{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Тема 1. Первичный анализ данных с Pandas</center>\n",
    "## <center>Практическое задание. Анализ данных пассажиров \"Титаника\"</center>\n",
    "\n",
    "**Заполните код в клетках (где написано \"Ваш код здесь\") **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Считаем данные из файла в память в виде объекта Pandas.DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/titanic_train.csv',\n",
    "                  index_col='PassengerId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Данные представлены в виде таблицы. Посмотрим на первые 5 строк:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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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>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",
       "  </tbody>\n",
       "</table>\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",
       "                                                          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",
       "             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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Для примера отберем пассажиров, которые сели в Cherbourg (Embarked=C) и заплатили более 200 у.е. за билет (fare > 200).**\n",
    "\n",
    "Убедитесь, что Вы понимаете, как эта конструкция работает. <br>\n",
    "Если нет – посмотрите, как вычисляется выражение в квадратных в скобках."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>119</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\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>300</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Baxter, Mrs. James (Helene DeLaudeniere Chaput)</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Widener, Mr. Harry Elkins</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>113503</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>C82</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "119                 0       1   \n",
       "259                 1       1   \n",
       "300                 1       1   \n",
       "312                 1       1   \n",
       "378                 0       1   \n",
       "\n",
       "                                                        Name     Sex   Age  \\\n",
       "PassengerId                                                                  \n",
       "119                                 Baxter, Mr. Quigg Edmond    male  24.0   \n",
       "259                                         Ward, Miss. Anna  female  35.0   \n",
       "300          Baxter, Mrs. James (Helene DeLaudeniere Chaput)  female  50.0   \n",
       "312                               Ryerson, Miss. Emily Borie  female  18.0   \n",
       "378                                Widener, Mr. Harry Elkins    male  27.0   \n",
       "\n",
       "             SibSp  Parch    Ticket      Fare            Cabin Embarked  \n",
       "PassengerId                                                              \n",
       "119              0      1  PC 17558  247.5208          B58 B60        C  \n",
       "259              0      0  PC 17755  512.3292              NaN        C  \n",
       "300              0      1  PC 17558  247.5208          B58 B60        C  \n",
       "312              2      2  PC 17608  262.3750  B57 B59 B63 B66        C  \n",
       "378              0      2    113503  211.5000              C82        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['Embarked'] == 'C') & (data.Fare > 200)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Можно отсортировать этих людей по убыванию платы за билет.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>259</th>\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>680</th>\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>738</th>\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>312</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Susan Parker \"Suzette\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass                                   Name     Sex  \\\n",
       "PassengerId                                                                    \n",
       "259                 1       1                       Ward, Miss. Anna  female   \n",
       "680                 1       1     Cardeza, Mr. Thomas Drake Martinez    male   \n",
       "738                 1       1                 Lesurer, Mr. Gustave J    male   \n",
       "312                 1       1             Ryerson, Miss. Emily Borie  female   \n",
       "743                 1       1  Ryerson, Miss. Susan Parker \"Suzette\"  female   \n",
       "\n",
       "              Age  SibSp  Parch    Ticket      Fare            Cabin Embarked  \n",
       "PassengerId                                                                    \n",
       "259          35.0      0      0  PC 17755  512.3292              NaN        C  \n",
       "680          36.0      0      1  PC 17755  512.3292      B51 B53 B55        C  \n",
       "738          35.0      0      0  PC 17755  512.3292             B101        C  \n",
       "312          18.0      2      2  PC 17608  262.3750  B57 B59 B63 B66        C  \n",
       "743          21.0      2      2  PC 17608  262.3750  B57 B59 B63 B66        C  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['Embarked'] == 'C') & \n",
    "     (data['Fare'] > 200)].sort_values(by='Fare',\n",
    "                               ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Пример создания признака.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_category(age):\n",
    "    '''\n",
    "    < 30 -> 1\n",
    "    >= 30, <55 -> 2\n",
    "    >= 55 -> 3\n",
    "    '''\n",
    "    if age < 30:\n",
    "        return 1\n",
    "    elif age < 55:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_categories = [age_category(age) for age in data.Age]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age_category'] = age_categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Другой способ – через `apply`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age_category'] = data['Age'].apply(age_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Сколько мужчин / женщин находилось на борту?**\n",
    "- 412 мужчин и 479 женщин\n",
    "- 314 мужчин и 577 женщин\n",
    "- 479 мужчин и 412 женщин\n",
    "- 577 мужчин и 314 женщин"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Выведите распределение переменной `Pclass` (социально-экономический статус) и это же распределение, только для мужчин / женщин по отдельности. Сколько было мужчин 2-го класса?**\n",
    "- 104\n",
    "- 108\n",
    "- 112\n",
    "- 125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "      <td>108</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144</td>\n",
       "      <td>347</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>314</td>\n",
       "      <td>577</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex     female  male  sum\n",
       "Pclass                   \n",
       "1           94   122  216\n",
       "2           76   108  184\n",
       "3          144   347  491\n",
       "sum        314   577  891"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data['Pclass'], data['Sex'], margins=True, margins_name='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Каковы медиана и стандартное отклонение платежей (`Fare`)? Округлите до 2 десятичных знаков.**\n",
    "- Медиана – 14.45, стандартное отклонение – 49.69\n",
    "- Медиана – 15.1, стандартное отклонение – 12.15\n",
    "- Медиана – 13.15, стандартное отклонение – 35.3\n",
    "- Медиана – 17.43, стандартное отклонение – 39.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медиана - 14.45 \n",
      "Стандартное отклонение - 49.69\n"
     ]
    }
   ],
   "source": [
    "print(f\"Медиана - {round(data['Fare'].median(), 2)} \\nСтандартное отклонение - {round(data['Fare'].std(), 2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Правда ли, что люди моложе 30 лет выживали чаще, чем люди старше 60 лет? Каковы доли выживших в обеих группах?**\n",
    "- 22.7% среди молодых и 40.6% среди старых\n",
    "- 40.6% среди молодых и 22.7% среди старых\n",
    "- 35.3% среди молодых и 27.4% среди старых\n",
    "- 27.4% среди молодых и 35.3% среди старых"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выжило молодых: 40.6%\n",
      "Выжило старых: 22.7%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Выжило молодых: {round(data[data['Age'] < 30]['Survived'].mean() * 100, 1)}%\")\n",
    "print(f\"Выжило старых: {round(data[data['Age'] > 60]['Survived'].mean() * 100, 1)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Правда ли, что женщины выживали чаще мужчин? Каковы доли выживших в обеих группах?**\n",
    "- 30.2% среди мужчин и 46.2% среди женщин\n",
    "- 35.7% среди мужчин и 74.2% среди женщин\n",
    "- 21.1% среди мужчин и 46.2% среди женщин\n",
    "- 18.9% среди мужчин и 74.2% среди женщин"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выжило мужчин: 18.9%\n",
      "Выжило женщин: 74.2%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Выжило мужчин: {round(data[data['Sex'] == 'male']['Survived'].mean() * 100, 1)}%\")\n",
    "print(f\"Выжило женщин: {round(data[data['Sex'] == 'female']['Survived'].mean() * 100, 1)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. Сравните графически распределение стоимости билетов и возраста у спасенных и у погибших. Средний возраст погибших выше, верно?**\n",
    "- Да \n",
    "- Нет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as mpl\n",
    "data[data['Survived'] == 1]['Fare'].hist(color=\"green\", label='Выжило')\n",
    "data[data['Survived'] == 0]['Fare'].hist(color=\"black\", label='Погибло', alpha=.5)\n",
    "mpl.title('Стоимость билетов')\n",
    "mpl.xlabel('Цена')\n",
    "mpl.ylabel('Частота')\n",
    "mpl.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pJr74xS9y4403cuihh3Lbbbd96PKtDZMNcPvtt3PJJZewbds2PvaxjzFz5kyeeOIJLrvsslav6PalL32Jr33tawwbNoyePXsya9askhTMcn4l9U6gDjhEUiMwjaQY3CXpImAVcA5ARDwn6S7geeB94JKIaH2sWjOzLrZly5adnk+ZMiUb7nrw4MGtXrO5tesgFK7XcplRo0Zlw2k3O+GEE3jqqadazVJVVdXqPjqrbEUhIibsZtbY3Sx/HXBdufKYmVnbPMyFmZllXBTMrOK1dqF6a1tHjpuLgplVtKqqKjZs2ODC0E4RwYYNG3b5fUNbKuV3CmZmrRo4cCCNjY2sX78+l/03NTW1+z/WrlBMrqqqKgYOHNiu7boomFlF69WrF0OGDMlt/w0NDe36nn9XKVcudx+ZmVnGRcHMzDIuCmZmlnFRMDOzjIuCmZllXBTMzCzjomBmZhkXBTMzy7gomJlZxkXBzMwyLgpmZpZxUTAzs4yLgpmZZVwUzMws46JgZmYZFwUzM8u4KJiZWcZFwczMMi4KZmaWcVEwM7OMi4KZmWVcFMzMLOOiYGZmGRcFMzPL5FIUJF0p6TlJz0q6U1KVpH6S5klakd73zSObmVl31uVFQdLhwGVAbUQcC/QAzgWuBh6KiKOAh9LnZmbWhfLqPuoJ9JbUE+gDrAHOAman82cDZ+cTzcys+1JEdP1OpcuB64CtwK8i4nxJb0TEQQXLbIqIXbqQJE0FpgL079//+Dlz5rR7/1u2bKG6urqj8cumXLnWrl3bqfX3228/3n333RKlKa2amppu9bfsLOdqv0rN1plcY8aMWRIRta3N69mpVB2Qnis4CxgCvAH8QtIFxa4fEbcCtwLU1tZGXV1duzM0NDTQkfXKrVy56uvrO7X+0KFDWb58eWnClFhdXV23+lt2lnO1X6VmK1euPLqP/hB4OSLWR8Q24G7gJOA1SQMA0vt1OWQzM+vW8igKq4BRkvpIEjAWWAbcC0xOl5kM/DKHbGZm3VqXdx9FxGJJc4GngPeBX5N0B1UDd0m6iKRwnNPV2czMursuLwoAETENmNZi8rskrQYzM8uJf9FsZmYZFwUzM8u4KJiZWcZFwczMMi4KZmaWcVEwM7OMi4KZmWVcFMzMLOOiYGZmGRcFMzPLuCiYmVnGRcHMzDIuCmZmlnFRMDOzjIuCmZllXBTMzCzjomBmZhkXBTMzyxR9OU5Jfwz8AVDVPC0ivlmOUGZmlo+iWgqS/h74AvAVQMA5wJFlzGVmZjkotvvopIiYBGyKiOnAicCg8sUyM7M8FFsUtqb370g6DNgGDClPJDMzy0ux5xTuk3QQcCPwFBDAj8sVyvY+0xuml2W7NUNrGDN9zG7nx7Qoy37N9lZFFYWI+Fb68J8l3QdURcSb5YtlZmZ5KKooSHoqIkYCRMS7wLtlTWVWrM3Agt3Pro/6ku6uvr602zOrNMWeU1BZU5iZWUUo9pzCUElPFzwXEBExvAyZrEw62q8/47AZZTsnYGaVpdii8DLw+XIGMTOz/BVbFN6LiFfKmsTMzHJX7DmFr5Ryp5IOkjRX0guSlkk6UVI/SfMkrUjv+5Zyn2Zm1rZii8Izkn4g6cn09teSDuzEfn8EPBARRwOfApYBVwMPRcRRwEPpczMz60LFFoWZwFvAn6e3t4DbOrJDSQcApwI/BYiI9yLiDeAsYHa62Gzg7I5s38zMOk4Rbf/iU9LSiBjR1rSidiiNAG4FnidpJSwBLgdWR8RBBcttiohdupAkTQWmAvTv3//4OXPmtDcCW7Zsobq6ut3rlVu5cq1duxaANZvXdGj9gf0G0rixsZSRSqatbIfVHFbS/Q0YMKCo5brbe6yzKjUXVG62zuQaM2bMkoiobW1esSeat0oaHRGPAkg6mQ/GQ2qvnsBI4CsRsVjSj2hHV1FE3EpSVKitrY26urp2B2hoaKAj65VbuXI1/+Cqw19JPW8GV91xVQkTlU5b2abVTSvp/iZMmFDUct3tPdZZlZoLKjdbuXIVWxT+NzC74DzCJmByB/fZCDRGxOL0+VySovCapAERsVbSAGBdB7dvZmYdVGxR+H1EfCo9H0BEvNXRHUbE7yW9KmloRCwHxpJ0JT1PUmhuSO9/2dF9mJlZxxRbFP4NGNmZYtDCV4CfS9oXeAm4kOSk912SLgJWkVzIx8zMulDRl+MspYhYCrR2kmNsF0cxM7MCxRaF4ZIKWwnNYx8dUIZMZmaWk2KLwjMRcVxZk5iZWe6K/fGamZl1A8UWhT8tawozM6sIxRaFaek1mgGQ1FfSzPJEMjOzvBRbFIan4xMBEBGbAJ9jMDPbyxRbFPYpHMpaUj9y+jqrmZmVT7H/sf818Jikuenzc4DryhPJzMzyUlRRiIjbJS0BxpD8RuFPIuL5siYzM7MuV3QXUEQ8J2k9UAUg6YiIWFW2ZGYl0NGRYXe7venFbW/GJ2ZQR11J923WFYo6pyDpTEkrgJeBhcBK4N/LmMvMzHJQ7InmbwGjgBcjYgjJGEX/WbZUZmaWi2KLwraI2EDyLaR9ImIBMKJ8sczMLA/FnlN4Q1I18AjJkNfrgPfLF8vMzPJQbEvhTOAd4ArgAeC3wLgyZTIzs5x8aEtB0stAtJyc3l8JfLQcoczMLB9tdR8VXghHwHyS3yqYmdle6EOLQnpyOSPp/ZbTzMxs71H09RQkfZQPuo7MzGwv1NY5hWdIzinsB/QB/qIrQpmZWT7aOqfQ/A2jpoh4rdxhzMwsX22dU3ilq4KYmVn+fE0Es/ZYUORyA6C+vr7TuyvFNszao+gTzWZmtvdzUTAzs4yLgpmZZVwUzMws46JgZmaZ3IqCpB6Sfi3pvvR5P0nzJK1I7/vmlc3MrLvKs6VwObCs4PnVwEMRcRTwUPrczMy6UC5FQdJA4I+BnxRMPguYnT6eDZzdxbHMzLo9RbS8XEIX7FSaC1wP1ABXRcQ4SW9ExEEFy2yKiF26kCRNBaYC9O/f//g5c+a0e/9btmyhurq6o/HLply51q5dC8CazWs6tP7AfgNp3NhYykglU6nZBvYbyI5tOzq9nQEDBpQgzQe623u/FCo1W2dyjRkzZklE1LY2r8t/0SxpHLAuIpZIqmvv+hFxK3ArQG1tbdTVtXsTNDQ00JH1yq1cuZp/FTu9YXqH1p9x3gyuuuOqEiYqnUrNNuO8GWxes7nT25kwYUIJ0nygu733S6FSs5UrVx7DXJwMnCnpDKAKOEDSz4DXJA2IiLWSBgDrcshmZtatdfk5hYi4JiIGRsRg4FxgfkRcANwLTE4Xmwz8squzmZl1d5U0IN4NwF2SLgJWAefknMcsd6UeEG/o0KEfuk0PwGe5FoWIaAAa0scbgLF55jEz6+78i2YzM8u4KJiZWaaSzil0OU1XLvuNaV3/2xAzs2K4pWBmZhkXBTMzy7gomJlZxkXBzMwyLgpmZpZxUTAzs4yLgpmZZVwUzMws46JgZmYZFwUzM8u4KJiZWcZFwczMMi4KZmaWcVEwM7OMi4KZmWVcFMzMLOOiYGZmGRcFMzPLuCiYmVnGRcHMzDIuCmZmlnFRMDOzTM+8A5jtraY3TM9lv9PqpuWyX9s7uKVgZmYZFwUzM8u4KJiZWabLi4KkQZIWSFom6TlJl6fT+0maJ2lFet+3q7OZmXV3eZxofh/4akQ8JakGWCJpHjAFeCgibpB0NXA18H9zyGe2R/uwE9wzDpvxofOnT+/cyfGYFp1a3/LX5S2FiFgbEU+ljzcDy4DDgbOA2elis4GzuzqbmVl3p4j8KrukwcDDwLHAqog4qGDepojYpQtJ0lRgKkD//v2PnzNnTrv3u2XLFqqrq1mydkkHk3fO8QOOb3V6c65SW7t2LQBrNq/p0PoD+w2kcWNjKSOVTKVm22Nz1XRu+7t7b7elXO/9UqjUbJ3JNWbMmCURUdvavNyKgqRqYCFwXUTcLemNYopCodra2njyySfbve+Ghgbq6urQdLV73VLYXRO7OVep1dfXAx3/3vyM82Zw1R1XlTBR6VRqtj0215jObb+j3Ufleu+XQqVm60wuSbstCrn8eE1SL+CfgZ9HxN3p5NckDYiItZIGAOvyyNYVdleMZnxiBmOmd/JfZWsaSr9JM9s75fHtIwE/BZZFxPcLZt0LTE4fTwZ+2dXZzMy6uzxaCicDE4FnJC1Np/0VcANwl6SLgFXAOV2WaEGX7SlRhsaAmVkpdHlRiIhHgd115o/tyixmZrYz/6LZzMwyLgpmZpbx0Nl52N05jAEfMs/MrAu4pWBmZhm3FMzsA51sqdZHffuWr2/f8lZ+bimYmVnGRcHMzDLuPjKz3DR3Hw0dOrRLupLcXdU2txTMzCzjomBmZhkXBTMzy7gomJlZxkXBzMwyLgpmZpZxUTAzs4x/p2BmJdPh64AfNqPD6wJMq5vW4XVtZ24pmJlZxkXBzMwyLgpmZpZxUTAzs4yLgpmZZfztIzPrNjoySmpnRnDdE0dldUvBzMwyLgpmZpZxUTAzs4yLgpmZZVwUzMws46JgZmYZFwUzM8tUXFGQ9DlJyyX9VtLVeecxM+tOKurHa5J6AH8L/E+gEXhC0r0R8Xy+yczMdtXWcN/Tp3d8OPC2LDhtQVm2W2kthU8Dv42IlyLiPWAOcFbOmczMug1FRN4ZMpL+DPhcRHwpfT4R+ExEXFqwzFRgavp0KLC8A7s6BHi9k3HLwbnar1KzOVf7VGouqNxsncl1ZEQc2tqMiuo+AtTKtJ2qVkTcCtzaqZ1IT0ZEbWe2UQ7O1X6Vms252qdSc0HlZitXrkrrPmoEBhU8HwisySmLmVm3U2lF4QngKElDJO0LnAvcm3MmM7Nuo6K6jyLifUmXAg8CPYCZEfFcGXbVqe6nMnKu9qvUbM7VPpWaCyo3W1lyVdSJZjMzy1eldR+ZmVmOXBTMzCzTrYpCJQ2hIWmmpHWSni2Y1k/SPEkr0vu+OeQaJGmBpGWSnpN0eSVkk1Ql6XFJv0lzTa+EXAX5ekj6taT7KizXSknPSFoq6clKySbpIElzJb2QvtdOzDuXpKHpcWq+vSXpirxzpdmuTN/3z0q6M/33UJZc3aYoFAyh8UfAMcAEScfkGGkW8LkW064GHoqIo4CH0udd7X3gqxHxSWAUcEl6nPLO9i5wekR8ChgBfE7SqArI1exyYFnB80rJBTAmIkYUfKe9ErL9CHggIo4GPkVy7HLNFRHL0+M0AjgeeAe4J+9ckg4HLgNqI+JYki/hnFu2XBHRLW7AicCDBc+vAa7JOdNg4NmC58uBAenjAcDyCjhuvyQZi6pisgF9gKeAz1RCLpLf0zwEnA7cV0l/S2AlcEiLablmAw4AXib9okul5GqR5X8B/1kJuYDDgVeBfiTfGL0vzVeWXN2mpcAHB7ZZYzqtkvSPiLUA6f1H8gwjaTBwHLCYCsiWdtEsBdYB8yKiInIBPwS+DuwomFYJuSAZEeBXkpakQ8RUQraPAuuB29Iut59I2r8CchU6F7gzfZxrrohYDcwAVgFrgTcj4lflytWdikKbQ2jYByRVA/8MXBERb+WdByAitkfStB8IfFrSsTlHQtI4YF1ELMk7y26cHBEjSbpNL5F0at6BSD7tjgRuiYjjgLfJt3ttJ+kPZ88EfpF3FoD0XMFZwBDgMGB/SReUa3/dqSjsCUNovCZpAEB6vy6PEJJ6kRSEn0fE3ZWUDSAi3gAaSM7J5J3rZOBMSStJRvU9XdLPKiAXABGxJr1fR9I//ukKyNYINKYtPYC5JEUi71zN/gh4KiJeS5/nnesPgZcjYn1EbAPuBk4qV67uVBT2hCE07gUmp48nk/TndylJAn4KLIuI71dKNkmHSjoofdyb5B/KC3nniohrImJgRAwmeU/Nj4gL8s4FIGl/STXNj0n6oZ/NO1tE/B54VdLQdNJY4Pm8cxWYwAddR5B/rlXAKEl90n+fY0lOzJcnV14ncvK4AWcALwK/A/5fzlnuJOkf3Ebyyeki4GCSE5Yr0vt+OeQaTdKt9jSwNL2dkXc2YDjw6zTXs8D/T6fnfswKMtbxwYnm3HOR9N3/Jr091/yer5BsI4An07/nvwB9KyRXH2ADcGDBtErINZ3kQ9CzwD8C+5Url4e5MDOzTHfqPjIzsza4KJiZWcZFwczMMi4KZmaWcVEwM7OMi4JZStL2dHTM30h6StJJeWcy62r+SqpZStKWiKhOH38W+KuIOC3nWGZdyi0Fs9YdAGyC5Ffekm5Mx7J/RtIX0unDJT2ZDur2hKSj0+krJX1XyfUfHpf08XT65yUtTpf/D0n90+nVkm5Lt/20pD9Nx89fKmmVpPXp45/kdCysG3FLwSwlaTvwDFBFMhTx6RGxRNKfAl8mGWvpEJIhUz4T6QiV6brfIfn3dE06DtKPI+I6SZOAP4+IcenAZm9EREj6EvDJiPiqpO8C+0XEFem2+kZEc0GaQjKO/qVdchCs2+uZdwCzCrI1klFYkXQicHs6Euto4M6I2E4yCNlC4ATgXklnkFy8aTvJQGrN7iy4/0H6eCDwT+ngZfuSXFMAknGczm1esbkgmOXB3UdmrYiIRSStgkNpfdj15uX+LSKGkAwieHbhrFYe/w1wc0QMA/6CpEVCun032a0iuCiYtSI9P9CDZHC0h4EvpBf5ORQ4FXhc0oEFqzQBhdd3+ELB/aL08YHA6vTx5IJlfwVk3UN5XAPYrJm7j8w+0Du9shskn94nR8R2SfeQXM71NySf6L8eEb+XNF7SN9PltwAXFmxrP0mLST54TUin1QO/kLQa+C+Si6YAfBv4W0nPknRDTScZM9+sy/lEs1mJpSeaayPi9byzmLWXu4/MzCzjloKZmWXcUjAzs4yLgpmZZVwUzMws46JgZmYZFwUzM8v8N2DLiW8InWYDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data['Survived'] == 1]['Age'].hist(color=\"green\", label='Выжило')\n",
    "data[data['Survived'] == 0]['Age'].hist(color=\"black\", label='Погибло', alpha=.5)\n",
    "mpl.title('Возраст пассажиров')\n",
    "mpl.xlabel('Возраст')\n",
    "mpl.ylabel('Частота')\n",
    "mpl.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Возраст погибших: 30.62617924528302\n",
      "Возраст выживших: 28.343689655172415\n"
     ]
    }
   ],
   "source": [
    "# data.groupby('Survived')['Age'].mean()\n",
    "print(f\"Возраст погибших: {data[data['Survived'] == 0]['Age'].mean()}\\nВозраст выживших: {data[data['Survived'] == 1]['Age'].mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7. Как отличается средний возраст мужчин / женщин в зависимости от класса обслуживания? Выберите верные утверждения:**\n",
    "- В среднем мужчины 1-го класса старше 40 лет\n",
    "- В среднем женщины 1-го класса старше 40 лет\n",
    "- Мужчины всех классов в среднем старше женщин того же класса\n",
    "- В среднем люди в 1 классе старше, чем во 2-ом, а те старше представителей 3-го класса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.611765</td>\n",
       "      <td>41.281386</td>\n",
       "      <td>38.233441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.722973</td>\n",
       "      <td>30.740707</td>\n",
       "      <td>29.877630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.750000</td>\n",
       "      <td>26.507589</td>\n",
       "      <td>25.140620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>27.915709</td>\n",
       "      <td>30.726645</td>\n",
       "      <td>29.699118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex        female       male        All\n",
       "Pclass                                 \n",
       "1       34.611765  41.281386  38.233441\n",
       "2       28.722973  30.740707  29.877630\n",
       "3       21.750000  26.507589  25.140620\n",
       "All     27.915709  30.726645  29.699118"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data['Pclass'], data['Sex'], values=data['Age'], margins=True, aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.233440860215055"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Pclass'] == 1]['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "name": "seminar02_practice_pandas_titanic.ipynb"
 },
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}