video_24-How do I create dummy variables in pandas?.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How do I create dummy variables in pandas?\n",
    "https://www.youtube.com/watch?v=oH3wYKvwpJ8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"http://bit.ly/kaggletrain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
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      "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  "
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# birinci yöntem yeni bir alana category map gibi maplemektir. \n",
    "train['Sex_male'] = train.Sex.map({'female':0, 'male':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "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  Sex_male  \n",
       "0      0         A/5 21171   7.2500   NaN        S         1  \n",
       "1      0          PC 17599  71.2833   C85        C         0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S         0  \n",
       "3      0            113803  53.1000  C123        S         0  \n",
       "4      0            373450   8.0500   NaN        S         1  "
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tekrar orjinal datayı çağırıyoruz\n",
    "train = pd.read_csv(\"http://bit.ly/kaggletrain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>25</th>\n",
       "      <td>0</td>\n",
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       "      <th>26</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
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       "      <th>29</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>865</th>\n",
       "      <td>0</td>\n",
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       "      <th>866</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>869</th>\n",
       "      <td>1</td>\n",
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       "      <th>870</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>875</th>\n",
       "      <td>0</td>\n",
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       "      <th>876</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>891 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex_male\n",
       "0           1\n",
       "1           0\n",
       "2           0\n",
       "3           0\n",
       "4           1\n",
       "5           1\n",
       "6           1\n",
       "7           1\n",
       "8           0\n",
       "9           0\n",
       "10          0\n",
       "11          0\n",
       "12          1\n",
       "13          1\n",
       "14          0\n",
       "15          0\n",
       "16          1\n",
       "17          1\n",
       "18          0\n",
       "19          0\n",
       "20          1\n",
       "21          1\n",
       "22          0\n",
       "23          1\n",
       "24          0\n",
       "25          0\n",
       "26          1\n",
       "27          1\n",
       "28          0\n",
       "29          1\n",
       "..        ...\n",
       "861         1\n",
       "862         0\n",
       "863         0\n",
       "864         1\n",
       "865         0\n",
       "866         0\n",
       "867         1\n",
       "868         1\n",
       "869         1\n",
       "870         1\n",
       "871         0\n",
       "872         1\n",
       "873         1\n",
       "874         0\n",
       "875         0\n",
       "876         1\n",
       "877         1\n",
       "878         1\n",
       "879         0\n",
       "880         0\n",
       "881         1\n",
       "882         0\n",
       "883         1\n",
       "884         1\n",
       "885         0\n",
       "886         1\n",
       "887         0\n",
       "888         0\n",
       "889         1\n",
       "890         1\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#diğer get_dummies() metodudur. \n",
    "pd.get_dummies(train.Sex,prefix='Sex').iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# başka birinde deneyelim.\n",
    "train.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eğer n tane category varsa n-1 tane dummied variable olmak zorundadır. \n",
    "# budurumda 1den sonraki columnları alıyoruz.\n",
    "# yeni bir df yapıyoruz.\n",
    "embarked_dummies = pd.get_dummies(train.Embarked, prefix='Embarked').iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# orjinal df le yer değiştiriyoruz.\n",
    "train = pd.concat([train,embarked_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <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",
       "      <td>0</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            Ticket     Fare Cabin Embarked  Embarked_Q  Embarked_S  \n",
       "0      0         A/5 21171   7.2500   NaN        S           0           1  \n",
       "1      0          PC 17599  71.2833   C85        C           0           0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S           0           1  \n",
       "3      0            113803  53.1000  C123        S           0           1  \n",
       "4      0            373450   8.0500   NaN        S           0           1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# yeni alanların eklendiğini görürüz. \n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aynısı sex alanı içinde yapalım.\n",
    "train.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sex_dummies = pd.get_dummies(train.Sex, prefix='Sex').iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# orjinal df le yer değiştiriyoruz.\n",
    "train = pd.concat([train,sex_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "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  Embarked_Q  Embarked_S  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S           0           1   \n",
       "1      0          PC 17599  71.2833   C85        C           0           0   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S           0           1   \n",
       "3      0            113803  53.1000  C123        S           0           1   \n",
       "4      0            373450   8.0500   NaN        S           0           1   \n",
       "\n",
       "   Sex_male  \n",
       "0         1  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sex dummy variable ları ekledik. \n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tekrar orjinal datayı çağırıyoruz\n",
    "train = pd.read_csv(\"http://bit.ly/kaggletrain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#orjinal alanların gittiğini yerine dummyied alanların geldiğini görürüz.\n",
    "# drop_first ise ilk dummy yi kaldırır. bu sayede manual iş yapmamış oluruz.\n",
    "train = pd.get_dummies(train,columns=['Sex','Embarked'], drop_first=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</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>1</td>\n",
       "      <td>0</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   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Sex_male  Embarked_Q  Embarked_S  \n",
       "0         A/5 21171   7.2500   NaN         1           0           1  \n",
       "1          PC 17599  71.2833   C85         0           0           0  \n",
       "2  STON/O2. 3101282   7.9250   NaN         0           0           1  \n",
       "3            113803  53.1000  C123         0           0           1  \n",
       "4            373450   8.0500   NaN         1           0           1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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