{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "source": [
                "# from IPython.display import display\r\n",
                "from IPython.core.interactiveshell import InteractiveShell\r\n",
                "import numpy as np\r\n",
                "import pandas as pd\r\n",
                "import seaborn as sns\r\n",
                "\r\n",
                "# general options\r\n",
                "pd.options.display.float_format = '{:,.2f}'.format\r\n",
                "InteractiveShell.ast_node_interactivity = \"all\""
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "source": [
                "# let's load our dataset, the kaggle Titanic training set\r\n",
                "titan = pd.read_csv('./data/titanic/train.csv')\r\n",
                "titan.head(3)\r\n",
                "titan.tail(4)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "   PassengerId  Survived  Pclass  \\\n",
                            "0            1         0       3   \n",
                            "1            2         1       1   \n",
                            "2            3         1       3   \n",
                            "\n",
                            "                                                Name     Sex   Age  SibSp  \\\n",
                            "0                            Braund, Mr. Owen Harris    male 22.00      1   \n",
                            "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.00      1   \n",
                            "2                             Heikkinen, Miss. Laina  female 26.00      0   \n",
                            "\n",
                            "   Parch            Ticket  Fare Cabin Embarked  \n",
                            "0      0         A/5 21171  7.25   NaN        S  \n",
                            "1      0          PC 17599 71.28   C85        C  \n",
                            "2      0  STON/O2. 3101282  7.92   NaN        S  "
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>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>22.00</td>\n",
                            "      <td>1</td>\n",
                            "      <td>0</td>\n",
                            "      <td>A/5 21171</td>\n",
                            "      <td>7.25</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>38.00</td>\n",
                            "      <td>1</td>\n",
                            "      <td>0</td>\n",
                            "      <td>PC 17599</td>\n",
                            "      <td>71.28</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>26.00</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>STON/O2. 3101282</td>\n",
                            "      <td>7.92</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>S</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 2
                },
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "     PassengerId  Survived  Pclass                                      Name  \\\n",
                            "887          888         1       1              Graham, Miss. Margaret Edith   \n",
                            "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
                            "889          890         1       1                     Behr, Mr. Karl Howell   \n",
                            "890          891         0       3                       Dooley, Mr. Patrick   \n",
                            "\n",
                            "        Sex   Age  SibSp  Parch      Ticket  Fare Cabin Embarked  \n",
                            "887  female 19.00      0      0      112053 30.00   B42        S  \n",
                            "888  female   NaN      1      2  W./C. 6607 23.45   NaN        S  \n",
                            "889    male 26.00      0      0      111369 30.00  C148        C  \n",
                            "890    male 32.00      0      0      370376  7.75   NaN        Q  "
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>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>887</th>\n",
                            "      <td>888</td>\n",
                            "      <td>1</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Graham, Miss. Margaret Edith</td>\n",
                            "      <td>female</td>\n",
                            "      <td>19.00</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>112053</td>\n",
                            "      <td>30.00</td>\n",
                            "      <td>B42</td>\n",
                            "      <td>S</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>888</th>\n",
                            "      <td>889</td>\n",
                            "      <td>0</td>\n",
                            "      <td>3</td>\n",
                            "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
                            "      <td>female</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>1</td>\n",
                            "      <td>2</td>\n",
                            "      <td>W./C. 6607</td>\n",
                            "      <td>23.45</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>S</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>889</th>\n",
                            "      <td>890</td>\n",
                            "      <td>1</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Behr, Mr. Karl Howell</td>\n",
                            "      <td>male</td>\n",
                            "      <td>26.00</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>111369</td>\n",
                            "      <td>30.00</td>\n",
                            "      <td>C148</td>\n",
                            "      <td>C</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>890</th>\n",
                            "      <td>891</td>\n",
                            "      <td>0</td>\n",
                            "      <td>3</td>\n",
                            "      <td>Dooley, Mr. Patrick</td>\n",
                            "      <td>male</td>\n",
                            "      <td>32.00</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>370376</td>\n",
                            "      <td>7.75</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>Q</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 2
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "source": [
                "# let's review the data\r\n",
                "titan.describe()"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "       PassengerId  Survived  Pclass    Age  SibSp  Parch   Fare\n",
                            "count       891.00    891.00  891.00 714.00 891.00 891.00 891.00\n",
                            "mean        446.00      0.38    2.31  29.70   0.52   0.38  32.20\n",
                            "std         257.35      0.49    0.84  14.53   1.10   0.81  49.69\n",
                            "min           1.00      0.00    1.00   0.42   0.00   0.00   0.00\n",
                            "25%         223.50      0.00    2.00  20.12   0.00   0.00   7.91\n",
                            "50%         446.00      0.00    3.00  28.00   0.00   0.00  14.45\n",
                            "75%         668.50      1.00    3.00  38.00   1.00   0.00  31.00\n",
                            "max         891.00      1.00    3.00  80.00   8.00   6.00 512.33"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>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>891.00</td>\n",
                            "      <td>891.00</td>\n",
                            "      <td>891.00</td>\n",
                            "      <td>714.00</td>\n",
                            "      <td>891.00</td>\n",
                            "      <td>891.00</td>\n",
                            "      <td>891.00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>mean</th>\n",
                            "      <td>446.00</td>\n",
                            "      <td>0.38</td>\n",
                            "      <td>2.31</td>\n",
                            "      <td>29.70</td>\n",
                            "      <td>0.52</td>\n",
                            "      <td>0.38</td>\n",
                            "      <td>32.20</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>std</th>\n",
                            "      <td>257.35</td>\n",
                            "      <td>0.49</td>\n",
                            "      <td>0.84</td>\n",
                            "      <td>14.53</td>\n",
                            "      <td>1.10</td>\n",
                            "      <td>0.81</td>\n",
                            "      <td>49.69</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>min</th>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.42</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25%</th>\n",
                            "      <td>223.50</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>2.00</td>\n",
                            "      <td>20.12</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>7.91</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>50%</th>\n",
                            "      <td>446.00</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>3.00</td>\n",
                            "      <td>28.00</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>14.45</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>75%</th>\n",
                            "      <td>668.50</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>3.00</td>\n",
                            "      <td>38.00</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>31.00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>max</th>\n",
                            "      <td>891.00</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>3.00</td>\n",
                            "      <td>80.00</td>\n",
                            "      <td>8.00</td>\n",
                            "      <td>6.00</td>\n",
                            "      <td>512.33</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 3
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Looking at the above you we know that in the training set:\r\n",
                "\r\n",
                "  - there are 891 passengers\r\n",
                "  - 38% of those survived the sinking of the Titanic\r\n",
                "  - their ages ranged from 0.4 to 80\r\n",
                "  - we are missing data in at least the 'Age' column"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "source": [
                "# let's get the pivot to work for the 'Sex' column\r\n",
                "by_sex = pd.pivot_table(data=titan, index=['Sex'])\r\n",
                "by_sex"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "         Age  Fare  Parch  PassengerId  Pclass  SibSp  Survived\n",
                            "Sex                                                            \n",
                            "female 27.92 44.48   0.65       431.03    2.16   0.69      0.74\n",
                            "male   30.73 25.52   0.24       454.15    2.39   0.43      0.19"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>Age</th>\n",
                            "      <th>Fare</th>\n",
                            "      <th>Parch</th>\n",
                            "      <th>PassengerId</th>\n",
                            "      <th>Pclass</th>\n",
                            "      <th>SibSp</th>\n",
                            "      <th>Survived</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sex</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>female</th>\n",
                            "      <td>27.92</td>\n",
                            "      <td>44.48</td>\n",
                            "      <td>0.65</td>\n",
                            "      <td>431.03</td>\n",
                            "      <td>2.16</td>\n",
                            "      <td>0.69</td>\n",
                            "      <td>0.74</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>male</th>\n",
                            "      <td>30.73</td>\n",
                            "      <td>25.52</td>\n",
                            "      <td>0.24</td>\n",
                            "      <td>454.15</td>\n",
                            "      <td>2.39</td>\n",
                            "      <td>0.43</td>\n",
                            "      <td>0.19</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 4
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "source": [
                "# pivot_table by default calculates the mean for each column, ignoring non-numeric columns\r\n",
                "# let's plot a few of those means\r\n",
                "by_sex[['Age', 'Fare', 'Survived']].plot.bar(figsize=(10,6))"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "<AxesSubplot:xlabel='Sex'>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 5
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 720x432 with 1 Axes>"
                        ],
                        "image/png": "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"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "source": [
                "# let's add another feature to the index\r\n",
                "by_s_pcl = pd.pivot_table(data=titan, index=['Sex', 'Pclass'])\r\n",
                "by_s_pcl\r\n",
                "by_s_pcl_2 = titan.pivot_table(index=['Sex', 'Pclass'])\r\n",
                "by_s_pcl_2"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "                Age   Fare  Parch  PassengerId  SibSp  Survived\n",
                            "Sex    Pclass                                                  \n",
                            "female 1      34.61 106.13   0.46       469.21   0.55      0.97\n",
                            "       2      28.72  21.97   0.61       443.11   0.49      0.92\n",
                            "       3      21.75  16.12   0.80       399.73   0.90      0.50\n",
                            "male   1      41.28  67.23   0.28       455.73   0.31      0.37\n",
                            "       2      30.74  19.74   0.22       447.96   0.34      0.16\n",
                            "       3      26.51  12.66   0.22       455.52   0.50      0.14"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th>Age</th>\n",
                            "      <th>Fare</th>\n",
                            "      <th>Parch</th>\n",
                            "      <th>PassengerId</th>\n",
                            "      <th>SibSp</th>\n",
                            "      <th>Survived</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sex</th>\n",
                            "      <th>Pclass</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
                            "      <th>1</th>\n",
                            "      <td>34.61</td>\n",
                            "      <td>106.13</td>\n",
                            "      <td>0.46</td>\n",
                            "      <td>469.21</td>\n",
                            "      <td>0.55</td>\n",
                            "      <td>0.97</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>28.72</td>\n",
                            "      <td>21.97</td>\n",
                            "      <td>0.61</td>\n",
                            "      <td>443.11</td>\n",
                            "      <td>0.49</td>\n",
                            "      <td>0.92</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>21.75</td>\n",
                            "      <td>16.12</td>\n",
                            "      <td>0.80</td>\n",
                            "      <td>399.73</td>\n",
                            "      <td>0.90</td>\n",
                            "      <td>0.50</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
                            "      <th>1</th>\n",
                            "      <td>41.28</td>\n",
                            "      <td>67.23</td>\n",
                            "      <td>0.28</td>\n",
                            "      <td>455.73</td>\n",
                            "      <td>0.31</td>\n",
                            "      <td>0.37</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>30.74</td>\n",
                            "      <td>19.74</td>\n",
                            "      <td>0.22</td>\n",
                            "      <td>447.96</td>\n",
                            "      <td>0.34</td>\n",
                            "      <td>0.16</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>26.51</td>\n",
                            "      <td>12.66</td>\n",
                            "      <td>0.22</td>\n",
                            "      <td>455.52</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.14</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 6
                },
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "                Age   Fare  Parch  PassengerId  SibSp  Survived\n",
                            "Sex    Pclass                                                  \n",
                            "female 1      34.61 106.13   0.46       469.21   0.55      0.97\n",
                            "       2      28.72  21.97   0.61       443.11   0.49      0.92\n",
                            "       3      21.75  16.12   0.80       399.73   0.90      0.50\n",
                            "male   1      41.28  67.23   0.28       455.73   0.31      0.37\n",
                            "       2      30.74  19.74   0.22       447.96   0.34      0.16\n",
                            "       3      26.51  12.66   0.22       455.52   0.50      0.14"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th>Age</th>\n",
                            "      <th>Fare</th>\n",
                            "      <th>Parch</th>\n",
                            "      <th>PassengerId</th>\n",
                            "      <th>SibSp</th>\n",
                            "      <th>Survived</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sex</th>\n",
                            "      <th>Pclass</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
                            "      <th>1</th>\n",
                            "      <td>34.61</td>\n",
                            "      <td>106.13</td>\n",
                            "      <td>0.46</td>\n",
                            "      <td>469.21</td>\n",
                            "      <td>0.55</td>\n",
                            "      <td>0.97</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>28.72</td>\n",
                            "      <td>21.97</td>\n",
                            "      <td>0.61</td>\n",
                            "      <td>443.11</td>\n",
                            "      <td>0.49</td>\n",
                            "      <td>0.92</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>21.75</td>\n",
                            "      <td>16.12</td>\n",
                            "      <td>0.80</td>\n",
                            "      <td>399.73</td>\n",
                            "      <td>0.90</td>\n",
                            "      <td>0.50</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
                            "      <th>1</th>\n",
                            "      <td>41.28</td>\n",
                            "      <td>67.23</td>\n",
                            "      <td>0.28</td>\n",
                            "      <td>455.73</td>\n",
                            "      <td>0.31</td>\n",
                            "      <td>0.37</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>30.74</td>\n",
                            "      <td>19.74</td>\n",
                            "      <td>0.22</td>\n",
                            "      <td>447.96</td>\n",
                            "      <td>0.34</td>\n",
                            "      <td>0.16</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>26.51</td>\n",
                            "      <td>12.66</td>\n",
                            "      <td>0.22</td>\n",
                            "      <td>455.52</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.14</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 6
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "source": [
                "# let's organize that table a little differently\r\n",
                "s_pcl_3 = pd.pivot_table(titan, index = 'Sex', columns = 'Pclass', values = ['Age', 'Fare', 'Survived'], aggfunc = 'mean')\r\n",
                "s_pcl_3"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "         Age               Fare             Survived          \n",
                            "Pclass     1     2     3      1     2     3        1    2    3\n",
                            "Sex                                                           \n",
                            "female 34.61 28.72 21.75 106.13 21.97 16.12     0.97 0.92 0.50\n",
                            "male   41.28 30.74 26.51  67.23 19.74 12.66     0.37 0.16 0.14"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
                            "      <th colspan=\"3\" halign=\"left\">Fare</th>\n",
                            "      <th colspan=\"3\" halign=\"left\">Survived</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Pclass</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sex</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>female</th>\n",
                            "      <td>34.61</td>\n",
                            "      <td>28.72</td>\n",
                            "      <td>21.75</td>\n",
                            "      <td>106.13</td>\n",
                            "      <td>21.97</td>\n",
                            "      <td>16.12</td>\n",
                            "      <td>0.97</td>\n",
                            "      <td>0.92</td>\n",
                            "      <td>0.50</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>male</th>\n",
                            "      <td>41.28</td>\n",
                            "      <td>30.74</td>\n",
                            "      <td>26.51</td>\n",
                            "      <td>67.23</td>\n",
                            "      <td>19.74</td>\n",
                            "      <td>12.66</td>\n",
                            "      <td>0.37</td>\n",
                            "      <td>0.16</td>\n",
                            "      <td>0.14</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 7
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "source": [
                "# what if we want different aggregates for different features\r\n",
                "# no problem\r\n",
                "s_pcl_4 = pd.pivot_table(titan, index = 'Sex', columns = 'Pclass', values = ['Age', 'Fare', 'Survived'], aggfunc = {'Age': np.mean, 'Fare': np.mean, 'Survived': np.sum})\r\n",
                "display(s_pcl_4)"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "         Age               Fare             Survived        \n",
                            "Pclass     1     2     3      1     2     3        1   2   3\n",
                            "Sex                                                         \n",
                            "female 34.61 28.72 21.75 106.13 21.97 16.12       91  70  72\n",
                            "male   41.28 30.74 26.51  67.23 19.74 12.66       45  17  47"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
                            "      <th colspan=\"3\" halign=\"left\">Fare</th>\n",
                            "      <th colspan=\"3\" halign=\"left\">Survived</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Pclass</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sex</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>female</th>\n",
                            "      <td>34.61</td>\n",
                            "      <td>28.72</td>\n",
                            "      <td>21.75</td>\n",
                            "      <td>106.13</td>\n",
                            "      <td>21.97</td>\n",
                            "      <td>16.12</td>\n",
                            "      <td>91</td>\n",
                            "      <td>70</td>\n",
                            "      <td>72</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>male</th>\n",
                            "      <td>41.28</td>\n",
                            "      <td>30.74</td>\n",
                            "      <td>26.51</td>\n",
                            "      <td>67.23</td>\n",
                            "      <td>19.74</td>\n",
                            "      <td>12.66</td>\n",
                            "      <td>45</td>\n",
                            "      <td>17</td>\n",
                            "      <td>47</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "source": [
                "titan.info()"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "<class 'pandas.core.frame.DataFrame'>\n",
                        "RangeIndex: 891 entries, 0 to 890\n",
                        "Data columns (total 12 columns):\n",
                        " #   Column       Non-Null Count  Dtype  \n",
                        "---  ------       --------------  -----  \n",
                        " 0   PassengerId  891 non-null    int64  \n",
                        " 1   Survived     891 non-null    int64  \n",
                        " 2   Pclass       891 non-null    int64  \n",
                        " 3   Name         891 non-null    object \n",
                        " 4   Sex          891 non-null    object \n",
                        " 5   Age          714 non-null    float64\n",
                        " 6   SibSp        891 non-null    int64  \n",
                        " 7   Parch        891 non-null    int64  \n",
                        " 8   Ticket       891 non-null    object \n",
                        " 9   Fare         891 non-null    float64\n",
                        " 10  Cabin        204 non-null    object \n",
                        " 11  Embarked     889 non-null    object \n",
                        "dtypes: float64(2), int64(5), object(5)\n",
                        "memory usage: 83.7+ KB\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Ok, a fair number of \"features\", with Age, Cabin and Embarked missing data. Much as we discovered in the previous post looking at `groupby()`. Now since we will eventually be interested in predicting the probability of a passenger's survival, let's perhaps look at which of the above features might contribute to a higher (or lower) rate of survival.\r\n",
                "\r\n",
                "Seems unlikely that PassengerId, Name, or Ticket are likely to correlate to survival in any way. I do expect that Sex, Age, Pclass likely have some correlation with survival. And, if travel class does, perhaps fare will also have some value as it would reflect the travel class as well as the passenger's status in general. The latter likely having some impact on accessability to the upper decks.\r\n",
                "\r\n",
                "But what about Cabin and Embarked. Since they are missing data do we really need to deal with that issue.\r\n",
                "\r\n",
                "Then there is the SibSp and Parch features.\r\n",
                "\r\n",
                "Let's have a look. Starting with `Embarked` first."
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "source": [
                "fgrid = sns.FacetGrid(titan, col='Embarked', col_wrap=2, height=4.0, aspect=1.2, sharex=False)\r\n",
                "fgrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=None, order=None, hue=\"Sex\", hue_order=None)\r\n",
                "fgrid.add_legend();"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 761.7x576 with 3 Axes>"
                        ],
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAI4CAYAAADnKmagAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABxfUlEQVR4nO3dd3hUVf7H8fc3PYTee1cUEVsAu6CsYFnpiKCg2Ne2a3cVe1ux/ywsKgKKIk3ABi4qWCmhg6DSe68ppJ7fHzOEEEISQiZ3Jvm8nmceZu65c+c7jrn55My555hzDhERERERCT1hXhcgIiIiIiJFozAvIiIiIhKiFOZFREREREKUwryIiIiISIhSmBcRERERCVEK8yIiIiIiIUphXoKKmWWa2YIct4eP4bntzezL43z96WYWX8TnDjeznsf5+mFm9qaZLTGzxWY2x8yaHM8xRSS0lPXzoP84J5rZ12a2wsyWmdkYM6t1vMcVKY0ivC5AJJcU59zpXrywmYV78bq5XA3UBVo757LMrD6Q5HFNIlKyyvR50MxigK+Ae51zX/i3dQBqAFu9rE0kGKlnXkKCma0xs+fN7DczSzCzM81sqpmtNLPbcuxa0cw+N7PfzWyImYX5n/+u/3lLzeypXMd93Mx+Bnrl2B5mZiPM7FkzCzezwf5e8kVmdqt/HzOzt/yv9RVQsxjeah1gs3MuC8A5t8E5t7sYjisiIa4MnQf7Ar8dDPIAzrkfnHNLiuHYIqWOeuYl2MSa2YIcj19wzn3mv7/eOXeOmb0GDAfOA2KApcAQ/z5tgZbAWmAK0B0YBzzqnNvl73X6zsxaO+cW+Z9zwDl3PoD/F2IEMApY4px7zsxuAfY659qYWTTwi5l9C5wBtABOBWoBvwPDcr8hM3sA6JfHe/3ROXd3rm1jgJ/N7ALgO+Bj59z8Av6biUjpUtbPg62AuQX+VxIRQGFegk9+Xy9P9v+7GCjvnNsP7DezA2ZW2d822zm3CsDMPgXOx/dLrLf/l1EEvt7vlsDBX2IHf0ke9F9gjHPuOf/jS4HWOcaBVgJOAC4EPnXOZQKbzOz7vIp2zg0GBhf4zn37bjCzFsDF/tt3ZtbLOfddYZ4vIqVCmT4PisixUZiXUJLq/zcrx/2Djw/+v+xyPcf5LyC9H2jjnNttZsPx9WQdlHtM+q9ABzN7xTl3ADDgLufc1Jw7mdnlebzeEY6xRwrnXCrwDfCNmW0FuuLrpRcRKQvnwaXARQUdU0R8NGZeSpu2ZtbEP0b0auBnoCK+X1R7/bMhXFbAMT4AvgbGmlkEMBW43cwiIXuWhTjgR6CPfyxpHaBDXgdzzg12zp2ex+2IIO8fA1vXfz8MaI3vq3IRkcIK6fMg8AlwrpldcXCDmXU2s1OP5T+CSFmhnnkJNrnHik5xzhV6WjbgN+BFfOM3fwQ+988KMx9fb88q4JeCDuKce9XMKgEf4etNagzMMzMDtuPrLf8c31CYxcCfwIxjqPNoagLv+cekAswG3iqG44pI6CjT50HnXIqZXQm8bmavA+n4hgPdc7zHFimNzLkCvx0TEREREZEgpGE2IiIiIiIhSmFeRERERCREKcyLiIiIiIQohXkRERERkRAVcrPZdO7c2U2ZMsXrMkREiosV5Uk6F4pIKVKk86D4hFzP/I4dO7wuQUTEczoXiogIhGCYFxERERERH4V5EREREZEQpTAvIiIiIhKiFOZFREREREKUwryIiIiISIgKuakpRUREJHAyMjL4fOJkxo4bz+YtW6hTuza9evage7cuhIeHe12eiOQSsDBvZsOAK4FtzrlWebQb8AZwOZAMXO+cmxeoekRERCR/GRkZ3POv+5j67bTsbZs3b2He/AVMnzGDN157hYgI9QOKBJNADrMZDnTOp/0y4AT/7Rbg3QDWIiIiInnIyMhg3759bNq8mf8Off+wIJ/T1G+nMXHSFyVcnYgUJGB/XjvnfjSzxvns0gUY6ZxzwEwzq2xmdZxzmwNVU7C57oNZbNidQv0qsXx0YzuvyxERKXEDBt7Mxo0bqVevHiOGved1OUEvMzOT5JQUkpOSSU5OJikpiaTkXPeTfPeTk5NJSk4m6eC+yYe2JycdepyWllbo1x87bjw9e3QL4DsUkWPl5Xdl9YD1OR5v8G87Isyb2S34eu9p2LBhiRRXEjbsTmH1jiSvyxCREFEaz4UbN25kzZq1XpcREM45UlJScgXuQ0E6rzB+cJ/s0J2cTKI/jCcnJ5OSkuLpe9q0ucz0t4mEDC/DvOWxzeW1o3NuKDAUID4+Ps99RERKu9J0Ljx4keXmzVsA37jsseMmeHaRpXOO1NTUo/Rs57qf3dOdkud+2c9PScH35XNwiIiIIC6uHHFxcZQrV464cofulytXjvJxcUz7/nu2bdt+1GNER0WXYMUiUhhehvkNQIMcj+sDmzyqRURESkheF1mmpqbyyKODCn2RZVpa2qFe7Hx6tg8L2cnJJOYI48nJKb7H/rbMzMxAv/VCCwsLyw7a5ePiKFcu1hfA4+KIiyuXHcDj4uKIy3k/7tD9Q4Hdty0qKqrA1z311FY88uigo7avWbuWe+9/iKeeeIwKFSoU51sWkSLyMsxPBu40s9FAO2BvWRovLyJSVn0+cXK+F1kOuOFmatWqmR2y8xqKkp6eUcJV5y+7lzvuKCE7RxgvF1cuV1u5I9qio6PxTfpWsrp368L0GTOO+vkATP7iS+bPX8ArL/+HM884veSKE5E8BXJqyk+B9kB1M9sAPAFEAjjnhgBf45uWcgW+qSlvCFQtIiISPMaOG59v+6zZswP6+rGxB4P1oVCdf892rl7ug/v422JiYggLKx1rMIaHh/PGa68wcdIXjB03nk2bN1O3Th169exB7Vo1eeiRx9i6bRvrN2zgmn79ufvOf3DbrTdr/nkRD1kwjecrjPj4eJeQkOB1GcclIzOLCfM2MmjSElIzsoiOCOOZLq3ocVZ9wsNKvidGDqdZhqSEFemHPpTPhRe0vyR7rHxBoqKiDvVeHzHeu9yRY7/j8grgh9rKxcYqeB6HXbt28+/HHmfad99nb2sTfxavDH6RunXreliZhDiFn+OglR9KWEZmFnd+Mp8pSw/9IkvNyOLB8Yv4fvk23up7BhHhpaOHJ1RpliGRwKpTu3a+Yf7UVqcw4sP3iY2NJTIysgQrk4JUrVqFd99+k09Hj+G5F/5DamoqcxLmcmWXHjz3zJNc1rmT1yWKlDlKjSVswryNhwX5nKYs3cK4eRtKuCIRkZLVq2ePfNv79b2GihUrKsgHKTOj7zVXM3HCGE5qcSIA+/bt46577uWRRx8nOTnZ4wpFyhb1zJewzxLW59v+yPjFvP6/v6hZMZqaFWL8/0ZTq2IMNSv4ttWqGE218tEakiMiISm/iyw7XdqRbl2v8qAqOVYnNG/O+LGjGfzyqwwf+THgux4iIWEur706mFantPS4QpGyQWG+hG3ek/+CHw7Ysu8AW/YdAPYedb8wg2rlo6l1MPRXiKamP/BnB/+K0VQvH02khu2ISBDJeZHlE089Q2pqKtHR0Tz1xCC6db1KY9pDSHR0NI89+gjnn38eDz78KLt27WL1mjX0uvoa7rv3Xwy8vn+puThYJFgpzJewOpVj2bT3wFHbK8ZEUKdSLFv3H2BPcvpR98tysH1/Ktv3pwL7jrqfGVSLi6KGv0c/Z+9+DX/Pf62KMdQoH01UhE64IlIyIiIi6NmjG0OGvseaNWupU6c2PXt087osKaL2F13IV5Mn8ODDj/LTz7+Qnp7Bi/8ZzE8//8LgF5+nZs0aXpcoUmopzJewq+MbMHft7qO2P3ZlS3rH+9bSSs3IZPv+VLbuS2X7/gNs25/K1n0H2LYvNfv+9v2p7ExKO+rxnIMdiWnsSExjWQGz+FeNi6JmhWhqHDasx3/f/w1AjQrRxESq10xERA5Xo0YNPnhvCMNHfsTgl18lPT2DX375lSuu6sZ/XniWizu097pEkVJJYb6E9TirPt8v35bnRbCdT6lNjzPrZz+OjginfpVy1K9SLt9jpmVksSPRF/C37TvA1v2pbN+XI/zv97XtSEwlv5lIdyWlsSspjeVb9uf7epViI3MN5zk0rCfn2P7YKIV+EZGyJCwsjIHXD6Bd27b8694HWLV6Nbt37+aW2+7gumv78vCD9xMdHe11mSKlisJ8CQsPM97qewYT5m9k0MQc88x3bUWPM4s2z3xURBh1K8dSt3JsvvtlZGaxMyntiN79bft9Pf9b96Wybf8BdiSmkZl19NS/NyWdvSnp/LUtMd/XqxATcdiwnpq5w7//D4K4aP1vKCJSmpzS8mQmThjD8y++xOjPxgLw0cefMGv2HF5/ZTAnnniCxxWKlB5KUR6ICA+jd3wD3p2+ktU7kqhbOTZ7aE2gX7dWxRhqVYzJd7/MLMfOpFS27Uv1D/M52Lt/MPAf6vnPyCf07z+Qwf4DGazcnv+c7XFR4b5x+/6gX8vfy39oNh/fvxWiIzxZ3lxERI5duXLlePbpJzn/vHN5dNAT7N27jz///ItuPa/mkYceoF/fPjqnixQDhXk5QniY+WfIyT/0Z2U5dienHT6cJ/vfVLbuP5D9B0FaZtZRj5OUlsmqHUmsKmChptjI8OypOo+YuSf7At8YKsYWLfQfXJl3k3/GoU17UhgzZ71W5hUROQ6dO11K69ancv+DjzB79hxSU1N58uln+fGnn3nx+WepWrWK1yWKhDSFeSmysDCjWnnfnPcn16l41P2cc+xJTs/Vu39wqM/hQ35SM44e+lPSM1m7M5m1O/NfkCQ6IuxQj74/8NfI42LeKuUis0O/VuYV8Ua9evUO+1dKp7p16vDR8A/473sf8Mabb5GZmcn3P0znyqu6MfilFzjv3HO8LlEkZJnL74rIIBQfH+8SEhK8LqNYdHh5Oqt3JNGkehw/3N/e63I855xj34GMw8bvb9uXekT437ovlZT0zON+vajwMP/QnmjSM7JYsunoU3y+1LN1iQyFkjKpSF/7lKZzoZQtCxYu4l/3Pcj69b5FFM2Mm2+8gX/ecxdRUVEeVyce0dffx0E98xI0zIxKsZFUio2kec0KR93POUdiakb2cJ7cQX/b/kNDfRJTM456nLTMLDbuSWFjAQt5AYyZs15hXkSkGJx+WmsmTxzHU08/x8RJk3HOMfT9Yfz62yxee+UlmjRp7HWJIiFFYV5CjplRISaSCjGRNKtRPt99k9My/L37h6bo3Jb7gt59B9h34OihH8geRy8iIsevQvnyvPzSC1xw/nk8/uTTJCUlsWTpUrp078njjz1Kj+5ddXGsSCEpzEupVi4qgsbVI2hcPS7f/bq9/Qvz1+85antB036KiMix63LVlZxxxmnce/9DLFiwkOTkFB7+92P8+NPPPPv0E1SsePTrsUTER1f0iQDXtG2Yb3tBfwyIiEjRNGzQgE8/HsEdt9+a3Rv/9TdTuLJLdxIS5npcnUjwU5gXwbcyb+dTah+1fcK8DUz7fWsJViQiUnZERkbyr3/ezccjP6R2bd+5eNOmzfS97npef/MtMjLyHwopUpYpzHuofpVYmlSPo34VDeHw2sGVeV/q2ZroCN+PRXREGFe0rgNAloM7PpnH3LW7vCxTRKRUa9e2DV9OGk+nTn8DICsri7fefpe+1w5gw4aNHlcnEpwU5j300Y3t+OH+9nx0YzuvSxEOrcx7cHx83cqxvN33TG6+oAngm3d+4PAE/tq638syRURKtcqVK/PWG6/x3LNPERvrOx/Pm7+AK7t058uvvva4OpHgozAvUoBHLjuZrqfXBWBvSjr9h83W7DYiIgFkZlzdqycTx4+hZcuTAUhMTOSf9z7AQ488SmJi/iuGi5QlCvMiBQgLM17qeRoXnFAdgM17DzBg2Gz2JKd5XJmISOnWrFlTxn72CTcOvD572/gJE+nSvSeLFi32rjCRIKIwL1IIURFhDLn2LE6rXwmAv7YlctOIBA4Uw0q0IiJydNFRUTzy0AN8+MFQqlevBsDatevofc21/Hfo+2RlZXlcoYi3FOZFCikuOoJh17ehiX+ayoS1u7nzk/lkZOoXiYhIoF1w/nl8NflzOrS/CICMjAwGv/IaA264iS1bNduYlF0K8yLHoFr5aEYObEuNCtEATFu2lccmLsE553FlIiKlX7Vq1Rg65G0eH/RvoqKiAPht5iyuvKob/5v2ncfViXhDYV7kGDWoWo7hN7ShfLRvAeXRc9bz6v/+9LgqEZGywczof20/JowdTfPmzQDYs2cvt99xN48/8TQpKZqgQMoWhXmRIjilbiWG9j+LqHDfj9D/fb+Cj35b421RIiJlyEkntWDi+DH069sne9snoz+ja4/eLFu+3MPKREqWwrxIEZ3brDqvXX06/tXHeXzyUr5evNnbokREypCYmBieemIQQ975P6pUrgzAypWr6N6zDyNGfqwhkFImKMyLHIcrWtfhqatOAcA5+OfoBfy2cqfHVYmIlC0dL7mYLyd/zrnnnA1Aeno6zzz3Ajff+g927tQ5WUo3hXmR49T/nMbc2aE5AGmZWdwyMoHfN+3zuCoRkbKlVq2aDB/2Hg89cB8REb5rmqbP+JErrurGjz/97HF1IoGjMC9SDO679ESujm8AwP7UDAZ8OJv1u5I9rkpEpGwJCwvj5psGMnb0KBo1agjAjh07GXjTrTz/4kukpmmxPyl9FOZFcqlfJZYm1eOoXyW20M8xM57r1oqOJ9cEYPv+VPoPm83OxNRAlSkiIkdx6qmtmPz5OHp275a9bdiHI+jZ+xpWrlzlYWXFY8DAm+nY6XIGDLzZ61IkCCjMi+Ty0Y3t+OH+9nx0Y7tjel5EeBj/d82ZnNmwMgCrdyQxcPgcklIzAlCliIjkJy4ujhdfeJY3XnuZChUqALBs2XK6dO/F6DFjQ/ri2I0bN7JmzVo2btzodSkSBBTmRYpRbFQ4w65vQ/Oa5QFYuGEvt4+aR7pWiRUR8cQVl1/Gl5MmcNaZZwBw4MABHhv0JHfe/U/27NnjbXEixSCgYd7MOpvZH2a2wswezqO9kpl9YWYLzWypmd0QyHpESkLlclGMHNiWOpViAPjxz+08NG4RWVmh2wskIhLK6tWry6iPhnP3XXcQFuaLPlO/ncYVV3Vn5qzZHlcncnwCFubNLBx4G7gMaAlcY2Ytc+12B/C7c+40oD3wiplFBaomkZJSt3IsIwa2pWKMb0aFCfM38p8pWsRERMQrERER3H3nP/j04xHUq1cXgK1bt3LdgIG88tobpKene1yhSNEEsme+LbDCObfKOZcGjAa65NrHARXMzIDywC5AA4ylVDixVgWGXd+G6Ajfj9l/f1zF+z+F/oVXIiKh7KyzzuSLieO54vLLAHDO8e6QofTp159169d7XJ3IsQtkmK8H5Pyp2ODfltNbwMnAJmAxcI9zToOLpdSIb1yVt/qeSZh/ldhnv1rGxPm6YElExEsVK1bk9VcH89KLz1GunG/msoULF/H3Lj2YOOkLj6sTOTaBDPOWx7bcg4Y7AQuAusDpwFtmVvGIA5ndYmYJZpawffv24q5TJKD+1rIWz3c7Nfvx/WMX8uOf+v9Yjp3OhSLFx8zo3q0rkz8fz6mtfCt5JyUlcf+DD3Pv/Q+xPzHR4wpFCieQYX4D0CDH4/r4euBzugGY4HxWAKuBk3IfyDk31DkX75yLr1GjRsAKFgmUPm0bct/fTgQgI8tx28dzWbRhj7dFScjRuVCk+DVu3IjPPv2YW2+5Cd+oX5j8xZdc1aUH8xcs9Lg6kYIFMszPAU4wsyb+i1r7AJNz7bMOuATAzGoBLQANKpZS6c6Lm9P/nEYAJKdlcsOHc1i9I8njqkREJCoqigfu+xcjPnyfWjV9i/+t37CBPn2v4+13hpCZmelxhSJHF7Aw75zLAO4EpgLLgDHOuaVmdpuZ3ebf7RngXDNbDHwHPOSc2xGomkS8ZGY88fdTuPzU2gDsTEqj/7BZbNt/wOPKREQE4NxzzuaLSRPoeMnFAGRmZvLaG//HtQMGsmnzZo+rE8lbQOeZd8597Zw70TnXzDn3nH/bEOfcEP/9Tc65S51zpzrnWjnnPg5kPSJeCw8zXu19Ou2aVAVg/a4Urh82h/0HNCWaiEgwqFq1Cu++/SZPP/k40dHRAMyZk8CVV3XnmylTPa5O5EhaAVakhMVEhvPegHhOqu1bXvz3zfu49aO5pGboa1wRkWBgZvS95momThhDixN91zvt27ePu+65l38/9jjJyckeVyhyiMK8iAcqxkQyYmBb6lfxTYn268qd3DtmoVaJFREJIic0b86EcaMZcN212dvGjB1Pl269WLL0dw8rEzlEYV7EI7UqxjByYFuqxvkWPf5q0Wae/vJ3nFOgFxEJFtHR0Qx67BHe++87VK3qGyK5es0ael19DR98OJysLC2PI95SmBfxUNMa5Rl2fRtiI8MBGP7rGt6ZvtLjqkREJLcO7S/iq8kTOP/8cwFIT8/ghRcHM/DmW9G6D+IlhXkRj53eoDLvXnsmEf5lYgdP/YMxc7SkuIhIsKlRowbD3vsv/37kQSIjIwD4+edfueKq7vwwfYbH1UlZpTAvEgTat6jJSz1bZz9+5PPFfLdsq4cViYhIXsLCwhh4/QDGjRlN0yZNANi1axc33/oPnn72eVJTUz2uUMoahXmRINH9zPr8+3LfAsiZWY47PpnH3LW7Pa5KRETyckrLk5k4YQx9ru6VvW3kR6Po3rMPf/61wsPKpKxRmBcJIrdc2Iybzvf19BxIz+LGEXNYsW2/x1WJiEheypUrx7NPP8lbb75GpUoVAfjjzz/p1qM3H4/6VBMaSIlQmBcJMv++/GS6nl4XgD3J6fT/YDab96Z4XJWIiBxN506X8sWkCbRt2waA1NRUnnz6WW77x13s2qVvWCWwFOZFgkxYmPFSz9O44ITqAGzae4ABw2azN1mrxIqIBKu6derw0fAPuPdf9xAe7puh7Lvvf+DKq7rx628zPa5OSjOFeZEgFBURxrvXnsWp9SoB8OfWRG4aOYcD6VolVkQkWIWHh/OP225h9Ccf0aB+fQC2bd/OgBtu4qXBr5CWluZxhVIaKcyLBKny0RF8eEMbGlcrB8CcNbu569P5ZGRqgRIRkWB2xumnMXnSeLpc9XcAnHMMfX8YV19zLWvWrPW4OiltFOZFglj18tGMHNiO6uWjAfjf71sZNGmJLqoSEQlyFcqX55XBL/LySy8SFxcHwOIlS7mqWw/GT/hc53EpNgrzIkGuYbVyDL+hDeWjfQuUfDp7Pa9N+8vjqkREpDC6dvk7X0waz2mn+dYSSU5O4aFHHuOf9z7Avn37PK5OSgOFeZEQ0KpeJYZedxZR4b4f2Te/+4uPZuqrWhGRUNCwQQNGjxrJHbffiplvte+vvv6GK7t0JyFhrsfVSahTmBcJEec2r86rV5+G//cAj09awjeLN3tblIiIFEpkZCT/+ufdfDzyQ2rXrg3Apk2b6Xvd9bzxf2+TkZHhcYUSqhTmRULIla3r8sSVLQFwDu4ZvYCZq3Z6XJWIiBRWu7Zt+HLSeDp1+hsAWVlZ/N9b79D32gFs2LDR4+okFCnMi4SY689rwj/aNwMgLTOLm0cksGyzxl2KiISKypUr89Ybr/Hcs08RGxsLwLz5C7iyS3e+/Oprj6uTUKMwLxKCHujUgt7xvjmM96dmMGDYbNbvSva4KhERKSwz4+pePZk4fgwnn3wSAImJifzz3gd46JFHSUxM8rhCCRUK8yIhyMx4vtupXHJSTQC27U9lwLDZ7ErSgiQiIqGkWbOmjBvzKTcOvD572/gJE+nSvSeLFi32rjAJGQrzIiEqIjyMt/qeyZkNKwOwakcSNwyfQ3KaLqISEQkl0VFRPPLQAwx7/79Ur14NgLVr19H7mmsZ+t4HZGVpsUA5OoV5kRAWGxXOBwPa0LxmeQAWrt/DP0bNI12rxIqIhJwLLzifryZ/TvuLLgQgIyODl15+lQE33MSWrVs9rk6ClcK8SIirEhfFiIFtqV0xBoDpf2znofGLtLqgiEgIqlatGu/99x0ef+zfREVFAfDbzFn8/aruTP32f4wdN4HNm7cAsHnzFsaOm0BmZqaXJYvHLNR+4cfHx7uEhASvyxAJOn9s2U+vIb+y74BvmM2tFzXlkctO9rgqKQQrypN0LhQp/ZYv/4N/3vcAK1aszHe/Tpd25I3XXiEiIqKEKit2RToPio965kVKiRa1K/DB9W2IjvD9WP93xire/2mVx1WJiEhRnXRSCyaOH0O/vn3y3W/qt9OYOOmLEqpKgo3CvEgp0qZxVf7vmjMI8/dxPPvVMiYt0CIkIiKhKiYmhqeeGETTpk3y3W/suPElVJEEG4V5kVLm0lNq81y3U7Mf3z92IT/9td3DikRE5HilpKTk275p8+YSqkSCjcK8SCl0TduG/KvjiQCkZzpu+2guizfs9bgqEREpqjq1a+fbXrdOnRKqRIKNwrxIKXX3Jc3p164hAElpmVz/4WzW7NCKgiIioahXzx7H1S6ll8K8SCllZjzdpRWdT/H15uxMSqP/sNls23/A48pERORYde/WhU6XdsyzrdOlHenW9aoSrkiChcK8SCkWHma83ud02jWpCsC6Xcnc8OEc9h9I97gyERE5FuHh4bzx2iu8+PyzREdHAxAdHc2Lzz/Lm6+/Snh4uMcVilcU5kVKuZjIcIb2j+ek2hUAWLppH7d+NJfUDC0yIiISSiIiIujZoxt16vi+ca1TpzY9e3RTkC/jFOZFyoBKsZGMGNiWepVjAfh15U7uHbOQrKzQWjROREREDqcwL1JG1KoYw8gb21KlXCQAXy3azNNf/k6orQItIiIihwQ0zJtZZzP7w8xWmNnDR9mnvZktMLOlZjYjkPWIlHXNapRn2PVtiI30fSU7/Nc1vDsj/2XCRUREJHgFLMybWTjwNnAZ0BK4xsxa5tqnMvAOcJVz7hSgV6DqERGfMxpW4Z1rzyTCv0zsS1P+YEzCeo+rEhERkaIIZM98W2CFc26Vcy4NGA10ybVPX2CCc24dgHNuWwDrERG/Di1q8p8erbMfPzJhMd8t2+phRSIiIlIUgQzz9YCc3X0b/NtyOhGoYmbTzWyumfXP60BmdouZJZhZwvbtWpZepDj0OKs+D192EgCZWY47PpnH3LW7Pa5K8qNzoYiI5BbIMG95bMt9pV0EcBZwBdAJGGRmJx7xJOeGOufinXPxNWrUKP5KRcqoWy9sysDzmgBwID2LG0fMYcW2/R5XJUejc6GIiOQWyDC/AWiQ43F9YFMe+0xxziU553YAPwKnBbAmEcnBzHjsipO56rS6AOxJTqf/B7PZslerxIqIiISCfMO8me03s31HuxVw7DnACWbWxMyigD7A5Fz7TAIuMLMIMysHtAOWFfXNiMixCwszXu51Guc3rw7Apr0HGDBsNnuTtUqsiIhIsMs3zDvnKjjnKgKvAw/jG/NeH3gIeLaA52YAdwJT8QX0Mc65pWZ2m5nd5t9nGTAFWATMBt53zi05rnckIscsKiKMIdedxan1KgHwx9b93DwygQPpWiVWREQkmBV2mE0n59w7zrn9zrl9zrl3gR4FPck597Vz7kTnXDPn3HP+bUOcc0Ny7DPYOdfSOdfKOfd6kd6FiBy38tERfHhDGxpVKwfA7DW7uPvT+WRkZnlcmYiIiBxNYcN8ppn1M7NwMwszs36AuuxESpnq5aMZObAt1ctHAfDt71sZNGmpVokVEREJUoUN832B3sBW/62Xf5uIlDKNqsUx/Ia2xEX5Von9dPY6Xp/2l8dViYiISF4KFeadc2ucc12cc9WdczWcc12dc2sCXJuIeKRVvUoM7R9PZLhvhtk3vvuLj2eu9bgqERERya1QYd7MTjSz78xsif9xazN7LLCliYiXzmtenVd7n475V4x4fNISpizZ7G1RIiIicpjCDrN5D3gESAdwzi3CN9WkiJRifz+tLo9f2RKALAd3j17AzFU7Pa5KSpPrPphFh5enc90Hs7wuRUQkJBU2zJdzzs3OtS2juIsRkeBzw3lNuL19MwDSMrK4eWQCy7cUtMyESOFs2J3C6h1JbNid4nUpIiIhqbBhfoeZNQMcgJn1BPR9u0gZ8WCnFvQ8qz4A+w9kMGDYbDbsTva4KhERESlsmL8D+C9wkpltBP4J3BaookQkuJgZL3Q/lQ4tagCwdV8q/YfNZldSmseViYiIlG2FDfNrnXMdgRrASc65851zmtpCpAyJDA/j7X5nckbDygCs2p7EwOFzSE7TiDsRERGvFDbMrzazocDZQGIA6xGRIFYuKoJhA9rQrEYcAAvW7+Efo+aRrlViRUREPFHYMN8CmIZvuM1qM3vLzM4PXFkiEqyqxEUx8sZ21KoYDcD0P7bz8PjFWiVWRETEA4VdNCrFOTfGOdcdOAOoCMwIaGUiErTqVY5lxMC2VIiJAGD8vA38Z8ofHlclIiJS9hS2Zx4zu8jM3gHmATFA74BVJSJB76TaFXm/fzxREb7TyJAZKxn282qPqxIRESlbCrsC7Gp8M9j8BLRyzvV2zo0PZGEiEvzaNa3Gm33OIMy/SuzTX/7O5IWbvC1KRESkDIko5H6nOee0SoyIHKFzq9o807UVj36+BID7xiygarkozj+huseVicjxuO6DWWzYnUL9KrF8dGM7r8sRkaPIN8yb2YPOuZeA58zsiKvbnHN3B6wyEQkZ/do1Yvv+VF6f9hfpmY5bP0pg9C3ncGr9Sl6XJiJFdHB1XhEJbgUNs1nm/zcBmJvHTUQEgHsuOYG+7RoCkJSWyQ3DZ7N2p4KAiIhIIOXbM++c+8J/d5Fzbn4J1CMiIcrMeKZLK3YmpjJ16VZ2JKZx3QezGX/7udSoEO11eSIiIqVSYWezedXMlpvZM2Z2SkArEpGQFR5mvNHnDNo2rgrAul3J3DB8NompWiVWREQkEAo7z3wHoD2wHRhqZovN7LFAFiYioSkmMpz3BsRzUu0KACzZuI9bP0ogNSPzuI993Qez6PDydK77YNZxH0tERKQ0KPQ88865Lc65N4HbgAXA44EqSkRCW6XYSEYMbEu9yrEA/LJiJ/eNWUhW1vGtEnvwgrwNu1OKo0wREZGQV9h55k82syfNbAnwFvArUD+glYlISKtVMYYRA9tSpVwkAF8u2swzX/2Oc8cX6EVEyrp69erRuHEj6tWr53UpEgQKO8/8h8CnwKXOOa0IIyKF0rxmeT64vg1935vJgfQsPvxlDTUrxHB7+2ZelyYiErJGDHvP6xIkiBTYM29m4cBK59wbCvIicqzObFiFd/qdSbh/mdj/TFnOuLkbPK5KRESkdCgwzDvnMoFqZhZVAvWUKQMG3kzHTpczYODNXpciElAXn1SLF7ufmv34ofGL+GH5Ng8rEhERKR0KO8xmLfCLmU0GsleBcc69GpCqyoiNGzeyZs1ar8sQKRG94huwIzGN/0xZTmaW4x+j5jHq5nac2bCK16WJiIiErMLOZrMJ+NK/f4UcNxGRQrvtoqbccF5jAFLSMxk4fA4rtiV6W5SIiEgIK1TPvHPuqUAXIiKln5kx6IqW7EhM44uFm9iTnM6AYb5VYmtXivG6PBERkZBT2KkpfzCz73PfAl2ciJQ+YWHGy71ac17zagBs3JPCgGGz2ZuS7nFlIiIioaeww2zuBx7w3wbhWzQqIUA1iUgpFx0RzpBrz+KUuhUB+GPrfm4emcCB9ONfJVZERKQsKVSYd87NzXH7xTl3L9AuwLWJSClWISaS4Te0pVG1cgDMXr2Le0bPJ/M4V4kVEREpSwo7zKZqjlt1M+sM1A5wbSJSytWoEM3IgW2pXt438+3UpVsZNGmJVokVEREppMIOs5mLb1hNAvArcC9wY6CKEpGyo1G1OIbf0Ja4qHAAPpm1jje++8vjqiTQMjKzGDNnPZv2pACwaU8KY+as1zczIiLHKN8wb2ZtzKy2c66Jc64p8BSw3H/7vaCDm1lnM/vDzFaY2cMFvE6mmfU81jcgIqGvVb1K/Pe6eCLDfavEvj7tL0bN0hoMpVVGZhZ3fjKfB8cvIjUjC4DUjCweHL+IO0bNIyMzy+MKRURCR0E98/8F0gDM7ELgBWAEsBcYmt8TzSwceBu4DGgJXGNmLY+y33+AqcdavIiUHuefUJ1Xep+e/XjQxCVMXbrFu4IkYCbM28iUo3y2U5ZuYcL8jSVckYhI6CoozIc753b5718NDHXOjXfODQKaF/DctsAK59wq51waMBroksd+dwHjAa3tLlLGXXVaXQZd6fubP8vBXZ/OZ9aqnR5XJcXts4T1+bZ//Ju+lRERKawCw7yZHVxY6hIg59zyBS04VQ/Iecbe4N+WzczqAd2AIQWXKiJlwY3nN+G2i5oBkJaRxU0jE1i+ZZ/HVUlx2uwfJ380izbu5faP5zJz1U5dDC0iUoCCwvynwAwzmwSkAD8BmFlzfENt8mN5bMt9Vn4deMg5l+/k0mZ2i5klmFnC9u3bC3hZEQl1D3VuQY8z6wOw/0AGA4bNZsPuZI+r8l5pORfWqRxb4D7fLNlCn6EzueyNn/hk1jqS0zJKoDIRkdCTb5h3zj0H3AcMB853h7pIwvANj8nPBqBBjsf1gU259okHRpvZGqAn8I6Zdc2jjqHOuXjnXHyNGjUKeFkRCXVmxos9TqVDC9/P+9Z9qXR7+1c2lvGZT0rLufDq+Ab5ttesEJ19f/mW/fz788Wc/fx3PPvl76zdmRTo8kREQkqBU1M652Y65z53ziXl2Panc25eAU+dA5xgZk3MLAroA0zOdewmzrnGzrnGwDjgH865icf6JkSk9IkMD+PtfmdyWoNKAGxPTCVNM5+UCj3Oqk/nU/JeqqTzKbX59eGLGXVTO/7WshZh/u949x3I4P2fV9P+5ekMHD6H6X9sI6uM/TEnIpKXgsa9F5lzLsPM7sQ3S004MMw5t9TMbvO3a5y8iOSrXFQEXU+rx8L1eY/qOzjzSe8CenoluISHGW/1PYMJ8zcyaOISUjOyiI4I45murehxZn3Cw4zzmlfnvObVWb8rmY9nreWzOevZk5yOc/D98m18v3wbTarHcd3ZjegZX5+KMZFevy0REU8UdtGoInHOfe2cO9E518w/ZAfn3JC8grxz7nrn3LhA1iMioefLxZvzbR8zJ/+ZUSQ4RYSH0Tu+AXX94+frVo6ld3wDwsMOv9yqQdVyPHLZycx85BJe6tmaU+pWzG5bvSOJp7/8nbOf/47HJi7mz637S/Q9iIgEg4CGeclbRkYGY8dNYPNm3zzLmzdvYey4CWRm5nsdsEiZVNDMJ5sKaJfSISYynN7xDfjyrvMZf/s5/P20ukT4g39yWiYfz1zHpa/9yDVDZzJlyRYNvxKRMiNgw2wkbxkZGdzzr/uY+u207G2pqak88uggps+YwRuvvUJEhD4WkYPqVI5l094DR22vW4iZUaT0MDPOalSVsxpVZdsVJ/PJ7HWMmrWO7ftTAfht1U5+W7WTepVj6Xd2Q/q0aUjVuCiPqxYRCRz1zJewzydOPizI5zT122lMnPRFCVckEtwKmvmkdxuNly+ralaM4Z8dT+SXhy7mzWvO4KxGVbLbNu5J4aUpf3D2C99x35iFLN5Q0GzKIiKhSWG+hI0dN/642kXKmoJmPjk4H72UXVERYVx1Wl3G334uX951Pr3Oqk9UhO/XW1pGFuPnbeDvb/1Mt3d+YdKCjdmzIomIlAYaz1HCNm/Zkm/72nXrSqgSkdBQmJlPRA5qVa8Sg3udxiOXn8xnc9bz8cy12esTzF+3h/nrFvBM+WX0bduAfmc3olbFGI8rFjl2130wiw27U6hfJZaPbmzndTniMfXMl7A6tfPuYTxox46d9Ly6L59PnERqamoJVSUS3Ao784nIQVXjori9fTN+fLADQ687i/OaV8tu25GYypvfr+C8F7/njk/mMWfNLg6tiSgS/DbsTmH1jiQ27NYEAKIwX+J69exR4D4LFizkgYf+zfkXXsx/XnpZvfUiIkUUHmZcekptRt10Nv/714Vcd3YjykWFA5CR5fhq0WZ6DfmNy9/8mdGz15GSplnFRCS0KMyXsO7dutDp0o55tp1+WmvOPefs7Me79+zhvQ8+pOOllzPwplv57vsfNH2liEgRnVCrAs90bcXMf1/CE39vSZPqcdltyzbv4+EJizn7he94/utlrN+V7GGlIiKFpzBfwsLDw3njtVd48flniY6OBiA6OpoXn3+Wzz79mJHDP+Dbb77k+gHXUbGib3EU5xw//vQzt95+Jx06duLd/77Hzp07vXwbIiIhq2JMJDec14Tv7r2IkQPbcslJNTH/iK29KekM/XEVFw7+gZtGzOGnv7ZrCI6IBDWFeQ9ERETQs0c36tTxjZ+vU6c2PXt0Izzc99Vv06ZNeOzfD/PLj9/zwnPPcMopLbOfu2nTZl559XXOv+hi/nXfAyQkzNUvGhGRIggLMy48sQYfXN+GGfd34OYLmlAxxjcvhHMwbdk2rvtgNpe8OoPhv6xm/4F0jysWETmSwnwQi42NpVfP7kwcP4bxYz+lR/eu2b356ekZfPHl1/Tp158rr+rOJ59+RmJikscVi4iEpobVyvHoFS2Z9e+OvNj9VE6qXSG7bdX2JJ784nfOfv47Hp+0hBXbEj2sVETkcArzIcDMOK11a/7zwnP8PON7Hn7oARo2PLRQzh9//snjTz7NeRd24ImnnuHPP//ysFoRkdAVGxVOn7YN+eaeCxhz6zlccWqd7FmTktIyGfnbWjq+OoNr35/Ft0u3kJmlb0ZFxFsK8yGmSpXK3DTweqZN/ZoPPxhKx0suJizM9zEmJSUx6pPRXP73rlxz7QC++vob0tLSPK5YRCT0mBltm1Tl7X5n8stDF3P3xc2pXj4qu/3nFTu45aO5XPjSDwyZsZLdSTrXiog3tGhUiAoLC+OC88/jgvPPY9OmTYz+bCxjxo1nxw7fhbFz5iQwZ04C1atXo3fPHvS5uhd169b1uGoRkdBTu1IM917agjsubs43i7cw/Nc1LFi/B4CNe1J48ZvlvPa/P+lyel36n9OYVvUqeVuwiJQp6pkvBerWrcu9/7qHH3+YxhuvvUybNvHZbTt27OSdIUNpf0knbvvHXfz08y9kZWkpcxGRYxUdEU7XM+ox8Y7zmHTHefQ4sz5R4b5fo6kZWYxJ2MCV//czPd/9lckLN5GWoXOtiASeeuZLkaioKK64/DKuuPwy/vzzL0Z9OpqJk74gKSmJrKwspn33PdO++56GDRvQ95o+9OjWlSpVKntdtohIyDmtQWVeaVCZf19+EqPnrOfjmWvZvPcAAAlrd5Owdjc1K0TTt11D+rZtSM2KMR5XLCKllXrmS6kTTzyBp54YxC8//sDTTz5OixNPzG5bt249L/5nMOdfdDEPPfIoixYt9rBSEZHQVa18NHd0aM5PD3ZgyLVnck7Tatlt2/an8vq0vzjvP99z96fzmbt2l6YSFpFipzBfypUvH0ffa67my8kTGD1qJH+/8nIiI31fyKSmpjJ+wkS69+pD1+69GTtuAikpKR5XLCISeiLCw+jcqg6f3nI2U/95If3aNSQ20rd2SHqmY/LCTfR49zf+/tbPjElYz4F0reYtIsVDYb6MMDPi48/itVcG89P077jv3n9St26d7PYlS5fyyKODOO/Ci3nuhf+wevUa74oVEQlhLWpX4LlupzLz35cw6MqWNK5WLrttycZ9PDhuEee88B0vfrOcDbuTPaxUREoDhfkyqHr16tx+6838MG0qQ4e8zUUXXoD51zLft28fHw4fyd86X8GAG27i2/9NIyMjw+OKRURCT6XYSG48vwnf39eeD29oQ/sWNbLbdienM2TGSi586QduGZnALyt2aAiOiBSJLoAtw8LDw7m4Q3su7tCetevWMXr0GMaOn8CePXsB+OXX3/jl19+oVasWfXr35OrevahZs0a+xxQRkcOFhRkdWtSkQ4uarNmRxEcz1zImYT37D2SQ5eDb37fy7e9baV6zPAPOaUS3M+tTPlq/nkWkcNQzLwA0atiQhx68n19+/IHB/3mB008/Lbtt69atvPF/b3Nhh47cdc+9zJw1Wz1IIiJF0Lh6HIOubMnMRy7huW6tOLFW+ey2FdsSGTRpKec8/x1PTl7Kqu2JHlYqIqFCYV4OEx0dTbeuVzHus0+YOGEsvXv1ICbGN6VaRkYG30yZyrX9b6DzFVcx8qNR7N+/3+OKRURCT1x0BP3aNWLqPy/k05vP5rJWtQkP8w133J+awfBf13DxKzPoP2w23y3bSmaWOlBEJG8K83JUrU5pyfPPPs2vP/3AoEcfoWmTJtltK1eu4ulnn+fcCzrw2ONP8vuyZR5WKmVF/SqxNKkeR/0qsV6XIlIszIxzmlXj3WvP4qcHO3BHh2ZUjYvKbv/xz+3cOCKBDi9P570fV7E3Od3DakUkGGlQnhSoYsWKDOh/Lf2v68fMmbP4+NPRTJv2PZmZmaSkpDD6s7GM/mwsZ5xxOv2u6cNlnS8lOjra67KlFProxnZelyASMHUrx/JAp5O46+IT+GrRZkb8toZFG3zXMK3blcxzXy/jlf/9Qbcz6tH/nMacXKeixxWLSDBQmJdCMzPOOedszjnnbLZs3cqYseP57LOxbN22DYD58xcwf/4CnnvhP/Tq2Z1rru5Ngwb1Pa5aRILZwW9Z9G3LITGR4fQ4qz49zqrP/HW7GfnbWr5ctIn0TMeB9Cw+nb2eT2evp23jqgw4tzGXnlKLyHB90S5SVlmoXcgYHx/vEhISvC6jWHTsdDlr1qylceNGTJv6tdflFEl6ejrfff8Doz4ZzW8zZx3WZmZcdOEF9OvbhwsvOJ/w8HCPqhQJalaUJ5Wmc6EUbPv+VEbPXsfHs9aydV/qYW21K8bQr11D+rRtSI0KxfetaIeXp7N6RxJNqsfxw/3ti+24UnQZmVlMmLeRQZOWkJqRRXREGM90aUWPs+pnX3MRokK6eK8pzHtowMCb2bhxI/Xq1WPEsPe8Lue4rVy5ik9Hf8b4zycdcWFs/Xr1uKZPb3r27E61qlU9qlAkKCnMS6GlZ2bx7dKtjPhtDbNX7zqsLTLcuOLUOgw4tzFnNKxy3K+lMB9cMjKzuPOT+UxZuuWIts6n1OatvmcQEbrf0CjMHweFeSl2ycnJfPHV14z6ZDS//374hbGRkZFc1rkT1/btwxlnnJ69WJVIGaYwL0Xy+6Z9fDRzDZ/P38iB9KzD2k6rX4n+5zTmitZ1iIks2reiCvPBZfTsdTw8YfFR21/q2Zre8Q1KsKJipTBwHBTmJWCccyxctJhRn3zKV19PIS0t7bD2k09qQb++ffj7lVcQFxfnUZUinlOYl+OyJzmNsQkbGDlzDet3pRzWVjUuij5tGnDt2Y2oW/nYrktQmC9+zjmS0jLZk5zG3pR09qaksy8lnT3J6dmP96aks8e/Pfuxvz0/8Y2qMO72c0vonRQ7hfnjoDAvJWLXrt2Mn/A5n4wew/r16w9rK1++PN26XkXfa67mhObNParwkNI2/EmCnsK8FIvMLMf0P7Yx4re1/Pjn9sPawgwubVmbAec25uymVQv1rajCfN6cc6SkZx4RtAsTzPelpJMRoDUD6laK4ddHLgnIsUuAwvxx0Gw2UiKqVq3CzTcN5MaB1/PTz78w6pPR/DB9Bs45EhMT+ejjT/jo409o17YNfa/pw986XkxUVFTBBw6AjRs3smbNWk9eW0SkqMLDjEtOrsUlJ9di5fZEPvptLePmbiAxNYMsB1OWbmHK0i20qFWB/uc2ouvp9YiLLrsx4ECOQL43JZ29yb7gfXgwTzt8H/8tPTPwHaERYUal2EjfrVwkK7clsu9AxlH3P9ZvXqT0KLs/xeKJsLAwLrrwAi668AI2btzE6DFj+WzMOHbt8l3INWv2HGbNnkONGtW5uldPrr66F3Vq1/a4ahGR0NKsRnmevOoU7u/Ugs/nbWDEb2tZsS0RgD+27ufRz5fw4jfL6XVWA/qf04jG1UNzqGNaRlaOkJ2WbzDP3YuempFV8AscpzDjUCCPjaRibCSVy0VRKTbisO2VYqMOC+6VYyMpFxV+2DcoY+as58Hxi476Wr3bhOx4eTlOGmYjnktNS+Pbb//HqE9GkzB33mFt4eHhXHJxB/peczXnnnM2YWGBv1K/NEwZKiFFw2wk4Jxz/LpyJyN+XcO0ZVvJPdKjfYsaDDi3MRedUIMs50p0+sOMzKw8e78PhvKDQ1XyCuYp6ZnFXk9uZlAhOoJK5Xxhu7I/eFf0h+/K5SJzBfNDobx8VARhxfTfLDPLcceoeUedzebtfmeG8vSUIVt4MAhomDezzsAbQDjwvnPuxVzt/YCH/A8TgdudcwvzO6Z+gZVuf/zxJ6M+Hc2kSV+QlJx8WFvjxo3oe83V9OjWlUqVKgWsBoV5KWEK81Ki1u9KZtSsdYyes449yYdfVNmwajnKRYazfOv+I56X3/SHmVmO/QeO7P3OK5jnHkeemHr0oSPFqXx07t7wQ0E8v2BeISYyaEJyRmYWE+ZvZNDEHH9odW1FjzM1z3xZFrAwb2bhwJ/A34ANwBzgGufc7zn2ORdY5pzbbWaXAU865/Jdr12/wMqG/YmJTJr0BaM+Hc1ff604rC0mJoYrL7+Mfn37cOqprYr9tRXmpYQpzIsnDqRnMnnhJkb8uoalm/YV6jntmlSlWvmowy/wTPYF8pL4or9cVPjhQ1byCOcVD3vs70WPiQjlOdiPUAovTlaYPw6BHDPfFljhnFsFYGajgS5Adph3zv2aY/+ZQP0A1iMhpEL58lzb7xr69e1Dwtx5jPrkU6Z++z/S0zM4cOAA4yZ8zrgJn9P61Fb069uHKy6/jJiYGK/LFhEJGTGR4fSOb0Cvs+ozb91uRvy6lskLN+X7nFm5FqoqiuiIsCN6wSvmGL5SKTbCP2486rBgXik2kqiI0hPIRYpLIMN8PSDnHIQbgPx63W8EvsmrwcxuAW4BaNiwYXHVJyHAzGgTfxZt4s9i+/btjB03gU8/G8Pmzb4xg4sWL2HRI4/x/Isv0aN7N/r2uZrGjRt5XLVIYOhcKIFgZpzVqCpnNarKrFU72bo/tcDnRIWH+YN2xGE94Hn2mGePNfe1FXURKxHJWyDDfF5fmeT5JZyZdcAX5s/Pq905NxQYCr6vlourQAktNWrU4B+338otN9/I9Bk/MuqT0fz08y8A7N27j2EfjmDYhyM4//xz6XfNNXRofyEREZqwSUoPnQsl0OpXLZdvmG9drxKjbz2b2MhwreAtEiQC+X3VBiDnPEn1gSO+vzOz1sD7QBfn3M4A1iOlREREBB0vuZgPPxjKtG+/5saB11O58qELYn/++Vduv+MuOnTsxNvvDGH79u35HE1ERA66Oj7/6Q2vPacR5aIiFORFgkggw/wc4AQza2JmUUAfYHLOHcysITABuM4592cAa5FSqnGjRjzy0AP8PON7XnrxOU5rfWp22+bNW3jtjf/jgvYduedf9zN7TgKhNhWriEhJ6nFWfTqfkvfaHp1PqU2PM3Vpm0iwCViYd85lAHcCU4FlwBjn3FIzu83MbvPv9jhQDXjHzBaYmaZmkCKJiYmhe7eujB87ms/HfUbPHt2zL4jNyMjgq6+/oe+1A7j87135aNQn7E9M9LhiEZHgEx5mvNX3DF7q2Zpo/8Wm0RFhvNSzdajPYy5SagX0snDn3NfOuROdc82cc8/5tw1xzg3x37/JOVfFOXe6/xYfyHqkbDj11Fa8+Pwz/PLj9zz6yEM0adw4u+2vv1bw1NPPcd4F7Xn8iadZvvwP7wqVYzZg4M107HQ5Awbe7HUpIqVWRHgYveMbULdyLAB1K8fSO76BgrxIkNIcT1JqVapUiRuu78+3U75k5PAP6HRpR8LDfbMoJCen8Mnoz7iyS3euvuZaJn/xJcnJyYwdNyF7ppzNm7cwdtwEMjMDv8KgFM7GjRtZs2YtGzdu9LoUERGRoKCpPqTUMzPOPedszj3nbDZv2cJnY8bx2ZixbN++A4C58+Yzd958oiIjSUs/tBpiamoqjzw6iOkzZvDGa69oZhwREREJOkonUqbUqV2bf959J3fcfivTvvueUZ+MZuas2QCHBfmcpn47jQcffpQLzj+X8uXLUz4uzvdv+fKUL++7HxMTo9kdREREpMQpzEuZFBkZyWWdO3FZ5078tWIF/a+/MbunPi+Tv/iSyV98edT2sLCww8J9XoE/+35cHtvjylG+fHni4uKyhwKJiIiIFERhXsq8E5o3P+4hNFlZWezbt499+/Yddz3lysXmHfjz+UMhLnvbobboqKjjrkVERESCm8K8CL7hNwcvfM1L82bNuOvOf5CYlEhiYiKJiUm5/k0kMenQ/aSkZBITE8nKyjrmWpKTU0hOTmHbcS52FRkZmce3A3FUyOMPhTj/NwN5fXtQrlw5z4cQZWRk8PnEyUdcnNy9Wxd9kyEiImWawrwI0KtnD+bNX3DU9ptuvIErLu98TMd0zpGSknLUwH/Ydv/9/UmH/hDIuT0tLe2Y31N6ejq7d+9m9+7dx/zcnMLCwvw9/zmHBR3+7UDObwYqlC/v/2PgyH2K8g1IRkYG9/zrPqZ+Oy17my5OFhER8dFvQBGge7cuTJ8x47DAeFCnSzvSretVx3xMM6NcuXKUK1eOmjVrHFd9qWlpJCUm5fHNwJHfDiQl5f7WIMc3BsnJx/zaWVlZ7N+/n/379x/XewCIjY3N0fN/tCFCh38zMHfu/Dw/F/BdnDxx0hf07NHtuGsTEREJRQrzIkB4eDhvvPYKEyd9wRNPPUNqairR0dE89cQgunW9yvOhHNFRUURXjaJq1SrHdZzMzEySk5PZf7Q/CJIS/X805DGMKNcfE0WZfz8lJYWUlJR8LzY+VmPHjVeYFxGRMkthXsQvIiKCnj26MWToe6xZs5Y6dWqXupAYHh5OhQoVqFChwnEdxznHgQMH8vhmIDn7j4L8vj3IuS01NfW4atm0efNxPV9ERCSUKcyLyDEzM2JjY4mNjaVGjeMbQpSWlpZ9nUD2EKEc1xa8O+Q9Nm7adNTn161T57heX0REJJQpzIuIp6KiooiKiqJKlcp5toeHR/DIo4OO+vxePXsEqDIREZHgF+Z1ASIi+enerQudLu2YZ1tRL04WEREpLRTmRSSoHbw4+cXnnyU6OhqA6OhoXnz+Wd58/VXPL04WESlp9avE0qR6HPWrxHpdigQBDbMRkaBXFi5OFhEprI9ubOd1CRJE1DMvIiIiIhKiFOZFREREREKUwryIiIiISIhSmBcRERERCVG6AFZEQka9evUO+1dERKSsU5gXyUWBMXiNGPae1yWIiIgEFYV5kVwUGEVERCRUaMy8iIiIiEiIUpgXEREREQlRCvMiIiIiIiFKYV5EREREJEQpzIuIiIiIhCiFeRERERGREKUwLyIiIiISohTmRURERERClMK8iIiIiEiIUpgXEREREQlRCvMiIiIiIiEqoGHezDqb2R9mtsLMHs6j3czsTX/7IjM7M5D1iIiIiIiUJgEL82YWDrwNXAa0BK4xs5a5drsMOMF/uwV4N1D1iIiIiIiUNoHsmW8LrHDOrXLOpQGjgS659ukCjHQ+M4HKZlYngDWJiIiIiJQagQzz9YD1OR5v8G871n1ERERERCQPgQzzlsc2V4R9MLNbzCzBzBK2b99eLMWJiIQanQtFRCS3QIb5DUCDHI/rA5uKsA/OuaHOuXjnXHyNGjWKvVARkVCgc6GUpPpVYmlSPY76VWK9LkVE8hERwGPPAU4wsybARqAP0DfXPpOBO81sNNAO2Ouc2xzAmkRERKQQPrqxndcliEghBCzMO+cyzOxOYCoQDgxzzi01s9v87UOAr4HLgRVAMnBDoOoRERERESltAtkzj3Pua3yBPee2ITnuO+COQNYgIiIiIlJaaQVYEREREZEQpTAvIiIiIhKiFOZFREREREKUwryIiIiISIhSmBcRERERCVHmm1AmdJjZdmCt13UUo+rADq+LkCPocwlepe2z2eGc63ysTypl58LS9pmWJvpsgldp+myKdB4Un5AL86WNmSU45+K9rkMOp88leOmzKX30mQYvfTbBS5+NHKRhNiIiIiIiIUphXkREREQkRCnMe2+o1wVInvS5BC99NqWPPtPgpc8meOmzEUBj5kVEREREQpZ65kVEREREQpTCvIiIiIhIiFKY94iZDTOzbWa2xOta5BAza2BmP5jZMjNbamb3eF2T+JhZjJnNNrOF/s/mKa9rkuOj82Dw0rkwOOk8KHnRmHmPmNmFQCIw0jnXyut6xMfM6gB1nHPzzKwCMBfo6pz73ePSyjwzMyDOOZdoZpHAz8A9zrmZHpcmRaTzYPDSuTA46TwoeVHPvEeccz8Cu7yuQw7nnNvsnJvnv78fWAbU87YqAXA+if6Hkf6beiNCmM6DwUvnwuCk86DkRWFe5CjMrDFwBjDL41LEz8zCzWwBsA34n3NOn41IgOlcGFx0HpTcFOZF8mBm5YHxwD+dc/u8rkd8nHOZzrnTgfpAWzPT0AyRANK5MPjoPCi5KcyL5OIfhzgeGOWcm+B1PXIk59weYDrQ2dtKREovnQuDm86DcpDCvEgO/ouLPgCWOede9boeOcTMaphZZf/9WKAjsNzTokRKKZ0Lg5POg5IXhXmPmNmnwG9ACzPbYGY3el2TAHAecB1wsZkt8N8u97ooAaAO8IOZLQLm4Bsr+qXHNclx0HkwqOlcGJx0HpQjaGpKEREREZEQpZ55EREREZEQFeF1ASIiIiIiRdW8xSkRQH/gRqABsB7fNR8jVvyxNNOLmsysPXC/c+7KQL+WeuZFREREJCT5g/xn+ML7ufjC/Ln+x2P87aWawryIiIiIhKr+QPejtHXHdyF3kZhZYzNbbmbvm9kSMxtlZh3N7Bcz+8vM2vpvv5rZfP+/LfI4TpyZDTOzOf79uhS1prwozIuIiIhIqCpoFqzjnSWrOfAG0Bo4CegLnA/cD/wb39SgFzrnzgAeB57P4xiPAt8759oAHYDBZhZ3nHVlK/VfPYiIiJRlZpYJLMb3O38ZMMA5l3yUfZ8EEp1zL5dchSLHpUEB7Q2P8/irnXOLAcxsKfCdc86Z2WKgMVAJGGFmJwAOiMzjGJcCV5nZ/f7HMf66lh1nbYB65kVEREq7FOfc6c65VkAacJvXBYkUo/UFtK87zuOn5rifleNxFr4/kJ8BfvD/fP0dX1DPzYAe/p/D051zDZ1zxRLkQWFeRESkLPkJ37ABzKy/mS0ys4Vm9lHuHc3sZv8Y34VmNt7Myvm39/KPH15oZj/6t51iZrP9i0st8vdSipSED46z/XhVAjb6719/lH2mAnf5V1bGzM4ozgIU5kVERMoAM4sALgMWm9kp+MbxXuycOw24J4+nTHDOtfG3L+PQ2OPHgU7+7Vf5t90GvOGcOx2IBzYE7p2IHGYEMOEobROAkQF+/ZeAF8zsFyD8KPs8g2/4zSIzW+J/XGy0AqyIiEgplmPMPPh65u8DbgVqO+cezbXvk/jHzJvZRcCzQGWgPDDVOXebmQ0BmgFj8AX+nWbWF98fByP92/4K/DsT8fFPP3kdvj84G+IbWvMBMNKreeZLki6AFRERKd1S/D3m2fxf9xfUmzcc6OqcW2hm1wPtAfyBvh1wBbDAzE53zn1iZrP826aa2U3Oue+L922I5G3FH0szgA/9tzJHw2xERETKnu+A3mZWDcDMquaxTwVgs5lFAv0ObjSzZs65Wc65x4EdQAMzawqscs69CUzGN42fiJQAhXkJGmaW6b946uDt4WN4bnsz+/I4X3+6mcUX8bnDzazn8by+/zinmNn3Zvanma00s6fMTD+nIlKsnHNLgeeAGWa2EHg1j90GAbOA/+GbS/ugwWa22D/290dgIXA1sMTMFuCbizvQ45RFxE9j5iVomFmic658EZ/bHrjfOXdlEZ8fjq+n6n7nXEIRnj8c+NI5N64or+8/RiywBLjdOfetf+aI8cC3zrnXinpcERERKb3U4ydBz8zWmNnzZvabmSWY2ZlmNtXfc51zvuSKZva5mf1uZkMO9mib2bv+5y01s6dyHfdxM/sZ6JVje5iZjTCzZ80s3MwG+6dnW2Rmt/r3MTN7y/9aXwE1i+Gt9gV+cc59C+Bf1OVO4IFiOLaIiIiUQroAVoJJrP8r2oNecM595r+/3jl3jpm9hu+irPPwLcywFBji36ct0BJYC0wBugPjgEedc7sO9r6bWWvn3CL/cw44584H8P9hEAGMApY4554zs1uAvc65NmYWDfxiZt8CZwAtgFOBWsDvwLDcb8jMHiDHWNMcfnTO3Z1r2ynA3JwbnHMrzSzWzCo75/bk/Z9NREREyiqFeQkmR8y4kMNk/7+LgfLOuf3AfjM7YGaV/W2znXOrAMzsU+B8fGG+tz+URwB18AX+g2H+4B8LB/0XGOOce87/+FKgdY7x8JWAE4ALgU+dc5nAJjPLc9YG59xgYHCB79znaLNLWCGfLyIiUuY0fvirCKA/vqkpG+BbFfYDYMSaF68o8tSUZnY3cDswzzmXV8fccck5FezxHEfDbCRU5Fw+OffSygf/KM0dhJ2ZNQHuBy5xzrUGvuLwpZaTcj3nV6CDmR3cx4C7cizB3OTgMJg8Xu8IZvZArot6D97ezGP3pfgWW8n5/KbADvXKi4iIHMkf5D/DF97PxRfmz/U/HuNvL6p/AJcHIsgXJ4V5KU3amlkT/1j5q4GfgYr4AvteM6uFb/XD/HwAfA2M9a+WOBW43T81G2Z2opnF4ZvBoY9/TH0doENeB3PODc7xh0DOW+4hNuAb3nO+mXX0v1Ys8CbwxLH9ZxARESkz+uMbVpuX7vgWkzpm/sXRmgKTzexRMxvmv35uvpl18e9zvZlNNLMvzGy1md1pZvf695l5cMpXM7vZ/9yFZjbeP8FF7tdrZmZTzGyumf1kZicVtlaFeQkmsbl6r188xuf/BryIb0aY1cDnzrmFwHx8vd7DgF8KOohz7lVgHvAR8D6+8fDz/NOw/RffNwGfA3/hG/bzLjDjGGvN63VT8C2N/qiZ/Ylv/uZfnHOjjvfYIiIipdSNx9meJ+fcbcAmfJ11ccD3zrk2/seD/R17AK3wTWDRFt90r8nOuTPwZZL+/n0mOOfaOOdOA5Ydpaah+EYCnIVvRME7ha1VU1OKBCkz64pv7ucOzrm1HpcjIiISdBo//NU6fENrjmb9mhevaFiUY5vZGnzDX6fgG6Kb4W+qCnQC2gHnOedu9u+/DjjHObfRzAYCrZ1z/zSzi4BngcpAeWCqfyXlJ4FEfBN5bAf+yPHy0c65kwtTpy6AFQlSzrmJwESPyxAREQlm68k/zK8rhtcwoIdz7o/DNpq148jr+HJe43cwZw8HujrnFprZ9UD7XMcPA/bkMwlIvjTMRkRERERC1QfH2V4YU4G7zMwAzOyMY3x+BWCz//q7Iy6mdc7tA1abWS//8c3MTivswRXmRURERCRUjQAmHKVtAjCyGF7jGSASWOS/fu6ZY3z+IGAW8D9g+VH26QfcaGYL8V3n16WwB9eYeREREREJWf7pJ6/Dd2FpQ3xDaz4ARh7PPPOhIuTCfOfOnd2UKVO8LkNEpLhoUTARESmykBtms2PHDq9LEBEREREJCiEX5kVERERExEdhXkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiIgrepWjMbBhwJbDNOdcqj3YD3gAuB5KB651z8wJVTzDJyMxiwryNfJawns17UqhTOZar4xvQ46z6hIdpljqv6HMRERGRUBOwMA8MB97i6CtvXQac4L+1A971/1uqZWRmcecn85mydEv2tk17DzB37W6+X76Nt/qeQUS4vjApafpcREREJBQFLMw75340s8b57NIFGOl8q1bNNLPKZlbHObc5UDUFgwnzNh4WGHOasnQLl7/5E7UqxpRwVbJ17wH+3JaYZ9uUpVuYMH8jveMblHBVIiIiIvkLZM98QeoB63M83uDfVqrD/GcJ6/Nt/3NrIn9uzTtUinfGzFmvMC8iIiJBx8swn9cgZJfnjma3ALcANGzYMJA1BdzmPSkF7hMZrvHZJS09M8//9bL9vnkfOxJTqV4+uoQqEhERESmYl2F+A5Czq7M+sCmvHZ1zQ4GhAPHx8fmnriBXp3Ism/YeOGp7fKMqjLv93BKsSAB6vPsrc9fuPmp7clomHQZP546Lm3PDeY2JjggvwepERERE8ublFX2Tgf7mczawt7SPlwe4uoChGr3baCiHFwr6XAD2p2bw4jfL6fjqDL5atBnf5R4iIiIi3glYmDezT4HfgBZmtsHMbjSz28zsNv8uXwOrgBXAe8A/AlVLMOlxVn06n1I7z7bOp9Smx5n1S7gigfw/l44n1eTODs2JjvD9uKzflcIdn8yj15DfWLB+TwlWKSIiInI4C7Xexfj4eJeQkOB1GcclIzOLCfM3MmbOejbtSaFu5Vh6t2lAjzM1n7mXCvpcNu1JYfDUP/h8/sbDntf19Lo82Pkk6laO9ahyCXH6oRcRkSJTmBc5RgvW7+HZL38nIccY++iIMG65sCm3XdSMuGgvL0WREKQwLyIiRaYwL1IEzjm+XryFF75Zxobdh2YoqlEhmgcubaFVY+VY6H8UEREpMoV5keNwID2T4b+u4e3vV7A/NSN7+8l1KjLoipM5t3l1D6uTEKEwLyIiRaYwL1IMdiSm8vq0P/lk1jqycvxIdTy5Fv++/CSa1ijvXXES7BTmRUSkyBTmRYrRn1v389xXy5jx5/bsbRFhxnXnNOKeS06gcrkoD6uTIKUwLyIiRaYwLxIA0//YxnNfLeOvbYnZ2yrFRnL3JSdw3dmNiIrwcokHCTIK8yIiUmQK8yIBkpGZxeg563ntf3+yMykte3uT6nE8ctlJ/K1lLcyU40RhXkREik5hXiTA9h1I5+0fVvDhz2tIy8zK3n5O02o8duXJnFK3kofVSRBQmBcRkSJTmBcpIet3JfPiN8v5avHm7G1m0Ous+tx/aQtqVozxsDrxkMK8iIgUmcK8SAlLWLOLZ778nYUb9mZvKxcVzm0XNePmC5oSGxXuYXXiAYV5EREpMoV5EQ9kZTkmL9zEf6YsZ/PeA9nb61SK4cHOLehyWj3CtOhUWaEPWkREikxhXsRDKWmZvP/TKt6dsZLktMzs7afVr8RjV7akTeOqHlYnJURhXkREikxhXiQIbNt3gFe+/ZMxc9eT80fy8lNr83Dnk2lYrZx3xUmgKcyLiEiRKcyLBJGlm/by7JfL+G3VzuxtUeFh3HBeY+64uDkVYyI9rE4CRGFeRESKTGFeJMg45/hu2Tae/3oZq3YkZW+vGhfFvzqewDVtGxIRrkWnShGFeRERKTKFeZEglZaRxahZa3l92l/sTUnP3t68ZnkeveJkOrSo6WF1UowU5kVEpMgU5kWC3J7kNN78bgUjf1tDRtahn9cLT6zBo5efTIvaFTysToqBwryIiBSZwrxIiFi1PZEXvlnO/37fmr0tzKBP24bc+7cTqV4+2sPq5DgozIuISJEpzIuEmF9X7ODZr5bx++Z92dvKR0dwR4fm3HBeY2IitehUiFGYFxGRIlOYFwlBmVmO8fM2MHjqH2zfn5q9vX6VWB6+7CSuOLUOZqUvI173wSw27E6hfpVYPrqxndflFJfS90GJiEiJ0ZQYIiEoPMzoHd+A6fe35+6LmxMT6ftR3rA7hTs/mU/PIb+xYP0eb4sMgA27U1i9I4kNu1O8LkVERCQoKMyLhLC46AjuvbQF39/Xnm5n1MvePnftbrq+/Qv3jJ7Pxj0KviIiIqWVwrxIKVC3ciyvXX06k+44jzaNq2Rvn7RgExe/PJ2Xp/5BYmqGhxWKiIhIICjMi5QipzWozJhbz+GdfmfSoGosAKkZWbz1wwo6vDydz+asIzMrtK6TERERkaNTmBcpZcyMy0+tw7R7L+KRy06iQnQEANv3p/LQ+MVc8eZP/LJih8dVioiISHFQmBcppaIjwrn1omZMf6A9157dkDD/nCnLt+yn3/uzuGnEHFZuT/S2SBERETkuCvMipVy18tE82/VUpvzzQtq3qJG9fdqybXR67UeenLyU3UlpHlYoIiIiRaUwL1JGnFirAsNvaMuIgW05sVZ5ADKyHMN/XcNFg3/g/Z9WkZaR5XGVIiIiciwU5kXKmItOrMHXd1/Ac91aUS0uCoB9BzJ49qtlXPraDKYu3UKoLSYnIiJSVinMi5RBEeFh9GvXiB8eaM9tFzUjKtx3KlizM5lbP5rLNe/NZMnGvR5XKSIiIgVRmBcpwyrGRPLwZSfx3X0XcUXrOtnbZ67axd/f+pn7xy5k674DHlYoIiIi+VGYFxEaVC3H233PZPzt53Bag8oAOAfj5m6g/eDpvDHtL1LSMr0tUkRERI6gMC8i2c5qVJXPbz+XN/qcTt1KMQCkpGfy2rQ/6fDydCbM20CWFp0SEREJGgrzInKYsDCjy+n1+O6+9tx/6YmUiwoHYMu+A9w7ZiFd3/mF2at3eVyliIiIQIDDvJl1NrM/zGyFmT2cR3slM/vCzBaa2VIzuyGQ9YhI4cVGhXPnxScw/f72XB3fAPMvOrVow156//c3bv94Lmt3JnlbpIiISBkXsDBvZuHA28BlQEvgGjNrmWu3O4DfnXOnAe2BV8wsKlA1icixq1kxhv/0bM1Xd13Auc2qZW//ZskW/vbqjzz/9TL2pqR7WKGIiEjZFcie+bbACufcKudcGjAa6JJrHwdUMDMDygO7gIwA1iQiRdSybkVG3dSO9/vH07R6HABpmVkM/XEV7Qf/wMjf1pCRqUWnRERESlIgw3w9YH2Oxxv823J6CzgZ2AQsBu5xzh2RBszsFjNLMLOE7du3B6peESmAmdGxZS2m/utCnvh7SyrFRgKwOzmdxyctpfMbP/HD8m1adEpERKSEBDLMWx7bcv+G7wQsAOoCpwNvmVnFI57k3FDnXLxzLr5GjRrFXaeIHKPI8DBuOK8JMx5oz43nNyEizPfjvmJbIjcMn0P/YbP5Y8t+j6sUEREp/QIZ5jcADXI8ro+vBz6nG4AJzmcFsBo4KYA1iUgxqlwuikFXtuR/917E31rWyt7+0187uOyNH/n354vZvj/VwwpFRERKt0CG+TnACWbWxH9Rax9gcq591gGXAJhZLaAFsCqANYlIADSpHsd7/eP55OZ2tKzj+3Ity8Ens9bR4eXpvDN9BQfSteiUiIhIcQtYmHfOZQB3AlOBZcAY59xSM7vNzG7z7/YMcK6ZLQa+Ax5yzu0IVE0iEljnNqvOF3edz0s9W1OzQjQAiakZvDTlDy55ZQZfLNyk8fQiIiLFyELtF2t8fLxLSEjwugwRKUBSagb//XEVQ39cyYH0Q9e1n9mwMoOubMkZDasc8zE7vDyd1TuSaFI9jh/ub1+M1Xoqr+uLRERECkUrwIpIQMRFR3Dv307k+/va0/2MQxNZzVu3h27v/Mrdn85nw+5kDysUEREJfQrzIhJQdSvH8urVpzP5zvNo0/hQb/zkhZu45JUZDJ66nMRULS8hIiJSFArzIlIiWtevzJhbz+HdfmfSsGo5AFIzsnj7h5W0Hzyd0bPXkZkVWsP+REREvKYwLyIlxsy47NQ6/O/eC/n35SdRIToCgB2JqTw8YTFXvPkTP/+la+BFREQKS2FeREpcdEQ4t1zYjOkPtOe6sxsR7l90avmW/Vz7wSxuHD6HFdsSPa5SREQk+CnMi4hnqpWP5pmurZhyzwW0b3Fodefvlm+j0+s/8sSkJexOSvOwQhERkeCmqSlFJGjM+HM7z331O39uPdQrXzEmgjs6NKdCTARPffE7qRlZREeE8UyXVvQ4q352r34IC/k3ICIi3lGYF5GgkpGZxWcJ63n12z/ZWUCvfOdTavNW3zOICA/pLxkV5kVEpMhC+jegiJQ+EeFh9GvXiOkPtOe2i5rl2/M+ZekWJszfWILViYiIBBeFeREJShViInn4spM4uXaFfPcbM2d9CVUkIiISfBTmRSSo7SpgqM2mPSklVImIiEjwUZgXkaBWp3Jsvu11C2gXEREpzRTmRSSoXR3fIN/23m3ybxcRESnNFOZFJKj1OKs+nU+pnWdb51Nq0+PM+iVckYiISPBQmBeRoBYeZrzV9wxe6tma6AjfKSs6IoyXerbm7X5nloZ55kVERIpMYV5Egl5EeBi94xtkj4+vWzmW3vENFORFRKTMU5gXEREREQlRCvMiIiIiIiFKYV5EREREJEQpzIuIiIiIhCiFeRERERGREKUwLyIiIiISohTmRURERERClMK8iIiIiEiIUpgXEREREQlRCvMiIiIiIiFKYV5EREREJEQpzIuIiIiIhCiFeRERERGREKUwLyIiIiISohTmRURERERClMK8iIiIiEiIUpgXEREREQlRAQ3zZtbZzP4wsxVm9vBR9mlvZgvMbKmZzQhkPSIiIiIipUlEoA5sZuHA28DfgA3AHDOb7Jz7Pcc+lYF3gM7OuXVmVjNQ9YiIiIiIlDaB7JlvC6xwzq1yzqUBo4EuufbpC0xwzq0DcM5tC2A9IiIiIiKlSiDDfD1gfY7HG/zbcjoRqGJm081srpn1D2A9IiIiIiKlSsCG2QCWxzaXx+ufBVwCxAK/mdlM59yfhx3I7BbgFoCGDRsGoFQRERERkdATyJ75DUCDHI/rA5vy2GeKcy7JObcD+BE4LfeBnHNDnXPxzrn4GjVqBKxgEREREZFQEsgwPwc4wcyamFkU0AeYnGufScAFZhZhZuWAdsCyANYkIiIiIlJq5DvMxsz2c+TQmGzOuYr5tGWY2Z3AVCAcGOacW2pmt/nbhzjnlpnZFGARkAW875xbUoT3ISIiIiJS5uQb5p1zFQDM7GlgC/ARvrHw/YAKBR3cOfc18HWubUNyPR4MDD6mqkVEREREpNDDbDo5595xzu13zu1zzr0L9AhkYSIiIiIikr/ChvlMM+tnZuFmFmZm/YDMQBYmIpJb/SqxNKkeR/0qsV6XIiIiEhQKOzVlX+AN/80Bv/i3iYiUmI9ubOd1CSIiIkGlUGHeObeGI1dvFRERERERDxVqmI2ZnWhm35nZEv/j1mb2WGBLExERERGR/BR2zPx7wCNAOoBzbhG+eeNFRERERMQjhQ3z5Zxzs3NtyyjuYkREREREpPAKG+Z3mFkz/AtImVlPYHPAqhIRERERkQIVdjabO4ChwElmthFYjW/hKBERERER8Uhhw/xa51xHM4sDwpxz+wNZlIiIiIiIFKyww2xWm9lQ4GwgMYD1iIiIiIhIIRU2zLcApuEbbrPazN4ys/MDV5aIiIiIiBSkUGHeOZfinBvjnOsOnAFUBGYEtDIREREREclXYXvmMbOLzOwdYB4QA/QOWFUiIiIiIlKgQl0Aa2argQXAGOAB51xSIIsSEREREZGCFXY2m9Occ/sCWomIiIiIiByTfMO8mT3onHsJeM7MXO5259zdAatMRERERETyVVDP/DL/vwmBLkRERERERI5NvmHeOfeF/+4i59z8EqhHREREREQKqbCz2bxqZsvN7BkzOyWgFYmIiIiISKEUdp75DkB7YDsw1MwWm9ljgSxMRERERETyV+h55p1zW5xzbwK34Zum8vFAFSUiIiIiIgUrVJg3s5PN7EkzWwK8BfwK1A9oZSIiIiIikq/CzjP/IfApcKlzblMA6xERERERkUIqMMybWTiw0jn3RgnUIyIiIiIihVTgMBvnXCZQzcyiSqAeEREREREppMIOs1kL/GJmk4Gkgxudc68GpCoRERERESlQYcP8Jv8tDKgQuHJERERERKSwChXmnXNPBboQERERERE5NoUK82b2A+Byb3fOXVzsFYmIiIiISKEUdpjN/TnuxwA9gIziL0dERERERAqrsMNs5uba9IuZzQhAPSIiIiIiUkiFHWZTNcfDMCAeqB2QikREREREpFAKO8xmLofGzGcAa4AbA1GQiIiIiIgUTr6LRplZGzOr7Zxr4pxrCjwFLPfffi/o4GbW2cz+MLMVZvZwAa+TaWY9j/UNiIiIiIiUVQWtAPtfIA3AzC4EXgBGAHuBofk90czCgbeBy4CWwDVm1vIo+/0HmHqsxYuIiIiIlGUFhflw59wu//2rgaHOufHOuUFA8wKe2xZY4Zxb5ZxLA0YDXfLY7y5gPLDtGOoWERERESnzCgzzZnZwXP0lwPc52goab18PWJ/j8Qb/tmxmVg/oBgzJ70BmdouZJZhZwvbt2wt4WRERERGRsqGgMP8pMMPMJgEpwE8AZtYc31Cb/Fge23IvPPU68JBzLjO/Aznnhjrn4p1z8TVq1CjgZUVEREREyoZ8e9edc8+Z2XdAHeBb59zBMB6Gb3hMfjYADXI8rg9syrVPPDDazACqA5ebWYZzbmLhyhcRERERKbsKnJrSOTczj21/FuLYc4ATzKwJsBHoA/TNdZwmB++b2XDgSwV5EREREZHCKew888fMOZdhZnfim6UmHBjmnFtqZrf52/MdJy8iIiIiIvmzQyNnQkN8fLxLSEjwugwRkeKS1/VFIiIihVLQBbAiIiIiIhKkFOZFREREREKUwryIiIiISIhSmBcRERERCVEK8yIiIiIiIUphXkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiFOZFREREREKUwryIiIiISIhSmBcRERERCVEK8yIiIiIiIUphXkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiFOZFREREREKUwryIiIiISIhSmBcRERERCVEK8yIiIiIiIUphXkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiFOZFREREREKUwryIiIiISIhSmBcRERERCVEK8yIiIiIiIUphXkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiAhrmzayzmf1hZivM7OE82vuZ2SL/7VczOy2Q9YiIiIiIlCYBC/NmFg68DVwGtASuMbOWuXZbDVzknGsNPAMMDVQ9IiIiIiKlTSB75tsCK5xzq5xzacBooEvOHZxzvzrndvsfzgTqB7AeEREREZFSJZBhvh6wPsfjDf5tR3Mj8E0A6xERERERKVUiAnhsy2Oby3NHsw74wvz5R2m/BbgFoGHDhsVVn4iIiIhISAtkz/wGoEGOx/WBTbl3MrPWwPtAF+fczrwO5Jwb6pyLd87F16hRIyDFioiIiIiEmkCG+TnACWbWxMyigD7A5Jw7mFlDYAJwnXPuzwDWIiIiIiJS6gRsmI1zLsPM7gSmAuHAMOfcUjO7zd8+BHgcqAa8Y2YAGc65+EDVJCIiIiJSmphzeQ5jD1rx8fEuISHB6zJERIpLXtcXiYiIFIpWgBURERERCVEK8yIiIiIiIUphXkREREQkRAVynnkRkWI1YODNbNy4kXr16jFi2HtelyMiIuI5hXkRCRkbN25kzZq1XpchIiISNDTMRkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiFOZFREREREKUwryIiIiISIhSmBcRERERCVEK8yIiIiIiIUphXkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiFOZFJOhlZGQwdtwENm/eAsDmzVsYO24CmZmZHlcmIiLiLXPOeV3DMYmPj3cJCQlelyEiJSQjI4N7/nUfU7+ddkRbp0s78sZrrxAREeFBZcXGvC5ARERCl3rmRSSojZ8wMc8gDzD122lMnPRFCVckIiISPEK6O0tESo/UtDTWrlnLipWrWLlyJStXrWLlqtUsX/5Hvs8bO248PXt0K6EqRUREgovCvIiUqP379/sD+8HQvpqVq1axfv0GsrKyjvl4mzZvDkCVIiIioUFhXkSKnXOOrdu2+QL7qoPB3Xd/+/YdhT5OpUoVycjIJCkp6aj71K1TpzhKFhERCUkK8yJSZBkZGaxbt97fu74yR2hfnW8Az61Ondo0a9aU5k2b0rRpU9/9Zk2pWrUq48Z/ziOPDjrqc3v17FEcb0VERCQkKcyLSIGSk5NZtXpNjqExvsC+du1a0tMzCnWMiIgIGjVqSPNm/sDe1BfYmzRpTFxc3FGf171bF6bPmHHU2Wy6db2qyO9LREQk1GlqShHJtnPXrsOGxBz8d9Omwo9LjytXjqZNm9CsWbPsHvamTZrQsGEDIiMji1RXRkYGEyd9wRNPPUNqairR0dE89cQgunW9ivDw8CIdM4hoakoRESkyhXmRMiYrK4uNGzf5e9cPjWdftWo1u/fsKfRxqlevRrPsITHN/AG+KbVr1cIsMPm0Y6fLWbNmLY0bN2La1K8D8hoeUJgXEZEi0zAbkVLqaFM9rl69hgMHDhTqGGFhYdSvX49mzZrSrIkvuDdr1pRmTZtQqVKlAL8DERERKYjCvEiIK46pHqOjo2napHH2xafNmvoCe5MmjYmOjg7wOxAREZGiUpgXCQHFOdWjb2hMs+we9mbNmlKvbt3SMPZcRESkzFGYFwkieU31uGrValauWk1iYmKhj5PfVI+BGs8uIiIiJU9hXsQDR0z1uHo1K1euKpGpHkVERKT0UJgXCaBgnepRRERESgeFeQ9kZGTw+cTJjB03ns1btlCndm169exB925dNG7ZQ0X9XA5O9bhq1WpW5FgFNRSmehQREZHQFtAwb2adgTeAcOB959yLudrN3345kAxc75ybF8iavJaRkcE9/7rvsNUsN2/ewrz5C5g+YwZvvPYKERH6G6ukFeZzyczKOmyqx1WrV7Ni5SpN9SgiIiKeCVhqNLNw4G3gb8AGYI6ZTXbO/Z5jt8uAE/y3dsC7/n9Lrc8nTs5zWXqAqd9OY8h/3+NvHS8p4ark2/9Ny/dzOe/Ci9m9e7emehQREZGgEsgu4LbACufcKgAzGw10AXKG+S7ASOdbhnammVU2szrOucIPKA4xY8eNz7f99Tff4vU33yqhaqSwdu7cmed2TfUoIiIiXgpkmK8HrM/xeANH9rrntU894LAwb2a3ALcANGzYsNgLLUmbt2zxugQpgrCwMM4992xN9SgiIiJBJZBhPq+E44qwD865ocBQgPj4+CPaQ0md2rXZvPnogb52rVp07XpVCVYkAJ9PnMTWrduO2n7G6acx/IP3SrAiyUu9evUO+1dERKSsC2SY3wA0yPG4PrCpCPuUKr169mDe/AVHbf/nPXfRs0e3kitIAGjUsCGPPDroqO29evYowWrkaEYM0x9UIiIiOYUF8NhzgBPMrImZRQF9gMm59pkM9Defs4G9pXm8PED3bl3odGnHPNs6XdqRbuqV94Q+FxEREQlF5rv2NEAHN7sceB3f1JTDnHPPmdltAM65If6pKd8COuObmvIG51xCfseMj493CQn57hL0MjIymDjpC8aOG8+mzZupW6cOvXr2oFvXq3TRpIf0uYhHdNGFiIgUWUDDfCCUhjAvIpKDwryIiBRZIIfZiIiIiIhIACnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiFOZFREREREJUyM1mY2bbgbVe11GMqgM7vC5CjqDPJXiVts9mh3Ous9dFiIhIaAq5MF/amFmCcy7e6zrkcPpcgpc+GxERkUM0zEZEREREJEQpzIuIiIiIhCiFee8N9boAyZM+l+Clz0ZERMRPY+ZFREREREKUeuZFREREREKUwryIiIiISIhSmPeImQ0zs21mtsTrWuQQM2tgZj+Y2TIzW2pm93hdk/iYWYyZzTazhf7P5imvaxIREfGaxsx7xMwuBBKBkc65Vl7XIz5mVgeo45ybZ2YVgLlAV+fc7x6XVuaZmQFxzrlEM4sEfgbucc7N9Lg0ERERz6hn3iPOuR+BXV7XIYdzzm12zs3z398PLAPqeVuVADifRP/DSP9NvREiIlKmKcyLHIWZNQbOAGZ5XIr4mVm4mS0AtgH/c87psxERkTJNYV4kD2ZWHhgP/NM5t8/resTHOZfpnDsdqA+0NTMNURMRkTJNYV4kF/947PHAKOfcBK/rkSM55/YA04HO3lYiIiLiLYV5kRz8F1l+ACxzzr3qdT1yiJnVMLPK/vuxQEdguadFiYiIeExh3iNm9inwG9DCzDaY2Y1e1yQAnAdcB1xsZgv8t8u9LkoAqAP8YGaLgDn4xsx/6XFNIiIintLUlCIiIiIiIUo98yIiIiIiIUphXkREREQkRCnMi4iIiIiEKIV5EREREZEQpTAvIiIiIhKiFOalVDOzTP/0kkvMbKyZlctn3yfN7P6SrE9ERETkeCjMS2mX4pw73TnXCkgDbvO6IBEREZHiojAvZclPQHMAM+tvZovMbKGZfZR7RzO72czm+NvHH+zRN7Ne/l7+hWb2o3/bKWY22/8NwCIzO6FE35WIiIiUWVo0Sko1M0t0zpU3swhgPDAF+BGYAJznnNthZlWdc7vM7Ekg0Tn3splVc87t9B/jWWCrc+7/zGwx0Nk5t9HMKjvn9pjZ/wEznXOjzCwKCHfOpXjyhkVERKRMUc+8lHaxZrYASADWAR8AFwPjnHM7AJxzu/J4Xisz+8kf3vsBp/i3/wIMN7ObgXD/tt+Af5vZQ0AjBXkREREpKRFeFyASYCnOudNzbjAzAwr6Smo40NU5t9DMrgfaAzjnbjOzdsAVwAIzO90594mZzfJvm2pmNznnvi/etyEiIiJyJPXMS1n0HdDbzKoBmFnVPPapAGw2s0h8PfP4923mnJvlnHsc2AE0MLOmwCrn3JvAZKB1wN+BiIiICOqZlzLIObfUzJ4DZphZJjAfuD7XboOAWcBaYDG+cA8w2H+Bq+H7o2Ah8DBwrZmlA1uApwP+JkRERETQBbAiIiIiIiFLw2xEREREREKUwryIiIiISIhSmBcRERERCVEK8yIiIiIiIUphXkREREQkRCnMi4iIiIiEKIV5EREREZEQ9f+bbT04cLvGgAAAAABJRU5ErkJggg=="
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Would appear that `Embarked` is somewhat correlated with survival, depending on the passenger's sex. So we should likely keep this feature in our eventual model."
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "source": [
                "# let's looks at a similar pivot table\r\n",
                "sp_e = pd.pivot_table(titan, index=['Pclass', 'Sex'], columns=['Embarked'], values=['Survived'], dropna=False, aggfunc='mean')\r\n",
                "sp_e"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "              Survived          \n",
                            "Embarked             C    Q    S\n",
                            "Pclass Sex                      \n",
                            "1      female     0.98 1.00 0.96\n",
                            "       male       0.40 0.00 0.35\n",
                            "2      female     1.00 1.00 0.91\n",
                            "       male       0.20 0.00 0.15\n",
                            "3      female     0.65 0.73 0.38\n",
                            "       male       0.23 0.08 0.13"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th colspan=\"3\" halign=\"left\">Survived</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Embarked</th>\n",
                            "      <th>C</th>\n",
                            "      <th>Q</th>\n",
                            "      <th>S</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Pclass</th>\n",
                            "      <th>Sex</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
                            "      <th>female</th>\n",
                            "      <td>0.98</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.96</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>male</th>\n",
                            "      <td>0.40</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.35</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
                            "      <th>female</th>\n",
                            "      <td>1.00</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.91</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>male</th>\n",
                            "      <td>0.20</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.15</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
                            "      <th>female</th>\n",
                            "      <td>0.65</td>\n",
                            "      <td>0.73</td>\n",
                            "      <td>0.38</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>male</th>\n",
                            "      <td>0.23</td>\n",
                            "      <td>0.08</td>\n",
                            "      <td>0.13</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 11
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "source": [
                "# Trouble. The above values don't seem to match those of the Seaborn pointplot??\r\n",
                "# Let's try groupby()\r\n",
                "sp_e_gb = titan.groupby([\"Sex\", \"Pclass\", \"Embarked\"])[\"Survived\"].mean().unstack()\r\n",
                "sp_e_gb"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "Embarked         C    Q    S\n",
                            "Sex    Pclass               \n",
                            "female 1      0.98 1.00 0.96\n",
                            "       2      1.00 1.00 0.91\n",
                            "       3      0.65 0.73 0.38\n",
                            "male   1      0.40 0.00 0.35\n",
                            "       2      0.20 0.00 0.15\n",
                            "       3      0.23 0.08 0.13"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>Embarked</th>\n",
                            "      <th>C</th>\n",
                            "      <th>Q</th>\n",
                            "      <th>S</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sex</th>\n",
                            "      <th>Pclass</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
                            "      <th>1</th>\n",
                            "      <td>0.98</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.96</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>1.00</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.91</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.65</td>\n",
                            "      <td>0.73</td>\n",
                            "      <td>0.38</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
                            "      <th>1</th>\n",
                            "      <td>0.40</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.35</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.20</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.15</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.23</td>\n",
                            "      <td>0.08</td>\n",
                            "      <td>0.13</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 12
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "source": [
                "titan.pivot_table('Survived', index=['Sex', 'Pclass'], columns=['Embarked'])"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "Embarked         C    Q    S\n",
                            "Sex    Pclass               \n",
                            "female 1      0.98 1.00 0.96\n",
                            "       2      1.00 1.00 0.91\n",
                            "       3      0.65 0.73 0.38\n",
                            "male   1      0.40 0.00 0.35\n",
                            "       2      0.20 0.00 0.15\n",
                            "       3      0.23 0.08 0.13"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>Embarked</th>\n",
                            "      <th>C</th>\n",
                            "      <th>Q</th>\n",
                            "      <th>S</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sex</th>\n",
                            "      <th>Pclass</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
                            "      <th>1</th>\n",
                            "      <td>0.98</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.96</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>1.00</td>\n",
                            "      <td>1.00</td>\n",
                            "      <td>0.91</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.65</td>\n",
                            "      <td>0.73</td>\n",
                            "      <td>0.38</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
                            "      <th>1</th>\n",
                            "      <td>0.40</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.35</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.20</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>0.15</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.23</td>\n",
                            "      <td>0.08</td>\n",
                            "      <td>0.13</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 13
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Not a clue what is going on. Will have to do some thinking and maybe some arithmetic."
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "source": [
                "#titan.pivot_table(\"Age\", index=[\"Sex\", \"Survived\", \"Embarked\"], columns = [\"Pclass\"], aggfunc='count')\r\n",
                "sp_e_gb_2 = titan.groupby([\"Sex\", \"Pclass\", \"Embarked\", \"Survived\"])[\"Ticket\"].count()\r\n",
                "sp_e_gb_2"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "Sex     Pclass  Embarked  Survived\n",
                            "female  1       C         0             1\n",
                            "                          1            42\n",
                            "                Q         1             1\n",
                            "                S         0             2\n",
                            "                          1            46\n",
                            "        2       C         1             7\n",
                            "                Q         1             2\n",
                            "                S         0             6\n",
                            "                          1            61\n",
                            "        3       C         0             8\n",
                            "                          1            15\n",
                            "                Q         0             9\n",
                            "                          1            24\n",
                            "                S         0            55\n",
                            "                          1            33\n",
                            "male    1       C         0            25\n",
                            "                          1            17\n",
                            "                Q         0             1\n",
                            "                S         0            51\n",
                            "                          1            28\n",
                            "        2       C         0             8\n",
                            "                          1             2\n",
                            "                Q         0             1\n",
                            "                S         0            82\n",
                            "                          1            15\n",
                            "        3       C         0            33\n",
                            "                          1            10\n",
                            "                Q         0            36\n",
                            "                          1             3\n",
                            "                S         0           231\n",
                            "                          1            34\n",
                            "Name: Ticket, dtype: int64"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 14
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "source": [
                "sp_e_gb_2 = titan.loc[titan[\"Embarked\"]==\"C\"].groupby([\"Sex\", \"Pclass\", \"Survived\"])[\"Ticket\"].count()\r\n",
                "sp_e_gb_3 = pd.pivot_table(titan.loc[titan[\"Embarked\"]==\"C\"], index=['Pclass', 'Sex', 'Survived'], values=['Ticket'], aggfunc='count')\r\n",
                "sp_e_gb_3"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "                        Ticket\n",
                            "Pclass Sex    Survived        \n",
                            "1      female 0              1\n",
                            "              1             42\n",
                            "       male   0             25\n",
                            "              1             17\n",
                            "2      female 1              7\n",
                            "       male   0              8\n",
                            "              1              2\n",
                            "3      female 0              8\n",
                            "              1             15\n",
                            "       male   0             33\n",
                            "              1             10"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th>Ticket</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Pclass</th>\n",
                            "      <th>Sex</th>\n",
                            "      <th>Survived</th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
                            "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
                            "      <th>0</th>\n",
                            "      <td>1</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>42</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
                            "      <th>0</th>\n",
                            "      <td>25</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>17</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
                            "      <th>female</th>\n",
                            "      <th>1</th>\n",
                            "      <td>7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
                            "      <th>0</th>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
                            "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
                            "      <th>0</th>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>15</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
                            "      <th>0</th>\n",
                            "      <td>33</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>10</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 15
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "source": [
                "pd.crosstab(titan.loc[titan[\"Sex\"]==\"female\"]['Survived'], titan['Embarked'], margins=True)\r\n",
                "pd.crosstab(titan.loc[titan[\"Sex\"]==\"male\"]['Survived'], titan['Embarked'], margins=True)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "Embarked   C   Q    S  All\n",
                            "Survived                  \n",
                            "0          9   9   63   81\n",
                            "1         64  27  140  231\n",
                            "All       73  36  203  312"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th>Embarked</th>\n",
                            "      <th>C</th>\n",
                            "      <th>Q</th>\n",
                            "      <th>S</th>\n",
                            "      <th>All</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Survived</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>9</td>\n",
                            "      <td>9</td>\n",
                            "      <td>63</td>\n",
                            "      <td>81</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>64</td>\n",
                            "      <td>27</td>\n",
                            "      <td>140</td>\n",
                            "      <td>231</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>All</th>\n",
                            "      <td>73</td>\n",
                            "      <td>36</td>\n",
                            "      <td>203</td>\n",
                            "      <td>312</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 16
                },
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "Embarked   C   Q    S  All\n",
                            "Survived                  \n",
                            "0         66  38  364  468\n",
                            "1         29   3   77  109\n",
                            "All       95  41  441  577"
                        ],
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th>Embarked</th>\n",
                            "      <th>C</th>\n",
                            "      <th>Q</th>\n",
                            "      <th>S</th>\n",
                            "      <th>All</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Survived</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>66</td>\n",
                            "      <td>38</td>\n",
                            "      <td>364</td>\n",
                            "      <td>468</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>29</td>\n",
                            "      <td>3</td>\n",
                            "      <td>77</td>\n",
                            "      <td>109</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>All</th>\n",
                            "      <td>95</td>\n",
                            "      <td>41</td>\n",
                            "      <td>441</td>\n",
                            "      <td>577</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 16
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "source": [
                "e_tst = titan.loc[(titan[\"Sex\"]==\"female\") & (titan['Embarked']=='C')].groupby([\"Pclass\"])\r\n",
                "print(e_tst[\"Survived\"].mean())"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "Pclass\n",
                        "1   0.98\n",
                        "2   1.00\n",
                        "3   0.65\n",
                        "Name: Survived, dtype: float64\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "source": [
                "sns.pointplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=titan.loc[titan[\"Embarked\"]==\"C\"]);"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ],
                        "image/png": "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"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "source": [
                "fgrid_2 = sns.FacetGrid(titan, col='Embarked', col_wrap=2, height=4.0, aspect=1.2, sharex=False)\r\n",
                "# from the single chart above, clearly something not working correctly in the first set of plots\r\n",
                "# let's specifically state the order for the sexes\r\n",
                "fgrid_2.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=None, order=None, hue=\"Sex\", hue_order=[\"female\", \"male\"] )\r\n",
                "fgrid_2.add_legend();"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 761.7x576 with 3 Axes>"
                        ],
                        "image/png": "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"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Bingo. The chart above matches the pivot table data. Finally! Took me awhile to sort the possible cause. No easy search results. Took some reading and guessing.\r\n",
                "\r\n",
                "Okay, now it looks like the port of embarcation is of less value in determining probability of survival. Likely can be dropped from the model."
            ],
            "metadata": {}
        }
    ],
    "metadata": {
        "orig_nbformat": 4,
        "language_info": {
            "name": "python",
            "version": "3.9.2",
            "mimetype": "text/x-python",
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "pygments_lexer": "ipython3",
            "nbconvert_exporter": "python",
            "file_extension": ".py"
        },
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.9.2 64-bit ('ds-3.9': conda)"
        },
        "interpreter": {
            "hash": "a27d3f2bf68df5402465348834a2195030d3fc5bfc8e594e2a17c8c7e2447c85"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}