Learn_Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AQeBWs5gQ66pZHNKbmkVEJEIlAAABRM0izIpQCQBAgFCzCDMjVAIA4CdqFgFCJQAAfqFm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\nKgEAAOA3QiUAAAD89v8D4rWYFoJdpN8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce5b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.plot(np.arange(10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JkiWecx181bVrV0VFRWnQoEF68cUXy11rWev8+te/liQtXrxYqampql+/vnr16qUxY8b4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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce2b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "g = sns.FacetGrid(tips, col=\"time\", row=\"smoker\")\n",
    "g = g.map(plt.hist, \"total_bill\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train_df = pd.read_csv('D:\\soft\\python_tools\\kaggle\\Titanic\\\\train.csv')\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hhx8qICBAkyZN0sSJE10ZAmh1uO0bAFoWlxZHTzzxhPLz85Wenq6ioiI99NBD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d735fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col=\"Survived\")\n",
    "g = g.map(plt.hist, \"Age\",bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xXX755YqNjVVYWJiSk5MlSevXr9cVV1yhsWPHqmvXrpozZ44OHTpU5ZUnAACAunbG4Wjb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\nAGBBOAIAALAgHAEAAFgQjgAAACz+P7+2C52ROm8rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf00cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col=\"Sex\")\n",
    "g = g.map(plt.hist, \"Age\")   #bins=20这个参数控制柱状的数目\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EIwAAADuEIwAAADuEIwAAADuEIwAAADt8Wg0AALiUxWJRQUGBS/cRHh7utG0R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\nh3AEAABgh3AEAABg5/8D3rpHz8RcLbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23061f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 是否获救与船舱等级的关系\n",
    "g = sns.FacetGrid(train_df, col=\"Survived\")\n",
    "g = g.map(plt.hist, \"Pclass\")   #bins=20这个参数控制柱状的数目\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot convert NA to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-4de1a90ca884>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# 是否获救与登录港口的关系\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Embarked'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'S'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m        \u001b[1;31m#填充空值\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Embarked'\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'Embarked'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'S'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'C'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Q'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m}\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;31m#把字母类型特征转换为数字\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, raise_on_error, **kwargs)\u001b[0m\n\u001b[1;32m   2948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2949\u001b[0m         mgr = self._data.astype(dtype=dtype, copy=copy,\n\u001b[0;32m-> 2950\u001b[0;31m                                 raise_on_error=raise_on_error, **kwargs)\n\u001b[0m\u001b[1;32m   2951\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2952\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;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   2936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2937\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2938\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'astype'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2939\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2940\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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\\internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, raw, **kwargs)\u001b[0m\n\u001b[1;32m   2888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2889\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mgr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2890\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2891\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2892\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;36mastype\u001b[0;34m(self, dtype, copy, raise_on_error, values, **kwargs)\u001b[0m\n\u001b[1;32m    432\u001b[0m                **kwargs):\n\u001b[1;32m    433\u001b[0m         return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,\n\u001b[0;32m--> 434\u001b[0;31m                             values=values, **kwargs)\n\u001b[0m\u001b[1;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m     def _astype(self, dtype, copy=False, raise_on_error=True, values=None,\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, raise_on_error, values, klass, mgr, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m                 \u001b[1;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_astype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mD:\\Anaconda2\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy)\u001b[0m\n\u001b[1;32m   1912\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1913\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1914\u001b[0;31m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot convert NA to integer'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1915\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobject_\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1916\u001b[0m         \u001b[1;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot convert NA to integer"
     ]
    }
   ],
   "source": [
    "# 是否获救与登录港口的关系\n",
    "train_df['Embarked'].fillna('S',inplace=True)        #填充空值\n",
    "train_df['Embarked'] = train_df['Embarked'].map({'S':0,'C':1,'Q':2}).astype(int)  #把字母类型特征转换为数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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37dsn1p85cwaRkZEICAhAdHQ0ysvLLTMLIiIiIgsx6/EhgiAgJiYG/v7+OHr0KMrKyvDa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\nn5ovFb5BvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3969e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col=\"Survived\")\n",
    "g = g.map(plt.hist, \"Embarked\")   #bins=20这个参数控制柱状的数目\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}