Titanic_with_name_sex_age_and_ticket_features-0.82275.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd #数据分析\n",
    "import numpy as np #科学计算\n",
    " \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pylab\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "sns.set_style(\"whitegrid\") \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 模型训练策略的开关\n",
    "WITH_MISSING_VALUES = True\n",
    "FIXED_CABIN_BY_TICKET_GROUP = False\n",
    "FIXED_CABIN_BY_TICKET_AND_SURNAME = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dealWithNameFeature(dataset):\n",
    "    # get Title feature\n",
    "    dataset['Title'] = dataset.Name.apply(lambda x:getTitle(x))\n",
    "    # get Surname feature\n",
    "    dataset['Surname'] = dataset.Name.apply(lambda x: x.split(',')[0].strip())\n",
    "\n",
    "def getSurnameGroupId(dataset): \n",
    "    dataset['SurnameGroupId'] = dataset.apply(lambda row : str(row.Surname + '-' + str(row.Pclass) + '-' + str(row.Embarked) + '-' + str(row.Fare) + '-' + row.Ticket[:-2] + '__'), axis = 1)\n",
    "\n",
    "def getTicketGroupId(dataset):    \n",
    "    dataset['TicketGroupId'] = dataset.apply(lambda row : str(str(row.Pclass) + '-' + str(row.Embarked) + '-' + str(row.Fare) + '-' + row.Ticket), axis = 1)\n",
    "\n",
    "def getSurnameGroupSize(dataset): \n",
    "    surname_group_value_count = dataset.SurnameGroupId.value_counts()\n",
    "    dataset['SurnameGroupSize'] = dataset.apply(lambda row : surname_group_value_count.loc[row.SurnameGroupId], axis=1)\n",
    "    \n",
    "def addNanniesCousinsFriends(dataset, only_boy_female = False, verbose = False):\n",
    "    '''\n",
    "    @dataset\n",
    "    @only_boy_female: only adding solo boys and females to surname groups if only_boy_female is True\n",
    "                otherwise: adding solo passengers to surname groups\n",
    "    '''\n",
    "    if only_boy_female:\n",
    "        solo_passengers = dataset.loc[(dataset.SurnameGroupSize == 1) & (index_boy | index_female)]\n",
    "    else:\n",
    "        solo_passengers = dataset.loc[(dataset.SurnameGroupSize == 1)]\n",
    "    if verbose:\n",
    "        print('Tottaly %d solo passengers satifies the condtions' % solo_passengers.shape[0])\n",
    "    nannies_cousins_firends = set()\n",
    "    count_nannies_cousins_firends = 0\n",
    "    for index, solo_passenger in solo_passengers.iterrows():\n",
    "        items = dataset.loc[(dataset.TicketGroupId == solo_passenger.TicketGroupId) & (dataset.PassengerId != solo_passenger.PassengerId)]\n",
    "        if items.shape[0] > 0:\n",
    "            count_nannies_cousins_firends += 1\n",
    "            nannies_cousins_firends.add(solo_passenger.SurnameGroupId)\n",
    "            nannies_cousins_firends.add(items.iloc[0,:].SurnameGroupId)\n",
    "            dataset.loc[dataset.PassengerId == solo_passenger.PassengerId, 'SurnameGroupId'] = items.iloc[0,:].SurnameGroupId\n",
    "    if verbose:\n",
    "        print('Totally adding %d  nannies, cousins and firends to surname groups' % count_nannies_cousins_firends)\n",
    "    return nannies_cousins_firends\n",
    "        \n",
    "def engineeringFeatures(dataset = None, verbose = False):\n",
    "    dealWithNameFeature(dataset)\n",
    "    getSurnameGroupId(dataset)\n",
    "    getTicketGroupId(dataset)\n",
    "    getSurnameGroupSize(dataset)\n",
    "    addNanniesCousinsFriends(dataset, verbose = verbose, only_boy_female=True)\n",
    "    # resize the surname group size \n",
    "    getSurnameGroupSize(dataset)\n",
    "    \n",
    "def getSurnameGroupSurvived(dataset, indicies_train_set):\n",
    "    '''\n",
    "    get the 'SurnameGroupSurvived' feature in train set \n",
    "    @dataset : combination of train set and test set \n",
    "    @indices_train : indices of train set \n",
    "    '''\n",
    "    dataset['SurnameGroupSurvived'] = np.NaN\n",
    "    surname_group_survived_mean = dataset.loc[indicies_train_set, ['SurnameGroupId','Survived']].groupby(['SurnameGroupId']).Survived.mean()\n",
    "    dataset.loc[indicies_train_set, ['SurnameGroupSurvived']] = dataset.loc[indicies_train_set,'SurnameGroupId'].apply(lambda x: surname_group_survived_mean.loc[x])\n",
    "    \n",
    "def setSurnameGroupSurvivedInTestSet(dataset, indices_test_set):\n",
    "    '''\n",
    "    set the 'SurnameGroupSurvived' feature in test set \n",
    "    @dataset : combination of train set and test set \n",
    "    @indices_test_set : indices of test set \n",
    "    ''' \n",
    "    for idx in indices_test_set:\n",
    "        tmp = dataset.loc[(dataset.SurnameGroupId == dataset.loc[idx, 'SurnameGroupId']) & (dataset.SurnameGroupSurvived.notnull()), ['SurnameGroupSurvived']]\n",
    "        if tmp.shape[0] > 0:\n",
    "            dataset.loc[idx, 'SurnameGroupSurvived'] = tmp.values[0]\n",
    "        if (pd.isna(dataset.loc[idx, 'SurnameGroupSurvived'])):\n",
    "            if dataset.loc[idx, 'Pclass']  == 3:\n",
    "                dataset.loc[idx, 'SurnameGroupSurvived'] = 1.0\n",
    "            else:\n",
    "                dataset.loc[idx, 'SurnameGroupSurvived'] = 0.0\n",
    "\n",
    "def getWomanChildrenGroupSurvived(dataset, indicies_train_set):\n",
    "    '''\n",
    "    get the 'SurnameGroupSurvived' feature in train set\n",
    "    @dataset : combination of train set and test set\n",
    "    @indices_train : indices of train set\n",
    "    '''\n",
    "    dataset['WomanChildGroupSurvived'] = np.NaN\n",
    "    indices_female = (dataset.Sex == 'female') \n",
    "    indices_boy = (dataset.Title == 'Master') | ((dataset.Sex == 'male') & (dataset.Age <= children_age_upper_limit))\n",
    "    indices_WCG = (dataset.SurnameGroupSize > 1) & (indices_female | indices_boy)\n",
    "    \n",
    "    # Not to group sb, according to analysis above\n",
    "    dataset['WomanChildGroupId'] = dataset.SurnameGroupId\n",
    "    dataset.loc[~indices_WCG, 'WomanChildGroupId'] = 'NoGroup'  \n",
    "    \n",
    "    ## woman-child-group \n",
    "    woman_child_group_survived_mean = dataset.loc[indicies_train_set, ['WomanChildGroupId','Survived']].groupby(['WomanChildGroupId']).Survived.mean()\n",
    "    dataset.loc[indicies_train_set, 'WomanChildGroupSurvived'] = dataset.loc[indicies_train_set, 'WomanChildGroupId'].apply(lambda x: woman_child_group_survived_mean.loc[x])\n",
    "    \n",
    "    \n",
    "def setWomenChildGroupSurvivedInTestSet(dataset, indices_test_set):\n",
    "    '''\n",
    "    set the 'SurnameGroupSurvived' feature in test set\n",
    "    @dataset : combination of train set and test set\n",
    "    @indices_test_set : indices of test set\n",
    "    '''\n",
    "    for idx in indices_test_set:\n",
    "        tmp = dataset.loc[(dataset.WomanChildGroupId == dataset.loc[idx,'WomanChildGroupId']) & (dataset.WomanChildGroupSurvived.notnull()), 'WomanChildGroupSurvived'] \n",
    "        if tmp.shape[0] > 0:\n",
    "            dataset.loc[idx, 'WomanChildGroupSurvived'] = tmp.values[0]\n",
    "        if (pd.isna(dataset.loc[idx, 'WomanChildGroupSurvived'])):\n",
    "            if dataset.loc[idx, 'Pclass'] == 3:\n",
    "                dataset.loc[idx, 'WomanChildGroupSurvived'] = 0\n",
    "            else:\n",
    "                dataset.loc[idx, 'WomanChildGroupSurvived'] = 1\n",
    "\n",
    "def predictionByWomanBoyRules(dataset):\n",
    "    '''\n",
    "    the results of prediction stored in 'Predict' feature in the dataset\n",
    "    @dataset : combination of train set and test set\n",
    "    '''\n",
    "    indices_female = (dataset.Sex == 'female') \n",
    "    indices_boy = (dataset.Title == 'Master') | ((dataset.Sex == 'male') & (dataset.Age <= children_age_upper_limit))\n",
    "    indices_male_adult = (all_data.SurnameGroupSize > 1) & (~index_boy) & (all_data.Sex == 'male')\n",
    "    \n",
    "    dataset['Predict'] = 0.0\n",
    "    dataset.loc[index_female, 'Predict'] = 1.0\n",
    "    dataset.loc[(dataset.WomanChildGroupSurvived == 0.0) & index_female & (dataset.SurnameGroupSize > 1), 'Predict'] = 0.0\n",
    "    dataset.loc[(dataset.WomanChildGroupSurvived == 1.0) & index_boy & (dataset.SurnameGroupSize > 1), 'Predict'] = 1.0\n",
    "    \n",
    "    #dataset.loc[index_female | index_boy, 'Predict'] = 1.0\n",
    "    #dataset.loc[(dataset.WomanChildGroupSurvived == 0.0) & (index_female | index_boy) & (dataset.SurnameGroupSize > 1), 'Predict'] = 0.0\n",
    "    #dataset.loc[indices_male_adult & (dataset.SurnameGroupSurvived == 1) & (dataset.Pclass == 1), 'Predict'] = 1.0\n",
    "    \n",
    "    \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('./data/train.csv')\n",
    "test = pd.read_csv('./data/test.csv')\n",
    "\n",
    "all_data = pd.concat([train, test], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"./data/train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "字段有：\n",
    "1. Passengerld => 乘客ID \n",
    "2. Pclass => 乘客等级(1/2/3等舱位) \n",
    "3. Name => 乘客姓名 \n",
    "4. Sex => 性别 \n",
    "5. Age => 年龄 \n",
    "6. SibSp => 堂兄弟/妹个数 \n",
    "7. Parch => 父母与小孩个数 \n",
    "8. Ticket => 船票信息 \n",
    "9. Fare => 票价 \n",
    "10. Cabin => 客舱 \n",
    "11. Embarked => 登船港口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看数据集基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练数据总共有891名乘客，但是很不幸，我们有些属性的数据不全\n",
    "1. Age(年龄)属性只有714名乘客有记录 \n",
    "2. Cain(客舱)只有204名乘客是已知的；\n",
    "3. Embarked 属性只缺2个\n",
    "\n",
    "看看测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.info()\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试数据中总共有481名乘客\n",
    "1. 有更多缺失值Age(年龄)：86个缺失\n",
    "2. Fare属性缺1位乘客的信息 \n",
    "3. Cabin(客舱)只有91名乘客是已知的；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             263\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看特征的分布信息"
   ]
  },
  {
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=\"red\">查看特征与标签关系</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticket_value_counts = dict(all_data.Ticket.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['TicketSize'] = all_data.Ticket.apply(lambda x : ticket_value_counts[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['SingleTicketFare'] = all_data.apply(lambda row : row.Fare / row.TicketSize, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     7.25000\n",
       "1    35.64165\n",
       "2     7.92500\n",
       "3    26.55000\n",
       "4     8.05000\n",
       "Name: SingleTicketFare, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['SingleTicketFare'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = all_data.iloc[:train.shape[0]]\n",
    "test = all_data.iloc[train.shape[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Survived\n",
      "CategoricalFare          \n",
      "(-0.001, 6.238]  0.300000\n",
      "(6.238, 7.245]   0.244898\n",
      "(7.245, 7.775]   0.302752\n",
      "(7.775, 7.925]   0.255556\n",
      "(7.925, 8.872]   0.214286\n",
      "(8.872, 13.0]    0.403614\n",
      "(13.0, 25.576]   0.500000\n",
      "(25.576, 30.0]   0.673469\n",
      "(30.0, 128.082]  0.622449\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "train['CategoricalFare'] = pd.qcut(train['SingleTicketFare'], 9)\n",
    "fare_survived = train.loc[:, ['CategoricalFare', 'Survived']].groupby(['CategoricalFare']).mean()\n",
    "print(fare_survived)\n",
    "plt.xticks(rotation=270)\n",
    "plt.rcParams['figure.figsize'] =(5, 5)\n",
    "sns.barplot(x='CategoricalFare', y='Survived', data=train)\n",
    "train.drop(['CategoricalFare'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex\n",
    "\n",
    "电影里面，援救过程中：女士，小孩优先"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Sex  Survived\n",
      "0  female  0.742038\n",
      "1    male  0.188908\n"
     ]
    }
   ],
   "source": [
    "print(train[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d588d6b240>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Sex\", y=\"Survived\", data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也就是说，依据性别全部判断女性幸存，男性遇难，训练集正确率都有 \n",
    "\n",
    "$$\\big[577 \\times (1 - 0.188908) + 314 \\times 0.742038\\big] / 891 = 78.67\\%$$\n",
    "\n",
    "当然这只是训练数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age\n",
    "\n",
    "由前面可知，Age缺失严重，但是不妨碍我们根据女士儿童优先探索一下，年龄分段到底影响结果与否？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "children_age_upper_limit = 12\n",
    "def getAgeSegment(age):\n",
    "    age_seg = 5\n",
    "    if age <= children_age_upper_limit:\n",
    "        age_seg = 1 # aldolescents\n",
    "    elif age <= 32 :\n",
    "        age_seg = 2\n",
    "    elif age <= 48 :\n",
    "        age_seg = 3\n",
    "    elif age <= 65 : # almost retirement age\n",
    "        age_seg = 4\n",
    "\n",
    "    return np.int32(age_seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Survived</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Survived     Sex\n",
       "0  22.0       0.0    male\n",
       "1  38.0       1.0  female\n",
       "2  26.0       1.0  female\n",
       "3  35.0       1.0  female\n",
       "4  35.0       0.0    male"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeNotNull = train.loc[train.Age.notnull(), ['Age', 'Survived', 'Sex']]\n",
    "AgeNotNull.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Survived</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Survived     Sex\n",
       "0     2       0.0    male\n",
       "1     3       1.0  female\n",
       "2     2       1.0  female\n",
       "3     3       1.0  female\n",
       "4     3       0.0    male\n",
       "6     4       0.0    male\n",
       "7     1       0.0    male\n",
       "8     2       1.0  female\n",
       "9     2       1.0  female\n",
       "10    1       1.0  female"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeNotNull.Age = AgeNotNull.Age.apply(lambda x: getAgeSegment(x))\n",
    "AgeNotNull.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.579710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.379310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.404255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Survived\n",
       "0    1  0.579710\n",
       "1    2  0.379310\n",
       "2    3  0.404255\n",
       "3    4  0.416667\n",
       "4    5  0.125000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeNotNull.groupby(by='Age', as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d588dd9048>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Age', y='Survived', data = AgeNotNull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.567568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0.744526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.170833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.794118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Sex  Survived\n",
       "0    1  female  0.593750\n",
       "1    1    male  0.567568\n",
       "2    2  female  0.744526\n",
       "3    2    male  0.170833\n",
       "4    3  female  0.794118\n",
       "5    3    male  0.183333\n",
       "6    4  female  0.916667\n",
       "7    4    male  0.166667\n",
       "8    5    male  0.125000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeNotNull.groupby(by=['Age', 'Sex'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上表我们观察到：\n",
    "1. 只要是女的，幸存率都很高\n",
    "2. 男的里面属于最小年龄段的幸存率是其他段的3倍多，其他年龄段男生直接预测死亡，准确率都有 $80\\%$ 出头\n",
    "\n",
    "<label align='center'><h3> 目标 </h3></label>\n",
    "\n",
    "那么我们得出这样的<font color='red'>**目标**</font>：<font color='blue'><strong>默认预测女性都存活，男性都遇难，再加上以下：</strong></font>\n",
    "\n",
    "1. <font color='blue'>预测什么样的女性会遇难，毕竟少数，如果能够预测对，正确率就会极大提高。</font>\n",
    "\n",
    "2. <font color='blue'>预测什么样的男孩（最小年龄段的男性）会幸存，也是少数，如果能够预测对，正确率就会极大提高。</font>\n",
    "\n",
    "3. <font color='blue'>不是第一个年龄段的男性什么条件下会存活？</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                              Braund, Mr. Owen Harris\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                               Heikkinen, Miss. Laina\n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                             Allen, Mr. William Henry\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Name'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name特征含3个字段：\n",
    "1. 姓氏\n",
    "2. 称谓Title\n",
    "3. 名字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提取称谓字段Title "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re as re # 正则包提取\n",
    "\n",
    "def getTitle(name):\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    # If the title exists, extract and return it.\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "\n",
    "all_data['Title'] = all_data['Name'].apply(getTitle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Master\n",
    "\n",
    "启发于 [Chris Deotte](https://www.kaggle.com/cdeotte) 的kernel: [Titanic using Name only [0.81818]\n",
    "](https://www.kaggle.com/cdeotte/titanic-using-name-only-0-81818)，于是探索了一下，`Master` 有何特点？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Title_Master_DataFrame = all_data.loc[all_data['Title']=='Master']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male    61\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Title_Master_DataFrame.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Master`全是男的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.00     5\n",
       "4.00     5\n",
       "1.00     5\n",
       "9.00     5\n",
       "6.00     4\n",
       "3.00     4\n",
       "7.00     3\n",
       "8.00     3\n",
       "13.00    3\n",
       "0.83     3\n",
       "11.00    2\n",
       "10.00    2\n",
       "11.50    1\n",
       "0.75     1\n",
       "14.50    1\n",
       "5.00     1\n",
       "12.00    1\n",
       "0.33     1\n",
       "0.42     1\n",
       "0.92     1\n",
       "0.67     1\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Title_Master_DataFrame.Age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Master`全是小于15岁的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    23\n",
       "0.0    17\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Title_Master_DataFrame.Survived.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看看是不是所有的男孩都叫 `Master`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(all_data.Title == 'Master')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy = all_data.loc[(all_data.Age <= children_age_upper_limit) & (all_data.Sex == 'male')]\n",
    "boy.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(boy.Title == 'Master')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <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>TicketSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>2</td>\n",
       "      <td>9.39375</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare                      Name  Parch  \\\n",
       "731  11.0   NaN        C  18.7875  Hassan, Mr. Houssein G N      0   \n",
       "\n",
       "     PassengerId  Pclass   Sex  SibSp  Survived Ticket  TicketSize  \\\n",
       "731          732       3  male      0       0.0   2699           2   \n",
       "\n",
       "     SingleTicketFare Title  \n",
       "731           9.39375    Mr  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.loc[boy.Title != 'Master']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>TicketSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>2</td>\n",
       "      <td>9.39375</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare                      Name  Parch  \\\n",
       "731  11.0   NaN        C  18.7875  Hassan, Mr. Houssein G N      0   \n",
       "\n",
       "     PassengerId  Pclass   Sex  SibSp  Survived Ticket  TicketSize  \\\n",
       "731          732       3  male      0       0.0   2699           2   \n",
       "\n",
       "     SingleTicketFare Title  \n",
       "731           9.39375    Mr  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data.Name.str.contains('Hassan')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>TicketSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>18.7875</td>\n",
       "      <td>Albimona, Mr. Nassef Cassem</td>\n",
       "      <td>0</td>\n",
       "      <td>208</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2699</td>\n",
       "      <td>2</td>\n",
       "      <td>9.39375</td>\n",
       "      <td>Mr</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>2</td>\n",
       "      <td>9.39375</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare                         Name  Parch  \\\n",
       "207  26.0   NaN        C  18.7875  Albimona, Mr. Nassef Cassem      0   \n",
       "731  11.0   NaN        C  18.7875     Hassan, Mr. Houssein G N      0   \n",
       "\n",
       "     PassengerId  Pclass   Sex  SibSp  Survived Ticket  TicketSize  \\\n",
       "207          208       3  male      0       1.0   2699           2   \n",
       "731          732       3  male      0       0.0   2699           2   \n",
       "\n",
       "     SingleTicketFare Title  \n",
       "207           9.39375    Mr  \n",
       "731           9.39375    Mr  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data.Ticket.str.match('^2699$')] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "无法确定他们是不是亲戚关系？如果不是就是这个11岁小男孩单独出行。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Title的性别分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
       "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'Countess',\n",
       "       'Jonkheer', 'Dona'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.Title.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the Countess {}\n",
      "Miss {'female': 260}\n",
      "Mme {'female': 1}\n",
      "Countess {'female': 1}\n",
      "Mrs {'female': 197}\n",
      "Lady {'female': 1}\n",
      "Mlle {'female': 2}\n",
      "Dona {'female': 1}\n",
      "Ms {'female': 2}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(466, 465)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_set = set([\"Dona\",'the Countess','Mme',\"Mlle\",\"Ms\",\"Miss\",\"Lady\",\"Mrs\", 'Countess'])\n",
    "tmp_dict = {'female': 0}\n",
    "for item in a_set:\n",
    "    new_dict = dict(all_data.loc[all_data.Title == item, :].Sex.value_counts())\n",
    "    print(item, new_dict)\n",
    "    if 'female' in set(new_dict.keys()):\n",
    "        tmp_dict['female'] +=new_dict['female'] \n",
    "    \n",
    "all_data.loc[all_data.Sex == 'female'].shape[0], tmp_dict['female'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "少了一位女性？看看以下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Major {'male': 2}\n",
      "Col {'male': 4}\n",
      "Capt {'male': 1}\n",
      "Master {'male': 61}\n",
      "Sir {'male': 1}\n",
      "Rev {'male': 8}\n",
      "Dr {'male': 7, 'female': 1}\n",
      "Mr {'male': 757}\n",
      "Jonkheer {'male': 1}\n",
      "Don {'male': 1}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(843, 843)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_set = set([\"Capt\",\"Don\",\"Major\",\"Col\",\"Rev\",\"Dr\",\"Sir\",\"Mr\",\"Jonkheer\",'Master'])\n",
    "\n",
    "tmp_dict = {'male': 0}\n",
    "for item in a_set:\n",
    "    new_dict = dict(all_data.loc[all_data.Title == item, :].Sex.value_counts())\n",
    "    print(item, new_dict)\n",
    "    if 'male' in set(new_dict.keys()):\n",
    "        tmp_dict['male'] +=new_dict['male'] \n",
    "    \n",
    "all_data.loc[all_data.Sex == 'male'].shape[0], tmp_dict['male'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里有一位女Dr。\n",
    "\n",
    "**前文我们已经看到女性幸存率和男孩较高，这里我们提取他们的位置：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_female = (all_data.Sex == 'female')\n",
    "\n",
    "index_boy = ((all_data.Title == 'Master') | ((all_data.Age <= children_age_upper_limit) & (all_data.Sex == 'male')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提取姓氏字段 Surname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Surname']=all_data['Name'].apply(lambda x:x.split(',')[0].strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提取FamilySize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按姓氏统计的家族成员数，这里属于**粗粒度划分**，因为不同家庭同姓氏也是有可能的！\n",
    "\n",
    "<font color='red'>**为什么要统计家庭成员？**</font>\n",
    "有句老话：在家靠父母，出门靠朋友。在援救过程中，势必一起出行的人或多或少会互相帮助，那么对生存预测就有影响。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Surname_Count = dict(all_data['Surname'].value_counts()) \n",
    "all_data['FamilySize'] = all_data['Surname'].apply(lambda x:Surname_Count[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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",
       "      <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>TicketSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>Title</th>\n",
       "      <th>Surname</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1</td>\n",
       "      <td>7.25000</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Braund</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2</td>\n",
       "      <td>35.64165</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "      <td>7.92500</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2</td>\n",
       "      <td>26.55000</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1</td>\n",
       "      <td>8.05000</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Allen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "3  35.0  C123        S  53.1000   \n",
       "4  35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                           Allen, Mr. William Henry      0            5   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket  TicketSize  \\\n",
       "0       3    male      1       0.0         A/5 21171           1   \n",
       "1       1  female      1       1.0          PC 17599           2   \n",
       "2       3  female      0       1.0  STON/O2. 3101282           1   \n",
       "3       1  female      1       1.0            113803           2   \n",
       "4       3    male      0       0.0            373450           1   \n",
       "\n",
       "   SingleTicketFare Title    Surname  FamilySize  \n",
       "0           7.25000    Mr     Braund           2  \n",
       "1          35.64165   Mrs    Cumings           2  \n",
       "2           7.92500  Miss  Heikkinen           1  \n",
       "3          26.55000   Mrs   Futrelle           2  \n",
       "4           8.05000    Mr      Allen           2  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = all_data.iloc[train.shape[0]:, :]\n",
    "train = all_data.iloc[:train.shape[0], :]\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "406"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data.FamilySize > 2, :].sort_values(['Surname', 'Fare', 'Ticket']).shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**姓氏一共多少种？**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(875, 667, 352)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_data.Surname.unique()), len(train.Surname.unique()), len(test.Surname.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 同姓却是不同家庭\n",
    "\n",
    "启发于：[Erik Bruin](https://www.kaggle.com/erikbruin) 的kernel: [Titanic: 2nd degree families and majority voting](https://www.kaggle.com/erikbruin/titanic-2nd-degree-families-and-majority-voting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surname</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abbing</th>\n",
       "      <th>C.A. 5547</th>\n",
       "      <th>7.5500</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Abbing, Mr. Anthony</th>\n",
       "      <th>42.0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Abbott</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">C.A. 2673</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">20.2500</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">S</th>\n",
       "      <th>Abbott, Master. Eugene Joseph</th>\n",
       "      <th>13.0</th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbott, Mr. Rossmore Edward</th>\n",
       "      <th>16.0</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbott, Mrs. Stanton (Rosa Hunt)</th>\n",
       "      <th>35.0</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Abelseth</th>\n",
       "      <th>348122</th>\n",
       "      <th>7.6500</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Abelseth, Mr. Olaus Jorgensen</th>\n",
       "      <th>25.0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348125</th>\n",
       "      <th>7.6500</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Abelseth, Miss. Karen Marie</th>\n",
       "      <th>16.0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Abelson</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">P/PP 3381</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">24.0000</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
       "      <th>Abelson, Mr. Samuel</th>\n",
       "      <th>30.0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abelson, Mrs. Samuel (Hannah Wizosky)</th>\n",
       "      <th>28.0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abrahamsson</th>\n",
       "      <th>SOTON/O2 3101284</th>\n",
       "      <th>7.9250</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Abrahamsson, Mr. Abraham August Johannes</th>\n",
       "      <th>20.0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abrahim</th>\n",
       "      <th>2657</th>\n",
       "      <th>7.2292</th>\n",
       "      <th>3</th>\n",
       "      <th>C</th>\n",
       "      <th>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</th>\n",
       "      <th>18.0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                 Survived\n",
       "Surname     Ticket           Fare    Pclass Embarked Name                                      Age  SibSp Parch          \n",
       "Abbing      C.A. 5547        7.5500  3      S        Abbing, Mr. Anthony                       42.0 0     0             1\n",
       "Abbott      C.A. 2673        20.2500 3      S        Abbott, Master. Eugene Joseph             13.0 0     2             0\n",
       "                                                     Abbott, Mr. Rossmore Edward               16.0 1     1             1\n",
       "                                                     Abbott, Mrs. Stanton (Rosa Hunt)          35.0 1     1             1\n",
       "Abelseth    348122           7.6500  3      S        Abelseth, Mr. Olaus Jorgensen             25.0 0     0             0\n",
       "            348125           7.6500  3      S        Abelseth, Miss. Karen Marie               16.0 0     0             0\n",
       "Abelson     P/PP 3381        24.0000 2      C        Abelson, Mr. Samuel                       30.0 1     0             1\n",
       "                                                     Abelson, Mrs. Samuel (Hannah Wizosky)     28.0 1     0             1\n",
       "Abrahamsson SOTON/O2 3101284 7.9250  3      S        Abrahamsson, Mr. Abraham August Johannes  20.0 0     0             0\n",
       "Abrahim     2657             7.2292  3      C        Abrahim, Mrs. Joseph (Sophie Halaut Easu) 18.0 0     0             0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame = all_data.groupby(['Surname', 'Ticket', 'Fare', 'Pclass', 'Embarked', 'Name', 'Age', 'SibSp', 'Parch'])[ 'Survived'].count().to_frame()\n",
    "data_frame.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**例1：姓氏为Andersson的乘客**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surname</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">Andersson</th>\n",
       "      <th>347075</th>\n",
       "      <th>7.7750</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Andersson, Mr. Johan Samuel</th>\n",
       "      <th>26.0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347091</th>\n",
       "      <th>7.7750</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Andersson, Miss. Ida Augusta Margareta</th>\n",
       "      <th>38.0</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350043</th>\n",
       "      <th>7.7958</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Andersson, Mr. August Edvard (\"Wennerstrom\")</th>\n",
       "      <th>27.0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101281</th>\n",
       "      <th>7.9250</th>\n",
       "      <th>3</th>\n",
       "      <th>S</th>\n",
       "      <th>Andersson, Miss. Erna Alexandra</th>\n",
       "      <th>17.0</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">347082</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">31.2750</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">S</th>\n",
       "      <th>Andersson, Master. Sigvard Harald Elias</th>\n",
       "      <th>4.0</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Miss. Ebba Iris Alfrida</th>\n",
       "      <th>6.0</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Miss. Ellis Anna Maria</th>\n",
       "      <th>2.0</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Miss. Ingeborg Constanzia</th>\n",
       "      <th>9.0</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Miss. Sigrid Elisabeth</th>\n",
       "      <th>11.0</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Mr. Anders Johan</th>\n",
       "      <th>39.0</th>\n",
       "      <th>1</th>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)</th>\n",
       "      <th>39.0</th>\n",
       "      <th>1</th>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                               Survived\n",
       "Surname   Ticket  Fare    Pclass Embarked Name                                               Age  SibSp Parch          \n",
       "Andersson 347075  7.7750  3      S        Andersson, Mr. Johan Samuel                        26.0 0     0             0\n",
       "          347091  7.7750  3      S        Andersson, Miss. Ida Augusta Margareta             38.0 4     2             0\n",
       "          350043  7.7958  3      S        Andersson, Mr. August Edvard (\"Wennerstrom\")       27.0 0     0             1\n",
       "          3101281 7.9250  3      S        Andersson, Miss. Erna Alexandra                    17.0 4     2             1\n",
       "          347082  31.2750 3      S        Andersson, Master. Sigvard Harald Elias            4.0  4     2             1\n",
       "                                          Andersson, Miss. Ebba Iris Alfrida                 6.0  4     2             1\n",
       "                                          Andersson, Miss. Ellis Anna Maria                  2.0  4     2             1\n",
       "                                          Andersson, Miss. Ingeborg Constanzia               9.0  4     2             1\n",
       "                                          Andersson, Miss. Sigrid Elisabeth                  11.0 4     2             1\n",
       "                                          Andersson, Mr. Anders Johan                        39.0 1     5             1\n",
       "                                          Andersson, Mrs. Anders Johan (Alfrida Konstanti... 39.0 1     5             1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = data_frame.loc[['Andersson']]\n",
    "tmp.sort_index(level=[0, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然这些人姓氏都是 `Andersson`， 但是他们却来自不同的家庭，证据：`Ticket, Fare` 差别很大，因此不能将他们简单地按姓氏分成一组。\n",
    "根据 `FamilySize = Parch + SipSp + 1`， 我们得到如下：\n",
    "1. 最后7个人数据能对上，铁定为至亲的一家人。\n",
    "2. 至于第2行与第4行的极有可能是他们的 `SibSp, Parch` 数据有误，有可能最后家庭成员取消出行，却这两列数据没有修改。\n",
    "    - 那么如果第2行的数据 `SibSp, Parch` 均为0，那么可能1,2行两个人有关系，可能是表亲或堂亲（毕竟只是票号最后两位不同），只是猜测，证据不足，。\n",
    "    - 然而 `3,4` 行的乘客数据与其他行差别较大。\n",
    "    \n",
    "<font color='red'>注意：</font> `SibSp, Parch` 两个特征数据经常有误，因此至亲的一家人的大小：`FamiliySize = SibSp + Parch + 1` 并不靠谱，后面例子也可以看到。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**例2：姓氏为Brown的乘客**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>Surname</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Brown</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>248733</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>F33</td>\n",
       "      <td>Brown, Miss. Amelia \"Mildred\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Brown</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>PC 17610</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B4</td>\n",
       "      <td>Brown, Mrs. James Joseph (Margaret Tobin)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>Brown</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>29750</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown, Mrs. Thomas William Solomon (Elizabeth ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>Brown</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>29750</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown, Mr. Thomas William Solomon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>Brown</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>29750</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown, Miss. Edith Eileen</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>Brown</td>\n",
       "      <td>51.4792</td>\n",
       "      <td>11769</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>C101</td>\n",
       "      <td>Brown, Mrs. John Murray (Caroline Lane Lamson)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Surname     Fare    Ticket  Pclass Embarked Cabin  \\\n",
       "345    Brown  13.0000    248733       2        S   F33   \n",
       "194    Brown  27.7208  PC 17610       1        C    B4   \n",
       "670    Brown  39.0000     29750       2        S   NaN   \n",
       "684    Brown  39.0000     29750       2        S   NaN   \n",
       "1066   Brown  39.0000     29750       2        S   NaN   \n",
       "1247   Brown  51.4792     11769       1        S  C101   \n",
       "\n",
       "                                                   Name  SibSp  Parch   Age  \\\n",
       "345                       Brown, Miss. Amelia \"Mildred\"      0      0  24.0   \n",
       "194           Brown, Mrs. James Joseph (Margaret Tobin)      0      0  44.0   \n",
       "670   Brown, Mrs. Thomas William Solomon (Elizabeth ...      1      1  40.0   \n",
       "684                   Brown, Mr. Thomas William Solomon      1      1  60.0   \n",
       "1066                          Brown, Miss. Edith Eileen      0      2  15.0   \n",
       "1247     Brown, Mrs. John Murray (Caroline Lane Lamson)      2      0  59.0   \n",
       "\n",
       "      PassengerId  \n",
       "345           346  \n",
       "194           195  \n",
       "670           671  \n",
       "684           685  \n",
       "1066         1067  \n",
       "1247         1248  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[(all_data.Surname == 'Brown')].sort_values(by=['Surname', 'Fare', 'Ticket'])\n",
    "\n",
    "tmp.loc[:, ['Surname', 'Fare', 'Ticket', 'Pclass', 'Embarked', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age', 'PassengerId']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然这些人姓氏都是 `Brown`，但是根据 `Fare, Ticket, Pclass, Embarked, SibSp, Parch` 差别明显，只有第3~5行为一个至亲家庭，即这里面有来自4个家庭的人。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**例3：姓氏为 Betros Attalah 的乘客**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Attalah</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>2694</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attalah, Mr. Sleiman</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Attalah</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>2627</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attalah, Miss. Malake</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Betros</td>\n",
       "      <td>4.0125</td>\n",
       "      <td>2648</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betros, Mr. Tannous</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>Betros</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>2622</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betros, Master. Seman</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Surname     Fare Ticket  Pclass Embarked Cabin                   Name  \\\n",
       "244   Attalah   7.2250   2694       3        C   NaN   Attalah, Mr. Sleiman   \n",
       "114   Attalah  14.4583   2627       3        C   NaN  Attalah, Miss. Malake   \n",
       "378    Betros   4.0125   2648       3        C   NaN    Betros, Mr. Tannous   \n",
       "1230   Betros   7.2292   2622       3        C   NaN  Betros, Master. Seman   \n",
       "\n",
       "      SibSp  Parch   Age  PassengerId  \n",
       "244       0      0  30.0          245  \n",
       "114       0      0  17.0          115  \n",
       "378       0      0  20.0          379  \n",
       "1230      0      0   NaN         1231  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[(all_data.Surname == 'Betros') | (all_data.Surname == 'Attalah')].sort_values(by=['Surname', 'Fare', 'Ticket'])\n",
    "\n",
    "tmp.loc[:, ['Surname', 'Fare', 'Ticket', 'Pclass', 'Embarked', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age', 'PassengerId']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同理，由于 `Fare, Ticket` 均差别较大，且`SibSp, Parch` 均为0，因此判断他们都是单独出行，不是一家人。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 堂兄弟堂姐妹（孙子辈）或者叔侄辈\n",
    "\n",
    "**姓氏相同，票价相同， 票号仅仅最后2位不同**，由于：`SibSp=0, Parch=0` 那么很有可能是**堂兄弟堂姐妹或者叔侄**？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <th>534</th>\n",
       "      <td>30.00</td>\n",
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       "      <td>Cacic, Miss. Marija</td>\n",
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       "      <td>3</td>\n",
       "      <td>female</td>\n",
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       "      <td>Miss</td>\n",
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       "      <th>928</th>\n",
       "      <td>21.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>Cacic, Miss. Manda</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
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       "      <td>315087</td>\n",
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       "      <td>Miss</td>\n",
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       "      <td>38.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>Cacic, Mr. Luka</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>Mr</td>\n",
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       "      <td>18.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>Cacic, Mr. Jego Grga</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315091</td>\n",
       "      <td>1</td>\n",
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       "      <td>Mr</td>\n",
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       "      <td>36.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Hocking, Mr. Samuel James Metcalfe</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242963</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>23.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>Hocking, Mr. Richard George</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29104</td>\n",
       "      <td>1</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>54.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>Hocking, Mrs. Elizabeth (Eliza Needs)</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29105</td>\n",
       "      <td>2</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>20.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>Hocking, Miss. Ellen Nellie\"\"</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29105</td>\n",
       "      <td>2</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>Miss</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>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29106</td>\n",
       "      <td>3</td>\n",
       "      <td>6.2500</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>24.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>Richards, Mrs. Sidney (Emily Hocking)</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29106</td>\n",
       "      <td>3</td>\n",
       "      <td>6.2500</td>\n",
       "      <td>Mrs</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>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29106</td>\n",
       "      <td>3</td>\n",
       "      <td>6.2500</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Cabin Embarked     Fare                                   Name  \\\n",
       "948   25.00  F G63        S   7.6500          Abelseth, Mr. Olaus Jorgensen   \n",
       "1236  16.00    NaN        S   7.6500            Abelseth, Miss. Karen Marie   \n",
       "210   24.00    NaN        S   7.0500                         Ali, Mr. Ahmed   \n",
       "784   25.00    NaN        S   7.0500                       Ali, Mr. William   \n",
       "534   30.00    NaN        S   8.6625                    Cacic, Miss. Marija   \n",
       "928   21.00    NaN        S   8.6625                     Cacic, Miss. Manda   \n",
       "471   38.00    NaN        S   8.6625                        Cacic, Mr. Luka   \n",
       "1201  18.00    NaN        S   8.6625                   Cacic, Mr. Jego Grga   \n",
       "1120  36.00    NaN        S  13.0000     Hocking, Mr. Samuel James Metcalfe   \n",
       "529   23.00    NaN        S  11.5000            Hocking, Mr. Richard George   \n",
       "774   54.00    NaN        S  23.0000  Hocking, Mrs. Elizabeth (Eliza Needs)   \n",
       "943   20.00    NaN        S  23.0000          Hocking, Miss. Ellen Nellie\"\"   \n",
       "407    3.00    NaN        S  18.7500         Richards, Master. William Rowe   \n",
       "437   24.00    NaN        S  18.7500  Richards, Mrs. Sidney (Emily Hocking)   \n",
       "831    0.83    NaN        S  18.7500        Richards, Master. George Sibley   \n",
       "\n",
       "      Parch  Pclass     Sex  SibSp  Survived              Ticket  TicketSize  \\\n",
       "948       0       3    male      0       NaN              348122           1   \n",
       "1236      0       3  female      0       NaN              348125           1   \n",
       "210       0       3    male      0       0.0  SOTON/O.Q. 3101311           1   \n",
       "784       0       3    male      0       0.0  SOTON/O.Q. 3101312           1   \n",
       "534       0       3  female      0       0.0              315084           1   \n",
       "928       0       3  female      0       NaN              315087           1   \n",
       "471       0       3    male      0       0.0              315089           1   \n",
       "1201      0       3    male      0       NaN              315091           1   \n",
       "1120      0       2    male      0       NaN              242963           1   \n",
       "529       1       2    male      2       0.0               29104           1   \n",
       "774       3       2  female      1       1.0               29105           2   \n",
       "943       1       2  female      2       NaN               29105           2   \n",
       "407       1       2    male      1       1.0               29106           3   \n",
       "437       3       2  female      2       1.0               29106           3   \n",
       "831       1       2    male      1       1.0               29106           3   \n",
       "\n",
       "      SingleTicketFare   Title  \n",
       "948             7.6500      Mr  \n",
       "1236            7.6500    Miss  \n",
       "210             7.0500      Mr  \n",
       "784             7.0500      Mr  \n",
       "534             8.6625    Miss  \n",
       "928             8.6625    Miss  \n",
       "471             8.6625      Mr  \n",
       "1201            8.6625      Mr  \n",
       "1120           13.0000      Mr  \n",
       "529            11.5000      Mr  \n",
       "774            11.5000     Mrs  \n",
       "943            11.5000    Miss  \n",
       "407             6.2500  Master  \n",
       "437             6.2500     Mrs  \n",
       "831             6.2500  Master  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[all_data.Surname.isin(['Ali', 'Cacic', 'Abelseth', 'Hocking', 'Richards']), :].sort_values(by=['Surname', 'Ticket', 'Fare'])\n",
    "\n",
    "tmp.drop(['Surname', 'FamilySize', 'PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>注意</font>： SibSp, Parch 两个特征数据经常有误，因此至亲的一家人的大小：FamiliySize = SibSp + Parch + 1 并不靠谱，后面例子也可以看到。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 至亲的一家人"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Surname</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Abbott</td>\n",
       "      <td>20.25</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.00</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>Abbott</td>\n",
       "      <td>20.25</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Abbott, Mr. Rossmore Edward</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.00</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>Abbott</td>\n",
       "      <td>20.25</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>West</td>\n",
       "      <td>27.75</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>West, Miss. Constance Mirium</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.00</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>West</td>\n",
       "      <td>27.75</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>West, Mr. Edwy Arthur</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>36.00</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>West</td>\n",
       "      <td>27.75</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>West, Mrs. Edwy Arthur (Ada Mary Worth)</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>33.00</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>West</td>\n",
       "      <td>27.75</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>West, Miss. Barbara J</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Surname   Fare      Ticket  Pclass Embarked  \\\n",
       "279   Abbott  20.25   C.A. 2673       3        S   \n",
       "746   Abbott  20.25   C.A. 2673       3        S   \n",
       "1283  Abbott  20.25   C.A. 2673       3        S   \n",
       "58      West  27.75  C.A. 34651       2        S   \n",
       "450     West  27.75  C.A. 34651       2        S   \n",
       "472     West  27.75  C.A. 34651       2        S   \n",
       "1141    West  27.75  C.A. 34651       2        S   \n",
       "\n",
       "                                         Name  SibSp  Parch    Age  \\\n",
       "279          Abbott, Mrs. Stanton (Rosa Hunt)      1      1  35.00   \n",
       "746               Abbott, Mr. Rossmore Edward      1      1  16.00   \n",
       "1283            Abbott, Master. Eugene Joseph      0      2  13.00   \n",
       "58               West, Miss. Constance Mirium      1      2   5.00   \n",
       "450                     West, Mr. Edwy Arthur      1      2  36.00   \n",
       "472   West, Mrs. Edwy Arthur (Ada Mary Worth)      1      2  33.00   \n",
       "1141                    West, Miss. Barbara J      1      2   0.92   \n",
       "\n",
       "      PassengerId  \n",
       "279           280  \n",
       "746           747  \n",
       "1283         1284  \n",
       "58             59  \n",
       "450           451  \n",
       "472           473  \n",
       "1141         1142  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[all_data.Surname.isin(['West', 'Abbott']), :].sort_values(by=['Surname', 'Fare', 'Ticket'])\n",
    "\n",
    "tmp.loc[:, ['Surname', 'Fare', 'Ticket', 'Pclass', 'Embarked', 'Name', 'SibSp', 'Parch', 'Age', 'PassengerId']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `West` 一家人，因为`Surname, Fare, Ticket, Pclass, Embarked, SibSp, Parch` 数据完全能对上。\n",
    "2. `Abbott` 也非常像一家人，但是 `SibSp, Parch` 数据却出现出入，第1行与第3行的 `SibSp, Parch` 数据似乎互换了（根据前面Master分析，那么Age无误，推测出来）。\n",
    "\n",
    "<font color='red'>注意</font>：1,3行的`SibSp, Parch` 2个特征数据似乎很有可能又错了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SurnameGroupId\n",
    "\n",
    "根据前面所说，如果至亲一家人和孙子辈、叔侄辈共同出行，我们得到如下一起出行的识别方案：\n",
    "\n",
    "1. `SurnameGroupId = Surname + Pclass + Embarked + Fare + Ticket[:-2]`\n",
    "2. 在叔侄辈和孙子辈中，Ticket最后2位不同，所以Ticket最后2位不加考虑，才能识别他们共同出行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "getSurnameGroupId(all_data)\n",
    "\n",
    "train = all_data.iloc[:train.shape[0], :]\n",
    "test = all_data.iloc[train.shape[0]:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来看看例子，叔侄关系（Cacic）与至亲一家（Brown）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Brown-2-S-13.0-2487__</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>248733</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>F33</td>\n",
       "      <td>Brown, Miss. Amelia \"Mildred\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Brown-1-C-27.7208-PC 176__</td>\n",
       "      <td>Brown</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>PC 17610</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B4</td>\n",
       "      <td>Brown, Mrs. James Joseph (Margaret Tobin)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>Brown-2-S-39.0-297__</td>\n",
       "      <td>Brown</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>29750</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown, Mrs. Thomas William Solomon (Elizabeth ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>Brown-2-S-39.0-297__</td>\n",
       "      <td>Brown</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>29750</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown, Mr. Thomas William Solomon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>Brown-2-S-39.0-297__</td>\n",
       "      <td>Brown</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>29750</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown, Miss. Edith Eileen</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>Brown-1-S-51.4792-117__</td>\n",
       "      <td>Brown</td>\n",
       "      <td>51.4792</td>\n",
       "      <td>11769</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>C101</td>\n",
       "      <td>Brown, Mrs. John Murray (Caroline Lane Lamson)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>Cacic-3-S-8.6625-3150__</td>\n",
       "      <td>Cacic</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>315084</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cacic, Miss. Marija</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>Cacic-3-S-8.6625-3150__</td>\n",
       "      <td>Cacic</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>315087</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cacic, Miss. Manda</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>Cacic-3-S-8.6625-3150__</td>\n",
       "      <td>Cacic</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>315089</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cacic, Mr. Luka</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>Cacic-3-S-8.6625-3150__</td>\n",
       "      <td>Cacic</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>315091</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cacic, Mr. Jego Grga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  SurnameGroupId Surname     Fare    Ticket  Pclass Embarked  \\\n",
       "345        Brown-2-S-13.0-2487__   Brown  13.0000    248733       2        S   \n",
       "194   Brown-1-C-27.7208-PC 176__   Brown  27.7208  PC 17610       1        C   \n",
       "670         Brown-2-S-39.0-297__   Brown  39.0000     29750       2        S   \n",
       "684         Brown-2-S-39.0-297__   Brown  39.0000     29750       2        S   \n",
       "1066        Brown-2-S-39.0-297__   Brown  39.0000     29750       2        S   \n",
       "1247     Brown-1-S-51.4792-117__   Brown  51.4792     11769       1        S   \n",
       "534      Cacic-3-S-8.6625-3150__   Cacic   8.6625    315084       3        S   \n",
       "928      Cacic-3-S-8.6625-3150__   Cacic   8.6625    315087       3        S   \n",
       "471      Cacic-3-S-8.6625-3150__   Cacic   8.6625    315089       3        S   \n",
       "1201     Cacic-3-S-8.6625-3150__   Cacic   8.6625    315091       3        S   \n",
       "\n",
       "     Cabin                                               Name  SibSp  Parch  \\\n",
       "345    F33                      Brown, Miss. Amelia \"Mildred\"      0      0   \n",
       "194     B4          Brown, Mrs. James Joseph (Margaret Tobin)      0      0   \n",
       "670    NaN  Brown, Mrs. Thomas William Solomon (Elizabeth ...      1      1   \n",
       "684    NaN                  Brown, Mr. Thomas William Solomon      1      1   \n",
       "1066   NaN                          Brown, Miss. Edith Eileen      0      2   \n",
       "1247  C101     Brown, Mrs. John Murray (Caroline Lane Lamson)      2      0   \n",
       "534    NaN                                Cacic, Miss. Marija      0      0   \n",
       "928    NaN                                 Cacic, Miss. Manda      0      0   \n",
       "471    NaN                                    Cacic, Mr. Luka      0      0   \n",
       "1201   NaN                               Cacic, Mr. Jego Grga      0      0   \n",
       "\n",
       "       Age  \n",
       "345   24.0  \n",
       "194   44.0  \n",
       "670   40.0  \n",
       "684   60.0  \n",
       "1066  15.0  \n",
       "1247  59.0  \n",
       "534   30.0  \n",
       "928   21.0  \n",
       "471   38.0  \n",
       "1201  18.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[(all_data.Surname == 'Brown') | (all_data.Surname == 'Cacic')].sort_values(by=['Surname', 'Fare', 'Ticket'])\n",
    "tmp.loc[:, ['SurnameGroupId', 'Surname', 'Fare', 'Ticket', 'Pclass', 'Embarked', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上表就很明显的看出 `GroupId` 比 `Surname` 在识别哪些人是一起出行这方面更有区分度。\n",
    "\n",
    "**`GroupId` 到底区分了多少一起出行的人？** 如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(991, 726, 373)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_data.SurnameGroupId.unique()), len(train.SurnameGroupId.unique()), len(test.SurnameGroupId.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 保姆和亲戚组\n",
    "\n",
    "来看看 `Ticket == 'PC 17608'`, `Fare Ticket Pclass Embarked`一模一样， `Cabin`也相近，根据 `SibSp Parch` 数据推测是保姆或者非同姓亲戚（比如：表亲），当然到底有木有说服力最终还是看预测的结果，我们先保持怀疑的态度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>SurnameGroupId</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Bowen-1-C-262.375-PC 176__</td>\n",
       "      <td>262.375</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bowen, Miss. Grace Scott</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Chaudanson-1-C-262.375-PC 176__</td>\n",
       "      <td>262.375</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B61</td>\n",
       "      <td>Chaudanson, Miss. Victorine</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>262.375</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>262.375</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>Ryerson, Miss. Susan Parker \"Suzette\"</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>262.375</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>Ryerson, Mrs. Arthur Larned (Emily Maria Borie)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>262.375</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>Ryerson, Master. John Borie</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>262.375</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>Ryerson, Mr. Arthur Larned</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SurnameGroupId     Fare    Ticket  Pclass Embarked  \\\n",
       "1266       Bowen-1-C-262.375-PC 176__  262.375  PC 17608       1        C   \n",
       "950   Chaudanson-1-C-262.375-PC 176__  262.375  PC 17608       1        C   \n",
       "311      Ryerson-1-C-262.375-PC 176__  262.375  PC 17608       1        C   \n",
       "742      Ryerson-1-C-262.375-PC 176__  262.375  PC 17608       1        C   \n",
       "915      Ryerson-1-C-262.375-PC 176__  262.375  PC 17608       1        C   \n",
       "955      Ryerson-1-C-262.375-PC 176__  262.375  PC 17608       1        C   \n",
       "1033     Ryerson-1-C-262.375-PC 176__  262.375  PC 17608       1        C   \n",
       "\n",
       "                Cabin                                             Name  SibSp  \\\n",
       "1266              NaN                         Bowen, Miss. Grace Scott      0   \n",
       "950               B61                      Chaudanson, Miss. Victorine      0   \n",
       "311   B57 B59 B63 B66                       Ryerson, Miss. Emily Borie      2   \n",
       "742   B57 B59 B63 B66            Ryerson, Miss. Susan Parker \"Suzette\"      2   \n",
       "915   B57 B59 B63 B66  Ryerson, Mrs. Arthur Larned (Emily Maria Borie)      1   \n",
       "955   B57 B59 B63 B66                      Ryerson, Master. John Borie      2   \n",
       "1033  B57 B59 B63 B66                       Ryerson, Mr. Arthur Larned      1   \n",
       "\n",
       "      Parch   Age  \n",
       "1266      0  45.0  \n",
       "950       0  36.0  \n",
       "311       2  18.0  \n",
       "742       2  21.0  \n",
       "915       3  48.0  \n",
       "955       2  13.0  \n",
       "1033      3  61.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[(all_data.Ticket == 'PC 17608'), ['SurnameGroupId', 'Fare', 'Ticket', 'Pclass', 'Embarked', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age']].sort_values(by=['SurnameGroupId'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 朋友团队组\n",
    "\n",
    "例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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",
       "      <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>TicketSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>Title</th>\n",
       "      <th>Surname</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>SurnameGroupId</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>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Icard</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard-1-nan-80.0-1135__</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>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Stone</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone-1-nan-80.0-1135__</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  TicketSize  \\\n",
       "61       0           62       1  female      0       1.0  113572           2   \n",
       "829      0          830       1  female      0       1.0  113572           2   \n",
       "\n",
       "     SingleTicketFare Title Surname  FamilySize           SurnameGroupId  \n",
       "61               40.0  Miss   Icard           1  Icard-1-nan-80.0-1135__  \n",
       "829              40.0   Mrs   Stone           1  Stone-1-nan-80.0-1135__  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[all_data.Ticket == '113572']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尽管这2位乘客：`Surname, Name` 都不一致，`SibSp, Parch` 均为0，但是 `Pclass, Embarked, Fare, Ticket, Cabin` 完全一致，因此我们怀疑他们是朋友或同事？唯一疑问点：年龄差距比较大。\n",
    "\n",
    "下面一组情况也类似："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>Surname</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Bing-3-S-56.4958-16__</td>\n",
       "      <td>Bing</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bing, Mr. Lee</td>\n",
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       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>Chip-3-S-56.4958-16__</td>\n",
       "      <td>Chip</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chip, Mr. Chang</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>Foo-3-S-56.4958-16__</td>\n",
       "      <td>Foo</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Foo, Mr. Choong</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>Hee-3-S-56.4958-16__</td>\n",
       "      <td>Hee</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hee, Mr. Ling</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>Lam-3-S-56.4958-16__</td>\n",
       "      <td>Lam</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lam, Mr. Ali</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>Lam-3-S-56.4958-16__</td>\n",
       "      <td>Lam</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lam, Mr. Len</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Lang-3-S-56.4958-16__</td>\n",
       "      <td>Lang</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lang, Mr. Fang</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Ling-3-S-56.4958-16__</td>\n",
       "      <td>Ling</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ling, Mr. Lee</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SurnameGroupId Surname     Fare Ticket  Pclass Embarked Cabin  \\\n",
       "74   Bing-3-S-56.4958-16__    Bing  56.4958   1601       3        S   NaN   \n",
       "838  Chip-3-S-56.4958-16__    Chip  56.4958   1601       3        S   NaN   \n",
       "643   Foo-3-S-56.4958-16__     Foo  56.4958   1601       3        S   NaN   \n",
       "930   Hee-3-S-56.4958-16__     Hee  56.4958   1601       3        S   NaN   \n",
       "692   Lam-3-S-56.4958-16__     Lam  56.4958   1601       3        S   NaN   \n",
       "826   Lam-3-S-56.4958-16__     Lam  56.4958   1601       3        S   NaN   \n",
       "509  Lang-3-S-56.4958-16__    Lang  56.4958   1601       3        S   NaN   \n",
       "169  Ling-3-S-56.4958-16__    Ling  56.4958   1601       3        S   NaN   \n",
       "\n",
       "                Name  SibSp  Parch   Age  \n",
       "74     Bing, Mr. Lee      0      0  32.0  \n",
       "838  Chip, Mr. Chang      0      0  32.0  \n",
       "643  Foo, Mr. Choong      0      0   NaN  \n",
       "930    Hee, Mr. Ling      0      0   NaN  \n",
       "692     Lam, Mr. Ali      0      0   NaN  \n",
       "826     Lam, Mr. Len      0      0   NaN  \n",
       "509   Lang, Mr. Fang      0      0  26.0  \n",
       "169    Ling, Mr. Lee      0      0  28.0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[(all_data.Ticket == '1601'), ['SurnameGroupId', 'Surname', 'Fare', 'Ticket', 'Pclass', 'Embarked', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age']].sort_values(['Surname'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TicketGroupId\n",
    "\n",
    "**为了识别出保姆、表亲（甚至远亲）、朋友一起出行的情况，构建新的特征**：`TicketGroupId = Pclass-Embarked-Fare-Ticket` 如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "getTicketGroupId(all_data)\n",
    "\n",
    "train = all_data.iloc[:train.shape[0], :]\n",
    "test = all_data.iloc[train.shape[0]:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(932, 684, 363)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_data.TicketGroupId.unique()), len(train.TicketGroupId.unique()), len(test.TicketGroupId.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再来看看经过 `TicketGroupId` 处理后前面2种情况：\n",
    "\n",
    "1.可能是保姆的情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>1266</th>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>Bowen-1-C-262.375-PC 176__</td>\n",
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       "      <th>950</th>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>Chaudanson-1-C-262.375-PC 176__</td>\n",
       "      <td>Chaudanson</td>\n",
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       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B61</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>female</td>\n",
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       "      <th>311</th>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>PC 17608</td>\n",
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       "      <td>1</td>\n",
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       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>742</th>\n",
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       "      <td>2</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>female</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>262.375</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>Ryerson, Mrs. Arthur Larned (Emily Maria Borie)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>female</td>\n",
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       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>Ryerson</td>\n",
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       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>PC 17608</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TicketGroupId    Ticket                   SurnameGroupId  \\\n",
       "1266  1-C-262.375-PC 17608  PC 17608       Bowen-1-C-262.375-PC 176__   \n",
       "950   1-C-262.375-PC 17608  PC 17608  Chaudanson-1-C-262.375-PC 176__   \n",
       "311   1-C-262.375-PC 17608  PC 17608     Ryerson-1-C-262.375-PC 176__   \n",
       "742   1-C-262.375-PC 17608  PC 17608     Ryerson-1-C-262.375-PC 176__   \n",
       "915   1-C-262.375-PC 17608  PC 17608     Ryerson-1-C-262.375-PC 176__   \n",
       "955   1-C-262.375-PC 17608  PC 17608     Ryerson-1-C-262.375-PC 176__   \n",
       "1033  1-C-262.375-PC 17608  PC 17608     Ryerson-1-C-262.375-PC 176__   \n",
       "\n",
       "         Surname     Fare  Pclass Embarked            Cabin  \\\n",
       "1266       Bowen  262.375       1        C              NaN   \n",
       "950   Chaudanson  262.375       1        C              B61   \n",
       "311      Ryerson  262.375       1        C  B57 B59 B63 B66   \n",
       "742      Ryerson  262.375       1        C  B57 B59 B63 B66   \n",
       "915      Ryerson  262.375       1        C  B57 B59 B63 B66   \n",
       "955      Ryerson  262.375       1        C  B57 B59 B63 B66   \n",
       "1033     Ryerson  262.375       1        C  B57 B59 B63 B66   \n",
       "\n",
       "                                                 Name  SibSp  Parch   Age  \\\n",
       "1266                         Bowen, Miss. Grace Scott      0      0  45.0   \n",
       "950                       Chaudanson, Miss. Victorine      0      0  36.0   \n",
       "311                        Ryerson, Miss. Emily Borie      2      2  18.0   \n",
       "742             Ryerson, Miss. Susan Parker \"Suzette\"      2      2  21.0   \n",
       "915   Ryerson, Mrs. Arthur Larned (Emily Maria Borie)      1      3  48.0   \n",
       "955                       Ryerson, Master. John Borie      2      2  13.0   \n",
       "1033                       Ryerson, Mr. Arthur Larned      1      3  61.0   \n",
       "\n",
       "         Sex  \n",
       "1266  female  \n",
       "950   female  \n",
       "311   female  \n",
       "742   female  \n",
       "915   female  \n",
       "955     male  \n",
       "1033    male  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[(all_data.Ticket == 'PC 17608'), ['TicketGroupId', 'Ticket', 'SurnameGroupId', 'Surname', 'Fare', 'Pclass', 'Embarked', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age', 'Sex']].sort_values(by=['TicketGroupId', 'SurnameGroupId'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.可能是朋友伴随出行的情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>Lam, Mr. Len</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>3-S-56.4958-1601</td>\n",
       "      <td>1601</td>\n",
       "      <td>Chip-3-S-56.4958-16__</td>\n",
       "      <td>Chip</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chip, Mr. Chang</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>3-S-56.4958-1601</td>\n",
       "      <td>1601</td>\n",
       "      <td>Hee-3-S-56.4958-16__</td>\n",
       "      <td>Hee</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hee, Mr. Ling</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         TicketGroupId  Ticket           SurnameGroupId Surname     Fare  \\\n",
       "61   1-nan-80.0-113572  113572  Icard-1-nan-80.0-1135__   Icard  80.0000   \n",
       "829  1-nan-80.0-113572  113572  Stone-1-nan-80.0-1135__   Stone  80.0000   \n",
       "74    3-S-56.4958-1601    1601    Bing-3-S-56.4958-16__    Bing  56.4958   \n",
       "169   3-S-56.4958-1601    1601    Ling-3-S-56.4958-16__    Ling  56.4958   \n",
       "509   3-S-56.4958-1601    1601    Lang-3-S-56.4958-16__    Lang  56.4958   \n",
       "643   3-S-56.4958-1601    1601     Foo-3-S-56.4958-16__     Foo  56.4958   \n",
       "692   3-S-56.4958-1601    1601     Lam-3-S-56.4958-16__     Lam  56.4958   \n",
       "826   3-S-56.4958-1601    1601     Lam-3-S-56.4958-16__     Lam  56.4958   \n",
       "838   3-S-56.4958-1601    1601    Chip-3-S-56.4958-16__    Chip  56.4958   \n",
       "930   3-S-56.4958-1601    1601     Hee-3-S-56.4958-16__     Hee  56.4958   \n",
       "\n",
       "     Pclass Embarked Cabin                                       Name  SibSp  \\\n",
       "61        1      NaN   B28                        Icard, Miss. Amelie      0   \n",
       "829       1      NaN   B28  Stone, Mrs. George Nelson (Martha Evelyn)      0   \n",
       "74        3        S   NaN                              Bing, Mr. Lee      0   \n",
       "169       3        S   NaN                              Ling, Mr. Lee      0   \n",
       "509       3        S   NaN                             Lang, Mr. Fang      0   \n",
       "643       3        S   NaN                            Foo, Mr. Choong      0   \n",
       "692       3        S   NaN                               Lam, Mr. Ali      0   \n",
       "826       3        S   NaN                               Lam, Mr. Len      0   \n",
       "838       3        S   NaN                            Chip, Mr. Chang      0   \n",
       "930       3        S   NaN                              Hee, Mr. Ling      0   \n",
       "\n",
       "     Parch   Age     Sex  \n",
       "61       0  38.0  female  \n",
       "829      0  62.0  female  \n",
       "74       0  32.0    male  \n",
       "169      0  28.0    male  \n",
       "509      0  26.0    male  \n",
       "643      0   NaN    male  \n",
       "692      0   NaN    male  \n",
       "826      0   NaN    male  \n",
       "838      0  32.0    male  \n",
       "930      0   NaN    male  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[ (all_data.Ticket == '113572') | (all_data.Ticket == '1601'),['TicketGroupId', 'Ticket', 'SurnameGroupId', 'Surname', 'Fare', 'Pclass', 'Embarked', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age', 'Sex']].sort_values(by=['TicketGroupId']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显然经过前面的分析，我们清楚 `SurnameGroupId` 比 `TicketGroupId` 更靠谱地识别出一起出行的人，毕竟 `TicketGroupId` 是由更少的特征组合起来的，那么来试试找出，哪些乘客可能是保姆远亲，甚至朋友一起出行。\n",
    "\n",
    "这些人的特征:\n",
    "\n",
    "1. 这些人（保姆、远亲、朋友）跟所在的至亲家庭几乎不可能是同一个 `SurnameGroupId`，因此他们这些人的 `SurnameGroupSize==1` \n",
    "2. 有了上一条推论，我们清楚有了 `SurnameGoupId` 以后，我们根据 `TicketGroupId` 找到保姆所在的家庭，远亲一起来的家庭，朋友团，然后修改保姆远亲朋友的 `SurnameGroupId`，或者重新定义一个字段 `TogetherId`（这个方法更麻烦一些）。\n",
    "\n",
    "首先，我们先创建出以下两个特征："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TicketGroupSize 和 SurnameGroupSize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>SurnameGroupSize</th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>1</td>\n",
       "      <td>Abbing-3-S-7.55-C.A. 55__</td>\n",
       "      <td>3-S-7.55-C.A. 5547</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abbing, Mr. Anthony</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>3</td>\n",
       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
       "      <td>3-S-20.25-C.A. 2673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>3</td>\n",
       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
       "      <td>3-S-20.25-C.A. 2673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abbott, Mr. Rossmore Edward</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>3</td>\n",
       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
       "      <td>3-S-20.25-C.A. 2673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>2</td>\n",
       "      <td>Abelseth-3-S-7.65-3481__</td>\n",
       "      <td>3-S-7.65-348122</td>\n",
       "      <td>F G63</td>\n",
       "      <td>Abelseth, Mr. Olaus Jorgensen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>2</td>\n",
       "      <td>Abelseth-3-S-7.65-3481__</td>\n",
       "      <td>3-S-7.65-348125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abelseth, Miss. Karen Marie</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>2</td>\n",
       "      <td>Abelson-2-C-24.0-P/PP 33__</td>\n",
       "      <td>2-C-24.0-P/PP 3381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>2</td>\n",
       "      <td>Abelson-2-C-24.0-P/PP 33__</td>\n",
       "      <td>2-C-24.0-P/PP 3381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abelson, Mr. Samuel</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SurnameGroupSize              SurnameGroupId        TicketGroupId  \\\n",
       "845                  1   Abbing-3-S-7.55-C.A. 55__   3-S-7.55-C.A. 5547   \n",
       "1283                 3  Abbott-3-S-20.25-C.A. 26__  3-S-20.25-C.A. 2673   \n",
       "746                  3  Abbott-3-S-20.25-C.A. 26__  3-S-20.25-C.A. 2673   \n",
       "279                  3  Abbott-3-S-20.25-C.A. 26__  3-S-20.25-C.A. 2673   \n",
       "948                  2    Abelseth-3-S-7.65-3481__      3-S-7.65-348122   \n",
       "1236                 2    Abelseth-3-S-7.65-3481__      3-S-7.65-348125   \n",
       "874                  2  Abelson-2-C-24.0-P/PP 33__   2-C-24.0-P/PP 3381   \n",
       "308                  2  Abelson-2-C-24.0-P/PP 33__   2-C-24.0-P/PP 3381   \n",
       "\n",
       "      Cabin                                   Name  SibSp  Parch   Age  \n",
       "845     NaN                    Abbing, Mr. Anthony      0      0  42.0  \n",
       "1283    NaN          Abbott, Master. Eugene Joseph      0      2  13.0  \n",
       "746     NaN            Abbott, Mr. Rossmore Edward      1      1  16.0  \n",
       "279     NaN       Abbott, Mrs. Stanton (Rosa Hunt)      1      1  35.0  \n",
       "948   F G63          Abelseth, Mr. Olaus Jorgensen      0      0  25.0  \n",
       "1236    NaN            Abelseth, Miss. Karen Marie      0      0  16.0  \n",
       "874     NaN  Abelson, Mrs. Samuel (Hannah Wizosky)      1      0  28.0  \n",
       "308     NaN                    Abelson, Mr. Samuel      1      0  30.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surname_group_value_count = all_data.SurnameGroupId.value_counts()\n",
    "all_data['SurnameGroupSize'] = all_data.apply(lambda row : surname_group_value_count[row.SurnameGroupId], axis=1)\n",
    "\n",
    "all_data.sort_values(['SurnameGroupId']).loc[:, ['SurnameGroupSize', 'SurnameGroupId', 'TicketGroupId', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age']].head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d588eacc18>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='SurnameGroupSize', y='Survived', data = all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>TicketGroupSize</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>3</td>\n",
       "      <td>1-C-106.425-PC 17761</td>\n",
       "      <td>Douglas-1-C-106.425-PC 177__</td>\n",
       "      <td>C86</td>\n",
       "      <td>Douglas, Mrs. Walter Donald (Mahala Dutton)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>3</td>\n",
       "      <td>1-C-106.425-PC 17761</td>\n",
       "      <td>LeRoy-1-C-106.425-PC 177__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LeRoy, Miss. Bertha</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>3</td>\n",
       "      <td>1-C-106.425-PC 17761</td>\n",
       "      <td>Douglas-1-C-106.425-PC 177__</td>\n",
       "      <td>C86</td>\n",
       "      <td>Douglas, Mr. Walter Donald</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3</td>\n",
       "      <td>1-C-108.9-PC 17758</td>\n",
       "      <td>Oliva y Ocana-1-C-108.9-PC 177__</td>\n",
       "      <td>C105</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>3</td>\n",
       "      <td>1-C-108.9-PC 17758</td>\n",
       "      <td>Penasco y Castellana-1-C-108.9-PC 177__</td>\n",
       "      <td>C65</td>\n",
       "      <td>Penasco y Castellana, Mrs. Victor de Satode (M...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>3</td>\n",
       "      <td>1-C-108.9-PC 17758</td>\n",
       "      <td>Penasco y Castellana-1-C-108.9-PC 177__</td>\n",
       "      <td>C65</td>\n",
       "      <td>Penasco y Castellana, Mr. Victor de Satode</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TicketGroupSize         TicketGroupId  \\\n",
       "1130                3  1-C-106.425-PC 17761   \n",
       "537                 3  1-C-106.425-PC 17761   \n",
       "544                 3  1-C-106.425-PC 17761   \n",
       "1305                3    1-C-108.9-PC 17758   \n",
       "307                 3    1-C-108.9-PC 17758   \n",
       "505                 3    1-C-108.9-PC 17758   \n",
       "\n",
       "                               SurnameGroupId Cabin  \\\n",
       "1130             Douglas-1-C-106.425-PC 177__   C86   \n",
       "537                LeRoy-1-C-106.425-PC 177__   NaN   \n",
       "544              Douglas-1-C-106.425-PC 177__   C86   \n",
       "1305         Oliva y Ocana-1-C-108.9-PC 177__  C105   \n",
       "307   Penasco y Castellana-1-C-108.9-PC 177__   C65   \n",
       "505   Penasco y Castellana-1-C-108.9-PC 177__   C65   \n",
       "\n",
       "                                                   Name  SibSp  Parch   Age  \n",
       "1130        Douglas, Mrs. Walter Donald (Mahala Dutton)      1      0  48.0  \n",
       "537                                 LeRoy, Miss. Bertha      0      0  30.0  \n",
       "544                          Douglas, Mr. Walter Donald      1      0  50.0  \n",
       "1305                       Oliva y Ocana, Dona. Fermina      0      0  39.0  \n",
       "307   Penasco y Castellana, Mrs. Victor de Satode (M...      1      0  17.0  \n",
       "505          Penasco y Castellana, Mr. Victor de Satode      1      0  18.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_group_value_count = dict(all_data.TicketGroupId.value_counts())\n",
    "all_data['TicketGroupSize'] = all_data.apply(lambda row : ticket_group_value_count[row.TicketGroupId], axis=1)\n",
    "\n",
    "all_data.sort_values(['TicketGroupId']).loc[:, ['TicketGroupSize', 'TicketGroupId', 'SurnameGroupId', 'Cabin', 'Name', 'SibSp', 'Parch', 'Age']].head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d588eac588>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='TicketGroupSize', y='Survived', data = all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "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>...</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>TicketSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>Title</th>\n",
       "      <th>Surname</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>SurnameGroupSize</th>\n",
       "      <th>TicketGroupSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1</td>\n",
       "      <td>7.25000</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Braund</td>\n",
       "      <td>2</td>\n",
       "      <td>Braund-3-S-7.25-A/5 211__</td>\n",
       "      <td>3-S-7.25-A/5 21171</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2</td>\n",
       "      <td>35.64165</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>2</td>\n",
       "      <td>Cumings-1-C-71.2833-PC 175__</td>\n",
       "      <td>1-C-71.2833-PC 17599</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "      <td>7.92500</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>1</td>\n",
       "      <td>Heikkinen-3-S-7.925-STON/O2. 31012__</td>\n",
       "      <td>3-S-7.925-STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>113803</td>\n",
       "      <td>2</td>\n",
       "      <td>26.55000</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>2</td>\n",
       "      <td>Futrelle-1-S-53.1-1138__</td>\n",
       "      <td>1-S-53.1-113803</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>373450</td>\n",
       "      <td>1</td>\n",
       "      <td>8.05000</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Allen</td>\n",
       "      <td>2</td>\n",
       "      <td>Allen-3-S-8.05-3734__</td>\n",
       "      <td>3-S-8.05-373450</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "3  35.0  C123        S  53.1000   \n",
       "4  35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                           Allen, Mr. William Henry      0            5   \n",
       "\n",
       "   Pclass     Sex  SibSp       ...                   Ticket TicketSize  \\\n",
       "0       3    male      1       ...                A/5 21171          1   \n",
       "1       1  female      1       ...                 PC 17599          2   \n",
       "2       3  female      0       ...         STON/O2. 3101282          1   \n",
       "3       1  female      1       ...                   113803          2   \n",
       "4       3    male      0       ...                   373450          1   \n",
       "\n",
       "   SingleTicketFare  Title    Surname FamilySize  \\\n",
       "0           7.25000     Mr     Braund          2   \n",
       "1          35.64165    Mrs    Cumings          2   \n",
       "2           7.92500   Miss  Heikkinen          1   \n",
       "3          26.55000    Mrs   Futrelle          2   \n",
       "4           8.05000     Mr      Allen          2   \n",
       "\n",
       "                         SurnameGroupId               TicketGroupId  \\\n",
       "0             Braund-3-S-7.25-A/5 211__          3-S-7.25-A/5 21171   \n",
       "1          Cumings-1-C-71.2833-PC 175__        1-C-71.2833-PC 17599   \n",
       "2  Heikkinen-3-S-7.925-STON/O2. 31012__  3-S-7.925-STON/O2. 3101282   \n",
       "3              Futrelle-1-S-53.1-1138__             1-S-53.1-113803   \n",
       "4                 Allen-3-S-8.05-3734__             3-S-8.05-373450   \n",
       "\n",
       "  SurnameGroupSize  TicketGroupSize  \n",
       "0                1                1  \n",
       "1                2                2  \n",
       "2                1                1  \n",
       "3                2                2  \n",
       "4                1                1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = all_data.iloc[train.shape[0]:, :]\n",
    "train = all_data.iloc[:train.shape[0], :]\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 寻找保姆、远亲、朋友"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tottaly 789 solo passengers satifies the condtions\n",
      "Totally adding 130  nannies, cousins and firends to surname groups\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.concat([train, test])\n",
    "nannies_firends_cousins = addNanniesCousinsFriends(all_data, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nannies_firends_cousins_data = tmp.loc[tmp.SurnameGroupId.isin(nannies_firends_cousins), ['SurnameGroupId', 'TicketGroupId', 'Pclass', 'Embarked', 'Sex', 'Fare', 'Ticket', 'SibSp', 'Parch', 'Age', 'Surname','Survived']].sort_values(['TicketGroupId'])\n",
    "\n",
    "len(nannies_firends_cousins_data.TicketGroupId.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>SurnameGroupId</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Douglas-1-C-106.425-PC 177__</td>\n",
       "      <td>1-C-106.425-PC 17761</td>\n",
       "      <td>male</td>\n",
       "      <td>PC 17761</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>LeRoy-1-C-106.425-PC 177__</td>\n",
       "      <td>1-C-106.425-PC 17761</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17761</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>LeRoy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>Douglas-1-C-106.425-PC 177__</td>\n",
       "      <td>1-C-106.425-PC 17761</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17761</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>Oliva y Ocana-1-C-108.9-PC 177__</td>\n",
       "      <td>1-C-108.9-PC 17758</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Oliva y Ocana</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>Penasco y Castellana-1-C-108.9-PC 177__</td>\n",
       "      <td>1-C-108.9-PC 17758</td>\n",
       "      <td>male</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Penasco y Castellana</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Penasco y Castellana-1-C-108.9-PC 177__</td>\n",
       "      <td>1-C-108.9-PC 17758</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Penasco y Castellana</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>Thayer-1-C-110.8833-174__</td>\n",
       "      <td>1-C-110.8833-17421</td>\n",
       "      <td>male</td>\n",
       "      <td>17421</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Thayer</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>Thayer-1-C-110.8833-174__</td>\n",
       "      <td>1-C-110.8833-17421</td>\n",
       "      <td>female</td>\n",
       "      <td>17421</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Thayer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Thayer-1-C-110.8833-174__</td>\n",
       "      <td>1-C-110.8833-17421</td>\n",
       "      <td>male</td>\n",
       "      <td>17421</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Thayer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>Fleming-1-C-110.8833-174__</td>\n",
       "      <td>1-C-110.8833-17421</td>\n",
       "      <td>female</td>\n",
       "      <td>17421</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fleming</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Spedden-1-C-134.5-169__</td>\n",
       "      <td>1-C-134.5-16966</td>\n",
       "      <td>female</td>\n",
       "      <td>16966</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Spedden</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>Spedden-1-C-134.5-169__</td>\n",
       "      <td>1-C-134.5-16966</td>\n",
       "      <td>male</td>\n",
       "      <td>16966</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Spedden</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>Spedden-1-C-134.5-169__</td>\n",
       "      <td>1-C-134.5-16966</td>\n",
       "      <td>male</td>\n",
       "      <td>16966</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Spedden</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>Burns-1-C-134.5-169__</td>\n",
       "      <td>1-C-134.5-16966</td>\n",
       "      <td>female</td>\n",
       "      <td>16966</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Burns</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>Wilson-1-C-134.5-169__</td>\n",
       "      <td>1-C-134.5-16966</td>\n",
       "      <td>female</td>\n",
       "      <td>16966</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>Ringhini-1-C-135.6333-PC 177__</td>\n",
       "      <td>1-C-135.6333-PC 17760</td>\n",
       "      <td>male</td>\n",
       "      <td>PC 17760</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Ringhini</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>White-1-C-135.6333-PC 177__</td>\n",
       "      <td>1-C-135.6333-PC 17760</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17760</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Young-1-C-135.6333-PC 177__</td>\n",
       "      <td>1-C-135.6333-PC 17760</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17760</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Young</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Spencer-1-C-146.5208-PC 175__</td>\n",
       "      <td>1-C-146.5208-PC 17569</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spencer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>Spencer-1-C-146.5208-PC 175__</td>\n",
       "      <td>1-C-146.5208-PC 17569</td>\n",
       "      <td>male</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Spencer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               SurnameGroupId          TicketGroupId     Sex  \\\n",
       "544              Douglas-1-C-106.425-PC 177__   1-C-106.425-PC 17761    male   \n",
       "537                LeRoy-1-C-106.425-PC 177__   1-C-106.425-PC 17761  female   \n",
       "1130             Douglas-1-C-106.425-PC 177__   1-C-106.425-PC 17761  female   \n",
       "1305         Oliva y Ocana-1-C-108.9-PC 177__     1-C-108.9-PC 17758  female   \n",
       "505   Penasco y Castellana-1-C-108.9-PC 177__     1-C-108.9-PC 17758    male   \n",
       "307   Penasco y Castellana-1-C-108.9-PC 177__     1-C-108.9-PC 17758  female   \n",
       "698                 Thayer-1-C-110.8833-174__     1-C-110.8833-17421    male   \n",
       "581                 Thayer-1-C-110.8833-174__     1-C-110.8833-17421  female   \n",
       "550                 Thayer-1-C-110.8833-174__     1-C-110.8833-17421    male   \n",
       "306                Fleming-1-C-110.8833-174__     1-C-110.8833-17421  female   \n",
       "319                   Spedden-1-C-134.5-169__        1-C-134.5-16966  female   \n",
       "1087                  Spedden-1-C-134.5-169__        1-C-134.5-16966    male   \n",
       "1133                  Spedden-1-C-134.5-169__        1-C-134.5-16966    male   \n",
       "337                     Burns-1-C-134.5-169__        1-C-134.5-16966  female   \n",
       "1262                   Wilson-1-C-134.5-169__        1-C-134.5-16966  female   \n",
       "373            Ringhini-1-C-135.6333-PC 177__  1-C-135.6333-PC 17760    male   \n",
       "1205              White-1-C-135.6333-PC 177__  1-C-135.6333-PC 17760  female   \n",
       "325               Young-1-C-135.6333-PC 177__  1-C-135.6333-PC 17760  female   \n",
       "31              Spencer-1-C-146.5208-PC 175__  1-C-146.5208-PC 17569  female   \n",
       "1207            Spencer-1-C-146.5208-PC 175__  1-C-146.5208-PC 17569    male   \n",
       "\n",
       "        Ticket  SibSp  Parch   Age               Surname  Survived  \n",
       "544   PC 17761      1      0  50.0               Douglas       0.0  \n",
       "537   PC 17761      0      0  30.0                 LeRoy       1.0  \n",
       "1130  PC 17761      1      0  48.0               Douglas       NaN  \n",
       "1305  PC 17758      0      0  39.0         Oliva y Ocana       NaN  \n",
       "505   PC 17758      1      0  18.0  Penasco y Castellana       0.0  \n",
       "307   PC 17758      1      0  17.0  Penasco y Castellana       1.0  \n",
       "698      17421      1      1  49.0                Thayer       0.0  \n",
       "581      17421      1      1  39.0                Thayer       1.0  \n",
       "550      17421      0      2  17.0                Thayer       1.0  \n",
       "306      17421      0      0   NaN               Fleming       1.0  \n",
       "319      16966      1      1  40.0               Spedden       1.0  \n",
       "1087     16966      0      2   6.0               Spedden       NaN  \n",
       "1133     16966      1      1  45.0               Spedden       NaN  \n",
       "337      16966      0      0  41.0                 Burns       1.0  \n",
       "1262     16966      0      0  31.0                Wilson       NaN  \n",
       "373   PC 17760      0      0  22.0              Ringhini       0.0  \n",
       "1205  PC 17760      0      0  55.0                 White       NaN  \n",
       "325   PC 17760      0      0  36.0                 Young       1.0  \n",
       "31    PC 17569      1      0   NaN               Spencer       1.0  \n",
       "1207  PC 17569      1      0  57.0               Spencer       NaN  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = None\n",
    "nannies_firends_cousins_data.drop(['Pclass','Embarked', 'Fare'], axis = 1).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**我们来看看之前的保姆、远亲、朋友组**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Surname</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Chaudanson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Chaudanson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
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       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Bowen-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Bowen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Icard-1-nan-80.0-1135__</td>\n",
       "      <td>1-nan-80.0-113572</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>113572</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Icard</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>Stone-1-nan-80.0-1135__</td>\n",
       "      <td>1-nan-80.0-113572</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>113572</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Stone</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>Chip-3-S-56.4958-16__</td>\n",
       "      <td>3-S-56.4958-1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Chip</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Lang-3-S-56.4958-16__</td>\n",
       "      <td>3-S-56.4958-1601</td>\n",
       "      <td>3</td>\n",
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       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Lang</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Ling-3-S-56.4958-16__</td>\n",
       "      <td>3-S-56.4958-1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Ling</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>Hee-3-S-56.4958-16__</td>\n",
       "      <td>3-S-56.4958-1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hee</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>643</th>\n",
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       "      <td>3</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Bing-3-S-56.4958-16__</td>\n",
       "      <td>3-S-56.4958-1601</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1601</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Bing</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SurnameGroupId         TicketGroupId  Pclass Embarked  \\\n",
       "915      Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "1033     Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "950   Chaudanson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "311      Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "742      Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "955      Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "1266       Bowen-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "61            Icard-1-nan-80.0-1135__     1-nan-80.0-113572       1      NaN   \n",
       "829           Stone-1-nan-80.0-1135__     1-nan-80.0-113572       1      NaN   \n",
       "838             Chip-3-S-56.4958-16__      3-S-56.4958-1601       3        S   \n",
       "509             Lang-3-S-56.4958-16__      3-S-56.4958-1601       3        S   \n",
       "169             Ling-3-S-56.4958-16__      3-S-56.4958-1601       3        S   \n",
       "930              Hee-3-S-56.4958-16__      3-S-56.4958-1601       3        S   \n",
       "643              Foo-3-S-56.4958-16__      3-S-56.4958-1601       3        S   \n",
       "74              Bing-3-S-56.4958-16__      3-S-56.4958-1601       3        S   \n",
       "\n",
       "         Sex      Fare    Ticket  SibSp  Parch   Age     Surname  Survived  \n",
       "915   female  262.3750  PC 17608      1      3  48.0     Ryerson       NaN  \n",
       "1033    male  262.3750  PC 17608      1      3  61.0     Ryerson       NaN  \n",
       "950   female  262.3750  PC 17608      0      0  36.0  Chaudanson       NaN  \n",
       "311   female  262.3750  PC 17608      2      2  18.0     Ryerson       1.0  \n",
       "742   female  262.3750  PC 17608      2      2  21.0     Ryerson       1.0  \n",
       "955     male  262.3750  PC 17608      2      2  13.0     Ryerson       NaN  \n",
       "1266  female  262.3750  PC 17608      0      0  45.0       Bowen       NaN  \n",
       "61    female   80.0000    113572      0      0  38.0       Icard       1.0  \n",
       "829   female   80.0000    113572      0      0  62.0       Stone       1.0  \n",
       "838     male   56.4958      1601      0      0  32.0        Chip       1.0  \n",
       "509     male   56.4958      1601      0      0  26.0        Lang       1.0  \n",
       "169     male   56.4958      1601      0      0  28.0        Ling       0.0  \n",
       "930     male   56.4958      1601      0      0   NaN         Hee       NaN  \n",
       "643     male   56.4958      1601      0      0   NaN         Foo       1.0  \n",
       "74      male   56.4958      1601      0      0  32.0        Bing       1.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nannies_firends_cousins_data.loc[(nannies_firends_cousins_data.Ticket == 'PC 17608') | (nannies_firends_cousins_data.Ticket == '113572') | (nannies_firends_cousins_data.Ticket == '1601')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Spedden-1-C-134.5-169__</td>\n",
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       "    <tr>\n",
       "      <th>1133</th>\n",
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       "      <td>1</td>\n",
       "      <td>45.00</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
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       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>48.00</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>61.00</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18.00</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21.00</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>Ryerson-1-C-262.375-PC 176__</td>\n",
       "      <td>1-C-262.375-PC 17608</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13.00</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Allison-1-S-151.55-1137__</td>\n",
       "      <td>1-S-151.55-113781</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>113781</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25.00</td>\n",
       "      <td>Allison</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Allison-1-S-151.55-1137__</td>\n",
       "      <td>1-S-151.55-113781</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>113781</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Allison</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Allison-1-S-151.55-1137__</td>\n",
       "      <td>1-S-151.55-113781</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>113781</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.92</td>\n",
       "      <td>Allison</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>Allison-1-S-151.55-1137__</td>\n",
       "      <td>1-S-151.55-113781</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>113781</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Allison</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>Wick-1-S-164.8667-369__</td>\n",
       "      <td>1-S-164.8667-36928</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>36928</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>57.00</td>\n",
       "      <td>Wick</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>Wick-1-S-164.8667-369__</td>\n",
       "      <td>1-S-164.8667-36928</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>36928</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Wick</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Wick-1-S-164.8667-369__</td>\n",
       "      <td>1-S-164.8667-36928</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>36928</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>31.00</td>\n",
       "      <td>Wick</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Panula-3-S-39.6875-31012__</td>\n",
       "      <td>3-S-39.6875-3101295</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>3101295</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Panula</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Panula-3-S-39.6875-31012__</td>\n",
       "      <td>3-S-39.6875-3101295</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>3101295</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>41.00</td>\n",
       "      <td>Panula</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>Panula-3-S-39.6875-31012__</td>\n",
       "      <td>3-S-39.6875-3101295</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>3101295</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Panula</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Panula-3-S-39.6875-31012__</td>\n",
       "      <td>3-S-39.6875-3101295</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>3101295</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Panula</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Panula-3-S-39.6875-31012__</td>\n",
       "      <td>3-S-39.6875-3101295</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>3101295</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Panula</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>Panula-3-S-39.6875-31012__</td>\n",
       "      <td>3-S-39.6875-3101295</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>3101295</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14.00</td>\n",
       "      <td>Panula</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SurnameGroupId         TicketGroupId  Pclass Embarked  \\\n",
       "319        Spedden-1-C-134.5-169__       1-C-134.5-16966       1        C   \n",
       "1087       Spedden-1-C-134.5-169__       1-C-134.5-16966       1        C   \n",
       "1133       Spedden-1-C-134.5-169__       1-C-134.5-16966       1        C   \n",
       "915   Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "1033  Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "311   Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "742   Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "955   Ryerson-1-C-262.375-PC 176__  1-C-262.375-PC 17608       1        C   \n",
       "498      Allison-1-S-151.55-1137__     1-S-151.55-113781       1        S   \n",
       "297      Allison-1-S-151.55-1137__     1-S-151.55-113781       1        S   \n",
       "305      Allison-1-S-151.55-1137__     1-S-151.55-113781       1        S   \n",
       "1197     Allison-1-S-151.55-1137__     1-S-151.55-113781       1        S   \n",
       "1108       Wick-1-S-164.8667-369__    1-S-164.8667-36928       1        S   \n",
       "856        Wick-1-S-164.8667-369__    1-S-164.8667-36928       1        S   \n",
       "318        Wick-1-S-164.8667-369__    1-S-164.8667-36928       1        S   \n",
       "50      Panula-3-S-39.6875-31012__   3-S-39.6875-3101295       3        S   \n",
       "638     Panula-3-S-39.6875-31012__   3-S-39.6875-3101295       3        S   \n",
       "824     Panula-3-S-39.6875-31012__   3-S-39.6875-3101295       3        S   \n",
       "164     Panula-3-S-39.6875-31012__   3-S-39.6875-3101295       3        S   \n",
       "266     Panula-3-S-39.6875-31012__   3-S-39.6875-3101295       3        S   \n",
       "686     Panula-3-S-39.6875-31012__   3-S-39.6875-3101295       3        S   \n",
       "\n",
       "         Sex      Fare    Ticket  SibSp  Parch    Age  Surname  Survived  \n",
       "319   female  134.5000     16966      1      1  40.00  Spedden       1.0  \n",
       "1087    male  134.5000     16966      0      2   6.00  Spedden       NaN  \n",
       "1133    male  134.5000     16966      1      1  45.00  Spedden       NaN  \n",
       "915   female  262.3750  PC 17608      1      3  48.00  Ryerson       NaN  \n",
       "1033    male  262.3750  PC 17608      1      3  61.00  Ryerson       NaN  \n",
       "311   female  262.3750  PC 17608      2      2  18.00  Ryerson       1.0  \n",
       "742   female  262.3750  PC 17608      2      2  21.00  Ryerson       1.0  \n",
       "955     male  262.3750  PC 17608      2      2  13.00  Ryerson       NaN  \n",
       "498   female  151.5500    113781      1      2  25.00  Allison       0.0  \n",
       "297   female  151.5500    113781      1      2   2.00  Allison       0.0  \n",
       "305     male  151.5500    113781      1      2   0.92  Allison       1.0  \n",
       "1197    male  151.5500    113781      1      2  30.00  Allison       NaN  \n",
       "1108    male  164.8667     36928      1      1  57.00     Wick       NaN  \n",
       "856   female  164.8667     36928      1      1  45.00     Wick       1.0  \n",
       "318   female  164.8667     36928      0      2  31.00     Wick       1.0  \n",
       "50      male   39.6875   3101295      4      1   7.00   Panula       0.0  \n",
       "638   female   39.6875   3101295      0      5  41.00   Panula       0.0  \n",
       "824     male   39.6875   3101295      4      1   2.00   Panula       0.0  \n",
       "164     male   39.6875   3101295      4      1   1.00   Panula       0.0  \n",
       "266     male   39.6875   3101295      4      1  16.00   Panula       0.0  \n",
       "686     male   39.6875   3101295      4      1  14.00   Panula       0.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nannies_firends_cousins_data.loc[nannies_firends_cousins_data.Surname.isin(['Spedden', 'Allison', 'Zabour', 'Panula', 'Wick', 'Ryerson'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000    60\n",
       "0.000000    46\n",
       "0.500000     5\n",
       "0.333333     1\n",
       "0.666667     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nannies_firends_cousins_data.groupby(['SurnameGroupId']).Survived.mean().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 多人一起出行的分组平均幸存率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**查看原来的数据，利用未调整 `SurnameGroupId` 前的 `SurnameGroupSize` 的值：**\n",
    "\n",
    "1.<font color='blue'>性别、年龄无差别统计一起出行的人平均幸存率：</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000    73\n",
       "1.000000    70\n",
       "0.500000    29\n",
       "0.666667     9\n",
       "0.333333     1\n",
       "0.750000     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[(all_data.SurnameGroupSize > 1)].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整组人全部幸存或全部遇难:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7814207650273224"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tmp.loc[0] + tmp.loc[1]) / (np.sum(tmp.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整组人幸存率大于等于$2/3$ 或 $0$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8360655737704918"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(tmp.loc[(tmp.index > 0.5) | (tmp.index == 0)].values) / (np.sum(tmp.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.<font color='blue'>只统计一起出行的人当中把女性小孩单独分组的平均幸存率：</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000    101\n",
       "0.000000     31\n",
       "0.333333      1\n",
       "0.750000      1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[(all_data.SurnameGroupSize > 1) & (index_boy | index_female)].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9850746268656716"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tmp.loc[0] + tmp.loc[1]) / (np.sum(tmp.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "女性男孩的数据按照 `SurnameGroupId` 分组以后，几乎所有的男孩和女性要么全遇难，要么全部幸存。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>**调整 `SurnameGroupId` 后的数据，`SurnameGroupSize` 需要重新计算**</font>：\n",
    "\n",
    "1.性别、年龄无差别统计一起出行的人平均幸存率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "surname_group_value_count = dict(all_data.SurnameGroupId.value_counts())\n",
    "all_data['SurnameGroupSize'] = all_data.apply(lambda row : surname_group_value_count[row.SurnameGroupId], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000    89\n",
       "1.000000    84\n",
       "0.500000    34\n",
       "0.666667    11\n",
       "0.750000     2\n",
       "0.800000     1\n",
       "0.333333     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[(all_data.SurnameGroupSize > 1)].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整组幸存率为$0$或$1$的概率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7792792792792793"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tmp.loc[0] + tmp.loc[1]) / (np.sum(tmp.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整组幸存率大于等于$2/3$或$0$ :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8423423423423423"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(tmp.loc[(tmp.index > 0.5) | (tmp.index == 0)].values) / (np.sum(tmp.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.只统计一起出行的人当中把**女性男孩单独分组**的平均幸存率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00    122\n",
       "0.00     34\n",
       "0.75      1\n",
       "0.50      1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[(all_data.SurnameGroupSize > 1) & (index_boy | index_female)].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整组幸存率为0或1的概率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9873417721518988"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tmp.loc[0] + tmp.loc[1]) / np.sum(tmp.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "回顾一下之前的<font color='red'>**目标**</font>：<font color='blue'><strong>默认预测女性都存活，男性都遇难，再加上以下：</strong></font>\n",
    "\n",
    "1. <font color='blue'>预测什么样的女性会遇难，毕竟少数，如果能够预测对，正确率就会极大提高。</font>\n",
    "\n",
    "2. <font color='blue'>预测什么样的男孩（最小年龄段的男性）会幸存，也是少数，如果能够预测对，正确率就会极大提高。</font>\n",
    "\n",
    "3. <font color='blue'>不是第一个年龄段的男性什么条件下会存活？</font>\n",
    "\n",
    "##### 遇难的女性所在的分组情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Vander Planke-3-S-18.0-3457__', 'Palsson-3-S-21.075-3499__',\n",
       "       'Turpin-2-S-21.0-116__', 'Arnold-Franchi-3-S-17.8-3492__',\n",
       "       'Goodwin-3-S-46.9-CA 21__', 'Zabour-3-C-14.4542-26__',\n",
       "       'Jussila-3-S-9.825-41__', 'Yousseff-3-C-14.4583-26__',\n",
       "       'Andersson-3-S-31.275-3470__', 'Robins-3-S-14.5-A/5. 33__',\n",
       "       'Boulos-3-C-15.2458-26__', 'Ford-3-S-34.375-W./C. 66__',\n",
       "       'Skoog-3-S-27.9-3470__', 'Sage-3-S-69.55-CA. 23__',\n",
       "       'Strom-3-S-10.4625-3470__', 'Lefebre-3-S-25.4667-41__',\n",
       "       'Rosblom-3-S-20.2125-3701__', 'Allison-1-S-151.55-1137__',\n",
       "       'Lahtinen-2-S-26.0-2506__', 'Barbara-3-C-14.4542-26__',\n",
       "       'Oreskovic-3-S-8.6625-3150__', 'Van Impe-3-S-24.15-3457__',\n",
       "       'Danbom-3-S-14.4-3470__', 'Canavan-3-Q-7.75-3648__',\n",
       "       'Cacic-3-S-8.6625-3150__', 'Caram-3-C-14.4583-26__',\n",
       "       'Lobb-3-S-16.1-A/5. 33__', 'Panula-3-S-39.6875-31012__',\n",
       "       'Bourke-3-Q-15.5-3648__', 'Ilmakangas-3-S-7.925-STON/O2. 31012__',\n",
       "       'Mudd-2-S-10.5-S.O./P.P.__', 'Carter-2-S-26.0-2442__',\n",
       "       'Rice-3-Q-29.125-3826__', 'Johnston-3-S-23.45-W./C. 66__'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perished_females_in_group = all_data.loc[(all_data.SurnameGroupSize > 1) & (all_data.Survived == 0) & (index_female), 'SurnameGroupId'].unique()\n",
    "perished_females_in_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    33\n",
       "0.5     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[all_data.SurnameGroupId.isin(perished_females_in_group), ['SurnameGroupId', 'Survived']].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9705882352941176"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[0] / tmp.values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "遇难的女性所在的分组不幸全军覆没，由此可以得出结论：如果整组都遇难那么，所在组的女性就会遇难。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 幸存的女性所在的分组情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000    75\n",
       "0.500000    28\n",
       "0.666667    10\n",
       "0.750000     2\n",
       "0.333333     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_females_in_group = all_data.loc[(all_data.SurnameGroupSize > 1) & (all_data.Survived == 1.0) & (index_female), 'SurnameGroupId'].unique()\n",
    "tmp = all_data.loc[all_data.SurnameGroupId.isin(survived_females_in_group), ['SurnameGroupId', 'Survived']].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.646551724137931"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[1] / tmp.values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 遇难的男孩所在的分组情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Palsson-3-S-21.075-3499__', 'Rice-3-Q-29.125-3826__',\n",
       "       'Panula-3-S-39.6875-31012__', 'Goodwin-3-S-46.9-CA 21__',\n",
       "       'Skoog-3-S-27.9-3470__', 'Sage-3-S-69.55-CA. 23__',\n",
       "       'Lefebre-3-S-25.4667-41__', 'Asplund-3-S-31.3875-3470__',\n",
       "       'Hassan-3-C-18.7875-26__', 'Andersson-3-S-31.275-3470__'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perished_boys_in_group = all_data.loc[ (all_data.SurnameGroupSize > 1) & (all_data.Survived == 0) & (index_boy), 'SurnameGroupId'].unique()\n",
    "perished_boys_in_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00    8\n",
       "0.50    1\n",
       "0.75    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[all_data.SurnameGroupId.isin(perished_boys_in_group), ['SurnameGroupId', 'Survived']].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[0] / tmp.values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "遇难的男孩所在的分组不幸全军覆没，由此可以得出结论：如果整组都遇难那么，所在组的男孩就会遇难。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 幸存男孩所在的分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000    12\n",
       "0.500000     4\n",
       "0.666667     2\n",
       "0.750000     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_boys_in_group = all_data.loc[ (all_data.SurnameGroupSize > 1) & (all_data.Survived == 1) & (index_boy), 'SurnameGroupId'].unique()\n",
    "tmp = all_data.loc[all_data.SurnameGroupId.isin(survived_boys_in_group), ['SurnameGroupId', 'Survived']].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.631578947368421"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[1] / tmp.values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 幸存成年男性所在分组情况\n",
    "\n",
    "由之前分析我们得到，成年男性幸存率非常低 $20\\%$不到"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ling-3-S-56.4958-16__', 'Greenfield-1-C-63.3583-PC 177__',\n",
       "       'Hassan-3-C-18.7875-26__', 'Hoyt-1-S-90.0-199__',\n",
       "       'Beckwith-1-S-52.5542-117__', 'Tornquist-3-S-0.0-LI__',\n",
       "       'McCoy-3-Q-23.25-3672__', 'Harder-1-C-55.4417-117__',\n",
       "       'Carter-1-S-120.0-1137__', 'Goldenberg-1-C-89.1042-174__',\n",
       "       'Bishop-1-C-91.0792-119__', 'Beane-2-S-26.0-29__',\n",
       "       'Thayer-1-C-110.8833-174__', 'Jonsson-3-S-7.8542-3504__',\n",
       "       'Frolicher-Stehli-1-C-79.2-135__',\n",
       "       'Duff Gordon-1-C-56.9292-PC 174__', 'Kimball-1-S-52.5542-117__',\n",
       "       'Nakid-3-C-15.7417-26__', 'Harper-1-C-76.7292-PC 175__',\n",
       "       'Frauenthal-1-S-133.65-PC 176__', 'Cardeza-1-C-512.3292-PC 177__',\n",
       "       'Dick-1-S-57.0-174__', 'Lam-3-S-56.4958-16__',\n",
       "       'Taylor-1-S-52.0-199__', 'Chambers-1-S-53.1-1138__'], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_male_adults_in_group = all_data.loc[(all_data.SurnameGroupSize > 1) & (all_data.Survived == 1) & (~index_boy) & (all_data.Sex == 'male')]\n",
    "survived_male_adults_in_group.SurnameGroupId.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 21)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_male_adults_in_group.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00    20\n",
       "0.50     3\n",
       "0.80     1\n",
       "0.75     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[all_data.SurnameGroupId.isin(survived_male_adults_in_group.SurnameGroupId), ['SurnameGroupId', 'Survived']].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.loc[1] / tmp.values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58901e9b0>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (4,4)\n",
    "sns.barplot(x='Pclass', y='Survived', data=all_data.loc[all_data.SurnameGroupId.isin(survived_male_adults_in_group.SurnameGroupId)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d588fcde80>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_adults_in_group = all_data.loc[(all_data.SurnameGroupSize > 1) & (~index_boy) & (all_data.Sex == 'male')]\n",
    "pylab.rcParams['figure.figsize'] = (4,4)\n",
    "sns.barplot(x='Pclass', y='Survived', data=male_adults_in_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_data.loc[all_data.SurnameGroupId.isin(survived_male_adults_in_group.SurnameGroupId)].sort_values(['SurnameGroupId']).to_csv('幸存的成年男性.csv', index = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 遇难成年男性所在分组情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000    76\n",
       "0.500000    32\n",
       "0.666667    11\n",
       "0.800000     1\n",
       "0.333333     1\n",
       "0.750000     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perished_male_adults_in_group = all_data.loc[(all_data.SurnameGroupSize > 1) & (all_data.Survived == 0) & (~index_boy) & (all_data.Sex == 'male'), 'SurnameGroupId'].unique()\n",
    "tmp = all_data.loc[all_data.SurnameGroupId.isin(perished_male_adults_in_group), ['SurnameGroupId', 'Survived']].groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6229508196721312"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.loc[0] / tmp.values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "perished_females_in_group = None\n",
    "survived_females_in_group = None\n",
    "perished_boys_in_group = None\n",
    "survived_male_adults_in_group = None\n",
    "perished_male_adults_in_group = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>**经过上面的分析，我们能用到的就是将女士和男孩单独区分出来，当他们所在的组是全部幸存或者全部遇难的话，就可以直接判断他们幸存与否**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 整组幸存情况都未知\n",
    "\n",
    "回顾一下之前的<font color='red'>**目标**</font>：<font color='blue'><strong>默认预测女性都存活，男性都遇难，再加上以下：</strong></font>\n",
    "\n",
    "1. <font color='blue'>预测什么样的女性会遇难，毕竟少数，如果能够预测对，正确率就会极大提高。</font>\n",
    "\n",
    "2. <font color='blue'>预测什么样的男孩（最小年龄段的男性）会幸存，也是少数，如果能够预测对，正确率就会极大提高。</font>\n",
    "\n",
    "3. <font color='blue'>不是第一个年龄段的男性什么条件下会存活？</font>\n",
    "\n",
    "我们这时候需要考虑**整组人是否幸存都未知的数据**，因为这样的数据我们没法利用组内的平均幸存率做预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = ['SurnameGroupId', 'Cabin', 'Pclass', 'Embarked', 'Sex', 'Fare', 'TicketGroupId', 'SibSp', 'Parch', 'Age', 'Survived', 'Title', 'SurnameGroupSize', 'SingleTicketFare']\n",
    "groups = all_data.loc[(all_data.SurnameGroupSize > 1), selected_features].sort_values(['SurnameGroupId', 'Survived']).groupby(['SurnameGroupId'])\n",
    "\n",
    "all_perished_groups = pd.DataFrame()\n",
    "one_more_survived_groups = pd.DataFrame()\n",
    "\n",
    "for index, group in groups:\n",
    "    indices = group.Survived.value_counts().index\n",
    "    if indices.shape[0] == 0:\n",
    "        all_perished_groups = all_perished_groups.append(group)\n",
    "    else:\n",
    "        one_more_survived_groups = one_more_survived_groups.append(group)\n",
    "        \n",
    "groups = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整组人幸存情况未知的组数为："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 222)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_perished_groups.SurnameGroupId.unique().shape[0], len(one_more_survived_groups.SurnameGroupId.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44, 606)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_perished_groups.SurnameGroupId.shape[0], one_more_survived_groups.SurnameGroupId.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "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>SurnameGroupId</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Fare</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>Abelseth-3-S-7.65-3481__</td>\n",
       "      <td>F G63</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>3-S-7.65-348122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>7.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>Abelseth-3-S-7.65-3481__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>3-S-7.65-348125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Miss</td>\n",
       "      <td>7.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>Andersson-3-S-7.775-3470__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>3-S-7.775-347091</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>38.00</td>\n",
       "      <td>Miss</td>\n",
       "      <td>7.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>Andersson-3-S-7.775-3470__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>3-S-7.775-347075</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>7.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>Clark-1-C-136.7792-135__</td>\n",
       "      <td>C89</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>136.7792</td>\n",
       "      <td>1-C-136.7792-13508</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>68.389600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>Clark-1-C-136.7792-135__</td>\n",
       "      <td>C89</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>136.7792</td>\n",
       "      <td>1-C-136.7792-13508</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>68.389600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Dyker-3-S-13.9-3470__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>13.9000</td>\n",
       "      <td>3-S-13.9-347072</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>6.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>Dyker-3-S-13.9-3470__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>13.9000</td>\n",
       "      <td>3-S-13.9-347072</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>6.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>Gibson-1-C-59.4-1123__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>1-C-59.4-112378</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>29.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>Gibson-1-C-59.4-1123__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>1-C-59.4-112378</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Miss</td>\n",
       "      <td>29.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>Howard-2-S-26.0-240__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>2-S-26.0-24065</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>Howard-2-S-26.0-240__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>2-S-26.0-24065</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>Jefferys-2-S-31.5-C.A. 310__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>31.5000</td>\n",
       "      <td>2-S-31.5-C.A. 31029</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>Jefferys-2-S-31.5-C.A. 310__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>31.5000</td>\n",
       "      <td>2-S-31.5-C.A. 31029</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>Jefferys-2-S-31.5-C.A. 310__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>31.5000</td>\n",
       "      <td>2-S-31.5-C.A. 31029</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Karnes-2-S-21.0-F.C.C. 135__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-F.C.C. 13534</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>Karnes-2-S-21.0-F.C.C. 135__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-F.C.C. 13534</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>Khalil-3-C-14.4542-26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>3-C-14.4542-2660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mr</td>\n",
       "      <td>7.227100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>Khalil-3-C-14.4542-26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>3-C-14.4542-2660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>7.227100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>Klasen-3-S-12.1833-3504__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>12.1833</td>\n",
       "      <td>3-S-12.1833-350405</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>36.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>6.091650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>Klasen-3-S-12.1833-3504__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>12.1833</td>\n",
       "      <td>3-S-12.1833-350405</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Miss</td>\n",
       "      <td>6.091650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>McCaffry-1-C-75.2417-130__</td>\n",
       "      <td>C6</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>75.2417</td>\n",
       "      <td>1-C-75.2417-13050</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>37.620850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>McCaffry-1-C-75.2417-130__</td>\n",
       "      <td>C6</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>75.2417</td>\n",
       "      <td>1-C-75.2417-13050</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>37.620850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>Peacock-3-S-13.775-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>3-S-13.775-SOTON/O.Q. 3101315</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>4.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172</th>\n",
       "      <td>Peacock-3-S-13.775-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>3-S-13.775-SOTON/O.Q. 3101315</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Master</td>\n",
       "      <td>4.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Peacock-3-S-13.775-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>3-S-13.775-SOTON/O.Q. 3101315</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Miss</td>\n",
       "      <td>4.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>Phillips-2-S-21.0-S.O./P.P.__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-S.O./P.P. 2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.00</td>\n",
       "      <td>Miss</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Phillips-2-S-21.0-S.O./P.P.__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-S.O./P.P. 2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>Pokrnic-3-S-8.6625-3150__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>3-S-8.6625-315095</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>8.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>Pokrnic-3-S-8.6625-3150__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>3-S-8.6625-315092</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>8.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>Schabert-1-C-57.75-132__</td>\n",
       "      <td>C78</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>57.7500</td>\n",
       "      <td>1-C-57.75-13236</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>28.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>Schabert-1-C-57.75-132__</td>\n",
       "      <td>C28</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>57.7500</td>\n",
       "      <td>1-C-57.75-13236</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>28.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>Smith-1-S-60.0-136__</td>\n",
       "      <td>C31</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>1-S-60.0-13695</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>Smith-1-S-60.0-136__</td>\n",
       "      <td>C31</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>1-S-60.0-13695</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>Snyder-1-S-82.2667-212__</td>\n",
       "      <td>B45</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>1-S-82.2667-21228</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>41.133350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>Snyder-1-S-82.2667-212__</td>\n",
       "      <td>B45</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>1-S-82.2667-21228</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>41.133350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>Stengel-1-C-55.4417-117__</td>\n",
       "      <td>C116</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>55.4417</td>\n",
       "      <td>1-C-55.4417-11778</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>27.720850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>Stengel-1-C-55.4417-117__</td>\n",
       "      <td>C116</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>55.4417</td>\n",
       "      <td>1-C-55.4417-11778</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>27.720850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>Thomas-3-C-6.4375-26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>6.4375</td>\n",
       "      <td>3-C-6.4375-2681</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mr</td>\n",
       "      <td>6.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>Thomas-3-C-6.4375-26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>6.4375</td>\n",
       "      <td>3-C-6.4375-2621</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mr</td>\n",
       "      <td>6.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>Ware-2-S-21.0-CA 313__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-CA 31352</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>Ware-2-S-21.0-CA 313__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-CA 31352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>Zakarian-3-C-7.225-26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>3-C-7.225-2656</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>Mr</td>\n",
       "      <td>7.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>Zakarian-3-C-7.225-26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>3-C-7.225-2670</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>Mr</td>\n",
       "      <td>7.225000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             SurnameGroupId  Cabin  Pclass Embarked     Sex  \\\n",
       "948                Abelseth-3-S-7.65-3481__  F G63       3        S    male   \n",
       "1236               Abelseth-3-S-7.65-3481__    NaN       3        S  female   \n",
       "1105             Andersson-3-S-7.775-3470__    NaN       3        S  female   \n",
       "1211             Andersson-3-S-7.775-3470__    NaN       3        S    male   \n",
       "1143               Clark-1-C-136.7792-135__    C89       1        C    male   \n",
       "1163               Clark-1-C-136.7792-135__    C89       1        C  female   \n",
       "981                   Dyker-3-S-13.9-3470__    NaN       3        S  female   \n",
       "1063                  Dyker-3-S-13.9-3470__    NaN       3        S    male   \n",
       "1259                 Gibson-1-C-59.4-1123__    NaN       1        C  female   \n",
       "1293                 Gibson-1-C-59.4-1123__    NaN       1        C  female   \n",
       "904                   Howard-2-S-26.0-240__    NaN       2        S    male   \n",
       "1104                  Howard-2-S-26.0-240__    NaN       2        S  female   \n",
       "922            Jefferys-2-S-31.5-C.A. 310__    NaN       2        S    male   \n",
       "1210           Jefferys-2-S-31.5-C.A. 310__    NaN       2        S    male   \n",
       "1229           Jefferys-2-S-31.5-C.A. 310__    NaN       2        S    male   \n",
       "956            Karnes-2-S-21.0-F.C.C. 135__    NaN       2        S  female   \n",
       "1137           Karnes-2-S-21.0-F.C.C. 135__    NaN       2        S  female   \n",
       "976                 Khalil-3-C-14.4542-26__    NaN       3        C    male   \n",
       "1140                Khalil-3-C-14.4542-26__    NaN       3        C  female   \n",
       "1044              Klasen-3-S-12.1833-3504__    NaN       3        S  female   \n",
       "1154              Klasen-3-S-12.1833-3504__    NaN       3        S  female   \n",
       "1009             McCaffry-1-C-75.2417-130__     C6       1        C    male   \n",
       "1161             McCaffry-1-C-75.2417-130__     C6       1        C    male   \n",
       "1050  Peacock-3-S-13.775-SOTON/O.Q. 31013__    NaN       3        S  female   \n",
       "1172  Peacock-3-S-13.775-SOTON/O.Q. 31013__    NaN       3        S    male   \n",
       "1300  Peacock-3-S-13.775-SOTON/O.Q. 31013__    NaN       3        S  female   \n",
       "1077          Phillips-2-S-21.0-S.O./P.P.__    NaN       2        S  female   \n",
       "1193          Phillips-2-S-21.0-S.O./P.P.__    NaN       2        S    male   \n",
       "1160              Pokrnic-3-S-8.6625-3150__    NaN       3        S    male   \n",
       "1194              Pokrnic-3-S-8.6625-3150__    NaN       3        S    male   \n",
       "925                Schabert-1-C-57.75-132__    C78       1        C    male   \n",
       "1013               Schabert-1-C-57.75-132__    C28       1        C  female   \n",
       "941                    Smith-1-S-60.0-136__    C31       1        S    male   \n",
       "1286                   Smith-1-S-60.0-136__    C31       1        S  female   \n",
       "903                Snyder-1-S-82.2667-212__    B45       1        S  female   \n",
       "1178               Snyder-1-S-82.2667-212__    B45       1        S    male   \n",
       "991               Stengel-1-C-55.4417-117__   C116       1        C  female   \n",
       "1068              Stengel-1-C-55.4417-117__   C116       1        C    male   \n",
       "1007                 Thomas-3-C-6.4375-26__    NaN       3        C    male   \n",
       "1024                 Thomas-3-C-6.4375-26__    NaN       3        C    male   \n",
       "1169                 Ware-2-S-21.0-CA 313__    NaN       2        S    male   \n",
       "1253                 Ware-2-S-21.0-CA 313__    NaN       2        S  female   \n",
       "1027                Zakarian-3-C-7.225-26__    NaN       3        C    male   \n",
       "1062                Zakarian-3-C-7.225-26__    NaN       3        C    male   \n",
       "\n",
       "          Fare                  TicketGroupId  SibSp  Parch    Age   Title  \\\n",
       "948     7.6500                3-S-7.65-348122      0      0  25.00      Mr   \n",
       "1236    7.6500                3-S-7.65-348125      0      0  16.00    Miss   \n",
       "1105    7.7750               3-S-7.775-347091      4      2  38.00    Miss   \n",
       "1211    7.7750               3-S-7.775-347075      0      0  26.00      Mr   \n",
       "1143  136.7792             1-C-136.7792-13508      1      0  27.00      Mr   \n",
       "1163  136.7792             1-C-136.7792-13508      1      0  26.00     Mrs   \n",
       "981    13.9000                3-S-13.9-347072      1      0  22.00     Mrs   \n",
       "1063   13.9000                3-S-13.9-347072      1      0  23.00      Mr   \n",
       "1259   59.4000                1-C-59.4-112378      0      1  45.00     Mrs   \n",
       "1293   59.4000                1-C-59.4-112378      0      1  22.00    Miss   \n",
       "904    26.0000                 2-S-26.0-24065      1      0  63.00      Mr   \n",
       "1104   26.0000                 2-S-26.0-24065      1      0  60.00     Mrs   \n",
       "922    31.5000            2-S-31.5-C.A. 31029      2      0  24.00      Mr   \n",
       "1210   31.5000            2-S-31.5-C.A. 31029      2      0  22.00      Mr   \n",
       "1229   31.5000            2-S-31.5-C.A. 31029      0      0  25.00      Mr   \n",
       "956    21.0000          2-S-21.0-F.C.C. 13534      0      0    NaN     Mrs   \n",
       "1137   21.0000          2-S-21.0-F.C.C. 13534      0      0  22.00     Mrs   \n",
       "976    14.4542               3-C-14.4542-2660      1      0    NaN      Mr   \n",
       "1140   14.4542               3-C-14.4542-2660      1      0    NaN     Mrs   \n",
       "1044   12.1833             3-S-12.1833-350405      0      2  36.00     Mrs   \n",
       "1154   12.1833             3-S-12.1833-350405      1      1   1.00    Miss   \n",
       "1009   75.2417              1-C-75.2417-13050      0      0  36.00      Mr   \n",
       "1161   75.2417              1-C-75.2417-13050      0      0  46.00      Mr   \n",
       "1050   13.7750  3-S-13.775-SOTON/O.Q. 3101315      0      2  26.00     Mrs   \n",
       "1172   13.7750  3-S-13.775-SOTON/O.Q. 3101315      1      1   0.75  Master   \n",
       "1300   13.7750  3-S-13.775-SOTON/O.Q. 3101315      1      1   3.00    Miss   \n",
       "1077   21.0000           2-S-21.0-S.O./P.P. 2      0      1  21.00    Miss   \n",
       "1193   21.0000           2-S-21.0-S.O./P.P. 2      0      1  43.00      Mr   \n",
       "1160    8.6625              3-S-8.6625-315095      0      0  17.00      Mr   \n",
       "1194    8.6625              3-S-8.6625-315092      0      0  24.00      Mr   \n",
       "925    57.7500                1-C-57.75-13236      1      0  30.00      Mr   \n",
       "1013   57.7500                1-C-57.75-13236      1      0  35.00     Mrs   \n",
       "941    60.0000                 1-S-60.0-13695      1      0  24.00      Mr   \n",
       "1286   60.0000                 1-S-60.0-13695      1      0  18.00     Mrs   \n",
       "903    82.2667              1-S-82.2667-21228      1      0  23.00     Mrs   \n",
       "1178   82.2667              1-S-82.2667-21228      1      0  24.00      Mr   \n",
       "991    55.4417              1-C-55.4417-11778      1      0  43.00     Mrs   \n",
       "1068   55.4417              1-C-55.4417-11778      1      0  54.00      Mr   \n",
       "1007    6.4375                3-C-6.4375-2681      0      0    NaN      Mr   \n",
       "1024    6.4375                3-C-6.4375-2621      1      0    NaN      Mr   \n",
       "1169   21.0000              2-S-21.0-CA 31352      1      0  30.00      Mr   \n",
       "1253   21.0000              2-S-21.0-CA 31352      0      0  31.00     Mrs   \n",
       "1027    7.2250                 3-C-7.225-2656      0      0  26.50      Mr   \n",
       "1062    7.2250                 3-C-7.225-2670      0      0  27.00      Mr   \n",
       "\n",
       "      SingleTicketFare  \n",
       "948           7.650000  \n",
       "1236          7.650000  \n",
       "1105          7.775000  \n",
       "1211          7.775000  \n",
       "1143         68.389600  \n",
       "1163         68.389600  \n",
       "981           6.950000  \n",
       "1063          6.950000  \n",
       "1259         29.700000  \n",
       "1293         29.700000  \n",
       "904          13.000000  \n",
       "1104         13.000000  \n",
       "922          10.500000  \n",
       "1210         10.500000  \n",
       "1229         10.500000  \n",
       "956          10.500000  \n",
       "1137         10.500000  \n",
       "976           7.227100  \n",
       "1140          7.227100  \n",
       "1044          6.091650  \n",
       "1154          6.091650  \n",
       "1009         37.620850  \n",
       "1161         37.620850  \n",
       "1050          4.591667  \n",
       "1172          4.591667  \n",
       "1300          4.591667  \n",
       "1077         10.500000  \n",
       "1193         10.500000  \n",
       "1160          8.662500  \n",
       "1194          8.662500  \n",
       "925          28.875000  \n",
       "1013         28.875000  \n",
       "941          30.000000  \n",
       "1286         30.000000  \n",
       "903          41.133350  \n",
       "1178         41.133350  \n",
       "991          27.720850  \n",
       "1068         27.720850  \n",
       "1007          6.437500  \n",
       "1024          6.437500  \n",
       "1169         10.500000  \n",
       "1253         10.500000  \n",
       "1027          7.225000  \n",
       "1062          7.225000  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_perished_groups.drop(['Survived', 'SurnameGroupSize'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "挑出女性和男孩的情况： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Title</th>\n",
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       "      <th>SingleTicketFare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>Abelseth-3-S-7.65-3481__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>3-S-7.65-348125</td>\n",
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       "      <td>0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miss</td>\n",
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       "      <td>7.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>Andersson-3-S-7.775-3470__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>3-S-7.775-347091</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>38.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miss</td>\n",
       "      <td>2</td>\n",
       "      <td>7.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>Clark-1-C-136.7792-135__</td>\n",
       "      <td>C89</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>136.7792</td>\n",
       "      <td>1-C-136.7792-13508</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>68.389600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Dyker-3-S-13.9-3470__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>13.9000</td>\n",
       "      <td>3-S-13.9-347072</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>6.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>Gibson-1-C-59.4-1123__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>1-C-59.4-112378</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>29.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>Gibson-1-C-59.4-1123__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>1-C-59.4-112378</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miss</td>\n",
       "      <td>2</td>\n",
       "      <td>29.700000</td>\n",
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       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>Howard-2-S-26.0-240__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>2-S-26.0-24065</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Karnes-2-S-21.0-F.C.C. 135__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
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       "      <td>2-S-21.0-F.C.C. 13534</td>\n",
       "      <td>0</td>\n",
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       "      <th>1137</th>\n",
       "      <td>Karnes-2-S-21.0-F.C.C. 135__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Mrs</td>\n",
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       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>Khalil-3-C-14.4542-26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>3-C-14.4542-2660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>7.227100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>Klasen-3-S-12.1833-3504__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>12.1833</td>\n",
       "      <td>3-S-12.1833-350405</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>36.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>6.091650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>Klasen-3-S-12.1833-3504__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>12.1833</td>\n",
       "      <td>3-S-12.1833-350405</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miss</td>\n",
       "      <td>2</td>\n",
       "      <td>6.091650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>Peacock-3-S-13.775-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>3-S-13.775-SOTON/O.Q. 3101315</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>3</td>\n",
       "      <td>4.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172</th>\n",
       "      <td>Peacock-3-S-13.775-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>3-S-13.775-SOTON/O.Q. 3101315</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "      <td>4.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Peacock-3-S-13.775-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>3-S-13.775-SOTON/O.Q. 3101315</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miss</td>\n",
       "      <td>3</td>\n",
       "      <td>4.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>Phillips-2-S-21.0-S.O./P.P.__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-S.O./P.P. 2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miss</td>\n",
       "      <td>2</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>Schabert-1-C-57.75-132__</td>\n",
       "      <td>C28</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>57.7500</td>\n",
       "      <td>1-C-57.75-13236</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>28.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>Smith-1-S-60.0-136__</td>\n",
       "      <td>C31</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>1-S-60.0-13695</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>Snyder-1-S-82.2667-212__</td>\n",
       "      <td>B45</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>1-S-82.2667-21228</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>41.133350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>Stengel-1-C-55.4417-117__</td>\n",
       "      <td>C116</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>55.4417</td>\n",
       "      <td>1-C-55.4417-11778</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>27.720850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>Ware-2-S-21.0-CA 313__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2-S-21.0-CA 31352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             SurnameGroupId Cabin  Pclass Embarked     Sex  \\\n",
       "1236               Abelseth-3-S-7.65-3481__   NaN       3        S  female   \n",
       "1105             Andersson-3-S-7.775-3470__   NaN       3        S  female   \n",
       "1163               Clark-1-C-136.7792-135__   C89       1        C  female   \n",
       "981                   Dyker-3-S-13.9-3470__   NaN       3        S  female   \n",
       "1259                 Gibson-1-C-59.4-1123__   NaN       1        C  female   \n",
       "1293                 Gibson-1-C-59.4-1123__   NaN       1        C  female   \n",
       "1104                  Howard-2-S-26.0-240__   NaN       2        S  female   \n",
       "956            Karnes-2-S-21.0-F.C.C. 135__   NaN       2        S  female   \n",
       "1137           Karnes-2-S-21.0-F.C.C. 135__   NaN       2        S  female   \n",
       "1140                Khalil-3-C-14.4542-26__   NaN       3        C  female   \n",
       "1044              Klasen-3-S-12.1833-3504__   NaN       3        S  female   \n",
       "1154              Klasen-3-S-12.1833-3504__   NaN       3        S  female   \n",
       "1050  Peacock-3-S-13.775-SOTON/O.Q. 31013__   NaN       3        S  female   \n",
       "1172  Peacock-3-S-13.775-SOTON/O.Q. 31013__   NaN       3        S    male   \n",
       "1300  Peacock-3-S-13.775-SOTON/O.Q. 31013__   NaN       3        S  female   \n",
       "1077          Phillips-2-S-21.0-S.O./P.P.__   NaN       2        S  female   \n",
       "1013               Schabert-1-C-57.75-132__   C28       1        C  female   \n",
       "1286                   Smith-1-S-60.0-136__   C31       1        S  female   \n",
       "903                Snyder-1-S-82.2667-212__   B45       1        S  female   \n",
       "991               Stengel-1-C-55.4417-117__  C116       1        C  female   \n",
       "1253                 Ware-2-S-21.0-CA 313__   NaN       2        S  female   \n",
       "\n",
       "          Fare                  TicketGroupId  SibSp  Parch    Age  Survived  \\\n",
       "1236    7.6500                3-S-7.65-348125      0      0  16.00       NaN   \n",
       "1105    7.7750               3-S-7.775-347091      4      2  38.00       NaN   \n",
       "1163  136.7792             1-C-136.7792-13508      1      0  26.00       NaN   \n",
       "981    13.9000                3-S-13.9-347072      1      0  22.00       NaN   \n",
       "1259   59.4000                1-C-59.4-112378      0      1  45.00       NaN   \n",
       "1293   59.4000                1-C-59.4-112378      0      1  22.00       NaN   \n",
       "1104   26.0000                 2-S-26.0-24065      1      0  60.00       NaN   \n",
       "956    21.0000          2-S-21.0-F.C.C. 13534      0      0    NaN       NaN   \n",
       "1137   21.0000          2-S-21.0-F.C.C. 13534      0      0  22.00       NaN   \n",
       "1140   14.4542               3-C-14.4542-2660      1      0    NaN       NaN   \n",
       "1044   12.1833             3-S-12.1833-350405      0      2  36.00       NaN   \n",
       "1154   12.1833             3-S-12.1833-350405      1      1   1.00       NaN   \n",
       "1050   13.7750  3-S-13.775-SOTON/O.Q. 3101315      0      2  26.00       NaN   \n",
       "1172   13.7750  3-S-13.775-SOTON/O.Q. 3101315      1      1   0.75       NaN   \n",
       "1300   13.7750  3-S-13.775-SOTON/O.Q. 3101315      1      1   3.00       NaN   \n",
       "1077   21.0000           2-S-21.0-S.O./P.P. 2      0      1  21.00       NaN   \n",
       "1013   57.7500                1-C-57.75-13236      1      0  35.00       NaN   \n",
       "1286   60.0000                 1-S-60.0-13695      1      0  18.00       NaN   \n",
       "903    82.2667              1-S-82.2667-21228      1      0  23.00       NaN   \n",
       "991    55.4417              1-C-55.4417-11778      1      0  43.00       NaN   \n",
       "1253   21.0000              2-S-21.0-CA 31352      0      0  31.00       NaN   \n",
       "\n",
       "       Title  SurnameGroupSize  SingleTicketFare  \n",
       "1236    Miss                 2          7.650000  \n",
       "1105    Miss                 2          7.775000  \n",
       "1163     Mrs                 2         68.389600  \n",
       "981      Mrs                 2          6.950000  \n",
       "1259     Mrs                 2         29.700000  \n",
       "1293    Miss                 2         29.700000  \n",
       "1104     Mrs                 2         13.000000  \n",
       "956      Mrs                 2         10.500000  \n",
       "1137     Mrs                 2         10.500000  \n",
       "1140     Mrs                 2          7.227100  \n",
       "1044     Mrs                 2          6.091650  \n",
       "1154    Miss                 2          6.091650  \n",
       "1050     Mrs                 3          4.591667  \n",
       "1172  Master                 3          4.591667  \n",
       "1300    Miss                 3          4.591667  \n",
       "1077    Miss                 2         10.500000  \n",
       "1013     Mrs                 2         28.875000  \n",
       "1286     Mrs                 2         30.000000  \n",
       "903      Mrs                 2         41.133350  \n",
       "991      Mrs                 2         27.720850  \n",
       "1253     Mrs                 2         10.500000  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_perished_groups.loc[(all_perished_groups.Sex == 'female') | ((all_perished_groups.Age <= children_age_upper_limit) & (all_perished_groups.Sex == 'male')) | (all_perished_groups.Title == 'Master')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查一下：`van Billiard` 家庭："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Title</th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>SurnameGroupSize</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>40.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>14.5</td>\n",
       "      <td>2</td>\n",
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       "      <td>van Billiard-3-S-14.5-A/5. 8__</td>\n",
       "      <td>3-S-14.5-A/5. 851</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>11.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>Master</td>\n",
       "      <td>van Billiard-3-S-14.5-A/5. 8__</td>\n",
       "      <td>3-S-14.5-A/5. 851</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>Master</td>\n",
       "      <td>van Billiard-3-S-14.5-A/5. 8__</td>\n",
       "      <td>3-S-14.5-A/5. 851</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age Cabin Embarked  Fare  Parch  Pclass   Sex  SibSp  Survived  \\\n",
       "153   40.5   NaN        S  14.5      2       3  male      0       0.0   \n",
       "1083  11.5   NaN        S  14.5      1       3  male      1       NaN   \n",
       "1235   NaN   NaN        S  14.5      1       3  male      1       NaN   \n",
       "\n",
       "      TicketSize  SingleTicketFare   Title                  SurnameGroupId  \\\n",
       "153            3          4.833333      Mr  van Billiard-3-S-14.5-A/5. 8__   \n",
       "1083           3          4.833333  Master  van Billiard-3-S-14.5-A/5. 8__   \n",
       "1235           3          4.833333  Master  van Billiard-3-S-14.5-A/5. 8__   \n",
       "\n",
       "          TicketGroupId  SurnameGroupSize  \n",
       "153   3-S-14.5-A/5. 851                 3  \n",
       "1083  3-S-14.5-A/5. 851                 3  \n",
       "1235  3-S-14.5-A/5. 851                 3  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data.Surname == 'van Billiard'].drop(['Ticket', 'FamilySize', 'Surname', 'Name', 'TicketGroupSize', 'PassengerId'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 至少一个人生存情况可知的组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>746</th>\n",
       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
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       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
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       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>3-S-20.25-C.A. 2673</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Abelson-2-C-24.0-P/PP 33__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>2-C-24.0-P/PP 3381</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>Abelson-2-C-24.0-P/PP 33__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>2-C-24.0-P/PP 3381</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>Aks-3-S-9.35-3920__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>9.3500</td>\n",
       "      <td>3-S-9.35-392091</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>4.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>Aks-3-S-9.35-3920__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>9.3500</td>\n",
       "      <td>3-S-9.35-392091</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master</td>\n",
       "      <td>2</td>\n",
       "      <td>4.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Ali-3-S-7.05-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>3-S-7.05-SOTON/O.Q. 3101311</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "      <td>7.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>Ali-3-S-7.05-SOTON/O.Q. 31013__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>3-S-7.05-SOTON/O.Q. 3101312</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "      <td>7.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>Allen-1-S-211.3375-241__</td>\n",
       "      <td>B5</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>1-S-211.3375-24160</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Miss</td>\n",
       "      <td>4</td>\n",
       "      <td>52.834375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SurnameGroupId Cabin  Pclass Embarked     Sex  \\\n",
       "746        Abbott-3-S-20.25-C.A. 26__   NaN       3        S    male   \n",
       "279        Abbott-3-S-20.25-C.A. 26__   NaN       3        S  female   \n",
       "1283       Abbott-3-S-20.25-C.A. 26__   NaN       3        S    male   \n",
       "308        Abelson-2-C-24.0-P/PP 33__   NaN       2        C    male   \n",
       "874        Abelson-2-C-24.0-P/PP 33__   NaN       2        C  female   \n",
       "855               Aks-3-S-9.35-3920__   NaN       3        S  female   \n",
       "1198              Aks-3-S-9.35-3920__   NaN       3        S    male   \n",
       "210   Ali-3-S-7.05-SOTON/O.Q. 31013__   NaN       3        S    male   \n",
       "784   Ali-3-S-7.05-SOTON/O.Q. 31013__   NaN       3        S    male   \n",
       "689          Allen-1-S-211.3375-241__    B5       1        S  female   \n",
       "\n",
       "          Fare                TicketGroupId  SibSp  Parch    Age  Survived  \\\n",
       "746    20.2500          3-S-20.25-C.A. 2673      1      1  16.00       0.0   \n",
       "279    20.2500          3-S-20.25-C.A. 2673      1      1  35.00       1.0   \n",
       "1283   20.2500          3-S-20.25-C.A. 2673      0      2  13.00       NaN   \n",
       "308    24.0000           2-C-24.0-P/PP 3381      1      0  30.00       0.0   \n",
       "874    24.0000           2-C-24.0-P/PP 3381      1      0  28.00       1.0   \n",
       "855     9.3500              3-S-9.35-392091      0      1  18.00       1.0   \n",
       "1198    9.3500              3-S-9.35-392091      0      1   0.83       NaN   \n",
       "210     7.0500  3-S-7.05-SOTON/O.Q. 3101311      0      0  24.00       0.0   \n",
       "784     7.0500  3-S-7.05-SOTON/O.Q. 3101312      0      0  25.00       0.0   \n",
       "689   211.3375           1-S-211.3375-24160      0      1  15.00       1.0   \n",
       "\n",
       "       Title  SurnameGroupSize  SingleTicketFare  \n",
       "746       Mr                 3          6.750000  \n",
       "279      Mrs                 3          6.750000  \n",
       "1283  Master                 3          6.750000  \n",
       "308       Mr                 2         12.000000  \n",
       "874      Mrs                 2         12.000000  \n",
       "855      Mrs                 2          4.675000  \n",
       "1198  Master                 2          4.675000  \n",
       "210       Mr                 2          7.050000  \n",
       "784       Mr                 2          7.050000  \n",
       "689     Miss                 4         52.834375  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_more_survived_groups.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 0.723404255319149, 2: 0.5567010309278351, 3: 0.27860696517412936}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d5890c9978>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_survived = dict(one_more_survived_groups.groupby(['Pclass']).Survived.mean())\n",
    "print(pclass_survived)\n",
    "plt.rcParams['figure.figsize'] =(5, 2)\n",
    "sns.barplot(x='Survived', y='Pclass', data=one_more_survived_groups, orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.6826923076923077, 'Q': 0.3181818181818182, 'S': 0.42443729903536975}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d5890c9ba8>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embarked_survived = dict(one_more_survived_groups.groupby(['Embarked']).Survived.mean())\n",
    "print(embarked_survived)\n",
    "plt.rcParams['figure.figsize'] =(3, 5)\n",
    "sns.barplot(x='Embarked', y='Survived', data=one_more_survived_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Survived\n",
      "CategoricalFare           \n",
      "(-0.001, 11.133]  0.148936\n",
      "(11.133, 15.5]    0.391304\n",
      "(15.5, 20.25]     0.522727\n",
      "(20.25, 25.467]   0.380952\n",
      "(25.467, 27.9]    0.468085\n",
      "(27.9, 39.688]    0.363636\n",
      "(39.688, 57.0]    0.577778\n",
      "(57.0, 78.85]     0.523810\n",
      "(78.85, 134.5]    0.795918\n",
      "(134.5, 512.329]  0.696970\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58919feb8>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SEoIKgoSEoIIgISGoIEhICCoIEhKCCoKEhKCCICEhqCBISAgqCBISggqChISggiAhIaggSEgIKggSEoIKgoSEoIIgISGoIEhICCoIEhKCCoKEhKCCICEhqCBISAgqCBISggqChISggiAhIaggSEgIKggSEoIKgoSEoIIgISGoIEhICCoIEhKCCoKEtKWgeoaNWGIbBEOFthTUG+O24M1V1uGNcVsMtitBsARt+RO/YOx6LBi73mC7EQRL0ZY1VBAMVUJQQZCQEFQQJCQEFQQJCUEFQUJCUEGQkKZ0m0saBlwAbAbMB440szkV5z8JfBpYAHzLzG5uhh9B0GqaVUPtC4w2s+2AE4GzyickrQMcB+wA7AGcJmmFJvkRBC2lWYLaEbgNwMw6ga0qzm0DzDCz+Wb2MjAHeFeT/AiCltLR09OT3KikS4DrzOzWbP8ZYCMzWyDp48CmZvbl7NzlwOVmdmelja6urvSOBUEiJk6c2FHteLNCj14BxlTsDzOzBf2cGwO81NdAfw4HwVCmWU2+GcDeAJImATMrzv0G2EnSaEljgQnArCb5EQQtpVlNvnIv37uADuBwXGBzzOzGrJfvU7igv2Nm1yV3IggGgaYIKgiWV9pu+oaklYFJwNrAC0Cnmb26PNoYCj4MFRtDwQdooxpK0n8AXwe2Bh7FOzLeAgj4HfBVM5u7PNgYCj70sbEN8PvBsDFU7kUvPT09bfFXKpUuKpVKm/dzbpNSqTRlGbHxzlo2Gi3fZjYubIf/afkv2Re+1X+lUmmHwfYhwWfYos9+R6lUGt2q8qlsZOXG9NkfVoeNVZtwj9/Syv9pOwfHfiuVIUnbSZo4CDZ+K+liSeUxt7WAmZIOblH5JDYkHQncVLE/DJgt6YACNj4GzJK0qqSvSXpC0hWSPp+z/Fr9nJqW14cqNgv/T9tZUCkf/p4BpkhaqcU27gdeB84AMLN/ApsAn2tR+VQ2jgTeByBpPzNbBEwEvlzAxueAd5jZK8D5+Njk2cAROcvfIWmOpGskfbDieCPfk8L/07br5avge/UWlHQ3Pj5Wpgf4T+AOPA6xJTYAzOw4STdI+qyZ/cDM5ktaULtkmvKJbHSbWXf2+nxJM/BeskUFbLyBCxs8mmZdM3tE0j/yFDazLcFrFeBTkk4GPsuS/6MBSfE/bTtBSVobOAnYQdK5wKvAA8A3zOzFnGaOSuBKChvlX8//Bn4haSd8SstjLSqfysZjki4AVgZuBqZnx88sYOMu4NrsS/1+4JYCZXsxs4eAhyS9HZgCbF6geMP/07bpNi8j6VbgauBXuJjGAnsCHzOzPQbTt6JI2sfMflmxvxs+7SXX/LBGyw9kA7jFzHJ9ObLnr4/jjxBX4D/Uq+Tual5sZ0+8qfiImf0qO7axmT1VxE4fvz5pZj+qp3w9tKOg7jeznaocv8/MdmmhHyPx9v1PgBKwEvComc0vYKMD2A+fG7Yq3tR5ALghz5dZ0gj8+aVuHwawfaiZXZbz2jWBjYHfAp/Aa4UngIsrmoL1+vFlMzs9x3XrAt8A1sDHlNYENgWmZs9led5rJWB/M7uy4tg+wE3Zc2FN2q7JB7wg6cvArXgNNQbYC/hbvQYl7Qe83cyKNFEuBV7Gf5W3BU4AXpPUUW7P5+ACYAWWrG0/iD/gH52j/GX4IGTdPkj6ImDA9Gx+WpnDMvt5mAZciDfxxuDNvr3we/SxnH78APgDcKeZza44tSdQU1DAJZm/Y4Ff4M89r2S+7Z3rU7i/j1T4NAz/f3yInJ0j7SiojwLH4t3mq+Ff6rvwANxcSBrf59BjwNclTTezX+c0M76ipvyxpIPMbI/sGSAv7zKzHfocu1bSgznLb2Bm5S9svT50A+/FZ04bcJqZ/Y5ivWMrm9kvJB1rZrtmx34p6aECNv4P2Ag4V9LqwHfN7BcF/FjFzK4FkHS8mX0me31fAR/WMbPvZuWmmtlhwJGSHshroO0EZWav47+ERWqTvlyN9+ZU/rP+DXwXeE9OG4skjTOzv0naGBhejyOSJmWzmsv72+I9Xi3xwczOq3jvXYAzJD0BrFjAzL+y8ZoZ5c8jqYT/2OX148bs5bmSNgJOkXQ4sHpOEyPk6RX+A1hd0mS8hirS/B0haVX8GXJfSYcCf8E7afIZKPBmQwJJl5nZoY3YMLPtE7jyJeBOSa/i7fVyTfHnAjYOB86RtCnexTwCjx07soU+9GJm9wG7SjoCfxbKyxH4mNFGwImS/gbMBo6p048/AYdmHSSX5yz2NeBBvOm8U+bTOLyzJC+n4U2+4cCB2d9+FGj9tGOnxHpm9tfB9gN629jrAs/lfWgdyFY9NlL60Mfuqnkf5vuU68jbOzgUkTQKfx6dUdf9bGWcU6LYrI5SqbR/qVQ6q1QqXZxt9yuVSh0FbBycwI/PD7aNoeBDQhunD7YPFbZmlkql60ul0ppFy7Zj6NEFwAeAh4CfA7/Ge2IuKGBj5QR+1DU2ktjGUPAhlY2fDQEfyvwTT393haQiA8Nt2eSbUaVnDEkPJno2KuLLO/Cu3pWA14o2dSTta2Y3VOwPx3vuCj0DSTodzxz1eJFyFeXXMrMXss+zKTDbzArl+ciCUyfiz3IvAA+a2b8L2tiSPmNyZvbIwKWWKL8j3olxa3Zo5T5DAbXKl3t/pwEH4z+83wd+YmZX5bHRjjVUOfFL5X6RnrFq9gpHFWfjN9NwMR0HPCfpfknfL2DmKkm3a3Giz7HANcoZYV3BzsA3VCC6u4ykKcAnJH0C+CmwPf7LnDuwVdJBeEvhM3iM5WF4+M9eBWycjI83rYALckXgzOx4nvKn4rXK8fh41F14SNQX8/qA9/5ejScOuhq4GP//HpvXQNv18rF0z9hwoIv8PWPVeAa4QdIuZjYvZ5mDgC3MrEfS2cCWeOKZ2wu8bydwJx7p8LEsVGcbSZ3AOQXsvIH3SH09+5X/aoHackszO0rSdGBnM3stiwL5DfkGVMF7G7c2s7ny2a9nArvg8Xi3DlhyMXsD2/Xx+wxJv8YjIGqxp5lNynx/GvgvvMfv1+QMpC63cCSdbWaTc/q9BG0nKDP7A/B+SavgTYNX68gdkCJSvBu/f29m23WyL1QRXzrM7ExJF0g608xOyMKR8sbQnZK9fCtwMj5esjv+Rc4bHTA/G3uZg4+/vIa3XHI3lTJ/yz2C8/CkpnMlFWkCdwDr4z9uAEhaH7+/eVhYYWcYsEqf47mpV0zQhoLKmncX4k2DV4BVJXUDR1cOkNYgRaT4ZcC92Sj6rhTrFClT/sIdA1yQ/Rq/Sf5f9eey7fyK15cW9OFEvFZ9Fngki9LYGBdoXq4BHshq1p3xFsQXgYcL2Dgej67owWuWVXAx5B3LekDSDXj0zGnAfXjr5fi8DqQY42zHTomHgI9WPrhLeiswzcwm9VuwOb5MwJt6j5Yf4iWNyVtjStq2MtRJPuVgdB0dAu8zszuKlOlTvgOfVLgWXkM9ZmaFnkkl/RfwTjxSfI6kNQtMp6m0swZZSFnR8tlA8EtZ6FRhUoxxtqOgfg1M6vuMoH6i0PuxUXdUcfaMAP7sUeR5qZqtscD/4tHRW+EDtF1m9sec5UcBn8SbqeXetduBK1r8OeqO9JY0Lrt+y7zR7f3YWQ2vbeu6l5mNhmcQtGMv3x3AzZKOk3R4tr0FD67My6XAeuUdLY4qvjhH2X/hN73Q+EQ//BR4EX/2WRufMn62pLy/khdmflyLN9lm4qmwzs5RNuXnuAS//7fhkd4fxsN+auZzMLO/4d3tjXIljd1LgKn4D0E5ev9y4EFJ+ZuuqUaXW/lXKpW2LpVKk0ul0jdKpdIX+ksBNUD5+ypeT614/UCLP8e9ffbvrtzmKP9Qn/2bs+39Lf4clffz8WrHh/q9rHbfSqXS7UVttF2nBICZ/RafzFYvDUcVJ+JNSZuZ2aOSdsB7yApRUX7nbH8c3mHTSvpGen8B7yVseKJjARq+lySI3m9LQVWi+iYHNhxVnIjPAJdJ2gCfIFmOjM47j+ho4KJshP8P+IDqgcCpad2sSbVI73UoFundKI3eS0gQvd+OnRJ9JweOAK7HcwfknRxYfqAfns2vqsePJGFDQwFJpWx8r7xfuIcuu58rFgn1yWl3j0Y7TQq+X0PR++1YQzU8OVCeaupEM9sn2+/AH6ZPMrMncvpxlaT7gQ9lvUDlsKGrzWzAKAdJ6wH74DVr0TCjmki6x8zy3ouv4R0TB2T7w/DxtSlmdn5OG/vg6yiPkXQa3gW/JfAl67My5QA2DsSn4s/q04NbHifrr1zD9zKzsTnwzix/xf+rxw60oaASBcCeiTePkLSBmT0j6XvARXiTJQ91hw2Z2V/l2ZtWGei6Wkh6Fvg7Hrd2pZmVF7Yr0uzY23xxcSQdY2bnS9oM/3y5BIV3/W+BB5POxmcDnIn3vOUSFB5D+HFggqSbgXOyMaEBP0uKe5nZeDsV+STqpe0ElYj5ZjYne32npE3MbIakIjYaChvKZqU2hJmtL8+/sCfw/aztfzwFkjviD+Id+PPkqZLuwKMuijR3Xs8Gs1+V9ALwWzPrlpQ3bKg33EceKPwRfGjkSnIM7SS6l/c2agPaSFCpBgAz5skjpMvr+/5S0vP4UiZ5qQwbOj+rmRbgGYxahpn9iyxKWtJ78DCg9QuYuBXPUrQCcB4+x2x18mVdKvOSpOPxZ4/H8NwSb5C/duolaz5fJukqPPwpdxbeoUBbdUpIOgzoaVRQkt4CfAf/Vf48IHw9oBvzRmmnChtKTRYF8lUz+0qBMjvhC4sXyRBUWX4snsLsJTP7nnxe1fB652f1sS0zs0bttIq2EtRQQtKK+K943ySVF+aNg8uCOb9S7gjJxsYmA2cPFLIjT3DZA6xlZn9v8HO8Df9BmY53AGyBJ6n8Tlb7tQRJ2+MRF2viCwe8He/Y+LqZFWk5NOLDchl6VDeS1pP0LUlXJzB3Gf4gfBo+Ae10vAl55UCF+vAu4Dr53C7wgdC5OWysgfdqnlvE4X64HA+IPS97/6/gITw1P4ek/5R0jqR7EvhxBj71ZBe8M+TPeCfRlHqMSfqwPPd9ES5leQw9ajBEZcVSqbR2AjtVw3tKpdL0AjbuLpVKm2ZJQdbpL4ymyfdjRra9o97PkciPeypeP1FOupPXj1KptEuVvydKpdJ+9f5Pl5vQo0bIBnLrGsztw3xJH2FxGuVySugiqbc6zGxmFqrzK0n/g8+HGpnAv7z8VdL+wO2S/hv4Jd6MLTz1okG6Je2LB9W+jM+gfhWPAMnDp6sc+z0eOXJ9ThsReiRpb2CNyqkYLeKjwCl4E2k1XEh3AkUmqL0EYGZ3SJqHN3FWxMNoWsURwFdxEa2Phy09gE8LaSWfxDuKXsKbfe8GRppZrmVtzOyjCXz4Mstb6FFfss6B6cDBFWNLywWS1jazXAuSBfloNPSo7QQlT/NbbQD1RTPbtEqRPDZXA94oMktV0ulmVmTJy2o2dgN+jHdOHI6vutEFPGxmNZspkubjC819O9t/C3AvcKyZ3ZXThxSfY3O882APYF/8gb78OXJFH6jB6efyZUBPMLNy1P0wPEHLZDO7v06bm+Lp4XIPHLddL5+ZjTOzdbNt5V9dYsrYinyTCytpNDEjwLfxTD8vAzfgORQeJv9CCF3AFtmgKlkX+k7ANwv4kOJznI+L+GV8YLeEDyXcNGCpJTmpQR++gi87Ux4jXITHJzayqEQ3Psic+1mq7Z+h6kE+/6mSHjwO73gzy9vVuqOkWeVaLYu23r5gCMu8bMYqZvYXSWPN7HpJn81Zfj7ezr9T0tNmdr15wsrci5yZ2e8kbYI/Q5Wn0d9ZMJxnvmV5HMzXxR1pZudoycWja/EvSSfhQwLn4EMSJeBXZpZnntobZvZS9nqapE2zGM3czTZJP2HpbFjr4TOPP5zHRtsLqp7mGj6Q2Zfr8KnTeTkZ+B9J783+kSsCn5W0t5l9KaeNJ7Kg3LvwiOnfFHh/8KiR+ZI+hPfSbYeHP+V+rpJ0HN4xcS/eEXA/cLiki8xsak4zz0s6Fv8c+1FfWuSf4DXuG3hP45/xz/Lf5Fu07TlJX8J7Wx8H7pP0MsVCwapN2a85jb+SthcU3lw7FPifvAWKhOUMwO/xZuLPJe2ZNXcOUP47q4e8AAALRUlEQVTF0sAzzn4S2B+PdC4PYp7Sb4kluQY8nk+ehvgQfBZynsSQZQ7Bk1R2Z2FLl+LCug/PsZCHT+E9hefh9+Vz2fELC/ixrpmdAb1TOT5snkR0Rs7yRwJfwJvNB+Czh9csEmVhDWSOKtN2gkrUXKt7uZYKOszsakkbApdncYZQbOxiVTNbKhIg70O0mV1U8bobD91B0h4Uy2A7En9eWBmfadtNgedr8xzmJ1Y5VeT+jpS0Mj4EsSawf1bD5EpLYJ7x95tZhPqOWadMoZRg2bPolEaa8W3XKYE31yr/3kHB5pqkjwGzJK0q6WuSnpB0hYrlFO8BMLPv4L/Ks/GmRpHOjaIdIUsg6UBJm2bTLyqp9uXuj3OAh7O4whl4R8mpeLOrlX6ci2dtugefT3UAnpC06HjYeODAbLC8KCfjkfKrZfvlZvwZeQ20Xbd5CiT9Bni3mc2TJ1acgsfkXWFmm+S0Md7Mnq7YXwkfiCw8BVzSumb2XO0rlypXXjVwAj4F45xsstzdtnit2zx2VsOzxZqZvSJpuJnlTmGc0I8VgYXWwMrxymYry1dh3AHvfcyVsEWeovti/Jlyz3JniAqs7NJ2Tb5EvMHi8KNX8Pb7I5IKDZJKmoa33d+Df5m6JD1cR5T23fKUzscWiWxuZFJeGXn22/Pw3rUTlE0Hl3SOmT3bQj96x7IkHUzBsayKHrp3SLoUb0Fshte6W+R0o+FmfNs1+eRrITXKXfhq60fj4z+5wlv6cCne1JyHR2t/KPvLG3tWyXN4dPfPsy90IcxsfjZHbGs8t0WRSXk/xMeAvpz5sB7+Ga5osR+NjmVNwyda/j3bTsOn5hcZtG68Gd/KiOJEUclbJbKzZ6lUOqlUKu1dcWzjAuXv7rN/b9HI5Iqy92TbdUul0g2lUmnnBj+bClx7b8XrWdWOt8iPvvfzvnruZ6lUelcD/o7vs79SqVQaW8RG2zX5rM5E8JVImmBmt+GpgyttFxk/eS0buLwTH3spPNGvIoxqDUnPsTic6kpggxzlDzazpeZ2mRWb4SppCzx+bZSk8/Bm8NwC5VP40dBYlqT3mNk9ZvZYkXJ92AhfWwro7TksRNs1+foiaW9JRRMq5l0iZSCOxAccH8O/AMdlx/NOFegNowKu6xNOVVNMGSnWCj4O+AEe4b4tMAtvghaJ3k7hx6fw5uZ5eNd50bGspZaJrYOGbbR9L59aHG2uLNVWveerXD8S/2VcE3jBKhJO5iw/3MwWVvTU/cmKJ6lcH1jNFqchK4x8Wv4EFqcDmJ0nZEjSN83sa/WeH2q0naCUINpcDURHS/oTPv29Gj3A4Wa2YU4/3o1Hm/8bz6HwEC6sT5nnb69V/mRgFJ5D72z8AXoL4GtmlqtTQdKn8dphJbxz5p1479gJZvbTnDben73/HFxMY3FxTzazATsVsv9nf4PQPcBeWS1ey4eGIvczGw1HrLfjM9S4BGZ6e5QkPYaHLT2F9yjV6mX7bo3zpxXw43RgdzP7kyThuSnOwUN+ds5Rfl8z21K+Pu4OZvZPeQai6eTvpfsEMAlPI/Y0vrTMv4C78eV28nAKsG1FcGo54cyd1O6lO6zG+byxdL2R+9kg9XtZHLmftxn+FXzWdW9WK/lC4Nfi96gmbSeoRNQdHW1mP0rpSEVU9x+BTTNx5S3+etZk/CvedQ9eQ7zUf5GlmG9mb0pagNeUc/ExuiIrkXSw9GoXr5NjfdwU8XMZjUbuQ4KI9bYTVPZLfpyZHVNx7Bzgu5Y/pVaK6OgUPCjpOjzCe098QPQTwPM5y5+FL3T2JB5dfRc+BjS1gA9/lGcHWhvP796VHc+1cnrGj/HPMp3F+TV2xKf0t4pGI/chQcR6O/byTSGbFJc9CAP8jvzNE2i8RykJ5sntL8F/2E43s+/h4srVa2lmv8Aj1W/BF81+GH+GK7Jw9Sfxnr3rMn8mAltVBt7m8GMKvgLkA3izcQbwATP7cQE/GuU4/Bluf7zntTyFJm/kPnjP7Qp47XwALszPm9m3cltodPCu1X+lUumuitePl0qlYZUDgTXKfrOR8y38jIfWOH9TI+ezay5u5HwqPxLcq4Y/R8W1x5dKpdEV+6NKpdK7i/jTdk0+PNXTtviMzhWAH8lXocgTXHrEAKE9PfgDacu6aCV9EV/CZXqfoNrD6L8nEWBXSX/Gfe4b4d2DD9LW4gB5+uhq9OC9hbUivVP40SgpPkeZ8qTR3bJ4zMKTRttRUMfizZvheFDqDnhe8sNylK11TaHZmQnoxnujTpNkwGlZZ0mtsYzVE7x3rl6rGqTwo1FSfI4y5UmjP1Odk0bbbhwKQNIY8+VTyvvDrI6UT0MJSbvg7f0ngImWrdkUtI6KqR9fwReNOyw7db+ZbZvHRtt1Skg6koqxjWzwbXY2XtC2mNl92dyhR/CB1aD1NBxt3naCwnti3gcgab+sZppIsTD9QUW+SnlVsp6x3VroTrCY3kXLzfNbbIr3eF6S10A7PkN1V8zoPF+exOMFiq24N9j8UD7dvr/29rnknxQXpOMtknbGx/a+R7a0j6QTzOwveQy0o6Aek3QBHuF8Mx5mA74cSrvwBD4W1h+zW+VIsATn47XUD/GcGsfgA+6X4fnWa9KOgvosPvA5DF+/ZySwivmi0W2BmeXJMxe0njfMbJaklSqCi6+RlHvxhrbp5ZN0sZn1O55Q63wQ1ELSFHz6/7Ds70Z8WGYXMzskj412qqFSDuAFQTWOwb9DuwNr4Qk/Z1B97amqtJOgUg7gBUEv5Umh5qnTptBnGVJJx+DPVzVpmyZfEDSLGpNGAQ7LO2m0nWqoIGgWySaNRg0VBAlpx0iJIBiyhKCCICHxDNVEJL0Tj+BYCZ+/9SvgVDNbqp0taQNgs1pZgur040TgbjPLPS1c0r346hfr4ElKnqg4fVXq3BrLCiGoJpHlyZsG7G9mT8nXaf0ZPqax1JpQwK740jzJBWVmtR66a3G3mX0kiTPLOCGo5rEP/kV8CiBLRnkI8KakS4D18Rx8t+LrMZ0IrJRNZvsznu+iA3gRT/X1Cj4WshWexGVDPI8DeJKUkfgA93Fm9qikp/HkLbPxvBnT8FUJL8XXUBqJh3E9jue1WA0fzLzYzGrm1sjShC1VLqvZ/olPPnw/Phl0Y/zx4qtWbA3itiOeoZrHOGCJhZ/NV/pbF+g0sz3wzEBHZwOK38WbUjfi82+OMbN3483EL+Ere6xpZtvg6xetn5n9HnBelpzxc7i4yM5/1MyOr3DhKOAv2eTFw/AEn28HppnZ+/CFziZX+Sy7Srq34m94jXJXmdlu+A/BC5lv+5BzcLSdiRqqeTwNbFl5IFt3aH1ga0nvwWudFaqUnQBckOXnG4nHl03AM8uSJbR8suLa6dnx32dplcG/yH1TMguvETGzWfgqjuOA4yXtn/kzsoo/SzX5JD0/QLnyIgGbAjtlOUAARkhas2iq6HYiaqjmcTOwp6S3QW8O87OBzYGXsojzs/BmXgc+n6v8/zDgkKyG+hKeJmwWsF1ma3V8/STwJt1O2fHNWZzTr9r8sNl43j4kbSTpKuCLwENm9nH8Ga9vspX+GKhc+b2fBK7OPsde2XVFF6NrK6KGahLmS2seClycTdMfg3c43IVnJd0Jz/b6FN48nAmcJOlh4Gh8Bb3yynlHZNftlT1jPY9nan0T/2JfnGVQGpld2x8XAT+RdB+e5Ob4zK8L5esOvwgskK9EWIubcpS7KPPtPnwRgQvaPfdHLSJSok2Q9A5gczObJmlNvDNhvBVYQjRoPiGoNkHSysBVeMq04cAPzZffDIYQIaggSEh0SgRBQkJQQZCQEFQQJCQEFQQJCUEFQUJCUEGQkP8PCN5pr9+3gMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_more_survived_groups['CategoricalFare'] = pd.qcut(one_more_survived_groups['Fare'], 10)\n",
    "fare_survived = one_more_survived_groups.loc[:, ['CategoricalFare', 'Survived']].groupby(['CategoricalFare']).mean()\n",
    "print(fare_survived)\n",
    "plt.xticks(rotation=270)\n",
    "plt.rcParams['figure.figsize'] =(5, 5)\n",
    "sns.barplot(x='CategoricalFare', y='Survived', data=one_more_survived_groups)\n",
    "#one_more_survived_groups.drop(['CategoricalFare'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Survived\n",
      "CategoricalFare            \n",
      "(-0.001, 4.65]     0.242424\n",
      "(4.65, 5.231]      0.346154\n",
      "(5.231, 6.238]     0.448276\n",
      "(6.238, 6.938]     0.321429\n",
      "(6.938, 7.25]      0.266667\n",
      "(7.25, 7.775]      0.428571\n",
      "(7.775, 8.05]      0.212121\n",
      "(8.05, 9.188]      0.384615\n",
      "(9.188, 11.0]      0.500000\n",
      "(11.0, 13.0]       0.527778\n",
      "(13.0, 23.539]     0.538462\n",
      "(23.539, 26.55]    0.647059\n",
      "(26.55, 28.99]     0.916667\n",
      "(28.99, 37.482]    0.653846\n",
      "(37.482, 43.833]   0.615385\n",
      "(43.833, 128.082]  0.846154\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d5892217b8>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_more_survived_groups['CategoricalFare'] = pd.qcut(one_more_survived_groups['SingleTicketFare'], 16)\n",
    "fare_survived = one_more_survived_groups.loc[:, ['CategoricalFare', 'Survived']].groupby(['CategoricalFare']).mean()\n",
    "print(fare_survived)\n",
    "plt.xticks(rotation=270)\n",
    "plt.rcParams['figure.figsize'] =(5, 5)\n",
    "sns.barplot(x='CategoricalFare', y='Survived', data=one_more_survived_groups)\n",
    "#one_more_survived_groups.drop(['CategoricalFare'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Survived\n",
      "OneBitCabin          \n",
      "A            0.666667\n",
      "B            0.815789\n",
      "C            0.619048\n",
      "D            0.818182\n",
      "E            0.736842\n",
      "F            0.833333\n",
      "G            0.500000\n",
      "U            0.373770\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d5892d8208>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_more_survived_groups.Cabin.fillna('Unknown', inplace=True)\n",
    "one_more_survived_groups['OneBitCabin'] = one_more_survived_groups.Cabin.apply(lambda x: str(x)[0])\n",
    "cabin_survived = one_more_survived_groups.loc[:, ['OneBitCabin', 'Survived']].groupby(['OneBitCabin']).mean()\n",
    "print(cabin_survived)\n",
    "sns.barplot(x='OneBitCabin', y='Survived', data=one_more_survived_groups)\n",
    "#one_more_survived_groups.drop(['OneBitCabin'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'female': 0.7361111111111112, 'male': 0.23766816143497757}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d5892d1c18>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_survived = dict(one_more_survived_groups.groupby(['Sex']).Survived.mean())\n",
    "print(sex_survived)\n",
    "plt.rcParams['figure.figsize'] =(5, 1)\n",
    "sns.barplot(x='Survived', y='Sex', data=one_more_survived_groups, orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13861386138613863\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58927ae80>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.sum(one_more_survived_groups.Age.isna()) / one_more_survived_groups.shape[0])\n",
    "plt.xticks(rotation=270)\n",
    "pylab.rcParams['figure.figsize'] =(24, 8)\n",
    "sns.barplot(x='Age', y='Survived', data=one_more_survived_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAgeSegment(age):\n",
    "    age_seg = 3 # 退休\n",
    "    if age <= 15:\n",
    "        age_seg = 1 # 小孩\n",
    "    elif age <= 60:  \n",
    "        age_seg = 2 # 未退休\n",
    "\n",
    "    return np.int32(age_seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 0.5822784810126582, 2: 0.48109965635738833, 3: 0.37681159420289856}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58956b278>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_more_survived_groups['age_segment'] = one_more_survived_groups.Age.apply(lambda x: getAgeSegment(x))\n",
    "age_survived = dict(one_more_survived_groups.groupby(['age_segment']).Survived.mean())\n",
    "print(age_survived)\n",
    "plt.rcParams['figure.figsize'] = (5, 4)\n",
    "sns.barplot(x='age_segment', y='Survived', data=one_more_survived_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age_segment  Sex   \n",
      "1            female    0.641026\n",
      "             male      0.525000\n",
      "2            female    0.773973\n",
      "             male      0.186207\n",
      "3            female    0.677419\n",
      "             male      0.131579\n",
      "Name: Survived, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d5895e2940>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUMAAAEBCAYAAADio8dgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFu5JREFUeJzt3XmUVOWZx/FvdTeLIIugERlQHAyPGFwQk7RLcEMWJ8aQuGRFk+gYlzGRGDBzgq3G5ajBxC2uSTqaDC4YI5OMSxRjFOxoIEERfEITVNSoICJICd21zB+3wG7o7roNdetWV/0+53huV9Wttx4vfX791q1b75PIZrOIiFS6qrgLEBEpBQpDEREUhiIigMJQRARQGIqIAApDEREAauIuoD0LFizQNT8iEokxY8Yktr6vZMMQYMyYMXGXICJlZsGCBW3er7fJIiIoDEVEAIWhiAigMBQRARSGIiKAwlBEBFAYiogACkORLRoaGpg6dSoNDQ1xlyIxUBiK5NTX17No0SLq6+vjLqVilNIfoJL+BopIMSWTyVZbiV59fT3Lli0jmUxSW1sbay2aGYpIbErpD5DCUEQEhaGICKAwFBEBFIYiIkBEnyabWRXwM+BAYBNwhrs3tnj8QuDLQAa40t0fjKIOEZGwopoZfh7o6e6HAhcBMzc/YGb9gfOBQ4HxwE8jqkFEJLSowvAI4BEAd28ADmnx2AbgVaB37r9MRDWIiIQWVRj2Bd5vcTttZi3fkq8ElgALgRsiqkFEJLSovoGyDujT4naVu6dyP08C9gD2zt1+1MzmuftzWw+ydOnSiMorfYsXL+bxxx9n3LhxjBo1Ku5yKkJTU9OWbSX/7hVTKR3zqMJwHnACcJ+Z1QIvtnjsPeBDYJO7Z81sLdC/rUFGjhwZUXml7/rrr6exsZFEIsHJJ58cdzkVoXv37lu2lfy7V0xxHPP2GkJFFYYPAseZ2XwgAXzDzKYCje4+x8zGAQ1mlgGeAf4YUR1dVil9TUmkEkQShu6eAb691d0vt3i8DqiL4rVFRLaHLroWEUFhKCICaD1DKQOvXbZ/QcZJrRkA1JBa8+oOj7nnxS/m30lKimaGIiIoDEVEAIWhiAigMBQRARSGIiKAwlBEBFAYiogACkMREUBhKCICKAxFRACFoYgIoDAUEQEUhiIigMJQRASIoYm8mR1E617JtcDn3f2RKGoREQkjqvUMtzSRzzWEmgmcCODufweOAjCzk4E3FYQiErc4msgDYGa9gUuB8yOqQaRTelZnW22lskQ1M2yziXyL3skA3wLud/fV7Q0Sdx/VOJVSP9lS17tA40wetoFHVvZi4tAd70iof7NwSun3PI4m8pt9FTipo0EquXeteviG91qBxjlwYBMHDmwqyFj6NwunlPomR/U2eR5wPEAbTeQxs35AD3dfGdHri4h0SixN5IERwCsRvbaISKfF1UT+eYJPnEVESkLFX3Td0NDA1KlTaWhoiLsUEYlRxfdNrq+vZ9myZSSTSWpra+MuR6RLKMde1RU/M0wmk622IlKZKj4MRURAYSgiAigMRUQAhaGICKAwFBEBFIYiIoDCUEQE0EXXBVeOF6OKVALNDEVEUBiKiAAKQxERQGEoIgIoDEVEAIWhiAgQQxP53OOTgLrczYXAue6u/owiEpuoZoZbmsgDFxE0kQfAzPoA1wKfdfdagl4ou0ZUh4hIKHE0kT+MoFveTDN7Gnjb3VdFVIeISChxNJHfFTgaOAj4AHjazJ51939sPUgxmkoXuol1oRqaF1LczbmjpmNefOV4zONoIv8u8Ly7vwVgZn8mCMZtwrAYTaUL3cS6UA3NC6ncG5rrmBdfVz7mpdREfgEwysx2NbMaoBZYElEdIiKhxNJE3sx+ADya2/c+d18cUR0iIqHE1UT+HuCeKF5bRGR76KJrEREUhiIigMJQRARQGIqIAApDERFAYSgiAigMRUQAhaGICKAwFBEBFIYiIoDCUEQEUBiKiAAKQxERILolvIpizPfv2uEx+qxeTzXw2ur1BRnvwT759xGR0qOZoYgIXXxmKJXj9ttvZ/78+VRVVZFIJLjgggsYNWpU3GXJDupZnW21jVOHYWhmY9t7zN3/3MHz8vVNvgE4HFifu+tEd39/m4FEgMbGRubOncusWbNIJBIsXbqU6dOnM2fOnLhLkx00edgGHlnZi4lDk3GXkndmeHZuOxzoDjwPjCboandUB8/b0jc51wNlJnBii8cPBia4++rtKVoqy4ABA3jzzTeZPXs2Y8eOZeTIkcyePRt35/LLL2fjK8307QEX1tbw4jtZ7l2SZuZxNdz9YpqmFJx5sN4AlaoDBzZx4MCmuMsA8pwzdPcvu/uXgVXAIe5+JvBpYGOecdvtm5ybNX4cuN3M5pnZN3egfqkAAwYM4JZbbmHhwoWceuqpTJw4kSeffJIZM2ZQV1fHzOO68anBVdy7JE3tkCr2GZDgmvlpXng7yzcPqo67fOkiwv7J3GOr53wsz/4d9U3uDdwIXAdUA0+a2V/d/YWtByn33rPF0tWP47/+9S8ApkyZwpQpU2hsbGTGjBls2rSJadOmUb2qmVQGhvRNAHDqftV89XfN/PCIGqqrErHU3NWPeT6V3Df558BLZrYY2A+oy7N/R32Tk8D17p4EMLO5BOcWtwnD/H1Qnw9RunT1Hr4rV65k1qxZ3HrrrfTo0YOhQ4dy44030qtXL2666SZSd05g8TsZ1uTer1z/XIpzxlRz1wtpDto9QZ8exQ/Ern7M8ynHvsmhwtDdbzazu4F9gX+GONc3DzgBuK+NvskjgHvM7GCCt+lHAL8KU4dUpvHjx7N8+XJOPvlkevXqRTabZdq0aQwaNIjp06eTXNEMwPdqa/jty2n690xwolXTswau+0uKurHdYv4/kK4gVBia2SeAW4H+wG/MbLG7/76Dp+Trm/wboAFoBu5y95d26P9Cyt7ZZ5/N2Wefvc39d999N69dtv+W20P6fnSOcMLwaiYM1zlDCSfs2+QbgG8AdxC8ZX4YaDcMQ/RNvga4plOVVphSuv5KpBKE/gZK7jrBrLuv4qPrAyUik4dtYN9+TUwetiHuUkQqQtiZ4RozOwvobWZfAtZGWJNQWtdfiVSCsDPDbwF7A6sJrhn8VmQViYjEIOzM8FLgDndfEmUxIiJxCRuG84BrzKwP8EvgXnf/MLqyRESKK+x1hrOB2Wa2B/AT4KcEl9mItFKINSFbWnDtlIKOt9mjy9OsXJfljNH63rIEwl5nuCdwGvBFYCEwKcqiRESKLeyfxQeAO4HPuLsuq5GS8ujyNA1vZNiUhjUfwmSr4tnXM6x4P8tZo2t4J5ll3soMqQz07gZ1Y1v/2v/O08x9JUMCOGqvKibvqwu1K1G+9QyHuPvrwNeALLBH7q0y7v6PItQnEkqyGa4+thtPvpLmty9nuGFCDYvezvLAy2lGDKji6mNrqEokuGhuM/7uRxeyv/p+lj+9muEnx9WQSMD0J1IcMriKoX3jWeBB4pNvZjg199+tW92fBY6JpCKR7bDPgCC8du6eYGi/BIlEgp27Z0lloKYKrnwmzU7dYHUS0i2+1PPK2gzvbMgy7YlgHZEPmuDN9VmFYQXqMAzdfWrux+uBObmv2YmUnPaiK5WB+a9nuHFiNzamspzzcDPZFmE4pG+CvfoluPLoGhKJBA8sTbN3fwVhJQp7znAc8CMzmwP83N3/GWFNIgVTlYCeNXDOw810q4KBOyV498OP0nD4LlWMHpTlu4+laM7AvgMTDNwpxoIlNmEvrTnPzLoTLN1/k5l1d/dx0ZYmXVFUl8J0pOXKNJ8cXMUnBwdfrNpnQBVXH5v/S1an7FfNKfvpQ5NK15lWoZ8CJgC7A09EU46ISDzCXme4BFgE3OnuZ0RbkohI8YU9Z/hLd7820kpERGIU9m3yJDPTSRURKVthZ4a7AW+a2QqCawyz7n5YezvnayLfYp8/AA+5+9bXMYqIFFXYMPxsJ8fN10Qe4HJgQCfHFRGJRNgwPK2N+y7rYP9WTeTN7JCWD5rZSUCGoJeKlJGWzZkKYc+LX+zw8XQ6zUVzm9mYgh8dWVOwtqCnPNDEfV/sXpCxpGsIG4Zv57YJYHOLz46020TezEYBXwFOAi7uaJByb8RdLMU8joVuLp6v9tWrV7NuE/xsUmm1Ay33392KbSLv7re1vG1m+WZ0HTWRnwL8GzAXGAY0mdkr7v7I1oOoiXxhFLOheaGbi+er/cwzz+SNdVmufTbFh81Z1uXaxpw7ppq9d6nitIea2G+3Kt5Yn+Wg3RNsaAZ/N8uQPgkuOryGFWsz3LogTZbge8nnHlLNJ3b76G/9ivcy3LwgTTYLfXvAhbU19O6ef/apJvLFV5Qm8mY2osXNwcCeeZ7SbhN5d5/WYtxLgLfaCkKRMOrq6jj3lHH07wn7DqzihBHVvL4uy48bUvx0fBVvbYBrx1UzYCf4wv3N3DixG3v2hSkPNfNBU5ZX12b59sFBcM5dkebR5ZlWYXjdX9JceGgNe/VL8HBjmnuXpPnmQVoQthyF/Ve9jeBT5AHAu8D38uzfYRP57S1WpD0r1mb5+1vBclwQzPIgmM19rHcwk+tZA3v1C37u1T1BUxp27ZXg14sz9KjOkExl6dWt9azvtXVZbngueFOTygQLO0h5yree4cEETeM/TfCJ8i1AL6DDM8v5msi32O+STtQq0q49+yYYMSzBMXtX897GLA83BqGYL7pu/muaiw4PZn6/eiHF2x+0fnxo3wTTD6vhY70TLH4nw5qN0dQv8cs3M7wCOM3dm8zscoLl/hsJPgXWDE9KxldGVTOzIcUfGjMkm+HrB4T7jsCxe1dR91Qzu/RMsGuvBOs2ZVs9fv4nq7l6fopM7u7v1eotcrnK9y9b5e4vmNlgoLe7LwQws7JZ1zBbVdNqKzsm36UwhTZkyBBunBh8knzpkdt+otzy8piWP992fLDvSSOrOWnktsG5ed8RA6uYeVxn1jORrirfv/LmxycCjwOYWQ9af1LcpW0cPJrmnQexcfDouEsRkRjlmw49bmbzgKHA58xsOMF5w3sjr6xIUv2GkOo3JO4yRCRmHc4M3f1q4AxgtLv/PXf3Le5+VeSViYgUUd4TZe6+tMXPy4HlkVYkIhIDnRkWEUFhKCICKAxFRACFoYgIoDAUEQEUhiIigMJQRARQGIqIAApDERFAYSgiAoRf6bpT8vVNNrNzgdMJVs++zN1/H0UdIiJhRTUz3NI3GbiIoG8yAGa2K3AOcBhwLHCLmWktdRGJVVRh2KpvMrClb7K7rwYOdPdmYBCw1t2zbY4iIlIkUYVhm32TN9/I9U8+D2gAZkdUg4hIaFGtdd9R32QA3P0mM7sdeNjMjnb3J7cepNwbcRdLuR/HcmxoXurK8ZhHFYbt9k02MwOuAr4INBN8wNJmTxU1kS8MNTQvPh3z4itKE/nt0GHfZDNbBDxL8Gnyw+7+VER1iIiEEkkY5uub7O6XApdG8doiIttDF12LiKAwFBEBFIYiIoDCUEQEUBiKiAAKQxERQGEoMWhoaGDq1Kk0NDTEXYrIFlFddC3Srvr6epYtW0YymaS2tjbuckQAzQwlBslkstVWpBQoDEVEUBiKVASdp81P5wxFKoDO0+anmaFIBdB52vwUhiIiKAxFRACFoYgIoDAUEQHiayJ/AfCl3M3/y618LSISmziayP878FWCJvKHAuPN7ICI6hARCaXoTeSBlcBEd0/neqV0AzZGVIeISChRXXTdZhN5d0+5ezOw2swSwLXA39z9H20NUu69Z4ul1I5jU1PTlm0haivHHr6FpmOeXyxN5M2sJ/ALYD1wTnuDqG9yYZRaD9/u3btv2Raitq7cw7dYdMw/0l7f5KjeJs8Djgdoo4l8AngIWOTuZ7l7OqIaRERCK3oTeaAaOBLoYWaTcvv/wN2fjagWEZG8YmkiD/SM4nUlemO+f9cOj9Fn9XqqgddWry/IeA/2yb+PSD666FpEBIWhiAigMBQRARSGIiKAwlBEBFAYiogACkMREUBhKCICKAxFRACFoYgIoDAUEQEUhiIiQHSr1ohIgWhxjOLQzFBEBIWhxCBbVdNqK1IKFIZSdBsHj6Z550FsHDw67lJEttCfZim6VL8hpPoNibsMkVZiaSKf22c3YD6wv7urVaiIxKroTeQBzGwC8Biwe0SvLyLSKXE0kQfIAOOANRG9vohIpxS9iTyAu/8RwMw6HKTUGnF3VTqOxadjXnxdsol8WGoiXxiFbWiuYx6Gjnnxdbkm8iIipajoTeTdfU5Erykist3iaiK/eb9hUby+iEhn6RsoIiIoDEVEAIWhiAigMBQRARSGIiKAwlBEBFAYiogACkMREUBhKCICKAxFRACFoYgIoDAUEQEUhiIigMJQpCKoV3V+CkORCqBe1fnpz4RIBVCv6vxi6ZtsZmcCZwEp4HJ3/30UdYiIhFX0vslmNgg4HzgcmABcZWY9IqpDRCSUOPomfwqY5+6b3P19oBE4IKI6RERCiaNv8taPrQf6tTVIey39Nrv9S5/YwTILbxX1cZewjVV5jmNn6JiHo2NefDt6zOPom7z1Y32AtVsPMGbMmEREtYmIbCOOvsnPAZ8xs55m1g8YCSyOqA4RkVAS2Wy24IO2+DT5AHJ9kwnCsdHd5+Q+Tf5PgjC+0t0fKHgRIiKdEEkYVioz+zRwtbsfFXctlcDMugG/AIYBPQgu05oTa1FlzsyqgTsAA9LAN9x9ebxVFYa+gVIgZjYNuBPoGXctFeRrwLvu/hlgEnBTzPVUghMA3P1w4GLgunjLKRyFYeEsB74QdxEV5n5gRovbqfZ2lMJw998RnOIC2At4O8ZyCkpfxysQd3/AzIbFXUclcfcPAMysDzAb+GG8FVUGd0+Z2a+AycBJcddTKJoZSpdmZkOBJ4G73f1/4q6nUrj7acAI4A4z6x13PYWgmaF0WWa2O/AYcJ67PxF3PZXAzL4ODHH3q4AkkCH4IKXLUxhKV/bfwC7ADDPbfO5wkrt/GGNN5e63wC/N7M9AN+C77r4x5poKQpfWiIigc4YiIoDCUEQEUBiKiAAKQxERQGEoIgIoDEU6zczOi7sGKTyFoUjn6Wt/ZUjXGUrBmFlfgpV7+gO7Eiz1tAC4maC9wzvARnc/3cz+C/gKkAXucfcbOhi3HhhOsCLQj939XjM7EriC4NsPywm6LdYAdwGDgZXAWHcfbGZ/AhYBo4APgKcJmpH1B8bn7rsV+DjBBOGH7v4nM3sBeIpgXc4scCJwHlAH3Onu5+zgIZMSopmhFNI+BME2HvgsMJUgZE5392MIQgsz2w84laBx2BHA583M2howtwjD0QQrAk0Cqs0sQRC0X3D3I4E3gNMJVlNZkVte6hJg9xZDPefuxxKse5h09+OAJcCRwBnAancfSxB4N+ee0xeY1eI1Jrn7FcAaBWH5URhKIb1FEGy/Jngr2Q0Y7O4v5R5/OrcdRbD80xPAXGAgQZBuw93XE8zGbgfuJQiz3YA9gPtys77xwJ4ELSTm5573MrCqxVALc9u1BCEI8B7BbHN/4PjcWA8ANWY2MLfP33LblWityrKmMJRCuhB41t2/RrDWYAJYmZsJAtTmtg68BBydWxW8ntZ9crYwsz2AMe4+GfgP4BqCQHsdODH3/CsIVq5ZDByae95wgrfqm3V0PuhlghngUQSzz/sJgrK956lZWRlSGEoh/S/wHTN7BvguwWKr5wG/MLPHCXpmN7v7IoJZ4TNm9leCc3VvtDPmW8AgM/sb8EeCc4ZNwHeAP5jZfOAcgiD8OTAst4jAJUDYBQRuA/Y1s6cIZpavunumg/2X5Ga/Ukb0AYpEyszOBe5z91VmdjnQ5O6XRfRahwE7u/tjZvZx4BF3Hx7Fa0n50RJeErW3gcfM7APgfeC0tnYys88RfOCytevd/cGQr/VPYJaZ1RGcrzx3O+qVCqWZoYgIOmcoIgIoDEVEAIWhiAigMBQRARSGIiKAwlBEBID/BzDg8vKE0vvoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import mean\n",
    "sex_age_survived = one_more_survived_groups.groupby(['age_segment', 'Sex']).Survived.mean()\n",
    "print(sex_age_survived)\n",
    "sns.barplot(x='age_segment', y='Survived', hue='Sex', data=one_more_survived_groups, estimator=mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SurnameGroupId', 'Cabin', 'Pclass', 'Embarked', 'Sex', 'Fare',\n",
       "       'TicketGroupId', 'SibSp', 'Parch', 'Age', 'Survived', 'Title',\n",
       "       'SurnameGroupSize', 'SingleTicketFare', 'CategoricalFare',\n",
       "       'OneBitCabin', 'age_segment'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_more_survived_groups.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SurnameGroupId', 'Cabin', 'Pclass', 'Embarked', 'Sex', 'Fare',\n",
       "       'TicketGroupId', 'SibSp', 'Parch', 'Age', 'Survived', 'Title',\n",
       "       'SurnameGroupSize', 'SingleTicketFare'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_perished_groups.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### women-children-group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "358"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices_boy_groups = ((one_more_survived_groups.Age <= children_age_upper_limit) & (one_more_survived_groups.Sex == 'male')) | (one_more_survived_groups.Title == 'Master')\n",
    "indices_female_groups = (one_more_survived_groups.Sex == 'female')\n",
    "\n",
    "more_one_survived_wcg = one_more_survived_groups.loc[indices_boy_groups|indices_female_groups]\n",
    "more_one_survived_wcg.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_female_groups = None\n",
    "indices_boy_groups = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>SurnameGroupId</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Fare</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Title</th>\n",
       "      <th>SurnameGroupSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>CategoricalFare</th>\n",
       "      <th>OneBitCabin</th>\n",
       "      <th>age_segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>20.25</td>\n",
       "      <td>3-S-20.25-C.A. 2673</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>3</td>\n",
       "      <td>6.750</td>\n",
       "      <td>(6.238, 6.938]</td>\n",
       "      <td>U</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>Abbott-3-S-20.25-C.A. 26__</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>20.25</td>\n",
       "      <td>3-S-20.25-C.A. 2673</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "      <td>6.750</td>\n",
       "      <td>(6.238, 6.938]</td>\n",
       "      <td>U</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>Abelson-2-C-24.0-P/PP 33__</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2-C-24.0-P/PP 3381</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>12.000</td>\n",
       "      <td>(11.0, 13.0]</td>\n",
       "      <td>U</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>Aks-3-S-9.35-3920__</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>9.35</td>\n",
       "      <td>3-S-9.35-392091</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>4.675</td>\n",
       "      <td>(4.65, 5.231]</td>\n",
       "      <td>U</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>Aks-3-S-9.35-3920__</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>9.35</td>\n",
       "      <td>3-S-9.35-392091</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master</td>\n",
       "      <td>2</td>\n",
       "      <td>4.675</td>\n",
       "      <td>(4.65, 5.231]</td>\n",
       "      <td>U</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  SurnameGroupId    Cabin  Pclass Embarked     Sex   Fare  \\\n",
       "279   Abbott-3-S-20.25-C.A. 26__  Unknown       3        S  female  20.25   \n",
       "1283  Abbott-3-S-20.25-C.A. 26__  Unknown       3        S    male  20.25   \n",
       "874   Abelson-2-C-24.0-P/PP 33__  Unknown       2        C  female  24.00   \n",
       "855          Aks-3-S-9.35-3920__  Unknown       3        S  female   9.35   \n",
       "1198         Aks-3-S-9.35-3920__  Unknown       3        S    male   9.35   \n",
       "\n",
       "            TicketGroupId  SibSp  Parch    Age  Survived   Title  \\\n",
       "279   3-S-20.25-C.A. 2673      1      1  35.00       1.0     Mrs   \n",
       "1283  3-S-20.25-C.A. 2673      0      2  13.00       NaN  Master   \n",
       "874    2-C-24.0-P/PP 3381      1      0  28.00       1.0     Mrs   \n",
       "855       3-S-9.35-392091      0      1  18.00       1.0     Mrs   \n",
       "1198      3-S-9.35-392091      0      1   0.83       NaN  Master   \n",
       "\n",
       "      SurnameGroupSize  SingleTicketFare CategoricalFare OneBitCabin  \\\n",
       "279                  3             6.750  (6.238, 6.938]           U   \n",
       "1283                 3             6.750  (6.238, 6.938]           U   \n",
       "874                  2            12.000    (11.0, 13.0]           U   \n",
       "855                  2             4.675   (4.65, 5.231]           U   \n",
       "1198                 2             4.675   (4.65, 5.231]           U   \n",
       "\n",
       "      age_segment  \n",
       "279             2  \n",
       "1283            1  \n",
       "874             2  \n",
       "855             2  \n",
       "1198            1  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_one_survived_wcg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00    122\n",
       "0.00     34\n",
       "0.75      1\n",
       "0.50      1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = more_one_survived_wcg.groupby(['SurnameGroupId']).Survived.mean().value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9873417721518988"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tmp[1] + tmp[0]) / tmp.values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58a638d68>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (2.0, 4.0) \n",
    "plt.yticks(np.linspace(0.1, 1.0, 10))\n",
    "sns.barplot(x='Pclass', y='Survived', data=more_one_survived_wcg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58a695e48>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (5.0, 2.0) \n",
    "plt.xticks(ticks=np.linspace(0.1, 1.0, 10))\n",
    "sns.barplot(x='Survived', y='Embarked', data=more_one_survived_wcg, orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58917f6a0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "more_one_survived_wcg['Cabin'] = more_one_survived_wcg.Cabin.fillna(\"Unknown\")\n",
    "more_one_survived_wcg['CabinOneBit'] = more_one_survived_wcg.Cabin.apply(lambda x: str(x)[0])\n",
    "pylab.rcParams['figure.figsize'] = (8.0, 5.0) \n",
    "plt.xticks(ticks=np.linspace(0.1, 1.0, 10))\n",
    "sns.barplot(x='Survived', y='CabinOneBit', data=more_one_survived_wcg, orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58a760eb8>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "more_one_survived_wcg['CategoricalFare'] = pd.qcut(more_one_survived_wcg['Fare'], 10)\n",
    "fare_survived = more_one_survived_wcg.loc[:,['CategoricalFare', 'Survived']].groupby(['CategoricalFare']).mean()\n",
    "plt.xticks(rotation=270)\n",
    "plt.yticks(ticks=np.linspace(0.1, 1.0, 10))\n",
    "pylab.rcParams['figure.figsize'] =(10, 5)\n",
    "sns.barplot(x='CategoricalFare', y='Survived', data=more_one_survived_wcg, )\n",
    "#one_more_survived_groups.drop(['CategoricalFare'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Survived\n",
      "CategoricalFare             \n",
      "(7.749, 14.454]     0.517241\n",
      "(14.454, 15.98]     0.700000\n",
      "(15.98, 21.075]     0.645161\n",
      "(21.075, 26.0]      0.727273\n",
      "(26.0, 29.125]      0.473684\n",
      "(29.125, 39.458]    0.583333\n",
      "(39.458, 61.899]    0.653846\n",
      "(61.899, 82.046]    0.846154\n",
      "(82.046, 146.521]   1.000000\n",
      "(146.521, 512.329]  0.900000\n"
     ]
    }
   ],
   "source": [
    "print(fare_survived)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12569832402234637"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_one_survived_wcg.Age.isna().sum() / more_one_survived_wcg.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAgeSegment(age):\n",
    "    age_seg = 5 # \n",
    "    if age <= 12: # children\n",
    "        age_seg = 1 \n",
    "    elif age <= 18: # adults\n",
    "        age_seg = 2\n",
    "    elif age <= 45:\n",
    "        age_seg = 3\n",
    "    elif age <= 60: # retirement\n",
    "        age_seg = 4\n",
    "    return np.int32(age_seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 0.5797101449275363, 2: 0.7272727272727273, 3: 0.7767857142857143, 4: 0.8421052631578947, 5: 0.6571428571428571}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58918e6d8>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAE3CAYAAABhDq8HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAF25JREFUeJzt3X2UZHV95/F3D00zzgiDgIIGUFzhmyI8uBlXWkHAs0YlG9T1uBPFOBnwIbOKD5tdNbCRbDyaHIOIgoobxegkZDcjPsaAGMgBIj6RiTGQLb6IESGjiIwC8tgzTO8ft4iTOdPV1b/uW7duzft1zpzTVVN17+d7qqfn07+6de/E7OwskiRJWrhlTQeQJElqK4uUJElSIYuUJElSIYuUJElSIYuUJElSIYuUJElSockmdrpp0ybPuSBJklpj9erVE7u6v5EiBbB69eqmdl2LbrdLp9NpOsaSG8e5nKkdnKkdnKk9xnGuYc20adOmOf/Ot/YkSZIKWaQkSZIKWaQkSZIKNXeM1Fs3NLXrGl3fdICajONcztQOoz/TpnPXNh1BUoNckZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpUW5GKiOMi4uq6ti9JktS0yTo2GhFvA14F3F/H9iVJkkZBLUUK+C7wUuBPa9q+pBEzec+/sPwH32Ji+7amowzV2rVXDvzYmZkZpqamakwzPCtWrGDdunWsWrWq6ShSo2opUpn56Yh4Sh3bljSalv/oRiYf/EnTMYZu8+Z7m47QmI0bN/La17626RhSo+pakZK0m3nowKNY/sjW3W5F6tAD9h74seO2IrVmzZqmY0iNs0hJWhLbVh3MfasObjrG0G04d+3Aj+12u3Q6nRrTDF+32206gtQoT38gSZJUqLYVqcy8FZiua/uSJElNc0VKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSpkEVKkiSp0GRTO9507tqmdl2LbrdLp9NpOsaSG8e5nKkdxnEmSePHFSlJkqRCFilJkqRCFilJkqRCFilJkqRCFilJkqRCFilJkqRCFilJkqRCFilJkqRCFilJkqRCFilJkqRCjV0i5rZ3Ht3UrmuxErit6RA1GMe5nKkdnKkdFjPToefcsJRRpEa4IiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklTIIiVJklRocqk3GBF7Ah8HngLsBbwrM7+w1PuRJElqWh0rUr8BbMnM5wCnAB+sYR+SJEmNW/IVKeBTwKU73N5Wwz4kqdi3t0zx2VtX8tAjE01H2a1Nrl3bdIRdmpmZYWpqqukYS27nuVasWMG6deuYnp5uMFX7LXmRysz7ACJib6pC9btLvQ9JWozLb1/B9+/bs+kY2ry56QS7vY0bN1qkFqmOFSki4hDgs8CHM/PP69iHJJU65ZAHeOiRCVekGja535ObjrBLu9OK1Jo1axpMNB7qONj8QODLwJmZedVSb1+SFuvY/Wc4dv+ZpmPs9g4955qmI+xSt9ul0+k0HWPJjetcTatjReps4HHAOyLiHb37TsnMB2vYlyRJUmPqOEbqzcCbl3q7kiRJo8YTckqSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWabGrHh55zQ1O7rkW326XT6TQdY8mN41zO1A7O1A7jOJO0EK5ISZIkFbJISZIkFbJISZIkFbJISZIkFZq3SEXE7+50+w/riyNJktQec35qLyJeDbwG6ETEr/bu3gPYEzhrCNkkSZJGWr/TH/wZcBVwNvDu3n3bgTvrDiVJktQGc761l5kPZ+atwHrgQODJwGHAccOJJkmSNNoGOSHnpcATgNt7t2eBa2tLJEmS1BKDFKmDMvPZtSeRJElqmUGK1E0R8aTM/MFS7vj4C49fys2NhiubDlCTcZzLmdrBmdphxGe67o3XNR1BY2yQInUCcFtE3EX1tt5sZj6p3liSJEmjb94ilZlHDCOIJElS28xbpCLil4CPAPsClwA3ZuYX6w4mSZI06ga5RMwFwOnAXcDFwP+qM5AkSVJbDHStvcy8herYqB8DP6s3kiRJUjsMUqR+EhG/BayMiJcDd9ecSZIkqRUGKVKvpjqj+V3AM3q3JUmSdnuDnP7gfuDPgeW9208DvllbIkmSpJYYpEhdBkwBPwUmqM4l9dI6Q0mSJLXBIEVqeWaeVHsSSZKklhmkSF0bES8Auo/ekZm31RdJkiSpHQYpUgcC7+fnn9abBbyIsSRJ2u0NUqQiMzu1J5EkSWqZQYrUDRExDXyLajWKzJypNZUkSVILDFKkTgT+0w63Z4Gn1hNHkiSpPeYtUpl5zDCCSJIktc28RSoivgPsscNdW4Hbgbdl5t/XFUySJGnUDXKJmL8BXgd0gDOA64E/BC6oMZckSdLIG+QYqSMy88re11dHxDsy86qI+L25nhARewAfBQJ4BDg9M7+7+LiSJEmjY5AiNRMR64GvUp0/6uGIWD3Pc08FyMzjI+Jk4H3AixeZVZI0ApbdsYzJ7iRsazrJYNZev3ZBj5+ZmWFqaqqmND+3YsUK1q1bx/T0dO37Un0GKVKnAf8TeBFwI/Aq4JlUb/PtUmZ+LiK+2Lv5ZOBHi8wpSRoRk9+ZZNk9gxwZMho237+56Qhz2rhxo0Wq5Qb51N6WiLiM6hIx3wDuz8zLB3jetoj4JPCfgZctOqkkaSRsO3wbk9vasyJ1yL6HLOjxw1yRWrNmTe37Ub0G+dTeHwAHUx1sPgOcBbxikI1n5m9GxNuBb0TEkZl5/2LCSpKat/2g7cwc1J7zMm9444YFPb7b7dLpeEEPDWaQtdkTMnMtcF9mfhI4bL4nRMSrIuKs3s0HgO1UB51LkiSNjUGOkZqMiOXAbO/TeIMUos8AfxIR1wJ7Am/JzIcWkVOSJGnkDFKk3gdsAh5PdYzU+fM9ofcWnm/8SpKksTbIweaXRsRVwNOAf87MLfXHkiRJGn1zHiMVEftFxPkRsQz4BeAi4PMREUNLJ0mSNML6HWz+YeB7va8vAC4E3gR8oO5QkiRJbdCvSO2bmRcAK4FjgA29ixSvHEoySZKkETfI6Q9OAv42M2d7ty1SkiRJ9D/Y/Ie9k3E+H3hXROwDvBX4x6EkkyRJGnH9VqT+K3A7cE5mfg44EtgHOHMYwSRJkkbdnCtSvRNoXgQQEYcDjwPOBbzMiyRJEoNda+9MqgsP7wd8AjgcV6UkSZIGOtj85cDzgLsz8wPAcfVGkiRJaodBitSjj3n0U3sP15RFkiSpVQa51t7/Aa4FnhwRlwGfqzeSJElSOwxyrb0LI+JK4Cjgpsy8of5YkiRJo2+Qg80/vsPNUyJiK9VpET6UmT+tLZkkSdKIG+QYqccAPwD+Avg+1QWM9wI+WWMuSZKkkTfIMVKPz8xX9L6+IiK+nJnviIhr6wwmSZI06gYpUvtExC9m5k0R0QEeGxH7A49dzI6ve+N1i3n6yOl2u3Q6naZjLLlxnMuZ2sGZ2mEcZ5IWYpAidSZwSUQ8EXiQ6qScvw68u8ZckiRJI2/eY6Qy85tU1927ElgJHJiZH87MT9cdTpIkaZTNuSIVEVPAK4A3UJ2Ecx/gsMx8cEjZJEmSRlq/FalbgWOAV2bmc4AfWKIkSZJ+rt8xUh8ATgOeEhEfAyaGE0mSJKkd5lyRysz3ZOaxwAVUheo/RMR7IuKooaWTJEkaYYMcbH5NZr4K+HfAvwB/WnsqSZKkFhjk9AcAZObdwIW9P5IkSbu9QS4RI0mSpF2wSEmSJBUa+K29pXbNiSc1teva3Nl0gJqM41zO1A7O1A7ONLiTrr2mpi2rKa5ISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFbJISZIkFZqsa8MR8QRgE/ArmXlTXfuRJElqSi0rUhGxJ/C/gQfr2L4kSdIoqGtF6r3AR4Czatq+JKlmN09McM0ey3i46SBj5OK1axvb98zMDFNTU43tvw7Lli1j/fr1TE9PN5ZhyYtURKwDfpyZV0SERUqSWupry5bxw4mJpmOMl82bm04wdjZu3DheRQo4A5iNiOcBTwc2RMSLMvOOGvYlSarJs7ZvZ2bCFaml9JiDD25s3+O6IrVmzZpGMyx5kcrMEx/9OiKuBtZboiSpfY6YneWIbY80HWOsnLRhQ2P77na7dDqdxvZfh1GYydMfSJIkFart9AcAmXlynduXJElqkitSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSxSkiRJhSab2vFJ117T1K5r0e126XQ6TcdYcuM4lzO1gzO1gzNpd+eKlCRJUiGLlCRJUiGLlCRJUiGLlCRJUiGLlCRJUiGLlCRJUiGLlCRJUiGLlCRJUiGLlCRJUqHGzmz+wf/+l03tujZXcUvTEWoxjnM5Uzs409I587xTG9mvNO5ckZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSpkkZIkSSo0WdeGI+JbwD29m9/LzNPr2pckSVITailSEbEcIDNPrmP7kiRJo6CuFaljgRUR8eXePs7OzK/XtC9JLXPHPd+je8fX2Lp9pukou41vrv1ULdudmZlhamqqlm03ZdmyZaxfv57p6emmo6gF6ipSDwDvBT4GHA5cHhGRmdtq2p+kFvnOnX/H3Q/e2XSM3cr9m+9uOkKrbNy40SKlgdRVpG4GbsnMWeDmiNgCPBG4vab9SWqRw5/wDLbdsdUVqSHa94CVtWx3XFek1qxZ03QMtURdReoM4Gjg9RHxJGAf4Ic17UtSyxy06jAOWnVY0zF2K2eed2ot2+12u3Q6nVq23ZRxnEn1qatIXQx8IiK+AswCZ/i2niRJGje1FKnMnAFOq2PbkiRJo8ITckqSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWySEmSJBWabGrHZ553alO7rkW326XT6TQdY8mN41zO1A7OJKkNXJGSJEkqZJGSJEkqZJGSJEkqZJGSJEkqNDE7Ozv0nW7atGn4O5UkSSq0evXqiV3d30iRkiRJGge+tSdJklTIIiVJklTIIiVJklSosTObt1VELAM+DBwLPAy8JjNv2ekxjwe+ChydmQ8NP+XCzDdTRPw34OW9m5dl5u8PP+XCDDDTG4B1wCzwzsz8YhM5F2LA771lwF8Bn8/Mjww/5cIN8FpdABwP/Kx314sz856hB12AAWY6Bfi93s2/B96QmSN9wGq/mSLi6cD7d3j4NPCSzPzS0IMuwACv0/8AXgFsB/4gMz/bSNAFGGCmt1PNdC/wR2342feoiDgOeE9mnrzT/acC5wDbgI9n5keHmcsVqYV7CbA8M58F/A5w3o5/GREvAL4MHNhAtlJzzhQRTwVeCTwbeBbw/Ig4ppGUC9NvpgOA11PN9B+BiyJil5/GGDF9v/d63gXsN9RUizffXL8MvCAzT+79GekS1dPv+29v4Fzg1zJzGrgVOKCJkAs050yZ+Q+Pvj7Ah4DPjHqJ6un3Ou0LvInezz3+bVEcZf1mOho4jaroPh94Z0SsaCTlAkXE24CPAct3un9P4HyqeU4CXhcRBw0zm0Vq4U4AvgSQmV8HnrHT328Hngf8ZMi5FqPfTLcDL8zMRzJzO7AnMPKrbPSZKTPvAo7NzK3AQcDdo74a0NP3ey8iXkb1/Xf58KMtypxz9X67Phz444i4LiLOaCbigvV7rZ4N3ACcFxF/C/woM388/IgLNt/PPiJiJfD7VAWkDfrNdD/wfWBl78/2oacr02+mDnB1Zj7Ue7fkO0AbfjEG+C7w0l3c3wFuycyfZuYM8BXgOcMMZpFauH2AHX8jfiQi/vUt0sz868zcMvxYizLnTJm5NTPvioiJiHgv8K3MvLmRlAsz3+u0LSLOBL4OXDrscIXmnCkijqL6TfOcJoItUr/XaiVwIfAbwAuB17dkRbTfTAcAzwXeDpwCvCUijhhyvhJ9/031vBr4VO+XlTaYb6bbgf9H9fbrBcMMtgj9ZroBODEi9o6I/alK/cphByyRmZ8Gtu7ir3ae92fAqqGE6rFILdy9wN473F6WmduaCrNE+s4UEcuBS3qPef2Qs5Wa93XKzA8CT6T6wfLcYYYr1G+mtcAvAH9DdezXb0fEC4cbr1i/uR4APpCZD2Tmz6jmO3bYAQv0m2kLcH1m3pGZ9wHXAk8fdsACg/zseyXV2y9t0W+mU6h+PhwGHAq8JCKeOeR8JeacKTO7wAepVq3PA74BtKX0zmXnefcG7h5mAIvUwl0H/CpARExTNfy2m3Om3rFDnwe+nZm/lZmPNBNxwfrNFBHxmd5sW6kOyGzDsv2cM2Xm2zLzuN4xKp8A3teSY1Sg/7+pI4CvRMQevWMhTqBaHRh1/WbaBBwVEQf0VgqmqVY9Rl3fn30RsQrYKzNvbyBbqX4z/RR4EHi49zbY3cC+Q0+4cP1+9j0eOCAzTwDeDBwC3NhEyCXUBQ6PiP0iYgo4EfjaMAP4qb2F+yzwKxHxVWACOD0ifpvqPdovNBut2JwzAXtQHcC3V++TRgBnZeZQv1EL9H2dIuLbVP/YZoHLM/OaBrMOahy/92D+1+oSqrdgtwIbMvOfGsw6qPlmOgu4ovfYjZnZhv/M5vv+O4LqwPk2me91eh7w9YjYTnXszV83mHVQ/X6e/yXw1Ii4HpgB3tqiX47/jYg4DXhsZv5xb74rqBaHPp6Zm4eZxUvESJIkFfKtPUmSpEIWKUmSpEIWKUmSpEIWKUmSpEJ+ak9S4yLid6iuCLCd6pOUZ2fmpsJtvZ/q9A+3FT7//wIfycyrS54vafdikZLUqIg4EngRcHxmzvYugPtJCk+8mZlvWcp8ktSPRUpS0+6kOnP0GRHxpcz8h4h4ZkRcDazPzJsiYj3VdRE/QXUunC3AZcDpwJG9AvYh4EqqEw2uB/4MeFlm3hoR/4XqZJ7nABcD+/f2/abMvCEi3gC8Bvgh8IShTC1pLHiMlKRG9a7L9iLgeOBrEXET8Gt9nnIQ8PzM/CPgH4HnRMRewMlUJetRF1NdOgeqy+Z8FDgbuCoznwu8Driod0buN1OdYfzFwNTSTCZpd2CRktSoiHgacG9mnpGZh1JdoPgiYL8dHjaxw9ff613lHapy9JtUBegLO1377RLgZRHxJGCf3tnDj6Za+bq699zHAb8I/FNmPpyZW4FvLvmQksaWRUpS046hWhla3rt9M9XV3LdQXTQW4Jd3ePyO10W8Cvj3wBlUK1D/KjPvpbqu3fnAn/Tuvgk4v3dNwjVUZeufgSMj4jERsUdve5I0EIuUpEZl5meAq4FvRMR1VNfMeitwLvChiLiC6pqPu3ruLHApMJWZt+ziIR8FTgH+onf73cCa3orUl4AbM/PHVMdOfRW4HLh/aSaTtDvwWnuSJEmFXJGSJEkqZJGSJEkqZJGSJEkqZJGSJEkqZJGSJEkqZJGSJEkqZJGSJEkqZJGSJEkq9P8BeN6As3LjrFMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "more_one_survived_wcg['AgeSegment'] = more_one_survived_wcg.Age.apply(lambda x: getAgeSegment(x))\n",
    "age_survived = dict(more_one_survived_wcg.groupby(['AgeSegment']).Survived.mean())\n",
    "print(age_survived)\n",
    "plt.xticks(ticks=np.linspace(0.1, 1.0, 10))\n",
    "plt.rcParams['figure.figsize'] = (6, 3)\n",
    "sns.barplot(x='Survived', y='AgeSegment', data=more_one_survived_wcg, orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 整组幸存情况未知的预测策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "606"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_more_survived_groups[one_more_survived_groups.SurnameGroupId.notna()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单独出行\n",
    "\n",
    "单独出行即 `SurnameGroupSize==1` 的乘客："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of solo passengers: 659\n"
     ]
    }
   ],
   "source": [
    "solo_passengers = all_data.loc[all_data.SurnameGroupSize == 1].drop(['Ticket', 'Title', 'Surname', 'SurnameGroupSize', 'TicketGroupSize', 'FamilySize'], axis=1)\n",
    "\n",
    "print('the number of solo passengers: %d' % solo_passengers.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58a84e160>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] =(4, 4)\n",
    "sns.barplot(x='Sex', y='Survived', data=solo_passengers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: nan, 2: 0.41935483870967744, 3: 0.2964426877470356, 4: 0.2894736842105263, 5: 0.23846153846153847}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58a8b4b70>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solo_passengers['age_segment'] = solo_passengers.Age.apply(lambda x: getAgeSegment(x))\n",
    "age_survived = dict(solo_passengers.groupby(['age_segment']).Survived.mean())\n",
    "print(age_survived)\n",
    "plt.rcParams['figure.figsize'] =(5, 4)\n",
    "sns.barplot(x='age_segment', y='Survived', data=solo_passengers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sex and Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age_segment  Sex   \n",
      "1            male           NaN\n",
      "2            female    0.785714\n",
      "             male      0.117647\n",
      "3            female    0.745098\n",
      "             male      0.183168\n",
      "4            female    0.875000\n",
      "             male      0.133333\n",
      "5            female    0.720000\n",
      "             male      0.123810\n",
      "Name: Survived, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58a91b208>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import mean\n",
    "sex_age_survived = solo_passengers.groupby(['age_segment', 'Sex']).Survived.mean()\n",
    "print(sex_age_survived)\n",
    "sns.barplot(x='age_segment', y='Survived', hue='Sex', data=solo_passengers, estimator=mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d58a947d30>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Pclass', y='Survived', hue='Sex', data=solo_passengers, estimator=mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Survived\n",
      "CategoricalFare           \n",
      "(-0.001, 7.25]    0.144928\n",
      "(7.25, 7.75]      0.339623\n",
      "(7.75, 7.879]     0.333333\n",
      "(7.879, 8.05]     0.157895\n",
      "(8.05, 9.5]       0.142857\n",
      "(9.5, 13.0]       0.387500\n",
      "(13.0, 26.55]     0.513514\n",
      "(26.55, 135.633]  0.431818\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solo_passengers['CategoricalFare'] = pd.qcut(solo_passengers['Fare'], 8)\n",
    "fare_survived = solo_passengers.loc[:, ['CategoricalFare', 'Survived']].groupby(['CategoricalFare']).mean()\n",
    "print(fare_survived)\n",
    "plt.xticks(rotation=270)\n",
    "sns.barplot(x='CategoricalFare', y='Survived', data=solo_passengers)\n",
    "solo_passengers.drop(['CategoricalFare'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 单独出行的男孩\n",
    "\n",
    "借此查看一下，所有`Master`的随行成员数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     23\n",
       "7      9\n",
       "6      8\n",
       "2      6\n",
       "4      5\n",
       "5      4\n",
       "8      3\n",
       "11     2\n",
       "1      2\n",
       "Name: SurnameGroupSize, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = all_data.loc[index_boy, 'SurnameGroupSize']\n",
    "tmp.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "称谓是 `Master` 有1个是单独出行的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>...</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>TicketSize</th>\n",
       "      <th>SingleTicketFare</th>\n",
       "      <th>Title</th>\n",
       "      <th>Surname</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>SurnameGroupId</th>\n",
       "      <th>TicketGroupId</th>\n",
       "      <th>SurnameGroupSize</th>\n",
       "      <th>TicketGroupSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3.1708</td>\n",
       "      <td>Olsen, Master. Artur Karl</td>\n",
       "      <td>1</td>\n",
       "      <td>913</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>C 17368</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1708</td>\n",
       "      <td>Master</td>\n",
       "      <td>Olsen</td>\n",
       "      <td>4</td>\n",
       "      <td>Olsen-3-S-3.1708-C 173__</td>\n",
       "      <td>3-S-3.1708-C 17368</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Betros, Master. Seman</td>\n",
       "      <td>0</td>\n",
       "      <td>1231</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2622</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Master</td>\n",
       "      <td>Betros</td>\n",
       "      <td>2</td>\n",
       "      <td>Betros-3-C-7.2292-26__</td>\n",
       "      <td>3-C-7.2292-2622</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked    Fare                       Name  Parch  \\\n",
       "912   9.0   NaN        S  3.1708  Olsen, Master. Artur Karl      1   \n",
       "1230  NaN   NaN        C  7.2292      Betros, Master. Seman      0   \n",
       "\n",
       "      PassengerId  Pclass   Sex  SibSp       ...          Ticket TicketSize  \\\n",
       "912           913       3  male      0       ...         C 17368          1   \n",
       "1230         1231       3  male      0       ...            2622          1   \n",
       "\n",
       "      SingleTicketFare   Title Surname FamilySize            SurnameGroupId  \\\n",
       "912             3.1708  Master   Olsen          4  Olsen-3-S-3.1708-C 173__   \n",
       "1230            7.2292  Master  Betros          2    Betros-3-C-7.2292-26__   \n",
       "\n",
       "           TicketGroupId SurnameGroupSize  TicketGroupSize  \n",
       "912   3-S-3.1708-C 17368                1                1  \n",
       "1230     3-C-7.2292-2622                1                1  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[(index_boy) & (all_data.SurnameGroupSize == 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 单独出行的女性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    74\n",
       "0.0    24\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = train.loc[(train.Sex == 'female') & (train.SurnameGroupSize == 1), 'Survived'].value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24489795918367346"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[0]/ tmp.values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最后的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Cabin', 'Embarked', 'Fare', 'Name', 'Parch', 'PassengerId',\n",
       "       'Pclass', 'Sex', 'SibSp', 'Survived', 'Ticket', 'TicketSize',\n",
       "       'SingleTicketFare', 'Title', 'Surname', 'FamilySize', 'SurnameGroupId',\n",
       "       'TicketGroupId', 'SurnameGroupSize', 'TicketGroupSize'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_cv(dataset, num_fold = 5, verbose = False, num_trial = 5):\n",
    "    sum_accuracy_all_trial = 0.0\n",
    "    size_test_set_in_cv = dataset.shape[0] // num_fold    \n",
    "    engineeringFeatures(dataset, verbose)\n",
    "    \n",
    "    for i in range(0, num_trial):\n",
    "        sum_accuracy_per_trial = 0.0\n",
    "        all_set_in_cv = dataset.sample(n = train.shape[0], random_state= i)\n",
    "        for j in range(0, num_fold):\n",
    "            \n",
    "            # get indices of test set and train set in cross validation\n",
    "            start_index_test_set = j * size_test_set_in_cv\n",
    "            indices_test_set_in_cv = np.linspace(0, size_test_set_in_cv - 1, num = size_test_set_in_cv, dtype=np.int32) + start_index_test_set\n",
    "            indices_train_set_in_cv = all_set_in_cv.drop(labels=indices_test_set_in_cv).index\n",
    "            \n",
    "            # get WomanChildGroupSurvived in train set \n",
    "            getWomanChildrenGroupSurvived(all_set_in_cv, indices_train_set_in_cv)\n",
    "            # set WomanChildGroupSurvived in test set\n",
    "            setWomenChildGroupSurvivedInTestSet(all_set_in_cv, indices_test_set_in_cv)\n",
    "            \n",
    "            # get SurnameGroupSurvived\n",
    "            #getSurnameGroupSurvived(all_set_in_cv, indices_train_set_in_cv)\n",
    "            #setSurnameGroupSurvivedInTestSet(all_set_in_cv, indices_test_set_in_cv)\n",
    "            \n",
    "            # female and boy rules\n",
    "            all_set_in_cv = predictionByWomanBoyRules(all_set_in_cv)\n",
    "\n",
    "            accuracy_per_time = np.sum(all_set_in_cv.iloc[indices_test_set_in_cv, :].Predict.astype(np.int32).values == all_set_in_cv.iloc[indices_test_set_in_cv, :].Survived.astype(np.int32).values) / size_test_set_in_cv\n",
    "            sum_accuracy_per_trial += accuracy_per_time  \n",
    "            # the end of for j in range(0, num_fold)\n",
    "            \n",
    "        sum_accuracy_per_trial = sum_accuracy_per_trial / num_fold\n",
    "        sum_accuracy_all_trial += sum_accuracy_per_trial\n",
    "        if verbose == True:\n",
    "            print(\"Trial %d has %d-fold CV accuracy = %f\" % (i + 1, num_fold, sum_accuracy_per_trial)) \n",
    "        # the end of for i in range(0, num_trial)\n",
    "        \n",
    "    print('Average accuracy in trials of total %d times  = %f' % (num_trial, sum_accuracy_all_trial / num_trial)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tottaly 179 solo passengers satifies the condtions\n",
      "Totally adding 46  nannies, cousins and firends to surname groups\n",
      "Trial 1 has 10-fold CV accuracy = 0.865169\n",
      "Trial 2 has 10-fold CV accuracy = 0.866292\n",
      "Trial 3 has 10-fold CV accuracy = 0.866292\n",
      "Trial 4 has 10-fold CV accuracy = 0.860674\n",
      "Trial 5 has 10-fold CV accuracy = 0.865169\n",
      "Trial 6 has 10-fold CV accuracy = 0.864045\n",
      "Trial 7 has 10-fold CV accuracy = 0.866292\n",
      "Trial 8 has 10-fold CV accuracy = 0.865169\n",
      "Trial 9 has 10-fold CV accuracy = 0.862921\n",
      "Trial 10 has 10-fold CV accuracy = 0.864045\n",
      "Average accuracy in trials of total 10 times  = 0.864607\n"
     ]
    }
   ],
   "source": [
    "custom_cv(num_fold = 10, verbose = True, num_trial = 10, dataset = pd.read_csv('./data/train.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getSurnameGroupSurvived(all_data, train.index)\n",
    "#setSurnameGroupSurvivedInTestSet(all_data, test.index)\n",
    "\n",
    "all_data['WomanChildGroupSurvived'] = np.NaN\n",
    "getWomanChildrenGroupSurvived(all_data, train.index)\n",
    "setWomenChildGroupSurvivedInTestSet(all_data, test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Cabin', 'Embarked', 'Fare', 'Name', 'Parch', 'PassengerId',\n",
       "       'Pclass', 'Sex', 'SibSp', 'Survived', 'Ticket', 'TicketSize',\n",
       "       'SingleTicketFare', 'Title', 'Surname', 'FamilySize', 'SurnameGroupId',\n",
       "       'TicketGroupId', 'SurnameGroupSize', 'TicketGroupSize',\n",
       "       'SurnameGroupSurvived', 'WomanChildGroupSurvived', 'WomanChildGroupId'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8720538720538721"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = predictionByWomanBoyRules(all_data)\n",
    "np.sum(all_data.Predict[:train.shape[0]] == all_data.Survived[:train.shape[0]]) / train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "PassengerId = np.array(range(892, 892 + test.shape[0]))\n",
    "submission = pd.DataFrame({\"PassengerId\": PassengerId, \"Survived\": all_data.Predict[train.shape[0]:].astype(np.int32)})\n",
    "submission.index=range(0, test.shape[0])\n",
    "submission.to_csv('./data/' + \"predictions.csv\", index=False)"
   ]
  }
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