P2_investigate_dataset/P2investivate_dataset.ipynb

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   "source": [
    "# 一. 重返泰坦尼克号\n",
    "\n",
    "本次数据分析的对象是泰坦尼克号数据，包括泰坦尼克号上2224名乘客和船员中891名的人口学数据和乘客基本信息，由Kaggle网站提供。下表中展示了前5条数据的基本情况："
   ]
  },
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   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# format_percent formats a decimal fraction into percentage string.\n",
    "def format_percent(frac):\n",
    "    return str(round(frac, 4) * 100) + '%'\n",
    "\n",
    "# render a bar plot for group-by data\n",
    "def bar_group_by(group_by, column, xlabel=None):\n",
    "    plt.figure(figsize=(14, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    group_by[column].count().plot(kind='bar')\n",
    "    plt.ylabel('number of passengers')\n",
    "    if xlabel is not None:\n",
    "        plt.xlabel(xlabel)\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    group_by[column].mean().plot(kind='bar')\n",
    "    plt.ylabel('survived rate')\n",
    "    if xlabel is not None:\n",
    "        plt.xlabel(xlabel)\n",
    "\n",
    "# render a pie plot for group-by data\n",
    "def pie_group_by(group_by, column, ylabel, labels):\n",
    "    plt.figure(figsize=(14, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    group_by[column].count().plot(kind='pie', autopct='%.2f%%', labels=labels)\n",
    "    plt.ylabel('number of passengers')\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    group_by[column].mean().plot(kind='pie', autopct='%.2f%%', labels=labels)\n",
    "    plt.ylabel(ylabel)\n",
    "    \n"
   ]
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       "<div>\n",
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <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",
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       "      <td>53.1000</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
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       "   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  "
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   ],
   "source": [
    "# take a look at data\n",
    "titanic_df = pd.read_csv('titanic-data.csv')\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "每个乘客包含12项基本信息：\n",
    "\n",
    "1. PassengerId：乘客唯一ID\n",
    "2. Survived：是否生还，0表示遇难，1表示生还\n",
    "3. Pclass：客舱等级，1表示头等舱，2表示二等舱，3表示下等舱\n",
    "4. Name：乘客姓名\n",
    "5. Sex：性别，男（male）或女（female）\n",
    "6. Age：年龄\n",
    "7. SibSp：同在船上的兄弟姐妹/配偶数量\n",
    "8. Parch：同在船上的双亲/子女数量\n",
    "9. Ticket：票号\n",
    "10. Fare：船票票价\n",
    "11. Cabin：客舱号\n",
    "12. Embarked：登船港口，C = Cherbourg, Q = Queenstown, S = Southampton\n",
    "\n",
    "从表格中展示的基本情况可以看出，有一部分乘客的客舱号数据已经遗失（NaN）。另外根据Kaggle上的说明，乘客的年龄为浮点数，小于1岁的乘客年龄为分数，如果不知道确切年龄，那么乘客的年龄格式为xx.5。sibsp数据忽略了未婚夫/未婚妻关系，parch数据包括了继子/继女关系，一些儿童乘客是跟保姆一起坐船的，对他们而言parch=0。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
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   "source": [
    "# 二. 感兴趣的问题\n",
    "\n",
    "对于泰坦尼克号数据，我所感兴趣的问题是：\n",
    "\n",
    "1. 乘客汇总统计信息和总体生还情况\n",
    "2. 乘客的年龄分布大致情况，最大年龄和最小年龄的生还情况是怎样的？\n",
    "3. 不同年龄段乘客的生还情况是怎样的？\n",
    "4. 乘客的登船地点和生还率之间的关系？\n",
    "5. 不同客舱等级乘客的生还率和平均船票花费分别是怎样的？\n",
    "6. 不同性别乘客的生还率和船票花费分别是怎样的？\n",
    "7. 带着兄弟姐妹/配偶一同登船的乘客，他们的生还率如何？其中的男性乘客呢？女性乘客呢？\n",
    "8. 带着双亲/子女一同登船的乘客，他们的生还率如何？其中的男性乘客呢？女性乘客呢？\n",
    "9. 0-10岁儿童乘客的生还情况如何，这时性别因素还有影响吗？\n",
    "10. 独自一人登船的乘客生还情况如何，性别因素会有影响吗？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 三. 对数据进行整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    "deletable": true,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of rows: 891; # of unique passengers: 891\n",
      "\n",
      "investigate the missing data of each columns:\n",
      "             missing percentage(%)\n",
      "PassengerId        0          0.0%\n",
      "Survived           0          0.0%\n",
      "Pclass             0          0.0%\n",
      "Name               0          0.0%\n",
      "Sex                0          0.0%\n",
      "Age              177        19.87%\n",
      "SibSp              0          0.0%\n",
      "Parch              0          0.0%\n",
      "Ticket             0          0.0%\n",
      "Fare               0          0.0%\n",
      "Cabin            687         77.1%\n",
      "Embarked           2         0.22%\n",
      "\n",
      "investigating Pclass\n",
      "[1, 2, 3]\n",
      "\n",
      "investigating Sex\n",
      "['male', 'female']\n",
      "\n",
      "investigating Embarked\n",
      "['Q', 'C', 'S']\n"
     ]
    }
   ],
   "source": [
    "# check whether number of rows and unique number of passengers match\n",
    "num_of_rows = len(titanic_df['PassengerId'])\n",
    "num_of_uniq = len(titanic_df['PassengerId'].unique())\n",
    "print('# of rows: {}; # of unique passengers: {}\\n'.format(num_of_rows, num_of_uniq))\n",
    "\n",
    "# check if there exists missing data.\n",
    "print('investigate the missing data of each columns:')\n",
    "missing = titanic_df.isnull().sum()\n",
    "ratio = titanic_df.isnull().sum() / float(len(titanic_df))\n",
    "round_ratio = ratio.apply(lambda x: format_percent(x))\n",
    "print(pd.concat([missing.rename('missing'), round_ratio.rename('percentage(%)')], axis='columns'))\n",
    "\n",
    "# are Pclass, Sex and Embarked data avaiable?\n",
    "print('\\ninvestigating Pclass')\n",
    "print(titanic_df.groupby('Pclass').groups.keys())\n",
    "\n",
    "print('\\ninvestigating Sex')\n",
    "print(titanic_df.groupby('Sex').groups.keys())\n",
    "\n",
    "print('\\ninvestigating Embarked')\n",
    "print(titanic_df.groupby('Embarked').groups.keys())"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "从以上对数据的初步调查可以看出，数据集的行数与乘客的数量相等，也就是说**不存在重复的数据**。然而年龄、客舱和登船港口数据存在缺失。有177名乘客的年龄数据缺失，占比19.87%，那么**在进行针对年龄的数据分析时必须把这部分数据筛除，否则势必影响结论的可靠性**；有687项数据缺失了客舱信息，占比77.1%，是数据缺失的重灾区，好在这对后面要进行的数据分析不存在影响：我们只需要知道客舱的等级就好，不需要知道具体客舱号，客舱等级数据不存在缺失。登陆港口只有两项数据是缺失的，占比不大，因此对结论的影响较小。最后，检查了一下客舱等级，性别和登陆港口数据，因为这些数据是有限取值范围的枚举值，检查下有没有错误数据，**结论是没有**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    {
     "name": "stdout",
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     "text": [
      "valid age 714, valid embark 889\n"
     ]
    },
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       "   PassengerId  Survived  Pclass  \\\n",
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       "1            2         1       1   \n",
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       "3            4         1       1   \n",
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       "                                                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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</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>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>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>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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filter out NaN 'Age' data, drop 'Cabin' too.\n",
    "titanic_df_age = titanic_df[pd.notnull(titanic_df['Age'])].drop('Cabin', axis=1)\n",
    "# filter out NaN 'Embarked' data, drop 'Cabin' too.\n",
    "titanic_df_embark = titanic_df[pd.notnull(titanic_df['Embarked'])].drop('Cabin', axis=1)\n",
    "# check the filtered data.\n",
    "print('valid age {}, valid embark {}'.format(len(titanic_df_age), len(titanic_df_embark)))\n",
    "display(titanic_df_age.head())\n",
    "display(titanic_df_embark.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "为方便后面的分析工作，我们对数据进行了清洗。对于后面要进行的有关乘客年龄的数据分析，我们将缺失数据滤除，对登陆港口也做同样处理，由于只需要知道客舱等级而不再需要客舱号，因此顺便将客舱号这列删除。最后打印了一下清洗后的数据进行观察，跟预期的一致，没有问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 四. 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1. 乘客汇总统计信息和总体生还情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f19f5502a50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f5576f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(titanic_df.describe())\n",
    "titanic_df.groupby('Survived')['PassengerId'].count().plot(kind='pie', autopct='%.2f%%', labels=['Victim','Survivor'])\n",
    "plt.title('Survivors and Victims')\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "891名乘客平均年龄约29.70周岁，标准差为14.53，表明年龄分布较为分散，最大年龄乘客80岁，最小的不足1岁；有乘客最多带了8位家属（兄弟姐妹/配偶）登船，还有的乘客最多带了6位直系血亲（父母/子女）登船；这些乘客购买船票平均花费了约32.20货币单位，标准差49.69，表明不同乘客的船票开销差异较大。这891人的生还率约为38.38%，不到一半的人在这次灾难中幸存下来。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2. 乘客的年龄分布大致情况，最大年龄和最小年龄的生还情况是怎样的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Details of number of passengers per age range\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Age\n",
       "(0, 10]      64\n",
       "(10, 20]    115\n",
       "(20, 30]    230\n",
       "(30, 40]    155\n",
       "(40, 50]     86\n",
       "(50, 60]     42\n",
       "(60, 70]     17\n",
       "(70, 80]      5\n",
       "Name: PassengerId, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>631</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "\n",
       "      Sex   Age  SibSp  Parch Ticket  Fare Embarked  \n",
       "630  male  80.0      0      0  27042  30.0        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Age</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>832</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Richards, Master. George Sibley</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29106</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                             Name     Sex  \\\n",
       "78            79         1       2    Caldwell, Master. Alden Gates    male   \n",
       "305          306         1       1   Allison, Master. Hudson Trevor    male   \n",
       "469          470         1       3    Baclini, Miss. Helene Barbara  female   \n",
       "644          645         1       3           Baclini, Miss. Eugenie  female   \n",
       "755          756         1       2        Hamalainen, Master. Viljo    male   \n",
       "803          804         1       3  Thomas, Master. Assad Alexander    male   \n",
       "831          832         1       2  Richards, Master. George Sibley    male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket      Fare Embarked  \n",
       "78   0.83      0      2  248738   29.0000        S  \n",
       "305  0.92      1      2  113781  151.5500        S  \n",
       "469  0.75      2      1    2666   19.2583        C  \n",
       "644  0.75      2      1    2666   19.2583        C  \n",
       "755  0.67      1      1  250649   14.5000        S  \n",
       "803  0.42      0      1    2625    8.5167        C  \n",
       "831  0.83      1      1   29106   18.7500        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f5576e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get categories by Age ranges.\n",
    "cat_age = pd.cut(titanic_df_age[\"Age\"], np.arange(0, 90, 10))\n",
    "print('Details of number of passengers per age range')\n",
    "display(titanic_df_age.groupby(cat_age).count()['PassengerId'])\n",
    "\n",
    "# ages are between 0 and 80.\n",
    "plt.title('Histogram of passengers age')\n",
    "titanic_df_age['Age'].hist(bins=8)\n",
    "\n",
    "display(titanic_df_age[titanic_df_age.Age == 80])\n",
    "display(titanic_df_age[titanic_df_age.Age < 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "由于乘客最大年龄为80岁，最小不足1岁，所以从乘客年龄范围统计数据和直方图的角度观察，发现20岁以上30岁以下的乘客数量最多，达230人，其次是30岁以上40岁以下，有155人。然后我们调查了最大年龄为80岁的乘客和所有不足1岁乘客的生还情况，发现他们都生还了，真是幸运！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3. 不同年龄段乘客的生还情况是怎样的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 714 passengers with age, 40.62% are survived\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f2026790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# investigate data with available 'Age'\n",
    "survived_rate_age = titanic_df_age['Survived'].mean()\n",
    "print('there are {} passengers with age, {} are survived'.\n",
    "      format(len(titanic_df_age), format_percent(survived_rate_age)))\n",
    "\n",
    "# investigate data group by age ranges\n",
    "group_by_age = titanic_df_age.groupby(cat_age)['Survived'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上是对具有有效年龄数据的乘客进行的统计分析。观察到这部分乘客总体生还率约为40.62%。因为最大年龄为80岁，因此这里我们对年龄进行了从0到80岁的分段分析，发现10岁以下的乘客生还率最高，其次是30岁以上40岁以下的。超过60岁的乘客生还率相对最低，远远不及其他年龄段乘客。可以看的出来年龄还是能够影响生还率的。在巨大的灾难面前，小孩子的求生能力是肯定不如成年人的，这么高的生还率只能解释为：在救生船资源有限的情况下，成年人把生的机会让给了儿童。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 乘客的登船地点和生还率之间的关系？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1fdd1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B9nWWLFk+yVE02tDQsqYjqAZe1/7VxLW18JIkTVXdnh3uyYjYony9E8VQucUU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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1fdded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9gKsBMvMeYCAiNm87/2ngqjqCSZK6zyJIkiSYDwy1HQ+VbQBk5qqeJ5IkVcZn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\nJElqFIsgSZIkSY3y/wGU6WM8J870lAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f20b5190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_embark = titanic_df_embark.groupby('Embarked')\n",
    "bar_group_by(group_by_embark, 'Survived')\n",
    "\n",
    "# investigate passengers from southampton group by Pclass\n",
    "sa_df = titanic_df_embark[titanic_df_embark.Embarked == 'S']\n",
    "sa_group_by_pclass = sa_df.groupby('Pclass')\n",
    "bar_group_by(sa_group_by_pclass, 'Survived')\n",
    "\n",
    "sa_group_by_gender = sa_df.groupby('Sex')\n",
    "bar_group_by(sa_group_by_gender, 'Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从第一组图中可以看到，绝大部分乘客是从英国南安普顿登船的。查了泰坦尼克号的维基百科，这艘游轮正是**1912年4月10日从英国南安普顿**出发的，这也就不足为奇了，但奇怪的是南安普顿登船的乘客生还率最低，为什么会这样呢？对这部分乘客进一步调查可以发现，南安普顿的三等舱乘客最多，他们的生还率是所有乘客中最低的，因此拉低了平均水平。再从性别上观察，南安普顿乘客男性是女性的两倍多，生还率却不及后者的三分之一，可以初步看出客舱等级和年龄均是影响生还率的重要因素，看来后续分析中还要重点关注一下这两个指标。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 不同客舱等级乘客的生还率和平均船票花费分别是怎样的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f19f18fb210>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1fe7590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1ad3f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_pclass = titanic_df.groupby('Pclass')\n",
    "bar_group_by(group_by_pclass, 'Survived')\n",
    "\n",
    "titanic_df.boxplot(column = 'Fare', by = 'Pclass', showfliers = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从第一组图中可以看出，三等舱乘客数量是最多的，其次是头等舱，二等舱乘客最少。然而头等舱乘客的生还率最高，其次是二等舱乘客，最后是三等舱乘客。这充分说明客舱等级是影响生还率的重要因素。第二组图分析了三种客舱船票花销，头等舱乘客的消费能力远在二等舱和三等舱之上，客舱等级越高花的钱越多，那么也可以说，船票开销也是影响生还率的一个因素。一个合理的解释是：头等舱靠近甲板，处于船体的上层；二等舱处于船体中层；三等舱处于船体下层，发生海难时，海水是从船体下部灌入的，因此三等舱乘客逃生难度最大，其次是二等舱，最后是头等舱。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 不同性别乘客的生还率和船票花费分别是怎样的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Axes(0.1,0.15;0.363636x0.75)\n",
       "1    Axes(0.536364,0.15;0.363636x0.75)\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nxv0ncEJmXtvjbJKkKeY9QZIkwbXAwQARsSuwIjO7v549FTgtM69uIpwkaWo5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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1b4a590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f17c7950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f159af10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_gender = titanic_df.groupby('Sex')\n",
    "bar_group_by(group_by_gender, 'Survived')\n",
    "\n",
    "titanic_df.boxplot(column='Fare', by='Sex', showfliers=False)\n",
    "\n",
    "titanic_df.groupby('Survived').boxplot(column = 'Fare', by = 'Sex', showfliers = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一组图对比分析了性别与生还率的关系，首先船上的男乘客几乎是女乘客的一倍，然而女乘客的生还率几乎是男乘客的三倍，这说明性别对生还率的影响很大。男人们发扬了真正的绅士风度，在救生资源有限的情况下把生的希望让给了女人和孩子，自己选择葬身鱼腹，这是令人肃然起敬的。第二组箱线图观察了不同性别乘客在船票上的消费情况，可以看到女性乘客船票花费的最大值和最小值都分别比男性乘客要高，均值，上下四分位数，中位数都高于男性，所以女人们要比男人们更舍得花钱享受更好的乘坐环境。第三组箱线图进一步对乘客按生还情况和性别类别进行分析，可以看到生还组的乘客，无论男性还是女性，其船票花费都要高于遇难组的乘客。因为客舱等级越高船票肯定越贵，这也从侧面佐证了上面分析中关于客舱等级对生还率影响的结论。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 带着兄弟姐妹/配偶一同登船的乘客，他们的生还率如何？其中的男性乘客呢？女性乘客呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f168ff50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SJKlRLIIkSZIkNYpFkCRJkqRGsQiSJEmS1CgWQZIkSZIaxSJIkiRJUqNYBEmS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f17d4dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_sibsp = titanic_df.groupby(titanic_df['SibSp'] > 0)\n",
    "bar_group_by(group_by_sibsp, 'Survived', 'SibSp > 0')\n",
    "sibsp_group_by_gender = titanic_df[titanic_df['SibSp'] > 0].groupby('Sex')\n",
    "bar_group_by(sibsp_group_by_gender, 'Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面第一组图中可以看到，没有带兄弟姐妹/配偶的乘客要比带了的多一倍，然而前者的生还率却不及后者，一种较为合理的解释是，带了兄弟姐妹/配偶的乘客更容易相互扶持，彼此照顾，因而获救生还的概率要高一些。然而对这部分乘客进一步分析可以发现，虽然带了兄弟姐妹/配偶的乘客在数量上旗鼓相当，但其中女性乘客的生还率仍然远高于男性，性别仍然是一个相当重要的影响因素。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 带着双亲/子女一同登船的乘客，他们的生还率如何？其中的男性乘客呢？女性乘客呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DKpx3EPDV8pybgf6I2K7cXp+ZB2Xm78uCaHtcbEGSelqV1eHmR8QAcARwWDkFoH+sEyLi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\n/10IbJQFqMVZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1b3e590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NYhEkSZIkqVH6On3DiPi/wN7lvc8BlgBfAGYCy4FjMnNNp3NJkiRJaoaO9gRF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f187ec90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_parch = titanic_df.groupby(titanic_df['Parch'] > 0)\n",
    "bar_group_by(group_by_parch, 'Survived', 'Parch > 0')\n",
    "\n",
    "parch_group_by_gender = titanic_df[titanic_df['Parch'] > 0].groupby('Sex')\n",
    "bar_group_by(parch_group_by_gender, 'Parch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "带着父母/子女一同乘船的乘客在数量上要比不带的少很多，但前者的生还率比后者高。合理的解释仍然是带了父母子女的乘客更容易相互扶持，彼此照顾，因而获救生还的概率要高一些，且其中带着子女的父母，因为需要照顾子女是可以上救生船的，这无疑增加了生还的概率，对这类乘客进一步的分性别分析可以看出，女性的数量要比男性多，某种程度上获救的机会就更大了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 0-10岁儿童乘客的生还情况如何，这时性别因素还有影响吗？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIkdcqyYQeQJGkUJDkbOAiYBE6uqjV9xx4MnA/8ZlX9pyFFlCTNEUeCJEmd\nl+QwYL+qOhh4OXDutFP+EviXeQ8mSWqFRZAkSfAM4JMAVXULMJZkj77jbwQuH0YwSdLcswiSJAlW\nAhN9+xO9NgCqauO8J5IktabVZ4KS7A/8HXB2Vb0vySOBDwNLgTuA46tqU5LjgFOAzcAFVXVhm7kk\nSZrBkh25eGxsOcuWLZ2rLFoExsdXDDuCtMMW033cWhGUZDfgvcBn+prPAM6rqsuSvAM4McmlwGnA\nk4F7gDVJLq+qH7eVTZKkadbRN/ID7EPzx7rtsn79XTscSIvLxISDiVr4Ftp9vK2irc3pcJuA59B0\nLFMOB67obV8JHAU8BVhTVRuq6m7gBuCQFnNJkjTdNcALAJIcAKxzCpwkLV6tjQRV1b3AvUn6m3er\nqk297R8Ce/PL87Cn2rfKaQaabjENz2rueF9oUFW1OsnaJKtppmaflOQEYENVXZ7kMuCRQJJcRzN1\n+2PDSyxJ2hHDfE/Q1uZbzzgP22kGmm6hDc+qfePjK7wvWrbYisyqOnVa0019x/5wnuNIklo036vD\n/TTJrr3tfWmmyk2fhz3VLkmSJElzbr6LoGuBY3vbxwJXAV8EDkzykCS70zwPdP0855IkSZLUEW2u\nDvc7wFnAY4CfJ3kBcBxwcZJXArcBl1TVz5OcClwNTAKnV9WGtnJJkiRJ6rY2F0ZYS7Ma3HTP3MK5\nq4BVbWWRJEmSpCnzPR1OkiRJkobKIkiSJElSp1gESZIkSeoUiyBJkiRJnWIRJEmSJKlTLIIkSZIk\ndYpFkCRJkqROsQiSJEmS1CkWQZIkSZI6ZdmwA0haGE4887PDjrDoXXTqkcOOIElSJzgSJEmSJKlT\nLIIkSZIkdYpFkCRJkqROsQiSJEmS1CkWQZIkSZI6xSJIkiRJUqdYBEmSJEnqFIsgSZIkSZ1iESRJ\nkiSpUyyCJEmSJHWKRZAkSZKkTrEIkiRJktQpFkGSJEmSOsUiSJIkSVKnWARJkiRJ6hSLIEmSJEmd\nYhEkSZIkqVMsgiRJkiR1ikWQJEmSpE6xCJIkSZLUKRZBkiRJkjpl2bADTElyNnAQMAmcXFVrhhxJ\nktQh2+qHkhwFvAO4D/h0Vb1tOCklSXNhJEaCkhwG7FdVBwMvB84dciRJUocM0A+dCxwLHAI8K8nj\n5zmiJGkOjUQRBDwD+CRAVd0CjCXZY7iRJEkdstV+KMmvAj+uqturajPw6d75kqQFalSmw60E1vbt\nT/Ta7txH4PX0AAAGxElEQVTSyePjK5bMR6i5cuVZRw87grTDvI+1yG2rH1rZ25/yQ+DXtvVhC62f\nAn/HtfB5D2s2RmUkaLoF13lIkhaVbfVD9lGStMCNShG0juYvbVP2Ae4YUhZJUvdsqx+afmzfXpsk\naYEalSLoGuAFAEkOANZV1cbhRpIkdchW+6Gq+g6wR5LHJFkGPK93viRpgVoyOTk57AwAJDkTOBTY\nDJxUVTcNOZIkqUOm90PAk4ANVXV5kkOBP++d+omqeteQYkqS5sDIFEGSJEmSNB9GZTqcJEmSJM0L\niyBJkiRJnWIRJEmSJKlTLIIkSZIkdYpFkO6XZP8k1yT5597+63tLxUoLgvewtLj5O66Fznt4dFgE\nqd97gZOB/9vbvxo4d3hxpFnzHpYWN3/HtdB5D48IiyD1u7eqbpnaqaqv0bwvQ1oovIelxc3fcS10\n3sMjYtmwA2ik/CTJicBuSZ4CHAP8cMiZpNnwHpYWN3/HtdB5D48IR4LU72XAPsCPgFOBnwAnDDOQ\nNEvew9Li5u+4Fjrv4RFhEaR+OwGXVdVzgHNo5qvuOtxI0qwcAPwTcCZwNrC61yZpcbCf0kJnPzUi\nnA6nfn8N/HmSZcBfAu8BPgQ8b6ippMG9rm/7QcCTgC/RdDiSFj77KS109lMjwiJI/XapquuSnA6c\nXVUfS/KyYYeSBlVVf9i/n2Q5cOGQ4kiae/ZTWtDsp0aHRZD6PTjJccCLgf+U5DHAnsONJO2QzcDj\nhx1C0pyxn9JiYz81JBZB6vcamgf2Xl1VG5O8BHjzkDNJA0syAUz2dpfQdC4fGF4iSXPMfkoLmv3U\n6FgyOTk581la1JJs8y8QvTXspZGX5ICq+vKwc0iaW/ZTWizsp0aHI0ECOG8bxyaBI+criLSD3pXk\nWVV177CDSJpT9lNaLOynRoRFkKiqI7Z2LMlb5jOLtIN+BnwjyU3APVONVfXC4UWStKPsp7SI2E+N\nCIsg3S/Jc4AzgL16TTsD3wPeNrRQ0gCSrKqqFwDvGnYWSe2xn9JCZT81eiyC1O+twB8ClwDHAMcC\nG4cZSBrQXgBV9flhB5HUqrdiP6WFyX5qxFgEqd/PqurbSXaqqv8ALkjyj8DHhx1MmsHjkvzF1g5W\n1Z/OZxhJrbGf0kJlPzViLILU7/tJjge+kuQjwLeBhw05kzSInwFfHXYISa2zn9JCZT81YiyCRJJ3\nV9V/A15KM1z7WGAN8CvA84eZTRrQD6rqkmGHkNQO+yktAvZTI8YiSABPBKiq+4CJJIdXlcuNaiFZ\nO+wAklplP6WFzn5qxOw07AAaCUtm2JdGWlW9YdgZJLXKfkoLmv3U6LEIEjQvmtvWviRJw2Q/JWlO\nLZmc9L8jXZfkTuD/9HaXAOntLwEmq+rJw8omSZL9lKS55jNBAnjCsANIkrQN9lOS5pQjQZIkSZI6\nxWeCJEmSJHWKRZAkSZKkTvGZIGkASfYGbgfeXFVnzvP3XgH8T+BpVfWIvvadgb8ATgYeVFX3zmcu\nSdJosa+SBudIkDSYlwJfA05o6xskeUiSp27h0EXA9Vtofzfwr23lkSQtOPZV0oAcCZIGcyLwauDi\nJE+tqtUASX4POBP4MXA18NqqekSSMeCvgHFgT+CsqvrYlj44yf7Aa2neiP72LZzycmAv4M+mtb+x\nqu5McuEO/3SSpMXAvkoakCNB0gySHErzB4PPApcCL+u1LwHOB15SVUfQdCBT3g5cVVVHAocCZyQZ\nn/a5z05yLXAG8NdVdVBV/f30719Vd24p19baJUndY18lzY5FkDSzlwMXV9Uk8CHghUmWAw8Fdq+q\nm3rnreq75gjg1UmuAz4F/Bx47LTPfQPwVeDEqvpci/klSYuffZU0C06Hk7YhyR7AscB3k/xBr3lp\nr+1qYHPf6ff1bW8CXlNVX9rGxx9DM3/7H5OsAd5bVbfMWXhJUifYV0mz50iQtG1/BHy+qh5fVU+s\nqicCf0wzzeBHwOYk6Z37B33XfQF4IUCSXZO8P8kD/uhQVRur6n1VdSDwCeDtSd7S9g8kSVp07Kuk\nWXIkSNq2l9PMg+63ima1m0cBpwCfTPJd4J+AqaU/3wp8MMkXgF2AC7a1LGhVfQb4TJJd+tt7S4te\nAzwYGO9NWVhbVf89yd/SPIRK79rvVdVx2/2TSpIWKvsqaZaWTE5ODjuDtGAlORr416r6dm8Kwiur\n6neHnUuSpCn2VdIvcyRI2jFLgb9Ncmdv+9VDziNJ0nT2VdI0jgRJkiRJ6hQXRpAkSZLUKRZBkiRJ\nkjrFIkiSJElSp1gESZIkSeoUiyBJkiRJnWIRJEmSJKlT/n+/k02yNfNTNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1cb0c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ksSNJkiSpkSx2JEmSJDWSxY4kSZKkRrLYkSRJktRIFjuSJEmSGsliR5IkSVIj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\nO8EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f1c6ec10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_teenager = titanic_df_age.groupby(titanic_df_age['Age'] < 11)\n",
    "bar_group_by(group_by_teenager, 'Survived', 'Age < 11')\n",
    "\n",
    "teenager_group_by_gender = titanic_df_age[titanic_df_age['Age'] < 11].groupby('Sex')\n",
    "bar_group_by(teenager_group_by_gender, 'Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从问题3的分析中看到，0-10岁乘客的生还率要高于其他年龄段，又从上面关于性别的分析得知，性别对于生还率具有重要影响。那对于这类儿童乘客来说性别因素仍然具有影响吗？让我们来分析一下，首先，从第一组图中可以看出，0-10岁乘客的数量不足100人，然而他们的生还率却高于其他年龄的乘客。进一步对0-10岁乘客进行分性别分析，可以发现男女比例差别不大，生还率差别也不大。也就是说，对于儿童乘客性别不是决定性因素，影响不大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. 独自一人登船的乘客生还情况如何，性别因素会有影响吗？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+J7AVsCWwtGubifVrtWDBxsybN7eP0Y6usbH5dYfQaLbvmmyP0eTvTZKkNRW9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f17e11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+M9pFmbkQWBwRC4EzgHkRcVREHJqZK4GrKIaH30TxvJBFkCRNYO30BH0BWAnsVW7vArwX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\ngAQVzm+Iyy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f16fd690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_alone = titanic_df.groupby([titanic_df.SibSp == 0, titanic_df.Parch == 0])\n",
    "bar_group_by(group_by_alone, 'Survived', 'SibSp == 0 & Parch == 0')\n",
    "\n",
    "alone_group_by_gender = titanic_df_age.loc(titanic_df.SibSp == 0)[titanic_df.Parch == 0].groupby('Sex')\n",
    "bar_group_by(alone_group_by_gender, 'Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "独自一人乘船的乘客要远远多于带着兄弟姐妹/配偶和带着父母/子女的乘客，但他们的生还率却是最低的，而且低很多。综合上面的分析来看，带不带亲属或者家人的确是影响生还率的一个重要因素。在独自一人乘船的乘客中，性别优势再一次发挥了它的威力，女性生还率仍然高于男性，高了三倍多！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五. 得出结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的分析中我们可以得出一些初步的结论。年龄，客舱等级，性别，是否带着兄弟姐妹/配偶或者父母/子女都是影响生还率的重要因素。首先**年龄是影响生还率的因素之一**，儿童乘客的生还率要比其他年龄段的乘客高；其次**客舱等级越高生还率就越高**，这也许是因为越高级的客舱离上层甲板越近的缘故；从船票开销上看，**女人们还是比男人们更愿意花钱**；然后，**性别是影响生还率的最大因素**，女性乘客在任何情况下都比男性乘客生还率高很多，推测这是因为男人们更有绅士风度，把有限的救生船名额先让给了女人和小孩；**带着家人的乘客生还率高于独自一人乘船的乘客**，可以合理推测，**一家人团结在一起互相帮助，能提高彼此生还的几率**；最后，**16周岁以下的儿童乘客，生还率比其他年龄段的乘客要高**。\n",
    "\n",
    "然而，以上对数据的调查和探索得出的结论都只是暂时的。首先样本数据只有891条，如果有更多的数据，也许能得出不太一样或者截然相反的结论。其次，有177位乘客的年龄数据是缺失的，这势必会对上文基于年龄的各项分析产生影响，这是数据固有的局限性。最后，如果严肃的对待数据分析，那么以上结论必须经过统计检验后才能对统计显著性下定论。"
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