу несколько колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:18:07.362815Z",
     "start_time": "2022-02-12T11:18:07.352050Z"
    }
   },
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "...               ...     ...   \n",
       "887                 0       2   \n",
       "888                 1       1   \n",
       "889                 0       3   \n",
       "890                 1       1   \n",
       "891                 0       3   \n",
       "\n",
       "                                                          Name  \n",
       "PassengerId                                                     \n",
       "1                                      Braund, Mr. Owen Harris  \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  \n",
       "3                                       Heikkinen, Miss. Laina  \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  \n",
       "5                                     Allen, Mr. William Henry  \n",
       "...                                                        ...  \n",
       "887                                      Montvila, Rev. Juozas  \n",
       "888                               Graham, Miss. Margaret Edith  \n",
       "889                   Johnston, Miss. Catherine Helen \"Carrie\"  \n",
       "890                                      Behr, Mr. Karl Howell  \n",
       "891                                        Dooley, Mr. Patrick  \n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Survived', 'Pclass', 'Name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Имена всех колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:19:10.540185Z",
     "start_time": "2022-02-12T11:19:10.523209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список всех индексов таблицы (т.е. ключей)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:19:15.744187Z",
     "start_time": "2022-02-12T11:19:15.724115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "            ...\n",
       "            882, 883, 884, 885, 886, 887, 888, 889, 890, 891],\n",
       "           dtype='int64', name='PassengerId', length=891)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Название колонки-индекса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.index.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тип данных в каждой из колонок таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:24:12.884381Z",
     "start_time": "2022-02-12T11:24:12.880278Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Строку таблицы, соответствующую *ключу 1*, можно получить через `loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:19:28.055175Z",
     "start_time": "2022-02-12T11:19:28.029116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived                          0\n",
       "Pclass                            3\n",
       "Name        Braund, Mr. Owen Harris\n",
       "Sex                            male\n",
       "Age                              22\n",
       "SibSp                             1\n",
       "Parch                             0\n",
       "Ticket                    A/5 21171\n",
       "Fare                           7.25\n",
       "Cabin                           NaN\n",
       "Embarked                          S\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обратите внимание, что *обычный численный индекс* этой строки равен 0. По нему также можно извлекать строку с мопощью `iloc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:20:35.065219Z",
     "start_time": "2022-02-12T11:20:35.055782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived                          0\n",
       "Pclass                            3\n",
       "Name        Braund, Mr. Owen Harris\n",
       "Sex                            male\n",
       "Age                              22\n",
       "SibSp                             1\n",
       "Parch                             0\n",
       "Ticket                    A/5 21171\n",
       "Fare                           7.25\n",
       "Cabin                           NaN\n",
       "Embarked                          S\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если же попробовать запустить"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T12:12:51.837534Z",
     "start_time": "2022-02-12T12:12:51.781444Z"
    }
   },
   "source": [
    "data.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "то возникнет исключение `KeyError: 0`, поскольку ключа `0` в этой таблице нет.\n",
    "\n",
    "Можно извлечь несколько строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T12:12:07.104570Z",
     "start_time": "2022-02-12T12:12:07.058060Z"
    }
   },
   "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>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>PassengerId</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>1</th>\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>2</th>\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>3</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно извлечь конкретное значение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:20:58.239239Z",
     "start_time": "2022-02-12T11:20:58.231280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Braund, Mr. Owen Harris'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]['Name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Или так"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:21:08.656521Z",
     "start_time": "2022-02-12T11:21:08.648244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Braund, Mr. Owen Harris'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Примеры анализа данных из таблицы\n",
    "\n",
    "#### 3.1 Простые операции из `pandas`\n",
    "\n",
    "Все описательные статистики по каждой колонке с вещественным типом данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:22:31.045659Z",
     "start_time": "2022-02-12T11:22:30.951759Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Идентификация пропусков в таблице"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:22:52.069434Z",
     "start_time": "2022-02-12T11:22:52.045704Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "             Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "PassengerId                                                                \n",
       "1               False   False  False  False  False  False  False   False   \n",
       "2               False   False  False  False  False  False  False   False   \n",
       "3               False   False  False  False  False  False  False   False   \n",
       "4               False   False  False  False  False  False  False   False   \n",
       "5               False   False  False  False  False  False  False   False   \n",
       "...               ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "887             False   False  False  False  False  False  False   False   \n",
       "888             False   False  False  False  False  False  False   False   \n",
       "889             False   False  False  False   True  False  False   False   \n",
       "890             False   False  False  False  False  False  False   False   \n",
       "891             False   False  False  False  False  False  False   False   \n",
       "\n",
       "              Fare  Cabin  Embarked  \n",
       "PassengerId                          \n",
       "1            False   True     False  \n",
       "2            False  False     False  \n",
       "3            False   True     False  \n",
       "4            False  False     False  \n",
       "5            False   True     False  \n",
       "...            ...    ...       ...  \n",
       "887          False   True     False  \n",
       "888          False  False     False  \n",
       "889          False   True     False  \n",
       "890          False  False     False  \n",
       "891          False   True     False  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно посчитать долю пропущенных значений в каждой колонке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:23:02.671998Z",
     "start_time": "2022-02-12T11:23:02.650301Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.000000\n",
       "Pclass      0.000000\n",
       "Name        0.000000\n",
       "Sex         0.000000\n",
       "Age         0.198653\n",
       "SibSp       0.000000\n",
       "Parch       0.000000\n",
       "Ticket      0.000000\n",
       "Fare        0.000000\n",
       "Cabin       0.771044\n",
       "Embarked    0.002245\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сортировка по столбцу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:24:54.319897Z",
     "start_time": "2022-02-12T11:24:54.282200Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.83</td>\n",
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       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Razi, Mr. Raihed</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass                                      Name  \\\n",
       "PassengerId                                                               \n",
       "804                 1       3           Thomas, Master. Assad Alexander   \n",
       "756                 1       2                 Hamalainen, Master. Viljo   \n",
       "645                 1       3                    Baclini, Miss. Eugenie   \n",
       "470                 1       3             Baclini, Miss. Helene Barbara   \n",
       "79                  1       2             Caldwell, Master. Alden Gates   \n",
       "...               ...     ...                                       ...   \n",
       "860                 0       3                          Razi, Mr. Raihed   \n",
       "864                 0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n",
       "869                 0       3               van Melkebeke, Mr. Philemon   \n",
       "879                 0       3                        Laleff, Mr. Kristo   \n",
       "889                 0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "\n",
       "                Sex   Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                                  \n",
       "804            male  0.42      0      1        2625   8.5167   NaN        C  \n",
       "756            male  0.67      1      1      250649  14.5000   NaN        S  \n",
       "645          female  0.75      2      1        2666  19.2583   NaN        C  \n",
       "470          female  0.75      2      1        2666  19.2583   NaN        C  \n",
       "79             male  0.83      0      2      248738  29.0000   NaN        S  \n",
       "...             ...   ...    ...    ...         ...      ...   ...      ...  \n",
       "860            male   NaN      0      0        2629   7.2292   NaN        C  \n",
       "864          female   NaN      8      2    CA. 2343  69.5500   NaN        S  \n",
       "869            male   NaN      0      0      345777   9.5000   NaN        S  \n",
       "879            male   NaN      0      0      349217   7.8958   NaN        S  \n",
       "889          female   NaN      1      2  W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Графики с помощью `seaborn`\n",
    "\n",
    "Посмотрим, как можно работать с библиотекой `seaborn`. Это популярная библиотека готовых шаблонов для статистической визуализации, написанная на бэкенде `matplotlib`. Она имеет высокоуровневый интерфейс и по умолчанию может строить более эстетичные графики.\n",
    "\n",
    "Построим гистограмму по возрасту отдельно для мужчин и женщин."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T12:22:02.932948Z",
     "start_time": "2022-02-12T12:22:02.530128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    plt.figure(figsize=(9, 5))\n",
    "    sns.histplot(data=data, x='Age', hue='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим боксплоты (ящики с усами) для стоимости билета в зависимости от класса.\n",
    "\n",
    "<i><a href=\"https://mipt-stats.gitlab.io/courses/python/09_seaborn.html#4.-Box-plot\" target=\"_blank\">Подробнее про боксплоты.</a></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T12:22:12.652327Z",
     "start_time": "2022-02-12T12:22:12.479681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    plt.figure(figsize=(9, 4))\n",
    "    sns.boxplot(data=data, x='Fare', y='Pclass', orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ограничим значения по горизонтальной оси"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T12:22:18.868870Z",
     "start_time": "2022-02-12T12:22:18.642275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    plt.figure(figsize=(9, 4))\n",
    "    sns.boxplot(data=data, x='Fare', y='Pclass', orient='h')\n",
    "    plt.xlim((-5, 170))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 Более сложные операции из `pandas`\n",
    "\n",
    "<i><a href=\"https://mipt-stats.gitlab.io/courses/python/10_pandas2.html\" target=\"_blank\">Подробнее про группировку данных.</a></i>\n",
    "\n",
    "Сгруппируем данные по классу и усредним по всем численным колонкам "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:40:01.122526Z",
     "start_time": "2022-02-12T11:40:01.088858Z"
    }
   },
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</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>1</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>38.233441</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.356481</td>\n",
       "      <td>84.154687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "      <td>29.877630</td>\n",
       "      <td>0.402174</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>20.662183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "      <td>25.140620</td>\n",
       "      <td>0.615071</td>\n",
       "      <td>0.393075</td>\n",
       "      <td>13.675550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived        Age     SibSp     Parch       Fare\n",
       "Pclass                                                    \n",
       "1       0.629630  38.233441  0.416667  0.356481  84.154687\n",
       "2       0.472826  29.877630  0.402174  0.380435  20.662183\n",
       "3       0.242363  25.140620  0.615071  0.393075  13.675550"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by='Pclass').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Похожим образом можно для каждого класса посчитать функции минимума и максимума"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:40:41.379070Z",
     "start_time": "2022-02-12T11:40:41.310982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Survived</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Name</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sex</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SibSp</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Parch</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ticket</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>Young, Miss. Marie Grice</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>110152</td>\n",
       "      <td>WE/P 5735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Abelson, Mr. Samuel</td>\n",
       "      <td>del Carlo, Mr. Sebastiano</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11668</td>\n",
       "      <td>W/C 14208</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Abbing, Mr. Anthony</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>12460</td>\n",
       "      <td>W./C. 6609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.5500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived                               Name  \\\n",
       "            min max                            min   \n",
       "Pclass                                               \n",
       "1             0   1  Allen, Miss. Elisabeth Walton   \n",
       "2             0   1            Abelson, Mr. Samuel   \n",
       "3             0   1            Abbing, Mr. Anthony   \n",
       "\n",
       "                                        Sex         Age       SibSp     Parch  \\\n",
       "                                max     min   max   min   max   min max   min   \n",
       "Pclass                                                                          \n",
       "1          Young, Miss. Marie Grice  female  male  0.92  80.0     0   3     0   \n",
       "2         del Carlo, Mr. Sebastiano  female  male  0.67  70.0     0   3     0   \n",
       "3       van Melkebeke, Mr. Philemon  female  male  0.42  74.0     0   8     0   \n",
       "\n",
       "            Ticket             Fare            \n",
       "       max     min         max  min       max  \n",
       "Pclass                                         \n",
       "1        4  110152   WE/P 5735  0.0  512.3292  \n",
       "2        3   11668   W/C 14208  0.0   73.5000  \n",
       "3        6   12460  W./C. 6609  0.0   69.5500  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by='Pclass').aggregate([min, max])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<i><a href=\"https://mipt-stats.gitlab.io/courses/python/10_pandas2.html#4.-Таблицы-сопряженности-(Crosstab)-и-сводные-таблицы-(Pivot-table)\" target=\"_blank\">Подробнее про агрегирующие функции.</a></i>\n",
    "\n",
    "С помощью функции `crosstab` можно очень легко получить информацию о том, сколько человек определенного пола ехали в таком-то классе, а также сумму по строкам и столбцам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:41:35.656502Z",
     "start_time": "2022-02-12T11:41:35.568563Z"
    }
   },
   "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>94</td>\n",
       "      <td>76</td>\n",
       "      <td>144</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>122</td>\n",
       "      <td>108</td>\n",
       "      <td>347</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>216</td>\n",
       "      <td>184</td>\n",
       "      <td>491</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass    1    2    3  All\n",
       "Sex                       \n",
       "female   94   76  144  314\n",
       "male    122  108  347  577\n",
       "All     216  184  491  891"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data['Sex'], data['Pclass'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А с помощью похожей функции `pivot_table` можно легко получить информацию о том, каков максимальный возраст среди пассажиров определенного пола для каждого класса."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-12T11:44:26.857156Z",
     "start_time": "2022-02-12T11:44:26.826116Z"
    }
   },
   "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>63.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>80.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass     1     2     3\n",
       "Sex                     \n",
       "female  63.0  57.0  63.0\n",
       "male    80.0  70.0  74.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(\n",
    "    data, \n",
    "    values='Age', \n",
    "    index='Sex',\n",
    "    columns='Pclass', \n",
    "    aggfunc=np.max\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Почитайте также про <i><a href=\"https://mipt-stats.gitlab.io/courses/python/10_pandas2.html#2.-Объединение-таблиц\" target=\"_blank\">операции объединения таблиц</a></i>, например, `merge` и `join`."
   ]
  }
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