03_cross_tabulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 生存フラグと特徴量のクロス集計"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from titanic_package.titanic_plot import plot_survival_rate\n",
    "from titanic_package.titanic_preprocess import is_age_estimated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data load\n",
    "titanic_train = pd.read_csv('./data/train.csv')\n",
    "titanic_test = pd.read_csv('./data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 学習/評価データをマージ\n",
    "titanic_train['Type'] = 'train'\n",
    "titanic_test['Type'] = 'test'\n",
    "titanic_test['Survived'] = np.NaN\n",
    "\n",
    "titanic_all = pd.concat([titanic_train, titanic_test], ignore_index=True, sort=True)\n",
    "train_flg = titanic_all['Type'] == 'train'\n",
    "test_flg = titanic_all['Type'] == 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pclass（1-3）を1st-3rdに変換\n",
    "pclass_name_list = ['', '1st', '2nd', '3rd']\n",
    "titanic_all['Pclass_name'] = titanic_all['Pclass'].apply(lambda x : pclass_name_list[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Passenger class\n",
    "* 1st(63.3%) > 2nd(47.3%) > 3rd(24.2%)の順に生存率が高い"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3rd\n",
      "1       1st\n",
      "2       3rd\n",
      "3       1st\n",
      "4       3rd\n",
      "       ... \n",
      "1304    3rd\n",
      "1305    1st\n",
      "1306    3rd\n",
      "1307    3rd\n",
      "1308    3rd\n",
      "Name: Pclass_name, Length: 1309, dtype: object\n",
      "0       3rd\n",
      "1       1st\n",
      "2       3rd\n",
      "3       1st\n",
      "4       3rd\n",
      "       ... \n",
      "1304    3rd\n",
      "1305    1st\n",
      "1306    3rd\n",
      "1307    3rd\n",
      "1308    3rd\n",
      "Name: Pclass_name, Length: 1309, dtype: object\n",
      "0        True\n",
      "1        True\n",
      "2        True\n",
      "3        True\n",
      "4        True\n",
      "        ...  \n",
      "1304    False\n",
      "1305    False\n",
      "1306    False\n",
      "1307    False\n",
      "1308    False\n",
      "Name: Type, Length: 1309, dtype: bool\n",
      "0      3rd\n",
      "1      1st\n",
      "2      3rd\n",
      "3      1st\n",
      "4      3rd\n",
      "      ... \n",
      "886    2nd\n",
      "887    1st\n",
      "888    3rd\n",
      "889    1st\n",
      "890    3rd\n",
      "Name: Pclass_name, Length: 891, dtype: object\n",
      "            Survived  count\n",
      "Pclass_name                \n",
      "1st            63.0%    216\n",
      "2nd            47.3%    184\n",
      "3rd            24.2%    491\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Pclass_name'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 性別\n",
    "* 女性(74.2%)の方が男性(18.9%)より高い"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Survived  count\n",
      "Sex                   \n",
      "female    74.2%    314\n",
      "male      18.9%    577\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Sex'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 年齢\n",
    "## 5才刻み\n",
    "* 0-5才は70.5%と生存率が高い\n",
    "* 5-10才は35.0%と平均並だが10-15才は57.9%と高い\n",
    "  * 0-15才は詳細を確認する\n",
    "* 15-60才は34-41%で平均(38%)とそれほど変わらない\n",
    "* 60-65才で28.6%, 65才以上で12.5%と生存率が低い\n",
    "  * 60才以上は「高齢者」としてまとめる\n",
    "* 欠損値も29.4%と低い\n",
    "\n",
    "## 0-15才\n",
    "* 5才以下: Pclassや性別に関わらず生存するケースが多い。死者の大半は3rdで2nd以上だと1人以外生存\n",
    "* 5-10才: Pclassで生存かどうかがほぼ分かれている。生存者の大半は2ndで死者の大半は3rd\n",
    "* 10-15才: 2nd以上だと生存。3rdでも42.9%が生存。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Survived  count\n",
      "Age                     \n",
      "00_0-5      70.5%     44\n",
      "01_5-10     35.0%     20\n",
      "02_10-15    57.9%     19\n",
      "03_15-20    34.4%     96\n",
      "04_20-25    34.4%    122\n",
      "05_25-30    38.9%    108\n",
      "06_30-35    46.6%     88\n",
      "07_35-40    41.8%     67\n",
      "08_40-45    36.2%     47\n",
      "09_45-50    41.0%     39\n",
      "10_50-55    41.7%     24\n",
      "11_55-60    38.9%     18\n",
      "12_60-65    28.6%     14\n",
      "13_65-99    12.5%      8\n",
      "nan         29.4%    177\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Age'\n",
    "\n",
    "# 5才刻み\n",
    "age_ctgr_list = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 99]\n",
    "plot_survival_rate(feature, titanic_all, bins=age_ctgr_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Type</th>\n",
       "      <th>Pclass_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.00</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SC/Paris 2123</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>27.7500</td>\n",
       "      <td>West, Miss. Constance Mirium</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248738</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>Johnson, Miss. Eleanor Ileen</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347742</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1.00</td>\n",
       "      <td>F4</td>\n",
       "      <td>S</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>Becker, Master. Richard F</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230136</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>Kink-Heilmann, Miss. Luise Gretchen</td>\n",
       "      <td>2</td>\n",
       "      <td>185</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>315153</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>3.00</td>\n",
       "      <td>F2</td>\n",
       "      <td>S</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>Navratil, Master. Michel M</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230080</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>Asplund, Miss. Lillian Gertrud</td>\n",
       "      <td>2</td>\n",
       "      <td>234</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347077</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>Asplund, Master. Edvin Rojj Felix</td>\n",
       "      <td>2</td>\n",
       "      <td>262</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347077</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>0.92</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>2</td>\n",
       "      <td>306</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2.00</td>\n",
       "      <td>F2</td>\n",
       "      <td>S</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>Navratil, Master. Edmond Roger</td>\n",
       "      <td>1</td>\n",
       "      <td>341</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230080</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>15.9000</td>\n",
       "      <td>Coutts, Master. William Loch \"William\"</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C.A. 37671</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>15.7417</td>\n",
       "      <td>Nakid, Miss. Maria (\"Mary\")</td>\n",
       "      <td>2</td>\n",
       "      <td>382</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2653</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>Richards, Master. William Rowe</td>\n",
       "      <td>1</td>\n",
       "      <td>408</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29106</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>4.00</td>\n",
       "      <td>A34</td>\n",
       "      <td>S</td>\n",
       "      <td>81.8583</td>\n",
       "      <td>Dodge, Master. Washington</td>\n",
       "      <td>2</td>\n",
       "      <td>446</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33638</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>Baclini, Miss. Marie Catherine</td>\n",
       "      <td>1</td>\n",
       "      <td>449</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2666</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>1</td>\n",
       "      <td>470</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2666</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>Hirvonen, Miss. Hildur E</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3101298</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>Quick, Miss. Phyllis May</td>\n",
       "      <td>1</td>\n",
       "      <td>531</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26360</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>4.00</td>\n",
       "      <td>F4</td>\n",
       "      <td>S</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>Becker, Miss. Marion Louise</td>\n",
       "      <td>1</td>\n",
       "      <td>619</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230136</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>1</td>\n",
       "      <td>645</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2666</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>13.4167</td>\n",
       "      <td>Karun, Miss. Manca</td>\n",
       "      <td>1</td>\n",
       "      <td>692</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>349256</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>Wells, Miss. Joan</td>\n",
       "      <td>1</td>\n",
       "      <td>751</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29103</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>0.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>1</td>\n",
       "      <td>756</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250649</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>Emanuel, Miss. Virginia Ethel</td>\n",
       "      <td>0</td>\n",
       "      <td>778</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>364516</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>Dean, Master. Bertram Vere</td>\n",
       "      <td>2</td>\n",
       "      <td>789</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C.A. 2315</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>0.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>1</td>\n",
       "      <td>804</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>Mallet, Master. Andre</td>\n",
       "      <td>2</td>\n",
       "      <td>828</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S.C./PARIS 2079</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>Richards, Master. George Sibley</td>\n",
       "      <td>1</td>\n",
       "      <td>832</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29106</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>1</td>\n",
       "      <td>870</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347742</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age    Cabin Embarked      Fare  \\\n",
       "10   4.00       G6        S   16.7000   \n",
       "43   3.00      NaN        C   41.5792   \n",
       "58   5.00      NaN        S   27.7500   \n",
       "78   0.83      NaN        S   29.0000   \n",
       "172  1.00      NaN        S   11.1333   \n",
       "183  1.00       F4        S   39.0000   \n",
       "184  4.00      NaN        S   22.0250   \n",
       "193  3.00       F2        S   26.0000   \n",
       "233  5.00      NaN        S   31.3875   \n",
       "261  3.00      NaN        S   31.3875   \n",
       "305  0.92  C22 C26        S  151.5500   \n",
       "340  2.00       F2        S   26.0000   \n",
       "348  3.00      NaN        S   15.9000   \n",
       "381  1.00      NaN        C   15.7417   \n",
       "407  3.00      NaN        S   18.7500   \n",
       "445  4.00      A34        S   81.8583   \n",
       "448  5.00      NaN        C   19.2583   \n",
       "469  0.75      NaN        C   19.2583   \n",
       "479  2.00      NaN        S   12.2875   \n",
       "530  2.00      NaN        S   26.0000   \n",
       "618  4.00       F4        S   39.0000   \n",
       "644  0.75      NaN        C   19.2583   \n",
       "691  4.00      NaN        C   13.4167   \n",
       "750  4.00      NaN        S   23.0000   \n",
       "755  0.67      NaN        S   14.5000   \n",
       "777  5.00      NaN        S   12.4750   \n",
       "788  1.00      NaN        S   20.5750   \n",
       "803  0.42      NaN        C    8.5167   \n",
       "827  1.00      NaN        C   37.0042   \n",
       "831  0.83      NaN        S   18.7500   \n",
       "869  4.00      NaN        S   11.1333   \n",
       "\n",
       "                                         Name  Parch  PassengerId  Pclass  \\\n",
       "10            Sandstrom, Miss. Marguerite Rut      1           11       3   \n",
       "43   Laroche, Miss. Simonne Marie Anne Andree      2           44       2   \n",
       "58               West, Miss. Constance Mirium      2           59       2   \n",
       "78              Caldwell, Master. Alden Gates      2           79       2   \n",
       "172              Johnson, Miss. Eleanor Ileen      1          173       3   \n",
       "183                 Becker, Master. Richard F      1          184       2   \n",
       "184       Kink-Heilmann, Miss. Luise Gretchen      2          185       3   \n",
       "193                Navratil, Master. Michel M      1          194       2   \n",
       "233            Asplund, Miss. Lillian Gertrud      2          234       3   \n",
       "261         Asplund, Master. Edvin Rojj Felix      2          262       3   \n",
       "305            Allison, Master. Hudson Trevor      2          306       1   \n",
       "340            Navratil, Master. Edmond Roger      1          341       2   \n",
       "348    Coutts, Master. William Loch \"William\"      1          349       3   \n",
       "381               Nakid, Miss. Maria (\"Mary\")      2          382       3   \n",
       "407            Richards, Master. William Rowe      1          408       2   \n",
       "445                 Dodge, Master. Washington      2          446       1   \n",
       "448            Baclini, Miss. Marie Catherine      1          449       3   \n",
       "469             Baclini, Miss. Helene Barbara      1          470       3   \n",
       "479                  Hirvonen, Miss. Hildur E      1          480       3   \n",
       "530                  Quick, Miss. Phyllis May      1          531       2   \n",
       "618               Becker, Miss. Marion Louise      1          619       2   \n",
       "644                    Baclini, Miss. Eugenie      1          645       3   \n",
       "691                        Karun, Miss. Manca      1          692       3   \n",
       "750                         Wells, Miss. Joan      1          751       2   \n",
       "755                 Hamalainen, Master. Viljo      1          756       2   \n",
       "777             Emanuel, Miss. Virginia Ethel      0          778       3   \n",
       "788                Dean, Master. Bertram Vere      2          789       3   \n",
       "803           Thomas, Master. Assad Alexander      1          804       3   \n",
       "827                     Mallet, Master. Andre      2          828       2   \n",
       "831           Richards, Master. George Sibley      1          832       2   \n",
       "869           Johnson, Master. Harold Theodor      1          870       3   \n",
       "\n",
       "        Sex  SibSp  Survived           Ticket   Type Pclass_name  \n",
       "10   female      1       1.0          PP 9549  train         3rd  \n",
       "43   female      1       1.0    SC/Paris 2123  train         2nd  \n",
       "58   female      1       1.0       C.A. 34651  train         2nd  \n",
       "78     male      0       1.0           248738  train         2nd  \n",
       "172  female      1       1.0           347742  train         3rd  \n",
       "183    male      2       1.0           230136  train         2nd  \n",
       "184  female      0       1.0           315153  train         3rd  \n",
       "193    male      1       1.0           230080  train         2nd  \n",
       "233  female      4       1.0           347077  train         3rd  \n",
       "261    male      4       1.0           347077  train         3rd  \n",
       "305    male      1       1.0           113781  train         1st  \n",
       "340    male      1       1.0           230080  train         2nd  \n",
       "348    male      1       1.0       C.A. 37671  train         3rd  \n",
       "381  female      0       1.0             2653  train         3rd  \n",
       "407    male      1       1.0            29106  train         2nd  \n",
       "445    male      0       1.0            33638  train         1st  \n",
       "448  female      2       1.0             2666  train         3rd  \n",
       "469  female      2       1.0             2666  train         3rd  \n",
       "479  female      0       1.0          3101298  train         3rd  \n",
       "530  female      1       1.0            26360  train         2nd  \n",
       "618  female      2       1.0           230136  train         2nd  \n",
       "644  female      2       1.0             2666  train         3rd  \n",
       "691  female      0       1.0           349256  train         3rd  \n",
       "750  female      1       1.0            29103  train         2nd  \n",
       "755    male      1       1.0           250649  train         2nd  \n",
       "777  female      0       1.0           364516  train         3rd  \n",
       "788    male      1       1.0        C.A. 2315  train         3rd  \n",
       "803    male      0       1.0             2625  train         3rd  \n",
       "827    male      0       1.0  S.C./PARIS 2079  train         2nd  \n",
       "831    male      1       1.0            29106  train         2nd  \n",
       "869    male      1       1.0           347742  train         3rd  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (0, 5]才(生存)\n",
    "f = (titanic_all['Age'] > 0) & (titanic_all['Age'] <= 5) & (titanic_all['Survived'] == 1)\n",
    "titanic_all[f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Type</th>\n",
       "      <th>Pclass_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349909</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382652</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>Skoog, Master. Harald</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347088</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Miss. Ellis Anna Maria</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>Panula, Master. Eino Viljami</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3101295</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Rice, Master. Arthur</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382652</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>2.0</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>10.4625</td>\n",
       "      <td>Strom, Miss. Telma Matilda</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347054</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>2</td>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Miss. Stina Viola</td>\n",
       "      <td>1</td>\n",
       "      <td>375</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349909</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>Goodwin, Master. Sidney Leonard</td>\n",
       "      <td>2</td>\n",
       "      <td>387</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>Skoog, Miss. Margit Elizabeth</td>\n",
       "      <td>2</td>\n",
       "      <td>643</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347088</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>Panula, Master. Urho Abraham</td>\n",
       "      <td>1</td>\n",
       "      <td>825</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3101295</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Master. Sigvard Harald Elias</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age    Cabin Embarked      Fare                                     Name  \\\n",
       "7    2.0      NaN        S   21.0750           Palsson, Master. Gosta Leonard   \n",
       "16   2.0      NaN        Q   29.1250                     Rice, Master. Eugene   \n",
       "63   4.0      NaN        S   27.9000                    Skoog, Master. Harald   \n",
       "119  2.0      NaN        S   31.2750        Andersson, Miss. Ellis Anna Maria   \n",
       "164  1.0      NaN        S   39.6875             Panula, Master. Eino Viljami   \n",
       "171  4.0      NaN        Q   29.1250                     Rice, Master. Arthur   \n",
       "205  2.0       G6        S   10.4625               Strom, Miss. Telma Matilda   \n",
       "297  2.0  C22 C26        S  151.5500             Allison, Miss. Helen Loraine   \n",
       "374  3.0      NaN        S   21.0750               Palsson, Miss. Stina Viola   \n",
       "386  1.0      NaN        S   46.9000          Goodwin, Master. Sidney Leonard   \n",
       "642  2.0      NaN        S   27.9000            Skoog, Miss. Margit Elizabeth   \n",
       "824  2.0      NaN        S   39.6875             Panula, Master. Urho Abraham   \n",
       "850  4.0      NaN        S   31.2750  Andersson, Master. Sigvard Harald Elias   \n",
       "\n",
       "     Parch  PassengerId  Pclass     Sex  SibSp  Survived   Ticket   Type  \\\n",
       "7        1            8       3    male      3       0.0   349909  train   \n",
       "16       1           17       3    male      4       0.0   382652  train   \n",
       "63       2           64       3    male      3       0.0   347088  train   \n",
       "119      2          120       3  female      4       0.0   347082  train   \n",
       "164      1          165       3    male      4       0.0  3101295  train   \n",
       "171      1          172       3    male      4       0.0   382652  train   \n",
       "205      1          206       3  female      0       0.0   347054  train   \n",
       "297      2          298       1  female      1       0.0   113781  train   \n",
       "374      1          375       3  female      3       0.0   349909  train   \n",
       "386      2          387       3    male      5       0.0  CA 2144  train   \n",
       "642      2          643       3  female      3       0.0   347088  train   \n",
       "824      1          825       3    male      4       0.0  3101295  train   \n",
       "850      2          851       3    male      4       0.0   347082  train   \n",
       "\n",
       "    Pclass_name  \n",
       "7           3rd  \n",
       "16          3rd  \n",
       "63          3rd  \n",
       "119         3rd  \n",
       "164         3rd  \n",
       "171         3rd  \n",
       "205         3rd  \n",
       "297         1st  \n",
       "374         3rd  \n",
       "386         3rd  \n",
       "642         3rd  \n",
       "824         3rd  \n",
       "850         3rd  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (0, 5]才(死亡)\n",
    "f = (titanic_all['Age'] > 0) & (titanic_all['Age'] <= 5) & (titanic_all['Survived'] == 0)\n",
    "titanic_all[f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Type</th>\n",
       "      <th>Pclass_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>20.525</td>\n",
       "      <td>Goldsmith, Master. Frank John William \"Frankie\"</td>\n",
       "      <td>2</td>\n",
       "      <td>166</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>363291</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>26.250</td>\n",
       "      <td>Collyer, Miss. Marjorie \"Lottie\"</td>\n",
       "      <td>2</td>\n",
       "      <td>238</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C.A. 31921</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>15.900</td>\n",
       "      <td>Coutts, Master. Eden Leslie \"Neville\"</td>\n",
       "      <td>1</td>\n",
       "      <td>490</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C.A. 37671</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>26.250</td>\n",
       "      <td>Hart, Miss. Eva Miriam</td>\n",
       "      <td>2</td>\n",
       "      <td>536</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F.C.C. 13529</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>36.750</td>\n",
       "      <td>Davies, Master. John Morgan Jr</td>\n",
       "      <td>1</td>\n",
       "      <td>550</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C.A. 33112</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>33.000</td>\n",
       "      <td>Harper, Miss. Annie Jessie \"Nina\"</td>\n",
       "      <td>1</td>\n",
       "      <td>721</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248727</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>6.0</td>\n",
       "      <td>E121</td>\n",
       "      <td>S</td>\n",
       "      <td>12.475</td>\n",
       "      <td>Moor, Master. Meier</td>\n",
       "      <td>1</td>\n",
       "      <td>752</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>392096</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Cabin Embarked    Fare  \\\n",
       "165  9.0   NaN        S  20.525   \n",
       "237  8.0   NaN        S  26.250   \n",
       "489  9.0   NaN        S  15.900   \n",
       "535  7.0   NaN        S  26.250   \n",
       "549  8.0   NaN        S  36.750   \n",
       "720  6.0   NaN        S  33.000   \n",
       "751  6.0  E121        S  12.475   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "165  Goldsmith, Master. Frank John William \"Frankie\"      2          166   \n",
       "237                 Collyer, Miss. Marjorie \"Lottie\"      2          238   \n",
       "489            Coutts, Master. Eden Leslie \"Neville\"      1          490   \n",
       "535                           Hart, Miss. Eva Miriam      2          536   \n",
       "549                   Davies, Master. John Morgan Jr      1          550   \n",
       "720                Harper, Miss. Annie Jessie \"Nina\"      1          721   \n",
       "751                              Moor, Master. Meier      1          752   \n",
       "\n",
       "     Pclass     Sex  SibSp  Survived        Ticket   Type Pclass_name  \n",
       "165       3    male      0       1.0        363291  train         3rd  \n",
       "237       2  female      0       1.0    C.A. 31921  train         2nd  \n",
       "489       3    male      1       1.0    C.A. 37671  train         3rd  \n",
       "535       2  female      0       1.0  F.C.C. 13529  train         2nd  \n",
       "549       2    male      1       1.0    C.A. 33112  train         2nd  \n",
       "720       2  female      0       1.0        248727  train         2nd  \n",
       "751       3    male      0       1.0        392096  train         3rd  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (5, 10]才(生存)\n",
    "f = (titanic_all['Age'] > 5) & (titanic_all['Age'] <= 10) & (titanic_all['Survived'] == 1)\n",
    "titanic_all[f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Type</th>\n",
       "      <th>Pclass_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>Panula, Master. Juha Niilo</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3101295</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>Ford, Miss. Robina Maggie \"Ruby\"</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W./C. 6608</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>Asplund, Master. Clarence Gustaf Hugo</td>\n",
       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347077</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Rice, Master. Eric</td>\n",
       "      <td>1</td>\n",
       "      <td>279</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382652</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>Van Impe, Miss. Catharina</td>\n",
       "      <td>2</td>\n",
       "      <td>420</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345773</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>Goodwin, Master. Harold Victor</td>\n",
       "      <td>2</td>\n",
       "      <td>481</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Miss. Ingeborg Constanzia</td>\n",
       "      <td>2</td>\n",
       "      <td>542</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>Skoog, Miss. Mabel</td>\n",
       "      <td>2</td>\n",
       "      <td>635</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347088</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Rice, Master. George Hugh</td>\n",
       "      <td>1</td>\n",
       "      <td>788</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382652</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Miss. Ebba Iris Alfrida</td>\n",
       "      <td>2</td>\n",
       "      <td>814</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>Skoog, Master. Karl Thorsten</td>\n",
       "      <td>2</td>\n",
       "      <td>820</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347088</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>Boulos, Miss. Nourelain</td>\n",
       "      <td>1</td>\n",
       "      <td>853</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2678</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare                                   Name  \\\n",
       "24    8.0   NaN        S  21.0750          Palsson, Miss. Torborg Danira   \n",
       "50    7.0   NaN        S  39.6875             Panula, Master. Juha Niilo   \n",
       "147   9.0   NaN        S  34.3750       Ford, Miss. Robina Maggie \"Ruby\"   \n",
       "182   9.0   NaN        S  31.3875  Asplund, Master. Clarence Gustaf Hugo   \n",
       "278   7.0   NaN        Q  29.1250                     Rice, Master. Eric   \n",
       "419  10.0   NaN        S  24.1500              Van Impe, Miss. Catharina   \n",
       "480   9.0   NaN        S  46.9000         Goodwin, Master. Harold Victor   \n",
       "541   9.0   NaN        S  31.2750   Andersson, Miss. Ingeborg Constanzia   \n",
       "634   9.0   NaN        S  27.9000                     Skoog, Miss. Mabel   \n",
       "787   8.0   NaN        Q  29.1250              Rice, Master. George Hugh   \n",
       "813   6.0   NaN        S  31.2750     Andersson, Miss. Ebba Iris Alfrida   \n",
       "819  10.0   NaN        S  27.9000           Skoog, Master. Karl Thorsten   \n",
       "852   9.0   NaN        C  15.2458                Boulos, Miss. Nourelain   \n",
       "\n",
       "     Parch  PassengerId  Pclass     Sex  SibSp  Survived      Ticket   Type  \\\n",
       "24       1           25       3  female      3       0.0      349909  train   \n",
       "50       1           51       3    male      4       0.0     3101295  train   \n",
       "147      2          148       3  female      2       0.0  W./C. 6608  train   \n",
       "182      2          183       3    male      4       0.0      347077  train   \n",
       "278      1          279       3    male      4       0.0      382652  train   \n",
       "419      2          420       3  female      0       0.0      345773  train   \n",
       "480      2          481       3    male      5       0.0     CA 2144  train   \n",
       "541      2          542       3  female      4       0.0      347082  train   \n",
       "634      2          635       3  female      3       0.0      347088  train   \n",
       "787      1          788       3    male      4       0.0      382652  train   \n",
       "813      2          814       3  female      4       0.0      347082  train   \n",
       "819      2          820       3    male      3       0.0      347088  train   \n",
       "852      1          853       3  female      1       0.0        2678  train   \n",
       "\n",
       "    Pclass_name  \n",
       "24          3rd  \n",
       "50          3rd  \n",
       "147         3rd  \n",
       "182         3rd  \n",
       "278         3rd  \n",
       "419         3rd  \n",
       "480         3rd  \n",
       "541         3rd  \n",
       "634         3rd  \n",
       "787         3rd  \n",
       "813         3rd  \n",
       "819         3rd  \n",
       "852         3rd  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (5, 10]才(死亡)\n",
    "f = (titanic_all['Age'] > 5) & (titanic_all['Age'] <= 10) & (titanic_all['Survived'] == 0)\n",
    "titanic_all[f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Type</th>\n",
       "      <th>Pclass_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>237736</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>330923</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>Nicola-Yarred, Miss. Jamila</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2651</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>Nicola-Yarred, Master. Elias</td>\n",
       "      <td>0</td>\n",
       "      <td>126</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2651</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>14.0</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>Carter, Miss. Lucile Polk</td>\n",
       "      <td>2</td>\n",
       "      <td>436</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113760</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>19.5000</td>\n",
       "      <td>Mellinger, Miss. Madeleine Violet</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250644</td>\n",
       "      <td>train</td>\n",
       "      <td>2nd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>15.0</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>Madill, Miss. Georgette Alexandra</td>\n",
       "      <td>1</td>\n",
       "      <td>690</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Ayoub, Miss. Banoura</td>\n",
       "      <td>0</td>\n",
       "      <td>781</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2687</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>11.0</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>Carter, Master. William Thornton II</td>\n",
       "      <td>2</td>\n",
       "      <td>803</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113760</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
       "      <td>0</td>\n",
       "      <td>831</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2659</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>0</td>\n",
       "      <td>876</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2667</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age    Cabin Embarked      Fare  \\\n",
       "9    14.0      NaN        C   30.0708   \n",
       "22   15.0      NaN        Q    8.0292   \n",
       "39   14.0      NaN        C   11.2417   \n",
       "125  12.0      NaN        C   11.2417   \n",
       "435  14.0  B96 B98        S  120.0000   \n",
       "446  13.0      NaN        S   19.5000   \n",
       "689  15.0       B5        S  211.3375   \n",
       "780  13.0      NaN        C    7.2292   \n",
       "802  11.0  B96 B98        S  120.0000   \n",
       "830  15.0      NaN        C   14.4542   \n",
       "875  15.0      NaN        C    7.2250   \n",
       "\n",
       "                                        Name  Parch  PassengerId  Pclass  \\\n",
       "9        Nasser, Mrs. Nicholas (Adele Achem)      0           10       2   \n",
       "22               McGowan, Miss. Anna \"Annie\"      0           23       3   \n",
       "39               Nicola-Yarred, Miss. Jamila      0           40       3   \n",
       "125             Nicola-Yarred, Master. Elias      0          126       3   \n",
       "435                Carter, Miss. Lucile Polk      2          436       1   \n",
       "446        Mellinger, Miss. Madeleine Violet      1          447       2   \n",
       "689        Madill, Miss. Georgette Alexandra      1          690       1   \n",
       "780                     Ayoub, Miss. Banoura      0          781       3   \n",
       "802      Carter, Master. William Thornton II      2          803       1   \n",
       "830  Yasbeck, Mrs. Antoni (Selini Alexander)      0          831       3   \n",
       "875         Najib, Miss. Adele Kiamie \"Jane\"      0          876       3   \n",
       "\n",
       "        Sex  SibSp  Survived  Ticket   Type Pclass_name  \n",
       "9    female      1       1.0  237736  train         2nd  \n",
       "22   female      0       1.0  330923  train         3rd  \n",
       "39   female      1       1.0    2651  train         3rd  \n",
       "125    male      1       1.0    2651  train         3rd  \n",
       "435  female      1       1.0  113760  train         1st  \n",
       "446  female      0       1.0  250644  train         2nd  \n",
       "689  female      0       1.0   24160  train         1st  \n",
       "780  female      0       1.0    2687  train         3rd  \n",
       "802    male      1       1.0  113760  train         1st  \n",
       "830  female      1       1.0    2659  train         3rd  \n",
       "875  female      0       1.0    2667  train         3rd  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (10, 15]才(生存)\n",
    "f = (titanic_all['Age'] > 10) & (titanic_all['Age'] <= 15) & (titanic_all['Survived'] == 1)\n",
    "titanic_all[f]"
   ]
  },
  {
   "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></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Type</th>\n",
       "      <th>Pclass_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350406</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>Goodwin, Master. William Frederick</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>14.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2665</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Elias, Mr. Tannous</td>\n",
       "      <td>1</td>\n",
       "      <td>353</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2695</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Miss. Sigrid Elisabeth</td>\n",
       "      <td>2</td>\n",
       "      <td>543</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>Goodwin, Mr. Charles Edward</td>\n",
       "      <td>2</td>\n",
       "      <td>684</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>Panula, Mr. Jaako Arnold</td>\n",
       "      <td>1</td>\n",
       "      <td>687</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3101295</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>18.7875</td>\n",
       "      <td>Hassan, Mr. Houssein G N</td>\n",
       "      <td>0</td>\n",
       "      <td>732</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2699</td>\n",
       "      <td>train</td>\n",
       "      <td>3rd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare                                  Name  \\\n",
       "14   14.0   NaN        S   7.8542  Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "59   11.0   NaN        S  46.9000    Goodwin, Master. William Frederick   \n",
       "111  14.5   NaN        C  14.4542                  Zabour, Miss. Hileni   \n",
       "352  15.0   NaN        C   7.2292                    Elias, Mr. Tannous   \n",
       "542  11.0   NaN        S  31.2750     Andersson, Miss. Sigrid Elisabeth   \n",
       "683  14.0   NaN        S  46.9000           Goodwin, Mr. Charles Edward   \n",
       "686  14.0   NaN        S  39.6875              Panula, Mr. Jaako Arnold   \n",
       "731  11.0   NaN        C  18.7875              Hassan, Mr. Houssein G N   \n",
       "\n",
       "     Parch  PassengerId  Pclass     Sex  SibSp  Survived   Ticket   Type  \\\n",
       "14       0           15       3  female      0       0.0   350406  train   \n",
       "59       2           60       3    male      5       0.0  CA 2144  train   \n",
       "111      0          112       3  female      1       0.0     2665  train   \n",
       "352      1          353       3    male      1       0.0     2695  train   \n",
       "542      2          543       3  female      4       0.0   347082  train   \n",
       "683      2          684       3    male      5       0.0  CA 2144  train   \n",
       "686      1          687       3    male      4       0.0  3101295  train   \n",
       "731      0          732       3    male      0       0.0     2699  train   \n",
       "\n",
       "    Pclass_name  \n",
       "14          3rd  \n",
       "59          3rd  \n",
       "111         3rd  \n",
       "352         3rd  \n",
       "542         3rd  \n",
       "683         3rd  \n",
       "686         3rd  \n",
       "731         3rd  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (10, 15]才(死亡)\n",
    "f = (titanic_all['Age'] > 10) & (titanic_all['Age'] <= 15) & (titanic_all['Survived'] == 0)\n",
    "titanic_all[f]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 年齢 x PassengerClass\n",
    "* 0-5才: クラスに関わらず生存率が高い\n",
    "* 5-10才: 2ndは4人全員が生存。3rdは16人中3人生存(18.8%)と低い\n",
    "* 10-15才: 2nd以上は5人全員が生存。3rdは14人中6人生存(42.9%)と平均より高い"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Survived  count\n",
      "Pclass_name                \n",
      "1st            66.7%      3\n",
      "2nd           100.0%     13\n",
      "3rd            57.1%     28\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Pclass_name'\n",
    "\n",
    "f = (titanic_all['Age'] > 0) & (titanic_all['Age'] <= 5)\n",
    "titanic_age = titanic_all[f].reset_index(drop=True)\n",
    "\n",
    "plot_survival_rate(feature, titanic_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Survived  count\n",
      "Pclass_name                \n",
      "2nd           100.0%      4\n",
      "3rd            18.8%     16\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = (titanic_all['Age'] > 5) & (titanic_all['Age'] <= 10)\n",
    "titanic_age = titanic_all[f].reset_index(drop=True)\n",
    "\n",
    "plot_survival_rate(feature, titanic_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Survived  count\n",
      "Pclass_name                \n",
      "1st           100.0%      3\n",
      "2nd           100.0%      2\n",
      "3rd            42.9%     14\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = (titanic_all['Age'] > 10) & (titanic_all['Age'] <= 15)\n",
    "titanic_age = titanic_all[f].reset_index(drop=True)\n",
    "\n",
    "plot_survival_rate(feature, titanic_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 年齢推定フラグ\n",
    "* 年齢が推定された人の生存率は5.6%と低い"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_all['IsAgeEstimated'] = titanic_all['Age'].apply(lambda age: 1 if is_age_estimated(age) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Survived  count\n",
      "IsAgeEstimated                \n",
      "0                 39.1%    873\n",
      "1                  5.6%     18\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'IsAgeEstimated'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一緒に乗船した兄弟、配偶者の数(SibSp)\n",
    "* 0(34.5%)から1(53.6%)と増え、それ以降は単調に減少\n",
    "  * 0をフラグ化する\n",
    "* サンプル数が少ないので4以上はまとめる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Survived  count\n",
      "SibSp                \n",
      "0        34.5%    608\n",
      "1        53.6%    209\n",
      "2        46.4%     28\n",
      "3        25.0%     16\n",
      "4        16.7%     18\n",
      "5         0.0%      5\n",
      "8         0.0%      7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'SibSp'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4以上は4に丸める\n",
    "titanic_all['SibSp_rounded'] = titanic_all['SibSp'].apply(lambda x: min(4, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Survived  count\n",
      "SibSp_rounded                \n",
      "0                34.5%    608\n",
      "1                53.6%    209\n",
      "2                46.4%     28\n",
      "3                25.0%     16\n",
      "4                10.0%     30\n"
     ]
    },
    {
     "data": {
      "image/png": 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cqMysSVlqqaWa/Iy1Vj9u+jMzs4rmRGVmZhXNicrMzCqaE5WZmVU0JyozM6toTlRmZlbRnKjMzKyiOVGZmdk8JN0g6SNJYxeyXpKulDRR0hhJm5cyHicqMzOr7SZg9zrW9wW659cg4OpSBuNEZWZm84iIp4BP69ikP3BzJM8BHSStVqp4nKjMzKy+ugDvFSxX57KS8Fh/ZmYtT5WkkQXLgyNicNmiWQQnKjOzlmdWRPRegv0nAWsULHfNZSXhpj8zM6uvYcDBufffNsC0iJhcqpO5RmVmZvOQdDvQB+gkqRr4DbAUQERcAwwHfgRMBKYDh5UyHicqMzObR0QcsIj1ARzdSOG46c/MzCqbE5WZmVW0kiYqSe9KelXS6JqukJJWkjRC0pv554q5vFGH5DAzs6ahMWpUO0REr4KukKcAj0ZEd+DRvAyNPCSHmZk1DeVo+usPDMnvhwB7F5Q32pAcZmbWNJQ6UQXwiKRRkgblss4F/e0/ADrn9406JIeZmTUNpe6e/sOImCRpFWCEpNcKV0ZESIr6HDAnvEEAbdq0abhIzcysIpW0RhURk/LPj4D7gK2AD2ua9PLPj/LmRQ3JERGDI6J3RPSuqvJjYGZmzV3JEpWk9pKWq3kP7AqMJQ29cUje7BDg/vy+UYfkMDOzpqGUVZLOwH2Sas5zW0Q8JOlF4E5JRwD/AfbL2zfqkBxmZtY0lCxRRcTbQM8FlE8BdlpAeaMOyWFmZk2DR6YwM7OK5kRlZmYVzYnKzMwqmhOVmZlVNCcqMzOraE5UZmZW0ZyozMysojlRmZlZRXOiMjOziuZEZWZmFc2JyszMKpoTlZmZVTQnKjMzq2hOVGZmVtGcqMzMrKI5UZmZWUVzojIzs4rmRGVmZvORtLuk1yVNlHTKAtavKelxSS9LGiPpR6WKxYnKzMzmIak1cBXQF+gBHCCpR63NzgDujIjNgP2BP5cqHicqMzOrbStgYkS8HRHfAncA/WttE8Dy+f0KwPulCqaqVAc2M7MmqwvwXsFyNbB1rW3OBh6RdCzQHti5VMG4RmVm1vJUSRpZ8Bq0GMc4ALgpIroCPwJukVSSnOIalZlZyzMrInrXsX4SsEbBctdcVugIYHeAiHhWUjugE/BRQwYKrlGZmdn8XgS6S1pbUhtSZ4lhtbb5L7ATgKQNgXbAx6UIxonKzMzmERGzgGOAh4EJpN594ySdK6lf3uwk4GeSXgFuBw6NiChFPCVv+svdHEcCkyJiT0lrk3qQdARGAQMj4ltJbYGbgS2AKcCAiHi31PGZmdn8ImI4MLxW2VkF78cDP2iMWBqjRnUcKSPXuAi4PCLWBT4jtXOSf36Wyy/P25mZWTMgaWlJ6y/OviVNVJK6AnsA1+VlATsCd+dNhgB75/f98zJ5/U55ezMza8Ik7QWMBh7Ky70k1b7ntVClrlH9ATgZmJOXOwJTc/snpL75XfL77/rt5/XT8vZmZta0nU16iHgqQESMBtYudueSJSpJewIfRcSoBj7uoJq+/7NmzVr0DmZmVm4zI2JarbKiO16UsjPFD4B+eaDCdqShNq4AOkiqyrWmwr75Nf32qyVVkYbkmFL7oBExGBgM0L59+5L0MDEzswY1TtKBQGtJ3YFfAP8udueS1agi4tSI6BoR3Uh98B+LiIOAx4F98maHAPfn98PyMnn9Y6Xq6mhmZo3qWGAjYAZwG+nWznHF7lyO56h+DZwoaSLpHtT1ufx6oGMuPxGYb1h5MzNrkvaIiNMjYsv8OgPot8i9skYZQikingCeyO/fJt1Uq73NN8C+jRGPmZk1qlOBu4ooWyCP9WdmZiUhqS9pwNoukq4sWLU8UHRvOCcqMzMrlfdJIxP1I41EVOML4IRiD+JEZWZmJRERrwCvSLotImYu7nGcqMzMrNS6SbqANK19u5rCiPheMTt79HQzMyu1G4GrSfeldiANQH5rsTs7UZmZWaktHRGPAoqI/0TE2aRxYIvipj8zMyu1GXma+jclHUMaiWjZYnd2jcrMzErtOGAZ0tBJWwA/Ze5IRIvkGpWZmZVMnjx3QET8EvgSOKy+x3CNyszMSiYiZgM/XJJjuEZlZmal9nKeKPEu4Kuawoi4t5idi0pUkh6NiJ0WVWZmZrYA7UjTNu1YUBbAkicqSe1IN8A6SVoRqJkafnnmzsxrZma2UBFR7/tShRZVo/p/wPHA6qRxmmoS1efAn5bkxGZmZsWoM1FFxBXAFZKOjYg/NlJMZmZm3ynqHlVE/FHS94FuhftExM0lisvMzAwovjPFLcA6wGhgdi4O0nhNZmZm85F0Yl3rI+KyYo5TbPf03kCPiIgitzczM1uuIQ5SbKIaC6wKTG6Ik5qZWfMXEec0xHGKTVSdgPGSXgBmFATRryGCMDOzyiJpd+AKoDVwXURcuIBt9gPOJt0KeiUiDlzIsdoBRwAbMe98VIcXE0uxiersIrczM7MmLo/PdxWwC1ANvChpWESML9imO3Aq8IOI+EzSKnUc8hbgNWA34FzgIGBCsfEU2+vvyWIPaGZmTd5WwMSIeBtA0h1Af2B8wTY/A66KiM8AIuKjOo63bkTsK6l/RAyRdBvwdLHBFDUoraQvJH2eX99Imi3p82JPYmZmTUoX4L2C5WrmH41oPWA9Sf+S9FxuKlyYmfnnVEkbAysAddXA5lFsjeq7nhuSRMqs2xR7EjMzqyhVkkYWLA+OiMH1PQbQHegDdAWekrRJRExdwLaD8zB8ZwLDSJMmnlmfE9VL7qL+N0m/AU6p7/5mZlZ2syKidx3rJwFrFCx3zWWFqoHnI2Im8I6kN0iJ68UFHO/GPN3Hk8D36htssU1/Py547SPpQuCbRezTTtILkl6RNE7SObl8bUnPS5ooaaikNrm8bV6emNd3q++HMTOzBvEi0D1fr9sA+5NqQoX+RqpNIakTqSnw7YUc7x1JgyXtlFvl6qXYiRP3KnjtBnxBav6rywxgx4joCfQCdpe0DXARcHlErAt8RuqySP75WS6/PG9nZmaNLCJmAccAD5N6590ZEeMknSup5rGkh4EpksYDjwO/iogpCznkBsA/gaOBdyX9SVLRkymqMQabkLQM8Azwc+DvwKoRMUvStsDZEbGbpIfz+2clVQEfACvXNRpG+/bt46uvvlrY6jpdPuKNxdqvEp2wy3rlDsHMmhBJ0yOifZnOvSLp+ayDIqJ1MfsU2/TXVdJ9kj7Kr3skdS1iv9aSRgMfASOAt4CpOVvDvD1JvutlktdPAzoWE5+ZmVU2Sf8j6c+kKaPaAfsVu2+xTX83ktonV8+vB3JZnSJidkT0It2I24pU/VsikgZJGilp5KxZsxa9g5mZlZWkd0lzGz4NbBIR+0XEPcXuX2yvv5UjojAx3STp+GJPEhFTJT0ObAt0kFSVa02FPUlqeplU56a/FUhTF9c+1mBgMKSmv2JjMDOzstk0Ihb72dtiE9UUST8Fbs/LB7CAJFJI0srAzJykliYNxXER6abbPsAdwCHA/XmXYXn52bz+MY/WbmbWdEk6OSIuBs6XNN/1PCJ+Ucxxik1UhwN/JPXGC+DfwKGL2Gc1YEgeM6oVqdfIg7mHyB2Sfgu8DFyft78euEXSROBTUndIMzNrumrG8xtZ51aLUGyiOhc4pGZMJ0krAZeSEtgCRcQYYLMFlL9Nul9Vu/wbYN8i47FK9/bb8O67sPnm0KFDuaMxszKIiAfy21cj4qXFPU6xiWrTmiSVT/6ppPmSkBkffADnnAPXXguz82TQ664LW24JvXun1+abw7LLljdOM2tMv5e0KnA3MDQixtZn52ITVStJK9aqUdV7+CVrxr74An7/e7j0UpgxA448EvbYA15+GUaOhGeegdvzLU4JNtwwJa2aBNarF7RrV/c5zKxJiogdcqLaD/iLpOVJCeu3xexfbLL5PfCspLvy8r7A+fWO1pqfmTPhuuvg7LPho49gn33gd7+D7t3T+r595277wQcwahS8+GJKXg89BDffnNZVVcHGG8+bvDbeGNq0afSPZGYNLyI+AK7MPcBPBs4CikpURY9MIakHsGNefKxwAq1y8cgUSVlGpoiAe++F006DN96A7beHiy+Grbeu3zGqq1PSGjlybgL7LLcyt20LPXvOm7w23BBaF/Uwu5ktRGOPTCFpQ2AA8BNSj/GhwD2LmMNq7v5NuQe4E1XS6Inq6afh5JPhueegRw+48ELYc8/UpLekIuCdd+YmrRdfTLWwL79M65dZJt3jKkxe664LrYp9dt3MypConiU9knRXRLxf7/2dqJq+RktUEybAKafAsGGw+upw7rlwyCGp2a6U5sxJtbbC5PXyy/BNHsB/hRVgiy3mTV5rrdUwidOsGWrMRJUfUbolIg5c3GO4Q4Qt2vvvp3tQ118P7dvD+efD8cen2k1jaNUKNtggvQYOTGWzZsH48XOT18iRcPnl6Z4ZQKdOc3sZ1iSv1VdvnHjN7DsRMVvSGpLaRMS3i3MMJypbuM8/h0sugcsuSwngmGPgjDNg5ZXLHVmqxW26aXodkWeKmTEDXn113vtdF1wwt5v8aqvN202+d+/K+Cxmzd87wL8kDQO+awaLiMuK2dmJyub37bfwl7+kpr1PPoH994ff/hbWWafckdWtbdu5CejII1PZ9OkwevS8yeuBB9K9MEhNhIXJa4st/ICyWcN7K79aAcvVd2cnKpsrAu66K/Xke+st2GGH1JOvd10zVle4ZZaB738/vWp8/jm89NK8yevuu+eu7959/geU25dl6h6zZiEizlmS/Z2oLHnyydST74UX0vNLw4fD7rs3zw4Jyy8PffqkV41PP517r2vkSHjqKbjttrSuVavULb4wefXs6QeUzYqUn51a0KC0Oy5g8/k4UbV0Y8emnnx//zt07Qo33pg6LLS0Z5VWWgl23TW9anzwwbzJa/hwuOmmtK6qCjbZZP4HlJdaqizhm1W4Xxa8b0d6nqroCQXdPb0ZWKzu6dXV8JvfpAvvcsvBqafCL34BSy/d4PE1G4UPKBf2Nix8QLlXr5S0tt02jdLRtm15YzZbgHJORV8QwwsRMd8A5Qvc1omq6atXopo2DS66KHXlnjMn9eQ77TTo2LF0ATZnEWmk+MLkVfOA8gYbwJ//nO71mVWQMjzwu1LBYitgC+DKiFi/mP3d9NdSzJgBV1+deu9NmQIHHZTed+tW7siaNin1hlxnHRgwIJXNmQP/+Eeqoe64Y/quf/976Ny5vLGalc8o0j0qkZr83gGOKHZnjzvT3M2Zk0Yt33BDOOEE2Gyz9Bf/rbc6SZVKq1Zp5PixY+HMM1NPyvXXT7Wrmme6zFqQiFg7Ir6Xf3aPiF0j4pli93eias4eewy22goOPDD1dHv4YRgxInW3ttJbeun0LNqYMem+1dFHwzbbpOZBsxZA0pZ5eo+a5YMl3S/pylrNgXVyomqOxoxJ02vstBN8/HGaSuOll+bt0WaNZ/310x8It92WOmNstVW6Nzh1arkjMyu1vwDfAkjaHrgQuBmYBgwu9iBOVM3Jf/8Lhx6aep49/3yaxPD111N3c48uXl4SHHAAvPZaSlJXX506W9x229xRMsyan9YR8Wl+PwAYHBH3RMSZwLrFHsRXr+bgs8/g17+G9daDO+6AX/4yjSxx0kl+KLXSrLACXHllerB6zTVTR4udd04JzKz5aS2pptPeTsBjBeuK7sznRNWEtf52BpvffUPqcXbJJanX2RtvpGGPVlyx3OFZXbbYAp59NnWwGDUqDa57xhnw9dfljsysId0OPCnpfuBr4GkASeuSmv+K4kTVFM2Zwwb/vJ9DjujL/wy+KM2q+/LLMGRI+ivdmobWreHnP0/Ns/vvn6ZP2WijNEqIWZlJ2l3S65ImSjqlju1+IikkzTcoaEScD5wE3AT8MOY+uNsKOLbYWJyompg1R/2Lg47+MX0vPplvluvA3RfdlJ7Z6dmz3KHZ4urcOXV4efzx1FS7557w4x/De++VOzJrofJkh1cBfYEewAGSeixgu+WA44DnF3asiHguIu6LiMLpPd6IiJeKjceJqolYeeJ4fnzK4fzk1MNp++XnDD/lUm770928t9m25Q7NGkqfPmlKkt/9Dh56KD37dsklcyeDNGs8WwETI+LtPPXeISIAABGdSURBVNnhHUD/BWx3HnAR8E0pg3GiqnDLfTiJ3S/6FT896n9Z5c1xPHHkqQy5/iFe33Ev9+Rrjtq0SeMujh+fRrU4+eT03NszRT8badYQugCFVfrqXPYdSZsDa0REyduqPYRShWr7+VS2vv0aeg67FdSKFwYMYuSAnzFj2eXLHZo1hm7dYNgwuP/+NBTTdtvBYYelcRo9K7EtuSpJhU+eD46Iop9rktQKuAw4tKEDW5CSJSpJa5Ae7OpMGuNpcERckZ9GHgp0A94F9ouIzyQJuAL4ETAdOLQ+bZjNResZ37DZ/bew5R2DafvVF4zb9X959uDj+HLlVRe9cz00l0F5F2vk+Kakf//Uff2889J4gfffDxdeCEcc4Rq1LYlZEVHXjKiTgDUKlrvmshrLARsDT6RLN6sCwyT1i4gGH3qllP/SZwEnRUQPYBvg6Hwz7hTg0YjoDjyalyHdtOueX4OAq0sYW8XR7Nn0eOQ+Dj18d7a77lLe32hzbrlmGCNOuqDBk5Q1Me3bp+Q0enSa82rQIPjhD+GVV8odmTVfLwLdJa0tqQ2wPzCsZmVETIuIThHRLSK6Ac8BJUlSUMJEFRGTa2pEEfEFMIHUxtkfGJI3GwLsnd/3B26O5Dmgg6TVShVfxYig2wtPctBRe7PbpacwfaVO3HXJzdx/3l+YsnYzry1Y/Wy0ETzxRJpD7M0307NYJ54IX3xR7sismYmIWcAxwMOka/edETFO0rmS+jV2PI1yj0pSN2AzUhfGzhExOa/6gNQ0CAu/eTe5oAxJg0g1Ltq0aVOymBvDKm+MZbvrLmHN0c8xdbU1+Pvpl/PG9n2b5/Tv1jAkOOQQ2GuvNI/YH/4AQ4emn/vs43871mAiYjgwvFbZWQvZtk8pYyl5I7ekZYF7gOMj4vPCdfnhr3oNdBYRgyOid0T0rqpqmn1Blp/8Hn0vOImDjvkJnd55ncePOoMh1w3njf/5kS80VpyVVoJrroF//xtWWQX22y8NRDxxYrkjM2twJb3SS1qKlKT+GhH35uIPJa0WEZNz095HuXxRN++avHbTPmXr266h5wO3Mad1a54/4EhG7vczvm2/bLlDs6Zqm23SzMJXXZXmvtp441TTOvlkj/NozUbJalS5F9/1wISIuKxg1TDgkPz+EOD+gvKDlWwDTCtoImzSqr75mi1v/wuHH7ILve6/hfG7/C833vQI/z7sBCcpW3JVVXDccWlg2733ht/8Jo0dOGJEuSMzaxClrFH9ABgIvCppdC47jTQfyZ2SjgD+A+yX1w0ndU2fSOqeflgJY2sUmj2bHiPuY9ubr2S5Tz7krW135JnDT+LTtYoe3d6seKuvnkbPP/zwNEnjrrumgYovuyytM2uiSpao8jTDC7vhstMCtg/g6FLF09hWmPwe/c76OZ3+8yaTN+jJP069jEmb1PXYglkD2XVXePXVNIr+734Hw4en57COPjrVvsyaGD8xWCJfduzMl50688CZV3LHFUOdpKxxtWsHZ50FY8fC978Pxx+fZhZ+fqFjh5pVLCeqEpndpg33XXA9E7fbzT35rHzWXTeNrn/XXfDhh7DttnDkkWmyTbMmwonKrLmT0jNWr72WalbXXQfrr5/mL4t6PR1iVhZOVGYtxXLLpY4Vo0almtahh6apRcaNK3dkZnVyojJraXr2TNOGXHttuofVqxf8+tfw1VeL3tesDJyozFqiVq3g//4vNQcOHJh6CPbokUZnN6swTlRmLdnKK8MNN8DTT8Pyy6cHhvv1g3ffLXdkZt9xojKzNG3ISy/BJZfAY4+l2tUFF8C335Y7MjMnKjPLlloKfvlLmDABdt89jRnYq1eaWsSsjJyozGxea6wB994LDz4IX38NO+yQ7mN9+GG5I7MWyonKzBZsjz1S1/XTT09zXm2wAVx9NcyeXe7IrIVxojKzhVtmGfjtb2HMGNhsMzjqqDS6xUsvlTsya0GcqMxs0TbYAB59FG69Ff77X9hySzj2WJg2rdyRWQvgRGVmxZHgoIPSs1c//3marHGDDeD22z0Uk5WUE5WZ1U+HDvCnP8ELL0CXLnDggbDLLvD66+WOzJopJyozWzy9e6dpQ666CkaOTLMKn3lm6ilo1oCcqMxs8bVunTpYvPYa7Ltv6nix0Uapl+DMmeWOzpoJJyozW3Krrpo6Wjz6aJq0cf/9Ye210wzDH39c7uisiXOiMrOGs+OO8Oqr8MADqWZ1+unpAeLDDoOXXy53dNZEOVGZWcNq3Rr23BMefhjGj4cjjkgzDG++OWy3Hdx5p5sFrV6cqMysdDbcMHW2qK6Gyy+H99+HAQPcLGj14kRlZqXXoQMcfzy88UZqFuzRw82CVjQnKjNrPDXNgo884mbBCidpd0mvS5oo6ZQFrD9R0nhJYyQ9KmmtUsXiRGVm5VHYLHjZZW4WrCCSWgNXAX2BHsABknrU2uxloHdEbArcDVxcqnicqMysvDp0gBNOcLNgZdkKmBgRb0fEt8AdQP/CDSLi8YiYnhefA7qWKpiSJSpJN0j6SNLYgrKVJI2Q9Gb+uWIul6QrcxVzjKTNSxWXmVUoNwtWki7AewXL1blsYY4A/lGqYEpZo7oJ2L1W2SnAoxHRHXg0L0OqXnbPr0HA1SWMy8wqnZsFS61K0siC16DFPZCknwK9gUsaLrx5lSxRRcRTwKe1ivsDQ/L7IcDeBeU3R/Ic0EHSaqWKzcyaiLqaBQ8/3M2Ci29WRPQueA2utX4SsEbBctdcNg9JOwOnA/0iYkapgm3se1SdI2Jyfv8B0Dm/L7qaKWlQzV8Bs2bNKl2kZlY5CpsFx41LSerOO+c2C951l5sFG9aLQHdJa0tqA+wPDCvcQNJmwF9ISeqjUgZTts4UERFAvSexiYjBNX8FVFVVlSAyM6toPXrAn/88b7Pgfvu5WbABRcQs4BjgYWACcGdEjJN0rqR+ebNLgGWBuySNljRsIYdbYo2dqD6sadLLP2uycFHVTDOz77hZsKQiYnhErBcR60TE+bnsrIgYlt/vHBGdI6JXfvWr+4iLr7ET1TDgkPz+EOD+gvKDc++/bYBpBU2EZmYL52bBZq+U3dNvB54F1pdULekI4EJgF0lvAjvnZYDhwNvAROBa4KhSxWVmzdjCmgW/9z03CzZhpez1d0BErBYRS0VE14i4PiKmRMROEdE9Vxs/zdtGRBydq5ibRMTIUsVlZi1AYbPgsGGpu7ubBZssj0xhZs1X69aw115uFmzinKjMrGVws2CT5URlZi1L7WbBDTZws2CFc6Iys5appllwxIi5zYJDh7pZsAI5UZmZ1TQLTpoEv/+9mwUrjBOVmVmNDh3gxBPdLFhhnKjMzGqrq1lw++1Ts6DHGm00TlRmZnWp3SxYXT13bMELLoBPPil3hM2eE5WZWTFqmgXffHNus+Bpp0HXrm4WLDEnKjOz+nCzYKNzojIzW1wLahY86CA3BzYwJyozsyVV2Cz43HOw6qrljqhZcaIyM2sorVunJkBrUE5UZmZW0ZyozMysojlRmZlZRXOiMjOziuZEZWZmFa2q3AGYmTWWy0e8Ue4QGswJu6xX7hAajROVtTi+WJk1LW76MzOziuZEZWZmFc2JyszMKprvUZm1MM3lHp3vz7UcrlGZmVlFq6hEJWl3Sa9LmijplHLHY2bWUi3qeiypraShef3zkrqVKpaKSVSSWgNXAX2BHsABknqUNyozs5anyOvxEcBnEbEucDlwUaniqZhEBWwFTIyItyPiW+AOoH+ZYzIza4mKuR73B4bk93cDO0lSKYKppETVBXivYLk6l5mZWeMq5nr83TYRMQuYBnQsRTBNrtefpEHAoLwYkr4uZzxFqAJmlfIEJ5by4EvGn73EWvLnb8mfHZb48y8taWTB8uCIGLxkhyydSkpUk4A1Cpa75rJ55C+zYr/Q2iSNjIje5Y6jHPzZW+Znh5b9+ZvJZy/melyzTbWkKmAFYEopgqmkpr8Xge6S1pbUBtgfGFbmmMzMWqJirsfDgEPy+32AxyIiShFMxdSoImKWpGOAh4HWwA0RMa7MYZmZtTgLux5LOhcYGRHDgOuBWyRNBD4lJbOSqJhEBRARw4Hh5Y6jgTWZZsoS8GdvuVry528Wn31B1+OIOKvg/TfAvo0Ri0pUUzMzM2sQlXSPyszMbD5OVGZmVtEq6h5VUydpA9LT2jUPxk0ChkXEhPJFZY0h/+67AM9HxJcF5btHxEPli6xxSNoKiIh4MQ+1szvwWr7P0aJIujkiDi53HM2J71E1EEm/Bg4gDTVSnYu7knrC3BERF5YrtnKTdFhE3FjuOEpF0i+Ao4EJQC/guIi4P697KSI2L2d8pSbpN6Qx4aqAEcDWwOPALsDDEXF+GcMrKUm1u2wL2AF4DCAi+jV6UM2QE1UDkfQGsFFEzKxV3gYYFxHdyxNZ+Un6b0SsWe44SkXSq8C2EfFlHkH6buCWiLhC0ssRsVlZAyyx/Pl7AW2BD4CuEfG5pKVJNcxNyxpgCUl6CRgPXAcEKVHdTu6qHRFPli+65sNNfw1nDrA68J9a5avldc2apDELWwV0bsxYyqBVTXNfRLwrqQ9wt6S1SJ+/uZsVEbOB6ZLeiojPASLia0nN/d9+b+A44HTgVxExWtLXTlANy4mq4RwPPCrpTeYO5rgmsC5wTNmiajydgd2Az2qVC/h344fTqD6U1CsiRgPkmtWewA3AJuUNrVF8K2mZiJgObFFTKGkFmvkfaRExB7hc0l3554f4utrg/IU2kIh4SNJ6pOHxCztTvJj/2mzuHgSWrblYF5L0ROOH06gOptYgpHk06YMl/aU8ITWq7SNiBnx34a6xFHOH2GnWIqIa2FfSHsDn5Y6nufE9KjMzq2h+jsrMzCqaE5WZmVU0JyozM6toTlTWbEg6XdI4SWMkjZa0taTr8kgJSPpyIfttI+n5vM8ESWc3auD1JOkJSUVPzCepj6QHSxmTWSm51581C5K2BfYENo+IGZI6AW0i4v+K2H0IsF9EvCKpNbD+EsZSlXv9mVkDcI3KmovVgE8Kukl/EhHv1659SLo817oelbRyLl4FmJz3mx0R4/O2Z0u6RdKzkt6U9LOFnTzXWp7OQ+rU7H+ipLH5dXwu6yZpbMF+v6ypweVYL5L0gqQ3JG2Xy5eWdEeu7d0HLF2w/645vpck3SVp2Vy+u6TX8sgJP17C79asrJyorLl4BFgjX+D/LOl/FrBNe9LspBsBTwK/yeWXA69Luk/S/5PUrmCfTYEdgW2BsyStXkcMm5PG+VtP0hbAYaRx77YBfiapmKGUqiJiK9ID5DXx/RyYHhEb5rItAHKt8Qxg5zye4EjgxBz/tcBeedtVizivWcVyorJmIQ9htAUwCPgYGCrp0FqbzQGG5ve3Aj/M+55LGgrnEeBAoHC08/sj4uuI+IQ00OpWdYTxQkS8k9//ELgvIr7Ksd0LbFfER7k3/xwFdMvvt8/xEhFjgJrhqrYBegD/kjSa9HDtWsAGwDsR8WakByVvLeK8ZhXL96is2cgjgDwBPJEHSl3UqAjfPe0eEW8BV0u6FvhYUsfa2yxkudBXRYQ5i3n/QGxXa/2M/HM2i/7/KWBERBwwT6HUq4g4zJoM16isWZC0vqTCEep7Mf8Awa2AffL7A4Fn8r57SKoZPLY7KUlMzcv9JbXLiasP8GKRIT0N7C1pGUntgf/NZR8Cq0jqKKktqQPIojyV40XSxqTmSIDngB9IWjeva5+H8XoN6CZpnbzdAZg1Ya5RWXOxLPBHSR1ItZaJpGbAuwu2+QrYStIZwEfAgFw+kDSg6PS870ERMTvnrjGkJr9OwHkR8X4xwUTES5JuAl7IRddFxMsAks7N5ZNISWVRrgZulDSBNOfVqHyOj3Pz5u056QGcERFvSBoE/D1/pqeB5YqJ26wSeaw/s4XIvfG+jIhLyx2LWUvmpj8zM6torlGZ1YOkTYBbahXPiIityxGPWUvgRGVmZhXNTX9mZlbRnKjMzKyiOVGZmVlFc6IyM7OK5kRlZmYV7f8D4jufjMKfA2EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'SibSp_rounded'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一緒に乗船した親子の数(Parch)\n",
    "* サンプル数が少ないので3以上はまとめる\n",
    "* 0(34.4%)から1(55.1%)と増え、それ以降は減少\n",
    "  * 0をフラグ化する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Survived  count\n",
      "Parch                \n",
      "0        34.4%    678\n",
      "1        55.1%    118\n",
      "2        50.0%     80\n",
      "3        60.0%      5\n",
      "4         0.0%      4\n",
      "5        20.0%      5\n",
      "6         0.0%      1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Parch'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3以上は3に丸める\n",
    "titanic_all['Parch_rounded'] = titanic_all['Parch'].apply(lambda x: min(3, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Survived  count\n",
      "Parch_rounded                \n",
      "0                34.4%    678\n",
      "1                55.1%    118\n",
      "2                50.0%     80\n",
      "3                26.7%     15\n"
     ]
    },
    {
     "data": {
      "image/png": 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W5n1gs2y5CzAr5/jZ2TYzM2tYI4FDatjfoC1ghUxUZcBXgesiYjdgMaua+QCINFhXnQbskjRU0kRJE8vLy+stWDMzSyLiCWB+DUUatAWskIlqNjA7Ip7L1u8iJa4PKt5Q9vPDbP8coFvO8V2zbauJiBER0Tci+paVeQQoM7MiaNAWsIIlqoh4H5glabts0/7ANGAcMDjbNhi4P1seB5yUtX3uBSzMaSI0M7P6U1bRMpW9hhY7oJoUukpyFnCbpNbATOAUUnK8Q9IQ4B3gmKzsA8BhwAxgSVbWzMzqX3lE9F2H4/NqAasvBU1UETEZqOrD2L+KsgGcUch4zMysXowDzpQ0BtiTAreA+SaPmZmtRtJooD/QSdJs4BdAK4CIuJ4GbgFzojIzs9VExHG17G/QFjCP9WdmZiXNicrMzEqaE5WZmZU0JyozMytpTlRmZlbSnKjMzKykOVGZmVlJc6IyM7OS5kRlZmYlzYnKzMxKmhOVmZmVNCcqMzMraU5UZmZW0pyozMyspDlRmZlZSXOiMjOzkuZEZWZmJc2JyszMSpoTlZmZlTQnKjMzK2kFTVSS3pb0iqTJkiZm2zaR9IikN7KfG2fbJelaSTMkvSzpq4WMzczMGoeGqFHtGxF9IqJvtn4B8GhE9AQezdYBDgV6Zq+hwHUNEJuZmZW4YjT9DQRGZcujgG/nbL8lkmeBjSR1LkJ8ZmZWQgqdqAL4p6RJkoZm2zaLiLnZ8vvAZtlyF2BWzrGzs21mZtaMlRX4/F+PiDmSvgI8IunV3J0REZKiLifMEt5QgNatW9dfpGZmVpIKWqOKiDnZzw+Be4E9gA8qmvSynx9mxecA3XIO75ptq3zOERHRNyL6lpUVOs+amVmxFSxRSWovaYOKZeAgYAowDhicFRsM3J8tjwNOynr/7QUszGkiNDOzZqqQVZLNgHslVVzn9oh4SNIE4A5JQ4B3gGOy8g8AhwEzgCXAKQWMzczMGomCJaqImAn0rmL7PGD/KrYHcEah4jEzs/xJOgS4BmgJ/DUihlfavyWp5/ZGWZkLIuKBQsTikSnMzGw1kloCfyI937ojcJykHSsVuxi4IyJ2A44F/lyoeJyozMyssj2AGRExMyKWAWNIz7rmCmDDbLkD8F6hgnG3OTMzq6yq51r3rFRmGOk52bOA9sABhQrGNSozs+anTNLEnNfQ2g9Zw3HAyIjoSuoId6ukguQU16jMzJqf8pzxV6uSz3OtQ4BDACLiGUltgU6seja23rhGZWZmlU0AekraWlJrUmeJcZXKvEvWg1vSDkBb4KNCBONEZWZmq4mIcuBM4GFgOql331RJl0kakBX7CfB9SS8Bo4GTs8eM6p2b/szMbA3ZM1EPVNr285zlacA+DRGLa1RmZlZwktpJ2m5tjnWiMjOzgpJ0BDAZeChb7yOp8j2vajlRmZlZoQ0jPUS8ACAiJgNb53uwE5WZmRXa8ohYWGlb3h0v3JnCzMwKbaqk7wEtJfUEfgg8ne/BrlGZmVmhnQXsBHwB3A4sBM7O92DXqMzMrNC+FREXARdVbJB0NHBnPge7RmVmZoX2szy3Vck1KjMzKwhJh5IGrO0i6dqcXRsC5fmex4nKzMwK5T1gIjAAmJSz/TPgnHxP4kRlZmYFEREvAS9Juj0ilq/teZyozMys0LpL+jVpWvu2FRsjYpt8DnZnCjMzK7SbgetI96X2BW4B/pbvwQVPVJJaSnpR0vhsfWtJz0maIWlsNtcJktpk6zOy/d0LHZuZmTWIdhHxKKCIeCcihgHfyvfghqhRnU2az6TCb4CrIqIH8Alplkiyn59k26/KypmZWeP3RTZN/RuSzpT0HWD9fA8uaKKS1JWUNf+arQvYD7grKzIK+Ha2PDBbJ9u/f1bezMwat7OB9UhDJ+0OnAAMzvfgQnemuBo4D9ggW+8ILMhmjwSYDXTJlrsAsyDNLilpYVb+4wLHaGZmBSKpJTAoIs4FFgGn1PUcBatRSToc+DAiJtVauG7nHSppoqSJ5eV5Py9mZmZFEBErgK+vyzkKWaPaBxgg6TBSd8QNgWuAjSSVZbWqrsCcrPwcoBswW1IZ0AGYV/mkETECGAHQvn37vIeJNzOzonkxmyjxTmBxxcaIuCefg/OqUUl6NJ9tuSLiZxHRNSK6A8cC/46I44HHgKOyYoOB+7PlcaxqszwqK+9EZGbW+LUlVTz2A47IXofne3CNNSpJbUk3wDpJ2hio6NywIavuLdXV+cAYSZcDLwI3ZttvBG6VNAOYT0puZmbWyEVEne9L5aqt6e9/gR8BW5DGaapIVJ8Cf8z3IhHxOPB4tjyTNCVx5TKfA0fne04zM2seakxUEXENcI2ksyLiDw0Uk5mZ2Zfy6kwREX+Q9DWge+4xEXFLgeIyMzMD8kxUkm4FtgUmAyuyzUEar8nMzGwNkn5c0/6I+H0+58m3e3pfYEf3wjMzszrYoPYitcs3UU0BNgfm1sdFzcys6YuIS+vjPPkmqk7ANEnPA1/kBDGgPoIwM7PSIukQ0iANLYG/RsTwKsocAwwj3Qp6KSK+V8252pIGHt+J1eejOjWfWPJNVMPyLGdmZo1cNj7fn4ADSWOyTpA0LiKm5ZTpCfwM2CciPpH0lRpOeSvwKnAwcBlwPKvPqlGjfHv9/SffE5qZWaO3BzAje+4VSWNIM1xMyynzfeBPEfEJQER8WMP5ekTE0ZIGRsQoSbcDT+YbTL5DKH0m6dPs9bmkFZI+zfciZmbWqHw5m0Umd6aLCr2AXpL+K+nZrKmwOsuznwsk7Uway7WmGthq8q1RfdlzI5sjaiCwV74XMTOzklImaWLO+ohswO86nQPoCfQnDTD+hKRdImJBFWVHZMPwXUIa13X9bDnvC9VJ1kX9Pkm/AC6o6/FmZlZ05RHRt4b9FbNZVMid6aLCbOC5iFgOvCXpdVLimlDF+W7Opvv4D7BNXYPN94Hf7+astiA9V/V5XS9mZmaNwgSgp6StSQnqWKByj777gOOAmyV1IjUFzqzmfG9JeggYy1rMjJFvjeqInOVy4G1S85+ZmTUx2SzrZwIPk7qn3xQRUyVdBkyMiHHZvoMkTSONWPTTiFhjDsHM9qRpPc4AbpL0d2BMRDyVTzxqzINNtG/fPhYvXlx7wSpc9cjr9RxN43LOgb2KHYKZFYmkJRHRvkjX3pj0fNbxEdEyn2Py7fXXVdK9kj7MXndL6rouwZqZWfMh6ZuS/kyaMqotcEy+x+aVqICbST01tshef8+2mZmZ1UjS26S5DZ8EdomIYyLi7nyPz/ce1aYRkZuYRkr6Uf5hmtWzFSugZV6tBmZWfLtGxFo/e5tvopon6QRgdLZ+HFDdTTOzwnnmGRg+HP7+d9hoI9hyS+jWbc2f3bpBly7QqlWxIzZrtiSdFxFXAr+StEaHiIj4YT7nyTdRnQr8AbiKNPjg08DJeR5rtm4i4KGHUoJ64gnYZBM4+2xYtgxmzYJ334Wnn4b581c/ToLOnatOYhXLX/lKKmdmhVAxnt/EGkvVIt9EdRkwuGJMJ0mbAL8lJTCzwigvhzvvhN/8Bl56KSWWq6+G006D9lV0WFq8OCWuiuSV+/Oll2D8eFi6dPVj2rSBrl1rTmYbbtgw79esiYmIv2eLr0TEC2t7nnwT1a4VSSq7+HxJu63tRc1qtHQpjBwJ//d/8NZbsMMOaf2446B16+qPa98ett8+vaoSAfPmrZ7Acpcfewzeey/d/8rVoUP1zYtbbpmaGNu0qa93b9YU/U7S5sBdwNiImFKXg/NNVC0kbVypRlXn4ZfMarRgAVx3Xao1ffgh7LknXHUVHHEEtMi3g2oNJOjUKb12q+bvrPJymDu36lrZrFnw/PPw8cdrHrf55jUns802q5/3YNYIRcS+WaI6BviLpA1JCevyfI7PN9n8DnhG0p3Z+tHAr2o6IJso6wmgTXaduyLiF9mQHGOAjqT+9CdGxDJJbYBbgN1JHTUGRcTbecZnjdncuSk5XXcdfPYZHHIIXHABfOMbDX//qKxsVYL52teqLrNkCcyeXXUymzo13U+r/CB6q1apibG65sUtt0w1N98vsyYqIt4HrpX0GHAe8HOg/hJVRNySjbS7X7bpu7kTaFXjC2C/iFgkqRXwlKQHgR8DV0XEGEnXk2Z9vC77+UlE9JB0LPAbYFA+8VkjNWNGat4bOTLVZI45Bs4/H/r0KXZkNVtvPejVK72qEpFqh5WTWMXPJ5+EOXPSe861/vo13yvr1g3atq36mmYlTNIOpO/zI0kVkbHAT/I9Pu/muywx1ZaccssHsChbbZW9gpTsKgY3HEWaPfg60tiBw7LtdwF/lKS6Dl5ojcALL6QOEnfdlWoap54K554L225b7MjqhwQbb5xevXtXXWbFCnj//ervl734Ymr+rGzTTauvkXXrlno5+vkyKz03kVrSDo6I9+p6cEHvM2XTGU8CepCmNX4TWBARFX9K5k7G9eVEXdmAiAtJzYNV3BCwRicCHn88dTH/5z9TT7rzzkvdzDffvNjRNbyWLVMnjC5dYK9qpnb7/PPqmxhffx0efTQ1lVZ13pqS2SabuInRGkyWB96KiGvW9hwFTVTZ/CN9JG0E3EsaQXedSBoKDAVoXVMPMCsNK1fC/fenBPX886lTwfDh8IMfpHsyVr22baFHj/SqzsKF1TcxPvtsqrUuX776MeutB9tsk/4Nvv/9mntSmq2jiFghqZuk1hGxbG3O0SA99yJiQXYDbW9gI0llWa0qdzKuiom6ZksqI01VvMboF9kslCMgjZ7eEPHbWli2DG67Da68El59NX0xXn89DB7s+yz1qUMH2GWX9KrKypXwwQdrNi0+8wyceSb87ndw2WWp67+bDK1w3gL+K2kc8GVPo4j4fT4HF6y/rKRNs5oUktoBB5KeUn4MOCorNhi4P1sel62T7a/z5FpWAhYtSl3Kt9km3Xtq2xbGjIHXXoP//V8nqYbWokW6b7XHHnDkkXDOOfD736eRPB54ICW6E09MHVjGjUtNtGb1701gPCnnbJDzykvB5qOStCups0TLLLg7IuIySduQbqptArwInBARX2Td2W8FdgPmA8dGRHWzRQKej2pd1Pt8VB9/DH/4Q3p98gn075+6mB90kO+HlLKVK9PoH5dcAm+8AXvvDVdckf79rMkq5nxUa8MTJzZT9Zao3n03NR/dcEMaUeLb305dzKvrIGClafny9JjApZemrvMHHZQS1u67FzsyK4CGTlTZrZ+qBqXdr4ria/Cj8rZ2pk5N95u23Rb+/GcYNAimTYN773WSaoxatUodK954Iz3bNnEi9O2bnm177bViR2eN37nAT7PXJcBk6jBQrROV1c0zz8DAgbDzzqlH2ZlnwsyZcPPNaUw+a9zatUvPtM2cmZoDH3gAdtopDQQ8a1axo7NGKiIm5bz+GxE/Bvrne7wTldUuAh58EL75zTSs0FNPwbBhqdnvqqvSsznWtHTokHoDzpyZ/hi59Vbo2RN+/GP46KNiR2eNjKRNcl6dJB1M6tmdFycqq155OYwenXqEHXZYGsn86qtTgvrFL6Bjx2JHaIX2la+kf/PXX4fvfQ+uuSb16Bw2DD5d6wlbrfmZRGrqmwQ8Qxo+aUi+BztR2ZqWLk0DxPbqlb6cKm60z5iRRpKoai4oa9q22gpuugmmTEkdLS69NN2fvOqqNIKGWQ0iYuuI2Cb72TMiDoqIp/I93onKVlmwAH79a+jeHU4/Pf01fd996ctp8GCPYGDpPuTdd6dRRvr0SU2BvXrBjTeuOciuNXuS+mXTe1SsnyTpfknXZtNF5cWJytI0G+efn8aCu/BC+OpX07h8FR0nPI+SVdavHzzySBpvsHPn1Nli553TM1krVxY7OisdfwGWAUj6BjCcNJ3TQrIRhvLhb6DmbMaMNFpE9+7w29/Ct76VRu2u6DjhB3WtNvvtl8YUvPfeNATTMcekJPbwwx7lwgBaRsT8bHkQMCIi7o6IS0iDlefFiaoZ+sobU9NzT9ttB6NGwZAh6WZ5RccJs7qQ0oPeL7+cfp/mzUuTX1YkMWvOWmZjtwLsD/w7Z1/eY806UTUXEXSd/Czf+dkQjj/ju2kW2vPOg7ffTg/sNpW5oKx4WraEk05KD+/x3xMAABCYSURBVAhfe216AHzvvVPz8ZQpxY7OimM08B9J9wNLgScBJPUgNf/lxUMoNXUrV7Lt04/Sb+wIOr/2Mos37sQL3x3M//zuEk+zYYW1aFHqzn7llWnerOOPT70Ft9mm2JE1e/kMoSTpEOAa0nitf42I4dWUO5I02W2/iFhjtAlJewGdgX9GxOJsWy9g/Yh4Ia94naiaphbLl7H9v/9Ov7E3sMnst1jQuRsTjz6NaQd9hxWt29T/oLRm1Zk/P83ofO21aWbj738fLr44dcKwoqgtUWWTHb5OmvViNjABOC6b6T233AbAP4DWwJlVJar64Ka/JqbV0sXsdvdITh18AAf/7kLK27TlHxdexcibHuKVw49lRes2xQ7RmptNNkmJ6s030/3QESNSU/OFF6aR9q0U7QHMiIiZ2WSHY4CBVZT7JfAboKAP0zlRNRFtF85n71uu5bQT9qX/X37Ngi5bcc8Vf+W2P9/L6/0PI1o2yByZZtXbYov0IPn06anzxa9/nZoBhw+HJUuKHZ2trguQO7jj7GzblyR9FegWEf8odDD+9mrkNvhgDrvffTM7P3gnrb74nBlfO4AJg77P+zu4956VqB494Pbb07N7F10EP/tZupd1ySXpeSw/WN4QyiTlNtONyGZPz4ukFsDvgZPrO7CqOFE1Uh3ffoO+d/yV7R4bD8D0AwYw6ejTmL+le+9ZI9G7N4wfnwY5vvBCOOOMNLfZZZfBccf5QfPCKo+IvjXsnwPkjjbdNdtWYQNgZ+BxpectNwfGSRpQiPtUTlSNTOdpL9Jv7Ai2febfLG/TjpcGHM+kI09h0Vd8Y9oaqa9/Hf7zn/TIxIUXwgknpHtav/oVHH64HzwvjglAT0lbkxLUscD3KnZGxEKgU8W6pMeBcwvVmcKJqjGIoPuEJ+g39ga6vjKBpRtsxDMnnsXkgcfz+YYbFzs6s3UnwaGHwsEHwx13pGbAAQPStDJXXJFGSrEGExHlks4EHiZ1T78pIqZKugyYGBHjGjIed08vYVpRTq8nHqLf2BFsOvM1Pt20My8cdQqvHHI05e3WW6dzu3u6lbTly9NknJdeCu+9lxLYFVekcShtnTX0VPTryjWqEtTyi8/Z6Z/30PfOG+nw/mzmbbktD587nFf3/RYrW/lGszUDrVrB0KFw4onwpz+lHoK7757GEvzlL9OI7dZsuEZVQtos+pRd/z6a3e4dRfsF85i7fW+eP3YoM/far95vLLtGZY3KwoWpo8Xvf5/mvzrllDR5Z9euxY6sUWpsNSonqhLQft6H7HbvKHYdP5o2SxbzVt//YcKxQ5mzS7+C3Uh2orJG6cMPUyeL669P/zfOOCN1b+/UqfZj7UtOVBUnlrqR5h3ZDAhSP/1rssmyxgLdgbeBYyLiE6U+jtcAhwFLgJNrGweqsSeqDnPeoe+dN7LjI/fQYsUKXv/mYUw85jQ+2naHgl/bicoatXfegWHD4JZb0ozTP/lJmsRxgw2KHVmj4ERVcWKpM9A5Il7IxoOaBHyb9IDY/IgYLukCYOOIOF/SYcBZpES1J3BNROxZ0zUaa6LqNPNV9hh9PT2ffJiVLcuYevCRTDrqVBZusWWDxeBEZU3CtGmph+A996Ra1UUXwQ9+AG3bFjuyktbYElXBnqiLiLkVNaKI+AyYThqCYyAwKis2ipS8yLbfEsmzwEZZsmtyNnt9Ct0nPMnEo0/jxlv/zb9/OKxBk5RZk7HjjnD33fD882kutXPOSR0tbroJysuLHZ3VkwZ59FtSd2A34Dlgs4iYm+16n9Q0CHmMLZWda6ikiZImljfSX8Tp+w/gr7c9zn+H/IQlm2xa7HDMGr9+/eCRR+Bf/0qjsg8ZArvsAnfd5ZmGm4CCJypJ6wN3Az+KiE9z90Vqd6zTb1FEjIiIvhHRt6yscfauX9mqNcvauy3drN7tv3+aVfiee1JP2aOPTknsn/90wmrECpqoJLUiJanbIuKebPMHFU162c8Ps+21jS1lZlY7Cb7zHXj5ZRg5Ej7+OD0wXJHErNEpWKLKevHdCEyPiN/n7BoHDM6WBwP352w/SclewMKcJkIzs7pp2RIGD4bXXkuTNk6dCnvvnaYYmTKl2NFZHRSyRrUPcCKwn6TJ2eswYDhwoKQ3gAOydYAHgJnADOAG4PQCxmZmzUWbNnDWWWnixssvh8ceg113hZNOgrfeKnZ0lgc/8NtMuXu6NVvz5qXR2f/wB1ixIg3VdPHFsPnmxY6swbh7uplZKevYEa68EmbMgFNPTaNcbLttegZrwYJiR2dVcKIys+apS5eUpF59FQYOTKOzb7NNqm0tWVLs6CyHE5WZNW89esDtt8PkyWn+qwsuSNuuuy5NN2JF50RlZgbQuzeMHw9PPpmaAk8/HbbfHm67DVauLHZ0zZoTlZlZrq9/HZ54Av7xjzTI7QknpOGZxo/3Q8NF4kRlZlaZBIcdBi+8AKNHw9KlcMQRq5KYNSgnKjOz6rRoAccem0Zp/8tf4O234ZvfhEMPhVdeKXZ0zYYTlZlZbVq1Ss9bzZiRurY//zzMnFnsqJoNJyozs3y1awc//Wka0WLAgGJH02w0zuHHzcyKacMNix1Bs+IalZmZlTQnKjMzK2lOVGZmVtKcqMzMrKQ5UZmZWUlzojIzszVIOkTSa5JmSLqgiv0/ljRN0suSHpW0VaFicaIyM7PVSGoJ/Ak4FNgROE7SjpWKvQj0jYhdgbuAKwsVjxOVmZlVtgcwIyJmRsQyYAwwMLdARDwWERUTdz0LdC1UME5UZmZWWRdgVs767GxbdYYADxYqGI9MYWvlqkdeL3YIRXXOgb2KHYLZuiiTNDFnfUREjFibE0k6AegLfLNeIquCE5WZWfNTHhF9a9g/B+iWs94127YaSQcAFwHfjIgv6jfEVQrW9CfpJkkfSpqSs20TSY9IeiP7uXG2XZKuzXqXvCzpq4WKy8zMajUB6Clpa0mtgWOBcbkFJO0G/AUYEBEfFjKYQt6jGgkcUmnbBcCjEdETeDRbh9SzpGf2GgpcV8C4zMysBhFRDpwJPAxMB+6IiKmSLpNUMWz8/wHrA3dKmixpXDWnW2cFa/qLiCckda+0eSDQP1seBTwOnJ9tvyUiAnhW0kaSOkfE3ELFZ2Zm1YuIB4AHKm37ec7yAQ0VS0P3+tssJ/m8D2yWLde1h4mZmTUTReuentWeoq7HSRoqaaKkieXl5QWIzMzMSklDJ6oPJHUGyH5W3IDLq4cJQESMiIi+EdG3rMydFs3MmrqGTlTjgMHZ8mDg/pztJ2W9//YCFvr+lJmZQQE7U0gaTeo40UnSbOAXwHDgDklDgHeAY7LiDwCHATOAJcAphYrLzMwal0L2+juuml37V1E2gDMKFYuZmTVeHuvPzMxKmnsjmBVBcx8rETxeouXPNSozMytpTlRmZlbSnKjMzKykOVGZmVlJc6IyM7OS5kRlZmYlzYnKzMxKmhOVmZmVNCcqMzMraU5UZmZW0pyozMyspDlRmZlZSXOiMjOzkuZEZWZmJc2JyszMSpoTlZmZlTQnKjMzK2lOVGZmVtKcqMzMrKSVFTsAM7O1cdUjrxc7hKI658BexQ6hwZRUjUrSIZJekzRD0gXFjsfMrLmq7ftYUhtJY7P9z0nqXqhYSiZRSWoJ/Ak4FNgROE7SjsWNysys+cnz+3gI8ElE9ACuAn5TqHhKJlEBewAzImJmRCwDxgADixyTmVlzlM/38UBgVLZ8F7C/JBUimFJKVF2AWTnrs7NtZmbWsPL5Pv6yTESUAwuBjoUIptF1ppA0FBiarYakpcWMZx2UAeXFuviPi3Xh+uPPb935M1w3jfnzaydpYs76iIgYsW6nLJxSSlRzgG45612zbavJPsyS/UDzJWliRPQtdhyNlT+/defPcN008c8vn+/jijKzJZUBHYB5hQimlJr+JgA9JW0tqTVwLDCuyDGZmTVH+XwfjwMGZ8tHAf+OiChEMCVTo4qIcklnAg8DLYGbImJqkcMyM2t2qvs+lnQZMDEixgE3ArdKmgHMJyWzglCBEqDVQtLQUm4TLnX+/NadP8N148+v4ThRmZlZSSule1RmZmZrcKIyM7OSVjKdKZo6SduTnuSueGhuDjAuIqYXLyprLrLfvy7AcxGxKGf7IRHxUPEiaxwk7QFEREzIhhI6BHg1Ih4ocmjNgmtUDUDS+aQhSAQ8n70EjPbgu+tO0inFjqGUSfohcD9wFjBFUu5QOFcUJ6rGQ9IvgGuB6yT9Gvgj0B64QNJFRQ2umXBnigYg6XVgp4hYXml7a2BqRPQsTmRNg6R3I2LLYsdRqiS9AuwdEYuyEa7vAm6NiGskvRgRuxU1wBKXfX59gDbA+0DXiPhUUjtSDXXXogbYDLjpr2GsBLYA3qm0vXO2z2oh6eXqdgGbNWQsjVCLiua+iHhbUn/gLklbkT4/q1l5RKwAlkh6MyI+BYiIpZL8/7cBOFE1jB8Bj0p6g1UDPW4J9ADOLFpUjctmwMHAJ5W2C3i64cNpVD6Q1CciJgNkNavDgZuAXYobWqOwTNJ6EbEE2L1io6QO+A/NBuGmvwYiqQVp6PzczhQTsr/UrBaSbgRujoinqth3e0R8rwhhNQqSupJqBe9XsW+fiPhvEcJqNCS1iYgvqtjeCegcEa8UIaxmxYnKzMxKmnv9mZlZSXOiMjOzkuZEZWZmJc2JypoESSskTZY0RdKdktarh3OeLOmP9RFffZI0TNK5dTxmUe2lzEqTE5U1FUsjok9E7AwsA36Q74GSWtZ3MIU4p1lz5URlTdGTpGfUkHSfpEmSpkoaWlFA0iJJv5P0ErC3pH6Snpb0kqTnJW2QFd1C0kOS3pB0ZU0XreKc+0t6UdIrkm6S1CYr93bWtRlJfSU9ni0Py8o9LmlmNvRRxbkvkvS6pKeA7XK2b5vFN0nSk9mYfmQzsz6TXfvydf9IzYrHicqaFEllwKFAxbMtp0bE7kBf4IeSOmbb25OGv+lNGntxLHB2tn4AsDQr1wcYRHowdpCkbjVcPvecE4GRwKCI2IX0cP3/y+MtbE96sHkP4BeSWknanTR7ah/gMKBfTvkRwFnZezwX+HO2/Rrguuzac/O4rlnJcqKypqKdpMmkBPEuaZpsSMnpJeBZoBtQMa7iCuDubHk7YG5ETACIiE8jojzb92hELIyIz4FpwFY1xFD5nG9FxOvZ+ijgG3m8j39ExBcR8THwIWlEjv8B7o2IJdnwPeMAJK0PfA24M3vvfyENywWwDzA6W741j+ualSwPoWRNxdKI6JO7IRvT7gDSgKxLsia2ttnuz/McFSR3RIIV1Px/Jt9zlrPqj8S2lfbV5XotgAWV33cOP81vTYJrVNaUdQA+yZLU9sBe1ZR7DegsqR+ApA2yJsR18RrQXVKPbP1E4D/Z8tusGjPuyDzO9QTwbUntsntnR0Cq+QFvSTo6i1uSemfH/JfUXAhw/Lq8EbNic6KypuwhoEzSdGA4qflvDRGxjHQf6g9ZM+EjrFnTqZOsqfAUUrPcK6TBS6/Pdl8KXCNpIqnWVNu5XiDdQ3sJeBCYkLP7eGBIFvdU0uScAGcDZ2TX7oJZI+ax/szMrKS5RmVmZiXNnSnM6kjSc6TZXnOd6OkezArDTX9mZlbS3PRnZmYlzYnKzMxKmhOVmZmVNCcqMzMraU5UZmZW0v4/iUEUeVuRfJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Parch_rounded'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 運賃(Fare)\n",
    "* 運賃が上がると生存率も上がる傾向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Survived  count\n",
      "Fare                     \n",
      "00_0-8       14.1%     92\n",
      "01_8-8       29.9%     87\n",
      "02_8-8       17.9%    106\n",
      "03_8-10      23.1%     78\n",
      "04_10-14     42.9%     84\n",
      "05_14-22     42.0%     88\n",
      "06_22-27     51.7%     87\n",
      "07_27-42     38.1%     97\n",
      "08_42-78     52.9%     85\n",
      "09_78-512    75.9%     87\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Fare'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 乗船港(Embarked)\n",
    "* 生存に乗船港は関係なさそうだがC(55.4%) > Q(39.0%) > S(33.7%)の順"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Survived  count\n",
      "Embarked                \n",
      "C           55.4%    168\n",
      "NaN        100.0%      2\n",
      "Q           39.0%     77\n",
      "S           33.7%    644\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Embarked'\n",
    "plot_survival_rate(feature, titanic_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 乗船港 x Pclass\n",
    "* 乗船港 <-> 所得水準 <-> Passenger classといった関係があるのか？\n",
    "  * Cの半数以上(52.2%)は1st\n",
    "  * Qの9割以上が3rd\n",
    "  * Qは3rdが多い割に生存率が高い\n",
    "  * 乗船港によって客室の位置が違っておりそれが生存率に影響している？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked       C    Q    S\n",
      "Pclass_name               \n",
      "1st          141    3  177\n",
      "2nd           28    7  242\n",
      "3rd          101  113  495\n",
      "\n",
      "Embarked        C     Q     S\n",
      "Pclass_name                  \n",
      "1st         0.522 0.024 0.194\n",
      "2nd         0.104 0.057 0.265\n",
      "3rd         0.374 0.919 0.542\n"
     ]
    }
   ],
   "source": [
    "cross_df = pd.crosstab(titanic_all['Pclass_name'], titanic_all['Embarked'])\n",
    "print(cross_df.to_string())\n",
    "print()\n",
    "\n",
    "cross_df = pd.crosstab(titanic_all['Pclass_name'], titanic_all['Embarked'], normalize='columns')\n",
    "print(cross_df.to_string(float_format='%.3f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'C'), Text(0, 0, 'Q'), Text(0, 0, 'S')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 100%積み上げ棒グラフ\n",
    "fig, ax = plt.subplots()\n",
    "data_points = np.arange(len(cross_df.columns))\n",
    "\n",
    "# 一番下の層のbarplot\n",
    "ax.bar(data_points, cross_df.iloc[0])\n",
    "\n",
    "# 二番目以降のbarplot。bottomに1番目の層の値を指定している。\n",
    "ax.bar(data_points, cross_df.iloc[1], bottom=cross_df.iloc[0])\n",
    "ax.bar(data_points, cross_df.iloc[2], bottom=cross_df.iloc[0]+cross_df.iloc[1])\n",
    "\n",
    "ax.legend(cross_df.index)\n",
    "\n",
    "ax.set_xticks(data_points)\n",
    "ax.set_xticklabels(cross_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C    270\n",
       "Q    123\n",
       "S    914\n",
       "Name: PassengerId, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_all.groupby('Embarked').count()['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 欠損値\n",
    "* 欠損値が2件(100%)ある\n",
    "  * 2人ともPclassが1stなので条件付き確率からSで補完する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Type</th>\n",
       "      <th>Pclass_name</th>\n",
       "      <th>IsAgeEstimated</th>\n",
       "      <th>SibSp_rounded</th>\n",
       "      <th>Parch_rounded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>38.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113572</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>62.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113572</td>\n",
       "      <td>train</td>\n",
       "      <td>1st</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked  Fare                                       Name  \\\n",
       "61   38.0   B28      NaN  80.0                        Icard, Miss. Amelie   \n",
       "829  62.0   B28      NaN  80.0  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "     Parch  PassengerId  Pclass     Sex  SibSp  Survived  Ticket   Type  \\\n",
       "61       0           62       1  female      0       1.0  113572  train   \n",
       "829      0          830       1  female      0       1.0  113572  train   \n",
       "\n",
       "    Pclass_name  IsAgeEstimated  SibSp_rounded  Parch_rounded  \n",
       "61          1st               0              0              0  \n",
       "829         1st               0              0              0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = titanic_all['Embarked'].isnull()\n",
    "titanic_all[f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(S|1st)=0.549, P(C|1st)=0.436, P(Q|1st)=0.009\n"
     ]
    }
   ],
   "source": [
    "# チケットクラス = 1stの下での乗船港の条件付き確率\n",
    "p_s = 914 / 1307\n",
    "p_c = 270 / 1307\n",
    "p_q = 123 / 1307\n",
    "\n",
    "p_1st = (107+216) / 1307\n",
    "\n",
    "p_1st_s = 0.194\n",
    "p_1st_c = 0.522\n",
    "p_1st_q = 0.024\n",
    "\n",
    "p_s_1st = p_1st_s * p_s / p_1st\n",
    "p_c_1st = p_1st_c * p_c / p_1st\n",
    "p_q_1st = p_1st_q * p_q / p_1st\n",
    "\n",
    "print('P(S|1st)={:.3f}, P(C|1st)={:.3f}, P(Q|1st)={:.3f}'.format(p_s_1st, p_c_1st, p_q_1st))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "titanic",
   "language": "python",
   "name": "titanic"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}