all_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "# Anaconda下安装 seaborn : conda install seaborn\n",
    "# 安装报错，在cmd下加入国内镜像：\n",
    "# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/\n",
    "# conda config --set show_channel_urls yes\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "\n",
    "#* PassengerId => 乘客ID \n",
    "#* Pclass => 乘客等级(1/2/3等舱位) \n",
    "#* Name => 乘客姓名 \n",
    "#* Sex => 性别 \n",
    "#* Age => 年龄 \n",
    "#* SibSp => 堂兄弟/妹个数 \n",
    "#* Parch => 父母与小孩个数 \n",
    "#* Ticket => 船票信息 \n",
    "#* Fare => 票价 \n",
    "#* Cabin => 客舱 \n",
    "#* Embarked => 登船港口\n",
    "\n",
    "train_df = pd.read_csv('D:\\soft\\python_tools\\kaggle\\Titanic\\\\train.csv')\n",
    "test_df = pd.read_csv('D:\\soft\\python_tools\\kaggle\\Titanic\\\\test.csv')\n",
    "combine = [train_df,test_df]\n",
    "print train_df.columns.values\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看哪些字段是有空值\n",
    "train_df.info()\n",
    "print '_'*40\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda2\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
       "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
       "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Graham, Mr. George Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name   Sex    Ticket        Cabin Embarked\n",
       "count                         891   891       891          204      889\n",
       "unique                        891     2       681          147        3\n",
       "top     Graham, Mr. George Edward  male  CA. 2343  C23 C25 C27        S\n",
       "freq                            1   577         7            4      644"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按Pclass字段分组，计算每个Pclass字段的存活的概率的平均数\n",
    "train_df[['Pclass','Survived']].groupby(['Pclass'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看性别分组下的存活概率的平均数\n",
    "train_df[['Sex','Survived']].groupby(['Sex'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['SibSp','Survived']].groupby(['SibSp'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x4174978>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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djiFJktR1Hetpiog9gDcDr87MW4FbI+I84G3AFZ2KI0mS5pfx8XHWrbut8vYj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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4174be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化\n",
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tzme2etqcDdqdz2yjoc1lMYxsc037NZdOZ5x99immA1u9+m663ZnaN3Xt2LGj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\nJEEdZBERERGRhP8fAw5dUC0vy+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x412d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xd391860>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AlSJyh1huB44S5fbqSimlEsfxwQSMwxpyXBRUthCYGKbuRPtYsM+A\nyW3TNKWUUrGWDMGUDxwUkeCFzUqAdGNM9zB194aUlWDtequUUioJJEMwZQKh22I2vE6LsG5oPaWU\nUg6VDLPyqmkcLA2vKyOsG1qvSW63y1HbPSulVEeTDMG0B+hhjHGLSMOa/HlAlYgcCVM3L6QsDyiO\n9GI5OZ2ObxetlFIq/pIhmFYBdcAk4HO77BxgaZi6XwB3hJRNBe6O9GKlpRXaY1KqA8rO7pToJihb\nUqz8YIx5FCtgbsSayPAMcL2IzDbG5AJlIlJtjOkCbAFeAp4AfgBcCQwRkapIrqUrPyjVMenKD86R\nDJMfAH4CLAfmAw8Dd9nPM4E1THcVgIgcBS4BpgHLgLOACyMNJaWUUomXFD2meNIek1Idk/aYnCNZ\nekxKKaU6CA0mpZRSjqLBpJRSylE0mJRSSjmKBpNSSilH0WBSSinlKBpMSimlHEWDSSmllKNoMCml\nlHIUDSallFKOosGklFLKUZJh2wuMMX/AWlncDTwpIqFbWwTXnQQ8AIwFdgN/FJEn49JQpZRSp8zx\nPSZjzE+Ba4CvAlcA1xljftJE3VzgHaxVyE8Dfgs8bIy5MD6tVUopdaqSocd0K/ArEVkEYIy5A/gd\n8GCYupcDxSJyl/16mzHmXOBaYG48GquUUurUOLrHZIzJB/oBnwYVLwQK7N5RqLnADWHKu7VB85RS\nSrUBp/eY8oEAsDeorARwYe1kWxJcWUSKgKKG18aYXljDgL9u85YqpZSKiYQHkzEmHejTxOHOACJS\nG1RWY39Pi+C8r2OF2hOn2EyllFJxkvBgAiYCH2H1jELdAWCMSQ0Kp4ZAqmzqhMaYTsCbwBBgqohU\nR9oYt9uF260bWSqlVKIkPJhE5GOauNdl32O6F8jjxBBdHlaIFTfxni7Au8Ag4FwR2R5Ne7p376yp\npJRSCeToyQ8iUgzsAs4OKj4HKBKRktD6xhgX8C9gADBNRDbFo51KKaViJ+E9pgg8CtxrjNmDNenh\nHuD+hoPGmB5AlYhUAN8FZgCXAuVBM/dqReRwXFutlFKqVZIhmO4HegJvAD7g7yLy56DjS4Gngf8H\nzMIKr7dDzvExcF7bN1UppdSpcgUC4eYcKKWUUonh6HtMSimlOh4NJqWUUo6iwaSUUspRNJiUUko5\nigaTUkopR9FgUkop5SgaTEoppRxFg0kppZSjaDAppZRyFA0mpZRSjqLBpJRSylE0mJRSSjmKBpNS\nSilH0WBSSinlKBpMSimlHEWDSSmllKNoMCmllHKUZNhaXSURY8wCYFoThwNATxEpbcV5pwMfAQNE\npKj1LWx03gKgEJghIp/E8Lx+4Nsi8lyszmmf1wP8CPgmYIBqYCVwj4gsiOW1lEoU7TGpWAsArwC5\nQF7IV35rQink3G2hrc4bU8aYNGABcDvwZ+B04DxgA/ChMeYbiWudUrGjPSbVFqpE5ECiGxEFV6Ib\nEKHfAaOBUSKyN6j8P40xXYE/G2Nmi0hlYpqnVGxoMKmEMMYUAo9iDfudC+zH6gkEgPuAvsCnwLdE\n5GDQW79qjLkN6AN8AdwmImvsc2YB9wMXAr2Aw8Bs4FYRqbaHAz8Efgn8DNgOXB3SruFYQ4bvATeI\nSMAYcwnwW2AksAd4CbhbRGrt9/QBHrH/HEeAO1r4s18PPG3/WUNDcYeIDArzHi9wI/BUSCg1+KXd\nhqrmrq1UMtBgUol0F/AD4MfAg8BzwEbgWqAL8AbWh/x/2/VdwE+B7wJ7gT8A7xpjBolINfAM0Bu4\nHCvopmIFwDrgL/Y5PMBFwESgE+BvaIwxZghWcL0tIjfZZTOxhiZvs48NAR4GhgHX2Pd83sMKwXOA\ndKzAbW548GVgbhPH6psoHwTkAJ+HOygi+4B9zVxTqaShwaTawjeNMV8PKQsA/xKR64PK3haRFwCM\nMX8DLgPuFJEVdtkHWENXwX4oIh/ax78F7MYKsqeA94GPRWS9XbfIGHMrMCbkHPeLyDb7HAV22SDg\nebtNPwiqeyfwuIj83X69wxhzMzDfGPMzYIT9NVhEdtjnvAFrQkJYIlKDFZzRyLG/H47yfUolHQ0m\n1RZmYw2VhQ5THQt5vTXo5wr7+/agsiqsIbkGAeCzhhciUmaM2cyJ8HoUuMwOhqHAKGAAVi8s+BzB\n123wKJAC7AopHw+caYy5KajMhdXTGmFf+3BDKNntWm2MaXJIzRhzLfB4E4d3iEhokAI03LPr3tR5\nlWovNJhUWzgqIoUR1KsLU+YPUxYsdKjLA9QYY1zAHKz7QC9iDZetAP4W5hzhQqNhyO9BY8y/RGSD\nXe7Guuf1bJj3FGOFX7jZreH+bA1mY90fC6ep920HSrCGJ/8ZetC+N/Zn4HYR2Rh6XKlkosGkks0E\nrCnTGGN6Yt3ruQ84DZgJnCUiy+zjKVj3hLZFcN6XgIXAdcDTxphJIhLACisjIsd7csaYGcCtWPfH\nVgHdjDEjGgLBGDMU6NrUhUSkgpN7hi2yJ2E8CfzIGHO/iOwJqXIHcAawI5rzKuVEGkyqLWQYY3Kb\nOHa4YTZbGC1N23YBTxhjvo91r+UBYCfwKtATq7dxtTHmINAD6/5QLpAWwTVc9of/TVj3h+7Amlxx\nL/CKMeYurF5Yf+DvwFYR2W+M+QhYAvzDGPNDrB7dwzQ9ieFU/B64AFhot+dzrHtPt2A9cHuViOis\nPJX09AFb1Rauwpo1F/xVbH+/xK4TbtZaSw+6BrCe5XkG615TJXChiPhEpBi4HmsCxQassNoNPITV\nk2juGsfL7CG8PwC/NsYMF5HXsaaUXw6swZo5OBe4wq4fwJrltwlrdt5bWEOJMX+Oyw6d6VgTPe7A\n6q29jfXw8nQR+Vesr6lUIrgCgaR46B04/uT7MqyZWWGXjzHGXAzczYkhnLtE5K34tVIppdSpSJoe\nkx1KL2Hd3G6qzljgdayhlnHAE8Brxphws5yUUko5UFLcYzLGjMAaHmnJN4B5IvJ/9utHjDGXYQ0t\nrW2r9imllIqdpAgmrHH1ecCvsO4rNOUZIDVMebc2aJNSSqk2kBTBJCKPNfxsjGmungS/NsaMAs7H\nWkNMKaVUEkiae0zRMsb0wLrf9KmIvJno9iillIpMUvSYomU/Q/MB1jTg0DXbmhUIBAIuV7LsgqCU\niiH9h+8Q7S6Y7C0I5mM94DhDRA5F8/7S0grcbv37qVRHk53dKdFNULZ2FUzGmEzgXawVAM5tzWZ1\nfn8Avz95nu1SSqn2JumDyR62K7P34/klMBCYAbiDlsWpEpHyBDVRKaVUFJJx8kNod6YY6zklgFlA\nBrCYk5fD+VPcWqeUUuqUJNWSRPFw4MBR/Q+iVAfUs2cXvbnsEMnYY1JKKdWOaTAppZRyFA0mpZRS\njqLBpJRSylE0mJRSSjmKBpNSSilH0WBSSinlKBpMSimlHEWDSSmllKNoMCmllHKUpF/EValTVXyo\ngmWb9nPkWC0ej4s+PTpx1ohcMtL0n4dSiZBUa+UZY9KAZcAPReSTJuqcDjwKjAHWATeLyIpIr6Fr\n5XUcRSVHeWX+VjbuPNzoWFqKh7PH5nPF9EGkp2pAdQS6Vp5zJM1Qnh1KLwEjm6mTCcwBPgbGA4uA\nOcaYjLg0UiWNTTsPc8/zK8KGEkBNXT3zlu/mvhdXUlnti3PrlOrYkuJXQWPMCODFCKpeA1SKyB32\n69uNMRdhba/+XFu1L17KjtXwyeq9rNp6kKOVdXg9bgryujB9XG9M/yx0S/jIHCyr4uE31lBTV99i\n3R37jvLEW+u5/evj4tAypRQkSTAB04F5wK+AymbqTQQWhpR9BkwmiYPJHwjw5sJC5izaSX3I7rr7\nSitZvKGEAXlduOXy0fTI0s5hS95fuouqmpZDqcGabYcoLC5nYH7XNmyVUqpBUgSTiDzW8LMxprmq\n+Vj3lYKVAKPaoFlxEQgEeOGDzXy0Yk+z9XbsO8rvn1/OL785oUOEUyAQoN4foM7nP/FVH/Szrz5s\neVWtjwUr90Z9vY9W7GHgxRpMSsVDUgRTFDKBmpCyGiAtAW2JiaWb9rcYSg3KjtXy+JvrufNbE+Iy\nrBcIBPAFfejXRhkStT4/vuPl9Y3PY9f1HS+vP6k8nvN2mroXpZSKvfYWTNU0DqE0mh/+O4nb7cLt\nds69mveX7oqq/ra95SzdtJ+87p1OhEJDGNTZH/pBIXByoJxcfuJYSLDUnfi5o6iq8eH1Js1cIaWS\nWnsLpj1AXkhZHlAc6Qlycjo5ZhJB4d4ytu8tj/p9j81e3wat6djq6v3sKa1i9OAeiW6KUu1eewum\nL4A7QsqmAndHeoLS0grH9JjWbzmQ6Ca0Kx63ixSvm+rayCc+NKjz+fnFI58xrF8Wl04dwNjB3R3z\nC4yKjezsTolugrIlfTAZY3KBMhGpBl4D7jHGPAQ8AfwA677Tq5Gez+8P4Pc74xnbSKYzJ5sUr5tU\nrxuv102Kx02KN+jL4yY1xXO83HtSeUN9z0n1T3p/o/KT6zb8wvGP9yXi+3ahNu86wgMvr6J/r85c\nPGUAE4b1dMwvMkq1F8kYTKGpUQx8G3hORI4aYy4BHge+B6wBLhSRqvg2MTa6dkqN+TldLkgN/sAO\n+TBPtT/QQ4MjNUxd7/FjnmaCIihoPC5H9DK+cmY/Pl+775SCv2j/MR799zpyczK5aFJ/Jo/Kw+vR\ne1BKxUJSLUkUD05akqimtp6f/N/CqJ65AZg5sT9TRuWRktI4LDxu/fAEWLv9EH99Yy11vuYncPTK\nTqdvzy6s3HKg2VmAOV3TmHlWf6aN601qiifGrVXxoEsSOYcGUwgnBRPAix9s5sPluyOun5bi4cEf\nTdUFSCOwfW85L364OewEE6/HxeRReVx93lAy072UlFYyd/FOPlu7r9FDzsG6Zqbw5TP7ce7pfclM\n1/8HyUSDyTk0mEI4LZjKjtXwP88s5cix2ojqX/flYZw/oW8bt6p92bGvnGWbDnDkWA1ej4vePToz\naVQuXTMbD6WWllfz7pIiPlm1l9pmelsZaV7On9CHL53RL+x5lPNoMDmHBlMIpwUTwJ4Dx3jw1dUc\nPhr67PDJvnr2QL569sA4tapjK6+s5cNlu5i3fA9VNU0v8prqdTPttN7MPKs/OV3T49hCFS0NJufQ\nYArhxGACKK+otX9T30NlyD2nEQXZXDSpgFEDcxLUuo6rstrHRyt38/7SXRytrGuynsftYuqYPC6c\nWEBuTmYcW6gipcHkHBpMIZwaTA1q6+p5ad4WPlltrfd20aQCrpg+OMGtUjV19Xy6ei/vLimitLzp\nnq3LBWcO78XFkwfQr1fnOLZQtUSDyTk0mEI4PZgaVFZbv51npqckuCUqmK/ez6L1+3jniyJKSptf\nCWvc4O5cPGUAQ/p0i1PrVHM0mJxDgylEsgSTcja/P8DyzQeY8/kOivYfa7bu8P5ZXDxlACMLsh3x\nnFdHpcHkHBpMITSYVCwFAgHWbi/l7UU72Lq7rNm6A/O7cPHkAZw2tAduDai402ByDg2mEBpMqq1I\n0WHmLNrJusLSZuv17tGJiycVcNbIXvpAdBxpMDmHBlMIDSbV1nbsK2fOop2skAON1tcK1qNbOhdN\nKmDqmDxSvLqaRFvTYHIODaYQGkwqXvYerGDuFztZtL4EfzP/Drt1TuUrZ/Znxum9SU/V1STaigaT\ncyRFMBlj0oBHgFlYm/49ICIPNlH3a8DvgX7ASuA2EVkZ6bU0mFS8HTxSxdwlRXy6uhhfM5svdkr3\n8qUz+nH+hL50ztDZmLGmweQcyRJMDwNnY60iPgB4DrhBRN4IqTcSWAbcBHwO/AS4Ahhkb4vRIg0m\nlShlx2p4f+ku5q/cQ00ze0alpXo497Q+XHBWP7I6h27Y7CzJ9FiDBpNzOD6YjDGZwEHgKyLyqV32\nS+B8ETkvpO7twLUicpb9ujNQDpwhIisiuZ4Gk0q0iuo65i3fzQdLd1FR3fRyR16Pm7PH5nPhxP70\nzMqIYwsj8+7iIl5bsA2AK2cMZubE/gluUfM0mJwjGab8jMPaN2pRUNlCYGKYuoeAUcaYKcYYF3Aj\nUAZsa/NWKhUjndJTuGzqQO6/ZQrXnDeErM7hF4H11ftZsHIPv3j8C/721gb2HKyIc0ubVlXj441P\ntuEPBPAHArzxybZm1xRUKlgy3EnNBw6KSPDf6hIg3RjTXUQOBZW/AlyGFVz19tfFItL8AyRKOVB6\nqpcLzurPueP78tm6YuZ+sZMDRxqPSPsDARat38ei9fsYP6wnF08uYGB+1wS0+IQDR6rw1Z8YfPDV\nBzhwpIr+uV0S2CqVLJIhmDKB0MXHGl6HDrB3B/KAW4DFwM3AM8aY00XkYJu2Uqk2kuJ1M+O0Ppwz\nNp+lG/czZ9HOJntHKzYfYMXmA4wamMMlkwsY1i9LV5NQSScZgqmaxgHU8Dp0MbJ7gTUi8hiAMeb7\nwEbgBuD+SC7mdrtwu/UfsnIeL27OHtebKWPzWbXlIG8uLAy7ySHA+sJS1heWMrRvNy6dOpBxQ7rH\nNaA8YbaZ93jceL3JcPdAJVoyBNMeoIcxxi0iDXNp84AqETkSUncC8OeGFyISMMasBgoivVhOTif9\nDVM53vkTO3PeWQWs2XKQV+dtZs3W8AMCW3aX8eArqxjYuytfP28YU8b1xhOHX7wOVza+n9S1awbZ\n2Z3a/Noq+SVDMK0C6oBJWFPAAc4BloapuxcYGVJmgCWRXqy0tEJ7TCpp9O+ZyX9dcxpbd5fx1ueF\nrNwcPqAK95Zz3/PLyH0nk0smFzB1bD7eML2aWCkvrwpbdjjTuR85GprO4dy/JTYRqTLGPAc8Zoy5\nEegL/BS4HsAYkwuU2c8p/Q142hizDGsW301Af+DZSK/n9wfw+3XGuEouA/K68ONZY9m9/xjvfLGT\nxRtLCPckSElpJU/O2cgbn2xn5sT+TBvXm7SU2C93VB/mQeH6ej++ZrajV6pBsgz4/gRYDswHHgbu\nEpHZ9rFi4CoAEXkV+BFwJ7ACmAycqxMfVEfRt1dnvnfZKO753iSmn9Ybryd87//w0Rpe+nAL//3I\n57z9+Y7jD8Iq5QSOf8A23vQBW9WeHD5aw3tLiliwag+1dU33VjLSPJw3vi9fPqMfXTuFf24qGkUl\nR/nt0yePtv/2hjMdPV1cH7B1DscP5SmlWi+7SxrXnD+UiycX8OGy3cxbvpvKMA+6VtXUM2fRTj5Y\nuotp43ozc2J/crqmJ6DFSmkwKdUhdMlM5WvTBjFzYn8WrNzDe0t3UV5R26herc/Ph8t389HKPUwe\nncdFkwrIy8lMQItVR6bBpFQHkpHm5cJJBZw/oS8L1xYz94siDpU3Xk2i3h9g4ZpiPltTzBnDe3Hx\n5PJuELIAACAASURBVAJHD8Op9iXiYDLGTIu0roh80rrmKKXiITXFuqc0bVxvFm8oYc6inewrDX1e\nHQLA0k37WbppP2MHd+eSyQMY0rdb/BusOpRoekwLsP6euuzvDRpuGAaX6XabSiUBr8fN1DH5TB6V\nx4rNB5izaCc7S46Grbtm2yHWbDuE6ZfFxVMKGDUgRx9GV20immAaGPTz+cBdwO1YD73WAWcCf8Ja\nFkgplUTcbhdnDO/FBNOT9YWlvP35DjbvDr/2sew6grxyhIK8LlwyuYDTh/XEHRRQpeXVzF+xu9H7\nPlu7j5yu6brJoWpRq6aLG2M2AzeLyLyQ8unAsyIyIDbNiz+dLq6UZfOuI8xZtJO12w81Wy+/eyYX\nTSrgrBG9mLd8D69/vI36Jh5ST0v1cMOFwzlrRG5bNPmU6HRx52jt5IfeWGvYhToM5LS+OUoppxjW\nL4th/bLYue8oc77YyfJN+wkXN8WHrNUkXvpwS9ip6MFqaut5fPZ6AEeGk3KG1q78sBi4294hFgBj\nTA7WCt4fx6JhSilnKMjrwi2Xj+bumyZy9pj8JheBbSmUGgSAp97ZGHa6ulLQ+qG8UcA8rL2SNmMF\n3DCsDfzOE5GdsWxkPOlQnlLNO1RWzbtLivhk9V7qTmHtu1nTBnHJlAGxa9gp0qE852j1kkTGmK7A\nN4DRWL8ErQJeFpHGc06TiAaTUpEpq6jlg6W7mL9iN9W19VG/v2dWOvf+YEobtKx1NJic45TWyjPG\npGLN1tsOICJJvxKkBpNS0dm2p4zf/2N5q977t5/NwON2xlrSGkzO0arJD+b/s3fn8VFX5+LHPzPZ\nEwgkrGHf5GFTFFBwQUGta9EWq9h6e622tcVra39yW3vbWu1yr9aKXRW1tiq3vVbbaqlrXXBDBVmE\nouDDLgoBDFkIkH3m98f5JgyTbTLMZGaS5/165UXmO+f7/T5ByTPnfM95jogPuB34JpCJG8b7bxE5\nhJutF9MEJSJZwL3AXNyutQtV9e5W2h7vtZ0KbAZuVNVXYxmPMeaIY9k2IxAIEsdtoUyKivZ/iW8A\nXwSuB2q8Y38HPgvcduxhNXMXMAWY5d3zVhGZG97IG158AXgPN8T4JPCkiPSNQ0zGGKB3z6yozsvL\nTicj3dbim+aiTUxfA25Q1YeBAICqPgZ8BbgqNqE5IpILfBn4pqqu8/ZhuhO371K4LwGVqjpfVbep\n6m24yRnTYhmTMeaIHjkZTBxR0OHzpk+w6eKmZdGuYxoJvNvC8XXAwOjDadFkXJxvhxxbhtsMMNxZ\nwJLQA6o6PcbxGGPCnD1lCO/vKOvQObNPGhynaEyqi7bHtANXgijchXgTIWKoCChR1dBFEnuBbBHp\nE9Z2FFAiIveLSLGIvCUiyTPtx5gu6sTj+nLScZGPmF8wfRiD+/Vov6HplqLtMf0cuFdEinDJ7RwR\nuQ43GeKmWAXnyeXIc6xGja/DB7d7ADcDvwIuwE1nf0FERFVbqlTRjN/vw9/KAkJjTOuun3s89z65\nnnc3lbTZ7txpQ7jy3OOOqq9nTKioEpOqPiQiGcAPgBzgfuAT4Aeqel8M4wOopnkCanwdvmaqHnhX\nVX/kvV4nIufhJmrcEcnNCgvzrGKyMVG67aun8ea/dvPXpZvZtuvoIrATR/Vh3rljOUn6Jyg6kyqi\nnS7eQ1UfAB7wZrz5VXVfbENrsgvoKyJ+VW1cZj4QqFLV8rC2xcAHYcc2AUMjvVlp6SHrMRlzDCYN\n703PC8dxy4Mrjjp+5dljGN4/j7KyQwmKrG0FBXmJDsF4oh3K2yMifwMeVtVXYhlQC9bittWYgdti\nA2AmsLKFtsuB8A0NxwF/ivRmgUCQQCuVkY0xkWloaF6qqKEhQP0xlDAy3Ue0iel63LTwF0RkF/AI\nbruLWE98QFWrRGQxcJ+IXAsMARYAVwOIyACgQlWrgfuAG0Tkh7hkdDVuBuEfYx1Xoh2uqwIgNyMn\nwZEYY0xsRTUrT1UXq+r5uCTxK+AiYLOIvC4i18QyQM9NwGpgKfAb4BZvPRO44bsrvLh2AucDlwDr\ngYuBi1S1OA4xJcxLO1/j5mU/4uZlP+KlnVbM3RjTtRxTrbxG3kSIr+LKFPVQ1ZRdzp3stfKq6qv5\n7hs/oj7oimam+9K4Y+at5KRnJzgyY47YubeS2x46erT9tmtOZtiAngmKqH1WKy95RDuUB4CInIEb\n0rvcu9ZfgIdiEJdpRUlVaVNSAqgPNlBSVcrQnoMSGJUxxsROtLPybgeuxM12ew34f8BfVbUqhrEZ\nY4zphqLtMV2B6xk9ksqbAhpjTKxc8dj8NNwz7q/jZg73AMqBl3A7Hix7fN6iuDwqEJFLgHuAAuCz\nqvpiPO4Tds/hwHZghPd8P2aiXWA7OpZBGJMMbKajidYVj80fgtthYWrYW31xo0tXAi9c8dj8eY/P\nWxS+/jIWfgQ8B/wYV+ygs8Ql0UacmERkKTBXVcu971ulqmcfc2TGdKKXdr7Gkq3PAXDp6As5d9hZ\nCY7IpIorHps/AHgdtzSlLecBL17x2PxZj89bFOtVxr2AN1X14xhfNyE60mP6EGh86r6TOGVKYzpb\nVX01T219nkDQLf58auvznD5ous10NJG6h/aTUqNpuF7NgljdXES2A8OAh0TkVlyRgXuBc3AFrx8G\nfqKqQRG5Grc90IvAf+JKvn0HqAIW4hLc/ar6Xe/ag4BfA2fj6pa+D3xDVRuLHYTG0Qv4LW65TiXw\nBPAdb41ph0ScmFQ1dH3SDap6sKM3MyYZ2UxHE60rHps/HLdBakd85YrH5t/6+LxFsfodOg23DdGd\nwKO4Ib13cVsGDcLVMm0A/ttrfyqwxTvvBlxhgtXAp3G7RvxeRB5V1XW44gRlwHQgDVdz9F7gxBbi\n+ANubeypuCT2a9y606929AeKdtuLPSLyiIjMjvJ8Y4zpCq6m479H84HLYhWAqu7HJZ4DuGQ0TFW/\npqpbVPV14Nu4mdONfLhezzbgAVwS+aGqvqeqDwH7cKXcwO0C/g1V3ayqHwCLgInhMYjIKOBS4N9V\ndYOqrsJtKHuNiHR48VrSlyQyxpgkNq79Ji2SmEZxxHhc0evKkGN+IEtEGrcZ3hsyvFaFeywTOru6\niiM7ONwHXOntazcON7mjpUQ83ju+W6TZjzaGljeWbVW0s/IWA4u9OnVf8L5+ICJvAg95WdcYY7q6\naD/cZ8Q0iiPSgY245zzhlSwa9yGpp7lm1XVFxIeb6p4PPAb8A5ew/tbKfctxiSv8vhHthRcq2qE8\nAFR1r6r+AjgN+AauG/ngsVzTtCwQDLC+ZAOP65PN3ntr9woqa+2RnzEJ0OFfusd4XnsUNxGiRFW3\neaNYo3ETLjo6YW0Cbj3WOap6h6o+h3tm1dp9ewGE3DcPuIvm++m1KyVKEolIFu6B21zc5oALVfXu\nds4ZgVfI1RtnTVlV9VU8uP6PfFC2ucX3X9/1Nu/seZdrJ13FxD7xGiEwxrTgMeBbHTynAfhrHGIB\neAE3a/pPIvI93ILb+4EXvFl5LZ3TWo3AclysXxCRfwCnALcBiEhm6Lmq+oGI/BP4PxH5Bq4H9gAu\nQR7o6A8RVY9JRG73pii+CozFPVgrUtWvqOqb0VyzHXcBU4BZuOdbt4rI3HbOWYR7qJfS6gL1LFr3\nUKtJqVF1QzX3/+thNpVt6aTIjDHACmBNB8/5x+PzFsV6vVEQwNtMdQ4uYSzHdRaeBm5s79wWrrUL\nmI+bTv4ecDNuZKweOKmFc/8N2IYb/nsBN6T4+Wh+mKQvSSQiucCXgfO96YvrRORO3DTHJ1o55ypc\nOZCU9+pHy9hasSOitg3BBhZveJwfnXozaf6ULfBuTMp4fN6i4BWPzb8eVzM0kiGr/bhZcjGlqqNC\nvt+BS04ttXsEN1mt8fWHuGngrV3rQZo/nnks5Pu0kLaluBG0YxbtM6b1wF86qU7eZFwCfTvk2DLc\nvPpmRKQPbq79dbTeRU0JgWCA13e93X7DEGU15azfvzFOERkTmX69c0hPO/LPLz3NR7/eXbPU0+Pz\nFq3ATTZo70HvXuC8x+ct2hr/qFJbtIlpFu5ZT2cowo1Ths4k2Qtke0ko3N24Ld9T/rfz1vLtlFaX\ndfi8d4pXxyEaYyKXk5XO3DNH4/f58Pt8zD1zNDlZx/RIO6k9Pm/RC8AkXPWE8H+0xbhnM8c/Pm9R\nR4f9uqVo/095GLhTRH4MbFHVmtiF1EwuEH79xtdHdZ1F5FzcDMEOrzRORiVVpVGdt7PyYw7UVpKf\nmbybspmu74LpwzhzchEAudnxmh2dPB6ft+hD4D+veGz+D3Az2nriktTGx+ctqktocCkm2sR0MW4K\n4ucAwmd6xHgH22qaj902vm7qtYlINm4x2HxVrY32Zn6/D78/OUYA/WnRxVFWU8F/LfsJw3oOZkJf\nYWIfYXTvEfbcqRXpLfw9p6f5SE8/ptUUBsjv0eGZwinv8XmLqun4hAgTItrE9NOYRtG2XbiVzH5v\nxgnAQKBKVUPLx5+CK6T4N29hWKPnROQRVb0+kpsVFubh8yVHYhpZN9iVTIzSzspd7KzcxfPbl5KT\nns3xA8ZxYtEEJg+cQL+8lkZBu6cKmj/7yM/PoaAgLwHRdC2Hat1nx7zMlJ8gazpRtJUfHmm/Vcys\nBeqAGUBjRduZwMqwdiuA48KObcHN6Hsp0puVlh5Kmh7TwPQiCrN7U1p97Nu3VNVX886utbyzay0A\nRXn9mdB3HJP6CMcVjCIjresPtbTmwIHmGy8fOFBFGbHemaB7eWHHqzy5+VkA5h53EZ8aMSuxAbXD\nPogkj2i3Vv9hW++r6o+jC6fFa1WJyGLgPhG5FhiCKxl/tRfLAKDCq/10VK0+b4hxt6qWRHq/QCBI\nIJA8O3rMHHxq0z5BkfDhIxjBAu/iQ/soPrSPlz98nQx/BscVjGJCoTChcCz9c/slTa+xM9Q3NP/7\nqm8IUl/frEqLiVBVfTVLNj/XtJXI3zc/x4yBp9hWIiYi0Q7lXRP2Oh0YgOvZxGOB7U24yg9LcfWe\nblHVJd57xbj9RRa3cF7yZJgozR5yButLNrCtov2Z+X6fn/844cv0yMxjQ6mycf8mtlbsoCFkS4eW\n1AXq2LBf2bBfAeiTXcD4PsKEQkEKRpNtv0xMB9lWIuZYRDuU12xTLBHJB37PkeG2mFHVKlwyDE+I\nqGqrT6hjPAkjITLSMph/wrU8+N7/om1UdchOy+KaiV9gXB83mjmk5yDOGz6b6vpqNpVtZUPpJjbs\n/4D9EUw/319dxrJdy1m2azl+n5/RvUYwwUtUg3sUdavelDHRmLNgiQ/IBqqfWnhpyn9A7mwxW1ig\nqge83RNfAH4Rq+sayM3I4YYTv8L6ko28+OErbD+w86j3Zw6awYUjP0WvrObTw7PTszmh30RO6DeR\nYDDIvqoS1zsqVTaXbaMu0PYs1kAwwObybWwu38aSrc+Rn9mT8YVjmdBHGFd4HD0ybFzeGIA5C5Zk\n4up5zgdOx1VFqJ2zYMlLuBGf559aeGnbwxcJ5pWau9XbQSJhYr3irRfQO8bXNLhhusn9JlKYXcAd\nK3951HunD57RYlIK5/P5GJDbjwG5/Zg99AxqG+rYWr6dDaXKhtJN7Dm0t91rHKitZMWe1azYsxof\nPobnD2WCl6iG5w/F77Mp1qb7mbNgyWjgKdy+RKEygYu8r7fmLFjy2acWXrqvs+NLNbGc/JAPzMM9\nBzIpIDMtg/F9xjK+z1guA0qry9i4fxMbSpUPSrdQ3VDd5vlBguw4sJMdB3by7I6XyE3PYXzhWO/5\n1Fh6ZeV3zg9iTALNWbBkKPA6rW8J0eg0YOmcBUvOeGrhpcc+1bYLi9XkB4Ba4GXge9GHYxKpMLuA\n0wdP5/TB02kINLD9wM6mYb+PKtvfPuZwfRWr961j9b51AAzuUeRm+vUZy6heI0j3d92SNKZbW0T7\nSanRRNw60BtidXMRGQ5sBz4N3AP0xT3v/x2uSs944BXgStzv6Z/hCnH3x60T/R9V/V0r174F+Dqu\nAs/rwA2q+lGsYm/NMU9+EJF+wJnAnjhteWESIM2fxpjeIxnTeySXjL6AA7WVbNy/iY2l7utgXftr\nfHYdLGbXwWJe3PkqWWmZjC0Y4yUqoW9OYSf8FMbE15wFS8bgKuF0xJfmLFjy/acWXlrRftMOuRlX\nVXwi8ChwIe55VxVumPEruMctFwKfBT7BLbv5rYj8XVU/Cb2Yt6/S53EJbS/wn8A/ReR4VY3rs7IO\nJSYve94IzFDVLSJyKvAcriYUIrIUuMSbRWe6kPzMnkwvmsr0oqkEggE+qtzFBm/Yb3vFh+2unapp\nqGV9yQbWl2wAoH9uXyYUCuMLxzK2YDSZaZltnm9Mkvr3KM7Jw5Vz+32MY/mxqr4HvCcivwL+T1WX\nAojIS8A43O/rl1R1pXf8DuBW3L56n4Rd79u4Em9veG3nA7uBC4BnYhz7USJOTCJyHfB93Iy7xod3\nD+Hq1Z2GW1/0N+C7uB/UdFF+n5/h+UMZnj+UC0eew+G6w3xQtoWN+90kivKa9j8I7jtcwr7DJbz6\n8Zuk+9MZ02ukm5LeRxiY29+mpJtUEV5tJlJjYhqFW7O5PeR1FfBh2OssVf2HiHxKRO7CJaop3rlH\nLa0RkTxcMYPHRCT0U2c2LoklR2LCdQMXqOo9ACIyDRfg91V1g3fsp7iy75aYupHcjFym9D+BKf1P\nIBgMUnxob9MC3y3l245aaNmS+kA9H5Rt5oOyzTyx5WkKsnozoc9Yt8C3cAw56V1zH5+uan9VGa9+\nvKzZ8eXFK+mddQ49M7vEHp6Nop2GGo81lvVhr5uVLhGRn+B2X/gDbsPA+RydwBo15obPAZvC3otu\n24MO6EhiGo9bo9TobFymfTbk2PvA8BjEZVKUz+djUI+BDOoxkHOHnUVNQy2by7a6Ken7lU+q9rd7\njbKact7c/Q5v7n4Hv8/PyPzh3gLfsQzpOcimpCepYDDIiztf5alt/2wqRRTq1Y/f5K3d73DVuM8x\nbeBJLVwhJUU7ESDuEwha4MNNZPi6qv4NQEQmhLzXRFUrRGQfUKSqz3ttM4A/A3fiapPGTUcSk4+j\nS/ycCZR62503yqfzNhA0KSArLZNJfcczqa9b3rHvcAkbSzexYb+yqWwLtREs8N1asZ2tFdt5atvz\n9MjIY7w302984diu9uk7pT2/42We3v5Cm21qA3U8tOFRgsDJXSM5/R+udmdH1AN/iXEckY59lwCX\niMgaYDDwS9zv9Zb2J7kb+B8R+QRQ4BbcY5sPjj3ctnUkMa3HrWbeIiK9gdnA38PaXO61M6ZF/XP7\n0j+3L2cNOY26QD3bync09aZ2H9rT7vkH6w6xcu8aVu5dgw8fQ3sObiqXNCJ/qO05lSAfHvio3aQU\n6k8f/JWxBWMiWhiezJ5aeOmaOQuWLMftfhCpvz618NL2/2fvmPDZRy3NRgoC1+L2rXsPN1X8d7ga\npyfhRsRCz7sL6AHcj+t0rALOU9VYzyZsxhcMRlbGSUSuwv1AD+Ky5lTgNFV9R0QGAVfh5ud/WVX/\nGMsgRSQLV9JjLq5HtlBV726l7cVeHGOArbiCr09Feq9PPqlM6rpWVfXVfPeNHzU9t0n3pXHHzFu7\nRNXmsupyNpZu9hb4bqaqvmOTO3PSs5GC45qeTxVkt12EJBAMsHrvOl7c+Sq7DhYf9d7xfSZw8ahP\nMbTn4A7/HN3R4g2PsWLP6g6dM2fU+Vww4pw4RdRx/fr1jGrGzZwFS04EluFm27VnD3DKUwsvTcRQ\nXsqIODEBeNtOzMc9VLszZJzyt7gHaj9T1Ta3xIiGiPwGOANXRXwErpL4Nar6RFi7E4B3cF3r53DT\nGn8BTFPViHpyyZ6YAF7a+VrTVhiXjr6Qc4edleCIYq8h0MCHlR95Vc83sbPy44i28whVlDegad3U\n6N4jyQhZ4FtVX8Xv1rddGNeHj8+MuahL/v02CgaD1AXqqGmopaahltqGWmoaao56feRYHTUNNdQG\naqmpr6Um4I5X19ewrWJHh+/dJ7uQH5/23dj/UFGKNjEBzFmw5AxgCdDWAr0PgYueWnjphmjv0110\nKDG1RkQGA9Wq2v6T7Y5fOxc3Lnp+yHz67wPnqOrZYW1vB05Q1YtDjj0PrFTVWyK5XyokJoDDda43\nkZvRPWasHaw9xAelm7wq6Upl3cEOnZ/pz2BswWjG9xHG9R7Dnzc9yebybe2fCMwb+1nOHHJqNGHH\nTEOgwSWEpkRR2yyZ1LZwrKahjlov0TSeXxPSvrahrsMJP5Z+Pev2pBl+PZbEBDBnwZL+uMkFX+Po\nShBbcCM+v39q4aUHjuUe3UVMasSoavv1aqI3GRfn2yHHltFy6aOHcUUTw/WKfViJ1V0SUqMemXlM\nG3gS0waeRCAYYNfB4qZySdsqPmxxFlio2kAd7+3/gPf2d/y57RNbnmJK/xPokdn2SE0wGKQ+UE9N\n4EjyaKkH0lZCca/rXG+kvqYpmdQHwmcCdw0BgnGZN50IXnHWH89ZsOS/cSM7PYFy4EPb+qJjYtJj\niicRmQv8VlUHhRwbh5ua3r+tXpqITMRtzX6Zqv4jkvulSo/JHFFVX4WWbW3a7LCsJvb1MUfmD6N/\nbr+w3kZYzyVQ226CNEfkZeRy58zbEh1Gk2PtMZnYSYWqmrlATdixxtctTXEEQET64ipRvBFpUjKp\nKSc9hxP7TeLEfpMIBoPsPbyvachvc/m2mPQ2th/Y2WwfrO7C7/OT6c8kK819ZR71ZxZZaZlsKttC\nWQQVP0JNG9AlpoubOEiFxFRN8wTU+LrFNVMiMgB4ETf18fKO3Mzv9+H32wenVDakVxFDehVx3siz\nqG2oZVPZNt4vUd4v+YC9h8PLgXUd6f70puTRmDAak0hWetZR7zUdb+H9zLBrpPvT2y0RtW7f+9y7\n9qEOxTt72Gmkp9tiadNcKiSmXUBfEfGrauM4yUCgSlWbjdl4EzGWAg3ArI5OyCgszLM6bV1KHgP6\nTmXmcVMBeG37cu5555GERePDR1a6SwTZ6Vlkpx35Pis90/vTey/dJYfQ97Kb2oYcS3PfJ3ISwVm9\nT2Z1yVpWfPxuRO0/M/58JgwdFeeoTKpKhcS0FrcAbAbwlndsJrAyvKE3g+95r/3s8DLukSgtPWQ9\npi5scFZ065Iy0zIZnj8krFeRdVSPIzMtk+yW3vOOZadnkeHPiN0Hn3r3VUOAGtre1LEzfFGuoK62\nnjX72l6Zcc6wmZw/5BzKytrfOqUzFRREsgzJdIakn/wAICKLcFUnrsVVvH0YuFpVl3jDdhWqWi0i\n/43blmMWR9eiqlLViKZp2uSHru9Xa+5nU/nWDp1z1bjLOW3QyXGKqOsIBoOsK3mfF3a8woeVR68h\nHVswmgtHnMvYgtEJiq5tNvkheaTKAO9NwGrcEN1vcNUclnjvFeN2YwRXGSIHV2Bwd8jXLzs1WpPU\nZg09vUPte2TkMW3A5DhF07X4fD5O7DeJz4+7rNl7c8fMSdqkZJJLKgzl4W08eA0tbOmuqv6Q78d3\nZlwmNZ3QdyKnFZ3MW8XNRoOb8fv8fGnC520jQ2M6Uar0mIyJGZ/Px+fHXcbsIWe02S4nPYevn/Al\nxvcZ20mRGWMgRXpMxsSa3+fnc2Mv4bRBp/Dc9pdZ88m6o94/d9hZnD/87G5XYcOYZGA9JtOtDeox\nkPNGzG52fNqAkywpGZMglpiMMcYkFUtMxhhjkoolJmNMzPXNKSTdd6QSRbovjb45bW1VZMwRlpiM\nMTGXk57NnNEX4Pf58fv8zBl9QZfYZdl0DpuVZ4yJi3OHncVpRacA3W//MHNsLDEZY+LGEpKJhg3l\nGWOMSSqWmIwxxiQVS0zGGGOSSko8YxKRLOBeXPXww8BCVb27lbYnAYuA44H3gPmquqazYjXGGHNs\nUqXHdBcwBbfP0vXArSIyN7yRt1HgM8BrXvu3gWdExJ7AGmNMikj6xOQlmy8D31TVdd4+THcCN7TQ\n/ErgsKrerM63gErg8s6L2BhjzLFI+sQETMYNOb4dcmwZML2FttO990K9CZwan9CMMcbEWiokpiKg\nRFXrQ47tBbJFpE8LbXeHHduL247dGGNMCkiFyQ+5QE3YscbXWRG2DW/XKr/fh9/v61CAJrWlpzX/\n752e5iM9PRU+txnT9aRCYqqmeWJpfH04wrbh7VpVWJiHz2eJqTvJ6jGUdH869QHXKU/3pzNm0FCr\nWmBMgqRCYtoF9BURv6oGvGMDgSpVLW+h7cCwYwOB4khvVlp6yHpM3dBnxlzAE5ufbfq+5mCAGg4l\nOCrTmQoK8hIdgvGkQmJaC9QBM4C3vGMzgZUttF0O3Bx27HTgp5HeLBAIEggEowjTpLLZQ85k+oCT\nAVffrb4+0M4Zxph48QWDyf9LWEQW4RLMtbiJDA8DV6vqEhEZAFSoarWI9AQ2A48CDwBfBz4HjFHV\nqkju9cknlcn/F2KMibl+/XraUEmSSJWnuzcBq4GlwG+AW7z1TOCG6a4AUNVK4NPAmcAq4BTgwkiT\nkjHGmMRLiR5TZ7IekzHdk/WYkkeq9JiMMcZ0E5aYjDHGJBVLTMYYY5KKJSZjjDFJxRKTMcaYpGKJ\nyRhjTFKxxGSMMSapWGIyxhiTVCwxGWOMSSqWmIwxxiQVS0zGGGOSSipse4GI3IGrLO4Hfq+q4Vtb\nhLadASwETgA+Bu5S1d93SqDGGGOOWdL3mERkAXAlcClwGXCViNzUStsBwLO4KuQnArcBvxGRCzsn\nWmOMMccqFXpM3wR+oKpvA4jIzcBPgLtbaPsZoFhVb/FebxWR2cAXgOc6I1hjjDHHJql7TCJSBAwF\n3gg5vAwY7vWOwj0HXNPC8V5xCM8YY0wcJHuPqQgIArtDju0FfLidbPeGNlbVncDOxtci0h83DPjD\nuEdqjDEmJhKemEQkGxjcyts9AFS1NuRYjfdnVgTX/RsuqT1wjGEaY4zpJAlPTMB04BVczyjc75ol\nKwAAIABJREFUzQAikhmSnBoT0uHWLigiecA/gDHA6apaHWkwfr8Pv982sjTGmERJeGJS1ddo5VmX\n94zpZ8BAjgzRDcQlseJWzukJPA+MAmar6raOxNOnTw/LSsYYk0BJPflBVYuBj4AzQg7PBHaq6t7w\n9iLiA54ERgBnquoHnRGnMcaY2El4jykCi4Cficgu3KSH24GfN74pIn2BKlU9BHwFmAXMAQ6EzNyr\nVdWyTo3aGGNMVFIhMf0c6Ac8AdQDD6rqr0LeXwk8BPwYmItLXk+HXeM14Oz4h2qMMeZY+YLBluYc\nGGOMMYmR1M+YjDHGdD+WmIwxxiQVS0zGGGOSiiUmY4wxScUSkzHGmKRiickYY0xSscRkjDEmqVhi\nMsYYk1QsMRljjEkqlpiMMcYkFUtMxhhjkoolJmOMMUnFEpMxxpikYonJGGNMUrHEZIwxJqlYYjLG\nGJNULDEZY4xJKqmwtbpJESLyKnBmK28HgX6qWhrFdc8CXgFGqOrO6CNsdt3hwHZglqq+HsPrBoAv\nqeriWF3Tu24mcCPweWAMUAOsA+5R1SdjeS9jEsl6TCaWgsBjwABgYNhXUTRJKeza8RCv68aUiPQA\n3gS+BtwFHA/MAl4HHhWRXycuOmNiy3pMJtaqVPWTRAfRAb5EBxChhbiEf2JYgn9fRFYBT4vIMlV9\nPDHhGRM7lphMpxOR7cAi3LDfbGAf8C1c7+VOYAjwBvBFVS0JOfVSEbkRGAwsB25U1X951+wN/By4\nEOgPlAFLgG+qarU3HPgS8H3gO8A2YF5YXONwQ4b/BK5R1aCIfBq4DZgA7AIeBX6qqrXeOYOBe72f\noxy4uZ2f/WrgIe9nDU+KO1R1VAvn5AP/DnynpV6nqj4rIi97f4eWmEzKs6E8kyi34H7JTwLWAouB\n7wFfAC4GTuHoX/I+YAHwdWAqUAk8LyLZ3vsPA5OBz+Cev3wL98v8upBrpAEXAdOBrwCBxjdEZAwu\ncT2tql/yktIFuKHJ+3CJaT5wuRcrIpKGS2KFwEzvvW/T9vDgn/GGNmk+3HlyK+ecAmTihvJa8zJw\niheTMSnNekwm1v5NRC4POxYEnlTVq0OOPa2qfwIQkd8BlwDfU9U13rEXcUkr1H+o6kve+18EPsYl\nsj8ALwCvqer7XtudIvJN3LOYUD9X1a3eNYZ7x0YBf/Ri+npI2+8B96vqg97rHSIyH1gqIt8Bxntf\no1V1h3fNa4B3W/vLUdUaXA+xI/p6f5a30aYEl7z7Ans7eH1jkoolJhNrS3BDZeHDVAfDXm8J+f6Q\n9+e2kGNVuCG5RkFCegyqWiEimziSvBYBl3iJ4ThgIjAC2Bh2jdD7NloEZAAfhR2fApwsIl8NOebD\n9bTGe/cua0xKXlzrRKSqhXsAICJfAO5v5e0dqhqeSAEan9kVcvTfUahC78+K1u5tTKqwxGRirVJV\nt0fQrq6FY4EWjoVqCHudBtSIiA94Bjfc9n+44bI1wO9auEZLSeMh4D3gbhF5UlU3eMf9uGdej7Rw\nTjEu+bU0HN7Sz9ZoCe75WEtaO28lbmr4WcCqVtrMBtaranUb9zYmJVhiMqlkKvAqgIj0A8biEseJ\nwAXAKaq6yns/A/esaWsE130UWAZcBTwkIjNUNYhLVqKqTb0UEZkFfBP3rGst0EtExqvqRu/944D8\n1m6kqodovdfT2jkHROQhYIGI/ElV93jJ+D3gKeBt4Dzgq21dx5hUYYnJxFqOiAxo5b2yxtlsLWhv\n2rYPeEBEvoabcbcQ+BA3C60frrcxT0RKcM9ZvoebXp0VwT183mSHr+KeD90M3AH8DHhMRG7B9cKG\nAQ8CW1R1n4i8ArwD/K+I/AeuR/cbmvfsYuHbuOdlb4nID3GJ9Ke4YcjvAG+o6h/icF9jOl1KzcoT\nkSwRWS8irVUXQEQuFpF3RaRSRNaKyJzOjNFwBbA77KvY+/PTXpuWZq21t9A1CPwEN/vuTeAwcKGq\n1qtqMXA1bgLFBlyy+hj4BTCtnXs0HfOG8O4Afigi41T1b7gp5Z8B/oWbjfcccJnXPoib5fcBbnbe\nU7ihxJiv41LVw7jhul/iZhz+C/gtrvLD/wDjReQJERkY63sb09l8wWBKLHxHRLJwQy6XArNbKiEj\nIifgPsEuwP0CuQDvl5Oqru/EcI3pVCJSgBvK+42qtjr5wphUkBJDeSIyHvdJtD2fB15W1Xu81/eK\nyCW4T/GWmEyXpapluOdtxqS8lEhMuNlILwM/wA3htOZh3ELEcL3iEJMxxpg4SInEpKr3NX4vIm21\n09DXIjIROAdXMsYYY0wKSKnJDx0hIn2Bv+FmK/0j0fEYY4yJTJdMTN505aW4GVfh5XGMMcYksZQY\nyusIr9rzUtxaklmqur8j5weDwaDPlyo7IRhjYsj+4SeJLpWYRCQXeB632HJ2NPsClZYewu+3/z+N\n6W4KCvISHYLxpHxi8obtKrwaYd8HRuJ29vSHVCCoUtUDkVwvEAgSCKTG2i5jjOmKUvEZU3jWKMat\nUwKYC+QAKzi68sAvOy06Y4wxxyRlKj90lk8+qbS/EGO6oX79etoYfpJIxR6TMcaYLswSkzHGmKRi\nickYY0xSscRkjDEmqVhiMsYYk1QsMRljjEkqlpiMMcYkFUtMxhhjkoolJmOMMUnFEpMxxpikYonJ\nGGNMUkn56uLdSWXtQd4uXsn6kg1U1h4kw5/B0J6DOX3QdEb1Go7tI9VxVfVVrNizhnf3/YsDNZX4\n/WkU5Q3g9KJTkMIx+H322c2YzpZSRVxFJAtYBfyHqr7eSpuTgEXA8cB7wHxVXRPpPZKxiGswGOT5\nHS/z/I6XqQ82tNhmZP4wrp10FYXZBZ0cXep6Y9dyntjyNLUNtS2+PzBvAF+eeBWDegzs5MhMIlgR\n1+SRMh8HvaT0KDChjTa5wDPAa8AU4G3gGRHJ6ZQg4yAYDPKXzf/g6e0vtJqUALYf2MnC1fdSWl3W\nidGlrhc+fIU/6xOtJiWAPYf2cveaRew6WNyJkRljUiIxich4YDluE8C2XAkcVtWb1fkWUAlcHu8Y\n4+XdT9bz2sdvRtS2vKaCh95/lFTqBSfC1vIdLNn6XERtq+qr+N36xTQEWv9QYIyJrZRITMBZwMvA\nqUBb3e3pwLKwY29656WkpTtbHLFs1baKHXxY+VGcoukaln70Rofaf1K1n/X7N8YpGmNMuJSY/KCq\n9zV+LyJtNS3CPVcKtReYGIew4m7XwWK2H9jZ4fPuXr2IzLTMOESU+oLBINUN1R0+781dKzix36Q4\nRGSMCZcSiakDcoGasGM1QFakF/D7ffj9yfEMtPjwnqjOawg2UFVfFeNourePD+4mPT1VBhiMSW1d\nLTFV0zwJZQGHI71AYWFe0ky7ziy1X4TJ4nB9FfsDnzC60KblGxNvXS0x7QLC5/YOBCKeVlVaeihp\nekxp9TYclyzqA/V876WfUZTXn1MHncz0oin0zu6V6LBMDBUU5CU6BOPpaolpOXBz2LHTgZ9GeoFA\nIEggkByz2kbnjyI7LYvqhvDRybZN7jeJMb1GxCeoFBcgyD93LOVwlEOdxYf28cTmZ3hy87OMLxzL\njKKpnNB3IhlpGTGO1JjuK+UTk4gMACpUtRr4K3C7iPwCeAD4Ou650+MJDDFq2elZTC+aymsfvxXx\nOZlpmXxx/OXkpKfs0q24CwQCLNkW2XTx1gQJsqFU2VCq5KTnMHXAZGYMnMqI/GE21GfMMUrFhxjh\n3Zli4AoAVa0EPg2ciasQcQpwoaqm7EyA84bPJj+zZ8Tt54w635JSO84ccioDcvtF3L5PdiG+NlYp\nVNVXsWzXcu5afQ8/WXEXL+x4hfKailiEaky3lFIliTpDMpYk2nWwmHvWPkhFbWWb7S4YcQ6fHnme\nfWKPwP6qUn677kH2HS5ps91pRSfz+XGXcaC2kpV73mV58Sr2HN7X7vV9+BhXeBwzBk7lhH6TyLSh\nvqRnJYmShyWmMMmYmAAqaip5cecrLC9e3WwquBSM4eyhM5nUd3yCoktNh+uqePmj13lz9woqaw8e\n9d6I/GHMHnoGU/tPPirRB4NBdlZ+zPLiVazauzaiZ1XZadluqK9oGiNtqC9pWWJKHpaYwiRrYmpU\n21DLlvLtrrp4WgZDegyif27fRIeV0uoD9Wwt30FF7QHSfK66eCSFW+sa6li/fyMrilfx/n4l2GyU\nubn+OX2ZXjSN6QOnUJDdOxbhmxixxJQ8LDGFSfbEZJJTRc0BVu51Q33Fh/a2296HDykYw/SiqZzY\nb5JV6kgClpiShyWmMJaYzLFoHOpbsWc1q/as5VB9+2u7s9OymNLfDfXZvlqJY4kpeVhiCmOJycRK\nXaCe90o2smKPG+oLBAPtntMvpw/TB05jetEU21urk1liSh6WmMJYYjLxEDqrb/eh9msg+vAxtmA0\n0wdO5cT+x5OVokN9h+vc5JDcjORfwmCJKXlYYgpjicnEUzAY5KODu1hevJpVe9/lUF37Q31ZaZlN\nQ32je41ImaG+l3a+1rTv1aWjL+TcYWclOKK2WWJKHpaYwlhiMp2lPlDPe/s/YHnxKt7f/0FEQ319\nswuZXjSV6QOn0iensBOijE5VfTXffeNHTbsup/vSuGPmreSkZyc4stZZYkoeKV+SyJhUle5P58R+\nkzix3yQqaw82zeprayv3kupSntn+Is9sf5GxvUczo2gak/tNIjs94p1dOkVJVWlTUgKoDzZQUlXK\n0J6DEhiVSRWWmIxJAj0ze3D20JmcPXQmH1XuZkXxKlbufZeDdYdaPWdT+VY2lW8lc9OTTOl3AjOK\npjK690j8vlSsNGbMEZaYjEkyQ3sOYmjPS/jMmIt4f7+yongV6/dvbHWor7ahluV7VrF8zyr6ZBcy\nfeAUphdNpW9On06O3JjYSInEJCJZwL3AXNymfwtV9e5W2n4W+G9gKPAucKOqvttZsRoTK+n+dCb3\nm8jkfhOprD3Iqr1rWVG8io8O7m71nP3VpTy74yWe3fESx/UexfSiaZzU7/ikG+ozpi0pkZiAu4Ap\nwCxgBLBYRHao6hOhjURkAvAn4KvAW8BNwDMiMsrbFsOYlNQzswezh57B7KFn8HHlblbsWc07e9a0\nOdS3uXwbm8u38bg+yUn93VDfmN6jbKjPJL2kT0wikgt8GThfVdcB60TkTuAG4Imw5ucB76nqn7xz\n/wv4D2ACsKbzojYmfob0HMSQnoP4zOiL2FCqLC9exfqSjTSETDYIVRuoY8We1azYs5rC7AI31Ddw\nGv1ybajPJKekT0zAZFycb4ccWwZ8r4W2+4GJInKa1/5aoALYGu8gjelsaf40ju87geP7TuBg7SE3\n1LdnFTsrd7V6Tml1Gc/teJnndrzM6F4jmVE0jSn9jyc7iadxm+4nFRJTEVCiqvUhx/YC2SLSR1X3\nhxx/DLgEl7gavK+LVdV2bTNdWo/MPGYNPZ1ZQ09n18FiVhSv5p29a5pt5xFqa8V2tlZs5y+b/s7k\nfsczo2gqYwtG21CfSbhUSEy5QE3YscbX4U90+wADgeuBFcB84GEROUlV294RzpguYnCPIuYe92ku\nHX0hG0qVFcWrWV+y4ah1RaFqA3Ws3LuGlXvXUJDVu2kBr22nYhIlFRJTNc0TUOPr8HouPwP+par3\nAYjI14CNwDXAzyO5md/vw++3BeAm9aXj56SBkzhp4CQO1h5i5Z61vL17JR8e+LjVc8pqynl+x8s8\nv+NlRvcewWmDTmbqgBPI6WCtu/S05v+G0tN8pKdbb8y0LxUS0y6gr4j4VbVxIcdAoEpVy8PaTgV+\n1fhCVYMisg4YHunNCgvzUqYWmTGRKiCPoQPOY+7k8/ioYjev7VjO6ztWUF59oNVztpbvYGv5Dh7T\nv3PK4BOZNfJUJvUX/P72k0sFzRNZfn4OBQV5x/RzmO4hFRLTWqAOmIGbAg4wE1jZQtvduBl4oQR4\nJ9KblZYesh6T6dJ60IuLh53PBUPOZcP+Tby9eyXr9r3f+lBfQx3Ldq5k2c6VFGT1YsagaZw6aBoD\n8vq12P6TwyU8vfXFZsef2fgqF486l15Z+TH9eWLFkmbySIkiriKyCDgdN8tuCPAwcLWqLhGRAUCF\nqlaLyBXAQ8DXcLPyvgpcB4yN9BmTFXE13dGhusOs3ruO5XtW8eGBjyI6Z2T+cGYUTWVK/8nkZuQQ\nDAZ5fsdSntn+QqvbzGf40/m8XMb0oqmxDD8mrIhr8kiVxJSDq/xwGW76952q+hvvvQDwJVVd7L2+\nBvg2MBjX2/qmt/4pIpaYTHe359Belhev5p09q6morWy3fYY/ncn9JgGwau/aiO7x7+PnJV1yssSU\nPFIiMXUmS0zGOA2BBj4o28KK4lWsK3mf+kB9+ydFKMOfzo9O/W5SDetZYkoelpjCWGIyprnDdYdZ\nve9frChexfYDO2NyzU+PPI8LR54bk2vFgiWm5GGJKYwlJmPatufQPlfiqHg1FbWtz+prT5/sAn58\n2n/FMLJjY4kpeVhiCmOJyZjIBIIBtHQLr3y8jPf3fxDVNX4963bS/Gkxjiw6lpiSR8TTxUXkzEjb\nqurr0YVjjEkVfp+f8X3G0isrP+rEFCBIcqQlk0w6so7pVSAI+Lw/GzV+ygg9Zv+vGdNN9MrKx4ev\n1SniremRkUeGPxWWUprO1pH6ICOBUd6fXwV24jbuGwAUAhcAH+DK/xhjuom8jFzG9xnb4fNOHnBS\nHKIxXUHEH1dU9cPG70Xku8BXVPXlkCYvisj1wCPA4tiFaIxJdmcNPo0N+zXi9j58nDF4RhwjMqks\n2oqKg3A17MKV4XpPxphuZGKfcUztPzni9uePOJuBef3jGJFJZdEmphXAT0WkR+MBESnEVfB+LRaB\nGWNSh8/n44sT5jGl/wnttj1n2Jl8euR5nRCVSVVRTRcXkYnAy7i9kjbhEtxY3AZ+Z4cO+6Uamy5u\nTPSCwSDv7d/IP3csbbYQd1zBcVw48lzG9B6ZoOjaZtPFk0dUPSZVfR+XiL6NK5a6DPgmcHwqJyVj\nzLHx+Xwc33cC82Rus/c+M+bipE1KJrlEPVdTVQ+IyEO4WXrbvGN1sQoslIhk4Yq4zsVtDrhQVe9u\npe3xXtupwGbgRlV9NR5xGWOMib2oekwi4hORO4By4H1gKLBYRB4UkYxYBui5C5gCzMJtm36rSPOP\nZCKSD7wAvAdMAp4EnhQR2yPaGGNSRLSTH74BfBGXJGq8Y38HPgvcduxhHSEiucCX8bavUNUlwJ3A\nDS00/xJQqarzVXWbqt6GewY2LZYxGWOMiZ9oE9PXgBtU9WEgAKCqjwFfAa6KTWhNJuOGHN8OObYM\nmN5C27OAJaEHVHW6qj4f45iMMcbESbSJaSTwbgvH1wEDow+nRUVAiaqGbgazF8gWkT5hbUcBJSJy\nv4gUi8hbInJajOMxxhgTR9Emph3AyS0cvxBvIkQM5XJkuLBR4+ussOM9gJuB3bgSSa8DL4jI4BjH\nZIwxJk6inZX3c+BeESnCJbdzROQ63JTxm2IVnKea5gmo8fXhsOP1wLuq+iPv9ToROQ/3POyOSG7m\n9/vw+205gzHHIj2t+b+h9DQf6enRfhY23UlUiUlVH/Jm3/0AyAHuBz4BfqCq98UwPnClj/qKiF9V\nA96xgUCVqpaHtS3GFZINtQk3azAihYV5+HyWmIw5FhXkNDuWn59DQUFeAqIxqSaqxCQiPVT1AeAB\nbyq2X1X3xTa0JmuBOmAG8JZ3bCawsoW2y4HwfaPGAX+K9GalpYesx2TMMTpwoKrFY2UcSkA0kbGk\nmTyiHcrbIyJ/Ax5W1VdiGVA4Va0SkcXAfSJyLTAEWABcDSAiA4AKVa0G7gNuEJEf4pLR1biJGn+M\n9H6BQJBAwKoSGXMs6hua/xuqbwhSXx9oobUxR4t2wPd63HDaCyKyQ0R+JCKjYhhXuJuA1cBS4DfA\nLd56JnDDd1cAqOpO4HzgEmA9cDFwkaoWxzE2Y0yYvjmFpPuO7Bea7kujb45tPGAiE1UR10Zeb+UL\n3tcU4E3gIVV9KDbhdT4r4mpMbLy08zWWbH0OgEtHX8i5w85KcERtsyKuyeOYElMjbyLEV4HbgR6q\nmrJbq1tiMiZ2Dte5Z025Gc0nQyQbS0zJI+oirgAicgau0sPl3rX+AqRsb8kYE1upkJBM8ol2Vt7t\nwJW4adivAf8P+KuqNp+KY4wxxnRAtD2mK3A9o0ds/yVjjDGxFJNnTF2JPWMypnuyZ0zJI+Iek4gs\nBeaqarn3fatU9exjjswYY0y31JGhvA+BBu/7nYD1LIwxxsRcVEN5Xkmig3GIJ+FsKM+Y7smG8pJH\ntJUf9ojIIyIyO6bRGGOM6fZSpSSRMcaYbsJKEoWxoTxjuicbykseVpIojCUmY7onS0zJIyVKEolI\nFnAvMBe3a+1CVb27nXNG4FUYV9XXYx2TMcaY+EiVkkR34YYKZwEjgMUiskNVn2jjnEVAbpziMcYY\nEydJX5JIRHKBLwPnq+o6YJ2I3AncALSYmETkKqBHPOMyxhgTH9HOylsP/KWT6uRNxiXQt0OOLQOm\nt9RYRPoAdwDXATZmbIwxKSbaxDQL96ynMxQBJapaH3JsL5DtJaFwd+O2fN/YKdEZY4yJqWiH8h4G\n7hSRHwNbVLUmdiE1kwuEX7/xdVboQRE5FzgNN0MwKn6/D7/fOlrGGJMo0Sami4HRwOcAROSoN2M8\nXbyasAQU8rqp1yYi2cB9wHxVrY32ZoWFefh8lpiMMSZRok1MP41pFG3bBfQVEb+qBrxjA4EqVS0P\naXcKMBL4m4iEZpbnROQRVb0+kpuVlh6yHpMx3VBBQV6iQzCeqBKTqj4S60DasBaoA2YAb3nHZgIr\nw9qtAI4LO7YFN6PvpUhvFggECQRsja0xxiRKtOuYftjW+6r64+jCafFaVSKyGLhPRK4FhgALgKu9\nWAYAFapaDWwLixNgt6qWxCoeY4wx8RXtUN41LVxnAK5n8+YxRdSym3CVH5YCFcAtqrrEe68Y+BKw\nuIXzrOtjjDEpJmZbq4tIPvB74C1V/UVMLpoAVivPmO7JauUlj2jXMTWjqgeAW3HDbMYYY0xUYpaY\nPL2A3jG+pjHGmG4klpMf8oF5uOdAxhhjTFRiNfkBoBZ4Gfhe9OEYY4zp7o558oOI9APOBPaoajxm\n5HUqm/xgTPdkkx+SR4eeMYnILSJSIiJjvNenApuBx4HXReRFEcmJQ5zGGGO6iYgTk4hcB3wf+B2w\nzzv8EK5e3fHAMKAn8N0Yx2iMMaYb6cgzpq8AC1T1HgARmQaMBb6vqhu8Yz8FFuKmjRtjjDEd1pGh\nvPHACyGvz8ZVVng25Nj7wPAYxGWMMaab6khi8nF0iZ8zgVJvu/NG+XTeBoLGGGO6oI4kpvXA6QAi\n0huYzdE9KIDLvXbGGGNMVDryjOm3uArfJ+J2ic0CfgUgIoOAq4Bv47aZiCkRycIVcZ2L65EtVNW7\nW2l7MW6/qDHAVlzB16diHZMxxpj4iLjHpKp/Am4EzvAOzVPVd7zvv4dLBj9T1T/GNkQA7gKmALOA\n64FbRWRueCMROQH4G/AgMBl4APiriBwfh5iMMcbEQUyqi4vIYKBaVfcfe0jNrp0LlADnq+ob3rHv\nA+eo6tlhbW8HTlDVi0OOPQ+sVNVbIrmfLbA1pnuyBbbJI9qSREdR1V2xuE4rJuPifDvk2DJaLn30\nMJDZwvFesQ/LGGNMPMS6ung8FAElqlofcmwvkC0ifUIbqtM0+UJEJgLn0IGt1Y0xxiRWKiSmXKAm\n7Fjj66zWThKRvrjnTW+o6j/iFJsxxpgYi8lQXpxV0zwBNb5ucc2UiAwAXsStu7q8Izfz+334/TbU\nbIwxiZIKiWkX0FdE/Koa8I4NBKpUtTy8sTcRYynQAMzq6ISMwsI8fD5LTMYYkyipkJjWAnXADOAt\n79hMYGV4Q28G3/Ne+9mq+klHb1Zaesh6TMZ0QwUFeYkOwXiSPjGpapWILMYt7r0WGAIsAK6GpmG7\nClWtxlU/H4lb7+T33gPXuzoQyf0CgSCBgM0YNyYWDlfXAZCbnZHgSEwqSfrE5LkJV/lhKVCBq+aw\nxHuvGPgSsBhXGSIHWBF2/iPAtZ0SqTEGgOdX7OSvr24F4HOzRnPB9GEJjsikipgssO1KbIGtMceu\nqqaeG3/9BvUN7p9TepqPX31zJjlZyftZ2BbYJo9UmC5ujEkxn5RXNSUlgPqGIJ+UVyUwIpNKLDEZ\nY4xJKpaYjDExtb34AE++vq3Z8eeWf0jx/kMJiMikGnvGFMaeMRkTnUAgyJ9f3sxLqz9utY3PB1fM\nHsP5pyTfRAh7xpQ8rMdkjImJ/3tpU5tJCSAYhMeWbuHFlR91UlQmFVliMsYcM91ZxtI1kW8y8Pgr\nWyipsMkQpmWWmIwxx6y9nlK4hkCQ19bujlM0JtUl76ICY0xSCgSCHDhcS1llDeWVNewtq2J1x6t/\n8eb6Yi47a3QcIjSpzhKTMaZJTV0D5ZU1lFXWUHawpvn3B2uoOFhLQwzKdpUfrKW+IUB6mg3cmKNZ\nYjKmGwgEg1Qerjsq0TT2eEIT0OGa+vYvZkycWWJKQdW19RysqiMjzU9+XqZt0xEDNXUNVB6uJT3N\nT35uZkpVmK+ta2jWuwlPOuUx6uXEUq8emdZbMi1KicQkIlm4Iq5zcZsDLlTVu1tpexKwCDgeeA+Y\nr6prOivWeAkEgry7uYSlaz5m44dlTcd798jkzMmDmHXSYHr3aHVDX9OCYDDI+ztKeWXNLtZt2U/A\nW9PXIyeDM44vYvaUwfTrnZOw+ALBIAcP17U5rFZeWcOh6sT2cvw+H2lpPurqA+03DnHapIFxisik\nupRYYCsivwHOwFURH4GrJH6Nqj4R1i4X2AL8L/AHYD4wDxilqhHNTU3GBbaHquu454n1fLCz2b6I\nTbIy0/j6JROZPKZvJ0aWumrrGnjw6Q2sauOhfZrfx9UXjOOME4pifv+6+gaXZJoSTG1T6HVZAAAM\nY0lEQVSzBFR+sCbhvZycrDR698iioGcWBT2y6N2z+ff5uZnoR+X8/NF3I75umt/H7dfNoG8CE384\nW2CbPJK+x+Qlmy8D56vqOmCdiNwJ3AA8Edb8SuCwqt7svf6WiFyE2159cWfFHEt19Q388vF1bN3d\n9nZSNbUN/PaJ9XzrislMHFHYSdGlpkAgyH1L3mftlpI22zUEgvzh2Y2kp/mYMTGyT/fBYJDKqrq2\nJxAkSS+nV4/MsKST2SzpZGdG9iti/PACZp00mFffjWwt0+dmjU6qpGSSS9InJmAyLs63Q44tA77X\nQtvp3nuh3gROJUUT0wsrP2o3KTVqCAT5wzMb+dnXT7Wx+zYs37Cn3aQU6pF/KieM7ktGuo+yg7VH\nEo3Xqyk76llOzVFVtRMhOzONgp5ZR5JOC9/3yov9c7R/+9RY/D7aXGjr88HnzhrNeScPjem9TdeS\nCompCChR1dCPmHuBbBHpo6r7w9q+F3b+XmBinGOMi0AgGPEn0EZllTW8sPIjJowoiFNUqe/Z5Ts7\n1L6mtoEF9yyjpq5jz1BizeeDXnmZ7SadRO155Pf7+LfzhFMnDuSpN3fwr237j3r/lHH9ueSMkQzq\na1uYm7alQmLKBWrCjjW+Dn/a31rblJwVsOmjcvYfCP9x2te4a6iJnXgnpazMNAp6tJxoGr/Pz8sg\nzZ/8PeHRg3sx96xRzRLTRacOt6RkIpIKiama5oml8fXhCNuGt2uV3+9LmqnCpZUdT0omufiA/B6Z\nTcmloGc2hT2PJJuCfPc6mXd2jUZaC0PJaWl+0tOTP7GaxEuFfw27gL4i4lfVxo+tA4EqVQ2fprbL\ney/UQKA40psVFuYlzbqgvLzMRIdg2pCVmUaf/Gz69MqhT69s+vTKprBXyOv8HArys7rl876sHLdG\nqb7B/ZNNT/MzdmQfcrMzEhyZSQWpkJjWAnXADOAt79hMYGULbZcDN4cdOx34aaQ3Ky09lDQ9przM\ntESHYDwFPbO47KzRXi/H9XZys9Lb+RAToPJA962gffns0Tz28pam72uqaqmpqk1wVK0rKLBhxmSR\nKuuYFuESzLXAEOBh4GpVXSIiA4AKVa0WkZ7AZuBR4AHg68DngDGpuI4pEAjy3fvfpqSiukPnXXnO\ncUwYbpMfWvPA0+/z8b6O7aR61afGcs7UIXGKqOs6XF0HkBI9JVvHlDxSoccEcBOu8sNSoAK4RVWX\neO8V4xbeLlbVShH5NHA/cB3wL+DCSJNSsvH7fcyeMpi/vBL5ZIY++VmcM3VwSjwkT5SLpg/ngac2\nRNw+JyvNqhREKRUSkkk+KdFj6kzJ1GMCqKsPsPDP77Lp44p226b5ffznlSciw6y31JZAMMiiv78X\n8VYN139mEtPG9Y9zVCbRrMeUPOxjdZLLSPdz4+WTmTSy7WoOOVnp3Hj5CZaUIuD3+bhuzkRmTBzQ\nZruMdD/XzZlgScmYTmY9pjDJ1mNqFAgGeW/bfpau2cV720qbCo727ZXNWScOYubkQeTn2iy+jggG\ng2z6qJxX3t3Fmk2fNFVs6JWXyczJg5h14iAK87MTHOX/b+/eY+woyziOf3dpuUSUJtxvCljyeLdG\nBEyVWv+CGC4WE2qbqBjSNGCq4AULWwTbqBVUoAiECBo1FkFASbkoEsRCgYCXGKE8EgiuQKncBCxU\nWrr+8c7Gw7otB7bbmTn9fpKT3Zl5z+xz/tj97fu+M/NqS7HH1BwG0whNDaZO69Zv4IW165g4oZ8d\nXvXKMHVj/csbWLN2PRO26eviajv1IoOpOdpy8YM6TJzQz04ucbFZTdimn528b0xqBOeYJEmNYjBJ\nkhrFYJIkNYrBJElqFINJktQoBpMkqVEMJklSoxhMkqRGacUNthHxLcqSF/3ApZk5cs2lzraHAt8B\n3gM8ApyTmZdukUIlSWPW+B5TRHwRmAkcDRwLzI6IUzbSdnfgesryGFOAM4ElEXHElqlWkjRWbegx\nzQMGMvMOgIg4FVgIfHeUtscAqzJzQbX9YERMB2YBN2yJYiVJY9PoHlNE7AnsCyzv2H0b8JaqdzTS\nDcDxo+zfaRzKkySNg6b3mPYEhoDHOvatBvooS6yv7mycmYPA4PB2ROxGGQY8Y9wrlSRtFrUHU0Rs\nD+y9kcM7AmTmSx37/lN93eTjtavzXkUJtUu6rae/v4/+fp9+L0l1qT2YgEOAWyg9o5FOBYiIbTvC\naTiQXtjYCSPiDcC1wGRgamau7baYnXfe0VSSpBrVHkyZeSsbmeuq5pgWA3vwvyG6PSghtmoj73kj\ncCNwADA9Mx/a3DVLksZPoy9+yMxVwD+AD3Xs/jAwmJmrR7aPiD7gGmA/4LDMvH9L1ClJ2nxq7zF1\n4SJgcUQ8Srno4ZvA2cMHI2IX4MXMXAOcAHwEOBJ4ruPKvZcy85ktWrUk6XVpQzCdDewKXA2sB36Q\nmed1HL8b+CHwdWAGJbyWjTjHrcBHx79USdJY9Q0NjXbNgSRJ9Wj0HJMkaetjMEmSGsVgkiQ1isEk\nSWoUg0mS1ChtuFxco4iI7YB7gJMy8/d119NmEbEXcD4wnfKoqyuA+SOe0ajXKCLeCnwfmAo8BVyQ\nmefUW5XawB5TC1WhtBR4R9219IirgO0pf0BnUm7QXlhrRS1XPYXlOsoKAFOAucBARMystTC1gsHU\nMhHxduBOYP+6a+kFERHAwcBnMvP+zLydskzKrHora73dgT8BJ2bmg5l5I3Azr3y8mDQqh/LaZxrl\nF3yATTxhXV17HDg8M5/s2NeHi0uOSWY+DnxyeDsipgKHUXpO0iYZTC2TmRcPf1/+2ddYZOazwE3D\n29UQ1OeA39ZWVI+JiIcpK1EvozxaTNokh/KkVzqbMidyet2F9JAZlHm79wHn1lyLWsBgkioRsRiY\nB8zOzJV119MrMvOPmXk9cDIwJyIcqdEmGUwSEBFLKH84Z2fmL+uup+0iYreIOHrE7vuAbYE31VCS\nWsRg0lYvIr4GzAGOy8wr666nR+wPXF2tQj3sIOCJzHy6pprUEnaptVWrLr8fAL4BrOhYXJLRVklW\n1+6m3AB+WUScQgmqbwOLaq1KrWCPqd1cTGvsjqL8HgwAj1WvVdVXvU6ZuQE4GlgDrAAuAc7NzAtq\nLUyt4EKBkqRGscckSWoUg0mS1CgGkySpUQwmSVKjGEySpEYxmCRJjWIwSZIaxWCSJDWKjyRSK1Vr\n/Ly5Y9cQ8G/KqqkLMnP5q7x/GnALsF9mDo5TmZJeB3tMaqshytpJe1SvvYAPAs8CN0bEPl2eQ1LD\n2GNSm63JzH92bK+OiLnAo8DHgSX1lCVpLAwm9ZqXq69rqwXpzgA+BexKWQ9ofmb+37LpETGJ0gM7\nAtgNeAb4FTAvM9dWbb4EzAX2oTzk9bLMXFQd24EShB8DJgErgYWZec04fU6pZzmUp54REXsDF1Dm\nmm4Azqess3Qy8C7g18C1EXHgKG//EfBe4BhgMvAFSqDNqc59JDC/2p4MnAqcHhGzqvcvqn7G4cDb\nqp9/eUR0zoNJ6oI9JrXZaRHx5er7CZTVUVcCnwD+BXwWOKmj1zIQETD6Cqq/AW7NzHur7cGImAe8\nu9o+AFgLDGbmI8CVEfEoMNhx/Hng4cx8NiIWAL+j9LwkvQYGk9rsYkqvCMoQ3tOZ+TxARLwfmAjc\n1fmGzByojk8bca6LgKMi4njgQOCdwH6UoAP4KXA88LeIuA+4CfhFFVIAi4FrgSci4i5K0P1suB5J\n3XMoT232dGY+VL3+PiIE1gF93ZwkIvqA64DzgJeAyylzRSuG22TmU5k5BZgKXAkcAiyPiIHq+J3A\nvsAM4A+UYcCVETF9jJ9R2urYY1KveoASTh8A/jq8MyLuBJYCf+5oO4UyN3RwZt5TtZtImUt6sNqe\nBUzKzAuBO4CzIuISYCawKCLOBG7LzGXAsmo58XuBYyn3S0nqksGknpSZL0bEEkpoPEkJiRMoQ3TX\nU+57Gu5RPU4JseOqtrsApwG7A9tVbbYHzomI54DllN7RNMo8EpQ5ptkRMYcSZodSbgC+fRw/ptST\nHMpTW3Vzc+xXgR9T5o/+QgmSIzLzgc5zZOYq4NPAUZRLyq8AHgG+BxxUtbmMcun5Asq8088pV959\nvjrXicDNwE+ABM4CvpKZS8fyIaWtUd/QkDe/S5Kawx6TJKlRDCZJUqMYTJKkRjGYJEmNYjBJkhrF\nYJIkNYrBJElqFINJktQoBpMkqVEMJklSoxhMkqRGMZgkSY3yX9dLRz2KaakwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd2214e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xbdb2be0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p59nS0nRMnEpXJMgR8W7gRcBzMvNT1eJbgCPaNl0L3DaTstesWUlfX2d/ZLfc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\noD+B8vDNayktNZ+hPG18QmZ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kedFpgrwV2L/6/39R+iID/BRYO5OCqpbixwPntq1aR+le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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbdc5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Before', (891, 12), (418, 11), (891, 12), (418, 11))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('After', (891, 10), (418, 9), (891, 10), (418, 9))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'combine' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8fa6cd27ab2f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcombine\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Title'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mName\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' ([A-Za-z]+)\\.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexpand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcrosstab\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Title'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Sex'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'combine' is not defined"
     ]
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把类型特征转换为数字特征\n",
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建了一个2行3列的全为0的数组\n",
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>7.2500</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "\n",
    "            # age_mean = guess_df.mean()\n",
    "            # age_std = guess_df.std()\n",
    "            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "\n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       AgeBand  Survived\n",
      "0  (-0.08, 16]  0.550000\n",
      "1     (16, 32]  0.337374\n",
      "2     (32, 48]  0.412037\n",
      "3     (48, 64]  0.434783\n",
      "4     (64, 80]  0.090909\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title   AgeBand\n",
       "0         0       3    0   22      1      0   7.2500        S      1  (16, 32]\n",
       "1         1       1    1   38      1      0  71.2833        C      3  (32, 48]\n",
       "2         1       3    1   26      0      0   7.9250        S      2  (16, 32]\n",
       "3         1       1    1   35      1      0  53.1000        S      3  (32, 48]\n",
       "4         0       3    0   35      0      0   8.0500        S      1  (32, 48]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把年龄字段分成6个阶段，并计算这个6个阶段与是否获救的相关性\n",
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "print train_df[['AgeBand','Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title   AgeBand\n",
       "0         0       3    0    1      1      0   7.2500        S      1  (16, 32]\n",
       "1         1       1    1    2      1      0  71.2833        C      3  (32, 48]\n",
       "2         1       3    1    1      0      0   7.9250        S      2  (16, 32]\n",
       "3         1       1    1    2      1      0  53.1000        S      3  (32, 48]\n",
       "4         0       3    0    2      0      0   8.0500        S      1  (32, 48]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把age字段转换为数字等级\n",
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age']\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>2</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除 AgeBand特征\n",
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.303538</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#* SibSp => 堂兄弟/妹个数\n",
    "#* Parch => 父母与小孩个数\n",
    "# 把上面这两个字段合并成家庭成员数量字段\n",
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "#查看家庭成员字段与是否获救的关系\n",
    "train_df[['FamilySize','Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'FamilySize'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-3c8754ca2ef8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcombine\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'IsAlone'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'FamilySize'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'IsAlone'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'IsAlone'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Survived'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'IsAlone'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1995\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1997\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1998\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1999\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2002\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2003\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1348\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3289\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3290\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3291\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3292\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1945\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1946\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1949\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:4154)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:4018)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12368)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12322)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'FamilySize'"
     ]
    }
   ],
   "source": [
    "# 再创建另一个特征，是否孤独一人\n",
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone','Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "labels ['SibSp' 'Parch' 'FamilySize'] not contained in axis",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-f67eb6859ca2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# 特征转换之后，把SibSp，Parch，FamilySize字段删除\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SibSp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Parch'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'FamilySize'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtest_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SibSp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Parch'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'FamilySize'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcombine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_df\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, level, inplace, errors)\u001b[0m\n\u001b[1;32m   1875\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1876\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1877\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1878\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   3049\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3050\u001b[0m                 raise ValueError('labels %s not contained in axis' %\n\u001b[0;32m-> 3051\u001b[0;31m                                  labels[mask])\n\u001b[0m\u001b[1;32m   3052\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3053\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: labels ['SibSp' 'Parch' 'FamilySize'] not contained in axis"
     ]
    }
   ],
   "source": [
    "# 特征转换之后，把SibSp，Parch，FamilySize字段删除\n",
    "train_df = train_df.drop(['SibSp','Parch','FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['SibSp','Parch','FamilySize'], axis=1)\n",
    "\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
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      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "    \n",
    "train_df.loc[:,['Age*Class','Age','Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 填充Embarked为空的数据\n",
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "\n",
    "#查看登录港口(Embarked)与存活（Survived）的关系\n",
    "train_df[['Embarked','Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         1      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        0          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        0          6"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进行类型特征的转换,把类型转换为数字\n",
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map({'S':0,'C':1,'Q':2}).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2   7.8292         2      1        1          6\n",
       "1          893       3    1    2   7.0000         0      3        0          6\n",
       "2          894       2    0    3   9.6875         2      1        1          6\n",
       "3          895       3    0    1   8.6625         0      1        1          3\n",
       "4          896       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数字类型的特征如果为空，那么填充这个字段的平均值\n",
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FareBand  Survived\n",
       "0       [0, 7.91]  0.197309\n",
       "1  (7.91, 14.454]  0.303571\n",
       "2    (14.454, 31]  0.454955\n",
       "3   (31, 512.329]  0.581081"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把票价字段分阶段\n",
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "labels ['FareBand'] not contained in axis",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-5cddc1f26e77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Fare'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Fare'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mtrain_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'FareBand'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mcombine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_df\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, level, inplace, errors)\u001b[0m\n\u001b[1;32m   1875\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1876\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1877\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1878\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   3049\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3050\u001b[0m                 raise ValueError('labels %s not contained in axis' %\n\u001b[0;32m-> 3051\u001b[0;31m                                  labels[mask])\n\u001b[0m\u001b[1;32m   3052\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3053\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: labels ['FareBand'] not contained in axis"
     ]
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     0         1      3        0          2\n",
       "2         1       3    1    1     0         0      2        0          3\n",
       "3         1       1    1    2     0         0      3        0          2\n",
       "4         0       3    0    2     0         0      1        0          6\n",
       "5         0       3    0    1     0         2      1        0          3\n",
       "6         0       1    0    3     0         0      1        0          3\n",
       "7         0       3    0    0     0         0      4        0          0\n",
       "8         1       3    1    1     0         0      3        0          3\n",
       "9         1       2    1    0     0         1      3        0          0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     0         2      1        1          6\n",
       "3          895       3    0    1     0         0      1        1          3\n",
       "4          896       3    1    1     0         0      3        0          3\n",
       "5          897       3    0    0     0         0      1        1          0\n",
       "6          898       3    1    1     0         2      2        1          3\n",
       "7          899       2    0    1     0         0      1        0          2\n",
       "8          900       3    1    1     0         1      3        1          3\n",
       "9          901       3    0    1     0         0      1        0          3"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891L,), (418, 8))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型训练\n",
    "X_train = train_df.drop('Survived',axis=1)\n",
    "Y_train = train_df['Survived']\n",
    "X_test = test_df.drop('PassengerId', axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
      "0       3    0    1     0         0      1        0          3\n",
      "1       1    1    2     0         1      3        0          2\n",
      "2       3    1    1     0         0      2        0          3\n",
      "3       1    1    2     0         0      3        0          2\n",
      "4       3    0    2     0         0      1        0          6\n",
      "5       3    0    1     0         2      1        0          3\n",
      "6       1    0    3     0         0      1        0          3\n",
      "7       3    0    0     0         0      4        0          0\n",
      "8       3    1    1     0         0      3        0          3\n",
      "9       2    1    0     0         1      3        0          0\n",
      "0    0\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    0\n",
      "5    0\n",
      "6    0\n",
      "7    0\n",
      "8    1\n",
      "9    1\n",
      "Name: Survived, dtype: int64\n",
      "   Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
      "0       3    0    2     0         2      1        1          6\n",
      "1       3    1    2     0         0      3        0          6\n",
      "2       2    0    3     0         2      1        1          6\n",
      "3       3    0    1     0         0      1        1          3\n",
      "4       3    1    1     0         0      3        0          3\n",
      "5       3    0    0     0         0      1        1          0\n",
      "6       3    1    1     0         2      2        1          3\n",
      "7       2    0    1     0         0      1        0          2\n",
      "8       3    1    1     0         1      3        1          3\n",
      "9       3    0    1     0         0      1        0          3\n"
     ]
    }
   ],
   "source": [
    "print X_train.head(10)\n",
    "print Y_train.head(10)\n",
    "print X_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.57"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression，逻辑回归\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100 , 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.170120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.361900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.283289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.255405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.279689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.713728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.170120\n",
       "5      Title     0.361900\n",
       "4   Embarked     0.283289\n",
       "2        Age     0.255405\n",
       "3       Fare     0.000000\n",
       "6    IsAlone     0.000000\n",
       "7  Age*Class    -0.279689\n",
       "0     Pclass    -0.713728"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各个字段在模型中预测的相关性\n",
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df['Correlation'] = pd.Series(logreg.coef_[0])\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.38"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines \n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100 , 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.82"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn \n",
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.97"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes 贝叶斯分类\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.56"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.01"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear SVC (线性支持向量机)\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.34"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent (随机梯度下降法)\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.18"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree （决策树）\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.18"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest (随机森林)\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>84.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>84.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>81.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>79.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>74.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  84.18\n",
       "8               Decision Tree  84.18\n",
       "0     Support Vector Machines  82.38\n",
       "1                         KNN  81.82\n",
       "2         Logistic Regression  79.57\n",
       "7                  Linear SVC  79.01\n",
       "5                  Perceptron  78.56\n",
       "6  Stochastic Gradient Decent  78.34\n",
       "4                 Naive Bayes  74.97"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对比各个模型的性能\n",
    "models = pd.DataFrame({'Model':['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "\n",
    "models.sort_values(by='Score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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