titanic/titanic-project.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created by [SmirkyGraphs](http://smirkygraphs.github.io/). Source: [GitHub](https://github.com/SmirkyGraphs/Python-Notebooks).\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Titanic Survivors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting basic info on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previewing the Data\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "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": [
    "# Overall general info\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the data types of each column\n",
    "titanic_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 891 rows ~100 missing Age and ~700 missing Cabin information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try and answer some important questions like\n",
    "* Who was on the titanic\n",
    "* Who survived the titanic\n",
    "* What factors may have lead to their survival"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who was on the titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50ba0df208>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x50ba0df978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphing by Gender\n",
    "sns.factorplot('Sex',data=titanic_df,kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50ba0dfef0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZkgpjMEtSYQxmSSqMwSxJhTGYJakwBrMkFcZglqTC7JAPmOhP50M6UntsMUtSYRptMUfE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50ba1451d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphing by class\n",
    "sns.factorplot('Pclass',data=titanic_df,hue='Sex',kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets find out how many children were onboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def male_female_child(passenger):\n",
    "    age,sex = passenger\n",
    "    if age < 16:\n",
    "        return 'child'\n",
    "    else:\n",
    "        return sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df['person'] = titanic_df[['Age','Sex']].apply(male_female_child,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50ba0df9e8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dJ0055eVt1WUfsyRVxmCWpMoYzJJUGYNZkipjMEtSZVodlRERhwPvzszFEfFbwCrKAwnX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\ng1mSKmMwS1Jl/g+PxmTY5k18qgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50ba230b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=titanic_df,hue='person',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surprisingly most of the children are located in 3rd class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x50ba2422b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d+74feGrKuQC+BHw2Ir5Jb+P9SJNn2s/ZWrrwWn4GOBgRL9HbI2EP8KNp58rMr0bE7cA/\n0hvo7aW3J8y0n69VAfxL33IXXss/Aw5ExHF6I+5HgJcnkcvD4yWpoK5Om0iSBrC8Jakgy1uSCrK8\nJakgy1uSCrK8Jakgy1uSCvpfyrtXGGP+t9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50ba426da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphing age\n",
    "titanic_df['Age'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "Name: person, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['person'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "83 people under the age 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bf86af98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JcuTMDjq0jpygh/9w1xpO6AHePNPLN356lltULv/xHkWGTwbUEkIIIW4m82mC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\n5iMxMZ/lvgigtZ4GvniZov8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KtholRIdCtPXEOdcyzLmWYb793HnyMj1srslhU00OtSVB\n6Ya7jHJzAyv63Gs1kpiYj4kvAvwh8GNgADimtf6mUqoW+DYzvVcrgDuBXmAE2An8MdCilAoB64A/\nnU1QPwt8npkk9T0PM5NonldK/R7wF8Bfzadi80k6DwEPAI/N3tN5Zk5ZAzPZdBYQZqZr7ZevsK53\nlVL7tdYHgfuA1+ZTSbMzDIOx+Dhd4R66JnvpDvfQHe5lMDqMgfG+ZW0WK0FXkCJ/AaHZBDPTFSTk\nCuK1e+RKL3CxbYrnDw4TjadRVS62bPBilX8XIYQwNZvFRq4rn1xXPmRsIZaK0h/vZSDey0C8j4bJ\n08BpLG4L+XWF3OqswJ0oorPFQntfhJeOd/LS8U68Ljsbq7PZXJvDhqpsPC65h18IsfLMtkheqVVy\nKW0A7lRKfWb2ddbs47DWugNAKRXRWp+ffT4OuIFu4L8ppaLMNBBOfGC9dcA3lFIADuDifCs0nyP5\nU8DdSqnDzDSlfn428/Vrrb+llPqvwIuAlZnRa7uvsK4/BR5WSjmZSVh/Mt+Kmkk4EaFtvIPW8XZa\nJjromuxmKhl93zIum4tifyHZ7kwy3aGZBNMVJOD0Y7XIFdzLSSTTvPbWKO+cD2OzwvZNXmorpSuc\nEEKsRG6bh3JvFeXeKgwjzVhilP54D/3xPvrnjIjrKnGzpqYCX7KQyEAm7V0Jjpzv58j5fmxWC2vL\nQmyuzWVzTQ7ZQflOEEKIeWgEfqC1/qFSKg/4wuz7xhU+AzNdZ///9u48SNLzoO/49z367fuc6bl2\n9tLu6pFk2bJiOfi2qoK5UsaupFKpACGYEAeKIhBIOYkDJCQhlVRhSOGUIRiEbY5AIBHBJmASYxtL\nliPZloWO3WdX0t67c589M32/+ePt6ZnZnV3tyjvTrZnfp6r1Xv12P72Pprt//Rzv91prTxpjfo6o\nZXQzC3y/tfaCMebtwOitFugVQ2fnkiY/fM3uU5uOfxr49A3OPQe8ZdP2aeDdt1q4fhCGIVdXJjsB\n8zxnF88zuTq95T6FeJ6xzCjl5ACDyRLl5CCZmKaJvx3Tc3X+1/+dYWa+QT7r8fY3pynk9Ou2iMhe\n4DguxWCAYjDAPdnXU2/Xma5NMNee4nLlEi+tngJOQQFK5TLHvIO0FgaZvJTg+XPzPH9unt/5P6c5\nNJzhjccHefBEmUPDGgcqInIDPw/8hjHmg0Rz8PybWzzvt4E/MMbMA5eIxnVu9iPApzqz5IbAP7zV\nAjlh+EqBt6fCXvRhX6gtcmruTHSbP8NyvdI9FrgxRtLDjKSHGEuPMJIaIu7Hd72MvXKnx3SGYcjT\nL1T4iyfmabZCThyN8+D9KXxPXyRuh8aq9R/VSf9RnfSfTCbB8vIay80lpmpXmaxdZaY2RZsWAL7j\nMxwcJFgbZnkiz+UrDu3O7BDFbJw3nhjkwROD3HOoqHGgd4jGD/Yf1Un/KZez+qJ6m9SUBNRbdU7P\nv8SpuTOcnD/DxMpk91jKT2GKxzmQGWU0PUwpUVT32Dtkda3F//7iLC+eXyMeOLztoQzjo0GviyUi\nIrvIcRxysTy5WJ7jmXtohU1matPRhES1CS7XzoJ7FsagdDBDvn2AxkKJqYtpPv/1Gp//+uWNy7Gc\nGOQNxwZIJ3Q5FhGRfrJvQ+dKY5VnZ17gmennOTl3unuZEt/xOZw9yKHcOIeyBxhIlNR9Zwecu7zG\nZ/5ilspqi+FBn7e+KUMqqTAvIrLfeY7PcGKU4UQ0VGi1tcJUbYLp2gRTtQmuhBby4ORh2B0ktjbE\n4tUcT9k6T52awnXg7oOdcaAnBhkqJHv8ikREZF+FzvnqAs/MPM8z08/z4vzLtDtXcCklCtyVP8Kh\n7Dij6RF81+txSfeuhaUGX/h/C5x6eRXHgQfuS3LviYRmpxURkW2lvDRHUsc4kjpGGIYsNuaZqk0w\nVbvKbH2adnwGjkDmiEeqNUxjoYS9usSpC/P83ufOcKCc7o4DPTKa1eeNiEgP7PnQuVyv8LWpZ3hq\n4mnOLV3o7h9ODXEsf4TjhaMUE4UelnB/qNbafPnri3ztuSVabRgoejz0hjQDxT3/v6CIiNwhjuNQ\nCEoUghJ3Z++j2W4yW59mqnaV6doEi1yBgSskBiAWJvDWhpiazPMnX53lT544Tz4d8MDxQR44NsC9\nR4okAn0GiYjshj35bltv1fmrmRd4auLrvDB7mjZtHBwOZg5wrHCEu/JHyAaZXhdzX2i1Qp4+uczj\nX11krdYmnXR54HVJDh8I1G1ZRES+Kb67tStutbXWaQWNuuNWUxfwj0ZfdmLNHPX5Eo+dL/GXz5bw\niHH3wQL331Xi9XcNcGBQs86LiOyUPRM622Gb0/Mv8eTE1/nG9LPUWnUAhpKD3FM6wd3F46RjqR6X\ncv8Iw5AXz6/x+a/MM7fYJOY7vPG+JOZYAk8z04qIyA5IeEkOpY5yKHWUMAxZbi52u+LOOFNQXiJe\nPvTHA4AAAByVSURBVIcTurjVImdmS5x6coA/+HyeYjbB6+8qcf/RAe47UiKV2DNfkUREeu41/466\nUFvkiStf5YmrTzJbnQcgG2R4w+DruKd0glKi2OMS7i9hGHL2YpWvPLPIhSs1HAdOHI3z+nuSJOKa\nKEhERHZHNCtugVys0JkVt8VcfaYbQhecWWLjs8TGz+C2A6pLJR6/UuJLpwahnuLISI77jhS593CR\n4wfyBDHN9yAi8mq9JkNnO2zz/OwpHr/yJM/PnKJNm5jr87qBe7i3dDdj6RF1kdllK2st/upUhW+c\nXGZxObq+2thwjAfvT5HP6oNaRER6y3M8yvFhyvFhXscD1No1ZmqTTNWuMlWbYLUwQVCYAMBtpLk8\nX+KCLfEnTw3gh3GOH8hz75ES9x0ucmQ0i+fqh1QRkVv1mgqd89UFHr/yJE9cfYqF2iIQdZ+9f/Be\n7i4eJ+7pGo+7KQxDLl6t8fQLy9izq7Tb4Hlw7HCc40fimiRIRET6VtyNcyB5iAPJQ4RhyEpreWM8\nqDOJN3QRb+giAG41z0sLBU4/M8Cjj5VI+HGOHchz93ieE+MFjo7liKslVETkhvo+FYRhyMuL5/nC\npcf4xtRztGkTuDFeP3gf9w/cw1Cq3Osi7jsrqy1eeGmFp19YZm6hCUA+63H8aJyj4wFBoF9/RUTk\ntcNxHDJ+joyf46703bTDNguNOaZrk0zXJphlGn9kEX/kPIQO7loBO1/i5DdKtB8r4Do+R0aynOiE\n0BPjebIp/RAuIrKur0PnF84+wadf+BwXK5cBGEyUeKB8P6Z4nJgX63Hp9o9Gs82lqzXOXqpy4eoE\nE9M1AFwXjowHHD8ap1zy1aVZRET2BNdxKQWDlIJBTPZ1nfGg00zXJpmqTbDgzBFLzcOBl3BCD2+t\nxMW5AmefH+CzT+YAl6FCkiOjWY6O5jg6muPwcJZ4oNZQEdmf+jp0fuzJT+HgcLxwlDeW72csPapg\nswvCMGRytsG5S2ucvVTl0kSVVjRME8+FkbLP2HDAkYOBJgcSEZE9LxoPOkI5PsJ9PECj3WCmPsV0\nbYKZ2iSLzjR+ahp//AxuGCO2VmZprsBT54o8eTIDODgOjA2mOTqS4+hYFEIPlNPqlisi+0Jfh863\nHXoIk72bXJDtdVH2pFYrZH6pyexCg7mFBrPrt/kG9UbYvV8h5zE6FGNkKMbRQxmq1VoPSy0iItJb\nMTfGaOIAo4kDANRaVabrk53uuJOspK7gpK6QGIcYSZL1YVqLJaYmclx+doXHnr0KgAMMlVIcHMpE\nt3K0LOXi+pFdRPaUvg6d7zn2ThYWVnf0Oaq1NouVJkvLTZZXWtTq7e6tWltfD7v72u2QEAjD6AZR\ny+D6tuOA7zl4nnPd0vOiYzHfxfcdYr7T2XbwfQffd7vr1x6LbTq2/jm08fydZWej1Y5e15ZbvUW1\nGq2vVtvMLzVYWGzSDrf+e7gOZDMeB0Y8RoZijJRjJBMbrZm+rw9BERGRzeJegvHkYcaThwFYba4w\nXZ/ohtCl4ByUz+GXIedmybZHcFcGWJsrMDdTZXJula+emuo+XiruM15OMzqYZnQgzdhAitGBtMKo\niLxm9XXovBPCMGR5pcXkTJ25xSZLlSaLy9FtqdKkVg9f8TEcB2K+Qyzm4HrRL5Prb/rRYmsQbLdD\nWu2Qar1NqwWtdki7vREO+0EQcygVfXIZl1zWI5fxyGU9MikX19UHmoiIyKuV8tMc9o9xOHWMMAyp\nNJeYrkfjQWfr00xyBtJnIA25I1mGYuOkmsOElQGW5n2mF6qcvrTI6UuLWx43HnMZ2RRCh0sphgpJ\nhopJkvE9/5VORF7DXvEdyhjjAh8DHgBqwA9Za1/cdPy9wM8CTeARa+3Hb3SOMeZB4DPAmc7pv2Kt\n/f079WJarZDZhQZTs3UmZ+tMzTSYnK1TrbWvu6/vQzrpMVB0SaeiWyrhEgRRuAx8N1rGohbKO/HL\nYhRGo3K2WiHN1sZ6qwXNbfavrzc792m1olZVOsXplsrZWHfdqNxBsHnpdtfjseg16tdSERGRneU4\nDtlYnmwsz13puwnDkKXmIjP1SWZqU8zUp3i5ehI4CQlIj2c4dOIQfz04SKY1TK2SZG65xuxSlbnF\nKpemKpyfWL7uebKpWDeAlgtJhospysVoO5uM6TNfRHrqVn4Wez+QsNa+1RjzFuAjwPsAjDEx4JeA\nNwMrwOPGmD8G3n6Dc94E/KK19iN3ovDVWouLV2tcuFrl4tUa07N1Wtfky2zapTwQo5j3yWe9bsAM\nehC6XNfBdaNWUxEREdl/HMchHyuQjxU4ljZRj6zm0pYQerryAqd5AYCUl+bA0CHuOnSIdyQPUvAG\nWFptMLtUZWG5xkKlxnylxkKlztmrS7x0Zem650wEHkPFZCeUpjatJylk4urhJCI77lZC5zuAPwOw\n1n7FGPPQpmP3Ai9aa+cBjDGPAe8C3nqDc94U3c28j6i18yestdf/XHcDa9UWF67WuHilyoWrVaZm\nG91jrhtNeFPM+xTzHsW8RyHnE4vpjVRERET6k+M45GJ5cptaQivNJWbqU50Zcic5UznJmcpJAOJu\ngrHEOGOpgxwYGOfB+BieE82A226HLK3WWViuMV+ps1DphNLlGldmVrgwWbnu+X3PYSCfpFxIUC4k\nOXqgQNJ3u9vqtisid8KtvJPkgM2DClrGGN9a29zm2DKQv9E5wJPAr1trv2aM+VfAvwb+2Y2euNUO\nmVuGUy8uc/rsSvf6kBBdumN0KGB0OGBsOM7QYKBJbnZJJpPodRFkG6qX/qM66T+qk/6jOrleliSj\nDAOduSkaS0yuTjBVnWRqbYKzqy9ydjUa6eQ7PgfSBzmYOcSh7BHGRw9yZLx43WOGYcjyap3ZxSpz\nS1VmF6vMLlWZ79wm56KJGz//9ctbzsulA0YGUoyU0gwPpBgZSHe3BwpJPLWS7opyWVdykNe2Wwmd\nS8Dm/9PdTuDc7lgWWLjROcaYR621C519jwIfvdkT/7tfPt0dj+m5MDzoMzQYY2jQZ7Do43nrb3Sh\nLuOxSzKZBJVKtdfFkGuoXvqP6qT/qE76j+rk1rjEGfUPM5o5DBlYa60yW59mtjbFbH2a85WznK+c\nhYkv4uAwGAwzlhxnLHGQscRBUn66+1jFVIxiKsaxka0hptZosVip0QgdLk8usbiy3lJa58VLi5y+\nsHBtsXBdh8F81CIa3RKU88nudiqhVtI7oVzOMj19yx0DZRfoR4DbdyvvBo8D7wX+e2d85rObjp0E\nThhjSkCFqGvtLxBdvWO7cz5rjPkxa+2TwN8AvnbTwvkOJ8bijA3HGC7H8D39miYiIiL7W9JLbblE\nS71dZ64+HQXR+jSz9Smm6xM8s/hVAPJ+kdHEAUYSBxhNjFMKBnEdd8tjxmMeQ8UUhUKKA6XklmPt\ndkhlrcHCSo3FykYYXazUWFipMzW/tm050wmfciHJSCkaRzpSSjFcSjFcTJJKxHbgX0ZE+tWthM5H\ngfcYY75MNEHqB4wx3wNkrLW/Zoz5SeCzgEs0e+1lY8x153Qe60eAjxpjGsAE8MGbPfHfe/8QKytq\nwRQRERG5kcANGOmESoBW2GK+PtsNoHP1WU5VnuNU5TkAYk7ASGKsG0JH4mPEvRt3c3Zdh1w6IJcO\nYOj64/VGa0vL6HoYXajUuDhV4dwNZtsdLqYYLkUz7a4H0+Fiinjg3Zl/GBHpG07YTxePvMann34q\nVLeb/qKuUP1J9dJ/VCf9R3XSf1Qnu2N9hty5+jRzjRnm6jMsN7fOcluKDTKaGOdY6Si5dplCrHTH\nLhe3vFpnbjma0Gj9NrdcY3Gltu01zIuZeBRGS6luMB0ppSgXkviee/0Je5y61/afcjmr7pe3SZ3t\nRURERPawzTPkHuE4ALV2jfl6FEBn6zPMN2aYW57h+eVvAJBwk53W0zGG42MMx0dv2hp6I67rkM/E\nyWfiHB3deqzVarO4cm0grTK/XOPUhQVOXTOO1HFgIJeIuumut5J2uuwO5hK69ItIH1PoFBEREdln\n4m58S5fcdthmqbFAxVngauUqc/UZzq2+yLnOLLkAhViJkfgYw50gOhgf6l6u5dXwPJdSLkEpd32Y\nbTTb3cu9RC2j1e76c2fneO7s3NbHcp0t40fXx46OlFIUsnHcXb42u4hspdApIiIiss+5jkshKDGe\nGWM8dhcA1dYac/VZ5hszzNdnmW/McaqxMTbUczzKwXA3hI4kxsj5hTvSLTfmu91ZcK9Va7S2DaPz\nyzUmOpd+ufaxhopJRoophjpjSNcDaS4d3JHyisjNKXSKiIiIyHUSXjK69EpyHNgYGzrfmO2E0Bkm\na1eZqF3ZOMdNMpwYZTg+xlB8hHJ8hIx/Zy8vEY95jJSiyYeutVZrbhtIp+fXuDy9su1jDZeSDHUC\n7uCmS78M5BP7cgypyE5Q6BQRERGRV7R5bOjhVNQa2gqbLDTmOyE0CqPnV1/m/OrL3fNSXrobQIc6\nt7SX3ZEWxmTcJxn3GRtMb9kfhiEr1eZ1Y0fnlmtcmVnhwmTl+tcLFHNxyvkkg4XO9Ujz6+E0QV6t\npCK3TKFTRERERF4Vz/EZCMoMBOXuvlqrynxjjoVNt3OrL3Fu9aXufZJeqhtAy/FRhoJhMn5ux0Kc\n4zhkkjEyyRgHhzJbjoVhSGWtyeL6dUi71yOts7hSw15cwF68/jFjvstgfiOMlnJxirk4pWyCgVyC\nQjbAc9VSKgIKnSIiIiJyB8W9BCNeNMZzXa1V7QTQ+W4QvbZFNOmmKMeHGYwPUw6GGIgPUYiVvqnJ\nim6F4zhkUzGyqRjj5euPN1ttllbrG0G0Uutel3R+ucbV2evHkUaPC4V0vBNGE5Sy8WjipGycgXy0\nzKYDTXIk+4JCp4iIiIjsqLiXYNiLZr5dFwXR+S0tohfWznJh7Wz3Pi4epWCQwfgQg8EQA0GZwWCI\nlJ/e7ml2hO+5lLIJStntLxlTrTdZXKmzvNpgebXO0kqdpc768mqDs1eXeOnK0rbneq5DcVMYLWTi\nFDIBhfX1bJxc4fqxqyKvNQqdIiIiIrLroiA6ynBi4wKe9XaNxcYCS40FFpsLLDbmmavPMFOf3HJu\nykt3Augwg/EyA8EQpWAAz9n9r7aJwCcR+AwXtz++Pp50u0C61Fmevriw/ckdqYRPcT2QdsJoFFDj\nFLIBxUycXDrQxEfStxQ6RURERKQvBG6ccnyYcny4uy8M21RalSiINuajUNpc4OLaOS6unevez8Eh\nHytSjA1QDAYodZbF2ABxb/tWyt2weTzp6MD2LbStdsjKWoPKNrdqo83CcpXZpSqXZ66fgbf7PEA2\nFdsUSoNNwTROMRMnnwnIpmIaayq7TqFTRERERPqW47hk/RxZP8eB5KHu/nq7zlIngK63ji43l1ho\nzHF29cyWx0h7mU4YLVEMBinGBigFAzs2i+7t8lyHXDoglw6uO1YopFhYiMaNNprtbYPp5tvVuVUu\nTF0/G+86x4FsKiCfDshnomWh01JayMS37E8EigpyZ+j/JBERERF5zQncIBrrGR/q7gvDkHq7xnJz\nieXmIsvNJSrNJZabS1yqnudS9fyWx4g5AcWgRD5WJO8Xo0vC+AVysQJZP4fr9FeLYMx3KWbjFLPx\nG94nDEPq6+F09fpQulJtUFlrMjm3ysWbhFOIrmOazwQU0gG5TJxCN5DGu8E0n4mTTcU0IZLclEKn\niIiIiOwJjuMQ9xLEvcSWMArQbDeptJZZbix2g+hyc4mZ2hRTtYnrHwuHrJ8nFyuQ6yzzfoFcLE8+\nViThJvuilfRajuMQj3nEYx4DuZt3K643WqxUm50g2ojW1xpRV99qg5W1aHtqfu2mj+M6kE0HFNJx\nsukY2WTQnRE4mwq2LpMBybjXl/92snMUOkVERERkz/Ndn4JbpBDbOuNPGLZZa6+x2qyw0lphpVlh\ntVXpLFe4tGnc6JbHc2LkYwXSXpaMnyHtZ0h72c4yQ9rPkvLSfddaulkQ8whi3k1bTgHa7ZDVWnNr\nGK2uh9NOUK02uDxToTkZvuLz+l40znVLIE1uhNRUwiediJFK+NEt7pOM+5oo6TVMoVNERERE9i3H\ncUl5aVJemsFtjrfCJivNlSiItiqsNlc6ywqLjQVm69M3ffyUl94Io9cE0oSbJOElSXpJ4m6ibwOq\n625MhjT8CvetN1usVZus1jq3zvrmfWud/VEX3/YtlyMe8zqBNAqiqfVgGu+E00SMdCIKqPEgau1N\nxLzuejzwCHxXraw9oNApIiIiInIDnuNHYz1j+W2PN9sNqu01qq011lprVNtrrLVWqXbWq6015hrT\nTNev78J7rbgb7wbRhJck4SbJL+dwGv6WfYEbJ3ADYm5A4ETLfgmsge8RZDzymZu3nq5rttobwbQT\nSmv1FtV6i1qjRbW+sV1ttKjVW8wsVqnWW6+qfA5sCaHXhtJEzCMIPOK+h++7xHyXmNdZdm7f/XD2\nVT33fvaKodMY4wIfAx4AasAPWWtf3HT8vcDPAk3gEWvtx290jjHmOPAJIASeA37UWnvrP2+IiIiI\niPQR342RcWNk/NwN7xOGIY2wsSmIrlJtV6m369Tbtc4tWq91JkJq0/mKfPO5fjbK4fjE3ICYE2wE\n0mu2Y06A7/p4jo/nePhOtO53tjfWO0t34z4uLq7j4TouLneutdD33BvO3HszYRhS64TQaqMTUjeF\n1UazTaMZLevNdme5dXu12mShUqPRbBO+cq/gru9++MRtvkq5lZbO9wMJa+1bjTFvAT4CvA/AGBMD\nfgl4M7ACPG6M+WPg7Tc45xeBn7bWfsEY86udfY/e6RclIiIiItIvHMch6IS/HNu3mG4WhiGtsEm9\nXcdLhCxWlqmHG8G00W7SDBvRrd2kGa5vN6m3a6y2VmiGjZ19TTg4uFEI7QTRaN3bWMfFcdzOfR2i\nnOrgrJ/vdNc2HrO7b9N/neu3txWLbr7j8y3Fd1IISrf0WsIwpNkKaTRb3UDabLVptkJarTbNdkiz\n1abVCmm01F72atxK6HwH8GcA1tqvGGMe2nTsXuBFa+08gDHmMeBdwFtvcM6bgC921v8U+DZuEjof\nPHTXrb8SEREREREB1i8f06DWrFFr1ag169Rb9Siwtpvd5Zb1VuO64412k0arQTts0wrbtMNWtGx3\nlt391x5r0gjbtNptICQMO7doC8KQdmc/6/vuEAeHbzVv4Q0jrzQCVXbLrYTOHLC4abtljPGttc1t\nji0D+RudAzjW2vCa+97Q+MAA09PLt1BE2S3lclZ10odUL/1HddJ/VCf9R3XSf1Qn/eebqZOEE4dY\nJmoBfA2IQml4zZLOejtaD6M9bcJuC+m1PMcl4Seor9V3pqC3OF5VNtxK6FwCNo+WdTuBc7tjWWDh\nRucYY9rb3FdERERERPa5qButww2ypLyG3co0V48D3wXQGZ/57KZjJ4ETxpiSMSYg6lr7xE3OedoY\n83Bn/TuBL32zL0BERERERET61620dD4KvMcY82Wi3x0+YIz5HiBjrf01Y8xPAp8lCrCPWGsvG2Ou\nO6fzWD8FfLwTUE8Cf3iHX4+IiIiIiIj0ESe8nfmBd1+ocQX9RWM9+pPqpf+oTvqP6qT/qE76j+qk\n/6hO+k+5nFUH4NvUH1eRFRERERERkT1JoVNERERERER2TL93rxUREREREZHXMLV0ioiIiIiIyI5R\n6BQREREREZEdo9ApIiIiIiIiO0ahU0RERERERHaMQqeIiIiIiIjsGIVOERERERER2TEKnSIiIiIi\nIrJj/F4XYDvGGBf4GPAAUAN+yFr7Ym9LtX8ZY74F+E/W2oeNMceBTwAh8Bzwo9badi/Lt58YY2LA\nI8ARIA78e+AFVCc9Y4zxgI8DhqgOfhioojrpOWPMEPA14D1AE9VJTxljvg4sdTbPAj+P6qSnjDH/\nEvhuICD63vVFVCc9ZYz5AeAHOpsJ4I3AO4D/jOqlJzrfvT5J9N2rBfwj9Jly2/q1pfP9QMJa+1bg\nXwAf6XF59i1jzIeAXyd64wP4ReCnrbXvBBzgfb0q2z71fcBs59//O4D/guqk194LYK19O/DTRF+k\nVSc91vmS8F+Btc4u1UkPGWMSgGOtfbhz+wCqk54yxjwMvA14O/Bu4CCqk56z1n5i/e+E6EezfwL8\nLKqXXvouwLfWvg34t+hz/lXp19D5DuDPAKy1XwEe6m1x9rWXgL+1aftNRL+EAvwp8K27XqL97Q+A\nn+msO0S/tKlOesha+0fABzubh4EFVCf94BeAXwWudLZVJ731AJAyxvy5MeYvjDFvQXXSa98OPAs8\nCnwa+Ayqk75hjHkIeJ219tdQvfTaacDv9MTMAQ1UJ7etX0NnDljctN0yxvRlV+C9zlr7P4j+uNY5\n1tqws74M5He/VPuXtbZirV02xmSBPyRqWVOd9Ji1tmmM+STwUeB3UJ30VKd72rS19rObdqtOemuV\n6IeAbyfqgq6/k94bJPpR/++wUSeu6qRvfBj4uc66/lZ6q0LUtfYU0XCaX0Z1ctv6NXQuAdlN2661\nttmrwsgWm/urZ4ladWQXGWMOAp8Hfsta+7uoTvqCtfYfAHcTfSAlNx1Sney+HwTeY4z5AtF4qE8B\nQ5uOq05232ngt621obX2NDALDG86rjrZfbPAZ621dWutJRqLvvmLs+qkR4wxBcBYaz/f2aXP+d76\np0R/K3cT9dr4JNE46HWqk1vQr6HzcaL+03S64Dzb2+LIJk93xoEAfCfwpR6WZd8xxgwDfw78c2vt\nI53dqpMeMsb8/c5kHBC15rSBr6pOesda+y5r7bs7Y6K+AXw/8Keqk576QTrzMxhjxoh6NP256qSn\nHgO+wxjjdOokDXxOddIX3gV8btO2Pud7a56NHphzQAzVyW3r1y6rjxL9Sv1lonFrH+hxeWTDTwEf\nN8YEwEmiLp6yez4MFIGfMcasj+38ceCXVSc98z+B3zTG/CXRB9FPENWD/k76i967eus3gE8YYx4j\nmu3xB4EZVCc9Y639jDHmXcCTRI0QP0o0q7DqpPcM8PKmbb1/9dYvAY8YY75E1ML5YeCrqE5uixOG\n4SvfS0RERERERORV6NfutSIiIiIiIrIHKHSKiIiIiIjIjlHoFBERERERkR2j0CkiIiIiIiI7RqFT\nREREREREdoxCp4iI7BnGmPuNMaEx5m/3uiwiIiISUegUEZG95ANE10v74V4XRERERCK6TqeIiOwJ\nxhgfuAy8E/gy8C3W2peMMQ8DHwWawBPAfdbah40xx4FfAQaAVeDHrLVP96TwIiIie5haOkVEZK/4\nm8B5a+1p4I+Af2yMiQG/BXyvtfZBoLHp/p8EPmSt/WvAB4Hf2+0Ci4iI7AcKnSIisld8APhvnfXf\nB34AeBCYstb+VWf/IwDGmAzwZuA3jTHfAH4XyBhjBna1xCIiIvuA3+sCiIiIfLOMMUPAdwEPGWN+\nHHCAIvCdbP8DqwdUrbVv3PQY48DcLhRXRERkX1FLp4iI7AXfB3zOWjturT1irT0M/Dzw7UDRGPP6\nzv2+BwittYvAGWPM9wEYY94D/GUvCi4iIrLXqaVTRET2gg8AH75m38eADwHfBnzKGNMGLLDWOf69\nwK8aYz4E1IG/a63V7HoiIiJ3mGavFRGRPcsY4wL/Efg5a+2KMeYngQPW2p/qcdFERET2DXWvFRGR\nPcta2yYap/lUZ8KgdwH/obelEhER2V/U0ikiIiIiIiI7Ri2dIiIiIiIismMUOkVERERERGTHKHSK\niIiIiIjIjlHoFBERERERkR2j0CkiIiIiIiI75v8D3vYKqxreLBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bf8816d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Sex',aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bf9e5f28>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LsrzU1uAMADfYtj06S+ftt227490er9ApC17YWkt5cJCJZ5+m/5v/TNd//N15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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50ba1de128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='person',aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bf9ddda0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+AP1la/F/sRX3gUNY+sP03H8fqss18i9wCLwWH6uDd7Ol72P2RneQyCe4u+o+\nTIbLv+UYDAYcNjMOm5nKskv33mfzBQYS58JiJJ4lHMvQH0vT3BalqTV6wf2NRgNBv50p1T4CbusF\nodHjtMiwayGEEOI8EgzFhKNmMnS+9ByoKmXrN2C0jZ19/o6m23g5/BEFrcAGxwJmWmpKXdKEpWoQ\nSUJ3vBgAe+LQl8zSlwDtawHQYtQoc6j47Cp+u4bPXvzZY9W43g4t1emkf+1qvDt24mg5TfVPXqXn\nwc3kqiqH4dXdOIfJyW0Vd7G1/1OaE4dJtyfZWP0QNtP1r6hqNZuo8Dmo8F0cHPMFlUg8Q/9Ahv6B\nNP2xc9fbD3VedH+nzXxBUKwZ/LmyzInlsolcCCGEGL8M2vAsSTcctJ6eS88vEaUTDHoYb+3S9dMf\nE/34I9yLl+Bfd2epyxmyQ+kWftz/MQAPBZZRm/eXuKKJI5WD7hh0xYpBsCde7AnMqxcGQKtJo8yu\n4neo+O3Fi8+u4bRojFjnlKbhPHIU9/4DaGYz/evvIjlLGaGDXbuCluer8BY60q1UWCvZVPMYbvPo\nbQmjaRoWu5WTreGLQmMknkH92lugwQBBn+NcaCx3UhNwUl3uwiu9jMNqPL6/jAfSLvok7aJPwaBn\nXL0pSI+hmFDie/cQ/fgjzBUV+G5bXepyhmxf6iQ/DX+CESObnIuYYa8mEU+XuqxxR9MgmoKu+GAI\nHAyDscyF532TQSsGP4dKmUOlzF7sESz3Wkins6NbtMFAcrZC3ufBt/UrKt5+l2hvH9FVKxm5NDp0\nJoOZm8tuZW90ByeSzbzW+mM21TxKhW10ejYNBgNuh4X6oJv6oPuC2wqqRjSeuaB3sX8gTf9Ahn3H\n+th3rO+C+5/fy1hzNji6qPQ7pJdRCCHEmCfBUEwY+WiUrpdfBJOJwL2bMFjGxkqGu5LH+FnkD1gw\nstm5hDpzWalLGhfyheIQ0K7zegK7Y5AtXBimHGaVOm+BgEMtXpwqXpvGpXZPKGUOy9bW0n/HWvyf\nb8G3dTuW3l76Nq5Hs1pLV9Qgg8HIQt8ynCY3B2N7eL3tJ2ysfpDJztKupGsyGgh47QS8dqjzXXBb\nKpM/LyieCY0ZTnbGON4+cMF9DQYI+h1nh6VWljmoHFwxtdxnl30ahRBCjAkSDMWEoGkaXS+/SCEW\nw7dmHdagPuZhXc325FFej3yBFTMPOJdQbZbho9cjnYPO2LlewO4Y9CYvnAtoQMNr16g/EwKdKgFH\nAcfY+P46G0BxAAAgAElEQVQAgILPS/+da/F9uQ1n83HMP/05PQ9upuD3Xf3BI8xgMDDTMwen2cXO\n8Je81fEL1gTXM897U6lLuySHzUydzUxdxYUL+qiqRiRxibmMAxn2hS/uZTQaDVT47OeFxWJwrCor\nzpWUnkYhhBB6IcFQTAjRjz8ksX8ftikNuJcsLXU5Q7IlcYQ3ol9iN1h40LmUSpO31CWNCdkCdA1A\nxwB0Dl6HUxd25ZmNGkGnel4ALA4JHQ+f0TWbjcjtt+LZsw9n8zGq//1VejffS2bypFKXBkC9YwoO\no5Ot4c/4uOddBnIRVgZWj5m5e0ajgYDHTsBz6V7GSDxDOJYhHM8QiQ2unBrP0B1OXfRcBiDgtZ0N\nixeER78Dm9U0Sq9KCCHEaFIUZQ3wCnAU0Chmsv8VCoW+/Nr9/hSoDoVC3xqNuiQYinEv095Gz2s/\nx+hwENiwcUx8AP0sfpDfDGzHabDyoHMpFabRW6xjLMkXikNAzw+BfQnQONfGVlNxO4hyZ4EKZzEA\nem0juBiMHhiNxBbfRN7nw7N7D5Wv/YrwujXEb1qgi3mH5bYgqyvu5su+T9gZ+ZKBfIQ7g/dhNo7t\nt6Qz223UlF+8bUgmVyByJjDGM4Rj2bMh8vCpMIdPXbw/o9dlpdLvIOh3EPSf6XV0EixzyEI4Qggx\n9r0VCoX+C4CiKLOAp4G1pSxobL8LC3EVai5Hx3PPoOVyBDbeh8mt/4D1UWwf78Z24jLYeMi5lIDJ\nffUHTQAFtTgn8EwA7Bgorg6qnjcc1GzUqHSrVDhVKgaDoGe8h8ArSDVOJe/14N/yJYEPP8bW0Un/\nnevQrKUfH+s2e7g9eDfb+j+jKX6YeH6Ae6sfwWFylrq0EWGzmKgKOKkKXPz6cnmVaGKwp3GwlzEy\nGCCPtUdpbote4vmMg4HxXE9j0O8gWOag3GvHfL37oAghhCgFP5BSFOVbwDqKGe2/nblRURQL8BJQ\nBVQC/zfwCfAa4ALywB8Ds4BvUeyF/DQUCv3vayniqsFQURQj8BSwEMgAfx4KhZrPu30T8DeDBb0U\nCoWeP6/4BsAG/F0oFHrrWgoTYjj0/fqXZFtP45q/AMeMmaUu54o0TeN3sT38Pr4Hj8HOQ65l+I3j\n80Py1WgaDGSgPQKt0WIY7IpduD2EyaBR7lApd50Lgj77pReFmchywQr67lyH/8ttuA4dxtLVTe/m\ne8mXB0pdGjajjVXl69gV2Upr6hQ/b32Ze6sfJmirKnVpo8piNl52f8aCqjGQyBKNZwifFxij8Szd\n4RStPYmLHmM0QMBb7GEM+s+FxjN/dtjkO2EhhNCBzYM9hSoQAf4W+JtQKLRcUZRJwAPAmT1KJgFv\nh0KhnyuKsgL4n8BpigFwPbAcKAM2Az8IhUI/VRTlzxVFMYRCoSHvTTiUd4cHAHsoFFo5WMh3gfvh\nbHr9HrAMSABfKIryFrAR6AuFQn+iKEoA2ANIMBSjKnn4EOH338NcVoZv7R2lLueKNE3jtwM7+DRx\nAJ/BwUOuZXiNF39IHK8KajH4tUWhbTAMxjMXLgxT5hgMgINB0G9Xr3tz+IlGdbnoX7saz979Z+cd\n9t9zpy72OzQZTCz134Lb7OVIbD+vt/2YOyrvZaZ7TqlL0wWT0UCZx0aZx0bD127TNI1kJn9BD2Nk\ncIhqJJHh0MkwcPEQVbfDckEvY1XAQU25i+qAU0KjEEKMnrNDSQEURfkGsB0gFAqdBr4/OMcQoB+4\nW1GUewf/bAmFQvsVRfk18AaQAv4K+EfgrxVF+c/ANsAIFIZa0FDeAW4F3hsscquiKOev3DEbaA6F\nQuHBF/Q5cDvFbs3XB+9joNibeFXBoP6H+U1EY7FdcgMxTv7wBTAamfz4ozgr9buap6Zp/KzrD3ya\nOEC52c0fld+Cx3T1UOhy20ehupGRzmm09Kmc7NNo6VdpC2vk1XO3OywaUwMq1V6Nao9G0KVhPrsO\nh3Hwok8OR+m3h7ic/KrlJGurcGzZTsXb75Lu6iJ5zzowlz4MLPMso9pTyecdn/F+15vEDP2srb0T\no2F42trvH5+972VA3WVuy+YK57bbOHOJpukbSHPqEttuAJT77NRXuqmv9DBp8Lq+yk3Aax+ROY1j\n8f1lIpB20Sdpl3EvBHwTQFGUeuAfgI8Gb/tT4FAoFPquoih/AjyiKMoCwBoKhTYoivIQ8JdAC/Bc\nKBQ6NNhZNxs4MNQChvJpwAucP8GhoCiKORQK5S9xWwzwhUKh+OCL8lAMiH89lGJ6emJXv5MYVcGg\nZ8y1i6ZptD/1fbL9/XhvvZ2su4xsJFnqsi5J1TR+Fd3CtuRRyo1uHrQvxZgykODKm9e73PYxtcF9\nKgunI8VLS7i4XcSZBWLO9AZWulQq3QUqXSpu64XzAnNZyJWo9mvhcFhJpUZ5g/trVV1D8s51+L7c\niv2rXRha2ujdvJGCr/RbWpRRxeqKu9na/xlbOv9Aa7SNe6o2Yx/CFyVX4vc7iej0HDDS7CYDtWUO\nassu/DdUVY1YKkcklqE/Vtxuo2+gGBr3NvWyt6n3gvs7bGbqgy7qK91MCrqpD7qpC7puqIdxLL6/\nTATSLvok7aJPwxnWQ6HQbkVR9g52tBmB/0Ex2EExIL46GABPA0GgCfh7RVEeozik9L9RnKv4sqIo\nMaANOHwtNQzljD4AnP+qjYOh8FK3eSiOkWVwbOwbwFOhUOiVaylKiBsRfu9dErt3YZs0Gc/ym0td\nzmWpmsovIp+zM3WMoNHDg86lOIz67W26FqkcnOovhsDTEeiJn0t5RkNxgZgqd4HqwSBokVX5R1XB\n66H/jrV4d+7GcaqF6h+/Qv+Gu0lNbyx1aXgtPtYE72FH+AtaUsf5ReuPuK/mEQLWilKXNq4YjQZ8\nLis+l5Up1Rd+sMnmCmf3ZyyGxQy90RTNbVGaWi9cCKfCZ6c+6Ka+0s3kSjcNNR7KR6h3UQghxotQ\nKPQJxcVjvv73f0Nx7ZYztp3389xLPNWmS/zd8uutayjB8IvBg/5icI7h/vNuOwzMGJxHGKc4jPQ7\niqJUAb8D/msoFPrweosT4loljxym91evYXS7Cdy3GYNRn0MOC5rKq+HP2Js+QZXRxwOuJdgNpV8p\n8nrl1eLcwJP9xUvHADDYI2gyatR4CmeDYNA1PvYLHPPMZgaWLyUXrMCzaw/BX/+G+Py5hNeuRrOW\n9gsKq9HKysBqDsX2cTR+iF+0vsxdVZtpdOl7AanxwmoxUR1wUv21FVRzeZW+gTQ9kdTgJU1PNMWe\n5l72NJ/rYfQ4LTRUe2mo9tBQ46Gh2kuZxzbaL0MIIcQ1MmjalReqOW9V0gUUP+n9GbAYcIdCoefO\nW5XUSHFV0icVRXkCeBw4ct5TbQiFQhfv8HuOJl3k+jOWhi7kwmFa/t//h0IiTvDxb2Krqy91SZeU\n1wr8e/gTDqZbqDX52excgs1wbcOxSj2UVNOKW0ec6CsGwdNhyA2uGGowaFS6VGo9BWo8xS0jJsoi\nMWNiKOklmKJRfNu+whKJkvd56dtwD5n6y81cG12tqVPsimyloBVY6FvKqvK1mK7x92UiDyUdDYlU\njp5Iiq5wis7+JJ39SaKJC38PfG4rU6u9TK3xML3ez7QaL/V1/jHz/jKRjKX3/YlE2kWfgkHPuBoe\ncdVgOIokGOrQWDkRafk8p7/9LdLHmvGtuwPP4qVXf1AJ5LQ8P+7/mCOZVupNATY7F2G5xg+5UJpg\nmCsUQ2BzLxzrvXDVUJ+9GATrvMVewYk6NHSsBkMACirug4dwho6CpjGwfCnRVSvBVPrGjOYifBX+\nnFh+gKC1ivVVD+C3Dn27DQmGoy+VyZ8NiWcuseS5mcJGA0yr89FQ5WF6vY/pdT4C3rG7oNZ4Mlbe\n9ycaaRd9kmA4ciQY6tBYORF1v/pTIh9+gGPWbAL3btLl/JasmuPl/o9oyrYzxVTBfc6bMBuu70P3\naAXDgXQxBDb3wKnwuX0E7WaNWk+BWm+BWk8Bl1U355GSGtPBcJCltxfvth2YEwmywQr6Nq4nFyz9\n/L68mmffwE5OJY9hMVhYG1yP4pk3pMdKMNSHeCpHR1+C1p4Ebb0JuvqTFNRz546A18b0Oh8zJ/mZ\nNbmMmnKnLs/l491Yed+faKRd9EmC4ciRYKhDY+FENLB9K53PPYO5vILKP/4TjCWeH3UpaTXHS/0f\ncCLbxTRzkA2OmzDfwDL8IxUMNa24mXzTYBjsPm/RGL9dZZIvzyRfcZ6gbCR/sfEQDAEMuRzuPftw\nnjiJZjISuXUVsSWLQAdzdk8nT7In+hV5LcdszwJWV9yF5SqLNkkw1CeX207oRC9tg0GxrTdBKnNu\ndyufy4oy2c+sKWXMnlxGZZlDguIoGAvv+xORtIs+jbdgWPrNq4S4AZn2Nrp+9EMMVivlmx/QZShM\nqRle6PuAllwPM8xV3ONYgGmY9mYbDpoG7QNwpAtC3TCQLp7jjAaNWk8xCE7yFfDYdPMlkhhhmsVC\nbNkSMnU1eL/aRdmnf8B1JET/nWvJ1tSUtLZJzgbKrOV8Ff6Cw7F9dKbbWF/1ABW2ypLWJa6dxWws\nrmgadAPFrYb6YxlOd8dp6YpxujvO9sPdbD/cDUCZx8asyWXMmuJnzpQA5T4ZeiqEEMNJegzFFen5\nG6pCKkXL3/8tuc5OApvux6nMKnVJF0moaZ7v+x1tuT5mWWq5yz53WDbsvtEeQ02DtmgxCB7pgtjg\nfEGLUWOyv8BkX54678SdK3i9xkuP4fkM6QyevftwnGpBA+IL5hG9bRWq48b2FrxRBa3AwYE9HEuE\nMBlM3Fx2O4v8yy/5+yU9hvp0tXbRNI3+gQwt3TFauuK0dMcv6FGsCTiZOzXAvGkBlEll2KxywhoO\nen7fn8ikXfRJ7z2GiqLcDPxTKBRaM5T7SzAUV6TXE5GmaXQ88yTxnTtwL12Gf826Upd0kVghxXN9\n79OZDzPXUsc6+1yMwzQM6nqC4ZkweKZn8EwYtJo0JvnyTC0rzhecKCuIjoTxGAzPsPT04N25B/PA\nAAW7ncjqW0nMmwslHtrXkW5jd2QbGTVNpa2GOyvvpdwavOA+Egz16VrbRdM0eqNpWrpinOyM0dId\nJ5dXATCbDMyo9zNvaoC5UwNMqnTLsNPrpNf3/YlO2kWf9BwMFUX5X8CfAIlQKLRiKI+RYCiuSK8n\not4336D/N29irZ9E8LFv6G6/wmghyXN979Gdj7LAMok19tnD+iHlWoJhOAkHOuBgJ0RS58LgZF+e\nBgmDw2o8B0MAVBVnUzOug4cx5vNkamvov3MtucrSDuPMqBn2R3dyOnUSIyaWB1ax2L8C0+DiThIM\n9elG2yVfUGnrTXCyY4ATnTG6w+d2xPK6rMyfGmDh9ArmTg3gsMnMmaHS6/v+RCftok9DCYab/ueb\n3wYeHeZDv/ab797/V1e6g6IoDwP7gJ8MNRjKmVKMOZFPP6b/N29i8vsp33S/7kJhOB/n2b736CvE\nWGxt4FbbzFH/5jqdK/YMHuiA1mjx2GajRmMgx7Sy4v6CEgbFNTMaSSozSU+qx7NnH/bWNqp/8irx\nBfMZWLmcgttdkrJsRhtLy26hzjGZPZGv2Nr/GcfiIe6ovJegraokNYmRZzYZmVLlYUqVh9UU91M8\n2RnjROcAJztjfHGgky8OdGI2GVAml7GwsZybpldQ4S/tMGghhBgNoVDol4qiNFzLY6THUFyR3r6h\niu/eSftTP8DocFD5zf+Auays1CVdoCcf5dm+94kWEiyzTmOlbfqIhMJL9RgWVDjeVwyDzT1Q0AyA\nRq1HpTGQZ4o/L3MGR9i47zH8GmtnF57dezDH4qgmE/FFCxlYvhTV6SxZTVk1y/7oLlpSxzFiZEnZ\nSu5quJPYwMRpl7FiJHtyNU2jsz9Jc9sAx9qjF/Qm1lW4uGlGBQsbK5hW68UoSyxfQG/v+6JI2kWf\n9DyUFGAwGP5MhpKKYaGnE1GqqYnWf/lnAIKPfRNriVdH/LrOXJhn+94jrqa5xTaDZbZpI3as84Nh\nfxL2tsH+dkjmiucnv70YBhsDedljcBRNtGAIgKriOHkK16HDmJIpVIuF2JJFDCxdjGYv3aqRXel2\ndke2k1KTBGzlrCq7gwZXY8nqERcbzSG+sWT2bEg81Rk7u3+ix2lhwbRyGXJ6Hj2974tzpF30SYLh\nyJFgqEN6ORFl2ts5/a2/Q02lKH/wYRzT9PUB73S2lxf6fkdSy7DGPouF1ikjejyb08ae4xn2tMGp\ncPGcZDNpTAvkmV6ep9yhlnpNkAlpQgbDMwoFHMdP4DocwpROo9qsDCxdQmzxTWg2W0lKyqk5DsX2\nciLRhIZGg3M6t1Xcgd8SKEk94kKlmvuZzRc41RnnWHuUY21REuniSqcmo4FZk/0snF4xoYec6uV9\nX1xI2kWfJBiOHAmGOqSHE1EuHOb0P/x/5MP9lK3fiGve/JLW83UnMl282P8BWS3HHfZ5zLXWjdix\nwknY0wYHOgwkBvNHtbuAUpFjsr+AWeYNltSEDoZn5PM4m4/jOhLCmM1SsNuJ37SA+MIFFDylmYOY\nsyTZ2rGF3mw3Rkws8i9nadktWI362/d0ItHDokDFIacpjrVHaW67eMjpwukVLJpRwdRa77CtKq13\nenjfFxeTdtEnvQfDayXBUFxRqU9EhWSC0//0j2TbWvHedjvem1eWrJZLOZpu4+X+Dymgco9jPjMt\nwz+8taBCUw/sbj2vd9CsMT2QR6nI4bPr5nd4wpNgeI4hl8PZ1IzzaBPGbA7NaCQ5o5HY4kVka2tG\ndZsLt9tOLJaiPX2a/QO7SBWSuExuVpWvZaZ7rmxrUCJ6CIZfF0tmOdY+QHNblJauGPlC8fzqc1tZ\nPCPIopkVzJpchnkcr95V6vd9cWnSLvokwXDkSDDUoVKeiNRcjrZ//S6p0BFcNy3Gf8eduvoAdzDd\nwk/6PwZgo2Mh0yzDu2R/KlvsHdzVem7PwWp3gZkVOWbVmMhlJIDojQTDS8jncbScxtF0DEs0CkC2\nqpLYoptIzJoJ5pGf0+V224kPzsnNq3ma4oc4Gj+MSoEqWy0rA6uZ5GwY8TrEhfQYDM+Xy6uc7IzR\n3BqhuT1KKlMAwGEzsXB6BYtnBJk/rRybdXyt6iUBRJ+kXfRJguHIkWCoQ6U6Eam5HJ3PPUN8904c\nM2YS0Nm2FHtSx3k1/BlGjGxyLmKyuXzYnrs7DjtbivsO5lUDFqPG9PI8s4I5/IO9gxJA9Ena5Qo0\nDUtPL86mZmztHRg0jYLDTmLeXBJzZpMLVozYoc8Phmck8nEODuyhLd0CQL19CivKb6fGXj9idYgL\n6T0Ynk9VNVp74jS1RjnaGiGWzAFgMRuZ2xBg8cwgN82owO2wlLjSGycBRJ+kXfRJguHIkWCoQ6U4\nEanpFG0/+DdSRw5jmzSZiocfxTAKvQpDtT1xlNejX2DBzP3OxdSab3zLDFWDY72wo+XccFGPVWV2\nZY4Z5Xm+/oW0BBB9knYZGmMigfPYcRzHT2LMFv+9shUVJGcrJGYpFHzeYT3epYLhGeFsP4dje+nK\ndAAwxdnIisDtVNqqh7UGcbGxFAzPp2kaXeEUTa0Rjp6O0jdQ/L9lNMDMSX4WzwyyeGaQgLd0q/Le\nCAkg+iTtok8SDEeOBEMdGu0TUT42QNsT3yNz8gT26TMov2+zrkLhH+IHeWtgO3aDhQecS6gy+W7o\n+TJ52NcOO09DJFU8t9R4CswJ5qj3Fbjc9loSQPRJ2uUaFQrY2juwt5zG1tGJQVUBSNfVkpw9i6Qy\nA9Vx4ytDXikYntGb6eZwbB+92W4AGl0KNwduo9wavOHji0sbq8Hw6/oH0md7Ejv6zr2ehmrP2ZBY\nW+EqYYXXRgKIPkm76JMEw5EjwVCHRvNElOvro/V73ybX2Ylz3nzK7l6vq+GjH8b28l5sF06DjYec\nSyk3Xf8Ki4lMMQzubIVM3oDJoNEYyDO7MkfAcfXfSQkg+iTtcv0M2Sz21jbsp05j6enBAGhGI5n6\nOlKN00g2TqPgv74vYoYSDKHYE9ST7eLQwF7CuT4Aprlmsth/swwxHQHjJRier7hfYpSjp6Oc7o4x\nuF0i1QHn2ZA4tcajq/nyXycBRJ+kXfRJguHIkWCoQ6N1Isq0t9H2L98hHwnjXnYzvttX6+aNU9M0\n3ovt4qP4PjwGOw+5luI3Xt+3v/1J2H4K9ndAQTVgN2vMDuaYFcxhv4aOUQkg+iTtMjyMyST2llbs\np09jCUfO/n22vJxU41RSjdPI1lTDEL84GmowPEPTNDozbYRiBwjn+gGosdex2L+Cqc4Zujk3jXXj\nMRieL53Nc6xtgKOtEU50DJxd4bTMbWPRzAqWzAwyc7Ifk46+AAUJIHol7aJPeg2GiqJYgJeABsAG\n/F0oFHrrao+TYCiuaDRORKnjx2h74l9QEwl8t6/Bs/zmET3etVA1jbcGtvFF4jA+o5OHnEvxGq99\naFt7FLadglA3gAGPVWVeVY7p5fnr2ntQAog+SbsMP2Myha2jA1t7B9buHgyF4sqQBYed1NSppBsm\nk54yGdV1+S9rrjUYnqFpGn3Zbo7GD9OVaQfAbwmwyH8zs9zzMBv1M8x9LBrvwfB8xRVOBzh6Osqx\n9ijpbPH/sctuLq5wOjPI3KkBbJbSr3AqAUSfpF30ScfB8M+AhaFQ6L8rihIA9oRCoclXe5wEQ3FF\nI30iShw8QPtT30fLZim7ez2u+QtG7FjXStVUXo9s4atUE+VGNw86l+Iy2ob8eE2D432w9SScjhTP\nG+XOAvOrckzxX37+4FBIANEnaZcRls9j7e7G1t6Brb0TU/pc2MtWVJwNiZn6OjTLudUhrzcYnm8g\nF6EpfoTTqZNoqDhMLuZ7FzHXuxC3eXgXy5koJlIwPF9B1WjtjnO0NUJTa5R4qrjCqdViZN7UchbP\nrGDh9Apc9tKscCoBRJ+kXfRpKMHwsZ//5beBR4f50K/94vGn/+pyNyqK4gYMoVAopihKOfBVKBSa\ndrUnla87RUlomkbkw9/T89rPACi//0Ec02eUuKpz8lqBV8OfsS99kkqjlwecS3AYrUN6rKYVN6T/\n4gR0xYrnizpvnnlVOWrc6mju6y3E+GI2k62tJVtbS0zTMEejWDu7sXZ1Ye3tw9rbi3fHLjSTiUxd\nLakpk0k3TAHnpBs+tNfiZ0nZCuZ4F3AsHuJEspnt4c/5KvwFU10zmO9dxCTHVBlmKq7KZDQwpdrD\nlGoPdy6pp7M/ydHTUZraIuw62sOuoz0YjQZmTS6ucLpoRpAyz9C/lBRCiFAoFAdQFMUDvA789VAe\nJz2G4opG4huqfDRC5w9fJHlgP0aHg/LND2CbdNXe7VGTVrP8qP8jmrMd1Jr8bHYuxma4+je3mgZH\ne+CL49AdNwAaDf4CC6pzlDvVYa1Reqb0SdqlhAoFrL29Z4OiJRI9e5PqdJCaNIl0wxTSUyZT8Hpu\n+HB5Ncfp1ClOJJuI5sIA+Mx+5nkXMdu7AIfJecPHGO8mao/hlfRF02d7Ejv7z/3bTKv1nl28pjow\nsv+3pGdKn6Rd9EmvQ0kBFEWZBLwBPBUKhV4aymMkGIorGu4TUXzPbrpefpFCPI6tYSqBDRsxua5/\ndc/hNlBI8mLfB7Tn+2k0V7LesQCz4cpzPjStOHfwixPQMxgIp5UVWFCdpWwIK4xeDwkg+iTtoh+G\ndBpbdw/Wzi5s3d0Yk6mzt+XKys4OO01PqkezXX9vjKZphHN9nEg005o6hUoBIyYa3TOZ5Z7HZOc0\njAZ9LS6iFxIMr2wgkaWpLUrT6Qine+Kc+bhWW+Fi8cwKFs0I0lA9/CucSgDRJ2kXfdJrMFQUpQr4\nBPivoVDow6E+ToKhuKLhOhGpmQw9r/2c6CcfgcmEb/Ua3IuW6GrYVW9+gOf73qe/EGeepZ619tlX\n/ECnanCkqxgI+xIGDGhMC+RZUJ3Dbx/Z3ysJIPok7aJPDruFbHcf1s5ubF1dWHp6MOaLi39oRiOZ\nmupiSGyYTLZ66Kudfl1WzdKSPM7JZDOx/EDx2CYninsuimceQWuVrs55pSbBcOhSmTzNbVGaWqOc\n7Dy3wmnAY2PRzCCLZ1QM2wqnEkD0SdpFn3QcDJ8AHgeOnPfXG0KhUOoyDwEkGIqrGI4TUbrlFJ3P\nP0u2ox1zRZDyezdhCepr0+jT2V5e7P+AhJpmubWRFbbGy36AU1U41AVbTkB/shgIGwcDoW+EA+EZ\nEkD0SdpFny5ql4KKpb9vcNhpN5ZwP4bBX13VaiU9ZVIxKE6ZQt7v41onBp/pRTydPEFruoWsmgEg\nYKlA8cxDcc/BY7m+PRnHEwmG1yebL3CyI8bR1gjH2gfIDPMKpxJA9EnaRZ/0GgyvlwRDcUU3ciJS\nMxnCv/8dfW/9GgoF3IuX4LttNQZLaVZau5yj6TZ+FP6IrJZnrX0OC6yXXqhCVeFgZzEQhlMGDAaN\n6YE8C6tzeGyj+3skAUSfpF306WrtYshmsQ4OO7V2dWFOnAsrea+XVEMxJGYmT0J12K/p2KpWoCvT\nQUvyBJ3pNlSK842rbLU0uhQa3TPxWwLX98LGOAmGN66gapzujtHUGr1ghVOL2ci8qQEWzwyycHoF\nbsfQ33clgOiTtIs+STAcORIMdeh6TkRqLkv000/pf+c3FAYGMLpclN2zAce0xhGq8vrtTh7n55E/\nwP/f3p0HR9Lf931/9zX3gfsGFnv2ns8lUqREmmKqTElWwlCxokqVLcWiSnFU5Uqc2BXaUdmqcipy\nrIpkpaxEtkWHIq1ElsNIIiXapFQmKfHhw/smQ7KfvXdx33PP9PXLHz0YDHbx7GJ3gZ0G8H1V9fb0\n9Ay2d78Auj/zOxr4sfQ1zltjD70mCOE7i/D527DV1NA1xflBn2ujzz8QbpMAEk9Sl3h60roY1erO\nbKcrq+hedLGtAHdslObsKRrnzuKOjjxRa6Ibusw37jHfuMuau4Ii+v0xmBjhbPYCZ7M2g4nhE9Pd\nVMVYMTwAACAASURBVILhwVJKsbRR5/pcidfnttgoRy3Vugb2TH97htMhBgqP/nBDAkg8SV3iSYLh\n4ZFgGENP8otI+T6l1z7Lxsc+ir+5iZZIkHvlB8i/6QfRU0/2KfthU0rxyeo3+dPK10lg8u7My0yZ\nuz+1D0L49gJ87g6U24HwwqDPtTGPXKK3PzcSQOJJ6hJPz1SXMMTa3Nzpdrq+jtY+b/q5HI1zZ6mf\nP0trahKM/XfdawVNllrzLDTus9Ja6rQkFs1+ZrNnOZU5w0RqBkuPVw+LgyTB8HCtl5tcb89wuri+\n8/88O5aPxiVeGGZiMPPQBxESQOJJ6hJPEgwPjwTDGNrPLyIVBJS/8HnW/+Qj+GtraKZJ9uVXyL/5\nLRiZ+E3Z7quAD2+9xtcaN8lrKd6deYVhY2f6ej+Eb83D5+9ApaVhaIoLQ1ELYbbHgXCbBJB4krrE\n00HWRfM8EssrJOcXSC4uortRa2KYTNI4c5r6ubM0T59CJfZ331MAL/RYbi2w0LjPUmuBQPkAGJrB\nRGqaU5mzzGROM2ANHavWRAmGz0+l7nYmr7m3XCFsn8pG+9NRS+KFYc5MFNA1TQJITEld4kmC4eGR\nYBhDj/pF5G2sU/nylyi9+hd4S0tgGOReeIn8W96KkYvPLSi6VYMGH9z4FHe9FcaMIv9J+mWyejRV\nvR/AN+bhC3eh2g6EF4ejG9NnrNj8nAASQOJK6hJPh1aXMMRaXSM1v0ByYQGjfUuM0DRpnjlNzb5A\n88ws6gnGVQcqYMNdZbm1yHJzkbK/1dmXM/JMZWaZSE0xnpqm3xo40kFRgmFvNF2fmwtlrs+VuL1Q\nxgvardW5BC+fH+Y/etMM431JTENusxInEgzjSYLh4ZFgGEMP/iLytzapfOXLVL78JZo3b0RP6jrZ\nq9fIv/WHMQuFHh3p4y15m3xg4z+wGVS5YI7xrvRVTM3A2w6Ed6Dmapi64uKQx9VRjycYr/9cSQCJ\nJ6lLPD2XuiiFuVUiOTdPam4Os1IFILQsGmfPUL94gcbsKTDNJ/qyzaDBcmuRldYiK62lzgynACk9\nzUR6mvHUFBOpKYaTYxiPue9qnEgw7D3PD7m7XOH6/S1uLJRotKIZTtMJgxfaM5xePT1AOvlk37fi\n4EkwjKcTFwxt29aB3wJeBFrALziOc6Nr/7uBXwZ84AOO47y/a99bgF91HOed+ziWngdDpRRBqYS7\ntIi/tUnYaBA2GgT1eudx2KgTNpug62iGgWaaaIYJhoFmRtt6IoGezWHk2ks2i5HNoedyGLk8eip1\nZD7lHRrKsfj6PWrf/DqVL3+JxvXXozu6axrJ6RnS9kXS5y/EsstoN6c5z+9ufpqW8nhL4ixvSZ7F\nCzW+PgdfvAN1T8PSFReHPa6OeKRiGgi3SQCJJ6lLPD33uiiFWSqRujdH8v4cZq0GRLfCqJ87S/3y\nRZoz0098v0SlFBW/xLq7yrq7ypq7QiPYCVaGZjCYGGY4McZwcpSh5AhDiREsff/dWp8nCYbxEoaK\nubUq91drfPvmOuVa9DNjGhqXZ6MZTl86N0QhG8/vp+NOgmE8xTUY2rZtAO8HbKK5037RcZzvPO59\n+/kI6CeBlOM4P2Tb9luBXwfe0/5LLeA3gDcDNeA127b/2HGcZdu23wf8bPv5WFFK4a+t0Vqcx11c\nxF1c6KzD+uGfpDTLwigWMQtFzGJf9LhY7HquiFHswywU0J7w0+VnoZTCW12lde8urXt3ad67y+37\n9/BKpc5rElNTZOxLpC9cwMjGs7vog16rfZePlr6EjsaPp19gVhvni3fhi3eh0Q6EL4y5XBnxSMmH\nokKIZ6Vp+H19VPv6qF67grm5Rer+fVL358l993vkvvs9/FyW+sWL1K5cxNvnfV01TaNg9VGw+jid\nPQ9A3a91guKmt8Zaa4WV1hK0rx81NPqsAYaTo/Rbg/QlBui3BihaAyRiGhhFb+i6xsxInhcujPLD\nl0dZ2Wq0b4OxxbdurvOtm+toGpyfLHYmrxnuS/f6sIUQe3s3gOM4b7Nt+53Ar9DOb4+yn8vgtwOf\naH/xL9i2/aaufZeAG47jbALYtv1Z4B3Ah4GbwF8Ffnf//4bDE1Sr1L/3XWr/33eof/c7+Bsbu1+g\n65h9fSQmp7AGBjEKBfRkEj2ZREsm0ROJaJ1Molntk2kQoMIAghAVBqgghDAgdD3CZmOnlbHzuEnY\nqEctkLUazY3b0c3xHkHP5jD7+jALRYxiASOXQ09nMNIZ9Ey0GO21nkyCpoMWXQygEU2lrmmARths\nEFSrBJVye13prP3NDVr37xE2Grv+fquvj9S58ySnZ8hcsDHy+T2PM4485fNHpS/w5fp1MlqCH028\nzPxcHx+/C01fI2EoXhpzuTziIb1khBCHQtPwB/qpDvRTfeEa1to6qbv3SN2fo/CVr1L4yldxh4eo\nXb5E/ZJN8ITjszNmloyZZTozC0CoQip+iS1vk5K3wZa3RcnbZNNbf+i9WSNHnzVAX2KAgtlH3syT\nMwvkzDw5M4+hyS/Gk0rTNEb7M4z2Z3j7tXE2Ky2uz21xY77E63PR8m8/dYOp4RyvXIi6nE6P5I5M\nbyghnqfX3vNT/yvw0wf8ZT/8to/+wf/wRjsdx/mIbdsfa2+eArbe6LXd9vNbvwCUurYD27ZNx3H8\nPfZVgGL7gP7Atu3Z/RzEtuHhgwsdoe9TcV5n6xvfZOvr36R640bUBRIw0mkKVy6TGhsjOTJMcniY\nxEA/+nNsnQNQYUjQaOBXqvjVKl6lil+tdLb9SiV6fn0Nd37ucA9G00gODZI6f470xDip8XFS42OY\nMe8i+kaWWlv8y7lPMNdaZ8QoMrn1Ch+5kaThQdJU/OBMwLXxkKSpAUfzU/N0+mge93EndYmn2NRl\nZgJ/ZoJqEGDOLWDdvI01v0j/X7xK32c+i3dmFvflF3Avnn/i8YjbCmSYZLyzrZSi6lUoe2XKbomK\nW6bslSi7Zeab95hv3tvz62TMLIVEkbyVbwfQDNn2OmNlO6E0baRIGkk07cknK+nrO5rnmOPuwbr0\n9WU4Pd3PjxLNcPr9Oxt89/YGN+a2+OPXqvzxa3cYHcjw1qvjvPXqGJdOD2LoEhIP2kFeJ4vjz3Ec\n37btDwH/GfCf7+c9+znrlIHu70S9HQr32pdnn4l0LwfRd7o1d5/SZz9D+fOfJ6xFg//RdRKTk6Rm\nT5M6dRprdBStPbZDAU2gWXWBXowN0iCVh1QebQgsouVByvOilsZmk7DVRLVahK0WYatJ2GyhWk1C\nz4vC7/a4UdX5A6VUNPYxnW63OKajx5lMZ1vrmjnPB6ou9GU4cmNAvtm4zYe3XqOlPIYa0yx93+au\nZ5I0FD8w4XFx2CNhQOhBw+v10T4dGcsWT1KXeIptXUZGYWQUrdUidX+O1N17JG7eJnHzNkEqRe3y\nRWrXruy7q+mjaCQoMkTRGtp1kgmUT82vUg9qNIL6Q8tqY5ml+sK+/o6EniSpp0i21wk9SdLY2e48\n335uqNiHW1Od10prUzzsZ+zn+YkC5ycKtLyA24tlXr+/xa3FMh/9zE0++pmb5DMWL50b4uULw1yZ\n7ccyj86kSHElYwzjaT9hvd2y94ate4fJcZy/Ydv23wO+aNv2ZcdxHjnEbz/B8DWifqr/T3uM4be7\n9n0POG/b9gBQJepG+mtPd+hPL6jXqXz5i5Re/QytO7cB0DMZsi+9TGr2NMnpmaib5RGmWRZmsQjF\nYq8PJbZ8FfCx8pd5rfY9dGUQ3r7G/bVJUqbiTRMuF4c9LDk3CSFiRiWTNM6dpXHuLEapTPrOHVJ3\n7lH42jcofO0btEZHqV27Qu2SjTrgc5mhmZ1xi3sem1J4ysMNW7TCZrQOuh6HLTzl4oUeXujiKZeG\nV8dX+/jU7YG8GQXL7RCZIm1kOkvGyHZtZ8kaWZJG6gD+B8SzSFoGF2f6uTjTjx+E3Fuudrqcvvqt\nRV791iJJS+famUFeuTDMC2cHycR9djchjgHbtn8WmHIc538B6kDYXh7pSWYlfYFo1Np7gVeAnOM4\nv901K6lONCvp/9H13lng9x3Hees+/g1PNCupUorG9dcpv/oZKl/5MspzQdNIzZ4me+0FUmfPoRnG\nQ+8phw1qYZNAhQQEBCrEVyEBIYGKpmnO6ilyRpq8niKlJU70p5hHZda4Tb/KB9c/zUKwhmpkaV1/\nmZSf5eqoiz3kH7tAGNsWkBNO6hJPR7IuYUhyYZH07TsklpbRlCI0DBr2eapXr9CanmqPH48npUI8\n5XfCYndw3H6MGVJrNfbcv59gaWkJ8p0xkdE6bxbIm0WKVj95s3Ciz99P6yDO+0opFtZq7clrSmxW\no9usGLrGxVP9vHJ+iJfOD9OfP9of2j9P0mIYTzGelTQL/A4wRtRP5J84jvPRx73vyN3HUIUh1a9/\nlY1/9zFa9+4CYPT1kb36AtkrVzHyecpBnRW/xLpfZtUvsx6UWfMrrAcVPOU/5m/YzUAnp6fIG2ny\nepphs8iENcCENcCI2YfxFGMqjpKjEAy/ULrNRyqvEege/toE5vxFrg0r7CEf85iW50he6J4AUpd4\nOup10RsNUnfukr59B7Ma9QLy+orUrl6hduUyQf5ozBD9oFwuRbXa3HNfqMJOi6QbNmmFzU5LZSts\n0ggane6untq7toZmUjT76EsM0Gf1U7SiGVkHEkOkDRnb+EYO+ryvlGK93OT1+yWuz2+xvLEzyd3Z\niUJnhtOxAanJo0gwjKe4BsOndWSCoQoCKl/6Ihv//mO4i1H/k/QFm+xLr+BODHHLW+JGa5GbrSXW\ngvJD77cw6NMzFPUMWS2JoenoaBjo6JqOgYaODigayqOuWtSVSz1sr5VL8EALrIHOmNXPhBkFxenE\nEFPW0LEKi3EOhrdKFf7N2hfYSs+hQh197iIvWCOcHwyObSDcdtQvdI8rqUs8HZu6KIW1tkb61h1S\nc/NoQYDSNJqzp6heu0Lj7Bkwjk73iEcFwyfhhx6NcDso1qj5NWpBhaofLXu1PmaNPIOJ4fa9HocZ\nTIzQnxjE0I7O/99hOezzfrnmcn1ui+tzJe6vVjvTIowPZnilHRJnx/LS2vsACYbxJMHw8OwZDEPP\no/z519j8+L/DW10FXSd16RLLL57ierbGjdYiy/7OfDcJDCbMAYb1PH16phMGM8/YJVQpRQuf9aDK\nWlhmNaiwGlRYD6u7AmNSsziTGON8cpxzyXFGzX70I/zLLY7B8O6myx/Ovc584ZtolotWL3C5fpEr\nxSQnZRK0Y3Ohe8xIXeLpONZFcz1S9++Tvn0Ha2MTgCCdpnblMtVrV/AHB3p8hI93UMHwUZRSuGEr\nColBhYpfpuxtUfa2aIS7z206OoOJEUZT44wlJxhNTdBvDZ64gPI8z/uNls/N9i0w7iyV8YPomrQ/\nn+SV88O8fGGIC9N9mMYx/7R3HyQYxpMEw8OzKxgq32frLz7N5sf/Pf7WJhgG3qUzfO1ylq9ZK7jt\nLqEmOhNGP1PmANPGACNGAf05ttiFKmQzrLMallnwt7gfrLPVdbLJ6inOJcY5n5zgUmqKwhHrvhKX\nYKiU4vqay8dvrXE3+02MgWUIdU41TvODiXGME3biPo4XuseB1CWejntdjFKJ9K07pO/eQ3ejf2dz\ncoLatSvUL1xAJeI52cfzCIaP4oYtyt4WJW+Lsh+tS94mYdeHvZaWYDQ5zkhqnLHUJBOpqWPfDbVX\n533XD7izWOH63BY3F8o03Wjeh0zK5MWz0b0Sr54eIJk4ma26EgzjSYLh4VGrq5Xofktf+TJrf/hh\nvNVVlGmyfGmMT50PWU1FYbCopblgjXPKHGLUKGLGrOtmJWxw39/gfrDBfX+dmmp19s1Yw1xJzXA5\nNc2o2Rf7TyJ7HQyDUPH1hSafvFFlwbiLder7aKZHwSvyds5T0NI9O7ZeOu4XukeV1CWeTkxdgiCa\nsObWbZLLKwCEiQS1Sza1a1dxR0diNWFNr4PhXkIVUPK22PTW2XTX2fTWqfi7h6f0W4NMpKeZSEVL\nwTpes4X3+rwP0bl/biWa4fT6fIlKPeoObJk6l07189L5IV48O3SiJq+RYBhPEgwPj7r3ua+y+uHf\np3nrFkrX+P6FAq9etmikdNJaggvmGLY1zphRjH2g2qaUYjOscddf45a/ynywiWrfW3DQyHMlNcOV\n1AyziZHn2tK5X706QTS8kM/dqfPnt2psUcKadjD61jCUwUvhLOfUOBpH43vgMJyYC90jRuoSTyex\nLnq1RvrOHdK372I0osk+3OEhqteuUrt8EZXq/a0e4hgM9+KFLpveBhvuKuvuKuvuGkHXRHZ5s8BE\napqp9CwzmVlyZqGHR/vs4hAMuymlWNpodG6DsVba+Z6ZHcvz0rkhXjo/xPRI7shcGz4NCYbxJMHw\nkHzvH/+q2vjilwC4PpPitRcz1PJJzlojXDTHmTYHj8WkLk3lcsdb46a/wl1/DY+dW2RcSc1wLXWK\nc8lxzJgMgH/eJ4iNesCf36zx2t06rl4nMXUdfXABNBgN+3hzeJ4cvb+g6bWTeKF7FEhd4ulE1yVU\nJJaXo1bEhcWd215cOE/1hau0piZ71op4VILhg0IVUvI2WXNX2kFxFTfc6RnUZw0wnZ5lJnOaydTM\nkbvfYtyC4YO2qi1uzpe4MV/i/kqVsH0ZO5BP8uL5IV4+N4Q90491zGahk2AYTxIMD8lr7/kpNT9s\n8erLOVojRV5KzHDJmiShmb0+tEPjq5C5YJ2b3gq3/BXq7Sm3k5rF5dQ0V1OnuJicJKH3bnzI8zpB\n3N10+eSNGt9YaBIaLpmpWzB8D6WFFFWWF8NZxlX/iW4l7HaiL3RjTOoST1KXiN5s7tz2olIF2re9\nuHaV6pXLhLnscz2eoxoMH6SUouyXWG0tsdJaYs1d6bQoamiMJseZzpxmOj3LWGoy9jOfxj0Ydmu6\nPrcXK9yYL3F7cWdcYtIyuHpmgJfODfHC2UHymUSPj/TZSTCMJwmGh+Tv/9rPq+DUOC8lZzllDh3r\n7gB7CZViKdjihr/MTW+Fsoq6/pgY2KlJrqVOcSk1TUZ/vv3pD/ME4QaKr801ePV2nbtbHug+xem7\nBMO3CXSfrEpyLZxlRg2jSyDcRS5040nqEk9SlwcohbW2TvrW7d23vTg9S/XqZRpnToN5+B/KHpdg\n+KBQBWy4652guOmtd4aQmJrFZHqa6XQUFAcTw7G73jlKwbBbECrmV6vcaLcmblWjn3lNg7OTRV5u\ndzkdG8jE7v98PyQYxpMEw0PyHz71YZVuxXPmtOdNKcVqWOGmt8wNf5mNMLqhsY7GueQ4V1OnuJKa\neS4znB7GCWKl6vPZ23W+cK9O3VNoiQZD0/O4/fdx9RZJZXElnOasGsfgeHUFOShyoRtPUpd4krq8\nMc11Sd27T/r2XazN9m0vUinqly5SvXoZb2T40LqaHtdg+CAvdFlzV1hpB8Vq12Q2GSPLdHqW6cxp\nZtKnyZq5Hh5p5KgGw25KKTbKrU5IXFivde6XONKf5qVzQ1w7M8iF6b4j0+VUgmE8STA8JN949ROq\ndgJOEE9jI6hy01/hhrfMShidUDRgNjHKldQMdnLy0GY4PagTRBAqvrPU4tXbNb6/6gIh6aE18hNz\nVJIroIGlDOxwEltNYnF8uxAfBLnQjSepSzxJXfbHKJVI375L6u49jFY0Zs4dHqJ25TK1SzZh9mC7\nmp6UYPigRlBnpbXUaVFshTv/B0OJEWYyp5lJn2EiPYXRg+E0xyEYPqje9Li1UI66nC5V8PzoliRJ\nS+fSqQGunR3k2pkBhorxnelcgmE8STA8JBIM96ccNrjprXDTX2Yh2GS7egU9w4XkBBeSk5xPTpA7\noMHuz3qC2Kj7fPFeg9fu1tlqhGiJBv2T84SDc7T0qN6DKs/ZcIwZNYxJvMdexIVc6MaT1CWepC5P\nKAxJLC2Rvn2X5OISWhhGXU1nT1G7fJHGubMo69l7+JzUYNgtGp+4xUprieXmIuvuSuc+iqZmMpme\nYSZ9hpnMafqtwefSBfI4BsNufhAyt1Ll1mKZW4tlNso7EwdNDmW5dmaQa2cHOT9VxDTi05oowTCe\nJBgeEgmGT64etrjjr3HPX+desE5D7Vz4TFqDXEhOcCoxwrQ19NTdTp/mBNHyQ76+0ORL9xq8vt5E\nz21hFdfJDmzQSG52Wgdn1Qhnw3H6eL4THhwHcqEbT1KXeJK6PD2t1Yq6mt69h7URdTUNLYv6+XPU\nL1+kOTMN+tNdPEswfJgf+qy7Kyy3FllpLVHxS519OaMQtSa2J7JJGYfTunXcg+GDtqotbi1EIfHe\ncgU/iK6Lk5bBxZk+rpwe4MrpgZ6PTZRgGE8SDA+JBMNnsz0u8Z6/xl1/ncVgi6D9qSNAUc8wlRhi\n2hpiyhpiOjG0r4ls9nuCCJXi+prLF+7V+ebWCmFuDb2wjpHfBD06Dk3BIAXOhKPSOviM5EI3nqQu\n8SR1ORhGuULq7j3S9+5h1KLzgp/NUr9oU794AXds9InGI0owfLx6UIvGJjajoOi1PwDW0BhJjjOT\nOc2pzBlGkxMHdi/kkxYMu3l+yP2VKrcXy9xe2t2aOJBPdkLi5dkBcunnOy+GBMN4kmB4SCQYHixP\n+SwEWywFJZaDEstBmbpq7XpNXk/TZ2TbS67rcZacnkJDo1BMUyk3oD0rqEYUAkthjU2/yv16meuV\nEituBd+soyWbaHpXIFUZRlUfo6qPYVUkIWMHD4Rc6MaT1CWepC4HrD2raeruPVL359A9DwCvWKR+\n8QL1ixfwhoYeGxIlGD4ZpUI2vQ1W2q2JG+5aZ7bThJ6M7p2YjloUC1bfU/89JzkYPqhcc7mzVOH2\nUpm7S5XO7TA04NRYniunB7g408+5qSJJ63A/7JZgGE8SDA+JBMPDpZSiplqdkLgclNgK61RVk5CD\n+R7QA4usSjGkZxhrh8EUR//eQXEkF7rxJHWJJ6nLIQoCkkvLJO/PkVxYRPej+/d5AwPULl6gbl/A\nHxzY860SDJ+NF7qstpZZaS2y3FqiHlQ7+4pWPzPp00ylZ5lKzzxRt1MJhnsLQ8XyZoM7S2XuLFWY\nX6sRhtH1k6FrnJ0ocPFUPxdn+jk7WcAyDzYoSjCMJwmGh0SCYW8opagrl4pqUgkbVMMmFdWkoaJP\ngE1Tx/MCGj6Um9Hi+qC8FForxYCZ5FTa4mzeIqVL19DnRS5040nqEk9Sl+fE90kuLpG6PxdNWhNE\nrSvu0CCN8+eonz+HN7zTkijB8OAopagF1XZIXGSttYyv/M7+4cQoU+lTTKVPMZGeJvGIoSQSDPfH\n9QLmVmvcW65wb6XK8ma9c0sMy9Q5N1nk4kwf9kw/p8fzzxwUJRjGkwTDQyLBMF7CEObLcHvT5Dvz\nPuVm9H1v6oqpQsCpPp/pYsAh95wQb0AudONJ6hJPUpfnT/M8kguLpO7PkVhaRgujIQZ+sRBNXHP+\nHInzp6nWW4/5SuJphCpg091g1V1itbXMhrvWme1UQ2M0Oc5EeobJ1DTjqSmSXTOZSzB8Ok3X5/5K\njXsrFe4vV1nZanT2mYbG6fEC56f6OD9V5PxUkUzqycYoSjCMJwmGh0SCYe+VGnB7HW6tw91NaPnR\n97qlK6aLAbP9PpOFgCNyL9hjTS5040nqEk9Sl97SPI/E0jKpuXkSi0ud7qZhLkvt7BkaZ87QmplG\nWTIG/bAEymfdXWO1tcxqa4ktb6MzPhFgKDHKZHqaidQ0l0Yv4NXkRP+s6i2f+ytV5laqzK1GQXH7\nklsDJoezUVCcLnJusshgIfXIWU8lGMaTBMNDIsHw+XMDuLcZhcHb67BR3/neziVCJgsBZ4c1hpIt\nYnQrH4Fc6MaV1CWepC4xEgQklldIzS9EYxJbUYthaBi0Ts3QODNL48xpgkKhxwd6vPmhx4a7xpq7\nwpq7ymZXiyJEYxTHk5OMpaJlMDF8YLOenlQtL2Bxrcb91Rrzq1UW1mudW2MAFLIJzowXODMRLafH\nC6STOx+WSDCMJwmGh0SC4eFr+rCwBXMlmNuC+S0I1E4X0fFcwEQhYLIQUEgqNE0uqOJK6hJPUpd4\nkrrEUzpp4s8tkVxYJLm4hFkud/a5Q0M0zp6mOXuK1sQ4GDJu4TAFKmDTXWfdXWEzWGe1sYLfnmsA\nwNISjKYmGE9NMJqcZDQ5TsaUexA/iyBULG/UmVutsrBeZ3G9RqW+83+uAeND2U5QfOXSGBlTw5RP\n6mNFguEhkWB4sJSKuobOlaIAOFeC1Sps33YCYDC9EwRHsuGerYJyQRVPUpd4krrEk9Qlnh6si16r\nkVxYIrm4SGJltTMuMbQsWlOTNE/N0Dw1gzc0+ET3SxRPJpdLUak0qPglNtw11t01Nrw1qn559+uM\nPCOpcUaSY+1lnLSR6dFRHw+Vusfieo3F9RoL63WWNup4/k5LrqFrTA3nmBnNcWosz8xonumR3KHf\nKkO8MQmGh0SC4dNTCsotWKlEy3IF5ktQc3e+Vw1dMZwJGckGjOSidXIfwznkgiqepC7xJHWJJ6lL\nPD2yLr5PYmWF5NIKiZUVzPJOF7ogm4lC4sw0zakpgmJBguIBeqPZYt2wxYa7zpa3zqa7waa3Tivc\n/bq8WWAoMcJgYoSh5AhDiRGKVr90Q31KYahYKzVZXK+xWXO5t1RhdatBEO5cu2sajA9mmRnNMTOS\nZ2o4y+Rwjr5c4pFjFsXBkGB4SCQY7k8QwlptJwCuVKPHTX/392XaChnNhozkotbAwUyI/hTfunJB\nFU9Sl3iSusST1CWenqQuer1OYmWVxNIyiZUVjObObKZ+LktrcpLW1CStyYldt8QQT+5JbiPSCOps\neRtsuRtsehtseRsPhUVTMxlIDDOUGGEgMUS/NUB/YoiCWZTg8gS2Z4sNQsVGucnyRp3lzQbLm3VW\nNhu4XS2LAJmUydRQFBK3w+LkcJbsE86GKh5NguEhkWC4Y7sFcLMGGw3YqMFmHTbqsNUEpXZ/84xQ\nEgAAEkNJREFUDxaSIQPp9pIJ6U+HZC11IOdFuaCKJ6lLPEld4knqEk9PXRelMMplEsurJNbWsNbW\ndgXFMJmgNTFBa2Icd2wUd2yUML3/G7yfdM96f8lW0KTkb1HyNil5W5S9LSp+adfkNgCGZrZD4iD9\n1iD91gAFq5+i1UdKT0tofMCjbiOilGKz0mJ1q8FqqclaqcnqVoOtaosHL/OLuQTjAxnGtpfBLGOD\nGYYKKfSnaUE44SQYHpKTFAyVgpq7c8P47mWzEYVAP3z4+yxlKgrJkL50yGA6CoAD6fBQ7yUoF1Tx\nJHWJJ6lLPEld4unA6qIURrWKtbreCYpmtbbrJX6xQGt0tBMU3dERVPKNb/J+kj1rMNxLqEKqfpmK\nX6bil9rrMlW/TKCCh16f0JMUzSgkFq1+ClYfebNA3iyQMwsk9MSBHt9R8DT3l/T8kI1yk9VSg7Wt\naL1eblGuPfxzZxoao/1RWBwdyDDcl2K4L81IX5r+QhJDl+7AezluwVBuGnSAwhDqXhT6ai7UWlB3\noepG6+3wV2lBqPb+PrJ0RTEZUki11+3HhWS4rzGBQgghxImiaQT5PEE+T/PMLAB6o4G1sYm5sYm1\nuYm1sUn29etkX7/eeZtfyOMNDeEODeINDeINDuIPDMj9FA+BrukUrD4KVt+u55VSNII6Fb9ENahQ\n86vRElRZd1dZdZf2/HpJPdUJiXkzT9bMkzFyZI0sWTNHxsiRNjInfmyjZeqMtoNeN88P2ay02Kg0\n2SjvrFe3Gsyv1R76OrquMVRIMdyfZrgvHYXGYpqBQorBQpJ8NoEuLbzHgvz2e4BS4IXQ9KLbOzS9\naGn5Xdtd61Z7XXOh4UH3rJ97fHUylmIwo8haimxCkU2E5BLb2yEpU4ZGCCGEEM8iTKdpTaZpTU5E\nTyiFXq9jbUQh0dzcxCxXSN+6TfrW7c77lKbh9xXxBgbw+4r4xWK07iviFwpgymXTQdI0jYyZJWNm\nGX1gn1KKZtjoBMVGUKcR1KgH9c7YxjV35Y2/NhppI0PayJLW06SM9tL1+MHnk/qjbzJ/XFimzkh/\nmpH+3V2slVLUmj6blRalaovNqkup2mKr5rJVbbFyu7Hn1zMNjYF8ioFCkoFCqhMYBwopitkEffkk\nubQl4fEIODa/4ZQCPwTXj27c3mqv3aD9nA+tYGf/9vPd4W477L1Ra95eNBQJI+rmWcwpUqYibSnS\n7fWD23L7GSGEEOI50zTCbJZWNktremrn6VYLs1zGLHUt5RLW5tZDX0IBQT6P31ckyOUIctloyUaP\n/VyOMJuVFscDomnbwS7DECMP7VdK4SmPRlCjGTRohk2aQYNW2KAZNGmGDZpBg5K3ybp64wC56+9E\n2xUck3qShJ4kqac6685zxoP7kpiadaSDpaZp5NIWubTF9Ejuof2uF1Bqh8RSzaVcc6nUPcr16PHK\n1t7BEaJbbWyHxL5ckmIuEa2zCQrZBIVMgkLGIp9NyO03eig2v71WyiFbla7g9sC69bjng4cnZdkv\nXVMkDUgYimxGkTDa26YiaUTbCQOSZtc+Q5EwFZYuLXxCCCHEUaSSSbzhYbzh4a4nFZrrYlRrmLUa\nRrWGUa1itB+n7s898muGCYswldq1BKlk57GyLJRlEVoWyjK7Hlso0wRDR+nRgmFEa/0ALza255ZQ\nqrNoavt5hdYErdncea79PAq0Xdvt51AoNNA10DSUpkedp7TomFX7+WjfzuPO8pQ0TSOhJUjoCYpW\n/yNfG6oQN2zhhm573epst7Yfq539taDKprcR/TufgI5OoitMJvUkCSO5Z8DcDpMPBkxDi28oSlhG\nuyvp3pM5+UFIpe5RaQfFSsOj1vCodi13liqEYXnP929LWjr5zE5gzGUscimLTMokm7bIpkyyKYts\n2iSTssilTFJJU1okD0BsguFvfsrj0d0wd9O1KJRZhiJjQjGpsIyd5x5eK8x2oOvelzAVxrP9bhJC\nCCHEcaFpqGQSP5nEHxx4eH8QoDebGI0merOJ3mjseqy3Wuiui7mxie77B3ZY6lkm/2iHQW2fEw4+\nOmYdHKVp0b/LNFDG7oXubdNAGWbXczrKMKPnTXOP5eHnk53HGZSZRyWi7Te6AFRK4SsfT7n4oYcb\nuvjKwws9POW21x5e5/md57bDZaCevP6mZj4UHhN6ikIpC76x576kvhM+E3qyZ62WpqHTn0/Sn3/j\niZ2UUjTcgGo9Cor1pket6VNv+dSbHvWmT63lU2/6bFQqhOH+vmc1DTLJB4NjO0ymoucyyShAphJG\ne9n92DKlW19sguGVsQAtDLCMNw523fukS6YQQgghnjvDIMxmCbPZx782CNE9F8110d1orfkBmu9H\n6+DBdYAWhlGQC8PocRhGgS4MH//3PeoDdq39h0YUfmEnFHW13umGQRCGu1v02mu1x+s7Oq2P6qFW\nSa2rdRLYta2FIQQhWhj92zXP3/l3b/8fHKIoeJqEpgmmQbifoGkY0GnV1VF6CvRM1NJr6O19BqEO\nvqYINIWnBfhaGK0J8JSPr7XXBHj4eMrHw8fFw6NBU/nUNYXSYKFdv1ADpUdtmUqDUItqGmrRNpr2\nUGhM6LtbLXdaMvcOmIZmHlq41DSNTDIKaQ+OcXyoNkrRdAMark/LDWi0ApquT9MN2svux41WQL3p\ns15qEuwzUHYzDe2hsPjQ46RB0jJIWNH6p/6y/bT/FbEUm2D4I2dDGtHsLUIIIYQQR5+hExopSKV4\n+KYM8RS727tsh+QghO3wGLSDYxDsWmgH6+2QTff+MIje98B7dl4XorkeZqO585pe/9uf0nZI3A6O\n2yFyJ1BqXfu3X6vhaeC2H0fhX0fT9fbaQGtva5oRbesGum6gmRaamUA3k+hWEsNKoZkWyrSiCZtM\nEzqP91rvvU/TDdJJk/RTTMvv+eFDwbHlBrh+iOtF65YX4Hohrr973fICqg2Plhc8dB/IB524YGjb\ntg78FvAi0AJ+wXGcG1373w38MuADH3Ac5/2Pe48QQgghhBCPpWmdLqRgPeGov2fQCaTb4bEriIYh\nqBAtbLfkdj3WdrX2KjQVhdidx+2xnGpnnObDrapq9xhPpTB1jcAP9mx13Wus6PbX3HlddGxKhZ39\nnSV88GuEaF3HoCnQFWjqSQZ9Pdlr9yyBpkXdfdsttTvBMREFRysB7VCKtTtoWqaFZZrkrXbwNMyd\n12QSYBrRc7oJRhKM9na7GzOGidJ0fDTcQO0Kjp4f4AcKzz/c1uxe2E8E/0kg5TjOD9m2/Vbg14H3\nANi2bQG/AbwZqAGv2bb9x8Db3ug9QgghhBBCxFpXIH1uYfQRetmSGxLiEeDiR+Mq291efeXhqYBA\neQSBSxj4hKFPGHioMIDAb7fiRi2yRqAwAoUZEq23twMwwu1tMLv2RftdzKCF2dh5n/mYTHZQrb0G\nYGkaaUMj1KMxsaGudbrt8tO/d0B/UzzsJxi+HfgEgOM4X7Bt+01d+y4BNxzH2QSwbfuzwDuAH3rE\ne/Z0+eXHvkQIIYQQQghxBPlhgBd6uKGHG7TXoYcbuLihv2u7rgKCMMBXAX7o46uAQAX4YXsd+IS+\nh/I8Qt9H8zyU54MfoPsBuh+18Op+iOFH3Y8NP0Rvr7VQoYcKPVAYYRRM9RD0sHs7eqx37TfCED2M\nWmz149dguK9gWABKXduBbdum4zj+HvsqQPEx79nTqdOzrK5W9n3g4vkYHs5LXWJI6hJPUpd4krrE\nk9QlnqQu8SR1ORyhCjtL1HN2u31Ydf3Zta12tuPQknzQ9hMMy0C+a1vvCngP7ssDW495jxBCCCGE\nEEL0lK7p6Jrc6mDbfv4nXgN+AqA9XvDbXfu+B5y3bXvAtu0EUTfSzz/mPUIIIYQQQgghYmQ/LYZ/\nBLzLtu3PEY3lfK9t238NyDmO89u2bf8d4E+JQuYHHMeZt237ofcc0vELIYQQQgghhHhGjw2GjuOE\nwC8+8PT3u/b/CfAn+3iPEEIIIYQQQogYkk61QgghhBBCCHHCSTAUQgghhBBCiBNO25mWVQghhBBC\nCCHESSQthkIIIYQQQghxwkkwFEIIIYQQQogTToKhEEIIIYQQQpxwEgyFEEIIIYQQ4oSTYCiEEEII\nIYQQJ5wEQyGEEEIIIYQ44SQYCiGEEEIIIcQJZ/byL7dtWwd+C3gRaAG/4DjOjV4e00ln2/ZbgF91\nHOedtm2fAz4IKOA7wN9yHCfs5fGdNLZtW8AHgFkgCfzPwHeRuvScbdsG8H7AJqrFLwJNpDY9Z9v2\nCPBV4F2Aj9Sk52zb/hpQbm/eBn4FqUvP2bb9PwL/KZAguh77C6QuPWXb9s8BP9feTAEvAW8H/jek\nLj3Tvh77ENH1WAD8VxzD80uvWwx/Ekg5jvNDwN8Hfr3Hx3Oi2bb9PuBfEf0iAvinwD9wHOcvARrw\nnl4d2wn2M8B6uwY/DvzvSF3i4t0AjuO8DfgHRBe6Upsea5+8/yXQaD8lNekx27ZTgOY4zjvby3uR\nuvScbdvvBH4YeBvwI8A0Upeecxzng9s/K0QfcP23wC8jdem1nwBMx3F+GPifOKbn/F4Hw7cDnwBw\nHOcLwJt6ezgn3k3gr3Zt/wDRp4cAHwf+8nM/IvFh4B+2H2tEn05JXWLAcZyPAH+zvXkK2EJqEwe/\nBvwLYKG9LTXpvReBjG3bf2bb9qds234rUpc4+DHg28AfAX8CfAypS2zYtv0m4IrjOL+N1CUOXgfM\ndm/HAuBxDOvS62BYAEpd24Ft2z3t3nqSOY7zB0Tf6Ns0x3FU+3EFKD7/ozrZHMepOo5TsW07D/y/\nRC1TUpeYcBzHt237Q8BvAv83UpueanfBWnUc50+7npaa9F6dKLD/GFGXa/lZiYchog/kf5qduuhS\nl9j4JeAftR/Lz0vvVYm6kX6faBjJP+MY1qXXwbAM5Lu2dcdx/F4djHhIdz/pPFGLiHjObNueBj4N\n/K7jOL+H1CVWHMf5G8AFohNFumuX1Ob5+3ngXbZt/znRuJx/DYx07Zea9MbrwP/lOI5yHOd1YB0Y\n7dovdemNdeBPHcdxHcdxiMZId1/YSl16xLbtPsB2HOfT7afkvN97/z3Rz8sFol4QHyIam7vtWNSl\n18HwNaI+u7S7lny7t4cjHvD19hgEgL8CvNrDYzmRbNseBf4M+HuO43yg/bTUJQZs2/7Z9sQNELWI\nhMBXpDa94zjOOxzH+ZH22JxvAP8l8HGpSc/9PO05BGzbniDqLfRnUpee+yzw47Zta+26ZIFPSl1i\n4R3AJ7u25bzfe5vs9HLcACyOYV163W3zj4g+3f0c0fip9/b4eMRufxd4v23bCeB7RF0ZxfP1S0A/\n8A9t294ea/i3gX8mdem5PwR+x7btzxCdIP47onrIz0y8yO+x3vs/gQ/atv1Zotn7fh5YQ+rSU47j\nfMy27XcAXyJqKPhbRDPGSl16zwZudW3L77He+w3gA7Ztv0rUUvhLwFc4ZnXRlFKPf5UQQgghhBBC\niGOr111JhRBCCCGEEEL0mARDIYQQQgghhDjhJBgKIYQQQgghxAknwVAIIYQQQgghTjgJhkIIIYQQ\nQghxwkkwFEIIceTZtn3Vtm1l2/ZP9fpYhBBCiKNIgqEQQojj4L1E95D6xV4fiBBCCHEUyX0MhRBC\nHGm2bZvAPPCXgM8Bb3Ec56Zt2+8EfhPwgc8Dlx3Headt2+eAfw4MAnXgv3Ec5+s9OXghhBAiJqTF\nUAghxFH3HwN3Hcd5HfgI8F/btm0Bvwv8dcdxXga8rtd/CHif4zivAH8T+P3nfcBCCCFE3EgwFEII\ncdS9F/g37cf/Fvg54GVgxXGcb7Wf/wCAbds54M3A79i2/Q3g94CcbduDz/WIhRBCiJgxe30AQggh\nxNOybXsE+AngTbZt/21AA/qBv8LeH34aQNNxnJe6vsYUsPEcDlcIIYSILWkxFEIIcZT9DPBJx3Gm\nHMeZdRznFPArwI8B/bZtX2u/7q8BynGcEnDdtu2fAbBt+13AZ3px4EIIIUScSIuhEEKIo+y9wC89\n8NxvAe8DfhT417Zth4ADNNr7/zrwL2zbfh/gAv+F4zgyE5sQQogTTWYlFUIIcezYtq0D/wT4R47j\n1Gzb/jvApOM4f7fHhyaEEELEknQlFUIIcew4jhMSjRv8cnuSmXcA/7i3RyWEEELEl7QYCiGEEEII\nIcQJJy2GQgghhBBCCHHCSTAUQgghhBBCiBNOgqEQQgghhBBCnHASDIUQQgghhBDihJNgKIQQQggh\nhBAn3P8PZ7/WUBnlJaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bfab3208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Pclass',aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets check what deck people were on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      C85\n",
       "3     C123\n",
       "6      E46\n",
       "10      G6\n",
       "11    C103\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck = titanic_df['Cabin'].dropna()\n",
    "deck.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bfc195c0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R8UJgc3vxvgngQ8BfD7qXHr1fm3Hg9ZmZQ+6l1/phfK+qyMxvRMRZwB9HxINpvkffA16e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\nmCSV8/8XpK5TmTec8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bfb73668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = []\n",
    "\n",
    "for level in deck:\n",
    "    levels.append(level[0])\n",
    "\n",
    "cabin_df = DataFrame(levels)\n",
    "cabin_df.columns = ['Cabin']\n",
    "cabin_df = cabin_df[cabin_df.Cabin != 'T']\n",
    "\n",
    "sns.factorplot('Cabin',data=cabin_df,order='ABCDEFG',kind='count',palette='winter_d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where did they depart from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bfa82630>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bfbcda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked',data=titanic_df,hue='Pclass',kind='count'\n",
    "               ,order=['C','Q','S'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets find out who was alone, and who was with family\n",
    "\n",
    "for this we will use SibSp (siblings) and Parch (parents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</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",
       "      <td>male</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>female</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>male</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  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index_1 = titanic_df.Alone > 0\n",
    "index_2 = titanic_df.Alone == 0\n",
    "\n",
    "titanic_df.loc[index_1, 'Alone'] = 'With Family'\n",
    "titanic_df.loc[index_2, 'Alone'] = 'Alone'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Alone</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",
       "      <td>male</td>\n",
       "      <td>With Family</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",
       "      <td>female</td>\n",
       "      <td>With Family</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",
       "      <td>female</td>\n",
       "      <td>Alone</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",
       "      <td>female</td>\n",
       "      <td>With Family</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>male</td>\n",
       "      <td>Alone</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  person        Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  With Family  \n",
       "1      0          PC 17599  71.2833   C85        C  female  With Family  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone  \n",
       "3      0            113803  53.1000  C123        S  female  With Family  \n",
       "4      0            373450   8.0500   NaN        S    male        Alone  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bfbcd6a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wy4j4vYh4HNVhhqE0+S4GbsjMBPZGxGkt81YCGzLzucAfAjfWv0vzgU9l5hLgR8DSiFhK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\n/wNwA1BXdGLPzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bfd766d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphing who was alone\n",
    "sns.factorplot('Alone',data=titanic_df,palette='Blues',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who Survived the Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50ba0a46d8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YD9NcXvMR4OmZ+RaAiLgJeIDmxEPa+/3YcbbfQnMN2nnAUeCmzPxweyH0Je1cf52Z7+j2\nj69B8hiTBiIzvw58Gvi3iNgZETcAC/v4qvdvZGbQBObiiFjYnt3+GuBjPff7i1m2v4smhD8OvAx4\nV8+lO88EftIozT2GSQOTmb8FnAP8CXA2cFd7UuGJfKV97P/QXLh8Ps1V9fdm5n/1PPds218GfKC9\nz4M0cZxoH/ZVv59wbvKlnAYiIn4eODMz/xK4DbgtItbRXNM3TfO1XNC83Ov1aM/ynwOvBR5rl491\nvO3H/uM6wvf+Xj+K5iT3mDQoB4DrI+IcgPbl1vOArwEP8r2PaVl7guf4NPBS4CLgE31u/zztBc3t\nR4ZcTPPxIprDDJMGIjO/AFwDbImIBP4ZWAhcC/wB8J6IuJsTfPBZZj5K863MOzPz231uvxb4oYj4\nJs3nVF2XmV8d3J9Mw+DpApLKcY9JUjmGSVI5hklSOYZJUjmGSVI5hklSOYZJUjn/B0BUuwtVAC9v\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bfe059e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df['Survivor'] = titanic_df.Survived.map({0:'no',1:'yes'})\n",
    "\n",
    "sns.factorplot('Survivor',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Women and Children first right?<br>\n",
    "Lets find out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bfea8588>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/yMfMuOD6Ju91+bYSRhwqZ2f/KGNCYjumlFoAzAQSgBjgDowl8NMw6on8Xmv9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\nZ0uzE6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bfc61d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphing by the sex/age\n",
    "sns.factorplot('Pclass','Survived',hue='person',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bfc483c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5ZgpIE1RA1AcV1F7QXraAvK64E9l7NpSAjTG/CbwTeBT4PeCfWGtrgwxsL1GPO5G9aaMj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\nlIBFRGKiBCwiEhMlYBGRmCgBi4jE5P8HEZDF55PUTp0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50bff2ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bff38240>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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psGaVzkVL1VGHjyajIRb2hqT8XAcjCA1TEDQERUcQNPihQxhFjZmKURQbpmTO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\nBIKS/P8awQAhN9cLowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c06596d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Pclass',palette='winter',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50c16c4128>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I6SaVPifBDGgX+8PntmduVBZ3X/CXScqLZG4518d1p3t57l2Nh5cl2N5s7hK+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\nRAHAEgUASxQALFEAsAK7kHaz///9DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c170fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations = [10,20,40,60,80]\n",
    "\n",
    "sns.lmplot('Age','Survived',hue='Pclass',palette='winter',data=titanic_df,x_bins=generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50c0654ba8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2WO0760/gl03Paa1vAN8C/Nu2japDEhGTnckYBwYSjMQtWeMRQog2Wm0AcrTW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\nAmRmZkX8A+AfL9SCNak4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c17ec828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Sex',data=titanic_df,palette='winter_d',x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</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",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  person        Alone Survivor  \n",
       "1       0  PC 17599  71.2833   C85        C  female  With Family      yes  \n",
       "3       0    113803  53.1000  C123        S  female  With Family      yes  \n",
       "6       0     17463  51.8625   E46        S    male        Alone       no  \n",
       "10      1   PP 9549  16.7000    G6        S   child  With Family      yes  \n",
       "11      0    113783  26.5500  C103        S  female        Alone      yes  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levels_df = titanic_df.dropna()\n",
    "levels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>freqs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "      <td>0.016835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>47</td>\n",
       "      <td>0.052750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>59</td>\n",
       "      <td>0.066218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>33</td>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>32</td>\n",
       "      <td>0.035915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>13</td>\n",
       "      <td>0.014590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>4</td>\n",
       "      <td>0.004489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "      <td>0.001122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>687</td>\n",
       "      <td>0.771044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            counts     freqs\n",
       "categories                  \n",
       "A               15  0.016835\n",
       "B               47  0.052750\n",
       "C               59  0.066218\n",
       "D               33  0.037037\n",
       "E               32  0.035915\n",
       "F               13  0.014590\n",
       "G                4  0.004489\n",
       "T                1  0.001122\n",
       "n              687  0.771044"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_cabin = titanic_df[\"Cabin\"].astype(str)\n",
    "\n",
    "new_Cabin = np.array([cabin[0] for cabin in char_cabin])\n",
    "\n",
    "new_Cabin = pd.Categorical(new_Cabin)\n",
    "\n",
    "new_Cabin.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</th>\n",
       "      <th>Deck</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",
       "      <td>male</td>\n",
       "      <td>With Family</td>\n",
       "      <td>no</td>\n",
       "      <td>n</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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</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",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</td>\n",
       "      <td>n</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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "      <td>C</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>male</td>\n",
       "      <td>Alone</td>\n",
       "      <td>no</td>\n",
       "      <td>n</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  person        Alone  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S    male  With Family   \n",
       "1      0          PC 17599  71.2833   C85        C  female  With Family   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone   \n",
       "3      0            113803  53.1000  C123        S  female  With Family   \n",
       "4      0            373450   8.0500   NaN        S    male        Alone   \n",
       "\n",
       "  Survivor Deck  \n",
       "0       no    n  \n",
       "1      yes    C  \n",
       "2      yes    n  \n",
       "3      yes    C  \n",
       "4       no    n  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Deck'] = new_Cabin\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</th>\n",
       "      <th>Deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "      <td>C</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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "      <td>C</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",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "      <td>no</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  person        Alone Survivor Deck  \n",
       "1       0  PC 17599  71.2833   C85        C  female  With Family      yes    C  \n",
       "3       0    113803  53.1000  C123        S  female  With Family      yes    C  \n",
       "6       0     17463  51.8625   E46        S    male        Alone       no    E  \n",
       "10      1   PP 9549  16.7000    G6        S   child  With Family      yes    G  \n",
       "11      0    113783  26.5500  C103        S  female        Alone      yes    C  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just getting values with a deck level\n",
    "deck_df = titanic_df[titanic_df.Deck != 'n']\n",
    "deck_df = deck_df[deck_df.Deck != 'T']\n",
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>203.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>203.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>455.970443</td>\n",
       "      <td>0.669951</td>\n",
       "      <td>1.197044</td>\n",
       "      <td>35.779457</td>\n",
       "      <td>0.443350</td>\n",
       "      <td>0.438424</td>\n",
       "      <td>76.341708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>251.869248</td>\n",
       "      <td>0.471393</td>\n",
       "      <td>0.527080</td>\n",
       "      <td>15.707436</td>\n",
       "      <td>0.629704</td>\n",
       "      <td>0.731133</td>\n",
       "      <td>74.520547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920000</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>260.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.206250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>458.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.441700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>686.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>89.552100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>890.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   203.000000  203.000000  203.000000  184.000000  203.000000   \n",
       "mean    455.970443    0.669951    1.197044   35.779457    0.443350   \n",
       "std     251.869248    0.471393    0.527080   15.707436    0.629704   \n",
       "min       2.000000    0.000000    1.000000    0.920000    0.000000   \n",
       "25%     260.500000    0.000000    1.000000   24.000000    0.000000   \n",
       "50%     458.000000    1.000000    1.000000   36.000000    0.000000   \n",
       "75%     686.000000    1.000000    1.000000   48.000000    1.000000   \n",
       "max     890.000000    1.000000    3.000000   80.000000    3.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  203.000000  203.000000  \n",
       "mean     0.438424   76.341708  \n",
       "std      0.731133   74.520547  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000   29.206250  \n",
       "50%      0.000000   55.441700  \n",
       "75%      1.000000   89.552100  \n",
       "max      4.000000  512.329200  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50c1920cc0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r07zfu7qX5u8/G6II7vtBChkJ7vt+ire9R+G0/OzZXrTTr18Eusb93AVcsGmg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\nF5eycY2Hi4uLi0vZuMbDxcXFxaVs/i/S6E+5O0MapQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c193a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue=\"Deck\", data=deck_df,palette='winter',\n",
    "           hue_order=['A','B','C','D','E','F','G'], x_bins=generations).set(ylim=[-0.4,1.4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bfe200b8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kitTF4nyqbg/m97487H5HxFvYLV6ecZpL/RSprx/MQjM718zGluXsgS15NstJ52Y485L+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\nYEQkGAWMiASjgBGRYBQwIhKMAkZEgvn/4Yl32ManpAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c1ac7f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Deck','Survived',data=deck_df, palette='winter', order=['A','B','C','D','E','F','G'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50bfd765c0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xB/gmcHBm/qjetg3weWBwvLauGiVJT9qdav3SH41syMybgB0namiYStKT/hL4+cibiLis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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c2128390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Survivor',hue='Deck', data=deck_df, palette='winter',\n",
    "               hue_order=['A','B','C','D','E','F','G'], kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50c218d588>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f/2O1r71Q1trbszZcdiGPs9Fhxn9puuufjDH/BrzxQp58MyZGwrb8kC3GMYtx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\nEcm9/x/cdkKw9GDkRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c218db00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Alone',data=titanic_df,palette='winter',x_bins=generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x50c1afc4a8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x50c21d0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone',hue='Survivor',data=titanic_df,palette='winter',kind=\"count\", hue_order=['yes','no'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You were more likely to survive if you went on the titanic with family"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "## Predicting Who Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import cross_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('train.csv')\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we have to take care of the nulls and convert our strings to numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replaicng missing values\n",
    "\n",
    "# Filling missing values\n",
    "train_df['Age'] = train_df['Age'].fillna(train_df['Age'].median())\n",
    "train_df['Fare'] = train_df['Fare'].fillna(train_df['Fare'].median())\n",
    "\n",
    "# Filling missing with the most common\n",
    "train_df['Embarked'] = train_df['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making Columns\n",
    "\n",
    "# Gender/Age\n",
    "train_df['person'] = train_df[['Age','Sex']].apply(male_female_child,axis=1)\n",
    "\n",
    "train_df['Alone'] = train_df.SibSp + train_df.Parch\n",
    "\n",
    "# Alone/With Family\n",
    "index_1 = train_df.Alone > 0\n",
    "index_2 = train_df.Alone == 0\n",
    "\n",
    "train_df.loc[index_1, 'Alone'] = 'With Family'\n",
    "train_df.loc[index_2, 'Alone'] = 'Alone'\n",
    "\n",
    "# Deck\n",
    "train_df['Deck'] = new_Cabin\n",
    "train_df['Deck'] = train_df['Deck'].astype('str')\n",
    "\n",
    "#############################################################################\n",
    "\n",
    "# Replacing sex\n",
    "train_df.loc[train_df['Sex'] == 'male','Sex'] = 0\n",
    "train_df.loc[train_df['Sex'] == 'female','Sex'] = 1\n",
    "\n",
    "# Replacing person\n",
    "train_df.loc[train_df['person'] == 'male','person'] = 0\n",
    "train_df.loc[train_df['person'] == 'female','person'] = 1\n",
    "train_df.loc[train_df['person'] == 'child','person'] = 2\n",
    "\n",
    "\n",
    "# Replacing Location\n",
    "train_df.loc[train_df['Embarked'] == 'S','Embarked'] = 0\n",
    "train_df.loc[train_df['Embarked'] == 'C','Embarked'] = 1\n",
    "train_df.loc[train_df['Embarked'] == 'Q','Embarked'] = 2\n",
    "\n",
    "# Replace Alone\n",
    "train_df.loc[train_df['Alone'] == 'With Family','Alone'] = 0\n",
    "train_df.loc[train_df['Alone'] == 'Alone','Alone'] = 1\n",
    "\n",
    "# Replace Deck 'A','B','C','D','E','F','G', 'T', 'n'\n",
    "train_df.loc[train_df['Deck'] == 'A','Deck'] = 0\n",
    "train_df.loc[train_df['Deck'] == 'B','Deck'] = 1\n",
    "train_df.loc[train_df['Deck'] == 'C','Deck'] = 2\n",
    "train_df.loc[train_df['Deck'] == 'D','Deck'] = 3\n",
    "train_df.loc[train_df['Deck'] == 'E','Deck'] = 4\n",
    "train_df.loc[train_df['Deck'] == 'F','Deck'] = 5\n",
    "train_df.loc[train_df['Deck'] == 'G','Deck'] = 6\n",
    "train_df.loc[train_df['Deck'] == 'T','Deck'] = 7\n",
    "train_df.loc[train_df['Deck'] == 'n','Deck'] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.806958473625\n"
     ]
    }
   ],
   "source": [
    "predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'person', 'Alone', 'Deck']\n",
    "\n",
    "alg = LogisticRegression(random_state=1)\n",
    "scores = cross_validation.cross_val_score(alg, train_df[predictors], train_df['Survived'], cv=3)\n",
    "\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test Data\n",
    "\n",
    "test_df = pd.read_csv('test.csv')\n",
    "\n",
    "# Replaicng missing values\n",
    "\n",
    "# Filling missing values\n",
    "test_df['Age'] = test_df['Age'].fillna(train_df['Age'].median())\n",
    "test_df['Fare'] = test_df['Fare'].fillna(train_df['Fare'].median())\n",
    "\n",
    "# Filling missing with the most common\n",
    "test_df['Embarked'] = test_df['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_cabin = test_df[\"Cabin\"].astype(str)\n",
    "\n",
    "test_Cabin = np.array([cabin[0] for cabin in test_cabin])\n",
    "\n",
    "test_Cabin = pd.Categorical(test_Cabin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making Columns\n",
    "test_df['person'] = test_df[['Age','Sex']].apply(male_female_child,axis=1)\n",
    "\n",
    "test_df['Alone'] = test_df.SibSp + test_df.Parch\n",
    "\n",
    "index_1 = test_df.Alone > 0\n",
    "index_2 = test_df.Alone == 0\n",
    "\n",
    "test_df.loc[index_1, 'Alone'] = 'With Family'\n",
    "test_df.loc[index_2, 'Alone'] = 'Alone'\n",
    "\n",
    "test_df['Deck'] = test_Cabin\n",
    "test_df['Deck'] = test_df['Deck'].astype('str')\n",
    "\n",
    "# Replacing sex\n",
    "test_df.loc[test_df['Sex'] == 'male','Sex'] = 0\n",
    "test_df.loc[test_df['Sex'] == 'female','Sex'] = 1\n",
    "\n",
    "# Replacing person\n",
    "test_df.loc[test_df['person'] == 'male','person'] = 0\n",
    "test_df.loc[test_df['person'] == 'female','person'] = 1\n",
    "test_df.loc[test_df['person'] == 'child','person'] = 2\n",
    "\n",
    "\n",
    "# Replacing Location\n",
    "test_df.loc[test_df['Embarked'] == 'S','Embarked'] = 0\n",
    "test_df.loc[test_df['Embarked'] == 'C','Embarked'] = 1\n",
    "test_df.loc[test_df['Embarked'] == 'Q','Embarked'] = 2\n",
    "\n",
    "# Replace Alone\n",
    "test_df.loc[test_df['Alone'] == 'With Family','Alone'] = 0\n",
    "test_df.loc[test_df['Alone'] == 'Alone','Alone'] = 1\n",
    "\n",
    "# Replace Deck 'A','B','C','D','E','F','G', 'T', 'n'\n",
    "test_df.loc[test_df['Deck'] == 'A','Deck'] = 0\n",
    "test_df.loc[test_df['Deck'] == 'B','Deck'] = 1\n",
    "test_df.loc[test_df['Deck'] == 'C','Deck'] = 2\n",
    "test_df.loc[test_df['Deck'] == 'D','Deck'] = 3\n",
    "test_df.loc[test_df['Deck'] == 'E','Deck'] = 4\n",
    "test_df.loc[test_df['Deck'] == 'F','Deck'] = 5\n",
    "test_df.loc[test_df['Deck'] == 'G','Deck'] = 6\n",
    "test_df.loc[test_df['Deck'] == 'T','Deck'] = 7\n",
    "test_df.loc[test_df['Deck'] == 'n','Deck'] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predict Test values\n",
    "\n",
    "alg.fit(train_df[predictors],train_df['Survived'])\n",
    "\n",
    "# Make predictions using the test set\n",
    "predictions = alg.predict(test_df[predictors])\n",
    "\n",
    "# Create a new dataframe with only the columns Kaggle wants from the dataset\n",
    "\n",
    "submission = pd.DataFrame({'PassengerId': test_df['PassengerId'], 'Survived': predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv')"
   ]
  }
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