02__Data_Visualization_Elliot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src='img/ms_logo.jpeg' height=40% width=40%></center>\n",
    "\n",
    "<center><h1>Data Visualization and Plotting</h1></center>\n",
    "\n",
    "Data by itself is useless; without any context, we have no way to understand it.  With different data science techniques, we can extract information from the data, and use this to drive decision making.\n",
    "\n",
    "The main way we've investigated data so far is with descriptive statistics.  Today, we're going to add data visualization to our toolbox, and see a real example of where descriptive statistics without accompanying visualization can be misleading.  \n",
    "\n",
    "Let's start by importing the first data set we'll be working with, and investigating it with some descriptive statistics.  \n",
    "\n",
    "The data set is a CSV, and can be found in the datasets subdirectory.  \n",
    "\n",
    "**Use pandas to read this data set into a dataframe below.  Call .describe() on the dataset to print the summary statistics.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "np.random.seed(1547)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00_Test_Installation.ipynb       README.md\r\n",
      "01_Descriptive_Statistics.ipynb  \u001b[34mdatasets\u001b[m\u001b[m/\r\n",
      "02__Data_Visualization.ipynb     \u001b[34mimg\u001b[m\u001b[m/\r\n",
      "Installation_Instructions.md\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.500909</td>\n",
       "      <td>7.500909</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.316625</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>2.031568</td>\n",
       "      <td>2.031657</td>\n",
       "      <td>2.030424</td>\n",
       "      <td>2.030579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>5.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.315000</td>\n",
       "      <td>6.695000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>6.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.580000</td>\n",
       "      <td>8.140000</td>\n",
       "      <td>7.110000</td>\n",
       "      <td>7.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.570000</td>\n",
       "      <td>8.950000</td>\n",
       "      <td>7.980000</td>\n",
       "      <td>8.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.840000</td>\n",
       "      <td>9.260000</td>\n",
       "      <td>12.740000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0         x1         x2         x3         x4         y1  \\\n",
       "count   11.000000  11.000000  11.000000  11.000000  11.000000  11.000000   \n",
       "mean     6.000000   9.000000   9.000000   9.000000   9.000000   7.500909   \n",
       "std      3.316625   3.316625   3.316625   3.316625   3.316625   2.031568   \n",
       "min      1.000000   4.000000   4.000000   4.000000   8.000000   4.260000   \n",
       "25%      3.500000   6.500000   6.500000   6.500000   8.000000   6.315000   \n",
       "50%      6.000000   9.000000   9.000000   9.000000   8.000000   7.580000   \n",
       "75%      8.500000  11.500000  11.500000  11.500000   8.000000   8.570000   \n",
       "max     11.000000  14.000000  14.000000  14.000000  19.000000  10.840000   \n",
       "\n",
       "              y2         y3         y4  \n",
       "count  11.000000  11.000000  11.000000  \n",
       "mean    7.500909   7.500000   7.500909  \n",
       "std     2.031657   2.030424   2.030579  \n",
       "min     3.100000   5.390000   5.250000  \n",
       "25%     6.695000   6.250000   6.170000  \n",
       "50%     8.140000   7.110000   7.040000  \n",
       "75%     8.950000   7.980000   8.190000  \n",
       "max     9.260000  12.740000  12.500000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in AQ_Data.csv as a dataframe.\n",
    "df = pd.read_csv('datasets/AQ_Data.csv')\n",
    "# Call .describe() on the dataframe. \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 different data subsets of data contained within this csv file.  The x and y cooridates for each point is denoted by the number following it--the points for the first data set are (x1, y1), (x2, y2) for the second data set, etc. \n",
    "\n",
    "The summary statistics suggest that these datasets are all almost exactly the same!  The mean and standard deviation of the points in each data set are essentially identical.  There are several other identical statistics:\n",
    "\n",
    "1.  The line of best fit for each data set is y = 3.0 + 0.5x\n",
    "1.  For each data set, the correlation between x and y values are 0.816\n",
    "1.  For each data set, the coefficient of determination is 0.67\n",
    "\n",
    "Looking purely at the summary statistics, the evidence is pretty clear.  These data sets are basically identical.  Let's graph them and confirm this suspicion. Run the cell below to plot each data set side-by-side.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-e43781ba8b79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean=7.50, std=1.94, r=0.82\n",
      "mean=7.50, std=1.94, r=0.82\n",
      "mean=7.50, std=1.94, r=0.82\n",
      "mean=7.50, std=1.94, r=0.82\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b4a5518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell--you do not need to write any code!\n",
    "\n",
    "x = df['x1']\n",
    "y1 = df['y1']\n",
    "y2 = df['y2']\n",
    "y3 = df['y3']\n",
    "x4 = df['x4']\n",
    "y4 = df['y4']\n",
    "\n",
    "def fit(x):\n",
    "    return 3 + 0.5 * x\n",
    "\n",
    "xfit = np.array([np.min(x), np.max(x)])\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.plot(x, y1, 'bo', xfit, fit(xfit), 'r-', lw=2)\n",
    "plt.axis([2, 20, 2, 14])\n",
    "plt.setp(plt.gca(), xticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))\n",
    "plt.text(3, 12, 'I', fontsize=20)\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(x, y2, 'bo', xfit, fit(xfit), 'r-', lw=2)\n",
    "plt.axis([2, 20, 2, 14])\n",
    "plt.setp(plt.gca(), xticks=(0, 10, 20), xticklabels=[],\n",
    "         yticks=(4, 8, 12), yticklabels=[], )\n",
    "plt.text(3, 12, 'II', fontsize=20)\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot(x, y3, 'bo', xfit, fit(xfit), 'r-', lw=2)\n",
    "plt.axis([2, 20, 2, 14])\n",
    "plt.text(3, 12, 'III', fontsize=20)\n",
    "plt.setp(plt.gca(), yticks=(4, 8, 12), xticks=(0, 10, 20))\n",
    "\n",
    "plt.subplot(224)\n",
    "xfit = np.array([np.min(x4), np.max(x4)])\n",
    "plt.plot(x4, y4, 'bo', xfit, fit(xfit), 'r-', lw=2)\n",
    "plt.axis([2, 20, 2, 14])\n",
    "plt.setp(plt.gca(), yticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))\n",
    "plt.text(3, 12, 'IV', fontsize=20)\n",
    "\n",
    "# verify the stats\n",
    "pairs = (x, y1), (x, y2), (x, y3), (x4, y4)\n",
    "for x, y in pairs:\n",
    "    print('mean=%1.2f, std=%1.2f, r=%1.2f' % (np.mean(y), np.std(y),\n",
    "          np.corrcoef(x, y)[0][1]))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Code pulled from Matplotlib's tutorial on Anscombe's Quartet:\n",
    "# https://matplotlib.org/gallery/specialty_plots/anscombe.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h2>WAIT, WHAT?</h2></center>\n",
    "<br>\n",
    "<br>\n",
    "<center><img src='http://www.reactiongifs.com/r/2013/11/betty-faint.gif'></center>\n",
    "\n",
    "Our summary statistics could not have been more wrong! When we graph these data sets, can see that they are qualititatively very, very different:\n",
    "\n",
    "1. Plot 1 is linear\n",
    "1. Plot 2 is non-linear.\n",
    "1. Plot 3 is mostly linear, with an outlier skewing the line of best fit.  \n",
    "1. Plot 4 has little relationship at all--all coordinates but 1 have the exact same X value, regardless of y value!\n",
    "\n",
    "## Anscombe's Quartet\n",
    "\n",
    "The misleading similarities of these data sets are no accident. These data sets are the famous [Anscombe's Quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet) (well, famous to statisticians and data scientists, anyway).  This data set was designed by the statistician Francis Anscombe in 1973 to illustrate how misleading summary statistics can be without an accompanying visualization.  Although the numbers tell us the data sets are the same, our eyes can easily see this is not the case.  \n",
    "\n",
    "This brings us to the main lesson Anscombe has so elegantly taught us: **use visualizations whenever possible!**.  \n",
    "<br>\n",
    "<br>\n",
    "\n",
    "<center><h1>Visualizing Data with Matplotlib</h1></center>\n",
    "\n",
    "For the second part of this lesson, we'll explore the different visualizations available in python's most popular data visualization library, [Matplotlib](https://matplotlib.org/index.html).  \n",
    "\n",
    "Matplotlib is a tried-and-true python framework that is one of the first tools data scientists reach for when working with a data set.  Data visualization is a crucial part of the data science process, whether we're doing our initial exploration to get a feel for our data in the beginning of a project, or communicating the results of our experiment to stakeholders in a clear, intuitive way.  \n",
    "\n",
    "### Which graph should I Use?\n",
    "\n",
    "Before we can visualize our data, we'll need to select the type of graph we'll want to use.  There are three main types of graphs we can use:\n",
    "\n",
    "1.  Histograms/bar charts\n",
    "1.  Scatterplots\n",
    "1.  Time Series\n",
    "\n",
    "These types of graphs are the most common (and most useful!) ways to visualize data. Luckily, pandas and matplotlib are designed to fit together effortlessly.  Let's code up some examples of each chart!\n",
    "\n",
    "\n",
    "# Histograms/Bar Charts\n",
    "\n",
    "Histograms are used to represent counts.  If the data you're trying to represent is categorical, or have repeated values across a dimension of your data set, then a histogram is the way to go.  \n",
    "\n",
    "Visualizing histograms is such a common task in pandas that they've actually built it right into the pandas library, eliminating extra code that would normally be needed to use matplotlib.  Every dataframe in pandas has access to the `.hist()` method, which will visualize the data in a histogram.  \n",
    "\n",
    "It's common to mix up bar charts and histograms.  We're going to build one of each to help exemplify the differences, and examine the best use case for each.  \n",
    "\n",
    "**TASK: Use the df.hist( ) method to create a histogram for the sample data (provided below)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blue</th>\n",
       "      <th>fish</th>\n",
       "      <th>one</th>\n",
       "      <th>red</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   blue  fish  one  red  two\n",
       "0     1     4    1    1    1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyHCXpAYZ7pLUIMNdkhr0fwHYHdQjPpUR/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110560588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data = {\n",
    "    'one': [1],\n",
    "    'two': [1],\n",
    "    'fish': [4],\n",
    "    'red': [1],\n",
    "    'blue': [1]\n",
    "}\n",
    "\n",
    "# Create a dataframe by calling pd.Dataframe() and passing the sample_data dictionary as the first argument.\n",
    "hist_df = pd.DataFrame(sample_data)\n",
    "\n",
    "# To create the histogram, we'll have to specify that we're calling it on the first (and only) row of data in \n",
    "# the dataframe.  To do this, we use .iloc[0].  We can chain the hist() method onto the iloc[0] method.\n",
    "#  \n",
    "# Call .iloc[0].hist() on the hist_df object.  \n",
    "hist_df.iloc[0].hist()\n",
    "hist_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this looks different than you were expecting, it's because you were thinking of a bar chart instead.  Histograms show us the number of times a value shows up.  In this case, 'one', 'two', 'red', and 'blue' all have values of 1, while only 'fish' has a value of 4.  The histogram has binned these examples to show us the total number of times each number shows up in the data set.  In this context, the graph above makes sense--the '1' column is at 4, and the '4' column is at 1.  \n",
    "\n",
    "A **Bar Chart** shows us separate values for different categories, side-by-side.  Let's create a bar chart of the same data and see how it looks.  \n",
    "\n",
    "**TASK: Create a bar chart using the .plot.bar() method on a new dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b469278>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b4698d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another dataframe called bar_df using pd.Dataframe. Pass in sample_data as the argument (this is already \n",
    "# in memory from the last cell ran, so you do NOT need to copy and paste sample data into this cell.)\n",
    "\n",
    "bar_df = pd.DataFrame(sample_data)\n",
    "\n",
    "# To create the chart, access the plot object stored in the dataframe's .plot attribute, and call this object's .bar() \n",
    "# method.  This can be chained together as .plot.bar()\n",
    "bar_df.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks more like what we expected! The chart color-codes each word differently for us, and even provides a nice key in the top-right corner for us. \n",
    "\n",
    "**TASK:  In your own words, how would you explain when you should use a histogram, versus when you should use a bar chart?**\n",
    "\n",
    "ANSWER:\n",
    "\n",
    "{WRITE ANSWER HERE!}\n",
    "\n",
    "\n",
    "<center><h2>Scatterplots</h2></center>\n",
    "\n",
    "Scatterplots are one of the simplest, but most effective visualizations out there! The idea behind a scatterplot is intuitive--make one column of the dataframe your x coordinate, make another one your y coordinate, and then plot everything to see how it looks.  \n",
    "\n",
    "Scatterplots are also easy to build use pandas/matplotlib.  \n",
    "\n",
    "**TASK: Create a scatterplot using the sample data provided below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11085f780>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11085f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We'll use the make_blobs function from Scikit-Learn to generate dummy data with a few clusters in it.  \n",
    "from sklearn.datasets import make_blobs\n",
    "X, _ = make_blobs()\n",
    "scatter_df = pd.DataFrame(X, columns=['x', 'y'])\n",
    "\n",
    "\n",
    "# To create the scatter plot, access the object stored in .plot, and then call the .scatter() method. You can\n",
    "# chain these together, as you did with the bar chart example.  In the .scatter() method, you'll need to pass in 'x'\n",
    "# and 'y' as positional arguments to tell the scatter plot which points belong on each axis.  \n",
    "scatter_df.plot.scatter(x='x', y='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was easy!  The scatterplot is a great way to get a quick, intuitive feel for our data set.  In this case, we can see that there are three clusters in our data.  In a real data science project, this is the type of thing that would likely warrant more investigation.  If we didn't visualize our data as a scatterplot, we might never have noticed this!\n",
    "\n",
    "\n",
    "<center><h2>Line Graphs/Time Series</h2></center>\n",
    "\n",
    "Line Graphs are most commonly used for Time Series analysis--that is, tracking how data changes over a time interval. A Time Series is a just a line graph where the x-axis is a sequence of datetimes, starting at the left and progressing as you move to the right.  Line graphs are just like a scatterplot, but with a continuous line drawn between the points, starting at the leftmost x value and connecting to each sequential x value.  \n",
    "\n",
    "Line graphs and Time Series plots are one the most important tools used in the financial world.  For our example chart, we're going to graph the price of the Ethereum cryptocurrency.  (If you're not familiar with ethereum, its a very popular cryptocurrency similar to bitcoin.  It's primarily used for HODLing, until it can traded for a lamborghinis on the moon).  \n",
    "\n",
    "<center><img src='https://i.redd.it/3zrf0i340iez.gif' height=25% width=25%></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1108e9588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sNStS7RxN9eWcu7iWaq9y/ieXrjittaUtrD215aSxcjBs15WnRjWeHMhzJScX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\nDmPPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108b3e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This \n",
    "eth_df = pd.read_csv('datasets/ethereum_price_data.csv')\n",
    "eth_df = eth_df.iloc[::-1]\n",
    "\n",
    "# call the .plot method on eth_df.  Be sure to pass in the \"Date\" column name (as a string) for the x keyword, \n",
    "# and the \"Close\" column name (also a string) for the y keyword.  Give the graph a label with the title with the \n",
    "# title keyword, and pass in a in the tuple (12,5) for the figsize keyword (this will set the size of the graph)\n",
    "eth_df.plot(x=\"Date\", y=\"Close\", title=\"Ethereum Price\", figsize=(12,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was pretty easy to do, and the graph is very easy to understand.  Note that we had to add a few keyword arguments to make the graph large enough to read without the X-axis items overlapping.  It's easy to adjust the looks of any plot with keyword arguments--after all, `Dataframe.plot` is just a wrapper around `matplotlib.pyplot`.  For a full list of keywords you can use to modify your plot, check out the [documentation!](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html#pandas.DataFrame.plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Challenge Section</h1></center>\n",
    "\n",
    "For the remainder of this lesson, you'll be using the Titanic data set to create several visualizations that communicate something interesting.  For instance, can you visualize survival rates of men vs. women?  How about by age? Fare price? \n",
    "\n",
    "To complete this section, you'll need to:\n",
    "\n",
    "1.  Think of at least 3 interesting questions to investigate in the data set.  \n",
    "1.  Select the most appropriate graph type to represent the data relevant to each question.  \n",
    "1.  Create a visualization that attempts to answer the questions you have posed!\n",
    "\n",
    "**TASK: Create at least 3 visualizations related to the Titanic data set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read in titanic.csv, which is contained within the datasets folder.\n",
    "\n",
    "titanic_df = pd.read_csv(\"datasets/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x103dc45f8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MT5p89vxKs1RV3V5VV7TLvwBuAHYGDgVObaudChzWLh8KnFaNS4HtOrctkyRJ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\nAyVNgDQK2aCkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10490a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axes = plt.subplots(2, 4, figsize=(16, 10), sharex=False, sharey=False)\n",
    "sns.countplot(x='Pclass', data=titanic_df, ax=axes[0,0])\n",
    "sns.countplot(x='Survived', data=titanic_df, ax=axes[0,1])\n",
    "sns.countplot(x='Parch', data=titanic_df, ax=axes[0,2])\n",
    "sns.countplot(x='Cabin', data=titanic_df, ax=axes[0,3])\n",
    "sns.countplot(x='Fare', data=titanic_df, ax=axes[1,0])\n",
    "sns.countplot(x='Ticket', data=titanic_df, ax=axes[1,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16602828>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6ZWxNadlLcjSwEfhpnv57+etja2oZ8jr6fnwQuJLBNcqvBq4GPjTWjqTB7+CPAr8M/BuD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\ng16SOmfQS1Ln/hfUTwBECDSLGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110def60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df.Sex.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[titanic_df.Sex.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0500      43\n",
       "13.0000     42\n",
       "7.8958      38\n",
       "7.7500      34\n",
       "26.0000     31\n",
       "10.5000     24\n",
       "7.9250      18\n",
       "7.7750      16\n",
       "26.5500     15\n",
       "0.0000      15\n",
       "7.2292      15\n",
       "7.8542      13\n",
       "8.6625      13\n",
       "7.2500      13\n",
       "7.2250      12\n",
       "16.1000      9\n",
       "9.5000       9\n",
       "24.1500      8\n",
       "15.5000      8\n",
       "56.4958      7\n",
       "52.0000      7\n",
       "14.5000      7\n",
       "14.4542      7\n",
       "69.5500      7\n",
       "7.0500       7\n",
       "31.2750      7\n",
       "46.9000      6\n",
       "30.0000      6\n",
       "7.7958       6\n",
       "39.6875      6\n",
       "            ..\n",
       "7.1417       1\n",
       "42.4000      1\n",
       "211.5000     1\n",
       "12.2750      1\n",
       "61.1750      1\n",
       "8.4333       1\n",
       "51.4792      1\n",
       "7.8875       1\n",
       "8.6833       1\n",
       "7.5208       1\n",
       "34.6542      1\n",
       "28.7125      1\n",
       "25.5875      1\n",
       "7.7292       1\n",
       "12.2875      1\n",
       "8.6542       1\n",
       "8.7125       1\n",
       "61.3792      1\n",
       "6.9500       1\n",
       "9.8417       1\n",
       "8.3000       1\n",
       "13.7917      1\n",
       "9.4750       1\n",
       "13.4167      1\n",
       "26.3875      1\n",
       "8.4583       1\n",
       "9.8375       1\n",
       "8.3625       1\n",
       "14.1083      1\n",
       "17.4000      1\n",
       "Name: Fare, Length: 248, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.Fare.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1671b6a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16849f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df.Fare.hist(bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Leonard, Mr. Lionel</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harrison, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B94</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Parkes, Mr. Francis \"Frank\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>303</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. William Cahoone Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>414</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cunningham, Mr. Alfred Fleming</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>467</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Campbell, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>482</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Frost, Mr. Anthony Wood \"Archie\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239854</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>598</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>634</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Parr, Mr. William Henry Marsh</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>675</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Watson, Mr. Ennis Hastings</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239856</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Knight, Mr. Robert J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>823</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Reuchlin, Jonkheer. John George</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                              Name   Sex  \\\n",
       "179          180         0       3               Leonard, Mr. Lionel  male   \n",
       "263          264         0       1             Harrison, Mr. William  male   \n",
       "271          272         1       3      Tornquist, Mr. William Henry  male   \n",
       "277          278         0       2       Parkes, Mr. Francis \"Frank\"  male   \n",
       "302          303         0       3   Johnson, Mr. William Cahoone Jr  male   \n",
       "413          414         0       2    Cunningham, Mr. Alfred Fleming  male   \n",
       "466          467         0       2             Campbell, Mr. William  male   \n",
       "481          482         0       2  Frost, Mr. Anthony Wood \"Archie\"  male   \n",
       "597          598         0       3               Johnson, Mr. Alfred  male   \n",
       "633          634         0       1     Parr, Mr. William Henry Marsh  male   \n",
       "674          675         0       2        Watson, Mr. Ennis Hastings  male   \n",
       "732          733         0       2              Knight, Mr. Robert J  male   \n",
       "806          807         0       1            Andrews, Mr. Thomas Jr  male   \n",
       "815          816         0       1                  Fry, Mr. Richard  male   \n",
       "822          823         0       1   Reuchlin, Jonkheer. John George  male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
       "179  36.0      0      0    LINE   0.0   NaN        S  \n",
       "263  40.0      0      0  112059   0.0   B94        S  \n",
       "271  25.0      0      0    LINE   0.0   NaN        S  \n",
       "277   NaN      0      0  239853   0.0   NaN        S  \n",
       "302  19.0      0      0    LINE   0.0   NaN        S  \n",
       "413   NaN      0      0  239853   0.0   NaN        S  \n",
       "466   NaN      0      0  239853   0.0   NaN        S  \n",
       "481   NaN      0      0  239854   0.0   NaN        S  \n",
       "597  49.0      0      0    LINE   0.0   NaN        S  \n",
       "633   NaN      0      0  112052   0.0   NaN        S  \n",
       "674   NaN      0      0  239856   0.0   NaN        S  \n",
       "732   NaN      0      0  239855   0.0   NaN        S  \n",
       "806  39.0      0      0  112050   0.0   A36        S  \n",
       "815   NaN      0      0  112058   0.0  B102        S  \n",
       "822  38.0      0      0   19972   0.0   NaN        S  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[titanic_df.Fare==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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aiUQms7Evv6k6NpjZUUUzkHXeY3nPnXOuKRuFAOItDod0RW87WUcAAJC6sRGp\nuVtqWjp3nZk0Nsy0HShk9Rrpr9PWOybUfm6twm31KqD5HhcMHp1zlbkqCBCvo6k230UAAADFKLIm\n+gMUo7Hhdm29Y1py0tY7nFpXtSvc9mK+ixWT7FQdAAAAAIDgRLOOg89MSZIGn5nSSH+tstT3MRsI\nHgEAAAAg305nHeNsvcNlq+9jNhA8AgCA4nRsIN8lKE3ZOq98PkAqZmYdY7KQfbz66qtXvuIVr+h9\n4YUXajo6OtbffPPNbenuy2/AHAAAgMIzPiJtv1/q3cTgJ9mUrfPK5wOkJlHWMSbDvo933333C5kU\nLR7BIwAAKD4jO6S+e6VIr1R/Sb5LUzqydV75fIBU1Gt8OKxFy6e1aFmClqEmjQ+HC2HkVYJHAABQ\nXE7P4+eiv1tXkd3KhmydVz4fIFWnFOndp0iv/3Z5Rp9HAABQXEZ2SMPPRR8PPyeN9Oe3PKUiW+eV\nzwdI1SlJh5L4IXgEAABI2umsVpxtd0vjB/NTnlKRrfPK5wNMT09PW74LkSnvGOb0wSR4BAAAxSM+\nqxVDditz2TqvfD7A1qGhoeZiDiCnp6dtaGioWdLW2evo8wgAAIpDoqxWDH3r0pet88rnA2hycvID\n+/fv/8b+/fvXqXgTddOStk5OTn5g9gqCRwAAUBzGRqTmbqlp6dx1ZtLYMMFJOrJ1Xvl8AF100UWD\nkt6c73IEheARRat/cFS/2j4kuQQrTbpsdUSr2htyXi4AQEAia6I/yK5snVc+H6DkETyiaLWGQxo4\nfEI7hkbnrOuJNKg1HMpDqQAAaTk2IDV25bsUuVVux1xuxwuUoGJthwtocTikq9Z1Jlx31bpOLSZ4\nBIDiMD4ibb+/vEbkLLdjLrfjBUoUwSOK2sq2sHoiM5um9kQatLItnKcSAQBSNrJD6ru3vEbkLLdj\nLrfjBUoUwSOKWqLsI1lHACgip0fodOUzH2C5HXO5HS9QwggeUfTis49kHQGgyMTPC1gu8wGW2zGX\n2/ECJYzgEUUvln00kXUEgKKSaF7AUs9Mldsxl9vxAiWO4BElYWVbWFeu7SDrCADFJD4jFVPqmaly\nO+ZyO16gxAUWPJrZMjN70My2mdnTZvaX3vIWM7vfzLZ7vxd7y83MvmRm/Wb2pJldGFTZUHoWh0O6\noredrCOAhKiTClCijFRMqWamyu2Yy+14gTIQ5DyPk5L+L+fcY2bWKOlRM7tf0nsl/dw59zkz+5Sk\nT0n6pKQ3SFrt/Vwq6avebyApHU21+S4CgMJFnVRoxkak5m6paencdWbS2LBU35L7cgWp3I653I4X\nKAOBBY/OuQFJA97jY2a2TdJZuDB0AAAgAElEQVRSSddIusLb7FuSHlK0or5G0redc07SI2a2yMy6\nvP0AAJA26qQCFFkT/Skn5XbM5Xa8QBnISZ9HM1sh6QJJv5PUEat8vd/t3mZLJe2Oe9keb9nsfX3Q\nzDab2eahoaEgiw0AKEHUSQAApCfw4NHMGiTdLumvnHNHF9o0wTI3Z4FzX3PObXTObYxEItkqJgCg\nDFAnAQCQvkCDRzOrVrSS/q5z7g5v8QEz6/LWd0ka9JbvkbQs7uXdkvYFWT4AQPmgTgIAIDNBjrZq\nkv5V0jbn3D/GrbpL0k3e45sk/Shu+Y3eCHcvlXSEviUAgGygTgIAIHNBjrb6cknvkfSUmT3uLftr\nSZ+TdJuZvV/SLknXeevulfRGSf2SxiX9cYBlAwCUF+okAAAyFORoqw8rcZ8RSXpNgu2dpD8LqjwA\ngPJFnQQAQOZyMtoqAAAAAKC4ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAA\nfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAFKJj\nA/kuQe6V4zEDRYTgEQAAoNCMj0jb75fGD+a7JLlTjscMFBmCRwAAgEIzskPqu1ca6c93SXKnHI8Z\nKDIEjwAAAIVkfETadrckF/1dDpm4cjxmoAgRPAIAABSSkR3S8HPRx8PPlUcmrhyPGShCBI8AAACF\n4nQGLk6pZ+LK8ZiBIkXwCAAAUCjiM3AxpZ6JK8djBopUVb4LAJSr/sFR/Wr7kOQSrDTpstURrWpv\nyHm5AAB5kigDF7Ptbql1lVTfktsyzefYgNTYlfl+iumYM5WtcwbkEcFjPP6pkUOt4ZAGDp/QjqHR\nOet6Ig1qDYfyUCoAhYSbTDniU//n7HMYG5Gau6WmpQnex6Sx4cIIpGJTavRuyrw8xXLMmcrmOQPy\niOAxhn9q5NjicEhXrevUVx6c2yznqnWdWkzwCJQ9bjLlQBL1f84+h8ia6E+hi02pEemV6i/JbF/F\ncsyZyuY5A/KIPo8xzC2EPFjZFlZPZObd6p5Ig1a2hfNUIgCFJHaTKRFuMmVJEvU/n0McptRIHecM\nJYTgUeKfGnmT6IKk7C5EACyIm0wBSqH+53PwMKVG6mLnbPIE5wxFj+BR4osQeRV/QVKWFyIAFsRN\npgClUP/zOYgpNdIRO2dTJ6P9N6dOcs5Q1Age+SJEnsUuSExleCECICncZApAGvV/2X8OTKmRutg5\nOzkujR6I/uacoYgRPPJFiAKwsi2sK9d2lN+FCICkcJMpAGnU/2X9OfhNqcFN97nis46j+6PLRveT\nfURRK+/RVstpbiEUtMXhkK7obS+vCxEAKeEmUxZlUP+X7edQLlNqZFPsnNUtklpXn1ne2CGFGjhn\nKErlHTzyRYgC0tFUm+8iAChg3GTKogzq/7L9HMplSo1siqyRmrqkvns0o7FfU5fU+yappjFvRQPS\nVd7BI1+EAIAiwk2mLMmw/udzQNIGt0lP/3Du8pazpWXM94jiU97BI3Kmf3BUv9o+JLkEK026bHVE\nq9obEqwEAAAZOTYgNXbN/7xQ9llq6B6FEkTwiJxoDYc0cPiEdgyNzlnXE2lQa7k1/yl1XEQAQGEY\nH5G23y/1booGKrOfF8o+SxHdo1CCCB6RE7ER6r7y4NxR7MpuxLpSx0UEABSOkR1S371SpFeqv2Tu\n80LZZymiexRKEFN1IGfi58eKKct5skpd7CKC6W4AIL9ON5t00d9HB2Y+T2eqiCD2CaBoEDwiZ2LZ\nx3hkHUvM7IsKLiIAIH/i57KcnpKG+s48T3dO6yD2CaBoEDwip+Kzj2QdS1D8RQUXEQCQP7MHa+m+\nWHr01ugE9TGp3uQLYp8AigrBI3Iqln00kXUsOYlGleMiAgDyI/5mXkuPNDUh7fyNdHL8zDap3uQL\nYp8AigrBI3JuZVtYV67tIOtYauIvKmK4iACA3EuUIdx6e/Tx6P70MoVB7BNA0WG0VeTc4nBIV/S2\nk3UsJcxlBQCFI36KiPo2qWmJ1LlB6lwvmaTWNdLxQ9Ftk50ywm+foUapqja1fQIoOgSPxayI59Lr\naKrNdxFyq4g/q6QwlxWAVJX692I+JZoiYvmlZx6nc+799gmgLBA8Fivm0ise5fBZMZcVgFSUw/di\noeLcA8gAfR6LFXPpFQ8+KwCYie/F/OHcA8gAwWMxYi694sFnBQAz8b2YP5x7ABmi2WoxSjSXXv0l\n+S0TEuOzyrn+wVH9avuQ5BKsNOmy1RGtam/IebkAeIrhe7FQ+mNmuxzFcO4BFDSCx2Iz31x6jGZZ\nePis8qI1HNLA4RPaMTQ6Z11PpEGtjPIL5E8xfC8WSp/AbJejGM49gIJH8FhsFppLL4i7h4Vy97UY\n5fqzyqFCzu4tDod01bpOfeXBuf15rlrXyRQxQD4Vw/dirE9gpDe/Zcp2OTI991wPABDBY3HJ9Vx6\n3l3Poyter9u3jRdkoFCwSnzew0LP7q1sC6sn0jCjfD2RBq1sC+exVECZy8b3YtABzOw+gfn6rs52\nOTI994WSjQWQdwSPxSTXc+l5dz3rW8+RXLse7h+es0khBAoFqcTnPZyd3Rs7OamDYyclJ73j4mV6\nePuQhkdPRjfOww2GRNlHso5AnmX6vZiLACbffQJjwXG2y5HpuS+UbCyAvCN4LCa5nEsv7q5n1bM/\n1psv+DM9nGBUby7I51EG8x7GZ/dClRU6cWpaqyJhnZya1p2P7zu9Xb5uMMSXj6wjUAAy/V4MOoDJ\nd5/AWHC86srslyOTc18o2VgABYGpOpDYrLuezeM79YpVbTM24YK8vMWye5JUXVmh9sYabdqwRFt2\nHp6xXb5uMMTKZ3ksA4AsycUUEwv1CcyFWHA81CdNT+WvHInKNTsLCqBsETxirgR3X6ue/bHefE7d\njGVckCOW3ZOk9d3NagvX6MWRsdPr832DYWVbWFeu7eAmB1Dsgg5g/PoEBj0fYuz9pyakR2+Vui/O\nTznmK1e+ywGgYNBsFXPNc/c1mn2M9n3Md1CAwhDL7t3yYL82rV+i+lDljPX5vsGwOBzSFb3t3OQA\nilkumpPmu596rN6dOikNPSdVhaTz3y6NDeW2HPOVK16hjY4LIKcCCx7N7JuSNkkadM6t85a1SPq+\npBWSXpT0dufcITMzSf8k6Y2SxiW91zn3WFBlwwIWuPsa6/v46/78BwUoHLOze4XWz7CjqTbfRUAB\noE4qYrkIYPLZTz2+3g01RH92PSK94qP5n2eyhEcNB5CeIJut/pukq2Yt+5SknzvnVkv6ufdckt4g\nabX380FJXw2wXFhI7O7r2a+a+9PcrUXTR/T2jd0FERSgMMRn9+hniAL2b6JOKj75bk6aC/nuazkf\nn+sBjc0dgR1A6Qss8+ic+6WZrZi1+BpJV3iPvyXpIUmf9JZ/2znnJD1iZovMrMs5NxBU+TAPn7uv\nlZIuqD9BUIAZ4rN79DNEIaJOKlL5bk4atELO7pXBqOEAUpfrPo8dscrXOTdgZu3e8qWSdsdtt8db\nNqeiNrMPKnonWMuXLw+2tEgo0kgzQMyPfoYoItRJha7UA5jZwfH0KamiOvq4FILjYhKbYxPAggpl\nwBxLsMwl2tA59zVJX5OkjRs3JtwGQH7RzxBFjjoJuREfHE8ck/Y+Ji29UKppzG+5yk1sjs3eTQTr\ngI9cB48HYk1/zKxL0qC3fI+kZXHbdUvaN+fVKG+lflcwoOPrHxzVr7YPJb70Nemy1RGtam/I+vsC\nRYA6qRQVa10xuE165KvSZR+TlqUwEFAyx1us5yRXYnNsRnoZRRbwket5Hu+SdJP3+CZJP4pbfqNF\nvVTSEfqWYIbYXcFSGBwhkQCPrzUc0sDhE3q4f3jOz8DhE2rNRvPSY/y7oihRJ5WaYq0rTvd9dKkN\nBDQ+Iu3bsvD2xXpOciXdcw+UqcCCRzP7nqTfSjrHzPaY2fslfU7SlWa2XdKV3nNJulfS85L6JX1d\n0keCKheKVOyuYL5HnwtKgMcXGwE1kayMiMqFCYoAdVKRSvXGVLHWFbERVydPpDbS6rH9Uu2ihc9T\nsZ6TXIkf7bYQRrkFClyQo62+c55Vr0mwrZP0Z0GVBUVu9l3BUptbKgfHt7ItfHr+xZiszcNIcx8U\nAeqkIpRqP7RirSti5Z46GR0gp6E9ufKPj0SDx92/k5ZdGm2WOnv7Yj0nuZJotFvOE7CgXDdbBVJX\n6ncFc3B88dnHsZOT2n1oXBtXLNbD24d068MvnPn59QvqHxz12VscmvsACEqqGbMgv0uDbJofK/fJ\ncWn0QPR3MuU/tl8aG5I23xr9naiM6WY0E75fCbbczvYcm6V4joBZCmW01eJFJ/RgpXhXsOgGh8nh\nXc9Y9rFv/1GtijTo5NS07nx85hggPZGG1PpAJrpYI/sIIFOpZsyC/C4NciTO+Kzj6P7ostH9Uqh+\n4fLHso5bb5fkor/DkZnZx3QzmvOVs9RGI832HJuleI6ABAgeM8EXRfAWuiuYIEiJDQ4T3zwzJuXA\nKBdSPL5MxLKPzw+N6l2XnqUH+wbVXFet1XHB9OtT6QNJcx8AQUn1xlSQ36VBNs2PzfNYt0hqXX1m\neWOHFGqYf57HWNZx35bo831bzmQfY9vPzmiGGpI/J7NvjJdi94TZc2zGS2eOzVyeIxIXyCOCx0yU\n4pdpniTKGF7WXaGGzf+lkwfH1RoOKVwT9+c6T5ASC5C+8uDcJidZGRwmSUllQBsmsnvXMwkr28K6\ncm2HVrU36JfPDam5rlrrupv1wLYDClVV6oG+Qf3i2aG5ZU2UrU3xYq1gs8JUwkBhSeXG1LEBqbou\nuO/SVDKg6XyXRNZITV1S3z2a0ZOoqUvqfVPi+R5nZB3jxGcfY+VNNaMZ23/8jfHYOZg8Xlo3COPn\n2JSk44ekusXzb7/Q55vLvqUkLpBnBI/pohP6glINFGZnDJe31CvcOakT9V2qWb1UNY01UkXcvN2J\n7gp6X+yBDg6TJL8MaHtjjTS6L7t3PZOwOBzSFb3t6miqPR1kHzl+SjtHxnX0xKSa66rnlDVhtvbI\nnuhk1onM8/+QTFY45wEmlTBQeJK9MRX7/13xytS+S1MJ8pLNgGbyXTK4TXr6h3OXt5ydeL7HidFo\nH8bDu6X6tjPLD++OLp+ckCaOpZfRlObeGB/ZIR3YGn3Nga3F0T3B7zOevX58JBrAz/f5+X2+uezC\nkShxwU1Q5BDBY7ro67WgVJuPzs4YHjl+SnfubpDq3qzXr+tUVcQnYIj7Yl8cbpmTfcxl1lHyz4A2\n1VVLdbPueuZIR1OtpDN9ILfsPKxrL+rW9/+we862Cc/b+Ij07E+krpckfoN5At9ks8I5bXZM6wGg\nsKTSDy3+//fSDyW//2SDvFQyoOl+l6TT764yJO16RFp81tzX7HpEWv6yaOYy1YzmjPJ4N8abl0V/\nxzd9LfQb5n6fcaL1fp/fQuuz3YUj1QynHDdBkVMEj+mgr5evdJqPxmcMjxw/pc07D6kn0qDF9UkE\nDLO+2OP3leusY0whZEAXEvuMbnmwX++8dLl6O5uSK+vIDqn/51LXBumlf5rSe/qdk5w2O6b1AFB4\nku2Hlu7/bypBXioZ0HS/S1Ltd3f8sHRkt9TY6f+aVDOa0sxjnp6Shvqi2cb4pq+Fnn1MNRD0+/z8\n1mezv206Gc5QmJugyCmCx3TkcJCTYpZq8JQocEgqYEjwxR7LPt7yYH/Os44xaR9PDsX6QLY31iRX\n1gwDrmTOSc6CbloPAIVndj+0+aTz/5vK91eqGdB0v0uSPd6Ygy9IL/5aOneT1LZ6/u3GDy5c/kiv\nVNs06zWzjrn7Yunx/5BOjkonx6LLTo5Fs5CFesMtnUDQ7/NbaH22R2xNNcP59B3SqivnP14gAASP\nqcr2F0UJSyd4SitjOM8XeywwymemrxAyoAuJ9YFsqqtOrqxZCLj83icnQXeRtB4o2EGGgHxK9/83\nle+v2RnByLnS0Lbo44QZ0BTLko7xkWiwMPycdPyg9IqPzv8eY8PzZzQb2qPB5ezgMf78NC3x+kv2\nRLfvuuDMdlW10b6TAfTLz1iqgeDQs9LAEzP3Ef/5+X2+2RyxNZ0M594t0S4kLT3cBEXOEDymKttD\nO5e4VIOn+KaUqWUd48RlH6/obc9rpi/l48mDWB9I37Jm6SIpmXMSeNBdJK0Him7qGSAX0vn/TfX7\na3ZG8PAuqefy7JQlXakEvwtlNGODw9Q2zZ0TUoqO0DqyIzow2orLok1fGzqkirhLxtWvWzjzmQ9+\nn3Gi9U/eJq185cxl8efW7/NNNXO8kFQznLGRdLfeLm14l3RwR0HeBEXpIXhMVTa/KMpAOsFTShlD\nny/2WGCUT4WQAU3WgmXN4kWS3zkJNOguotYDhTL1DFAw0v3/zeT76+g+6ek7pfOviw44k2lZ0pHN\nDGeippHxN8ZPjUnHBqXFK6RH/03a/5QUbo0OlhPjl/nMB7/PePb6qZPS8w9Iyy+NZu4O7jizLn6w\noERy/fkmSlycGvNG0rVolnjxyoK8CYrSQ/CIwKUaPMWaUqaVdYzxvnT7R0N5b/aX9PEUgHnLmuWL\npGTOSWBBd5G1Hij0gZeAnErn/zeZ76+piflHuBx6VvrNP0ud588MHnP5XZKtm3fzNY2M3RifOBbN\nSoY7pepaaclLpKUXzd1PoX1X+n3GiQLBqZNSVV10ALiN75vZfNUsus+ulxTG5zs7cRH7nGIj6e57\nPDpPpVRwN0FReggeEbikgqdZQ1MnlTFMouJub1xZEM3+CiEDmqyEZQ3gIsnvnAQWdBdZ64FiGHgJ\nyJl0/n/9vr9G90sjz0tn/dHc77Gj+6RHb5Xkor8jvWcCyFx9l2Tz5p1f09fYCK21i6SxQf+BeQrF\nQp9xOBI9hwv9DdS3Jh49vHNd9ss6Wzqf79EB6cQRqbruzLIlXr/UQgvsUXIIHpETCwYK6U6unETF\n3STR7C8b8hRwFVPQHeTgNoU+8BJQ0Py+v4a3S9t+HB0YZnYWb+hZaedvoo93/iY6dUVTjidjz9bN\nu1T6BJ44LO1+pDCbpyaSTB2Vi0AwHel8vkV2ExSlheAR+RfwRO00+1tYUEFPuY0UGuTgNsUw8BJQ\nlMZHpMe+LZ04NDfLczrrGGd29jEXshUopNoncPZ6BINAEEWG4BH5lYOJ2mn2t7Cggp5yGyk06MFt\nimngJaBoLNSMMz7rGJOv7GOm0ukTGL+ePnRzutcA5YrgEfmVo4naU2r2V2YVRFaCngTnbHE4pOsv\nWab7tu6XJG0fHNWR46dS22+RCTLLXUwDLwFZM/u7JZvfzws145w8MTfrGBNk9jGo+sevaWQuB4eJ\nVyz1bbrda4ASRPCI/El16PEMKpmkm/2VaQWRUdAzPqKJbfdpV/ur9PCe6RmrppzTkeOn9PLVbRo8\nNqEjx0+VdJPhoLPcxdQHFMjY7O/jbH8/x9+8bFsd7fs4/Jw0fjAaPLat8aZCmCUWbGU7eAyy/slG\nn8BsB3rFVN8G3L0GKCYEj8ifVPpXZKGSSarZX5lWELGg598f2anVXj/E1ycb9IzskD17r8L1Z2vP\noTa9ODI2Y3VnU62OHD+lXQfHJZVu1jGGwW2ALJn9fZzN7+f4m5ctPdI5b5Smfxyd6++5n0oXv196\n9d9kfgypKOT6J4hAr5CPN14OutcAxYTgESnLykAoqQ5NnUwl43NXdL5mfwNHjqtv4Kh6mydV99jt\nmhw9oarHbtfxqqXqO1Kl3q4mdTXXzbPX5MSfs7aGkF7WManfHqjS8OjJrA1Kk+l+V7aFdd6SZq3p\nbNCvtw/rgb5B/eLZoZkbmfTaczu0rPpI9Fx7n2Oo0tT0wk/06rP+WN8cmfmSt1ywVFv3HpHkn80s\nhUF2GNwGyILZF+yn++Rl6QI+vhln1wZp9++lc6+OzvVnJh3endtBTDIJUJLJCB7eJS1ann75sh3o\nBX282ZSj7jVAsSB4RMqyMhBKKkNTJ1PJJHlXNFGzv2PHJ7VzZFxrTu3SgR1PRReOPqWGs/u18+hy\nLV1Ur65m/0NaSPw5e9+FzZrY9qBa2l6lO/uPZG1Qmkz3uzgc0gXLF2ngyHEdOzGp7YOJP9/2qrEz\n5zquUg0d7tfKlfu1ovVM9rEn0qBV7Q2qrDD9un/YN5gqlUF2GNwGyFD8Bfv0VHSQmmxewMc349z9\ne2l8WKqoTDzXXy6kG6AkU/cd3Sc9fad0/nXpNbUNIvMW5PFmy7GB6DyKqXSvAcoAwSNSlpUBVlIZ\nmjqZSiaDu6KRxhqtb53S0K9/oKNHT5xefvz3P9D6l/+VIo01Ke0vkdg5u+fJAS2Z2quhP/xQS163\nRitaOzLKTmV7vyvbwhoZndA1FyzVv/xix5z1b71wqWqO9EXPdevq6F16T6iyYk72Mb4MyQRTQY9Y\nmitBDm5TCtlZYEGzW6Z0XxwdpGbqpFTp/U9l6wJ+fETa+5h0zhuivwPoc59UGdINUJKp+4aelX7z\nz1Ln+ekFj9nOvAV9vNkQC1KXvZTpS4BZCB6RlpzNnZhMJRN3V/T4k3dqz1THnIFbJM17Yb04HFJ1\n1aCe2PvMjOVje5/RhqpBNYR7snIoK9vCuuacWp36/V2SnE49fZeuueQvtCzDc5bN/S4Oh7R+2SKF\nKisSf77hCekR7w70k7dJK1854/Xx2cfKCjv995BKMFUq83IGNbhNqWRngXnFBystPdLURHSKjJYe\nqc77+87WBfzIDqmhQ3r+ISlybmB97n3LkE6AkkxG8PRclS69UWIzCfTmE+TxZsvIjmi2tqYp+nd3\ncNbNVLKPKGMV+S4AilMsQxQvkMzQQpVMgm0qD25XeHSn9hw6rof7h2f8DBw+kfjCenxENf33zFnX\nGg6ppv/H0ZH3smBxOKSeygMa94LU8b3PqKdyf8bnLNv77WiqTfj5RrOOL0TP9dRJ6fkHohd1LWeC\n6zPZx6o5fw/JBlM5+9sqUonOTwznCUUvUdZx6+3Rx6P7o989Mdvuzuz7OZZ1nJqQNt8a/b33sbn7\njGW7Rua2iMiYX///hY4vUUZwtvi5KmNzVKYimTo4FUEfbzbEyjg1IfXdE+0Le/arZv40d0e71wBl\niMwj0hb4qJLJDKoTuwPpSdR0MmbeC+uxEVUvXq7ujV2yw8c1Pe1UUWHqXlSn6trq7M1vNT6i0PYf\nKxyq1NjJKYVDlQpt/7HUdU5m+w9ov3M+39NZR0Uv4KrqpP6fSxvfN6P5au200/mLT0lt6TedZMTS\nhZVKdhaYI74/fH2b1LRE6twgda6XTFKoUarybkRlOv9gLOv4xH9IctEgdcO7ZmbAgs52pdL/P14y\nGcHTWcc4qWQfUx3YLhlBHm+2xILUUIM0cTS/fWGBAkTwiLQFPqqkXyUzcUwaPTDnruh8A7fMe2Ed\n63954pQee2KfHt99WC9ZskhLNyyRaquzdzwjO1R9sF/tTbV6cXhM7U21qj7Yn3nTq4D2G//5nu7r\nGDvXoYZ5K9YqSdPjJ7WoLv1zx4ilCwt6PkkgbxL1h19+afbfJ5Z1rFsk7dsSXbZvi7Tu2pl9H4Me\naTOV/v/xkmn6GZ91jIllH5MJHtMN9BYS5PFmQy6DVKBIETwiI4GOKulXyYyPSH/4xpzFfgO3zKeh\ntlo97Y36zm936m0XLVNDNgPHuAopHKrUspY61Ycqo+syqZji9lsXqlRbY43qsrFfz+nPNz7rOFuC\n91lUn3kQw4ilCyM7C2RgbCQ6Rcfmb0YznDH9P5c2vt9rkugKM5BIJiM4eWJu1jEm2exjuoFetgWR\nAZ1ProJUoIgRPCIjQY4q6WuBu6K1007nNE5oeUu9qisrkr6wXt3eoHe/9KyMR6uMHxGzNRzSK1sP\nabIyIrVHL1LCNVWqrvaCvEyaXsWdg5CkyNS0QpUVme/Xszgc0hvP71LN6AvZvwOdxHvn7W+rCJCd\nRVkIapTTyJroFB0TR6XFZ51ZPnFUqqg4s74QA4lkMoJWIbWtiY6KnWib8ZH0Rl7NB7/jPbY/a11L\nchakAkWM4BEZC2pUSV8L3BWtknR0aFSL6vfpstWRpC+sF4dDeu3ajowvxONHxGyuq9be9gap4o2S\noufrded1ZKdJ7KxzkI3wYfZUEHXVlepoapaa3iKZ1NvVpK7muiy8k7+8/W0VCbKzKGlBjnLqFyg0\nLyvcQCLZjOCr/yb4suTCQsc7ccwb5OiQVL84s/cJopkuUIIIHlGy2hpq9J51NapqTi2LmI2AJb5P\n2pHjp7R556HT6/7sVauy2yQ2RQvNE1gXqtRrzm1Xc221DnhzXp44NaVD49ERDjuaatVYU0BfG0HO\nvVYEyM6ipAU5p99CgUI4Eg0uCSQK3+A26ZGvSpd9LPO/kUJppgsUuAK6CgSyq8kd1eTuB1XV9GZJ\nua/kC3VEzIXmCfyjnjYdGjulgaMnNHTsxJz105L2H53QqjwGv6cFPfdagZod/EcaazR0bCL6ZJ65\nTIGiE/Qop8kECp3rsvd+yL5czvsI4DSCR5SukR2q2v5TqXNtXvqnFOqImInKFXPB8kWKNNZoZHRC\nD/QNaXxi8vS6+poqvfbc9sKZhD6LWYlnBo7qJ08NyCXIxppJbzi/S2u7mjJ6j2xZKPjviTQUzucD\nZCLoUU5R/PgbAfKC4BGlI74JYzbvSGbQNDKrI2JmsYnmQlnRxeGQNq1foif3HNGLccFje2ONNq1f\nkr3gN4njmZ1lm5ic0ujElK7qCWnRY7ep4fiEpv5wuw5OdejhvdO64pz2tM5xS31IOwZH9fsX5k5Q\nfcnKFrVkYfTYbFko+C+EmxNAxsp5uoQyb4qftHL+GwHyjOARxWt2sBjfhNG7Izk2MamR5x5X/bJn\n9NuJlRoePXnm9ck08cuwaWTsQv8L9/Vp44rFenj70Mwy5Kgc85Vrvqzoyraw1nc3a/DYhMYnJlVf\nU6X13c3Za3Kb5PHMzsUnFEUAABZnSURBVLKdmppWbXWl3rj4sIZfeErVzbUa2P64mlc+L7kVag2H\nFuzTOd+57myu1bsuXZ4weHzXpWeps7mwBu4p1CbRM3ARjHRlOl1Csf7tlWlT/LQwpQaQNwSPKE6J\ngsVYE8aKNafvSIY6ezXRt0UnHr1dLes/rO8+M6KDYyclJ63tatK6Jc269eEXovtMFFhkoWnkyraw\n/uq1qzVxyun7f9g9Z/18TQ1jQVBbOKSX1Tyv8d/8QPV1Z0eD4LGTGfdtW9ZSp1esbpNzTiZTU22V\nfr19SM111ZpyTtdfslzhUJUGj51Qe2Ot/o8Ll2Yvq5XkeZ0d5FZXVujd6xs0teXbWrKoVpNTTovq\nqlWz/R697bX/Q4111ZqadvM261zfvUidTTUJ3+u8Jc360OVna9fBce0cGdOh8VNat7RZvZ2F13+w\nUJtEn8ZFMNKV6XQJxfy3F+QAQYUqnUB/9t9I22ppeHv0sfc3sudEre7fdiClm4gAkkPwiOKUMFj0\nmqjWvid6B7KlR9XnvkldJ0+pv+8JLTlvr87piOjBZ6N9+V55TkQ/3bpfL46MSUoQxKXS9HV2BXh4\nl7RouaTohf7Fi8e139oSvnS+i/5Y1m1N4ykdfPQOjY9NnA6Cnzo8lbhvWwpNQaemncI1lbqsc0q/\nGKjQbZv3qLWhWi/radP9zwyqqa5aE5PTqqyo0MTktB7oG9QvnhvSa8/t0LLqI6lX+LGypdikOD7L\n1tFco87JvTo59KwqFtXp2IlTkqRT+55R1YHtOqxGLWupn7dZ5+vO69D+oxP61aN7El5UTExO61W9\n7d7fxbguW9Wmn249oP1HzwweZCa96fwuhWuq9LO4i5NYk1pJMklX9Ea09+BxjYydDORiJatNorOt\nHC+CkR2ZTpcwskPaekfx/e2V4+Av6Qb68X8j4Uh04KOh56SxoejfyMQxdVWekJz0cP/wnJfntW94\nsWbFgTgEjyg+syvZWLAoSQe2SoPPSC09UvfF0vMPqa7npQo/v1Wnnr5Lr1v/YT29r0ZNkbBCVRWn\nA0cpQRCXbGf82RXg0X3S03dK518XnYT56D5VPHOnOta9LaWmhovDIb31wqWq2f+ojux9JvpWe5/R\nkvP26q0XXjQ34EyjKeiNL2nUqb4H1dF6hW5+bI/Wdzd7I66e1LaBozpy/JSeHxrV2ZEGNddVqyfS\noPaqsdQr/PiypTjIQXyW7Y1n12ji0TvV1lCj3QfHdexEtE9mfahStU/8UO1v7JVUP2+zzk5vGpb5\nMpPLWuo1cWpaLwyPaVWkQSNjJ/VPP9s+Y5tLVrZoUX1INVUVc5rUvjgyrvGJSa3vbtY5XY360RP7\nTr93ti9WYufllgf7Cy/rWG4XwcieTKZLGB+Rnr4jGkQ8fUdx/e2V4+Av6d5kiv8bGR+R+u6ZWR+N\nj6iy7x69+ZzX6eG59xDz931ZzFlxIE5FvgsApCy+ko0PFiVp6mS0Ijn/OqkqJD1ztyqratR9+XsV\nalmms+uP69yuRl17Ube27Dx8epdzgrj5OuOPz+0T9/+3d/exddX3HcffX1/7+uHaThw/xklKnIcl\nmIwASRMgwHgsgbGwrmwQ9gea2rFJndpOk6qiSZOqqdI6TRtV1a1DDKpOFWwtDwULlSGg0zpUICEJ\nSwiGPJLnOAlxmpjYefjtj3Ouc+/1te917OPzu76fl3Rln3OfPr4+9vd+7+93zhkugMfDKtXXC299\nH/o+zFpOHOvl4dXzsu5aqIh1pQap7O3JWlfZ20NXamjkjXNzjCLddMxvTjHPHeL4xheYc/Eg3bMb\nuW95J3uOneH+a4NP/euSCdoba6hLJgCCZrZ/d1HPkzdbX29wQudMo72uGbpaUty4sIWFqbM0dy6g\n7so7qbvyLlqXr6X92ntoXr6W5s4FVA+eyPoZM6Vf63zXpa1b3snsGTX0D5zjkTXzeW3bkRG3Se8D\nmfs4VYkK2hqCKbH3Le/M2r6ierPS1ZLiru52/0Ydc98Ei0yF4zvhwCY4fST4Wirb3njqzXSR+yHT\n5f6s+epeuG7GwF5uWpQ94yfWWRpF1mgR36l5lNKSW2SHBmDzT4JRRoBkPQyegvNn4eQ+aPocfPJr\nKhffznvtf8SL+1K01NewpKOx+FHHtHxvhHML4Mn9sPHpYHnj00GGjOV5VadY2BpMXSymiFX376bx\nzO6sdY1ndlPdv2vsHEU0Y/cvqeH8tpcwYPD9F/mzVU0kKyuY01TH4rZ6FrbWU5WooLm+mqpERZA3\nNTj+gp+Z7f3/hPr27OuLaDCaUkk+39XEi/tSfHbtV9jU8SA9Nev40YW76alZx6aOB7m46tGsEYv0\n6COMfK0zr0tL36ZjRi0PrJjLorYG6muyJ2es6pqVNfU093HqkglWdc3KGtWO8s1KUyrJrUvbPBx1\nzDDd3wSLH9KjjqcPB8unDwfLpbDtFVtvppPJ+JApX93LWFfZ28O6JbVZd4l11HEymmURD6h5lNKS\nWXAuDAVvEA5ugguDl0YfIbtJOfYRVSd30d3ZyMa9n3Lz4hbaGqpHb+IKHbAh859+Zp76DjjWC3vf\nCpb3vhVcl86x963h0UejiCIW5qhNJkiFI3+pZILaZGLsHEU2YwsTRxg48AEVBmcOfEB39VHcRTd8\nuo70qFp1ZfBvYnjUcbwFP53twhDsemPk7wqKKqbzmuqYlUrSXF/NjLokW/af5P39/WzZf5L5LSnq\na6pG/Ixrl3Xkfa3HGpnsmFHD7149mzkza3l49eeybpN75NV8o48Pr75iSkYd09obPToSbDm+CRY/\npEcdh8IPBYfOlMbo43jqzXQxWR8y5at7vzmctS5z9DH2UUfNyJBpQs2jlI7cgnNhCCproa4FdrwO\nV/4eLLgN5t8MNQ1QOxOauoLbbn+ZrtTQ8BS/xtqqURuL4Z3xF9w28jJjbnDAhnx5rlgD7zwB7mKw\n7C7C2z+EZV+6dJtw9LGoqYZhjuTiO+hccS8zl32BzhX3klx8x9g5wp93zEI8cJzkxz2kkgnMjOZU\nEvfBy9w4J5F1uo6sBnt41HF8zzN8n/TvK/N3le91HUXmKFtXS4qlHY0011eztKNx1NdyrGmdY41M\nzmmqA2BRWwOruoJ9U3JHHUd7nEVt9SQqLO/jTmvl+CZY/JA76phWCqOPxdab6WQyPmTK9/9m2/NB\n85ghPfpY1Ae2UdGMDJlmdMAcKR2FjsJX1wzX/3lwyO7tPXBwM3z2aXD9sY+o7t/Fnd3Ls5qjvI1F\nsQdsyCyA828J3qjsfAOq6iBRARfPBzvHr/gTuOqLsO2F4dHHu5etYWahE89n5HBnz3Fg30la5s2E\nnBG2yzrf1fGdVJ3YQVtjDXuOnaFjRg3njvbSNLAXCEbSMg/IEow6fnhZzzN8n2T9pWnFFYngdzVO\n6VG2dLZdfafHfEMw1rTOYg44kz7/47u7T4x6vsfcx+mYUePngWyiNtGjZIpcrjPHIdUGi+8eeV2q\nze9tbyIHCCpFEz0VS1q+undgE8y+JpjZcmJnsC4cfXzw81f4MeqYVi4HRZJpSc2jlI5iiuzACXjv\nxyP/UQNsf5nmmxYBwUFNJrS/WL5Rx19+J/j+wiBYBZz7LFje8CTc8s2geQTY+DQz1y4Fij9cd31N\nFYvaG0ZMzbysQpxxn9pkgpaGauqqK3Eu2EeE1sXD9xlusFOD8OvLf56i7zMOxR4sZqxpncU8xqK2\nBv70lgVjnmYj93G8PJBN1MrtTbD4o3E2NHYSnCQn33U6NYI3JuNDpny1Jb0by9bnYPnDl5pHgtHH\nm1Z/jVpfRh3TdDRqKVFqHmV6OXNsXIXpsvcXyyyAzYugwqDtKmjrDp4Hg4sXLj1vZQ3c8/dwZFuw\nPHB83G9o8ma9nEKccZ8k0HrhIslERd77NKWS3NndTvVneyf0PEXfZxwm42AxxTxGeh/IfKOOoz2O\ndweyEZnOTh2Cs/1QVTvyurP9wfWtDVOfS0aajA+Z8tWW82dhznXgLPggYdkfwkA45deM2qFPgdaJ\nPe9kZU3TjAwpUeZcnjNll4iVK1e6DRs2xB1DRESil2dYyS+qSSIiZcP7mhQVHTBHREREREREClLz\nKCIiIiIiIgWpeRQREREREZGCdMAckQJ2HD3N/3zcB/l2Dza4eXHrmEfiFBERERGZDtQ8ihTQnEpy\n6ORZdvadHnHdwtZ6mnVETREREREpA5q2KlJA+iTw+ZTVSeBFREREpKxp5FGkCF0tKRa21meNPi5s\nrZ/QSeBzp8MOnr/A6cHg3JAG/M6SVj44eIqjvxnEgFuXtnLgxGccPzOk6bIiIiIiMuW8ah7NbC3w\nPSABPOmc+7uYI4kAl0Yff/DmjuF1Ex11zJ0Oe+7CRfYcH2Bg8DxXz53Bb7XX8+Svdg8vL5ndwM+3\nHAQ0XVZkKqgmiYiIZPNm2qqZJYAfAPcA3cB6M+uON5XIJenRR5j4qCOMnA5blaigraEagPuWd7J5\nX3/W8qa9J4dvq+myItFSTRIRERnJm+YRWAXscM7tcs4NAc8C98ecSWRYutkzJq95y2xIAeqSCVZ1\nzSJZWcGe42dGLMPkNK4iUpBqkoiISA6fmsc5wL6M5f3huixm9qiZbTCzDX19fVMWTgSCZu+u7vZJ\na97yjT4+vPqK4VHG3GXQqKPIFFFNEhERyeFT82h51o04s55z7gnn3Ern3MrW1tYpiCVySVMqya1L\n2ya1ecudDruorZ5EhY26rFFHkSmhmiQiIpLDp+ZxPzAvY3kucDCmLCKjam+smdTHy50O2zGjZsxl\njTqKTAnVJBERkRw+NY/vAovNrMvMksBDwEsxZxKZErnTYQsti0jkVJNERERyeHOqDufceTP7C+BV\ngsOiP+Wc2xZzLJEpkTsdttCyiERLNUlERGQkb5pHAOfcK8ArcecQiUPudNhCyyISLdUkERGRbD5N\nWxURERERERFPqXkUERERERGRgtQ8ioiIiIiISEFqHkVERERERKQgNY8iIiIiIiJSkJpHERERERER\nKUjNo4iIiIiIiBSk5lFEREREREQKUvMoIiIiIiIiBal5FBERERERkYLMORd3hstmZn3A3gk8RAtw\nbJLiRE1Zo6Gs0VDWaJRz1mPOubWT+HiTTjXJW8oaDWWNhrJGo+xqUlRKunmcKDPb4JxbGXeOYihr\nNJQ1GsoaDWWd3krpNVPWaChrNJQ1GspanjRtVURERERERApS8ygiIiIiIiIFlXvz+ETcAcZBWaOh\nrNFQ1mgo6/RWSq+ZskZDWaOhrNFQ1jJU1vs8ioiIiIiISHHKfeRRREREREREiqDmUURERERERAoq\n2+bRzNaaWa+Z7TCzb8WdJ5OZPWVmR81sa8a6WWb2mpl9HH5tijNjmpnNM7M3zWy7mW0zs6+H673L\na2Y1ZvaOmW0Js347XN9lZm+HWf/DzJJxZwUws4SZbTKznnDZy5wAZrbHzP7PzDab2YZwnXfbAICZ\nzTSzn5nZh+F2e4OPWc1sSfh6pi+nzOwbPmYFMLO/DP+utprZM+Hfm7fbrE98rkdQOjVJ9ShaqkmR\n5CyJegSlVZNUj6JVls2jmSWAHwD3AN3AejPrjjdVlh8BuSce/RbwunNuMfB6uOyD88BfOeeuBK4H\nvhq+lj7mHQRud84tB64B1prZ9cB3gX8Ks34KfDnGjJm+DmzPWPY1Z9ptzrlrMs6j5OM2APA94BfO\nuaXAcoLX2Luszrne8PW8BlgBDAAv4GFWM5sDfA1Y6ZxbBiSAh/B/m41dCdQjKJ2apHoULdWkyVcS\n9QhKpyapHk0B51zZXYAbgFczlh8DHos7V07G+cDWjOVeYHb4/WygN+6Mo+T+OXCX73mBOuA9YDVw\nDKjMt23EmG8uwT/h24EewHzMmZF3D9CSs867bQBoBHYTHizM56w5+b4A/K+vWYE5wD5gFlAZbrN3\n+7zN+nIphXoU5iq5mqR6NKkZVZMmP2NJ1qMwl7c1SfUo+ktZjjxyacNK2x+u81m7c+4QQPi1LeY8\nI5jZfOBa4G08zRtOu9kMHAVeA3YCJ51z58Ob+LItPA58E7gYLjfjZ840B/yXmW00s0fDdT5uAwuA\nPuDpcPrVk2aWws+smR4Cngm/9y6rc+4A8A/AJ8AhoB/YiN/brC9KsR6Bh9thJtWjSaeaNPlKtR6B\nxzVJ9Sh65do8Wp51OmfJBJhZPfAc8A3n3Km484zGOXfBBVMu5gKrgCvz3WxqU2Uzs/uAo865jZmr\n89zUp212jXPuOoKpd181s1viDjSKSuA64F+cc9cCZ/BkStBowv0y1gE/jTvLaMJ9XO4HuoBOIEWw\nLeTyaZv1he9/2yVH9WhyqSZFpuTqEfhfk1SPoleuzeN+YF7G8lzgYExZinXEzGYDhF+PxpxnmJlV\nERTqnzjnng9Xe5sXwDl3EvglwX4xM82sMrzKh21hDbDOzPYAzxJME3oc/3IOc84dDL8eJdgHYhV+\nbgP7gf3OubfD5Z8RFG8fs6bdA7znnDsSLvuY9U5gt3Ouzzl3DngeuBGPt1mPlGI9Aj+3Q9WjaKgm\nRaMU6xH4X5NUjyJWrs3ju8Di8MhLSYLh95dizlTIS8Aj4fePEOzLETszM+DfgO3OuX/MuMq7vGbW\namYzw+9rCf7BbAfeBB4IbxZ7VufcY865uc65+QTb5hvOuT/Gs5xpZpYys4b09wT7QmzFw23AOXcY\n2GdmS8JVdwAf4GHWDOu5ND0I/Mz6CXC9mdWF/xPSr6uX26xnSrEegYfboepRNFSTolGi9Qj8r0mq\nR1GLe6fLuC7AvcBHBPsY/HXceXKyPUMwT/scwSdTXybYv+B14OPw66y4c4ZZbyIY+n8f2Bxe7vUx\nL3A1sCnMuhX4m3D9AuAdYAfBNIzquLNmZL4V6PE5Z5hrS3jZlv578nEbCHNdA2wIt4MXgSaPs9YB\nx4EZGet8zfpt4MPwb+vfgWpft1nfLj7XozBfSdQk1aMpya2aNLlZS6YehXlLoiapHkV7sfBFFhER\nERERERlVuU5bFRERERERkXFQ8ygiIiIiIiIFqXkUERERERGRgtQ8ioiIiIiISEFqHkVERERERKQg\nNY8iHjKzL5qZM7OlcWcREZHypXokIpnUPIr4aT3wK4ITMouIiMRF9UhEhql5FPGMmdUDawhOxP1Q\nuK7CzP7ZzLaZWY+ZvWJmD4TXrTCz/zazjWb2qpnNjjG+iIhME6pHIpJLzaOIf34f+IVz7iPghJld\nB/wBMB/4beArwA0AZlYFfB94wDm3AngK+E4coUVEZNpRPRKRLJVxBxCREdYDj4ffPxsuVwE/dc5d\nBA6b2Zvh9UuAZcBrZgaQAA5NbVwREZmmVI9EJIuaRxGPmFkzcDuwzMwcQfF1wAuj3QXY5py7YYoi\niohIGVA9EpF8NG1VxC8PAD92zl3hnJvvnJsH7AaOAV8K9zVpB24Nb98LtJrZ8LQhM7sqjuAiIjKt\nqB6JyAhqHkX8sp6Rn+o+B3QC+4GtwL8CbwP9zrkhggL/XTPbAmwGbpy6uCIiMk2pHonICOacizuD\niBTBzOqdc6fDqUTvAGucc4fjziUiIuVF9UikfGmfR5HS0WNmM4Ek8Lcq1CIiEhPVI5EypZFHERER\nERERKUj7PIqIiIiIiEhBah5FRERERESkIDWPIiIiIiIiUpCaRxERERERESlIzaOIiIiIiIgU9P8m\naeoZ6o1k4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f40198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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obLA1G5u78sorRyORyGn/NefmlzyuM7NhMxuRtDb287CZjZjZcJr7/KykT5vZ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\nJEmSJPVl41GSJEmS1Nf/A9pswKt/BKzRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a176e2f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g9oYqPbWjT05SqKJUFWWlKS0LADB/+RSTgHyV7nUSgPyQ6oA590iaUOT2nr+R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\nRGRa/xfNrh47zx5w5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17c8fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of passangers by age and fare, grouped by sex and class and survival info.\n",
    "def scatter_plot_class(pclass):\n",
    "    g = sns.FacetGrid(titanic_df[titanic_df['Pclass'] == pclass],\n",
    "                     col='Sex',\n",
    "                     col_order=['male','female'],\n",
    "                     hue='Survived',\n",
    "                     hue_kws=dict(marker=['v','^']),\n",
    "                     size=6)\n",
    "    g = (g.map(plt.scatter, 'Age', 'Fare', edgecolor='w', alpha=0.7, s=80).add_legend())\n",
    "    plt.subplots_adjust(top=0.9)\n",
    "    g.fig.suptitle('CLASS {}'.format(pclass))\n",
    "    \n",
    "scatter_plot_class(1)\n",
    "scatter_plot_class(2)\n",
    "scatter_plot_class(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    0.629630\n",
       "2    0.472826\n",
       "3    0.242363\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_by_class = titanic_df.groupby('Pclass')['Survived'].mean()\n",
    "survived_by_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_by_sex = titanic_df.groupby('Sex')['Survived'].mean()\n",
    "survived_by_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "0.42     1.000000\n",
       "0.67     1.000000\n",
       "0.75     1.000000\n",
       "0.83     1.000000\n",
       "0.92     1.000000\n",
       "1.00     0.714286\n",
       "2.00     0.300000\n",
       "3.00     0.833333\n",
       "4.00     0.700000\n",
       "5.00     1.000000\n",
       "6.00     0.666667\n",
       "7.00     0.333333\n",
       "8.00     0.500000\n",
       "9.00     0.250000\n",
       "10.00    0.000000\n",
       "11.00    0.250000\n",
       "12.00    1.000000\n",
       "13.00    1.000000\n",
       "14.00    0.500000\n",
       "14.50    0.000000\n",
       "15.00    0.800000\n",
       "16.00    0.352941\n",
       "17.00    0.461538\n",
       "18.00    0.346154\n",
       "19.00    0.360000\n",
       "20.00    0.200000\n",
       "20.50    0.000000\n",
       "21.00    0.208333\n",
       "22.00    0.407407\n",
       "23.00    0.333333\n",
       "           ...   \n",
       "44.00    0.333333\n",
       "45.00    0.416667\n",
       "45.50    0.000000\n",
       "46.00    0.000000\n",
       "47.00    0.111111\n",
       "48.00    0.666667\n",
       "49.00    0.666667\n",
       "50.00    0.500000\n",
       "51.00    0.285714\n",
       "52.00    0.500000\n",
       "53.00    1.000000\n",
       "54.00    0.375000\n",
       "55.00    0.500000\n",
       "55.50    0.000000\n",
       "56.00    0.500000\n",
       "57.00    0.000000\n",
       "58.00    0.600000\n",
       "59.00    0.000000\n",
       "60.00    0.500000\n",
       "61.00    0.000000\n",
       "62.00    0.500000\n",
       "63.00    1.000000\n",
       "64.00    0.000000\n",
       "65.00    0.000000\n",
       "66.00    0.000000\n",
       "70.00    0.000000\n",
       "70.50    0.000000\n",
       "71.00    0.000000\n",
       "74.00    0.000000\n",
       "80.00    1.000000\n",
       "Name: Survived, Length: 88, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_by_age = titanic_df.groupby('Age')['Survived'].mean()\n",
    "survived_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kNY7JrCRJkiSpcUxmJUmSJEmNYzIrSZIkSWock1lJkiRJUuOYzEqSJEmSGsdkVpIkSZLU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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18742ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1,axis2,axis3) = plt.subplots(1, 3, figsize=(16,6))\n",
    "\n",
    "ax = survived_by_class.plot.bar(ax=axis1, color='#5975A4', title='Survival Rate by Class', sharey=True)\n",
    "ax.set_ylabel('Survival Rate')\n",
    "ax.set_ylim(0.0,1.0)\n",
    "ax = survived_by_sex.plot.bar(ax=axis2, color='#5F9E6E', title='Survival Rate by Sex', sharey=True)\n",
    "ax.set_ylim(0.0,1.0)\n",
    "ax = survived_by_age.plot.bar(ax=axis3, color='#B55D60', title='Survival Rate by Age Range', sharey=True)\n",
    "ax.set_ylim(0.0,1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">female</th>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">male</th>\n",
       "      <th>30.5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31.0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34.5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.0</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44.0</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55.5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>283 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Survived  Survived\n",
       "Pclass Sex    Age                     \n",
       "1      female 2.0   0.000000         1\n",
       "              14.0  1.000000         1\n",
       "              15.0  1.000000         1\n",
       "              16.0  1.000000         3\n",
       "              17.0  1.000000         2\n",
       "              18.0  1.000000         3\n",
       "              19.0  1.000000         3\n",
       "              21.0  1.000000         2\n",
       "              22.0  1.000000         4\n",
       "              23.0  1.000000         2\n",
       "              24.0  1.000000         5\n",
       "              25.0  0.000000         1\n",
       "              26.0  1.000000         1\n",
       "              29.0  1.000000         1\n",
       "              30.0  1.000000         5\n",
       "              31.0  1.000000         2\n",
       "              32.0  1.000000         1\n",
       "              33.0  1.000000         3\n",
       "              35.0  1.000000         6\n",
       "              36.0  1.000000         3\n",
       "              38.0  1.000000         3\n",
       "              39.0  1.000000         4\n",
       "              40.0  1.000000         2\n",
       "              41.0  1.000000         1\n",
       "              42.0  1.000000         1\n",
       "              43.0  1.000000         1\n",
       "              44.0  1.000000         2\n",
       "              45.0  1.000000         1\n",
       "              47.0  1.000000         1\n",
       "              48.0  1.000000         2\n",
       "...                      ...       ...\n",
       "3      male   30.5  0.000000         1\n",
       "              31.0  0.333333         3\n",
       "              32.0  0.454545        11\n",
       "              33.0  0.000000         7\n",
       "              34.0  0.000000         4\n",
       "              34.5  0.000000         1\n",
       "              35.0  0.000000         5\n",
       "              36.0  0.000000         5\n",
       "              37.0  0.000000         1\n",
       "              38.0  0.000000         3\n",
       "              39.0  0.250000         4\n",
       "              40.0  0.000000         4\n",
       "              40.5  0.000000         2\n",
       "              41.0  0.000000         2\n",
       "              42.0  0.000000         4\n",
       "              43.0  0.000000         2\n",
       "              44.0  0.250000         4\n",
       "              45.0  0.500000         2\n",
       "              45.5  0.000000         1\n",
       "              47.0  0.000000         2\n",
       "              48.0  0.000000         1\n",
       "              49.0  0.000000         1\n",
       "              50.0  0.000000         1\n",
       "              51.0  0.000000         3\n",
       "              55.5  0.000000         1\n",
       "              59.0  0.000000         1\n",
       "              61.0  0.000000         1\n",
       "              65.0  0.000000         1\n",
       "              70.5  0.000000         1\n",
       "              74.0  0.000000         1\n",
       "\n",
       "[283 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data = pd.concat(\n",
    "    [titanic_df.groupby(['Pclass', 'Sex', 'Age'])['Survived'].mean(),\n",
    "    titanic_df.groupby(['Pclass', 'Sex', 'Age'])['Survived'].count()],\n",
    "    axis=1)\n",
    "grouped_data.colums = ['Survived', 'Count']\n",
    "grouped_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p5tJA8yPgMODPU0o/Gm2asT5e+J/k/0mxoizoSvItd0mSJEl62CgJ1zfJL894PvBvEfEv\no00z1qTrESml/x7ot3K9SyhJkiRJk9tzgOemlL5VbkY9n3XkRmN6vBBYFBF7Ul6bWp5fvGVjSipJ\nkiRJk01K6fUD31cCfz/aNGNNut4OnAQ8OSJuAq4DjtmQQkqSJEnSw8lYk67rU0oviYitgGkppfta\nFkqSJEmSpoqx/qbruog4CdgXWNywPJIkSZI0pYw16XoS8B3yY4bXRcQnI+IF7YolSZIkSZuuiHhv\n/3M0Y0q6UkpLUkqnp5QOB54FbAv8cKNKKUmSJEmTV33HxavWNeJY73QRES+MiBOBXwJbAC/fsLJJ\nkiRJ0sPHmF6kERHXAZcDpwN/m1K6v2mpJEmSJGmKGOvbC5+RUrq3aUkkSZIkaQoaNemKiP+VUvog\n8M8RkQaHp5Te1axkkiRJkjQFrOtO12/K56WtCyJJkiRJk0h9seCP1jXiqElXSum/SueVKaXLNrZU\nkiRJkjQVpJT+uv85mrG+vfAjEfHbiPhARDx1o0onSZIkSQ8jY/0/XX8G7AcsBE6KiF9FxPtaFkyS\nJEmSpoIx/5+ulNKtKaX/AN5Cfn38+5uVSpIkSZKmiLH+n66nAEcDRwJ3AKcB72lYLm3CDjzrXwE4\n77DjJrgkk9f5X1sEwMxV3UtBX/yqWU2WdeuH5gOwy3v3aDJ/SdLk8sP/PAiAF77pmxNcErX23rMW\nPNj9ocNmT2BJppaIeOGw/imlHw7rD2P/P11fAL4KvDSldPMGlE2SJEmSpoL+zafNgecCV5J/jjXU\nOpOuiJgOXJtS+vjGlk6SJEmSJrOU0iH97xGxM/CJ0aZZ52+6UkqrgB0jYubGFU+SJEmSppy7gWeM\nNsJYHy+8HvhpRJwD3F97ppQ+suFlkyRJkqTJJSK+B0T5Og3YE/jSaNOMNem6ufxNA7bZ0AJKkiRJ\n0iT33l735sBLgd+PNsGYkq6U0v/diEJJkiRJ0pSQUvrlQK+fR8TFwFfWNs1YXxn/fSAN9k8pvWi9\nSihJkiRJk9jAK+OnAX8EbDHaNGN9vLB/C20L4Ahg5XqVTpIkSZImv/4r41cCfwBeNtoEY3288BcD\nvX4aEWv951+SJEmSNBUNvjJ+LNb5yniAiNih97dTRMwBdlnvEkqSJEmT0MFzv8DBc78w0cXQJiAi\nDomIX0TE9yPiaRGxXUQcPNo0Y3288Bd0v+mqt9DesOFFlSRJkqRJ6aPAkcBjgA+nlPaPiPcB565t\nglHvdEXEcyNil5TS41NKewD/F/ht+bt6/MotSZIkSZPCgpTSZSmlbwKPLv02G22CdT1e+BlgOUBE\n/CnwL8AXgXuAkzaurJIkSZI06VwUEZ8vbzHcLCLeANwx2gTrerxwekrpztJ9NHBSSmkuMDciLt/4\n8kqSJEnSpPLc8vkPwHXAU4BjRptgnUlXRMxIKa0EXgwcux7TSpIkSdKUsiH/q3hdidNXgR9GxCJg\nCfBjgIh4AvkRQ0mSJEl62IiI1442PKX0xcF+oyZdKaV/jojvkn8g9q2UUn2D4TTgnRtaUEmSJEma\npJ4zyrAgvwNjNet8RDCldNGQftesX7kkSZIkafJLKb1rfacZ0z9H3lARMScifhcR8yLiuFHGOzIi\nUkTs07I8kqYO44ukFowtktYlInaMiC9HxO0RcVtEnBoRO402TbOkKyKmAycABwB7A6+MiL2HjLcN\n8C7g4lZlkTS1GF8ktWBskTRGJwCXA7OBm8r3T482Qcs7Xc8D5qWU5qeUlgOnAYcOGe8DwAeBpQ3L\nImlqMb5IasHYImksnpJS+lCJE5FS+hmw22gTtEy6dgVu7H1fUPo9KCKeBeyWUjp3tBlFxLERcWlE\nXLpw4cLxL6mkycb4IqmFcYstZdwH48sdi+8d35JKmkjT+18iYtSEC9omXTGkX3pwYMQ04KPAe9Y1\no5TSSSmlfVJK+8yaNWsciyhpkjK+SGph3GILrB5fdtx623EqoqRNwI8i4hmle0fgQuBvRpug5T84\nXsDqt9lmAzf3vm8DPA34QUQA7AKcExGHpJQubVguSZOf8UVSC8YWSeuUUnpb7+tTUkr3r2ualne6\nLgH2iojHR8RM4BXAOXVgSumelNJOKaXHpZQeB1wEGLQkjYXxRVILxhZJ62UsCRc0TLpSSiuBd5Bv\nt/0GOD2ldFVEHB8Rh7RarqSpz/giqQVji6RWWj5eSErpPOC8gX7vX8u4+7Usi6SpxfgiqQVji6QW\nmv5zZEmSJGlTddCZH+egMz8+0cXQw4BJlyRJkiQ1ZNIlSZIkSQ2ZdEmSJElSQyZdkiRJktSQSZck\nSZIkNWTSJUmSJEkNmXRJkiRJUkMmXZIkSZLUkEmXJEmSJDU0Y6ILoIePg+Z+FoBgepP5H3rGBWX+\n3WH9jSNf8mD34XN/AsCZR7ygyfLX5eyvLwLg0KN22uh5/fbE2wB48tsetdHzkqaSg+d+AYBzj3j9\nBJdkdUfN/TUAXz/iaRNcEk0mt5z49wA8+m0fmOCSPHzVuss3j3jjBJdEk513uiRJkiSpIZMuSZIk\nSWrIpEuSJEmSGjLpkiRJkqSGTLokSZIkqSGTLkmSJElqyKRLkiRJkhoy6ZIkSZKkhky6JEmSJKkh\nky5JkiRJasikS5IkSZIaMumSJEmSpIZMuiRJkiSpIZMuSZIkSWrIpEuSJEmSGjLpkiRJkqSGTLok\nSZIkqSGTLkmSJElqaMZEF0CaLN571gIAPnTY7AkuidTOR7+yPwB/86oLJ7gkY3fQ3M8C8M0j3jjB\nJZEeWtd+4lAA9nzn2RNcEo23w+b+AICzjthvg6Z/7ZnXAzArpo9TibSxvNMlSZIkSQ2ZdEmSJElS\nQyZdkiRJktSQSZckSZIeMh88bX8+eNr+E10M6SFl0iVJkiRJDZl0SZIkSVJDJl2SJEmS1FDTpCsi\n5kTE7yJiXkQcN2T4uyPi6oi4MiK+GxGPbVkeSVOH8UVSC8YWSS00S7oiYjpwAnAAsDfwyojYe2C0\ny4B9UkpPB84APtiqPJKmDuOLpBaMLZJaaXmn63nAvJTS/JTScuA04ND+CCml76eUHihfLwJmNyyP\npKnD+CKpBWOLpCZaJl27Ajf2vi8o/dbmDcD5wwZExLERcWlEXLpw4cJxLKKkScr4IqmFcYstsHp8\nuWPxveNUREmTUcukK4b0S0NHjHg1sA/w78OGp5ROSintk1LaZ9asWeNYREmTlPFFUgvjFltg9fiy\n49bbjlMRJU1GMxrOewGwW+/7bODmwZEi4iXA3wEvTCkta1geSVOH8UVSC8YWSU20vNN1CbBXRDw+\nImYCrwDO6Y8QEc8CPgMcklK6vWFZJE0txhdJLRhbJDXR7E5XSmllRLwDuBCYDnw+pXRVRBwPXJpS\nOod8S35r4OsRAXBDSumQVmWSNDUYXyS1YGyZvA78xnt637YE4LyX/dPEFEYaouXjhaSUzgPOG+j3\n/l73S1ouX9LUZXyR1IKxRVILTf85siRJkiQ93Jl0SZIkSVJDJl2SpIeNg884jYPPOG2iiyFJepgx\n6ZIkSZKkhky6JEmSJKkhky5JkiRJasikS5IkSZIaMumSJEmSpIZMuiRJkiSpIZMuSZIkSWrIpEuS\nJEmSGjLpkiRJUhOfPHV/Pnnq/hNdjDE56MwTOOjMEya6GJqiTLokSZIkqSGTLkmSJElqyKRLkiRJ\nG+Vzp7yUz53y0odkWQecfTQHnH30Q7IsabyYdEmSJElSQyZdkiRJktSQSZckSZIkNWTSJUmSJEkN\nmXRJkiRJUkMmXZIkSWrqo1/Zn49+ZXL8k2SpBZMuSZIkSWpoxkQXQJIkSQ9vf/f1OQ92//NRF0xg\nSaQ2vNMlSZKkTca7587h3XPnrHtEaRIx6ZIkSZKkhky6JEmSJKkhky5JkiRJasikS5IkSZIaMumS\nJEmSpIZMuiRJkiSpIZMuSZIkSWrIpEuSJEmSGjLpkiRJkqSGTLokSZIkqSGTLkmSJElqyKRLkiRJ\nkhpqmnRFxJyI+F1EzIuI44YM3zwivlaGXxwRj2tZHklTh/FFUgvGFkktNEu6ImI6cAJwALA38MqI\n2HtgtDcAd6WUngB8FPi3VuWRNHUYXyS1YGyR1ErLO13PA+allOanlJYDpwGHDoxzKPDF0n0G8OKI\niIZlkjQ1GF8ktWBskdREpJTazDjiSGBOSumN5ftfAM9PKb2jN86vyzgLyvdryziLBuZ1LHBs+fok\n4A6gjrPTkO5h/TZ03IkevimVxbJa1g0p66KU0hzG0SYeXyZ6+KZUFstqWVuW9bebcmwpw8YSXybz\nPrCslnWqlnXc4wsppSZ/wFHAZ3vf/wL4xMA4VwGze9+vBXYcw7wvHa17XcPXZ9yJHr4plcWyWtYN\nLet4/7EJx5eJHr4plcWyWtaHoqzj+UfD2DKV94Fl3bTLYlk3fFnj+dfy8cIFwG6977OBm9c2TkTM\nALYD7mxYJklTg/FFUgvGFklNtEy6LgH2iojHR8RM4BXAOQPjnAO8tnQfCXwvlRRTkkZhfJHUgrFF\nUhMzWs04pbQyIt4BXAhMBz6fUroqIo4n37Y7B/gc8KWImEduJXrFGGd/0jq61zV8fcad6OGbUlks\nq2Xd0LKOq008vkz08E2pLJbVsj4UZR03jWMLTN19YFk37bJY1g1f1rhp9iINSZIkSVLjf44sSZIk\nSQ93Jl2SJEmS1FCz33RtqIiYA3yc/Cz1j4A/BrYBNgPu6fXbFtiF/E79HYD6nGSQk8lU/vqJ5Qiw\nvHRvXsYdKX8rgHvLvDYDVpb+m/XmO9Kb16resJHeclJv3FQ+a5mWAo/ojdMvc+r1H/wni2ngs44/\nbWCcEfJ2GyxTf9r+92llPVaW7dEftrZ/9Dhs2KqB5Q5bB3rD6/6p49TuflmWAzOHLDsNmb4/vC5/\nZZk+lfnOYPj+62+3WraVwG+Ap/a+zxiY/33kN1b112lQfz/3y1rH72+32r2yfMZaho+2fQePoWHL\nrd/7wxP5+K/bu7/cwXUZ7AZ4AJgHPB1YRt5/dwM3lGE7ANuXed4FXA48qyznXSmlC4esSxO9+DIs\npvRjzizgkeT9sZzuGNmc4cfvKlY/Vurx8ED5vop1x5c6z5XATcBjB4q/vDfvYfGFgTJAd8zHwGeV\nBrpHiyl9g8dS3yry8bRF+Yxeeeo8pw2ZbnD+tSyrWD2+ru1cG1aWfllXlPE2Hxjej9kMmX8/ZvW3\ndxoYp8acWoZlve/9c2wl+VqwNatv41W95ffj3CLy8bg2/f09rP+wGAfD48vg9DUWVYPHfTUsNtRt\ntayUY7CMo8WUakWZfj5rxpc7gUcBtwK/JMcUmKD4spa6y7Du/YHHAPeTj4cR8rapx8eGxpf7gR3p\nYsg0uu3Vn+cI+fjbsha9fPaP8/vL8LoPp/emXdabdm3Xo/6xN7ge9OY37LytdbK1HXujxZc63uA8\nB9XjfVg9cX3mU8vbv14Pi7Hrii/9bdE/P+u2rvFrGl3s7NcL+9f8VeWzXmemD0xfx5/eG3/YuVm3\ny9rqN1V/Wz4AbDVQrsF1rfMaFl/6y+4fV8OOp35c34xuGw/WxQeX23c/cBt5Oz+l9Kv1mVp3mU4X\nXx5Z1vduNjS2tHgP/Yb+lZW7FtiDfEItA+aUfr8pK72M/N/gf1w25A3AzsDvSv9fAb8tO+JnZZw7\nya98/S/gz8kBZ3n5uxe4BbiaXAF7G/Ad4LyyYVcB7y7TjAB/Blxfur9fpllUlj8CvJn8VqOVwFfK\nMj5N/lFuKmVZUrrPLcMXAZeVfkeX4SPkAH0ncGOvvFeU4cuB68p41wGLS1nnkw+i5WU97irj3AV8\nrZT93rJ9VpAvWDXInUQXkG8CDuwtYwX5jU0PlHktLfuiluWa0n956b+C/FrdO0q5/lfZN0tLWU8t\n491ZpvsssLAsa1XZD7Wy8Hvgr8q2rut9SZn+srL/FpfhtaxXlelvL8MWl/VeVbbPT8rwi8v0K3pl\nnQ/8vFeWO8vw28v3X5f9vQr4Nvk4qZWqui0vLPNZRj6xbyqfCTiOfDyuICdvJ5fuu0s563yWle8f\nLN13ABf1tu2NZbxLynZZXPrd1VveUvIxM1K2yf8p088v+/KBsj4jpSz/Vua1gnxuLSz7eEXZVnXY\nSNme9/fmvwR4V+n+fNmH15LP2/llOa8o6/Io4PFl+PSHOL48gTVjykvpYs5hZbutAt5Q1u/15IpS\njS/1fEnA4WVb/BHwFrpzo27U0UzyAAAgAElEQVSn7cu6jyW+PLdMu5wcXxaT40M9v/+MNePLZ8p+\nGCnj1uP2i6X7o6wZXxL5GL2W1ePL1WWa60s5VpHPvfvKNF8q09Tj7RS6+LK47Pd76ZKQ19Fd2OsF\nbm3x5X5yzL6j9KvH8PJS5p+W+SymOzduKsu5Hnh16X9vmcd9pf9N5HP3m3Tn58pSrhvI8eU64L9L\nOWrMvbbs75tYM77Uit99ZX3qebSKHE9qDL+u7OeV5Jizinzu16T7lt5nLd/tZTvdR45DdVn9+LKY\nXCGo++glpV8q++LFrBlf7umt9ydYPb7UuH1DGeclZZoHyDFnhBzzlpOvGb8r419ftttIKesdZZwf\nl+FXkyssqeyX68tfTaZOLft4RZluKd01KQFvB77FmvHlC+TYtAc5liwD/gewLxMQXxhed3npWrqv\nLJ/LgBeUbXlomc81dPWSjY0v9Vh6LV18+Su6a9ovyr4+jS6+PLesxx108eXU3nHzZrrKdY0v19Bd\nz44pwxeQj+elZfqRMs9lZV7nlf25klwvqwnGp8rn7eRjdzndNW1d8eUBuuvVzWW6Ob3y3k2O+TW+\nLiOf+7USfW/pV+tMd5LrbivJlfB5pQwLy/TfoIsvC8nH8WK6Y3dFGb62+DKffG25qZTh0jJdjX8j\n5HOn1k2+V/rNL+Mv6XUvLOW/j3wcrCrjnlHm+Styg8BgfPlDr7x139xb1uPist1qHDm/jHt32b9/\nKP3r9jurV+5/L/NaXMa7jtXrhc+niy9Ly7jfLut0K915cH3ZHyvI16th8WUpXf1pGbmuWffvEnL8\nW0V3XtVYMkI+B95PFzdrPfG9dOdzjS+vJJ9n84FnsIGxZVN7vPB5wLyU0nxyZeh6ciI1j3xxP7b0\n+1vgbPKGnJdSup28YfcmtwheRs5wH13mez65Yvoi4FWl3xJy5ltPxj3K51HAP5KD9+bknVEv7CPk\nCtfdZf7bkCs5d5dlBXkn/rBMtzd5J15P3kkrya2bvyvDdydXhLcmVyiW0bUsrAL2KcN3LMu4nfw/\nQ2oysVMZ73vkEzmRD8A7yAdETUZGyAFh11L+6XQn1nalfP0W4LuAy1JK55EvELXVqZ4kD5T51RaX\nW0u5tiIHmNrqUBNNgK+SKwlXlDLPJJ8E08kBAPLF46Yy7V/StVR8m/wPKpeWcXchvz1qOfmEuJTc\nGrRFGR6s3toyo/R7BPlkXlz2xwj5f63cX7bXNmUfbAU8u6xXkI+VheRWjmvoLqw3l+4HesuqLXk1\nIZtBDoLnlv61xWUz8j5cATy5bLfaInlNGfemsn0eT2693YZcKf19KeP8Ur6ryzpsRb6ozKS7y7e0\nTL+4rOsPetvwlrKc3UpZppV9tLLM7/xSzgfKMvcq86yVz7rvnkQ+hjYnJ/aryIH2NeRz98llW5xK\nvkheDrwopXRdGf48HhrPK8ubxZox5Tl0Mecf6VrSn0WulOySUrqZ1eNLbVE8i3z87QK8sCxrKV2L\n8z6MPb5A13K4DXn73k0+3iDvv8H4Uo+p+8hPANxSlr1TKceLWTO+1OXMY/X4smsZ/h26lvVH0DVi\n/IYcH64tn7vSJQX1OKnH9gjw/5fu2mq6mLXHl5ooLSWfczvTNZJsTj7+6gVzOjnu7VjKeDfwP8u2\noaz/zeTj+Rfl+76l3P27XjuQk9udyeddrSgsJ59fO5IrPoPxpbaqziSfD5uR9/E1ZTtsXtZzF+Dr\nZb2eUvbBrDJOrTDfUOb9nDK83kn/bSlXbaFeRRdfrk0p3UmOL0vKPPvH0J7k+NWPL1uTr1cAzyQf\n6zW+3EfXkLiEnHTdVtb7q6VMu5PPiR1L/xvJ14/ZZZs9nXxurCzl/hXwOHKMvr+U9Vq6BPxy4KBS\nzpt727Q2AtVj/LmsGV9WkF8ENp98Xb8c2I/cKDER8WVY3eU5Q7ofIFc6R8jn5J/Q1V0gn7+JfFzV\nY3RD4ssLyrRb0FVqR8r0i3vLGQH+hS6+1KRlKV182Ze830fIFdp6d2mn0m8B+bi5j3w+QXc+zaBL\n1r9d1ut28j6tCUOtu9RGw4XkWHA1+bg/j7HFlyDXa+4mn7O/pIvRM+kal5aWZcxkzf+tNkJ3h29p\n2Y4ryNfWb5EbwrekSx62I8eXGsv/i+7aMI0cw9cWX64ln7dXlHEfU4Zv1Sv3U+gSrX3JseLRZdi1\npfuaMs4WdPWYa8v6PI98rm1LPtf68aUu/44ybj0vZ/bmPaMse3nZZ4vL9v8ueb8G3bm9b9kXkM/5\nGhd/SBdf5pdt9Ui6+PIA3V32e8jxZYuyrtuRrz+/JzeqDMaXPegS/7qP31LmfV0p18Vl3e4o3+sT\nI0vJx+ur6OIqZd3eTHc+1/jyWnId5tvkc2PDYstE390aaC06kvKf4Ev3D8gVv8+SK93nkyvY88rw\n5eRKwE9Lv2+XjX1T2RH1saDr6AJYbYF4H11rQW3lrbcZv0I+OOodjJppLyE/JnBP6f8H8oXxu3St\nTi8mX2hq6+uq8lkz8+XAmXR3MmrmXVs0ry3T1pal2qp1X5lHDZ797uPoWo3qnayzyAfgA731rnei\nRsgXr3rnpyZHN/bGXUY+qW6lCwC1dbIG60QOXot7060cGKe2qi+mazW5v+yT1Pur26Ou000Dw1Ip\ne/1+B12SU7fVit44fzOwv+sdmhoAamvVMuAf6FpF6jR397bHojJubWWvLUcPlH4/K+P016ee5HX5\nN9Edg7eVbVDvTtR9UgP9vb15DK5jbVkaoau49Y/tJb3P2npYy7CUfNGorTwre9M/UL7XOyXL6M6j\nBeRKeN23i8jHfi3bUnIjyAhdQ8DNZdxvk1uN3kc+h68kt769t5znnwOOfCjjS++zxpQfAJ8s/S8l\nJy1/KOu5kBxbziE/7lfjS133mmDW/ToYX+qFZqzx5YjedH+giy91f+3EmvFlITlu3F2Wc2kZv7Z4\nLmXN+DLSG9aPL/WYO47unPtiWUYitzTfRY4ZK8mt2f1jflFZj3r347/JF8T+OLU19yJWjy+r6GJr\nPefrObai1+9XA+Mk8p2bhb3t2B+WyJWr2lgwOKzus3on+v5e/wfozvl+fKnnY40JNfGr8aVWru4k\nV2pXsXp8vbm3fes61mtBjX+3l3W5iTXjZU2Ya/cCunN/VdlH9Q5Sf3vUCm7d//W4qv3q+i2hi8f9\n+N6PwbUBbmVv29zA6rFlhO4uSP9Yv4suPn6fruFhGbkS+4fe8m5nzfhyZxn/ZeT48nXyOfxJJiC+\nMLzu8smB7veSj9Hzyefdj8mxph9f7qa7LmxMfLmX7o5hP75cSnfX6w9lWfv09tmzyEnvQronemp9\nosaP+8ty6rm6uCxrOTk+1P1Wz9n7yjT/VYbVulCtj/yArj71ebqnJ+r1rV8Xqsf5uuJLfRpnP1aP\nL/f2xu3XI+6mO0fq53297it622P+wDxqnLiX7rgfa3yp+/kBuvO33klPvWG18a0+gXNv6XcnOeb0\n6wgj5ES1nu+1/1298RbQJZ/1bnu/vPVuX91m/TrWCvI5vWhguiW9detvh7o+9a/Ofxlrbq8lvenv\npzsG6tMNg/ElkY/D+iTG/WXYNXTH+cfK58VlHjUOj5Bjy8/p7nCtJB/LK4Fvl/O5xpcryU9T/H3p\nt0GxZVO70xVr6e7bk/yYRh2+DfnEOhH4U/IBtCU5kL2xjPMouscRbyVnzX9J3lmQd8pm5I1eT+pH\nklsHbie3FC4hZ+ifIO/g0cr49vL5XfJJHeQDoGbZlLJcVsoF+ZGnJeTAVINrfQyjtmg+k9wisKxM\nU+8+vZmcfNZ1/RHwRHJrwBvL/H5JDribleU+uWy768jZ+lJyq9tKumdaH1+Wu6zM48tl+l+Rt/Pp\n5FaJ+ux4TR6nl/n+C91doIPLetRWvsPpHnGrF9R7y7yD/OjRZ8u0HymfqZSdMt3pdEG0nkC3lLIc\nR3dy1paYRG7ZqIGntqweQXcb/KrS7xJyK1AiVzBrcv6Hsvxt6VqS636ZR76tXstYf/N1D/mxrH7L\nZb0Q/ZocvK4sw24kt3yOkFv6flvW5yK6i9lP6FqkomyLr5Zl1Weztyjj1/VeST5+6+MDQdfqv5Tu\nEaiL6O4E1Fb9neieAV9Z1mtLusaKZeRW2OrD5H23iNz6tRNrSmvpbikGPvsS+Tjek7zNp5G37/fJ\nseVF5Lt0C8jrfgNdMrAl3d3vW8jx5U1lnr8DPsTY48v/pWt1W1s5h8WXeod2eW+8L9DdCevHl9ry\n3X/cpcaXaXQxpd41OYB8TEJuLT2PHGsXkR9XvI18Ll9Djge/J7fQJnKFYOey7NoquLQsZxZd7Lin\nlGdrcgy4h9yYMZPuDl19VHcncnz5JN1F/H+QGwbqnZRzyTHxDrq7wL+gi90vKtvqPvK+m06XlNXH\nuH9I3m9blHW9jS6+1MpF//ctq+jiS23tXgm8vGzTL9JVChaWaebTPW74G7pz4efk42Vm2SYzyDHp\n38kJCuQ7T3V7/iddheXU0l0buepxsLSsx31l3lfTPWJVKy3XkeNq/0mEL5MrGLD6b3fv7q37fWVb\nbVPW/TbyORJlPlfR3R2ov438fpn2meRjoR5vTy7zrRXfO1gzvny3rNPH6OJLYvjvzR6K+DKs7jJY\nntfT3YGAfK6cSRdf/pa8XU6muwu0IfFlGfmY2Zy8H/rx5Yl0MWJt26pe935QynBBma7/FMZy8nE1\n2PhaH81eUNajLncm+a5RtRndP5x+Fvn4hxxfLifHjyvLcm8jXxeD7g7HuuLLNuTH6U6mq/d9o2yX\nO8gN9ffSHWdblHW6o5Tl1NJdG5seXcrxvbIuN5DrBfUR6Jml3DeXMnyBLhEeLb7U+DWDfO2tDXG1\nsbRuv5oAbU93rVpeyvo08nHyrjLdUvKd6kSug9U7fL+nu0t/Wln/bcu2WkKuo32pTLcdXdJ8Dfk8\nq8nlcnJj24Ky/F+UeS0v67+KfI5eyuo/ZQhy3ejLZT/U+FIfW1xJd8d1hHwdrvHlqrKNB+MLZboT\n6K5l08t2qneCX1O6/1DGfwzdo4b16arqGWUb3A/sGxF7svbrcP9zzDa1pGsB+VGn2v0Y8obajfwI\nw03kjX4o8B/kE3dH8oaqj8Y9lVzhXl6GjQD/ChxCPhgeSW5R2r10TyMfBLUV+QbyQVQPxC3JFaNf\nl/kfRD4xIJ+Qm5X5LSHvnBeQ73bVVqHNybdH9yvL2orcOjed/Iz2Y+kSgHordGXpPrPM/0K6R4iW\nkE+iZ9NVRq4u5ZtWyraU/MjX98m3ROeTH6Wqjxw8gfwoCORK5jbkA/o35JPjJ+RgdwXdCwe2o/uh\n4Wnk5O7yUq5aAbm7rE9t3aiVI8gJ3LLS7+KyT7Yu3fWRyR3KNoGcJNZ/OPlWut927EYOXrPonmWv\nPyzeoQzfnK5CN6OUPcp6PK1837kM26Is85Hki1N9ecZL6F628ipycNqiDKcs53FlnC1LWXYj79va\nWndnKfO0Mv/ry7Q3kPfhDuRA+TPysT5S9sfry3L3pruLWtd7Zlm3X9BVgL5d1mcxXcJUg/Z95GNm\nBnnf7lbKuoTuEZNHkCs1m5F/ezBC3vd7lXmtLNum/ramtrquKuXZktxaGmW/7VKmuZEc2DanO4dv\nLt314jS7191ajS/1czY5vjymlOFO8vH+nNJvK/JvAvYgX7RfQz5WjiOvd/1Nw0nk/X0reZ8+jhxf\ngnw+/ISxx5fX012I+/FlpMzvYNaMLzPI+2dGKfNzyMfca0o562OyteK+BV1cGYwv/ZhSH/u4pKwD\npex/Uub3aWAuOb6cQz5eVpZt9Igyn/fSVQB/RHcezyzz3LqUYUu6x1dPIx+b59LFl/q5edl//XOi\nNlY8h+6xlT1Kv/r4YiInituWsn+nlGHzMt8l5XutZD6J1Ru3Hln+anyp89myrGcdZ29yfJlWxplV\nyrI5+VGorcp+qPH3SeRzfib58Z36yPbh5OOozucx5Nj1MvL5XituteJyDbliArmyclMpz+nk4+4R\nZb5PKNt8C3JF8hfls95d243ujml9RHte2ab1GldbnrejSyI3K/tk6zLs53QvuNq9lH162T61ge6P\n6B6dXFGmoyynPnK+JfkR1sH4cldZ7g/Ktt2dfA7Xc/uhji/D6i4397rvIK/Hc8hPy8wkXxNTKf8i\n8qPOd5HrI1ux4fGlHre3lfn048t3yucIXXypdw2CHFu2IR8D95D32X501/79ycfSI8jxBfI5Vte9\nNu49upR/elnmdLqfFjyHHFvqTx3q73d2IR+PTy3leSm5Mn09Oc5sVvrvw+jxZQY5lv+S7jr/VfKj\nlSNl/J3Jca02StbfMm5FvmbtSY5r9W4JZZt/r2zfHYDj6X7vOELer7PLdnxdKe+2jC2+zCjTb08X\nD+tPIqaVbfNIuuR3q7KsWaUsm5MboWp9pP7E49/Ix8hMunNoGvDXZdqZZV5bkn+ztITuN9r1iaDd\nyNeamgBeR36M93Hk83z3Ms+tybFuGrnOtmMZd7cyn5Wlu/6er8aXS8s2/2b5XhuXt6erW25X+g3G\nF8rn28t23LXss5llGfUYTuRzrDYy30f3SOhBpSxbkRPAp5LPmXvJDQIL6OJLvx6zQbFlU0u6LgH2\nioj6G5bHku+C7EU+wU8kH7RHk3duve17JfmOTk0cti3z+wdyMP9U+buP3Jq0ogyvj8JcSz7IppMf\nTXgP3YXh/jJuvWuxGTnhqLdudyLvvGtLv9fS/WDxXPLB/EHyxQFyS1V9tOTosm41664VgG3KuM8r\ny/sD+UDZnHwwbF7+6nPdjyX/MJEynzmle0vyhfTR5JaxW+h+6F1bMc6h+5HhmXR3bqALtCvL8IVl\neXuVad5ZttP2dI/U1Av4LeQW8seWefxp2W4zyYHjiWUeW5NP3vrYGmX6N9E9blNb4qeVZa0q2+nJ\n5EpEDXr1tv/vyRXJpXQ/3q+P+9XW1n8ry1pMrmAuIgfQW8p8vtSb7w10P85dUcp1SVnfG3rjXVHW\n79F0rd5BruAcSPdmqa3okst68d2B7gUo9WUaq8gXh7pdgxxE9iIf/5T1fTb5IlF/TFqTzfp7nAV0\nb7iqyei9pRwryjrXVrXrS/dWdK1/lOUvKN3TyMGtPh9fH2dbSj5H/6Ss01ml/D8mX0CPISf/zwS+\nX87zvch3OB4Kl5TlLaSLKf9JPkZ/QfeihpfTPTrxZnKloybVvybHl+3J+7BexL9DPt/+D/mcXUr3\nwon1jS+1ItSPL0vK8P/NmvHlX+nuXi2gexT2KLqLZI0v9dxN5IaFwfhSK2B70P1245/IFQzKNti6\n9P8zuor6C+nuON9I94a+U+jiy+l0b9/agnze19bIesdohFxJDfL5Ui++NdZPI8elX5AriItLv33J\nx/gjyRWUU8gXz0eSj+W76R6RHQHeQRcrHlXW7Y4yr/3IFdz6u89Utl+N8dfSvSSgPv6ytPSrj/rV\nVu36Y/X627pbyOdYvbNUH+m8mVzZHinT3kn3w/BU1vNXdL9DmVm2e21Muoa8b6Psnxpf9qF7BLQ+\nJj1Cd518EjlZ24ocg6aRGyj7b5vcjXwHoz6mCN3v1ijbYytWf1veEnLsqb/Tqa3k9S7u7eTYfVUZ\nb3NycjejrE+Nd39L1xLejy/1Lv8LydetZ5KvsT9gYuLLsLrLL3rdP6O7Nr6c7icE/0mOQ7uS9/sV\ndJXvDY0vx9DFjsH48jO63/atJO+zA+jiy1voHmv9XBnnI2V5kJOuRD5XjirT1ztZtYEW8nXxA3T1\npRHyfrmTfO6fR25wDLoncGaQz92ty3hfJ58ru5HPnXq3Yj5rjy9blnV+JPmlIlHK+yy6SvatZZ88\nie5uSf0t6/SyTnuQ48f3Sr9ty/LeQd6P9QmePeniy1LyeVwT2iWsPb7cTm54qfFlFfm3eKvI5/LF\nZZrP0TVof7j0+z3dY3/3keP0f5OP9VvoHn+vjbg1Jn+5N6/f073YpD4JcwK5nlLrCLVRdUdyY8kD\n5fv25Ni9tGyXHcj163t683obOSmpdaB6A6TG9HoHK5V9VedLb/vNLt+XlPUYFl/q+td3FEDepzUx\n/XkZD3LDT20om1vmcxn5OK2xen/yzYlTyXWXe8jHwDPJx9ox5LrMb9jA2BLlmcVNRkQcSL6VOZ3c\ngvM/6H44WG9h70S302rrbm2pqS0tw9TKRp0Ouhbe2ppad1DQZcP1Na5V/7GB9VGXtb76y6vz2NAy\nrKLL/GOg3+CyRpt+XeVbm9HmX2+h1xatGDLu4CMbNaiO9OYxuG+hez1pvTWfGL5PawJVf+/Wf+yl\nHl91+vra5/qY3dZD1mfYOgxbl1UD5QnWXNfav78Phu279Vluogv69YJ5N/kCOp3uOeyZZX23GBhe\n57GU7hX8M+h+T3IK+eK8fRnnLnLF4pnkbfnXKaXz11LOcdeLL/1XxvdjymXki/yj6X7QvIx8gZpN\n928f+upxViuUdbtNpzt2auVnLPEFhp9nYzUecWZj1bv19djqV+IHlzGWmAFrnhNj1X8UpL5EYHB4\nnXc/LtT+9W7MNLqXlUB3ztaYUNdzcF3686/HSI0d9amA2greP77q/G8gV1a2Yc31HyzzsOX25wVr\nxk9Ycx/UmLqENePa2rbXMPVRyxV01+XaKFAfUZ5J9yjlFkPKUhu+ZtOdUzVJ6D9lUePXhMSXtdRd\ndqKLKf3uPehiQ42Z9U7ixsaX6eTEv97RrE9s9I3nuT5Mv3yD59Pg+Vy7h02/NmuLL8OsK75saLys\n+udWTdzWtt7Dzpf6O6ktyOs1s/Tv34GsdaL+HaLq/l6/+pjmTXRPUdXYtYruRTi1njCNnPQ+d2Ce\n9fwfrCuOpl9/66937WZgPvVlbFuyulpXW9cya8I6GNfq9qg/55hJjtu1YWhw34yQGzX2L/1qA/tS\nckK4JfmpsWfQvTL+LjYwtmxySZckSZIkTSWb2uOFkiRJkjSlmHRJkiRJUkMmXZIkSZLUkEmXJEmS\nJDVk0iVJkiRJDZl0SZIkSVJDJl2SJEmS1JBJlyRJkiQ1ZNIlSZIkSQ2ZdEmSJElSQyZdkiRJktSQ\nSZckSZIkNWTSJUmSJEkNmXRJkiRJUkMmXZIkSZLU0IyJLsD6mjNnTrrgggsmuhiSxi4mugBjZXyR\nJpVJE1vA+CJNMuMeXybdna5FixZNdBEkTVHGF0mtGF+kh7dJl3RJkiRJ0mRi0iVJkiRJDZl0SZIk\nSVJDJl2SJEmS1JBJlyRJkiQ1ZNIlSZIkSQ01S7oi4vMRcXtE/HotwyMi/iMi5kXElRHx7FZlkTS1\nGF8ktWJ8kdRCyztdJwNzRhl+ALBX+TsW+FTDskiaWk7G+CKpjZMxvkgaZ82SrpTSj4A7RxnlUOCU\nlF0EbB8Rj25VHklTh/FFUivGF0ktzJjAZe8K3Nj7vqD0u2VwxIg4ltyaxO677/6QFK6lr38hN6Ad\n9foLHuz3xZNfCsBrX/ctPndK7l4Z8eDwFSU9fscxF/LRr+wPwN+86sKh8/+7r+f5//NRF/Duubl7\ncZn+pMMuWG3cA84+GoDzD/3a0Hkd+I33AXDey/5p1HU66MyP977lw+qbh7999XHmfhaAYDoA5x7x\n+tWGH3zGqbn/kccMXcafn3HmatOfc+ShQ8d72RnfebA7IpflrCP24/C5PwHgzCNe8ODwI+f+EoAz\njng2R83NT5J8/YincfTc3wOwRcwE4IuHP5b3nrUAgA8dNnvocj9z5u0AvPnwnTnlzIV5+pT34cuP\n2Gm1cc//2iIAZq5KD/bbbFX+/NO/mMVFX8zT7/vaWUOX9dsTbwPgyW971IP9Fnz4VgBmv2eX1ca9\n9UPzAdjlvXt0/T7yq9zv3X/EbR/L2+BRfz2lnpCZkPhy8wl/DcBj3v4xbvzEqwHY7Z1f5tpP5GN1\n+bS8v5/y9nO44lOHAPCMt57DJZ/5cwBWRB7+P489d72We9bnDwBgZXTHUz++DPOZL+U48ua/uJBP\nnpq71ye+LCkx5SNHXMCxZ+V+g/FlmAO/8R4AznvZh4cPP+tf8/DDjnuwX40v3zz8rzjozBNKdxdf\namz55hFv5OC5XwByfDn4jFPKGCXmHHkMB59xWul+xYPT19gC644va7M+8WWYdcWXqsYWgNcc3sWH\ns7+eY8qhR+20xjSDanzZbGU+Xp7zhp3XOY1WM27xZeGnPwPArLe8eeiCbjnx7wF49Ns+MHR4jS17\nvvNsfnNCjilPefs5Y1mHMamxBeCwvzx/6Dj9+sto1hVbWjnwrOMBOO+w9zeZ/1jrLv915OEccsbZ\nwLrrL9848iXjXcwpZzLWXSbyRRoxpF8a0o+U0kkppX1SSvvMmjW8EipJPcYXSa0YXyStt4lMuhYA\nu/W+zwZunqCySJpajC+SWjG+SFpvE5l0nQO8prwFaF/gnpTSGrfmJWkDGF8ktWJ8kbTemv2mKyK+\nCuwH7BQRC4B/ADYDSCl9GjgPOBCYBzwAvH74nCRpdcYXSa0YXyS10CzpSim9ch3DE/D20caRpGGM\nL5JaMb5IamEiHy+UJEmSpCnPpEuSJEmSGjLpkiRJkqSGTLokSZIkqSGTLkmSJElqyKRLkiRJkhoy\n6ZIkSZKkhky6JEmSJKkhky5JkiRJasikS5IkSZIaMumSJEmSpIZMuiRJkiSpIZMuSZIkSWrIpEuS\nJEmSGjLpkiRJkqSGTLokSZIkqSGTLkmSJElqyKRLkiRJkhoy6ZIkSZKkhky6JEmSJKkhky5JkiRJ\nasikS5IkSZIaMumSJEmSpIZMuiRJkiSpIZMuSZIkSWrIpEuSJEmSGjLpkiRJkqSGZkx0ASRJU88B\nZx/9YPf5h35tAksiSdLE806XJEmSJDVk0iVJkiRJDZl0SZIkSVJDJl2SJEmS1JBJlyRJkiQ1ZNIl\nSZIkSQ2ZdEmSJElSQyZdkiRJktRQ06QrIuZExO8iYl5EHDdk+O4R8f2IuCwiroyIA1uWR9LUYXyR\n1IKxRVILzZKuiJgOnJRLLQIAACAASURBVAAcAOwNvDIi9h4Y7X3A6SmlZwGvAE5sVR5JU4fxRVIL\nxhZJrbS80/U8YF5KaX5KaTlwGnDowDgJ2LZ0bwfc3LA8kqYO44ukFowtkpqY0XDeuwI39r4vAJ4/\nMM4/At+KiHcCWwEvaVgeSVOH8UVSC8YWSU20vNMVQ/qlge+vBE5OKc0GDgS+FBFrlCkijo2ISyPi\n0oULFzYoqqRJxvgiqYVxiy1gfJHUaZl0LQB2632fzZq34N8AnA6QUvo5sAWw0+CMUkonpZT2SSnt\nM2vWrEbFlTSJGF8ktTBusaUMN75IAtomXZcAe0XE4yNiJvnHpucMjHMD8GKAiHgKOXDZFCRpXYwv\nklowtkhqolnSlVJaCbwDuBD4DflNP1dFxPERcUgZ7T3AmyLiCuCrwOtSSoO38SVpNcYXSS0YWyS1\n0vJFGqSUzgPOG+j3/l731cAftyyDpKnJ+CKpBWOLpBaa/nNkSZIkSXq4M+mSJEmSpIZMuiRJkiSp\nIZMuSZIkSWrIpEuSJEmSGjLpkiRJkqSGTLokSZI0aRxw9tEccPbRE10Mab2YdEmSJElSQyZdkiRJ\nktSQSZckSZIkNWTSJUmSJEkNmXRJkiRJUkMmXZIkSZLUkEmXJEmSJDVk0iVJkiRJDZl0SZIkSVJD\nJl2SJEmS1JBJlyRJkiQ1ZNIlSZIkSQ2ZdEmSJElSQyZdkiRJktSQSZckSZIkNWTSJUmSJEkNmXRJ\nkiRJUkMmXZIkSZLUkEmXJGlCHXjW8Rx41vETXQxJG+Fzp7yUz53y0nGZ17vnzuHdc+ds8PQHfuN9\nHPiN941LWaRhIuLZEfHE3vetIuKwiNh3bdP8v/buPNyyqjzw//etKqqAYh4cQXHAOU4g0TaJOLRg\nTDRxCsa0Q5tgOtq2v0Q7pJOYtHYeW9MxphUTifMQbYkY0WAUFecJUERBwapiKoqhqqCoebj3rt8f\n613Zm1P33joUd1NVl+/nee5z9tnj2sN691prr32ulS5JkiRJGt87gSmAiAjgm8DLgb+JiD+dbgEr\nXZIkSZI0vkNLKcty+JeBA0opzwWeArx4ugWsdEmSJEnS+CZ6w08DvgxQSpkAJqdbYNFdkChJkiRJ\nmi+WRcSbgUuA04HTACLiCGaodPmkS5IkSZLGdzpwKPBS4IxSytdz/GLgVdMt4JMuSZIkSRpTKeWW\niPgj4KFAiYhFpZSJUsqNwI3TLeOTLkmSJEkaU0Q8GrgMeA/wbeBrEfH42Zax0iVJkiRJ43sn8NJS\nypOB5cBzgLfPtoCVLkmSJEka36GllO/mcJRS1gJLZ1vASpckSZIkjW9hRLTfxlgQES8C1sy2gJUu\nSZIkSRrfO4CH5PANwCnAy2dbYNBKV0ScGhFXRMSyiDhjhnleFBGXR8RlEfFPQ6ZH0vxhfJE0BGOL\npDH8SynlcoBSyqmllFeyiyddg/1kfEQsBM4E/iOwErgwIs5tCcx5jgf+BHhyKeXWiLjHUOmRNH8Y\nXyQNwdgiaUzfioj/UUo5ByAiHgF8EDhppgVmrXRFxAagzDS9lHLILIufBCwrpazIdX0CeC5weW+e\n3wPOLKXcmuu7ebb0SFIyvkgagrFF0jieBrw/Il4A/Bj4HeC/zLbArJWuUsrBABHxJuo/+voIEMBL\ngIN3kZj7Atf1vq8EfnFknofk+r8FLAT+spTyb6MriojTqf/5mfvd73672KykuwHji6QhzFlsyXmM\nL9I8VEpZFRGvAb4O/Cbw66WUr8+2zLjvdJ1SSnl3KWVDKWV9KeXvgefvYpmYLo0j3xcBxwMnAy8G\n3hsRh+20UClnlVJOLKWcePTRR4+ZZEnzmPFF0hDmLLaA8UWar7LC9a/UH8/4ReCtEfGW2ZYZt9I1\nGREviYiFEbEgIl4CTO5imZXAsb3vxwCrppnnM6WUHaWUq4ArqIFMkmZjfJE0BGOLpHGcADyhlPLF\nUsql1IrXxGwLjFvp+m3gRcBN+ffCHDebC4HjI+IBEbEYOA04d2SefwGeChARR1Ef2a8YM02S7r6M\nL5KGYGyRtEullFeUUtb3vk+UUv58tmXG+vXCUsrV1BdJ70hiJvLR2xeofZ7fX0q5LN8Pu6iUcm5O\ne2ZEXE59cvaG/I/OkjQj44ukIRhbJA1lrEpXRDwE+HvgnqWUR0XEo4HnlFL+12zLlVLOA84bGffG\n3nAB/jD/JGlsxhdJQzC2SBrCuN0L/5H6Pyl2AGTfxdOGSpQkSZIkzRfjVroOLKV8f2TcrC+LSZIk\nSdJ8FRGv73/OZtxK15qIeBD5s6n5j8Bu2O0USpIkSdK+7SX5uasfGBzvnS7g1cBZwMMi4nrgqt5G\nJEmSJEkzGLfSdU0p5RkRsRRYUErZMGSiJEmSJGm+GLd74VURcRbwRGDjgOmRJEmSpHll3ErXQ4Ev\nUbsZXhUR74qIXxouWZIkSZI0P4xV6SqlbCmlfLKU8jzgccAhwNcGTZkkSZIk7b1afejru5px3Cdd\nRMRTIuLdwA+A/YEX7V7aJEmSJGnfVkp5Xf9zNmP9kEZEXAVcAnwSeEMpZdOdSqEkSZIk3U2M++uF\njymlrB80JZIkSZI0D81a6YqI/15KeRvwVxFRRqeXUl47WMokSZIkaR7Y1ZOun+bnRUMnRJIkSZL2\nRRHxmVLKc2eaPmulq5Ty2Ry8tJTywzlNmSRJkiTtYyJiPRBA6X0eGBG3AWeVUt4wusy4v1749oj4\nWUS8OSIeOWcpliRJkqR9SCnlkFLKwf1P4FLgKODU6ZYZ9/90PRU4GVgNnBURP46IP5ujdEuSJEnS\nvuwtpZQdwHnTTRz7/3SVUm4spfxf4PepPx//xrlJnyRJkiTtGyLiyIj4aETcHBE3RcTHgAsASil/\nPN0yY1W6IuLhEfGXEfET4F3At4Fj5irh0t3BZ85ew2fOXrOnkyFJ0h129gdO5ewPTNtraifv+cgp\nvOcjp8zJdk//9Kmc/unxtjuEZ3/qvTz7U+/dY9vXXutM6kOoY4Dr8/s/zLbAuP+n6wPAx4FnllJW\n3ZkUSpIkSdI+7OGllNMAIiJKKd+OiL+dbYFdVroiYiGwvJTyd3OUSEmSJEnaVy3sf4mIY3e1wC67\nF5ZSJoEjI2LxnUiYJEmSJM0HX4+Ix+TwkcAXgNfNtsC43QuvAb4VEecCm9rIUsrbdyeVkiRJkrQv\nKqX8Qe/rw0spm2acOY1b6VqVfwuAg3cjbZIkSZK0z4uIvxj5DkAp5X/OtMxYla7ZViBJkiRJdyMb\nesNLgGcBK2ZbYKxKV0RcAJTR8aWUp92R1EmSJEnSvmz0FauIeCvw+dmWGbd74et7w/sDzwcm7lDq\nJEmSJGkeidq38FHA8bPNN273wotHRn0rIr62m2mTJEmSpH1SRKwHgtoTcD9gCvid2ZYZt3vhEb2v\nC4ATgXvtXjIlSZIkad9USjmk/z0ingk8Hfj0TMuM273wYrp3uiaAq4FX3vEkSpIkSdL8UUr5YkT8\nH+CPZ5pn1kpXRDwBuK6U8oD8/jLq+1xXA5fPXVIlSZIkae838pPxC4BfAFbOtsyCXazzPcD2XPmv\nAG8BPgTcBpy12ymVJEmSpH3Tht7fWuCDwK/PtsCuuhcuLKXcksO/BZxVSvkU8KmIuOTOpVWSJEmS\n9i2llLdHxKHA1lLKtnGW2dWTroUR0SpmTwe+0ps27vtgkiRJkjQvRMSbgZ8B10TE8yPisIj489mW\n2VWl6+PA1yLiM8AW4Bu5oQdTuxhKkiRJ0t3JacBxwOOBN5RS1gG/NtsCsz6tKqX8VUR8Gbg38MVS\nSvsFwwXAf73TyZUkSZKkfctVwH6llFURcWCOO2C2BXbZRbCU8t1pxl25e+mTJEmSpH3adcB3IuKf\ngcMj4sPAt2dbwPeyJEmSJGl81+QfwDuBy0op/zrbArt6p+tOiYhTI+KKiFgWEWfMMt8LIqJExIlD\npkfS/GF8kTQEY4ukXSmlvKn397ZdVbhgwCddEbEQOBP4j9R/FnZhRJxbSrl8ZL6DgdcC3xsqLZLm\nF+OLpCEYWySNIyLeD8RM00sprxgdN2T3wpOAZaWUFQAR8QngucDlI/O9GXgb8PoB0yJpfjG+SBqC\nsUXSOD53RxcYsnvhfakvmTUrc9y/i4jHAceWUmZNeEScHhEXRcRFq1evnvuUStrXGF8kDWHOYkvO\na3yR5qFSyjnAZ4ErgSuAc0sp57S/6ZYZstI13SO38u8TIxYAfwv80a5WVEo5q5RyYinlxKOPPnoO\nkyhpH2V8kTSEOYstYHyR5quIeDRwGfAe6q8Wfi0iHj/bMkNWulYCx/a+HwOs6n0/GHgU8NWIuBp4\nInCuL6RKGoPxRdIQjC2SxvFO4KWllCcDy4HnAG+fbYEhK10XAsdHxAMiYjH1Pzef2yaWUm4rpRxV\nSjmulHIc8F3gOaWUiwZMk6T5wfgiaQjGFknjOLT3v4yjlLIWWDrbAoNVukopE8BrgC8APwU+WUq5\nLCLeFBHPGWq7kuY/44ukIRhbJI1pYUS0HyRcEBEvAtbMtsCg/xy5lHIecN7IuDfOMO/JQ6ZF0vxi\nfJE0BGPL3HrXx06pAzP+uLa0T3oH8BDqL5uuAk4BXj7bAoNWuiRJkiRpPimlvK83/KxxlhnynS5J\nkiRJutuz0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJ\nkiRJA7LSJUmSpLulZ5/zdzz7nL/L4TN59jln7uEUab6y0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy\n0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LS\nJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIl\nSZIkSQNatKcTIEmSpNmtOvN1ANzn1e/YwykZxp+efWr3xUcCmoe8rCVJkiRpQFa6JEmSJGlAVrok\nSZIkaUBWuiRJkiRpQFa6JEmSJGlAVrokSZIkaUBWuiRJkiRpQINWuiLi1Ii4IiKWRcQZ00z/w4i4\nPCIujYgvR8T9h0yPpPnD+CJpCMYWSUMYrNIVEQuBM4FnAY8AXhwRjxiZ7YfAiaWURwP/DLxtqPRI\nmj+ML5KGYGyRNJQhn3SdBCwrpawopWwHPgE8tz9DKeWCUsrm/Ppd4JgB0yNp/jC+SBqCsUXSIIas\ndN0XuK73fWWOm8krgc9PNyEiTo+IiyLiotWrV89hEiXto4wvkoYwZ7EFjC+SOkNWumKacWXaGSN+\nBzgR+OvpppdSziqlnFhKOfHoo4+ewyRK2kcZXyQNYc5iCxhfJHUWDbjulcCxve/HAKtGZ4qIZwB/\nCjyllLJtwPRImj+ML5KGYGyRNIghn3RdCBwfEQ+IiMXAacC5/Rki4nHAe4DnlFJuHjAtkuYX44uk\nIRhbJA1isEpXKWUCeA3wBeCnwCdLKZdFxJsi4jk5218DBwFnR8QlEXHuDKuTpH9nfJE0BGOLpKEM\n2b2QUsp5wHkj497YG37GkNuXNH8ZXyQNwdgiaQiD/nNkSZIkSbq7s9IlSZIkSQOy0iVJkiRJA7LS\nJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIl\nSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJ\nkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmS\nJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIk\nSQOy0iVJkiRJA7LSJUmSJEkDstIlSZIkSQOy0iVJkiRJA7LSJUmSJEkDGrTSFRGnRsQVEbEsIs6Y\nZvqSiPh/Of17EXHckOmRNH8YXyQNwdgiaQiDVboiYiFwJvAs4BHAiyPiESOzvRK4tZTyYOBvgbcO\nlR5J84fxRdIQjC2ShjLkk66TgGWllBWllO3AJ4DnjszzXOBDOfzPwNMjIgZMk6T5wfgiaQjGFkmD\niFLKMCuOeAFwainld/P7fwJ+sZTymt48P8l5Vub35TnPmpF1nQ6cnl8fCqwF2jxHTTM83bjdnXdP\nT9+b0mJaTevupHVNKeVU5tBeHl/29PS9KS2m1bQOmdaf7c2xJaeNE1/25XNgWk3rfE3rnMcXSimD\n/AEvBN7b+/6fgHeOzHMZcEzv+3LgyDHWfdFsw7uafkfm3dPT96a0mFbTurtpnes/9uL4sqen701p\nMa2m9a5I61z+MWBsmc/nwLTu3Wkxrbu/rbn8G7J74Urg2N73Y4BVM80TEYuAQ4FbBkyTpPnB+CJp\nCMYWSYMYstJ1IXB8RDwgIhYDpwHnjsxzLvCyHH4B8JWSVUxJmoXxRdIQjC2SBrFoqBWXUiYi4jXA\nF4CFwPtLKZdFxJuoj+3OBd4HfCQillFbiU4bc/Vn7WJ4V9PvyLx7evrelBbTalp3N61zai+PL3t6\n+t6UFtNqWu+KtM6ZgWMLzN9zYFr37rSY1t3f1pwZ7Ic0JEmSJEkD/3NkSZIkSbq7s9IlSZIkSQMa\n7J2u3RURpwJ/R+1L/XXgycDBwH7Abb1xhwD3ov6m/hFA6ycZ1Mpkyb9+xXIK2J7DS3LeqfzbAazP\nde0HTOT4/Xrrneqta7I3baq3ndKbt+RnS9NW4MDePP00l9740X+yWEY+2/wLRuaZoh630TT1l+1/\nX5D7MZHHoz9tpn/0ON20yZHtTrcP9Ka389PmacP9tGwHFk+z7TLN8v3pbfsTuXzJ9S5i+vPXP24t\nbRPAT4FH9r4vGln/BuovVvX3aVT/PPfT2ubvH7c2PJGfMcP02Y7v6DU03Xbb9/70Qr3+2/Hub3d0\nX0aHATYDy4BHA9uo528dcG1OOwI4LNd5K3AJ8LjczmtLKV+YZl8G0Ysv08WUfsw5Gjicej62010j\nS5j++p3k9tdKux425/dJdh1f2jongOuB+48kf3tv3dPFF0bSAN01HyOfTRkZni2m9I1eS32T1Otp\n//yMXnraOhdMs9zo+ltaJrl9fJ0pr02Xln5ad+R8S0am92M206y/H7P6x7uMzNNiTkvDtt73fh6b\noN4LDuL2x3iyt/1+nFtDvR5n0j/f042fLsbB9PFldPkWi5rR676ZLja0Y7Ut0zGaxtliSrMjl1/B\nzvHlFuCewI3AD6gxBfZQfJmh7DLd8CnAfYBN1Othinps2vWxu/FlE3AkXQxZQHe8+uucol5/B7Sk\n52f/Ot+U09s5XNhbdltv2ZnuR/1rb3Q/6K1vunzbymQzXXuzxZc23+g6R7Xrfbpy4h1ZT0tv/349\nXYzdVXzpH4t+/mzHusWvBXSxs18u7N/zJ/Oz3WcWjizf5l/Ym3+6vNmOy0zlm6Z/LDcDS0fSNbqv\nbV3TxZf+tvvX1XTXUz+u70d3jEfL4qPb7dsE3EQ9zg/Pca0808ouC+niy+G5v+vY3dgyxO/Q7+5f\n7txy4IHUDLUNODXH/TR3ehv1v8F/Iw/ktcA9gCty/I+Bn+WJ+HbOcwv1J18/C/w6NeBsz7/1wA3A\n5dQC2B8AXwLOywM7CfxhLjMFPBW4JocvyGXW5PangFdRf9VoAvin3MY/UF/KLZmWLTn8uZy+Bvhh\njvutnD5FDdC3ANf10vujnL4duCrnuwrYmGldQb2Itud+3Jrz3Ar8v0z7+jw+O6g3rBbkzqILyNcD\nv9rbxg7qLzZtznVtzXPR0nJljt+e43dQf1Z3babrv+e52Zpp/VjOd0su915gdW5rMs9DKyz8HPhv\neazbfl+Yy/8wz9/GnN7Selkuf3NO25j7PZnH55s5/Xu5/I5eWlcA3+ml5ZacfnN+/0me70ngfOp1\n0gpV7Vh+IdezjZqxr8/PApxBvR53UCtvH8zhdZnOtp5t+f1tObwW+G7v2F6X812Yx2Vjjru1t72t\n1GtmKo/Jn+TyK/Jcbs79mcq0vDXXtYOat1bnOd6Rx6pNm8rjuam3/i3Aa3P4/XkOl1Pz7Yrczmm5\nL/cEHpDTF97F8eXB7BxTnkkXc34zj9sk8Mrcv1dQC0otvrT8UoDn5bH4BeD36fJGO06H5b6PE1+e\nkMtup8aXjdT40PL3U9k5vrwnz8NUztuu2w/l8N+yc3wp1Gt0ObePL5fnMtdkOiapeW9DLvORXKZd\nbx+miy8b87yvp6uEvJzuxt5ucDPFl03UmL02x7VreHum+Vu5no10eeP63M41wO/k+PW5jg05/npq\n3v1Xuvw5kem6lhpfrgK+n+loMXd5nu/r2Tm+tILfhtyflo8mqfGkxfCr8jxPUGPOJDXvt0r3Db3P\nlr6b8zhtoMahtq1+fNlILRC0c/SMHFfyXDydnePLbb39fie3jy8tbl+b8zwjl9lMjTlT1Ji3nXrP\nuCLnvyaP21SmdW3O842cfjm1wFLyvFyTf60y9bE8xztyua1096QCvBr4IjvHlw9QY9MDqbFkG/Ak\n4InsgfjC9GWXZ84wfGl+bgN+KY/lc3M9V9KVS+5sfGnX0svo4st/o7unXZzn+hN08eUJuR9r6eLL\nx3rXzavoCtctvlxJdz97SU5fSb2et+byU7nObbmu8/J8TlDLZa2C8ff5eTP12t1Od0/bVXzZTHe/\nWpXLndpL7zpqzG/xdRs177dC9Poc18pMt1DLbhPUQviyTMPqXP5f6OLLaup1vJHu2t2R02eKLyuo\n95brMw0X5XIt/k1R804rm3wlx63I+bf0hldn+jdQr4PJnPefc50/pjYIjMaXq3vpbedmfe7H9/K4\ntTjy+Zx3XZ7fq3N8O36f7qX7r3NdG3O+q7h9ufAX6eLL1pz3/NynG+nywTV5PnZQ71fTxZetdOWn\nbdSyZju/W6jxb5IuX7VYMkXNA2+ki5utnPh6uvzc4suLqflsBfAYdjO27G3dC08ClpVSVlALQ9dQ\nK1LLqDf303PcG4DPUA/kslLKzdQD+whqi+APqTXce+d6P08tmD4N+O0ct4Va822Z8YH5+ULgL6nB\newn1ZLQb+xS1wLUu138wtZCzLrcV1JP4tVzuEdSTeA31JE1QWzevyOn3oxaED6IWKLbRtSxMAifm\n9CNzGzdT/2dIq0wclfN9hZqRC/UCXEu9IFplZIoaEO6b6V9Il7EOzfT1W4BvBX5YSjmPeoNorU4t\nk2zO9bUWlxszXUupAaa1OrSKJsDHqYWEH2WaF1MzwUJqAIB687g+l/3PdC0V51P/QeXWnPde1F+P\n2k7NEBdRW4P2z+nB7VtbFuW4A6mZeWOejynq/1rZlMfr4DwHS4HH534F9VpZTW3luJLuxroqhzf3\nttVa8lqFbBE1CH4ux7cWl/2o53AH8LA8bq1F8sqc9/o8Pg+gtt4eTC2U/jzTuCLTd3nuw1LqTWUx\n3VO+rbn8xtzXr/aO4Q25nWMzLQvyHE3k+j6f6dyc2zw+19kKn+3cPZR6DS2hVuwnqYH2pdS8+7A8\nFh+j3iQvAZ5WSrkqp5/EXeOk3N7R7BxTTqCLOX9J15L+OGqh5F6llFXcPr60FsVPU6+/ewFPyW1t\npWtxPpHx4wt0LYcHU4/vOur1BvX8jcaXdk1toPYAuCG3fVSm4+nsHF/adpZx+/hy35z+JbqW9QPp\nGjF+So0Py/PzvnSVgnadtGt7CvgfOdxaTTcyc3xpFaWt1Dx3D7pGkiXU66/dMBdS496RmcZ1wH/I\nY0Pu/yrq9Xxxfn9iprv/1OsIauX2HtR81woK26n560hqwWc0vrRW1cXU/LAf9RxfmcdhSe7nvYCz\nc78enufg6JynFZivzXWfkNPbk/SfZbpaC/UkXXxZXkq5hRpftuQ6+9fQg6jxqx9fDqLerwAeS73W\nW3zZQNeQuIVa6bop9/vjmab7UfPEkTn+Our945g8Zo+m5o2JTPePgeOoMXpTpnU5XQX8EuDZmc5V\nvWPaGoHaNf4Edo4vO6g/BLaCel+/BDiZ2iixJ+LLdGWXE6YZ3kwtdE5R8+Qv05VdoObfQr2u2jW6\nO/Hll3LZ/ekKtVO5/MbedqaAt9DFl1Zp2UoXX55IPe9T1AJte7p0VI5bSb1uNlDzE3T5aRFdZf38\n3K+bqee0VRha2aU1Gq6mxoLLqdf9eYwXX4JarllHzbM/oIvRi+kal7bmNhaz8/9Wm6J7wrc1j+MO\n6r31i9SG8APoKg+HUuNLi+Wfpbs3LKDG8Jniy3Jqvv1RznufnL60l+6H01W0nkiNFffOactz+Mqc\nZ3+6cszy3J+TqHntEGpe68eXtv21OW/Ll4t7616U296e52xjHv8vU89r0OXtJ+a5gJrnW1z8Gl18\nWZHH6nC6+LKZ7in7bdT4sn/u66HU+8/PqY0qo/HlgXQV/3aOfz/XfVWm63u5b2vze+sxspV6vf42\nXVwl9+1VdPm5xZeXUcsw51Pzxu7Flj39dGuktegF5H+Cz+GvUgt+76UWuj9PLWAvy+nbqYWAb+W4\n8/NgX58nonULuoougLUWiD+jay1orbztMeM/US+O9gSj1bS3ULsJ3Jbjr6beGL9M1+r0dOqNprW+\nTuZnq5lvB86he5LRat6tRXN5Lttallqr1oZcRwue/eEz6FqN2pOsT1MvwM29/W5PoqaoN6/25KdV\njq7rzbuNmqlupAsArXWyBetCDV4be8tNjMzTWtU30rWabMpzUnp/7Xi0fbp+ZFrJtLfva+kqOe1Y\n7ejN8/+NnO/2hKYFgNZatQ34C7pWkbbMut7xWJPztlb21nK0Ocd9O+fp70/L5G3719NdgzflMWhP\nJ9o5aYF+fW8do/vYWpam6Apu/Wt7S++ztR62NGyl3jRaK89Eb/nN+b09KdlGl49WUgvh7dyuoV77\nLW1bqY0gU3QNpKkWvAAAE7JJREFUAaty3vOprUZ/Rs3Dl1Jb316f+fx9wAvuyvjS+2wx5avAu3L8\nRdRKy9W5n6upseVcane/Fl/avrcKZjuvo/Gl3WjGjS/P7y13NV18aefrKHaOL6upcWNdbueinL+1\neG5l5/gy1ZvWjy/tmjuDLs99KLdRqC3Nt1JjxgS1Nbt/za/J/WhPP75PvSH252mtud/l9vFlki62\ntjzf8tiO3rgfj8xTqE9uVveOY39aoRauWmPB6LR2ztqT6E298Zvp8nw/vrT82GJCq/i1+NIKV7dQ\nC7WT3D6+ruod37aP7V7Q4t/NuS/Xs3O8bBXmNrySLu9P5jlqT5D6x6MVcNv5b9dVG9f2bwtdPO7H\n934Mbg1wE71jcy23jy1TdE9B+tf6rXTx8QK6hodt1ELs1b3t3czO8eWWnP83qPHlbGoefhd7IL4w\nfdnlXSPDr6deo5+n5rtvUGNNP76so7sv3Jn4sp7uiWE/vlxE99Tr6tzWib1z9jhqpXc1XY+eVp5o\n8WNTbqfl1Y25re3U+NDOW8uzG3KZz+a0VhZq5ZGv0pWn3k/Xe6Ld3/ploXad7yq+tN44J3P7+LK+\nN2+/HLGOLo+0zw294R/1jseKkXW0OLGe7rofN76087yZLv+2J+mlN601vrUeOOtz3C3UmNMvI0xR\nK6otv7fxt/bmW0lX+WxP2/vpbU/72jHrl7F2UPP0mpHltvT2rX8c2v60v7b+bex8vLb0lt9Edw20\n3g2j8aVQr8PWE2NTTruS7jp/R35+L9fR4vAUNbZ8h+4J1wT1Wp4Azs/83OLLpdTeFH+e43Yrtuxt\nT7pihuG+B1G7abTpB1Mz1ruBX6FeQAdQA9nv5jz3pOuOeCO11vyfqScL6knZj3rQW6Y+nNo6cDO1\npXALtYb+TuoJni2Nr87PL1MzdVAvgFbLJtPyw0wX1C5PW6iBqQXX1g2jtWg+ltoisC2XaU+fXkWt\nfLZ9/TrwEGprwO/m+n5ADbj75XYflsfuKmptfSu11W2Crk/rA3K723IdH83lf0w9zp+ktkq0vuOt\n8rgw1/sWuqdAv5b70Vr5nkfXxa3dUNfnuoPa9ei9uezb87Nk2snlPkkXRFsGuiHTcgZd5mwtMYXa\nstECT2tZfT7dY/DLctyF1FagQi1gtsr51bn9Q+haktt5WUZ9rN7S2N75uo3aLavfctluRD+hBq9L\nc9p11JbPKWpL389yf75LdzP7Jl2LVOSx+Hhuq/XN3j/nb/s9Qb1+W/eBoGv130rXBeq7dE8CWqv+\nUXR9wCdyvw6ga6zYRm2Fbf6Geu7WUFu/jmJnZYbhIcXIZ1+hXscPoh7zBdTjewE1tjyN+pRuJXXf\nr6WrDBxA9/T7Bmp8+b1c5xXA/2H8+PI/6VrdZkrndPGlPaHd3pvvA3RPwvrxpbV897u7tPiygC6m\ntKcmz6Jek1BbS8+jxto11O6KN1Hz8pXUePBzagttoRYI7pHbbq2CW3M7R9PFjtsyPQdRY8Bt1MaM\nxXRP6FpX3aOo8eVddDfxJ1EbBtqTlM9RY+JauqfAF9PF7qflsdpAPXcL6SplrRv316jnbf/c15vo\n4ksrXPTfb5mkiy+ttXsCeFEe0w/RFQpW5zIr6Lob/pQuL3yHer0szmOyiBqT/ppaQYH65Kkdz3+k\nK7B8LIdbI1e7DrbmfmzIdV9O18WqFVquosbVfk+Ej1ILGHD7d3fX9fZ9Qx6rg3Pfb6Lmkcj1XEb3\ndKC9G3lBLvtY6rXQrreH5XpbwXctO8eXL+c+vYMuvhSmf9/srogv05VdRtPzCronEFDzyjl08eUN\n1OPyQbqnQLsTX7ZRr5kl1PPQjy8PoYsRMx2rdt/7aqbh33K5fi+M7dTrarTxtXXNXpn70ba7mPrU\nqNmP7h9OP456/UONL5dQ48elud2bqPfFoHvCsav4cjC1O90H6cp9/5LHZS21oX493XW2f+7T2kzL\nx3K4NTbdO9PxldyXa6nlgtYFenGme1Wm4QN0FeHZ4kuLX4uo997WENcaS9vxaxWgw+juVdszrY+i\nXievzeW2Up9UF2oZrD3h+zndU/pP5P4fksdqC7WM9pFc7lC6SvOV1HzWKpfbqY1tK3P7F+e6tuf+\nT1Lz6EXc/lWGoJaNPprnocWX1m1xgu6J6xT1Ptziy2V5jEfjC7ncmXT3soV5nNqT4Jfm8NU5/33o\nuhq23lXNY/IYbAKeGBEPYub7cP9zbHtbpWsltatTG74P9UAdS+3CcD31oD8X+L/UjHsk9UC1rnGP\npBa4t+e0KeB/A8+hXgyHU1uU7pfDC6gXQWtFvpZ6EbUL8QBqwegnuf5nUzMG1Ay5X65vC/Xk/BL1\naVdrFVpCfTx6cm5rKbV1biG1j/b96SoA7VHoRA6fk+v/Al0Xoi3UTPR4usLI5Zm+BZm2rdQuXxdQ\nH4muoHalal0OHkztCgK1kHkw9YL+KTVzfJMa7H5E94MDh9K9aPgJauXukkxXK4Csy/1prRutcAS1\nArctx30vz8lBOdy6TB6RxwRqJbH9w8n/Qvdux7HU4HU0XV/29mLxETl9CV2BblGmPXI/HpXf75HT\n9s9tHk69ObUfz3gG3Y+t/DY1OO2f08ntHJfzHJBpOZZ6bltr3S2Z5gW5/mty2Wup5/AIaqD8NvVa\nn8rz8Yrc7iPonqK2/V6c+3YxXQHo/NyfjXQVpha0N1CvmUXUc3tspnULXReTA6mFmv2o7x5MUc/9\n8bmuiTw27d2a1uo6mek5gNpaGnne7pXLXEcNbEvo8vCqHG43p2N6w0Nr8aV9HkONL/fJNNxCvd5P\nyHFLqe8EPJB6034p9Vo5g7rf7Z2Gs6jn+0bqOT2OGl+Cmh++yfjx5RV0N+J+fJnK9f0aO8eXRdTz\nsyjTfAL1mntpprN1k20F9/3p4spofOnHlNbt48LcBzLtv5zr+wfgU9T4ci71epnIY3Rgruf1dAXA\nr9Pl48W5zoMyDQfQdV/9BPXa/BxdfGmfS/L89fNEa6w4ga7bygNzXOu+WKgVxUMy7V/KNCzJ9W7J\n762Q+VBu37h1eP61+NLWc0DuZ5vnEdT4siDnOTrTsoTaFWppnocWfx9KzfOLqd13Wpft51Gvo7ae\n+1Bj129Q83sruLWCy5XUggnUwsr1mZ5PUq+7A3O9D85jvj+1IHlxfrana8fSPTFtXbSX5TFt97jW\n8nwoXSVyvzwnB+W079D9wNX9Mu0L8/i0BrpfoOs6uSOXI7fTupwfQO3COhpfbs3tfjWP7f2oebjl\n7bs6vkxXdlnVG15L3Y8TqL1lFlPviSXTv4ba1flWanlkKbsfX9p1e1Oupx9fvpSfU3TxpT01CGps\nOZh6DdxGPWcn0937T6FeSwdS4wvUPNb2vTXu3TvTvzC3uZDu1YITqLGlverQ3t+5F/V6fGSm55nU\nwvQ11DizX44/kdnjyyJqLP8B3X3+49SulVM5/z2oca01SrZ3GZdS71kPosa19rSEPOZfyeN7BPAm\nuvcdp6jn9Zg8ji/P9B7CePFlUS5/GF08bK9ELMhjczhd5XdpbuvoTMsSaiNUK4+0VzzeSr1GFtPl\noQXA63LZxbmuA6jvLG2he0e79Qg6lnqvaRXAq6jdeI+j5vP75ToPosa6BdQy25E577G5nokcbu/z\ntfhyUR7zf83vrXH5MLqy5aE5bjS+kJ+vzuN43zxni3Mb7Rou1DzWGpk30HUJfXamZSm1AvhIap5Z\nT20QWEkXX/rlmN2KLXtbpetC4PiIaO+w3J/6FOR4agZ/N/Wi/S3qyW2PfS+lPtFpFYdDcn1/QQ3m\nf59/G6itSTtyeusKs5x6kS2kdk34I7obw6actz212I9a4WiPbo+inrzlOe5ldC8sfo56Mb+NenOA\n2lLVupb8Vu5bq3W3AsDBOe9Jub2rqRfKEurFsCT/Wr/u+1NfTCTXc2oOH0C9kd6b2jJ2A92L3q0V\n41y6lwzPoXtyA12gncjpq3N7x+cy/zWP02F0XWraDfwGagv5/XMdv5LHbTE1cDwk13EQNfO2bmvk\n8r9H192mtcQvyG1N5nF6GLUQ0YJee+z/c2pBcivdy/utu19rbX1rbmsjtYC5hhpAb8j1fKS33mvp\nXs7dkem6MPf32t58P8r9uzddq3dQCzi/SvfLUkvpKpft5nsE3Q+gtB/TmKTeHNpxDWoQOZ56/ZP7\n+3jqTaK9TNoqm+19nJV0v3DVKqPrMx07cp9bq9o1ObyUrvWP3P7KHF5ADW6tf3zrzraVmkd/Offp\n05n+b1BvoC+hVv4fC1yQ+fx46hOOu8KFub3VdDHlH6nX6MV0P9TwIrquE6+iFjpapfon1PhyGPUc\ntpv4l6j57U+oeXYr3Q9O3NH40gpC/fiyJaf/MTvHl/9N9/RqJV1X2BfS3SRbfGl5t1AbFkbjSyuA\nPZDu3Y3/RS1gkMfgoBz/VLqC+lPonjhfR/cLfR+miy+fpPv1rf2p+b61RrYnRlPUQmpQ80u7+bZY\nv4Aaly6mFhA35rgnUq/xw6kFlA9Tb56HU6/ldXRdZKeA19DFinvmvq3NdZ1MLeC29z5LHr8W45fT\n/UhA6/6yNce1rn6tVbu9rN7erbuBmsfak6XWpXMVtbA9lcveQvdieMn9/DHdeyiL87i3xqQrqec2\n8vy0+HIiXRfQ1k16iu4++VBqZW0pNQYtoDZQ9n9t8ljqE4zWTRG699bI47GU2/9a3hZq7Gnv6bRW\n8vYU92Zq7L4s51tCrdwtyv1p8e4NdC3h/fjSnvI/hXrfeiz1HvtV9kx8ma7scnFv+Nt098YX0b1C\n8I/UOHRf6nn/EV3he3fjy0voYsdofPk23bt9E9Rz9iy6+PL7dN1a35fzvD23B7XSVah55YW5fHuS\n1Rpood4X30xXXpqinpdbqHn/PGqDY9D1wFlEzbsH5XxnU/PKsdS8055WrGDm+HJA7vPh1B8ViUzv\n4+gK2TfmOXko3dOS9i7rwtynB1Ljx1dy3CG5vddQz2PrwfMguviylZqPW4V2CzPHl5upDS8tvkxS\n38WbpObl7+Uy76Nr0P6bHPdzum5/G6hx+vvUa/0Guu7vrRG3xeSP9tb1c7ofNmk9Yc6kllNaGaE1\nqh5JbSzZnN8Po8burXlcjqCWr2/rresPqJWSVgZqD0BaTG9PsEqeq7ZeesfvmPy+JfdjuvjS9r/9\nRgHUc9oqpt/J+aA2/LSGsk/len5IvU5brD6F+nDiY9Syy23Ua+Cx1GvtJdSyzE/ZzdgS2WdxrxER\nv0p9lLmQ2oLzJLoXB9sj7KPoTlpr3W0tNa2lZTqtsNGWg66Ft7WmthMUdLXh9jOuTb/bwB3RtnVH\n9bfX1rG7aZikq/nHyLjRbc22/K7SN5PZ1t8eobcWrZhm3tEuGy2oTvXWMXpuoft50vZovjD9OW0V\nqPa+W7/bS7u+2vLtZ59bN7uDptmf6fZhun2ZHElPsPO+tvH9czDdubsj2y10Qb/dMNdRb6AL6fph\nL8793X9kelvHVrqf4F9E9z7Jh6k358NynlupBYvHUo/l60opn58hnXOuF1/6Pxnfjyk/pN7k7033\nQvM26g3qGLp/+9DXrrNWoGzHbSHdtdMKP+PEF5g+n41rLuLMndWe1rdrq1+IH93GODEDds4T4+p3\nBWk/IjA6va27Hxfa+PY0ZgHdj5VAl2dbTGj7Obov/fW3a6TFjtYroLWC96+vtv5rqYWVg9l5/0fT\nPN12++uCneMn7HwOWkzdws5xbabjNZ3W1XIH3X25NQq0LsqL6bpS7j9NWlrD1zF0eapVEvq9LFr8\n2iPxZYayy1F0MaU//EC62NBiZnuSeGfjy0Jqxb890Ww9NvrmMq9Pp5++0fw0mp/b8HTLz2Sm+DKd\nXcWX3Y2XTT9vtYrbTPs9XX5p70ntT92vxTm+/wSylYn6T4iaTb1xrZvm9XS9qFrsmqT7IZxWTlhA\nrfQ+YWSdLf+PlhVn0y+/9fe7DTOynvZjbAdwe62stqtttgrraFxrx6O9zrGYGrdbw9DouZmiNmqc\nkuNaA/tWaoXwAGqvscfQ/WT8rexmbNnrKl2SJEmSNJ/sbd0LJUmSJGlesdIlSZIkSQOy0iVJkiRJ\nA7LSJUmSJEkDstIlSZIkSQOy0qW7TET8ZkSUiHjYnk6LpPnD2CJpKMYXzRUrXborvZj6/0tO29MJ\nkTSvGFskDcX4ojlhpUt3iYg4CHgy8EoycEXEgoh4d0RcFhGfi4jzIuIFOe2EiPhaRFwcEV+IiHvv\nweRL2ksZWyQNxfiiuWSlS3eV3wD+rZRyJXBLRDweeB5wHPALwO8CTwKIiP2AdwIvKKWcALwf+Ks9\nkWhJez1ji6ShGF80Zxbt6QTobuPFwDty+BP5fT/g7FLKFHBjRFyQ0x8KPAo4PyIAFgI33LXJlbSP\nMLZIGorxRXPGSpcGFxFHAk8DHhURhRqICvDpmRYBLiulPOkuSqKkfZCxRdJQjC+aa3Yv1F3hBcCH\nSyn3L6UcV0o5FrgKWAM8P/tH3xM4Oee/Ajg6Iv79kX1EPHJPJFzSXs3YImkoxhfNKStduiu8mJ1b\nhj4F3AdYCfwEeA/wPeC2Usp2arB7a0T8CLgE+A93XXIl7SOMLZKGYnzRnIpSyp5Og+7GIuKgUsrG\nfIz/feDJpZQb93S6JO3bjC2ShmJ80e7wnS7taZ+LiMOAxcCbDVqS5oixRdJQjC+6w3zSJUmSJEkD\n8p0uSZIkSRqQlS5JkiRJGpCVLkmSJEkakJUuSZIkSRqQlS5JkiRJGtD/D1EeGoX7IqCQAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111bd37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " g = sns.factorplot(\n",
    "     x='Age',\n",
    "     y='Survived',\n",
    "     col='Pclass',\n",
    "     row='Sex',\n",
    "     data=titanic_df,\n",
    "     margin_titles=True,\n",
    "     kind='bar',\n",
    "     ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Sex   \n",
       "1       female    106.125798\n",
       "        male       67.226127\n",
       "2       female     21.970121\n",
       "        male       19.741782\n",
       "3       female     16.118810\n",
       "        male       12.661633\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_by_class_sex = titanic_df.groupby(['Pclass', 'Sex'])['Fare'].mean()\n",
    "fare_by_class_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Avarage Fare')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2kiRJkqReM9hKkiRJknrNYCtJkiRJ6jWDrSRJkiSp1wy2kiRJkqReM9hKkiRJknrNYCtJ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\npF4z2EqSJEmSeu3/BzlL5TTzXNHoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18b01438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = fare_by_class_sex.plot.bar(figsize=(16,4), title='Fare Avarage by Class and Sex')\n",
    "ax.set_ylabel('Avarage Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</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>C</th>\n",
       "      <td>445.357143</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>1.886905</td>\n",
       "      <td>30.814769</td>\n",
       "      <td>0.386905</td>\n",
       "      <td>0.363095</td>\n",
       "      <td>59.954144</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>417.896104</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>2.909091</td>\n",
       "      <td>28.089286</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.168831</td>\n",
       "      <td>13.276030</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>449.527950</td>\n",
       "      <td>0.336957</td>\n",
       "      <td>2.350932</td>\n",
       "      <td>29.445397</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.413043</td>\n",
       "      <td>27.079812</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
       "Embarked                                                                   \n",
       "C          445.357143  0.553571  1.886905  30.814769  0.386905  0.363095   \n",
       "Q          417.896104  0.389610  2.909091  28.089286  0.428571  0.168831   \n",
       "S          449.527950  0.336957  2.350932  29.445397  0.571429  0.413043   \n",
       "\n",
       "               Fare  Count  \n",
       "Embarked                    \n",
       "C         59.954144    168  \n",
       "Q         13.276030     77  \n",
       "S         27.079812    644  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked = titanic_df.groupby('Embarked').mean()\n",
    "embarked['Count'] = titanic_df['Embarked'].value_counts()\n",
    "embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a169d8eb8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+VT0xs0ebAsBacdXwvnuuVQCwLi33O0Zfobv/fKlVwQFgXrr7L4bNy7v73XMtBoB1Z7lT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\nooXlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a191bd630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1, axis2) = plt.subplots(1, 2, figsize=(14,6))\n",
    "\n",
    "sns.countplot(x='Embarked', data=titanic_df, order=['S', 'C', 'Q'], ax=axis1)\n",
    "sns.barplot(x=embarked.index, y='Survived', data=embarked, order=['S','C','Q'], ax=axis2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    0.629630\n",
       "2    0.472826\n",
       "3    0.242363\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_by_class = titanic_df.groupby('Pclass')['Survived'].mean()\n",
    "survived_by_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.694268\n",
       "male      0.429809\n",
       "Name: SibSp, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sibligns_survived = titanic_df.groupby('Sex')['SibSp'].mean()\n",
    "sibligns_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "0.42     1.000000\n",
       "0.67     1.000000\n",
       "0.75     1.000000\n",
       "0.83     1.000000\n",
       "0.92     1.000000\n",
       "1.00     0.714286\n",
       "2.00     0.300000\n",
       "3.00     0.833333\n",
       "4.00     0.700000\n",
       "5.00     1.000000\n",
       "6.00     0.666667\n",
       "7.00     0.333333\n",
       "8.00     0.500000\n",
       "9.00     0.250000\n",
       "10.00    0.000000\n",
       "11.00    0.250000\n",
       "12.00    1.000000\n",
       "13.00    1.000000\n",
       "14.00    0.500000\n",
       "14.50    0.000000\n",
       "15.00    0.800000\n",
       "16.00    0.352941\n",
       "17.00    0.461538\n",
       "18.00    0.346154\n",
       "19.00    0.360000\n",
       "20.00    0.200000\n",
       "20.50    0.000000\n",
       "21.00    0.208333\n",
       "22.00    0.407407\n",
       "23.00    0.333333\n",
       "           ...   \n",
       "44.00    0.333333\n",
       "45.00    0.416667\n",
       "45.50    0.000000\n",
       "46.00    0.000000\n",
       "47.00    0.111111\n",
       "48.00    0.666667\n",
       "49.00    0.666667\n",
       "50.00    0.500000\n",
       "51.00    0.285714\n",
       "52.00    0.500000\n",
       "53.00    1.000000\n",
       "54.00    0.375000\n",
       "55.00    0.500000\n",
       "55.50    0.000000\n",
       "56.00    0.500000\n",
       "57.00    0.000000\n",
       "58.00    0.600000\n",
       "59.00    0.000000\n",
       "60.00    0.500000\n",
       "61.00    0.000000\n",
       "62.00    0.500000\n",
       "63.00    1.000000\n",
       "64.00    0.000000\n",
       "65.00    0.000000\n",
       "66.00    0.000000\n",
       "70.00    0.000000\n",
       "70.50    0.000000\n",
       "71.00    0.000000\n",
       "74.00    0.000000\n",
       "80.00    1.000000\n",
       "Name: Survived, Length: 88, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_survival = titanic_df.groupby('Age')['Survived'].mean()\n",
    "age_survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SICL2AG4ZalSSJEmSJA3QZDTj91FGMX5cRJwCTAdeNdSoJEmSJEkaoMloxmdG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16a85ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, (axis1,axis2,axis3) = plt.subplots(1, 3, figsize=(16,6))\n",
    "\n",
    "ax = survived_by_class.plot.bar(ax=axis1, color='#5957A4', title='People that survived on each Class', sharey=True)\n",
    "ax.set_ylabel('Survivals on each class')\n",
    "ax.set_ylim(0.0,1.0)\n",
    "ax = sibligns_survived.plot.bar(ax=axis2, color='#EF9E6E', title='Sibligns that survived', sharey=True)\n",
    "ax.set_ylim(0.0,1.0)\n",
    "ax = age_survival.plot.bar(ax=axis3, color='#C0C0C0', title='Age of Survival', sharey=True)\n",
    "ax.set_ylim(0.0,1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}