Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8051597",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77dfc5b6",
   "metadata": {},
   "outputs": [
    {
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       "<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": [
    "# load data\n",
    "titanic_df = pd.read_csv('titanic.csv')\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57b1ffa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2fd55324",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived         Age       SibSp       Parch\n",
       "count  891.000000  714.000000  891.000000  891.000000\n",
       "mean     0.383838   29.699118    0.523008    0.381594\n",
       "std      0.486592   14.526497    1.102743    0.806057\n",
       "min      0.000000    0.420000    0.000000    0.000000\n",
       "25%      0.000000   20.125000    0.000000    0.000000\n",
       "50%      0.000000   28.000000    0.000000    0.000000\n",
       "75%      1.000000   38.000000    1.000000    0.000000\n",
       "max      1.000000   80.000000    8.000000    6.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[[\"Survived\",\"Age\", \"SibSp\", \"Parch\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97294cba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ns/h76j0lmj3f36gyjjdrtlhg1r0000gn/T/ipykernel_54199/1429244643.py:1: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  titanic_df.describe(include=[np.object])\n"
     ]
    },
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       "<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>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>B96 B98</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",
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       "</div>"
      ],
      "text/plain": [
       "                           Name   Sex  Ticket    Cabin Embarked\n",
       "count                       891   891     891      204      889\n",
       "unique                      891     2     681      147        3\n",
       "top     Braund, Mr. Owen Harris  male  347082  B96 B98        S\n",
       "freq                          1   577       7        4      644"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe(include=[np.object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23093564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.551066\n",
       "1    0.242424\n",
       "2    0.206510\n",
       "Name: Pclass, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the distribution of different classes\n",
    "titanic_df.Pclass.value_counts() / len(titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a5ceab",
   "metadata": {},
   "source": [
    "# Deal with missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e04083c7",
   "metadata": {},
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71c5c9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the statistic of age adn then fill in missing values in age \n",
    "titanic_df.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc77462f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.361582\n",
       "std       13.019697\n",
       "min        0.420000\n",
       "25%       22.000000\n",
       "50%       28.000000\n",
       "75%       35.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df=pd.read_csv(\"titanic.csv\")\n",
    "\n",
    "# get median age of all passengers\n",
    "age_median1 = titanic_df.Age.median()\n",
    "\n",
    "# use fillna to fill in，inplace=True means making changes on titanic_df上\n",
    "titanic_df.Age.fillna(age_median1, inplace=True)\n",
    "\n",
    "titanic_df.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba71928d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.441268\n",
       "std       13.018747\n",
       "min        0.420000\n",
       "25%       22.000000\n",
       "50%       29.000000\n",
       "75%       35.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df=pd.read_csv(\"titanic.csv\")\n",
    "\n",
    "age_median2 = titanic_df.groupby('Sex').Age.median()\n",
    "\n",
    "# set sex as index\n",
    "titanic_df.set_index('Sex', inplace=True)\n",
    "\n",
    "titanic_df.Age.fillna(age_median2, inplace=True)\n",
    "# cancel sex as index\n",
    "titanic_df.reset_index(inplace=True)\n",
    "\n",
    "titanic_df.Age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e5230d7",
   "metadata": {},
   "source": [
    "# The factors that will influence survival "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70dc2779",
   "metadata": {},
   "source": [
    "## Classes ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f7ce8f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# ratio of differenct classes \n",
    "titanic_df[['Pclass', 'Survived']].groupby('Pclass').mean().sort_values(by='Survived', ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "00a73656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Survived'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=titanic_df, x='Pclass', y='Survived', ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44dd2dd",
   "metadata": {},
   "source": [
    "### Conclusion 1: 1st class have the most survivals, followed by 2nd class, the 3rd class has the least survivals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d491b2e1",
   "metadata": {},
   "source": [
    "# Sex ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "adf3fbf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the survival ratio of two sex\n",
    "titanic_df[[\"Sex\", \"Survived\"]].groupby('Sex').mean() \\\n",
    "    .sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e33c9b15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='Survived'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=titanic_df, x='Sex', y='Survived', ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e96ea7",
   "metadata": {},
   "source": [
    "### Conclusion 2: All of the survivals, 74% are women while 18% are men."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04941227",
   "metadata": {},
   "source": [
    "# Both class and sex ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75a56bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>0.921053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.157407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Survived\n",
       "Pclass Sex             \n",
       "1      female  0.968085\n",
       "       male    0.368852\n",
       "2      female  0.921053\n",
       "       male    0.157407\n",
       "3      female  0.500000\n",
       "       male    0.135447"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[['Pclass','Sex', 'Survived']].groupby(['Pclass', 'Sex']).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd6bd047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>0.921053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.157407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Survived\n",
       "Pclass Sex             \n",
       "1      female  0.968085\n",
       "       male    0.368852\n",
       "2      female  0.921053\n",
       "       male    0.157407\n",
       "3      female  0.500000\n",
       "       male    0.135447"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or use pivot table \n",
    "titanic_df.pivot_table(values='Survived', index=['Pclass', 'Sex'], aggfunc=np.mean)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ef8e65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Survived'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJElSkxs7diwHHnggO+64IxHBrbfeWuyS6mzYsGFEBAsWLCh2KfXWstgFSJIkCd655uBil7BV3a76S8H6mjBhAsOHD+fwww/noosuonXr1vTr169g/av2DAOSJElqUr/97W83bffYY48iV5Nt3iYkSZKkJvX+++8DGASaAcOAJEmSmsTVV19NRDB58mQAImLTn3JvvPEGw4YNo2vXrrRu3Zrdd9+doUOH8uabb27RX/k9+/Pnz+e2227jwAMPZIcddqB79+78+Mc/JqUEwK9//WuOPPJI2rZtS+fOnbngggtYu3btFv099thjnHHGGfTo0YO2bduy00470bt3b8aOHUtZWVmdPuvLL7/MkCFD6NKlC9tvvz1du3ble9/73qYg1Fx4m5AkSZKaRP/+/QEYN24cCxcuZOTIkZu1P/XUU5xyyils2LCBE044gc9+9rMsWrSIRx55hCeffJLJkyfTq1evLfq97LLLePbZZznhhBMYNGgQjz/+OFdccQXr16+nQ4cOjBgxgpNOOokvfvGL/OEPf+D2229n48aN3HnnnZv1M2LECFq0aEHfvn3Zc889WbFiBc888wzDhw9n+vTp3H///bX6nP/zP//Dd7/7XVq3bs2JJ55I165dmTt3Lvfccw9PPPEEL730Et26davfX2KBGQYkSZLUJPr370///v159tlnWbhwIVdfffWmto8//ph/+Zd/oU2bNjz33HMceOCBm9pef/11+vbty7nnnsvMmTO36HfGjBm89tpr7LnnnkDuCsRnP/tZbrrpJtq0acOMGTPo2bMnAOvWrePwww/n3nvvZdSoUXTu3HlTP08++ST77rvvZn2XlZVx9tlnM378eC644AL69u271c84Z84cvve979G9e3emTJmyqSaAZ555huOOO47hw4fz6KOP1v4vrhF5m5AkSZKKbvz48SxfvpxRo0ZtFgQADjroIL773e8ya9Ys/va3v23x3iuvvHKzk+5ddtmFE088kdWrV/ODH/xgUxAAaN26Naeffjrr169n9uzZm/VTOQgAtGjRguHDhwMwceLEGj/HnXfeyYYNGxgzZsxmNQEMHDiQE088kSeeeIKVK1fW2FdT8MqAJEmSim7atGkAvPrqq5tdMSg3Z84cAGbPnr1FWOjTp88Wx5dPTu7du/cWbeUn6YsWLdps/9KlS7npppv43e9+x9tvv82qVas2a3/vvfdq/TmmTJnC9OnTt2j/8MMP2bhxI3PmzKmytqZmGJAkSVLRLV26FIC77757q8d98sknW+xr3779FvtatmxZY9uGDRs27Vu+fDlHHHEE8+fP58gjj+Sss86iQ4cOtGzZkuXLlzNmzBjWrVtX689x00031flzFENJhoGI2Au4BhgMdAQ+AB4DRqWUPq5DP18DhgMHVuhnBnBLSmlagcuWJElSNcpP2l999VUOOeSQJh//nnvuYf78+YwcOXKLKxPTpk1jzJgxteqn/HOsWLGCnXfeudBlFlzJzRmIiH3JnbCfDbwCjAbeJndSPy0iOtaynxuB3wK9gKeAMcBM4BvAixFxRuGrlyRJUlXKn0D8/PPPF2X8t956C4BTTz11i7YpU6bUup9if466KrkwANwBdAYuTCmdlFIakVIaSC4U7A9cX1MHEdEFuAz4O3BgSuncfD9DgK8AQe7KgyRJkprA2WefzS677MKoUaN45ZVXtmgvKyvj2WefbbTxu3fvDrDFGLNmzeInP/lJrfu54IILaNWqFRdffPGmeQ4VrV+/vlkFhZK6TSgi9gEGAQuA2ys1jwTOA86MiEtTSquo3j+RC0Ivp5Q+rNiQUpocESuB3QpWuCRJkraqY8eOPPTQQ5x88sn069ePY489loMOOogWLVrwzjvvMG3aNJYuXVrlw8IK4ayzzuKmm27ioosuYvLkyey3337MnTuX3/72t5xyyin86le/qlU/BxxwAPfeey/nnHMOBx10EIMHD6ZHjx5s2LCBd955h+eff57ddtuNN954o1E+R12VVBgABua3k1JKmz0GLqW0MiJeJBcW+gF/3Eo/c4H1wJER0SmltKS8ISKOAdqRm4MgSZKkJnLsscfy2muv8V//9V9MnDiR559/nu2335499tiDgQMHVnkLT6HssccePP/884wYMYIXXniBiRMncsABB3DHHXfw5S9/udZhAOCMM87g0EMP5eabb2by5MlMmjSJtm3bssceezBkyBBOP/30RvscdRXlj2kuBRFxE7nbey5LKd1cRfttwPnAD1NKd1Zur3TsRcAtwBJyJ/5LgX2BE4HngDMqXzWopp8Z1TQd0KtXrzYzZlTXrObunWsOLnYJ27xuV/2l2CWoGfK71/j87jW98vXsK653L9Wktv9uevfuzcyZM2emlOq8VmmpXRkoXxtqRTXt5ft3qamjlNKtEbEAuBf4boWmt4BxtQkCkiRJUikrxQnEWxP5bY2XOyLiR8BDwDhyVwTaAr3JrUz0i4j4f7UZMKXUu6o/QPO4EUySJEmqRqmFgfJf/rd8ekTOzpWOq1JE9AduBB5PKV2SUno7pbQ6pTQTOBl4D7g0P2FZkiRJ2iaVWhh4M7/tUU37fvntlus4be7r+e3kyg0ppdXknl/QAji8rgVKkiRJpaLUwkD5yfugiNis9ohoBxwFrAFeqqGf1vltdcuHlu9fX58iJUmSpFJQUmEgpTQPmAR0J7dqUEWjyN33P778GQMR0SoiDsg/tbii8ic9nBcRe1ZsiIjjyYWKtcDUwn4CSZIkqfkotdWEAH5I7iR9bEQcC8wG+gIDyN0edEWFY/fMty8kFyDKPQQ8DXwZmB0RjwKLgZ7kbiEKYERKaWmjfhJJkiSpCk21/H/JhYGU0ryI6ANcAwwGvgp8AIwFRqWUltWij7KI+Cq5qwvfJDdpuA2wDPgdMDalNKmRPoIkScqYiCClRFlZGS1alNSNGSqS8jAQETUc2TAlFwYAUkrvAmfX4rgF/N9yo5XbNgC35v9IkiQ1mtatW7N27VpWrVpFu3btil2OSsCqVauA3L+dxmQ0lSRJamTlAWDx4sWsXLmSsrKyJrsNRKWj/OrRypUrWbx4MUCjh8eSvDIgSZJUSjp06MCqVatYvXo1ixYtKnY5KhFt2rShQ4cOjTqGYUCSJKmRtWjRgq5du7Js2TJWrlzJunXrvDKgKkUErVu3pl27dnTo0KHR55gYBiRJkppAixYt6NSpE506dSp2KdImzhmQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKqJIMAxGxV0TcGxHvR8S6iFgQEbdGxK716OuLEfFwRHyQ7+uDiJgUEV9tjNolSZKk5qJlsQuoq4jYF5gKdAZ+A7wBHAkMBwZHxFEppaW17Os/gWuBJcBvgQ+ATsDhQH/gd4WuX5IkSWouSi4MAHeQCwIXppR+Wr4zIm4BLgauB75fUycR8c/kgsDTwCkppZWV2lsVsmhJkiSpuSmp24QiYh9gELAAuL1S80hgFXBmRLStoZ8WwI3AamBo5SAAkFLaUIiaJUmSpOaqpMIAMDC/nZRSKqvYkD+hfxFoA/SroZ8vAHuTuw3o44j4WkT8W0QMj4jPF7poSZIkqTlq0G1CEXFWfd+bUhpfj7ftn9/OqaZ9LrkrBz2AP26lnyPy278DM4GDKzZGxHPAkJTSRzUVFBEzqmk6oKb3SpIkScXU0DkD44BU4XVUel2V8mPqEwba57crqmkv379LDf10zm+/D8wHvgy8DPwTcDPwFeDX5CYRS5IkSdukhoaBs6vYdwpwAjAFeBZYDHQBBgDHAI8DjzZw3OpEfltTINmuwvFDUkqv5l+/HhEnk7vy8KWI+HxKadrWOkop9a6ykNwVg161K1uSJElqeg0KAyml+yq+zq/NPxj4RkrpiUqHj4qIbwD/C9xVzyHLf/lvX037zpWOq87H+e3bFYIAACmlNRExEfgOuSVLtxoGJEmSpFJV6AnEVwCPVhEEAEgp/QZ4DLiynv2/md/2qKZ9v/y2ujkFlftZXk17eVjYsXZlSZIkSaWn0GHgUOCtGo55Cziknv1Pzm8H5ZcH3SQi2gFHAWuAl2ro5zngU2C/iNi+ivbP5bcL6lmnJEmS1OwVOgysJxcItuZQoF5r+KeU5gGTgO7A+ZWaRwFtgfEppVWQe3BYRByQf2pxxX6WAL8id7vRVRXbIuI4chOIVwBP1adOSZIkqRQU+gnEfwROiYgLgNtTSpsm8kZEABcAxwMPN2CMHwJTgbERcSwwG+hLboLyHHK3KpXbM9++kFyAqOiS/PuuiIhjgFfIrSZ0MrAR+G5KaXkD6pQkSZKatUKHgRHkTsrHABdFxAvk1vLfHTia3IO+luWPq5eU0ryI6ANcQ26y8leBD4CxwKiU0rJa9vNhRPQF/pNcAOgHrASeBH6SUqrpViNJkiSppBU0DORP1PsBd5Bbu3+fSof8ATg/pfR2A8d5l6qXNa183AL+b7nRqtqXkbtCcElD6pEkSZJKUaGvDJBSeovcBN89gcPJ3Ze/ApiVUnqv0ONJkiRJqp+Ch4Fy+RN/T/4lSZKkZqrRwkBEHAD0BHZKKd3fWONIkiRJqp9CLy1KRBwWEX8CXgceAsZVaPtSRKyOiBMKPa4kSZKkuiloGIiIHsCzwP7kVhT6faVDniO3mtCQQo4rSZIkqe4KfWVgJLA9cGRK6RJgesXG/HMHpgFHFHhcSZIkSXVU6DBwLPBISmn2Vo55B9ijwONKkiRJqqNCh4FdgEW1GHP7Ao8rSZIkqY4KHQY+BD5bwzEHAe8WeFxJkiRJdVToMPAMcEJE7F9VY0QcQe5WookFHleSJElSHRU6DPwE+BR4LiJ+QH5uQEQclH/9BLAS+K8CjytJkiSpjgr60LGU0psRcSrwIHBbfncAr+W3y4FTUkrvFHJcSZIkSXVX8CcQp5Seioi9gW8D/YCOwArgJeB/UkrLCj2mJEmSpLoreBgASCktJ/fQsTGN0b8kSZKkhiv0E4iPj4hCz0OQJEmS1AgKfeL+JPBuRPy/iPhcgfuWJEmSVECFDgP/DewAXAa8GhHTI+KCiOhY4HEkSZIkNVBBw0BK6fvAZ4DTgd8Dh5KbN/BeRDwSESdGRKPMU5AkSZJUNwW/vz+ltD6l9OuU0teBvYDLgTeBk4BHgfcj4tZCjytJkiSpbhp1sm9K6cOU0i0ppUOBw4GxQHvgXxtzXEmSJEk1a5KVfyKiB3AacArQqinGlCRJkrR1jXb/fkTsAnyT3MPHjiT3BOJ/AD8HxjXWuJIkSZJqp6BhIP+MgePJBYATgO2BBPyRXAB4JKW0tpBjSpIkSaqfQl8ZeB/YjdxVgDnAfcD4lNJ7BR5HkiRJUgMVOgzsANwNjEspvVTgviVJkiQVUKHDwO4ppXUF7lOSJElSIyj0Q8cMApIkSVKJaNCVgYg4K/8/H00prazwukYppfENGVuSJElSwzT0NqFx5FYLeglYWeH11kT+GMOAJEmSVEQNDQPnkDux/yD/+uwG9idJkiSpiTQoDKSUxlV6fV+DqpEkSZLUZAo6gTj/0DFJkiRJJaDQJ++LIuLGiDiowP1KkiRJKrBCh4EdgcuB1yJiekScHxEdCzyGJEmSpAIo+EPHgJOAbwODgLHAzRHxJHAf8GRKaWOBx5QkSVIDvXPNwcUuYZvX7aq/FLuELRT6oWPrU0r/m1L6GrAX8CNgDnAy8CjwQUSMjojDCzmuJEmSpLprtAm/KaW/p5RuTikdAvQGfgqUAcOB6Y01riRJkqTaKfRtQlVKKc2KiE+AdcBFTTWuJEmSpOo16kl5RLQHvkluDkHf/O6VwK8bc1xJkiRJNSt4GMg/a2AwuQBwAtCa3FOK/wiMAx5JKa0t9LiSJEmS6qagYSAi/gv4FtAZCHKTh+8D7k8pLSrkWJIkSZIaptBXBi4BVgB3A/ellKYVuH9JkiRJBVLoMDAUeDSltK7A/UqSJEkqsEIvLXoecEWB+5QkSZLUCAodBvrhsqGSJElSSSh0GJgLdC1wn5IkSZIaQaHDwD3A1yKiW4H7lSRJklRghb6l5wngOODFiLgRmA4sJvecgc2klN4p8NiSJEmS6qDQYeBtcif+AYzZynGpEcaWJEmSVAeFPiEfTxVXASRJkiQ1PwUNAymlYYXsT5IkSVLjKfQEYkmSJEklwjAgSZIkZVRBbxOKiHtreWhKKX2nkGNLkiRJqptCTyAeVkN7+UpDCTAMSJIkSUVU6DCwdzX7dwGOAK4EpgIjCjyuJEmSpDoq9GpCC6tpWgi8GhETgdeAp4GfF3JsSZIkSXXTpBOIU0rvkntK8fCmHFeSJEnSloqxmtDfgf2KMK4kSZKkCpo0DETEdsBAYEVTjitJkiRpS4VeWvSYrYzTFTgbOAy4p5DjSpIkSaq7Qq8m9Cy5ZUOrE8BzwOUFHleSJElSHRU6DFxD1WGgDPgYeCWl9EqBx5QkSZJUD4VeWvTqQvYnSZIkqfEU+srAFiLiRHKThgOYklJ6pLHHlCRJklSzBq8mFBEnRMRzEfGlKtrGAY8CFwL/Cvw6Ih5u6JiSJEmSGq4QS4ueCPQCXq64MyK+DpwFrAauA/4NeBs4KSL+pQDjSpIkSWqAQtwmdCQwLaW0ttL+c8hNJj47pfQQQETcD8wDvgU8WICxJUmSJNVTIa4MdCF3gl/ZMcByYNNtQSmlxcCTwOEFGFeSJElSAxQiDOwKLKu4IyK6AR2AF1JKlZcanQ90LMC4kiRJkhqgEGFgJbBXpX2989tZ1byn8i1FdRIRe0XEvRHxfkSsi4gFEXFrROzagD7PjIiU/3NuQ+qTJEmSSkEhwsBfgK9FxE4V9p1Mbr7AC1UcvzfwQX0Hi4h9gRnA2cArwGhyE5OHA9Mios5XHSKiK/BT4JP61iVJkiSVmkKEgV+Qu1VoSkRcGBG3kZsgvBiYXPHAiAjgaOBvDRjvDqAzcGFK6aSU0oiU0kByoWB/4Pq6dJav6X+ApcBdDahLkiRJKimFCAM/ByaSmxQ8Gvgh8CkwPKW0sdKxx5KbcPx0fQaKiH2AQcAC4PZKzSOBVcCZEdG2Dt1eSO6haGfn3y9JkiRlQoPDQEqpDPgacCa5X9avA/qWLydaSSdgDPB4PYcbmN9Oyo9bsY6VwItAG6BfbTqLiJ7ADcCYlNJz9axJkiRJKkmFeM5AeSD4Rf7P1o6bAExowFD757dzqmmfS+7KQQ/gj1vrKCJaAvcD7wD/Ud+CImJGNU0H1LdPSZIkqSkUJAw0ofb57Ypq2sv371KLvq4id2vT0SmlNQ2sS5IkSSo5pRYGahL5beVnG2x+UMSR5K4G3JxSmtaQAVNKvavan79i0KshfUuSJEmNqRATiJtS+S//7atp37nScVuocHvQHODKwpUmSZIklZZSCwNv5rc9qmnfL7+tbk4BwE759/cE1lZ40FgityIRwN35fbc2tGBJkiSpuSq124TKn1swKCJaVFxRKCLaAUcBa4CXttLHOnLLoValF7l5BC+QCx4NuoVIkiRJas5KKgyklOZFxCRyKwadT+6pweVGAW2Bn6WUVgFERCtgX2BDSmlevo81wLlV9R8RV5MLA/ellO5prM8hSZIkNQclFQbyfghMBcZGxLHAbKAvMIDc7UFXVDh2z3z7QqB705YpSZIkNW+lNmeA/C/8fYBx5ELApeR+/R8LfD6ltLR41UmSJEmloxSvDJBSehc4uxbHLeD/lhutTb9XA1fXty5JkiSplJTclQFJkiRJhWEYkCRJkjLKMCBJkiRllGFAkiRJyijDgCRJkpRRJbmaUNb1vnx8sUvIhEfbFbsCSZKkxuWVAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjSjIMRMReEXFvRLwfEesiYkFE3BoRu9by/R0j4tyIeDQi3oqINRGxIiJeiIjvRERJ/r1IkiRJddGy2AXUVUTsC0wFOgO/Ad4AjgSGA4Mj4qiU0tIauvln4E7gA2Ay8A6wO3AKcA9wfET8c0opNc6nkCRJkoqv5MIAcAe5IHBhSumn5Tsj4hbgYuB64Ps19DEHOBF4MqVUVqGP/wBeAU4lFwweLmzpkiRJUvNRUrfDRMQ+wCBgAXB7peaRwCrgzIhou7V+UkrPpJSeqBgE8vsXA3flX/YvRM2SJElSc1VSYQAYmN9OquJEfiXwItAG6NeAMTbkt582oA9JkiSp2Su124T2z2/nVNM+l9yVgx7AH+vaeUS0BM7Kv3yqlu+ZUU3TAXUdX5IkSWpKpXZloH1+u6Ka9vL9u9Sz/xuAzwG/SylNrGcfkiRJUkkotSsDNYn8ts6rAEXEhcCl5FYnOrO270sp9a6mvxlAr7rWIUmSJDWVUrsyUP7Lf/tq2neudFytRMT5wBjgb8CAlNKy+pUnSZIklY5SCwNv5rc9qmnfL7+tbk7BFiLiIuA24K/kgsDielcnSZIklZBSCwOT89tBlZ8SHBHtgKOANcBLteksIv4NGA38mVwQ+LBwpUqSJEnNW0mFgZTSPGAS0B04v1LzKKAtMD6ltAogIlpFxAH5pxZvJiKuJDdheAZwbEppSWPWLkmSJDU3pTiB+IfAVGBsRBwLzAb6AgPI3R50RYVj98y3LyQXIACIiG8D1wAbgeeBCyOCShaklMY1yieQJEmSmoGSCwMppXkR0Yfcyfxg4KvAB8BYYFQtJ//und9uB1xUzTFTgHENKlaSJElqxkouDACklN4Fzq7FcQv4v+VGK+6/Gri60HVJkiRJpaSk5gxIkiRJKhzDgCRJkpRRhgFJkiQpowwDkiRJUkYZBiRJkqSMMgxIkiRJGWUYkCRJkjLKMCBJkiRllGFAkiRJyqiSfAKxJBVL78vHF7uEbd6j7YpdgSRlh1cGJEmSpIwyDEiSJEkZZRiQJEmSMsowIEmSJGWUYUCSJEnKKMOAJEmSlFGGAUmSJCmjDAOSJElSRhkGJEmSpIzyCcSSJKnZ8+nfjc+nf2eTVwYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRlVkmEgIvaKiHsj4v2IWBcRCyLi1ojYtRj9SJIkSaWoZbELqKuI2BeYCnQGfgO8ARwJDAcGR8RRKaWlTdWPJEmSVKpK8crAHeRO4C9MKZ2UUhqRUhoIjAb2B65v4n4kSZKkklRSYSAi9gEGAQuA2ys1jwRWAWdGRNum6EeSJEkqZSUVBoCB+e2klFJZxYaU0krgRaAN0K+J+pEkSZJKVqmFgf3z2znVtM/Nb3s0UT+SJElSySq1CcTt89sV1bSX79+lifohImZU03To7Nmz6d27d01d1Nns95zX3BS+1uLvxS5hm7f9bwr//Whsfv8an9+9xud3T1Xxu9f4Guu7N3v2bIDu9XlvqYWBmkR+m5pBPxvXrFmzYubMmQsaWIuK44C/5rZvFLeMbdwHM4tdgZofv3tNwe+etuR3ryk03nevO/CP+ryx1MJA+S/27atp37nScY3dDyml0vt5RTUqv+Lj/79S0/K7JxWH373sKrU5A2/mt9Xdy79fflvdXIBC9yNJkiSVrFILA5Pz20ERsVntEdEOOApYA7zURP1IkiRJJaukwkBKaR4widx9UedXah4FtAXGp5RWAUREq4g4IP+04Xr3I0mSJG2LIqWGzrVtWvkT+6nknh78G2A20BcYQO62ni+klJbmj+0OzAcWppS617cfZY/3TkrF4XdPKg6/e9lVUlcGYNOv+n2AceRO3i8F9gXGAp+v7Ql8ofqRJEmSSlXJXRmQJEmSVBgld2VAkiRJUmEYBiRJkqSMMgxIkiRJGWUYkCRJkjLKMCBJkiRllGFAkiRJyijDgCRJkpRRhgEpLyKGRMRPI+L5iPhHRKSIeKDYdUnbsojoGBHnRsSjEfFWRKyJiBUR8UJEfCci/O+U1Egi4saI+GNEvJv/7i2LiFkRMTIiOha7PjUNHzom5UXEn4FDgU+ARcABwC9SSmcUsy5pWxYR3wfuBD4AJgPvALsDpwDtgYeBf07+x0oquIhYD8wE/gZ8CLQF+gF9gPeBfimld4tXoZqCYUDKi4gB5ELAW8CXyJ2YGAakRhQRA8mdgDyZUiqrsL8L8ArQFRiSUnq4SCVK26yI2CGltLaK/dcD/wHcmVL6YdNXpqbk5VcpL6U0OaU0118gpaaTUnompfRExSCQ378YuCv/sn+TFyZlQFVBIO9/89v9mqoWFY9hQJLUXG3Ibz8tahVS9pyQ375W1CrUJFoWuwBJkiqLiJbAWfmXTxWzFmlbFxGXATuRm6fTBziaXBC4oZh1qWkYBiRJzdENwOeA36WUJha7GGkbdxm5ifvlngKGpZQ+KlI9akLeJiRJalYi4kLgUuAN4MwilyNt81JKXVJKAXQht5LXPsCsiOhV3MrUFAwDkqRmIyLOB8aQW+pwQEppWZFLkjIjpfT3lNKjwCCgIzC+yCWpCRgGJEnNQkRcBNwG/JVcEFhc3IqkbEopLSQXyA+KiE7FrkeNyzAgSSq6iPg3YDTwZ3JB4MPiViRl3h757caiVqFGZxiQJBVVRFxJbsLwDODYlNKSIpckbfMi4oD8w/0q72+Rf+hYZ2BqSunjpq9OTcknEEt5EXEScFL+ZRfgK8DbwPP5fUtSSpc1fWXStisivg2MI/fr40+BFVUctiClNK4Jy5K2efnb8m4CngPmAUvJrSj0JXITiBeTC+d/K1aNahqGASkvIq4GRm7lkIUppe5NU42UDbX43gFMSSn1b/xqpOyIiM8BPwCOAvYCdgFWAXOAJ4GxTuDPBsOAJEmSlFHOGZAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkqclFRPeISBExrti1SFKWGQYkSdXKn7BX/LMxIpZExDMR8a1i1ydJapiWxS5AklQSRuW3rYD9gZOAARHRO6V0SdGqkiQ1SKSUil2DJKmZiogEkFKKSvuPBf6Qf7lPSmlBHfvtDswH7kspDWtwoZKkevE2IUlSnaWU/gi8AQRwRMW2iDgyIn4VEe9FxLqI+CAiJkXEaTX1GxE9IuKGiPhTRHyUf//CiPjviNiriuMjIr4dEVPzx6+NiHcjYmJEnF7p2EMi4sGIWJDv96OImBkRt0ZEqwb+lUhSSfI2IUlSfZVfLdh0iTkivgvcCWwEHgfmAp2BPsAPgf+toc9TgO8Dk4GpwHrgIOBc4ISI6JNSeq/C8dcD/07uKsP/AiuAz5ALKP8M/Cpf1yHAy/laH88fvzPw2Xxd/wlsqOPnl6SSZxiQJNVZRHyZ3NyBBEzP7zsQuAP4B/DFlNLrld6zxS/7VbgfGJ1SWlfpvYOA35M7af9BhabvAe8Bn0spra70nk4VXn4b2AE4KaX0m0rH7Qps9l5JygrDgCSpRhFxdf5/VpxAHORO3Bfm235A7r8r11YOAgAppUU1jVPpV/+K+ydFxOvAV6po3kDuSkTl9yyp4tg1VRz3cU11SdK2yjAgSaqNkfltApYDzwM/Tyk9UOGYfvnt7+s7SEQE8C1gGHAosCuwXYVD1ld6yy+AfwVej4hfA1OAaSmlFZWO+xUwHHgsIh4CngZeTCnNq2+tkrQtcDUhSVK1qltNqJpj55K7B3/nlNLKGo7tThWrCUXEaOAi4APgGXK3AJX/mj8M+KeKtUTEdsAFwDnAIfndnwK/Ay5NKb1V4djPA1cAA4Ed87vfBEallB6s6fNJ0rbIMCBJqlYdw8B0chOFe6aU3qjh2O5UCgMR0ZlcCPgb8IXKgSIi3gR6VFdL/v1HA98kN3l4HnBQFfMPWgO9gcHkrirsAhyXUnq6ps8oSdsalxaVJBXKS/nt8fV8/z7k/rs0qYogsFe+vVoppQ9TSo+klE4jd1VhX+BzVRy3LqU0NaV0FXBhfvc36lmzJJU0w4AkqVDuJHeLzpX5lYU2U4vVhBbkt0fnb/8pf99OwN1UmucWEa0j4tj8PIOK+1sBHfIvV+f3fTEi2lcx5u4Vj5OkrHECsSSpIFJKf4uIHwJ3AbMi4jfknjPQkdztQyuBAVt5/+KImEDuNp8/R8QkoD1wHLAW+DNwWIW37EhuIvCCiHgZWEhu+dDjgJ7A4yml2fljLwUGRcSzwNvAJ+SeX3A88DHw3w38+JJUkgwDkqSCSSndHRF/BS4D+pNbgnQJ8BpwTy26+A65k/XTgfOBj8g9JOwq4OFKx64C/o1cwPhCfqyV5OYK/AC4t8Kxd5A76e8LHEXuv3+L8vtvrrA8qiRlihOIJUmSpIxyzoAkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUYYBSZIkKaMMA5IkSVJGGQYkSZKkjDIMSJIkSRllGJAkSZIyyjAgSZIkZZRhQJIkScoow4AkSZKUUf8f9HsmEy+cSMkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=titanic_df, x='Pclass', y='Survived', hue='Sex', ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a43fcfb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Survived'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(data=titanic_df, x='Pclass', y='Survived', hue='Sex', ci=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5af61d",
   "metadata": {},
   "source": [
    "# age?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe036030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fab66f15820>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use FacetGrid().map()\n",
    "sns.FacetGrid(titanic_df, col='Survived').map(plt.hist, 'Age', bins=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9340c73f",
   "metadata": {},
   "source": [
    "# Conclusions: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9d849f",
   "metadata": {},
   "source": [
    "- 1. In every classes, female has more survivals than male.\n",
    "- 2. The survivals of female in class 1 and 2 are very close. \n",
    "- 3. The survivals of male in class 1 are more than men in class 2 and 3.\n",
    "- 4. The survivals of male in class 2 and 3 are very close.\n",
    "- 5. Children have more chances to survive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd216d7e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "823d37aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}