1111/Titanic-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7aaed3bd",
   "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>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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "train = pd.read_csv('./train.csv')\n",
    "test = pd.read_csv('./test.csv')\n",
    "data = pd.concat([train,test],ignore_index = True)\n",
    "pd.options.mode.chained_assignment = None # 避免pandas报错\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # 忽略警告\n",
    "%matplotlib inline\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad428e1c",
   "metadata": {},
   "source": [
    "## 数据观察"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "7806d40f",
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "9d588c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived        418\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              1\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "36ba3492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    549\n",
       "1.0    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "3f76c4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='Survived'>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = \"Pclass\",y = \"Survived\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "41eeeeb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Survived'>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = \"Sex\",y = \"Survived\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "f0d658c0",
   "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": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "6ba9f76b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(39.11586516203704, 0.5, 'rate')"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 672.125x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = sns.FacetGrid(data, hue=\"Survived\",aspect=2)\n",
    "age.map(sns.kdeplot,'Age',shade= True)\n",
    "age.set(xlim=(0, data['Age'].max()))\n",
    "age.add_legend()\n",
    "plt.xlabel('Age') \n",
    "plt.ylabel('rate') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71859aa8",
   "metadata": {},
   "source": [
    "乘客0到10岁生存几率大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "07f0ee85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SibSp', ylabel='Survived'>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = \"SibSp\",y = \"Survived\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "1189a46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Parch', ylabel='Survived'>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Parch\",y=\"Survived\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "a65b5a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Family', ylabel='Survived'>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"Family\"]=data['SibSp']+data['Parch']+1\n",
    "sns.barplot(x=\"Family\", y=\"Survived\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844d6bbd",
   "metadata": {},
   "source": [
    "## 数据处理\n",
    "## 特征工程 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae2b172",
   "metadata": {},
   "source": [
    "### 1.名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "cd71d7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Title', ylabel='Survived'>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Title'] = data[\"Name\"].apply(lambda x:x.split(',')[1].split('.')[0].strip())\n",
    "data['Title'].replace(['Capt', 'Col', 'Major', 'Dr', 'Rev'],'Officer', inplace=True)\n",
    "data['Title'].replace(['Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty', inplace=True)\n",
    "data['Title'].replace(['Mme', 'Ms', 'Mrs'],'Mrs', inplace=True)\n",
    "data['Title'].replace(['Mlle', 'Miss'], 'Miss', inplace=True)\n",
    "data['Title'].replace(['Master','Jonkheer'],'Master', inplace=True)\n",
    "data['Title'].replace(['Mr'], 'Mr', inplace=True)\n",
    "\n",
    "sns.barplot(x=\"Title\", y=\"Survived\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "27114a58",
   "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",
       "      <th>Family</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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",
       "      <td>2</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</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",
       "      <td>2</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</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",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</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",
       "      <td>2</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</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",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "0               1       0.0       3   \n",
       "1               2       1.0       1   \n",
       "2               3       1.0       3   \n",
       "3               4       1.0       1   \n",
       "4               5       0.0       3   \n",
       "...           ...       ...     ...   \n",
       "1304         1305       NaN       3   \n",
       "1305         1306       NaN       1   \n",
       "1306         1307       NaN       3   \n",
       "1307         1308       NaN       3   \n",
       "1308         1309       NaN       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",
       "1304                                 Spector, Mr. Woolf    male   NaN      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "1307                                Ware, Mr. Frederick    male   NaN      0   \n",
       "1308                           Peter, Master. Michael J    male   NaN      1   \n",
       "\n",
       "      Parch              Ticket      Fare Cabin Embarked  Family    Title  \n",
       "0         0           A/5 21171    7.2500   NaN        S       2       Mr  \n",
       "1         0            PC 17599   71.2833   C85        C       2      Mrs  \n",
       "2         0    STON/O2. 3101282    7.9250   NaN        S       1     Miss  \n",
       "3         0              113803   53.1000  C123        S       2      Mrs  \n",
       "4         0              373450    8.0500   NaN        S       1       Mr  \n",
       "...     ...                 ...       ...   ...      ...     ...      ...  \n",
       "1304      0           A.5. 3236    8.0500   NaN        S       1       Mr  \n",
       "1305      0            PC 17758  108.9000  C105        C       1  Royalty  \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500   NaN        S       1       Mr  \n",
       "1307      0              359309    8.0500   NaN        S       1       Mr  \n",
       "1308      1                2668   22.3583   NaN        C       3   Master  \n",
       "\n",
       "[1309 rows x 14 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db72aaa1",
   "metadata": {},
   "source": [
    "可见Mrs,Miss,Master,Royalty等身份高的更容易获救，Mr,Officer这些身份较低的相反"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ecc3af",
   "metadata": {},
   "source": [
    "### 2.家庭特征 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "e9f281b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Family', ylabel='Survived'>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"F_size\"]=data['SibSp']+data['Parch']+1\n",
    "data.loc[(data[\"F_size\"] >= 1) & (data[\"F_size\"] <= 4),\"Family\"]  = 1\n",
    "data.loc[(data[\"F_size\"] >= 5) & (data[\"F_size\"] <=7 ),\"Family\"] = 2\n",
    "data.loc[data[\"F_size\"] >= 8,\"Family\"] = 3\n",
    "data[\"Family\"] = data[\"Family\"].astype(np.int32)\n",
    "sns.barplot(x = \"Family\",y = \"Survived\",data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "a94dca98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>Goodwin, Master. William Frederick</td>\n",
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       "      PassengerId  Survived  Pclass                                     Name  \\\n",
       "59             60       0.0       3       Goodwin, Master. William Frederick   \n",
       "71             72       0.0       3               Goodwin, Miss. Lillian Amy   \n",
       "159           160       0.0       3               Sage, Master. Thomas Henry   \n",
       "180           181       0.0       3             Sage, Miss. Constance Gladys   \n",
       "201           202       0.0       3                      Sage, Mr. Frederick   \n",
       "324           325       0.0       3                 Sage, Mr. George John Jr   \n",
       "386           387       0.0       3          Goodwin, Master. Sidney Leonard   \n",
       "480           481       0.0       3           Goodwin, Master. Harold Victor   \n",
       "678           679       0.0       3  Goodwin, Mrs. Frederick (Augusta Tyler)   \n",
       "683           684       0.0       3              Goodwin, Mr. Charles Edward   \n",
       "792           793       0.0       3                  Sage, Miss. Stella Anna   \n",
       "846           847       0.0       3                 Sage, Mr. Douglas Bullen   \n",
       "863           864       0.0       3        Sage, Miss. Dorothy Edith \"Dolly\"   \n",
       "1030         1031       NaN       3           Goodwin, Mr. Charles Frederick   \n",
       "1031         1032       NaN       3              Goodwin, Miss. Jessie Allis   \n",
       "1079         1080       NaN       3                          Sage, Miss. Ada   \n",
       "1233         1234       NaN       3                    Sage, Mr. John George   \n",
       "1251         1252       NaN       3              Sage, Master. William Henry   \n",
       "1256         1257       NaN       3           Sage, Mrs. John (Annie Bullen)   \n",
       "\n",
       "         Sex   Age  SibSp  Parch    Ticket   Fare Cabin Embarked  Family  \\\n",
       "59      male  11.0      5      2   CA 2144  46.90   NaN        S       3   \n",
       "71    female  16.0      5      2   CA 2144  46.90   NaN        S       3   \n",
       "159     male   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "180   female   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "201     male   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "324     male   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "386     male   1.0      5      2   CA 2144  46.90   NaN        S       3   \n",
       "480     male   9.0      5      2   CA 2144  46.90   NaN        S       3   \n",
       "678   female  43.0      1      6   CA 2144  46.90   NaN        S       3   \n",
       "683     male  14.0      5      2   CA 2144  46.90   NaN        S       3   \n",
       "792   female   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "846     male   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "863   female   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "1030    male  40.0      1      6   CA 2144  46.90   NaN        S       3   \n",
       "1031  female  10.0      5      2   CA 2144  46.90   NaN        S       3   \n",
       "1079  female   NaN      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "1233    male   NaN      1      9  CA. 2343  69.55   NaN        S       3   \n",
       "1251    male  14.5      8      2  CA. 2343  69.55   NaN        S       3   \n",
       "1256  female   NaN      1      9  CA. 2343  69.55   NaN        S       3   \n",
       "\n",
       "       Title  F_size  \n",
       "59    Master       8  \n",
       "71      Miss       8  \n",
       "159   Master      11  \n",
       "180     Miss      11  \n",
       "201       Mr      11  \n",
       "324       Mr      11  \n",
       "386   Master       8  \n",
       "480   Master       8  \n",
       "678      Mrs       8  \n",
       "683       Mr       8  \n",
       "792     Miss      11  \n",
       "846       Mr      11  \n",
       "863     Miss      11  \n",
       "1030      Mr       8  \n",
       "1031    Miss       8  \n",
       "1079    Miss      11  \n",
       "1233      Mr      11  \n",
       "1251  Master      11  \n",
       "1256     Mrs      11  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[\"Family\"] ==3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "613edd9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
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       "F_size            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "5c16682a",
   "metadata": {},
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       "1043         1044       NaN       3  Storey, Mr. Thomas  male  60.5      0   \n",
       "\n",
       "      Parch Ticket  Fare Cabin Embarked  Family Title  F_size  \n",
       "1043      0   3701   NaN   NaN        S       1    Mr       1  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22ae28e",
   "metadata": {},
   "source": [
    "### 把Pclass3的Fare众数填入Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "7640895f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Fare.fillna(data[data.Pclass == 3]['Fare'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "5573a558",
   "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",
       "      <th>Family</th>\n",
       "      <th>Title</th>\n",
       "      <th>F_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</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",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</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",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>Master</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass                            Name     Sex  \\\n",
       "0               1       0.0       3         Braund, Mr. Owen Harris    male   \n",
       "2               3       1.0       3          Heikkinen, Miss. Laina  female   \n",
       "4               5       0.0       3        Allen, Mr. William Henry    male   \n",
       "5               6       0.0       3                Moran, Mr. James    male   \n",
       "7               8       0.0       3  Palsson, Master. Gosta Leonard    male   \n",
       "...           ...       ...     ...                             ...     ...   \n",
       "1303         1304       NaN       3  Henriksson, Miss. Jenny Lovisa  female   \n",
       "1304         1305       NaN       3              Spector, Mr. Woolf    male   \n",
       "1306         1307       NaN       3    Saether, Mr. Simon Sivertsen    male   \n",
       "1307         1308       NaN       3             Ware, Mr. Frederick    male   \n",
       "1308         1309       NaN       3        Peter, Master. Michael J    male   \n",
       "\n",
       "       Age  SibSp  Parch              Ticket     Fare Cabin Embarked  Family  \\\n",
       "0     22.0      1      0           A/5 21171   7.2500   NaN        S       1   \n",
       "2     26.0      0      0    STON/O2. 3101282   7.9250   NaN        S       1   \n",
       "4     35.0      0      0              373450   8.0500   NaN        S       1   \n",
       "5      NaN      0      0              330877   8.4583   NaN        Q       1   \n",
       "7      2.0      3      1              349909  21.0750   NaN        S       2   \n",
       "...    ...    ...    ...                 ...      ...   ...      ...     ...   \n",
       "1303  28.0      0      0              347086   7.7750   NaN        S       1   \n",
       "1304   NaN      0      0           A.5. 3236   8.0500   NaN        S       1   \n",
       "1306  38.5      0      0  SOTON/O.Q. 3101262   7.2500   NaN        S       1   \n",
       "1307   NaN      0      0              359309   8.0500   NaN        S       1   \n",
       "1308   NaN      1      1                2668  22.3583   NaN        C       1   \n",
       "\n",
       "       Title  F_size  \n",
       "0         Mr       2  \n",
       "2       Miss       1  \n",
       "4         Mr       1  \n",
       "5         Mr       1  \n",
       "7     Master       5  \n",
       "...      ...     ...  \n",
       "1303    Miss       1  \n",
       "1304      Mr       1  \n",
       "1306      Mr       1  \n",
       "1307      Mr       1  \n",
       "1308  Master       3  \n",
       "\n",
       "[1014 rows x 15 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Cabin.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "b183b41c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cabin', ylabel='Survived'>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[data.Cabin.notnull(),\"Cabin\"] = 0\n",
    "data.loc[data.Cabin.isnull(),\"Cabin\"] = 1\n",
    "# data[\"Family\"] = data[\"Family\"].astype(np.int32)\n",
    "sns.barplot(x='Cabin',y='Survived',data = data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe5e02f",
   "metadata": {},
   "source": [
    "### 可把有无Cabin 作为一个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "ca35086c",
   "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",
       "      <th>Family</th>\n",
       "      <th>Title</th>\n",
       "      <th>F_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</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>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "0               1       0.0       3   \n",
       "1               2       1.0       1   \n",
       "2               3       1.0       3   \n",
       "3               4       1.0       1   \n",
       "4               5       0.0       3   \n",
       "...           ...       ...     ...   \n",
       "1304         1305       NaN       3   \n",
       "1305         1306       NaN       1   \n",
       "1306         1307       NaN       3   \n",
       "1307         1308       NaN       3   \n",
       "1308         1309       NaN       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",
       "1304                                 Spector, Mr. Woolf    male   NaN      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "1307                                Ware, Mr. Frederick    male   NaN      0   \n",
       "1308                           Peter, Master. Michael J    male   NaN      1   \n",
       "\n",
       "      Parch              Ticket      Fare Cabin Embarked  Family    Title  \\\n",
       "0         0           A/5 21171    7.2500     1        S       1       Mr   \n",
       "1         0            PC 17599   71.2833     0        C       1      Mrs   \n",
       "2         0    STON/O2. 3101282    7.9250     1        S       1     Miss   \n",
       "3         0              113803   53.1000     0        S       1      Mrs   \n",
       "4         0              373450    8.0500     1        S       1       Mr   \n",
       "...     ...                 ...       ...   ...      ...     ...      ...   \n",
       "1304      0           A.5. 3236    8.0500     1        S       1       Mr   \n",
       "1305      0            PC 17758  108.9000     0        C       1  Royalty   \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500     1        S       1       Mr   \n",
       "1307      0              359309    8.0500     1        S       1       Mr   \n",
       "1308      1                2668   22.3583     1        C       1   Master   \n",
       "\n",
       "      F_size  \n",
       "0          2  \n",
       "1          2  \n",
       "2          1  \n",
       "3          2  \n",
       "4          1  \n",
       "...      ...  \n",
       "1304       1  \n",
       "1305       1  \n",
       "1306       1  \n",
       "1307       1  \n",
       "1308       3  \n",
       "\n",
       "[1309 rows x 15 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b978213c",
   "metadata": {},
   "source": [
    "### 根据乘客的职业（tile）来进行对年龄赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "1bd38b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Survived'>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    for name in [\"Mr\",\"Mrs\",\"Miss\",\"Master\",\"Royalty\",\"Officer\"]:\n",
    "        index = data.loc[(data.Title == name,'Age')]\n",
    "        index.fillna(data[data.Title == name]['Age'].median(),inplace = True)\n",
    "        data.loc[(data.Title == name,'Age')] = index\n",
    "\n",
    "    data.loc[data.Age <=15 ,\"Age\"] = 1\n",
    "    data.loc[(data.Age > 15) & (data.Age <= 35) ,\"Age\"] = 2\n",
    "    data.loc[(data.Age > 35) & (data.Age <= 58),\"Age\"] = 3\n",
    "    data.loc[(data.Age > 58) ,\"Age\"] = 4\n",
    "    sns.barplot(x='Age',y='Survived',data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "ad6359bb",
   "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>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",
       "      <th>Family</th>\n",
       "      <th>Title</th>\n",
       "      <th>F_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked, Family, Title, F_size]\n",
       "Index: []"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Embarked==4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36f963d",
   "metadata": {},
   "source": [
    "### 处理Embarked的空缺值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "bf171854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Embarked', ylabel='Survived'>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    data.loc[data.Embarked == \"S\",\"Embarked\"] = 1\n",
    "    data.loc[data.Embarked == \"Q\",\"Embarked\"] = 2\n",
    "    data.loc[data.Embarked == \"C\",\"Embarked\"] = 3\n",
    "    data.loc[data.Embarked.isnull(),\"Embarked\"]=4\n",
    "    sns.barplot(x='Embarked',y='Survived',data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "fd93c93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived       418\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin            0\n",
       "Embarked         0\n",
       "Family           0\n",
       "Title            0\n",
       "F_size           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3d6b66",
   "metadata": {},
   "source": [
    "### 将Sex转化为可用参数 female:0 male:1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "1f24e584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Survived'>"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    data.loc[data.Sex == 'female',\"Sex\"] = 0\n",
    "    data.loc[data.Sex == 'male',\"Sex\"] = 1\n",
    "    sns.barplot(x='Sex',y='Survived',data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "8a6103c8",
   "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",
       "      <th>Family</th>\n",
       "      <th>Title</th>\n",
       "      <th>F_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "0               1       0.0       3   \n",
       "1               2       1.0       1   \n",
       "2               3       1.0       3   \n",
       "3               4       1.0       1   \n",
       "4               5       0.0       3   \n",
       "...           ...       ...     ...   \n",
       "1304         1305       NaN       3   \n",
       "1305         1306       NaN       1   \n",
       "1306         1307       NaN       3   \n",
       "1307         1308       NaN       3   \n",
       "1308         1309       NaN       3   \n",
       "\n",
       "                                                   Name Sex  Age  SibSp  \\\n",
       "0                               Braund, Mr. Owen Harris   1  2.0      1   \n",
       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...   0  3.0      1   \n",
       "2                                Heikkinen, Miss. Laina   0  2.0      0   \n",
       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)   0  2.0      1   \n",
       "4                              Allen, Mr. William Henry   1  2.0      0   \n",
       "...                                                 ...  ..  ...    ...   \n",
       "1304                                 Spector, Mr. Woolf   1  2.0      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina   0  3.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen   1  3.0      0   \n",
       "1307                                Ware, Mr. Frederick   1  2.0      0   \n",
       "1308                           Peter, Master. Michael J   1  1.0      1   \n",
       "\n",
       "      Parch              Ticket      Fare Cabin Embarked  Family    Title  \\\n",
       "0         0           A/5 21171    7.2500     1        1       1       Mr   \n",
       "1         0            PC 17599   71.2833     0        3       1      Mrs   \n",
       "2         0    STON/O2. 3101282    7.9250     1        1       1     Miss   \n",
       "3         0              113803   53.1000     0        1       1      Mrs   \n",
       "4         0              373450    8.0500     1        1       1       Mr   \n",
       "...     ...                 ...       ...   ...      ...     ...      ...   \n",
       "1304      0           A.5. 3236    8.0500     1        1       1       Mr   \n",
       "1305      0            PC 17758  108.9000     0        3       1  Royalty   \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500     1        1       1       Mr   \n",
       "1307      0              359309    8.0500     1        1       1       Mr   \n",
       "1308      1                2668   22.3583     1        3       1   Master   \n",
       "\n",
       "      F_size  \n",
       "0          2  \n",
       "1          2  \n",
       "2          1  \n",
       "3          2  \n",
       "4          1  \n",
       "...      ...  \n",
       "1304       1  \n",
       "1305       1  \n",
       "1306       1  \n",
       "1307       1  \n",
       "1308       3  \n",
       "\n",
       "[1309 rows x 15 columns]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "32017a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(39.11586516203704, 0.5, 'Survived')"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 672.125x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = sns.FacetGrid(data, hue=\"Survived\",aspect=2)\n",
    "age.map(sns.kdeplot,'Fare',shade= True)\n",
    "age.set(xlim=(0, 90))\n",
    "age.add_legend()\n",
    "plt.xlabel('Fare') \n",
    "plt.ylabel('Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e0e5cb2",
   "metadata": {},
   "source": [
    "### 可见  票价在 20元以下的生存率较低  20元 到 45元 生存率一般   45以上比较高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "984ccf56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fare', ylabel='Survived'>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zzz/X9OnTNWrUKB09elRPPfVUp7ZRX1+vlpYWJSUlhbUnJSWptra23TEffvihHnnkEa1fv149e3buWuiCggI1NjaGliNHjnRqHAAAuDBFdLfU8OHDtWfPHm3cuFHvv/++WltblZ2drXvvvTfsAuPO+PI28i9ZltWmTTr9Ys7p06friSee0OjRozu9fafTKafT2aWaAADAhSuicPPZZ5/pkksu0Zw5czRnzpyIvnjQoEGKi4trM0tTV1fXZjZHkk6cOKHdu3erurpa8+bNk3T62h/LstSzZ09t3bpVX/va1yKqBQAAmCOi01JDhgzRfffdpy1btkT8osz4+Hh5PB75fL6wdp/Pp/Hjx7fpn5iYqA8++EB79uwJLbm5uRozZoz27NmjG2+8MaI6AACAWSKauVm3bp02btyob37zm0pMTNS0adN03333ady4cV3aTn5+vmbMmKH09HRlZGRo5cqVqqmpUW5urqTT18scPXpU69atU48ePZSWlhY2fsiQIUpISGjTDgAALl4RhZupU6dq6tSpOnHihH7xi19o48aNGj9+vFJSUnTffffpBz/4Qae2M23aNDU0NGjp0qXy+/1KS0tTeXm5kpOTJUl+v/+sz7wBAAD4Sw7LsqxobGjfvn269957tXfv3nafgRMrAoGAXC6XGhsbedknjFKz9Fq7S7DVoncHqiEYJ0ka6GzRMzc12FyR/Ub+4AO7S9CE5ybYXQJiyM75OyMe25W/3xG/OFOSPv/8c/385z/XlClT9Nd//ddqaGjQokWLzmWTAAAA5ySi01Jbt27V+vXr9ctf/lJxcXG6++67tWXLFt1yyy3Rrg8AAKBLIgo3U6ZM0V133aWXXnpJd911l3r16hXtugAAACISUbipra3lehUAABCTOh1uAoFAWKA50wsoCT4AAMAunQ43f/VXfyW/368hQ4aof//+7b4i4ctXJ8Ty3VIAAMBsnQ43b731lgYMGBD6ub1wAwAAYLdOh5u/vBPq1ltvPR+1AAAAnLOInnNz2WWX6Z//+Z914MCBaNcDAABwTiIKN/PmzdMbb7yh1NRUeTweFRcXy+/3R7s2AACALoso3OTn5+u9997Tb3/7W/3d3/2dSktLNXLkSHm9Xq1bty7aNQIAAHTaOb1+YfTo0XriiSd04MABVVZW6vjx45o9e3a0agMAAOiyiB7i95f++7//Wxs2bFBZWZkaGxt19913R6MuAOiSAc6Wdn8GcPGJKNz87ne/0/r167Vhwwb94Q9/0G233aZly5Zp6tSp6tevX7RrBICzenTsn+wuAUCMiCjcXHXVVUpPT9fcuXP1j//4jxo6dGi06wIAAIhIl8NNS0uLVqxYobvvvjv0UD8AAIBY0eULiuPi4vTggw+qsbHxfNQDAABwTiK6W+raa6/VwYMHo10LAADAOYso3PzLv/yLFi1apF/96lfy+/0KBAJhCwAAgF0iuqB40qRJkqRvfOMbYS/Q5K3gAADAbhGFm7fffjvadQAAAERFROHmL98QDgAAEEsiCjfbt28/4+c333xzRMUAAACcq4jCza233tqm7S+vveGaGwAAYJeI7pb69NNPw5a6ujq98cYbGjdunLZu3RrtGgEAADotopkbl8vVpm3ixIlyOp1auHChqqqqzrkwAACASEQ0c9ORwYMH68CBA9HcJAAAQJdENHOzd+/esHXLsuT3+7Vs2TJdf/31USkMAAAgEhGFmxtuuEEOh0OWZYW133TTTVq9enVUCgMAAIhEROHm0KFDYes9evTQ4MGDlZCQEJWiAAAAItWla25+/etf6/XXX1dycnJoqaio0M0336yRI0fqO9/5joLB4PmqFQAA4Ky6NHPz+OOP69Zbb1VWVpYk6YMPPlB2drZmzZql1NRUPf300xo+fLgef/zx81ErYshDDz2k48ePSzp9Ifny5cttrggAgNO6FG727NmjH/7wh6H1l19+WTfeeKP+7d/+TZLkdrv12GOPEW4uAsePH9exY8fsLgMAgDa6dFrq008/VVJSUmi9oqIi9IZwSRo3bpyOHDkSveoAAAC6qEvhJikpKXQxcVNTk95//31lZGSEPj9x4oR69eoV3QoBAAC6oEvhZtKkSXrkkUdUWVmpgoICXXLJJcrMzAx9vnfvXl1++eVRLxIAAKCzunTNzY9+9CNNnTpVt9xyi/r27auXXnpJ8fHxoc9Xr14tr9cb9SIBAAA6q0vhZvDgwaqsrFRjY6P69u2ruLi4sM9feeUV9e3bN6oFAgAAdEVE75ZyuVxtgo0kDRgwIGwmpzNKSkqUkpKihIQEeTweVVZWdth3x44dmjBhggYOHKjevXvrqquu0rPPPtvl+gEAgLkiekJxtJSVlWnBggUqKSnRhAkT9MILLygrK0v79u3TyJEj2/Tv06eP5s2bp+uuu059+vTRjh079MADD6hPnz76zne+Y8MeAACAWBPVt4J3VVFRkbKzs5WTk6PU1FQVFxfL7XartLS03f5jx47Vt7/9bV1zzTUaNWqU7rvvPt15551nnO0BAAAXF9vCTVNTk6qqqtpcgOz1erVr165ObaO6ulq7du3SLbfc0mGfYDCoQCAQtgAAAHPZFm7q6+vV0tIS9lBA6fSzdGpra884dsSIEXI6nUpPT9fcuXOVk5PTYd/CwkK5XK7Q4na7o1I/AACITbaelpIkh8MRtm5ZVpu2r6qsrNTu3bu1YsUKFRcXa+PGjR32LSgoUGNjY2jhCcoAAJjNtguKBw0apLi4uDazNHV1dW1mc74qJSVFknTttdfq2LFjevzxx/Xtb3+73b5Op1NOpzM6RQMAgJhn28xNfHy8PB6PfD5fWLvP59P48eM7vR3LshQMBqNdHgAAuEDZeit4fn6+ZsyYofT0dGVkZGjlypWqqalRbm6upNOnlI4ePap169ZJkp5//nmNHDlSV111laTTz7155plnNH/+fNv2AQAAxBZbw820adPU0NCgpUuXyu/3Ky0tTeXl5UpOTpYk+f1+1dTUhPq3traqoKBAhw4dUs+ePXX55Zdr2bJleuCBB+zaBQAAEGNsDTeSlJeXp7y8vHY/W7t2bdj6/PnzmaUBAABnZPvdUgAAANFEuAEAAEYh3AAAAKMQbgAAgFFsv6D4QuX5/jq7S7BV4qcnQ8nY/+nJi/73UfX0TLtLAAD8P2ZuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCg97S4AF6bWXn3a/RkAALsRbhCRk2Oy7C4BAIB2cVoKAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCi2h5uSkhKlpKQoISFBHo9HlZWVHfbdvHmzJk6cqMGDBysxMVEZGRnasmVLN1YLAABina3hpqysTAsWLNCSJUtUXV2tzMxMZWVlqaampt3+27dv18SJE1VeXq6qqirddttt+vrXv67q6upurhwAAMQqW8NNUVGRsrOzlZOTo9TUVBUXF8vtdqu0tLTd/sXFxVq8eLHGjRunK6+8Uk8++aSuvPJK/cd//Ec3Vw4AAGKVbeGmqalJVVVV8nq9Ye1er1e7du3q1DZaW1t14sQJDRgwoMM+wWBQgUAgbAEAAOayLdzU19erpaVFSUlJYe1JSUmqra3t1DZ+8pOf6NSpU7rnnns67FNYWCiXyxVa3G73OdUNAABim+0XFDscjrB1y7LatLVn48aNevzxx1VWVqYhQ4Z02K+goECNjY2h5ciRI+dcMwAAiF097friQYMGKS4urs0sTV1dXZvZnK8qKytTdna2XnnlFd1xxx1n7Ot0OuV0Os+5XgAAcGGwbeYmPj5eHo9HPp8vrN3n82n8+PEdjtu4caNmzZqlDRs26K677jrfZQIAgAuMbTM3kpSfn68ZM2YoPT1dGRkZWrlypWpqapSbmyvp9Cmlo0ePat26dZJOB5uZM2dq+fLluummm0KzPr1795bL5bJtPwAAQOywNdxMmzZNDQ0NWrp0qfx+v9LS0lReXq7k5GRJkt/vD3vmzQsvvKDm5mbNnTtXc+fODbXff//9Wrt2bXeXDwAAYpCt4UaS8vLylJeX1+5nXw0s27ZtO/8FAQCAC5rtd0sBAABEE+EGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCi2h5uSkhKlpKQoISFBHo9HlZWVHfb1+/2aPn26xowZox49emjBggXdVygAALgg2BpuysrKtGDBAi1ZskTV1dXKzMxUVlaWampq2u0fDAY1ePBgLVmyRNdff303VwsAAC4EtoaboqIiZWdnKycnR6mpqSouLpbb7VZpaWm7/UeNGqXly5dr5syZcrlcnfqOYDCoQCAQtgAAAHPZFm6amppUVVUlr9cb1u71erVr166ofU9hYaFcLldocbvdUds2AACIPbaFm/r6erW0tCgpKSmsPSkpSbW1tVH7noKCAjU2NoaWI0eORG3bAAAg9vS0uwCHwxG2bllWm7Zz4XQ65XQ6o7Y9AAAQ22ybuRk0aJDi4uLazNLU1dW1mc0BAADoLNvCTXx8vDwej3w+X1i7z+fT+PHjbaoKAABc6Gw9LZWfn68ZM2YoPT1dGRkZWrlypWpqapSbmyvp9PUyR48e1bp160Jj9uzZI0k6efKkjh8/rj179ig+Pl5XX321HbsAAABijK3hZtq0aWpoaNDSpUvl9/uVlpam8vJyJScnSzr90L6vPvNm7NixoZ+rqqq0YcMGJScn6w9/+EN3lg4AAGKU7RcU5+XlKS8vr93P1q5d26bNsqzzXBEAALiQ2f76BQAAgGgi3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwiu3hpqSkRCkpKUpISJDH41FlZeUZ+1dUVMjj8SghIUGXXXaZVqxY0U2VAgCAC4Gt4aasrEwLFizQkiVLVF1drczMTGVlZammpqbd/ocOHdLkyZOVmZmp6upqPfroo3rwwQe1adOmbq4cAADEKlvDTVFRkbKzs5WTk6PU1FQVFxfL7XartLS03f4rVqzQyJEjVVxcrNTUVOXk5GjOnDl65plnurlyAAAQq3ra9cVNTU2qqqrSI488Etbu9Xq1a9eudse888478nq9YW133nmnVq1apS+++EK9evVqMyYYDCoYDIbWGxsbJUmBQOCc6m8J/vmcxsMs53o8RcOJz1vsLgExJhaOy+Y/N9tdAmLIuRyTX461LOusfW0LN/X19WppaVFSUlJYe1JSkmpra9sdU1tb227/5uZm1dfXa9iwYW3GFBYW6oknnmjT7na7z6F6IJzruVy7SwDaKnTZXQEQxvXwuR+TJ06ckMt15u3YFm6+5HA4wtYty2rTdrb+7bV/qaCgQPn5+aH11tZW/fGPf9TAgQPP+D04u0AgILfbrSNHjigxMdHucgCOScQkjsvosCxLJ06c0PDhw8/a17ZwM2jQIMXFxbWZpamrq2szO/OloUOHttu/Z8+eGjhwYLtjnE6nnE5nWFv//v0jLxxtJCYm8g8WMYVjErGI4/LcnW3G5ku2XVAcHx8vj8cjn88X1u7z+TR+/Ph2x2RkZLTpv3XrVqWnp7d7vQ0AALj42Hq3VH5+vl588UWtXr1a+/fv18KFC1VTU6Pc3NPXLxQUFGjmzJmh/rm5uTp8+LDy8/O1f/9+rV69WqtWrdKiRYvs2gUAABBjbL3mZtq0aWpoaNDSpUvl9/uVlpam8vJyJScnS5L8fn/YM29SUlJUXl6uhQsX6vnnn9fw4cP105/+VP/wD/9g1y5c1JxOpx577LE2p/0Au3BMIhZxXHY/h9WZe6oAAAAuELa/fgEAACCaCDcAAMAohBsAAGAUwg0AADAK4QYd2r59u77+9a9r+PDhcjgc+uUvf3nWMRUVFfJ4PEpISNBll12mFStWnP9CcVEoLCzUuHHj1K9fPw0ZMkRTpkzRgQMHzjqOYxLnU2lpqa677rrQA/oyMjL0+uuvn3EMx+T5R7hBh06dOqXrr79e//qv/9qp/ocOHdLkyZOVmZmp6upqPfroo3rwwQe1adOm81wpLgYVFRWaO3eu3n33Xfl8PjU3N8vr9erUqVMdjuGYxPk2YsQILVu2TLt379bu3bv1ta99TX//93+v3/zmN+3255jsHtwKjk5xOBx69dVXNWXKlA77PPzww3rttde0f//+UFtubq7+53/+R++88043VImLyfHjxzVkyBBVVFTo5ptvbrcPxyTsMGDAAD399NPKzs5u8xnHZPdg5gZR884778jr9Ya13Xnnndq9e7e++OILm6qCqRobGyWd/kPSEY5JdKeWlha9/PLLOnXqlDIyMtrtwzHZPQg3iJra2to2Lz1NSkpSc3Oz6uvrbaoKJrIsS/n5+frbv/1bpaWlddiPYxLd4YMPPlDfvn3ldDqVm5urV199VVdffXW7fTkmu4etr1+AeRwOR9j6l2c9v9oOnIt58+Zp79692rFjx1n7ckzifBszZoz27NmjP/3pT9q0aZPuv/9+VVRUdBhwOCbPP8INombo0KGqra0Na6urq1PPnj01cOBAm6qCaebPn6/XXntN27dv14gRI87Yl2MS3SE+Pl5XXHGFJCk9PV3vvfeeli9frhdeeKFNX47J7sFpKURNRkaGfD5fWNvWrVuVnp6uXr162VQVTGFZlubNm6fNmzfrrbfeUkpKylnHcEzCDpZlKRgMtvsZx2Q3sYAOnDhxwqqurraqq6stSVZRUZFVXV1tHT582LIsy3rkkUesGTNmhPofPHjQuuSSS6yFCxda+/bts1atWmX16tXL+sUvfmHXLsAg3/3udy2Xy2Vt27bN8vv9oeWzzz4L9eGYRHcrKCiwtm/fbh06dMjau3ev9eijj1o9evSwtm7dalkWx6RdCDfo0Ntvv21JarPcf//9lmVZ1v3332/dcsstYWO2bdtmjR071oqPj7dGjRpllZaWdn/hMFJ7x6Ika82aNaE+HJPobnPmzLGSk5Ot+Ph4a/Dgwdbtt98eCjaWxTFpF55zAwAAjMI1NwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbADFr1qxZcjgcbZaPPvrI7tIAxLCedhcAAGcyadIkrVmzJqxt8ODBXdpGS0uLHA6HevTg/3PAxYB/6QBimtPp1NChQ8OW5cuX69prr1WfPn3kdruVl5enkydPhsasXbtW/fv3169+9StdffXVcjqdOnz4sJqamrR48WJdeuml6tOnj2688UZt27bNvp0DcF4QbgBccHr06KGf/vSn+t///V+99NJLeuutt7R48eKwPp999pkKCwv14osv6je/+Y2GDBmi2bNna+fOnXr55Ze1d+9efetb39KkSZP04Ycf2rQnAM4H3goOIGbNmjVLP/vZz5SQkBBqy8rK0iuvvBLW75VXXtF3v/td1dfXSzo9czN79mzt2bNH119/vSTp97//va688kp9/PHHGj58eGjsHXfcob/5m7/Rk08+2Q17BKA7cM0NgJh22223qbS0NLTep08fvf3223ryySe1b98+BQIBNTc36/PPP9epU6fUp08fSVJ8fLyuu+660Lj3339flmVp9OjRYdsPBoMaOHBg9+wMgG5BuAEQ0/r06aMrrrgitH748GFNnjxZubm5+uEPf6gBAwZox44dys7O1hdffBHq17t3bzkcjtB6a2ur4uLiVFVVpbi4uLDv6Nu37/nfEQDdhnAD4IKye/duNTc36yc/+Uno7qef//znZx03duxYtbS0qK6uTpmZmee7TAA24oJiABeUyy+/XM3NzXruued08OBB/fu//7tWrFhx1nGjR4/Wvffeq5kzZ2rz5s06dOiQ3nvvPT311FMqLy/vhsoBdBfCDYALyg033KCioiI99dRTSktL0/r161VYWNipsWvWrNHMmTP1ve99T2PGjNE3vvEN/frXv5bb7T7PVQPoTtwtBQAAjMLMDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACM8n9oFtJSEnfuBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[data.Fare <= 20,\"Fare\"] = 1\n",
    "data.loc[(data.Fare > 20) & (data.Fare <= 40),\"Fare\"] = 2\n",
    "data.loc[(data.Fare > 40),\"Fare\"] = 3\n",
    "sns.barplot(x='Fare',y='Survived',data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "be4ee4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA. 2343        11\n",
       "CA 2144          8\n",
       "1601             8\n",
       "PC 17608         7\n",
       "S.O.C. 14879     7\n",
       "                ..\n",
       "113792           1\n",
       "36209            1\n",
       "323592           1\n",
       "315089           1\n",
       "359309           1\n",
       "Name: Ticket, Length: 929, dtype: int64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Ticket\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "3ef446d7",
   "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",
       "      <th>Family</th>\n",
       "      <th>Title</th>\n",
       "      <th>F_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "0               1       0.0       3   \n",
       "1               2       1.0       1   \n",
       "2               3       1.0       3   \n",
       "3               4       1.0       1   \n",
       "4               5       0.0       3   \n",
       "...           ...       ...     ...   \n",
       "1304         1305       NaN       3   \n",
       "1305         1306       NaN       1   \n",
       "1306         1307       NaN       3   \n",
       "1307         1308       NaN       3   \n",
       "1308         1309       NaN       3   \n",
       "\n",
       "                                                   Name Sex  Age  SibSp  \\\n",
       "0                               Braund, Mr. Owen Harris   1  2.0      1   \n",
       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...   0  3.0      1   \n",
       "2                                Heikkinen, Miss. Laina   0  2.0      0   \n",
       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)   0  2.0      1   \n",
       "4                              Allen, Mr. William Henry   1  2.0      0   \n",
       "...                                                 ...  ..  ...    ...   \n",
       "1304                                 Spector, Mr. Woolf   1  2.0      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina   0  3.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen   1  3.0      0   \n",
       "1307                                Ware, Mr. Frederick   1  2.0      0   \n",
       "1308                           Peter, Master. Michael J   1  1.0      1   \n",
       "\n",
       "      Parch              Ticket  Fare Cabin Embarked  Family    Title  F_size  \n",
       "0         0           A/5 21171   1.0     1        1       1       Mr       2  \n",
       "1         0            PC 17599   3.0     0        3       1      Mrs       2  \n",
       "2         0    STON/O2. 3101282   1.0     1        1       1     Miss       1  \n",
       "3         0              113803   3.0     0        1       1      Mrs       2  \n",
       "4         0              373450   1.0     1        1       1       Mr       1  \n",
       "...     ...                 ...   ...   ...      ...     ...      ...     ...  \n",
       "1304      0           A.5. 3236   1.0     1        1       1       Mr       1  \n",
       "1305      0            PC 17758   3.0     0        3       1  Royalty       1  \n",
       "1306      0  SOTON/O.Q. 3101262   1.0     1        1       1       Mr       1  \n",
       "1307      0              359309   1.0     1        1       1       Mr       1  \n",
       "1308      1                2668   2.0     1        3       1   Master       3  \n",
       "\n",
       "[1309 rows x 15 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "3fd91086",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        -0.338481\n",
       "Family        -0.133006\n",
       "Age           -0.079916\n",
       "SibSp         -0.035322\n",
       "PassengerId   -0.005007\n",
       "F_size         0.016639\n",
       "Parch          0.081629\n",
       "Fare           0.290931\n",
       "Survived       1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    corrDf=pd.DataFrame()\n",
    "    corrDf=data.corr()\n",
    "    corrDf['Survived'].sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "5959ea1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看各特征与Survived的相关性\n",
    "plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(corrDf,annot=True,vmax=1,cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "0c9e951f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        -0.338481\n",
       "Family        -0.133006\n",
       "Age           -0.079916\n",
       "SibSp         -0.035322\n",
       "PassengerId   -0.005007\n",
       "F_size         0.016639\n",
       "Parch          0.081629\n",
       "Fare           0.290931\n",
       "Survived       1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrDf['Survived'].sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fef3143",
   "metadata": {},
   "source": [
    "### 对Title参数化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "1c58f81f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Title', ylabel='Survived'>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name,number in zip ([\"Mr\",\"Mrs\",\"Miss\",\"Master\",\"Royalty\",\"Officer\"],[0,1,2,3,4,5]):\n",
    "    data.loc[data.Title == name,\"Title\"] = number\n",
    "sns.barplot(x='Title',y='Survived',data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "8e4ced4c",
   "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",
       "      <th>Family</th>\n",
       "      <th>Title</th>\n",
       "      <th>F_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "0               1       0.0       3   \n",
       "1               2       1.0       1   \n",
       "2               3       1.0       3   \n",
       "3               4       1.0       1   \n",
       "4               5       0.0       3   \n",
       "...           ...       ...     ...   \n",
       "1304         1305       NaN       3   \n",
       "1305         1306       NaN       1   \n",
       "1306         1307       NaN       3   \n",
       "1307         1308       NaN       3   \n",
       "1308         1309       NaN       3   \n",
       "\n",
       "                                                   Name Sex  Age  SibSp  \\\n",
       "0                               Braund, Mr. Owen Harris   1  2.0      1   \n",
       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...   0  3.0      1   \n",
       "2                                Heikkinen, Miss. Laina   0  2.0      0   \n",
       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)   0  2.0      1   \n",
       "4                              Allen, Mr. William Henry   1  2.0      0   \n",
       "...                                                 ...  ..  ...    ...   \n",
       "1304                                 Spector, Mr. Woolf   1  2.0      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina   0  3.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen   1  3.0      0   \n",
       "1307                                Ware, Mr. Frederick   1  2.0      0   \n",
       "1308                           Peter, Master. Michael J   1  1.0      1   \n",
       "\n",
       "      Parch              Ticket  Fare Cabin Embarked  Family Title  F_size  \n",
       "0         0           A/5 21171   1.0     1        1       1     0       2  \n",
       "1         0            PC 17599   3.0     0        3       1     1       2  \n",
       "2         0    STON/O2. 3101282   1.0     1        1       1     2       1  \n",
       "3         0              113803   3.0     0        1       1     1       2  \n",
       "4         0              373450   1.0     1        1       1     0       1  \n",
       "...     ...                 ...   ...   ...      ...     ...   ...     ...  \n",
       "1304      0           A.5. 3236   1.0     1        1       1     0       1  \n",
       "1305      0            PC 17758   3.0     0        3       1     4       1  \n",
       "1306      0  SOTON/O.Q. 3101262   1.0     1        1       1     0       1  \n",
       "1307      0              359309   1.0     1        1       1     0       1  \n",
       "1308      1                2668   2.0     1        3       1     3       3  \n",
       "\n",
       "[1309 rows x 15 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736d76a8",
   "metadata": {},
   "source": [
    "### 对数据进行划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "a5811b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "    X = data[[\"Age\",\"Pclass\",\"Sex\",\"Fare\",\"Cabin\",\"Embarked\",\"Title\",\"Family\"]]\n",
    "    y = data[[\"Survived\"]]\n",
    "    Xtrain = X.iloc[0:890,]\n",
    "    Xtest = X.iloc[891:,]\n",
    "    ytrain = y.iloc[0:890,]\n",
    "    ytest = y.iloc[891:,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "277903c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain = X.iloc[0:890,]\n",
    "Xtest = X.iloc[891:,]\n",
    "ytrain = y.iloc[0:890,]\n",
    "ytest = y.iloc[891:,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "59ad30fb",
   "metadata": {
    "scrolled": false
   },
   "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",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>890 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived\n",
       "0         0.0\n",
       "1         1.0\n",
       "2         1.0\n",
       "3         1.0\n",
       "4         0.0\n",
       "..        ...\n",
       "885       0.0\n",
       "886       0.0\n",
       "887       1.0\n",
       "888       0.0\n",
       "889       1.0\n",
       "\n",
       "[890 rows x 1 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602f38b9",
   "metadata": {},
   "source": [
    "### 导入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "e133ceb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "    from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,ExtraTreesClassifier\n",
    "    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.neighbors import KNeighborsClassifier\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    from sklearn.model_selection import GridSearchCV,cross_val_score,StratifiedKFold\n",
    "#设置kfold，交叉采样法拆分数据集\n",
    "kfold=StratifiedKFold(n_splits=10)\n",
    "\n",
    "#汇总不同模型算法\n",
    "classifiers=[]\n",
    "classifiers.append(DecisionTreeClassifier())\n",
    "classifiers.append(RandomForestClassifier())\n",
    "classifiers.append(ExtraTreesClassifier())\n",
    "classifiers.append(GradientBoostingClassifier())\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LogisticRegression())\n",
    "classifiers.append(LinearDiscriminantAnalysis())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "9ad37ef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results=[]\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier,Xtrain,ytrain,scoring='accuracy',cv=kfold,n_jobs=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "68545a14",
   "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>cv_mean</th>\n",
       "      <th>cv_std</th>\n",
       "      <th>algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.819101</td>\n",
       "      <td>0.024846</td>\n",
       "      <td>DecisionTreeCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.830337</td>\n",
       "      <td>0.031561</td>\n",
       "      <td>RandomForestCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.826966</td>\n",
       "      <td>0.031461</td>\n",
       "      <td>ExtraTreesCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.826966</td>\n",
       "      <td>0.036304</td>\n",
       "      <td>GradientBoostingCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.823596</td>\n",
       "      <td>0.033727</td>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.815730</td>\n",
       "      <td>0.030648</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.810112</td>\n",
       "      <td>0.026327</td>\n",
       "      <td>LinearDiscrimiAna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cv_mean    cv_std            algorithm\n",
       "0  0.819101  0.024846      DecisionTreeCla\n",
       "1  0.830337  0.031561      RandomForestCla\n",
       "2  0.826966  0.031461        ExtraTreesCla\n",
       "3  0.826966  0.036304  GradientBoostingCla\n",
       "4  0.823596  0.033727                  KNN\n",
       "5  0.815730  0.030648                   LR\n",
       "6  0.810112  0.026327    LinearDiscrimiAna"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_means=[]\n",
    "cv_std=[]\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "    \n",
    "#汇总数据\n",
    "cvResDf=pd.DataFrame({'cv_mean':cv_means,\n",
    "                     'cv_std':cv_std,\n",
    "                     'algorithm':['DecisionTreeCla','RandomForestCla','ExtraTreesCla',\n",
    "                                  'GradientBoostingCla','KNN','LR','LinearDiscrimiAna']})\n",
    "\n",
    "cvResDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "c25e3784",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x258f2bf59d0>"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 611.111x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvResFacet=sns.FacetGrid(cvResDf.sort_values(by='cv_mean',ascending=False),sharex=False,\n",
    "            sharey=False,aspect=2)\n",
    "cvResFacet.map(sns.barplot,'cv_mean','algorithm',**{'xerr':cv_std},\n",
    "               palette='muted')\n",
    "cvResFacet.set(xlim=(0.7,0.9))\n",
    "cvResFacet.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe97c523",
   "metadata": {},
   "source": [
    "### 可见 Gboost(梯度推进) 、RF(随机森林)、DT(决策树)效果比较好，接下来对其进行更细的调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "87b1ff6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n",
      "最佳参数 {'learning_rate': 0.05, 'loss': 'deviance', 'max_depth': 4, 'max_features': 0.3, 'min_samples_leaf': 150, 'n_estimators': 300}\n",
      "modelRF模型得分为：0.824\n"
     ]
    }
   ],
   "source": [
    "#Gboost模型\n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid = {'loss' : [\"deviance\"],\n",
    "              'n_estimators' : [100,200,300],\n",
    "              'learning_rate': [0.1, 0.05, 0.01],\n",
    "              'max_depth': [4, 8],\n",
    "              'min_samples_leaf': [100,150],\n",
    "              'max_features': [0.3, 0.1] \n",
    "              }\n",
    "modelgsGBC = GridSearchCV(GBC,param_grid = gb_param_grid, cv=kfold, \n",
    "                                     scoring=\"accuracy\", n_jobs= -1, verbose = 1)\n",
    "modelgsGBC.fit(Xtrain,ytrain)\n",
    "print('最佳参数',modelgsGBC.best_params_)\n",
    "print('modelRF模型得分为：%.3f'%modelgsGBC.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "a1f7a10c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.83707865 0.80898876 0.78089888 0.80337079 0.80337079]\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(classifier,Xtrain,ytrain,n_jobs=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "ff1a167e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n",
      "最佳参数 {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 100, 'n_estimators': 100}\n",
      "modelRF模型得分为：0.800\n"
     ]
    }
   ],
   "source": [
    "# RF\n",
    "RF = RandomForestClassifier()\n",
    "param_RF = {\"n_estimators\": [100,150,200,250],\n",
    "         \"max_depth\": [4,8,12],\n",
    "         \"min_samples_leaf\":[100,150,200],   \n",
    "        \"criterion\": [\"gini\",\"entropy\"]\n",
    "}\n",
    "modelRF = GridSearchCV(RF,param_grid = param_RF, cv=kfold, \n",
    "                                     scoring=\"accuracy\", n_jobs= -1, verbose = 1)\n",
    "modelRF.fit(Xtrain,ytrain)\n",
    "print('最佳参数',modelRF.best_params_)\n",
    "print('modelRF模型得分为：%.3f'%modelRF.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "85afb947",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 45 candidates, totalling 450 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [4, 8, 12],\n",
       "                         &#x27;min_samples_leaf&#x27;: [100, 150, 200],\n",
       "                         &#x27;min_weight_fraction_leaf&#x27;: [0.3, 0.4, 0.5, 0.6, 0.7]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=None, shuffle=False),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [4, 8, 12],\n",
       "                         &#x27;min_samples_leaf&#x27;: [100, 150, 200],\n",
       "                         &#x27;min_weight_fraction_leaf&#x27;: [0.3, 0.4, 0.5, 0.6, 0.7]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=None, shuffle=False),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [4, 8, 12],\n",
       "                         'min_samples_leaf': [100, 150, 200],\n",
       "                         'min_weight_fraction_leaf': [0.3, 0.4, 0.5, 0.6, 0.7]},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DT\n",
    "\n",
    "DT = DecisionTreeClassifier()\n",
    "param_DT = {\"max_depth\":[4,8,12],\n",
    "         \"min_weight_fraction_leaf\":[0.3,0.4,0.5,0.6,0.7],\n",
    "         \"min_samples_leaf\":[100,150,200]\n",
    "        }\n",
    "modelDT = GridSearchCV(DT,param_grid = param_DT, cv=kfold, \n",
    "                                     scoring=\"accuracy\", n_jobs= -1, verbose = 1)\n",
    "modelDT.fit(Xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "cc116875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "modelgsGBC模型得分为：0.826\n",
      "modelRF模型得分为：0.800\n",
      "modelDT模型得分为：0.782\n"
     ]
    }
   ],
   "source": [
    "print('modelgsGBC模型得分为：%.3f'%modelgsGBC.best_score_)\n",
    "print('modelRF模型得分为：%.3f'%modelRF.best_score_)\n",
    "print('modelDT模型得分为：%.3f'%m826odelDT.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "b7f820d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "GBCpreData_y=modelgsGBC.predict(Xtest)\n",
    "GBCpreData_y=GBCpreData_y.astype(int)\n",
    "#导出预测结果\n",
    "GBCpreResultDf=pd.DataFrame()\n",
    "GBCpreResultDf['PassengerId']=data['PassengerId'][data['Survived'].isnull()]\n",
    "GBCpreResultDf['Survived']=GBCpreData_y\n",
    "GBCpreResultDf\n",
    "#将预测结果导出为csv文件\n",
    "GBCpreResultDf.to_csv('./TitanicGBSmodle.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c57dfcf9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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