{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# warningsを無視する\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1 ライブラリのインポートとデータの読み込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"./titanic_csv/train.csv\")\n",
    "df_test = pd.readb_csv(\"./titanic_csv/test.csv\")\n",
    "df_gender_submission = pd.read_csv(\"./titanic_csv/gender_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 本文にはない、レイアウト設定用\n",
    "sns.set_palette(\"Blues_r\", 3) # 青３色のスタイル\n",
    "mpl.style.use('seaborn-white') # 背景が白いスタイル\n",
    "\n",
    "# jupyter notebook用\n",
    "% matplotlib inline  \n",
    "\n",
    "# 日本語表示用 ##カーネルでは日本語表示できない\n",
    "plt.rcParams[\"font.size\"] = 18\n",
    "plt.rcParams['font.family'] = 'IPAPGothic' \n",
    "\n",
    "# サイズの設定\n",
    "plt.rcParams['figure.figsize'] = (8.0, 6.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2 データの概要を確認する\n",
    "## 4.2.1 データフレームについて"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.2 データフレームの行数と列数を確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n",
      "(418, 11)\n",
      "(418, 2)\n"
     ]
    }
   ],
   "source": [
    "print(df_train.shape) # 学習用データ\n",
    "print(df_test.shape) # 本番予測用データ\n",
    "print(df_gender_submission.shape) # 提出データのサンプル"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.3 列の名前の確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n",
      "----------\n",
      "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
      "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_train.columns) # トレーニングデータの列名\n",
    "print('-'*10) # 区切りを挿入\n",
    "print(df_test.columns) # テストデータの列名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.4 df.info()で概要の確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.5 df.head()で概要の確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.6 欠損値がいくつあるか確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum() \n",
    "# isnull()は、欠損値に対しTrueを返し、欠損値以外にはFalseを返す\n",
    "# sum()は、Trueを1、Falseを0として合計する\n",
    "# よってdf.isnull().sum()で欠損値を算出することができる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.7 要約統計量の表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%            NaN          NaN     0.000000   328.000000     2.000000   \n",
       "50%            NaN          NaN     0.000000   655.000000     3.000000   \n",
       "75%            NaN          NaN     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000         NaN  \n",
       "50%       0.000000         NaN  \n",
       "75%       1.000000         NaN  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_trainとdf_Testを縦に連結\n",
    "df_full = pd.concat([df_train, df_test], axis = 0, ignore_index=True)\n",
    "\n",
    "print(df_full.shape) # df_fullの行数と列数を確認\n",
    "\n",
    "df_full.describe() # df_fullの要約統計量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.8 死亡者と生存者の可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RWJ6S7D8BbJeZlzRORMT3gD8Bx0fEjykJG/qebdzoBlymeuzvmtby0pBU37oNERpxmfmT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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c5f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 男女別の生存者数を可視化\n",
    "sns.countplot(x='Survived', data=df_train)\n",
    "\n",
    "plt.xlabel(\"Survived\", fontsize = 20)\n",
    "plt.ylabel(\"count\", fontsize = 20)\n",
    "plt.title(\"死亡者と生存者の数\",  fontsize = 20)\n",
    "plt.yticks(fontsize = 20)\n",
    "plt.xticks([0.0,1.0], ['死亡','生存'])\n",
    "plt.xticks([0,1],[\"死亡者\", \"生存者\"], fontsize = 20)\n",
    "plt.show()\n",
    "\n",
    "# 男女別の生存割合を表示する\n",
    "df_train[['Sex','Survived']].groupby(['Sex']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m1Vdf5c4772SDDTZg0qRJLF++nP79+zN9+nTuv/9+SktLufjii8uPX7FiBSef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\neLVz5E0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116318080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', hue='Sex', data=df_train)\n",
    "plt.xticks([0.0,1.0], [\"死亡\",\"生存\"])\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('Survived', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.title(\"男女別の死亡者と生存者の数\", fontsize = 20)\n",
    "plt.legend(fontsize = 20)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.9 チケットクラス"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZWPJW1azQY3y2JHjOT4kP3WtKtH0I0Vt2jaf1Ltz9gzTj4o/p98Vm9l2gD5EP0GJ+iZk1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\nHKCYmZlZ0/kfsFCrvQbcuakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116571dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# チケットクラス別の生存者数を可視化\n",
    "sns.countplot(x='Survived', hue='Pclass', data=df_train)\n",
    "plt.xticks([0.0,1.0], ['死亡','生存'])\n",
    "plt.title(\"チケットクラス別の死亡者と生存者の数\", fontsize = 20)\n",
    "\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('Survived', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.legend(fontsize = 20)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# チケットクラス別の生存割合を表示する\n",
    "df_train[['Pclass','Survived']].groupby(['Pclass']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.10 年齢の分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CREQkw9e//nV22WUXzjvvPGpra9liiy248MIL2WKLLZg4cWKvxMsDDjhgxAYR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\naZoSCREREWna/wPgtO0jp804XAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179c10f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 全体のヒストグラム\n",
    "sns.distplot(df_train['Age'].dropna(), kde=False, bins = 30 ,color = \"yellow\",label=\"全体\")\n",
    "plt.title(\"乗船者の年齢の分布\", fontsize = 20)\n",
    "\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('Age', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "\n",
    "# 死亡者のヒストグラム\n",
    "sns.distplot(df_train[df_train[\"Survived\"] == 0].Age.dropna(), kde = False,bins=30,color = \"green\", label=\"死亡\")\n",
    "\n",
    "# 生存者のヒストグラム\n",
    "sns.distplot(df_train[df_train[\"Survived\"] == 1].Age.dropna(), kde = False,bins=30,color = \"red\", label=\"生存\")\n",
    "\n",
    "plt.legend(fontsize = 20); # 凡例を表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CategoricalAge</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(0.34, 10.367]</td>\n",
       "      <td>0.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(10.367, 20.315]</td>\n",
       "      <td>0.382609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(20.315, 30.263]</td>\n",
       "      <td>0.365217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(30.263, 40.21]</td>\n",
       "      <td>0.445161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(40.21, 50.157]</td>\n",
       "      <td>0.383721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(50.157, 60.105]</td>\n",
       "      <td>0.404762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(60.105, 70.0525]</td>\n",
       "      <td>0.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(70.0525, 80]</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CategoricalAge  Survived\n",
       "0     (0.34, 10.367]  0.593750\n",
       "1   (10.367, 20.315]  0.382609\n",
       "2   (20.315, 30.263]  0.365217\n",
       "3    (30.263, 40.21]  0.445161\n",
       "4    (40.21, 50.157]  0.383721\n",
       "5   (50.157, 60.105]  0.404762\n",
       "6  (60.105, 70.0525]  0.235294\n",
       "7      (70.0525, 80]  0.200000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 年齢を８等分し、CategoricalAgeという変数を作成\n",
    "df_train['CategoricalAge'] = pd.cut(df_train['Age'], 8)\n",
    "\n",
    "# CategoricalAgeでグルーピングして、Survivedを平均\n",
    "df_train[['CategoricalAge', 'Survived']].groupby(['CategoricalAge'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.11 タイタニック号に乗っている兄弟・配偶者の数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kom4cM1vDzFasUHYwEWamlDk2CXgP2CuzbxegF3B9hetdS7wee7a20iIiIlJf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\nSeodExRJktQ7/wBCP9fXJFPSaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115afcda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp', data = df_train, color='cornflowerblue')\n",
    "\n",
    "plt.title(\"同乗している兄弟・配偶者の数\", fontsize = 20)\n",
    "\n",
    "\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('SibSp', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z31mEO78mxsw/HbjB3cdFy/yEMO7HFDO7zt1/E00/gNCW+6K732Fh6NzENnAG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\nMzMzK8sBg5mZmZXlgMHMzMzK+h9hvTFYEO0rJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11746b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp_0_1_2over</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.270270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Survived\n",
       "SibSp_0_1_2over          \n",
       "0                0.345395\n",
       "1                0.535885\n",
       "2                0.270270"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SibSpが0か1であればそのまま、2以上であれば2である特徴量SibSp_0_1_2overを作成\n",
    "df_train[\"SibSp_0_1_2over\"] = [i if i <=1 else 2 for i in df_train[\"SibSp\"]]\n",
    "\n",
    "# SibSp_0_1_2overごとに集計し、可視化 \n",
    "sns.countplot(x='Survived', hue='SibSp_0_1_2over', data=df_train)\n",
    "\n",
    "\n",
    "plt.legend([\"0人\",\"1人\",\"2人以上\"],fontsize =20)\n",
    "\n",
    "plt.xticks([0,1],[\"死亡\",\"生存\"],fontsize = 20)\n",
    "\n",
    "plt.title(\"同乗している兄弟・配偶者の数別の死亡者と生存者の数\", fontsize = 20)\n",
    "\n",
    "plt.xlabel(\"Survived\",fontsize = 20)\n",
    "plt.ylabel(\"count\",fontsize = 20)\n",
    "plt.show()\n",
    "\n",
    "df_train[[\"SibSp_0_1_2over\",\"Survived\"]].groupby([\"SibSp_0_1_2over\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.12 タイタニック号に乗っている両親・子供の数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qsTHb5lJgvYjYrk3ZiZSunEubtl0NPAbs1+H8+1Bm+Vw+oEpLkqQhV5uAEhFv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163d24a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Parch', data = df_train ,color='cornflowerblue')\n",
    "plt.title(\"同乗している両親・子供の数\", fontsize = 20)\n",
    "\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('Parch', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xrGMIiiZhE6x1T5XxSyXnghKxeSQuww4kqT5YzdkSvT2PBRQ7seWOmzdp+Xkz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\nPr2mq7Vd8KDEOeecy5K5c+c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59/OSKCABeO+99wCS9u2It2CBTX81Z07FEvCJghKAwYMH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\nqgVDiSRJqoX/D/WbSPHdbT4oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f240f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(\"Blues_r\", 4)\n",
    "\n",
    "# 2以下であればそのままの数、3以上は3という変換を行う\n",
    "df_train[\"Parch_0_1_2_3over\"] = [i if i <=2 else 3 for i in df_train[\"Parch\"]]\n",
    "\n",
    "# Parch_0_1_2_3overごとに集計し可視化\n",
    "sns.countplot(x=\"Survived\",hue=\"Parch_0_1_2_3over\", data = df_train)\n",
    "plt.title(\"同乗している両親・子供の数別の死亡者と生存者の数\", fontsize = 20)\n",
    "\n",
    "plt.legend([\"0人\",\"1人\",\"2人\",\"3人以上\"],fontsize=20)\n",
    "\n",
    "plt.xticks([0,1],[\"死亡\",\"生存\"],fontsize = 20)\n",
    "\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('Parch', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch_0_1_2_3over</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Survived\n",
       "Parch_0_1_2_3over          \n",
       "0                  0.343658\n",
       "1                  0.550847\n",
       "2                  0.500000\n",
       "3                  0.266667"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[[\"Parch_0_1_2_3over\",\"Survived\"]].groupby([\"Parch_0_1_2_3over\"]).mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.13 １人で乗船しているか２人以上で乗船しているか"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1174b52b0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fFHMvNm53RDCpzjxsTHF3bMhpSUwy5Jxzzu024k05m27iTaIzFpuNciU2Uc4t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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a37a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SibSpとParchが同乗している家族の数。1を足すと家族の人数となる\n",
    "df_train['FamilySize']=df_train['SibSp']+ df_train['Parch']+ 1\n",
    "\n",
    "# IsAloneを0とし、2行目でFamilySizeが1であれば1にしている\n",
    "df_train['IsAlone'] = 0\n",
    "df_train.loc[df_train['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "# IsAloneごとに可視化\n",
    "sns.countplot(x=\"Survived\",hue=\"IsAlone\",data=df_train)\n",
    "\n",
    "plt.legend([\"２人以上\",\"1人で乗船\"],fontsize = 20)\n",
    "plt.xticks([0,1],[\"死亡\",\"生存\"],fontsize=20)\n",
    "\n",
    "plt.xlabel(\"Survived\",fontsize=20)\n",
    "plt.ylabel(\"count\",fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "plt.title(\"１人or２人以上で乗船別の死亡者と生存者の数\",fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.14 運賃の分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NWTcB5XfA3hExsd3GiHgJsCfw391UKCLWj4hTIuLhiPj3bo4hSZLGt276oHwV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\n7RhQJElS7RhQJElS7RhQJElS7fz/TX7UHDb2nAgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f1e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_train['Fare'].dropna(),kde = False,hist=True);\n",
    "\n",
    "plt.title(\"運賃の分布\", fontsize = 20)\n",
    "\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('Fare', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NWTcB5XfA3hExsd3GiHgJsCfw391UKCLWj4hTIuLhiPj3bo4hSZLGt276oHwV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\n7RhQJElS7RhQJElS7RhQJElS7fz/TX7UHDb2nAgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b39588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_train['Fare'].dropna(), rug=False, kde = False,hist=True);\n",
    "\n",
    "plt.title(\"運賃の分布\", fontsize = 20)\n",
    "\n",
    "plt.tick_params(labelsize=20)\n",
    "plt.xlabel('Fare', fontsize = 20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CategoricalFare</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CategoricalFare  Survived\n",
       "0       [0, 7.91]  0.197309\n",
       "1  (7.91, 14.454]  0.303571\n",
       "2    (14.454, 31]  0.454955\n",
       "3   (31, 512.329]  0.581081"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['CategoricalFare'] = pd.qcut(df_train['Fare'], 4)\n",
    "df_train[['CategoricalFare', 'Survived']].groupby(['CategoricalFare'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.13 名前"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Kelly, Mr. James\n",
       "1                Wilkes, Mrs. James (Ellen Needs)\n",
       "2                       Myles, Mr. Thomas Francis\n",
       "3                                Wirz, Mr. Albert\n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['Name'][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Capt',\n",
       " 'Col',\n",
       " 'Countess',\n",
       " 'Don',\n",
       " 'Dr',\n",
       " 'Jonkheer',\n",
       " 'Lady',\n",
       " 'Major',\n",
       " 'Master',\n",
       " 'Miss',\n",
       " 'Mlle',\n",
       " 'Mme',\n",
       " 'Mr',\n",
       " 'Mrs',\n",
       " 'Ms',\n",
       " 'Rev',\n",
       " 'Sir'}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 敬称を抽出し、重複を省く\n",
    "set(df_train.Name.str.extract(' ([A-Za-z]+)\\.', expand=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Capt': 1,\n",
       "         'Col': 2,\n",
       "         'Countess': 1,\n",
       "         'Don': 1,\n",
       "         'Dr': 7,\n",
       "         'Jonkheer': 1,\n",
       "         'Lady': 1,\n",
       "         'Major': 2,\n",
       "         'Master': 40,\n",
       "         'Miss': 182,\n",
       "         'Mlle': 2,\n",
       "         'Mme': 1,\n",
       "         'Mr': 517,\n",
       "         'Mrs': 125,\n",
       "         'Ms': 1,\n",
       "         'Rev': 6,\n",
       "         'Sir': 1})"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# collections.Counterを使用して、数え上げる\n",
    "import collections\n",
    "collections.Counter(df_train.Name.str.extract(' ([A-Za-z]+)\\.', expand=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Capt        70.000000\n",
       "Col         58.000000\n",
       "Countess    33.000000\n",
       "Don         40.000000\n",
       "Dr          42.000000\n",
       "Jonkheer    38.000000\n",
       "Lady        48.000000\n",
       "Major       48.500000\n",
       "Master       4.574167\n",
       "Miss        21.773973\n",
       "Mlle        24.000000\n",
       "Mme         24.000000\n",
       "Mr          32.368090\n",
       "Mrs         35.898148\n",
       "Ms          28.000000\n",
       "Rev         43.166667\n",
       "Sir         49.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_trainにTitle列を作成、Title列の値は敬称\n",
    "df_train['Title'] = df_train.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "# df_testにTitle列を作成、Title列の値は敬称\n",
    "df_test['Title'] = df_test.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "# df_trainのTitle列の値ごとに平均値を算出\n",
    "df_train.groupby('Title').mean()['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 変換するための関数を作成\n",
    "def title_to_num(title):\n",
    "    if title == 'Master':\n",
    "        return 1\n",
    "    elif title == 'Miss':\n",
    "        return 2\n",
    "    elif title == 'Mr':\n",
    "        return 3\n",
    "    elif title == 'Mrs':\n",
    "        return 4\n",
    "    else:\n",
    "        return 5\n",
    "\n",
    "# リスト内包表記を用いて変換\n",
    "df_train['Title_num'] = [title_to_num(i) for i in df_train['Title']]\n",
    "df_test['Title_num'] = [title_to_num(i) for i in df_test['Title']]"
   ]
  }
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