index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA with Categorical Variables\n",
    "\n",
    "Whether EDA (exploratory data analysis) is the main purpose of your project, or is mainly being used for feature selection/feature engineering in a machine learning context, it's important to be able to understand the relationship between your features and your target variable.\n",
    "\n",
    "Many examples of EDA emphasize numeric features, but this notebook emphasizes categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Dataset\n",
    "\n",
    "This analysis uses the [Titanic dataset](https://www.kaggle.com/c/titanic/data) in order to predict whether a given person survived or not\n",
    "\n",
    "This dataset has the following columns:\n",
    "\n",
    "| Variable | Definition | Key |\n",
    "| -------- | ---------- | --- |\n",
    "| survival | Survival | 0 = No, 1 = Yes |\n",
    "| pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd |\n",
    "| sex | Sex | |\n",
    "| Age | Age in years | |\n",
    "| sibsp | # of siblings / spouses aboard the Titanic | |\n",
    "| parch | # of parents / children aboard the Titanic | |\n",
    "| ticket | Ticket number | |\n",
    "| fare | Passenger fare | |\n",
    "| cabin | Cabin number | |\n",
    "| embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |\n",
    "\n",
    "To get started, we'll open up the CSV with Pandas.\n",
    "\n",
    "(If you were using this for a machine learning project, you would additionally separate the dataframe into `X` and `y`, and then into train and test sets, but for the purposes of this example we'll assume that the entire `titanic.csv` contains training data.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")\n",
    "\n",
    "# PassengerId is a dataset artifact, not something useful for analysis\n",
    "df.drop(\"PassengerId\", axis=1, inplace=True)\n",
    "\n",
    "# We want to use Age as one of the main examples, drop rows that are missing Age values\n",
    "df.dropna(subset=[\"Age\"], inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numeric vs. Categorical EDA\n",
    "\n",
    "Here we are trying to see the relationship between a given numeric feature and the target, which is categorical.  Let's use the `Age` column as an example.\n",
    "\n",
    "### What Not to Do\n",
    "\n",
    "One thought we might have would be just to use a scatter plot, since the categorical target has already been encoded as 0s and 1s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "ax.scatter(df[\"Age\"], df[\"Survived\"], alpha=0.5)\n",
    "\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Survived\")\n",
    "\n",
    "fig.suptitle(\"Age vs. Survival for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we can see that age seems to matter some, but it's pretty hard to extract much useful information from this visualization.  Let's try some other visualizations that tell us more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Histograms\n",
    "\n",
    "Rather than using the y axis to represent the two categories, let's use two different colors.  That means that we can use the y axis to represent counts rather than trying to discern this information from the density of dots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.hist(df[df[\"Survived\"]==1][\"Age\"], bins=15, alpha=0.5, color=\"blue\", label=\"survived\")\n",
    "ax.hist(df[df[\"Survived\"]==0][\"Age\"], bins=15, alpha=0.5, color=\"green\", label=\"did not survive\")\n",
    "\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Count of passengers\")\n",
    "\n",
    "fig.suptitle(\"Age vs. Survival for Titanic Passengers\")\n",
    "\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Density Estimate Plots\n",
    "\n",
    "This is showing largely the same information as the histograms, except that it's a density estimate (estimate of the probability density function) rather than a count across bins.  Seaborn has nice built-in functionality for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.kdeplot(df[df[\"Survived\"]==1][\"Age\"], shade=True, color=\"blue\", label=\"survived\", ax=ax)\n",
    "sns.kdeplot(df[df[\"Survived\"]==0][\"Age\"], shade=True, color=\"green\", label=\"did not survive\", ax=ax)\n",
    "\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Density\")\n",
    "\n",
    "fig.suptitle(\"Age vs. Survival for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Box Plots\n",
    "\n",
    "Here we lose some of the information about the distribution overall in order to focus in on particular summary statistics of the distribution\n",
    "\n",
    "<a title=\"Dcbmariano / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0)\" href=\"https://commons.wikimedia.org/wiki/File:Box_plot_description.jpg\"><img width=\"256\" alt=\"Box plot description\" src=\"https://upload.wikimedia.org/wikipedia/commons/a/ac/Box_plot_description.jpg\"></a>\n",
    "\n",
    "Matplotlib and Seaborn both have methods for this.  The Seaborn one is built on top of the Matplotlib one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.boxplot(x=\"Age\", y=\"Survived\", data=df, orient=\"h\", palette={1:\"blue\", 0:\"green\"}, ax=ax)\n",
    "\n",
    "ax.get_yaxis().set_visible(False)\n",
    "\n",
    "fig.suptitle(\"Age vs. Survival for Titanic Passengers\")\n",
    "\n",
    "color_patches = [\n",
    "    Patch(facecolor=\"blue\", label=\"survived\"),\n",
    "    Patch(facecolor=\"green\", label=\"did not survive\")\n",
    "]\n",
    "ax.legend(handles=color_patches);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical vs. Categorical EDA\n",
    "\n",
    "Here we are trying to see the relationship between a given categorical variable and the target (which is also categorical).  Let's use the `Pclass` (passenger class) feature as an example.\n",
    "\n",
    "### What Not to Do\n",
    "\n",
    "Again, there is nothing _preventing_ us from just making a scatter plot, since the passenger class is encoded as a number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MrOKcCMzMKs6JwMys4pwIzMwqrtREIGmcpIclzZZ0YoPxG0n6paR7JM2SdHSZ8ZiZ2cpKSwSSBgJnA+OBHYEjJO1YN9mxwAMRsQswFvhPSWuVFZOZma2szDOCPYDZETEnIl4FpgIT6qYJYANJAtYHngWWlhiTmZnVKTMRDAfmFfrn52FFZwE7AAuA+4DPR8Tf6xckaZKkTkmdCxcuLCteM7NKKjMRqMGwqOt/HzAT2ALYFThL0oYrzRQxJSI6IqJj2LBhfR+pmVmFlZkI5gMjC/0jSEf+RUcDl0cyG5gLbF9iTGZmVqfMRDAdGCNpq3wB+HBgWt00jwPvAZC0GbAdMKfEmMzMrM6gshYcEUslHQdcDwwEzo+IWZKOyeMnA6cCF0i6j9SU9OWIWFRWTGZmtrLSEgFARFwDXFM3bHKhewFwYJkxmJlZ93xnsZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcWVmggkjZP0sKTZkk7sYpqxkmZKmiXp5jLjMTOzlQ0qa8GSBgJnA+8F5gPTJU2LiAcK0wwBzgHGRcTjkt5YVjxmZtZYmWcEewCzI2JORLwKTAUm1E3zMeDyiHgcICKeKTEeMzNroMxEMByYV+ifn4cVbQtsLOkmSTMkHVViPGZm1kBpTUOAGgyLBut/O/AeYB3g95LuiIhHVliQNAmYBDBq1KgSQjUzq65uE4GkJaz85f2aiNiwm9nnAyML/SOABQ2mWRQRLwEvSboF2AVYIRFExBRgCkBHR0eX8ZiZWe91mwgiYgMASd8EngL+h3SkfySwQQ/Lng6MkbQV8ARwOOmaQNEvgLMkDQLWAvYEvtfLbTAzs9eh2aah90XEnoX+cyXdCXynqxkiYqmk44DrgYHA+RExS9IxefzkiHhQ0nXAvcDfgfMi4v5V2hIzM1slzSaCZZKOJP3yJ4AjgGU9zRQR1wDX1A2bXNd/BnBGk3GYmVkfa/ZXQx8DPgo8nf8+wsrNPGZmtgZq6owgIh5l5XsAzMysH2jqjEDStpJ+I+n+3P9WSV8rNzQzM2uFZpuGfgR8BfgbQETcS/oVkJmZreGaTQTrRsRddcOW9nUwZmbWes0mgkWS3ky+uUzSh4EnS4vKzMxaptmfjx5LurN3e0lPAHNJN5WZmdkartlE8FhEHCBpPWBARCwpMygzM2udZpuG5kqaAuwFvFhiPGZm1mLNJoLtgF+TmojmSjpL0j7lhWVmZq3SVCKIiFci4tKI+AfgbcCGgF8raWbWDzT9YhpJ+0k6B/gDsDbpkRNmZraGa+pisaS5wEzgUuBL+f0BZmbWDzT7q6FdIuKFUiMxM7O26OkNZSdExHeAb0la6c1gEXF8aZGZmVlL9HRG8GD+31l2IGZm1h49varyl7nz3oi4uwXxmJlZizX7q6H/kvSQpFMl7VRqRGZm1lLN3kfwbmAssBCYIuk+v4/AzKx/aPo+goh4KiJ+ABxD+inpyaVFZWZmLdPsG8p2kHRKfkPZWcDtwIhSIzMzs5Zo9j6CHwOXAAdGxIIS4zEzsxbrMRFIGgj8KSLObEE8ZmbWYj02DUXEMmBTSWu1IB4zM2uxpl9MA9wmaRrw2nOGIuK/SonKzMxaptlEsCD/DQA2KC8cMzNrtaYSQUR8o+xAzMysPZp9DPWNQKOHzu3f5xGZmVlLNds09MVC99rAocDSvg/HzMxardmmoRl1g26T5FdVmpn1A83eWbxJ4W+opHHAm5qYb5ykhyXNlnRiN9PtLmmZpA/3InYzM+sDzTYNzWD5NYKlwKPAJ7ubId+IdjbwXmA+MF3StIh4oMF0pwPXNx+2mZn1lW7PCPKR+psiYquI2Br4BvBQ/nugu3mBPYDZETEnIl4FpgITGkz3OeAy4JleR29mZq9bT01DPwReBZC0L/Bt4CfA88CUHuYdDswr9M/Pw14jaTjwIWBydwuSNElSp6TOhQsX9rBaMzPrjZ4SwcCIeDZ3HwZMiYjLIuLfgW16mFcNhtX/BPX7wJfzYyy6FBFTIqIjIjqGDRvWw2rNzKw3erpGMFDSoIhYCrwHmNSLeecDIwv9I0h3Jxd1AFMlAQwFDpK0NCKu7DFyMzPrEz19mV8C3CxpEfAKcCuApG1IzUPdmQ6MkbQV8ARwOPCx4gQRsVWtW9IFwFVOAmZmrdXTy+u/Jek3wObADRFRa9oZQLrI2928SyUdR/o10EDg/IiYJemYPL7b6wJmZtYaWv7dvmbo6OiIzs7OdodhZrZGkTQjIjoajWv6ncVmZtY/ORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxZWaCCSNk/SwpNmSTmww/khJ9+a/2yXtUmY8Zma2stISgaSBwNnAeGBH4AhJO9ZNNhfYLyLeCpwKTCkrHjMza6zMM4I9gNkRMSciXgWmAhOKE0TE7RGxOPfeAYwoMR4zM2ugzEQwHJhX6J+fh3Xlk8C1JcZjZmYNDCpx2WowLBpOKL2blAj26WL8JGASwKhRo/oqPjMzo9wzgvnAyEL/CGBB/USS3gqcB0yIiD83WlBETImIjojoGDZsWCnBmplVVZmJYDowRtJWktYCDgemFSeQNAq4HPh4RDxSYixmZtaF0pqGImKppOOA64GBwPkRMUvSMXn8ZOBkYFPgHEkASyOio6yYzMxsZYpo2Gy/2uro6IjOzs52h2FmtkaRNKOrA23fWWxmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhU3qMyFSxoHnAkMBM6LiNPqxiuPPwh4GZgYEX8oK57RJ1690rBHTzu4rNVZhbhuWRlaVa9KOyOQNBA4GxgP7AgcIWnHusnGA2Py3yTg3LLiaVSg3Q03a5brlpWhlfWqzKahPYDZETEnIl4FpgIT6qaZAFwYyR3AEEmblxiTmZnVKTMRDAfmFfrn52G9nQZJkyR1SupcuHBhnwdqZlZlZSYCNRgWqzANETElIjoiomPYsGF9EpyZmSVlJoL5wMhC/whgwSpMY2ZmJSozEUwHxkjaStJawOHAtLpppgFHKdkLeD4iniwjmK6utPuXHfZ6uW5ZGVpZrxSxUktM3y1cOgj4Punno+dHxLckHQMQEZPzz0fPAsaRfj56dER0drfMjo6O6OzsdhIzM6sjaUZEdDQaV+p9BBFxDXBN3bDJhe4Aji0zBjMz657vLDYzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzq7hSbygrg6SFwGOvczFDgUV9EE5fWh1jAsfVW6tjXKtjTOC4eqMvYtoyIho+rG2NSwR9QVJnV3fYtcvqGBM4rt5aHeNaHWMCx9UbZcfkpiEzs4pzIjAzq7iqJoIp7Q6ggdUxJnBcvbU6xrU6xgSOqzdKjamS1wjMzGy5qp4RmJlZ1q8SgaTzJT0j6f4uxkvSDyTNlnSvpN0K48ZJejiPO7GFMR2ZY7lX0u2SdimMe1TSfZJmSurTlzA0EddYSc/ndc+UdHJhXCll1WRcXyrEdL+kZZI2yeNKKS9JIyXdKOlBSbMkfb7BNO2oW83E1fL61WRcLa1fTcbUjrq1tqS7JN2T4/pGg2nKr1sR0W/+gH2B3YD7uxh/EHAt6V3JewF35uEDgT8BWwNrAfcAO7YopncAG+fu8bWYcv+jwNA2ldVY4KoGw0srq2biqpv2EOC3ZZcXsDmwW+7eAHikfpvbVLeaiavl9avJuFpav5qJqU11S8D6uXswcCewV6vrVr86I4iIW4Bnu5lkAnBhJHcAQyRtDuwBzI6IORHxKjA1T1t6TBFxe0Qszr13kN7bXLomyqorpZXVKsR1BHBJX627KxHxZET8IXcvAR4EhtdN1o661WNc7ahfTZZXV0opr1WIqVV1KyLixdw7OP/VX7gtvW71q0TQhOHAvEL//Dysq+Gt9klS5q8J4AZJMyRNakM8e+dT1msl7ZSHrRZlJWld0itOLysMLr28JI0G3kY6citqa93qJq6iltevHuJqS/3qqaxaXbckDZQ0E3gG+FVEtLxulfqqytWQGgyLboa3jKR3k3bUfQqD3xkRCyS9EfiVpIfyEXMr/IF0S/qLSu+evhIYw2pQVtkhwG0RUTx7KLW8JK1P+nL4QkS8UD+6wSwtqVs9xFWbpuX1q4e42lK/mikrWly3ImIZsKukIcAVknaOiOI1stLrVtXOCOYDIwv9I4AF3QxvCUlvBc4DJkTEn2vDI2JB/v8McAXpVLAlIuKF2ilrpHdPD5Y0lDaXVcHh1J26l1lekgaTvkAuiojLG0zSlrrVRFxtqV89xdWO+tVMWWUtrVuFdTwH3EQ6Gykqv2711UWP1eUPGE3XF0APZsWLLnfl4YOAOcBWLL/oslOLYhoFzAbeUTd8PWCDQvftwLgWltWbWH6fyR7A47ncSi2rnuLK4zciXUdYrxXllbf7QuD73UzT8rrVZFwtr19NxtXS+tVMTG2qW8OAIbl7HeBW4P2trlv9qmlI0iWkXyMMlTQf+Drp4gsRMRm4hnQFfjbwMnB0HrdU0nHA9aQr8edHxKwWxXQysClwjiSApZEeLrUZ6TQR0gd+cURc1xcxNRnXh4HPSloKvAIcHqn2lVZWTcYF8CHghoh4qTBrmeX1TuDjwH25LRfgJNKXbNvqVpNxtaN+NRNXq+tXMzFB6+vW5sBPJA0ktdBcGhFXSTqmEFfpdct3FpuZVVzVrhGYmVkdJwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCW6PkJ0LWng75s/w4gH5B0raSrslPknxQ0qWSNlN6UudV7Y7P+i8nAlvTvBIRu0bEzsCrwDHtDmhVSBpU1782cDVwbkRsExE7AOeSbjgyK5UTga3JbgW2AZB0ZX4g2KzaQ8Hyw7wuyGcP90n6lzz8eEkP5Ge7T83D1lN6F8J0SXdLmpCHT5R0uaTrJP1R0ndqK5f0SUmPSLpJ0o8knZWHD5N0WV7WdEnvzMNPkTRF0g2ku1yLPgb8PiJ+WRsQETfGis+cQdIeSu8VuDv/3y4P30npufYz83aNydt0dX6w2/2SDuvLwrf+o1/dWWzVkY+oxwO1Ozw/ERHPSloHmC7pMtKjKobnswfyQ70ATgS2ioi/FoZ9lfT8+U/kYXdJ+nUetyvpaZV/BR6W9P+BZcC/k96dsAT4LekWf4Azge9FxO8kjSLd+blDHvd2YJ+IeKVuk3YGZjSx6Q8B++a7Sg8A/gM4lHRmdGZEXCRpLdKdpgcBCyLi4Lz9GzWxfKsgJwJb06xTeETArcB/5+7jJX0od48kPcnyYWDr/MV9NXBDHn8vcJGkK0lPvQQ4EPiApC/m/rXJjx8AfhMRzwNIegDYEhgK3Bz5CZWSfgZsm6c/ANgxP5IAYENJG+TuaQ2SQG9sRHokwRjSkyYH5+G/B74qaQRweUT8UdJ9wHclnU56Ccytr2O91o+5acjWNLVrBLtGxOci4lVJY0lfvntHxC7A3cDakV7IsgvpiY7Hkp7ACekhXmeTjs5n5LMLAYcWlj0qIh7M0/+1sP5lpAOoRo8ArhmQY6kta3ikl6EAvNTFPLNyPD05Fbgxn+UcQkpYRMTFwAdIz+25XtL+EfFIXuZ9wLdVeB2kWZETgfUHGwGLIydcHVQAAAE+SURBVOJlSduTntBIfqzxgIi4jNyMI2kAMDIibgROAIYA65Oabz6nfBgv6W09rPMuYD9JG+dEcmhh3A3AcbUeSbs2sQ0XA++QdHBhvnGS3tJgW5/I3RML024NzImIHwDTgLdK2gJ4OSJ+CnyX1IxlthInAusPrgMGSbqXdMR8Rx4+HLgpNyVdAHyF1Hb+09xscjepLf+5PN9g4F5J9+f+LkXEE6T2+TuBXwMPAM/n0ccDHfmi7QM08cum3Fz0flIy+mOebyLprVVF3yEd3d+Wt6XmMOD+vK3bky5Gv4V0rWMm6RrI/+spDqsmP33UbBVJWj/SG7YGkV5Wcn5EXNHuuMx6y2cEZqvulHy0fT8wl+UXns3WKD4jMDOrOJ8RmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxf0fB3cqDd7W0cwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(df[\"Pclass\"], df[\"Survived\"], alpha=0.5)\n",
    "\n",
    "ax.set_xlabel(\"Passenger Class\")\n",
    "ax.set_ylabel(\"Survived\")\n",
    "\n",
    "fig.suptitle(\"Passenger Class vs. Survival for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...but that plot is not really useful at all.  It's really just telling us that at least 1 person falls into each category\n",
    "\n",
    "### Grouped Bar Charts\n",
    "\n",
    "This shows the distribution across the categories, similar to the \"multiple histograms\" example for numeric vs. categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.catplot(\"Pclass\", hue=\"Survived\", data=df, kind=\"count\", \n",
    "            palette={1:\"blue\", 0:\"green\"}, ax=ax)\n",
    "\n",
    "plt.close(2) # catplot creates an extra figure we don't need\n",
    "\n",
    "ax.set_xlabel(\"Passenger Class\")\n",
    "\n",
    "color_patches = [\n",
    "    Patch(facecolor=\"blue\", label=\"survived\"),\n",
    "    Patch(facecolor=\"green\", label=\"did not survive\")\n",
    "]\n",
    "ax.legend(handles=color_patches)\n",
    "\n",
    "fig.suptitle(\"Passenger Class vs. Survival for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.catplot(\"Survived\", hue=\"Pclass\", data=df, kind=\"count\", \n",
    "            palette={1:\"yellow\", 2:\"orange\", 3:\"red\"}, ax=ax)\n",
    "\n",
    "plt.close(2) # catplot creates an extra figure we don't need\n",
    "\n",
    "ax.legend(title=\"Passenger Class\")\n",
    "ax.set_xticklabels([\"did not survive\", \"survived\"])\n",
    "ax.set_xlabel(\"\")\n",
    "\n",
    "fig.suptitle(\"Passenger Class vs. Survival for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Bar Charts\n",
    "\n",
    "These can be used for counts (same as grouped bar charts) but if you use percentages rather than counts, they show proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>270</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Pclass            \n",
       "1          64  122\n",
       "2          90   83\n",
       "3         270   85"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe with the counts by passenger class and survival\n",
    "counts_df = df.groupby([\"Pclass\", \"Survived\"])[\"Name\"].count().unstack()\n",
    "counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.150943</td>\n",
       "      <td>0.212264</td>\n",
       "      <td>0.636792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.420690</td>\n",
       "      <td>0.286207</td>\n",
       "      <td>0.293103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass           1         2         3\n",
       "Survived                              \n",
       "0         0.150943  0.212264  0.636792\n",
       "1         0.420690  0.286207  0.293103"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide by the total number and transpose for plotting\n",
    "pclass_percents_df = counts_df.div(counts_df.sum()).T\n",
    "pclass_percents_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "pclass_percents_df.plot(kind=\"bar\", stacked=True, color=[\"yellow\", \"orange\", \"red\"], ax=ax)\n",
    "\n",
    "ax.legend(title=\"Passenger Class\")\n",
    "ax.set_xticklabels([\"did not survive\", \"survived\"], rotation=0)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Proportion\")\n",
    "\n",
    "fig.suptitle(\"Passenger Class vs. Survival for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.344086</td>\n",
       "      <td>0.655914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.520231</td>\n",
       "      <td>0.479769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.760563</td>\n",
       "      <td>0.239437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived         0         1\n",
       "Pclass                      \n",
       "1         0.344086  0.655914\n",
       "2         0.520231  0.479769\n",
       "3         0.760563  0.239437"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the same counts df, but now survived + did not survive add up to 1\n",
    "survived_percents_df = counts_df.T.div(counts_df.T.sum()).T\n",
    "survived_percents_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "survived_percents_df.plot(kind=\"bar\", stacked=True, color=[\"green\", \"blue\"], ax=ax)\n",
    "\n",
    "ax.set_xlabel(\"Passenger Class\")\n",
    "ax.set_xticklabels([1, 2, 3], rotation=0)\n",
    "ax.set_ylabel(\"Proportion\")\n",
    "\n",
    "color_patches = [\n",
    "    Patch(facecolor=\"blue\", label=\"survived\"),\n",
    "    Patch(facecolor=\"green\", label=\"did not survive\")\n",
    "]\n",
    "ax.legend(handles=color_patches)\n",
    "\n",
    "fig.suptitle(\"Passenger Class vs. Survival for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numeric vs. Numeric vs. Categorical EDA\n",
    "\n",
    "Sometimes it's interesting to see the relationship between two different numeric features and the target\n",
    "\n",
    "### What Not to Do\n",
    "\n",
    "You could just make a scatter plot of the two numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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omT1jZl/23682s0fM7EX/eVXFdz9sZi+Z2XEze0ur0yYiIiKyGCxEDtm/AI5WvP8Q8A3n3K3AN/z3mNlu4F3A7cA+4DNmFl2A9ImIiIiEqqUBmZltAu4D/mvF5LcBB/zXB4C3V0z/vHMu55w7BbwE3N3K9ImIiIgsBq3OIfvPwAeBcsW0PufcIID/vN6fvhE4V/G98/40ERERkWWtZQGZmf0kcMk599R8Z6kyzVVZ7v1mdtjMDg8PD99UGkVEREQWg1bmkL0eeKuZnQY+D7zRzB4EhsysH8B/vuR//zywuWL+TcCF6xfqnHvAObfHObdn3bp1LUy+iIiIyMJoWUDmnPuwc26Tc24bXmX9v3XOvRt4GNjvf20/8CX/9cPAu8wsYWbbgVuB77YqfSIiIiKLRSyEdX4ceMjM3gucBd4B4Jx73sweAl4AisD7nXOlENInIiIisqDMuRuqaS0Ze/bscYcPHw47GSIiIiJzMrOnnHN7qn2mnvpFREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQqaATERERCRkCshEREREQtaygMzMkmb2XTN71syeN7OP+dNXm9kjZvai/7yqYp4Pm9lLZnbczN7SqrSJiIiILCatzCHLAW90zr0a+EFgn5n9EPAh4BvOuVuBb/jvMbPdwLuA24F9wGfMLNrC9ImIiIgsCi0LyJxnwn8b9x8OeBtwwJ9+AHi7//ptwOedcznn3CngJeDuVqVPREREZLFoaR0yM4ua2feAS8AjzrnvAH3OuUEA/3m9//WNwLmK2c/700RERESWtZYGZM65knPuB4FNwN1mdkeNr1u1RdzwJbP7zeywmR0eHh5uVlJFREREQrMgrSydc6PAY3h1w4bMrB/Af77kf+08sLlitk3AhSrLesA5t8c5t2fdunUtTbeIiIjIQmhlK8t1Ztbrv04BbwKOAQ8D+/2v7Qe+5L9+GHiXmSXMbDtwK/DdVqVPREREZLGItXDZ/cABv6VkBHjIOfdlMzsEPGRm7wXOAu8AcM49b2YPAS8AReD9zrlSC9MnIiIisiiYczdU01oy9uzZ4w4fPhx2MkRERETmZGZPOef2VPtMPfWLiIiIhEwBmYiIiEjIFJCJiIiIhEwBmYiIiEjIFJCJiIiIhEwBmYiIiEjIFJCJiIiIhGxeAZl53m1m/8Z/v8XM7m5t0kRERERWhvnmkH0G2Av8nP9+HPjDlqRIREREZIWZ79BJ9zjnXmNmzwA450bMrK2F6RIRERFZMeYbkBX8MSkdeAOHA+WWpUqkCY4Opjl4ZIiB0Qwbe1Psu6OPXf09gdMbWdZCrV9Elj799qWWeY1laWa/ALwTeA1wAPgZ4CPOuS+0Nnm1aSxLCXJ0MM0Dj5+iJxWnKxljPFsknSnwpl3rePTo8A3T7793e+AfY9CyGpmnkfWLyNLXyP+ILD83PZalc+4vgA8C/wEYBN4edjAmUsvBI0P0pOL0pOJEzGZeHzh0tur0g0eG6l5WI/M0sn4RWfoa+R+RlWXOgMzMImZ2xDl3zDn3h865P3DOHV2IxIk0amA0Q1dydol8VzLG0Fi26vSB0Uzdy2pknkbWLyJLXyP/I7KyzBmQOefKwLNmtmUB0iPSFBt7U4xni7OmjWeL9HUnq07f2Juqe1mNzNPI+kVk6Wvkf0RWlvl2e9EPPG9m3zCzh6cfrUyYyM3Yd0cf6UyBdKZA2bmZ1/v3bqk6fd8dfXUvq5F5Glm/iCx9jfyPyMoy30r9P1ZtunPu75ueojqoUr/UolaWIrKY6LcvtSr1z6vbi7ADL5FG7OrvqfpnFzS9kWU1ex6RsChYaD39J0gt8x066YfM7EkzmzCzvJmVzGys1YkTWW6mm76nMwX6e5KkMwUeePwURwfTYSdNVjCdlyLhm28dsj/AGzbpRSAFvM+fJiJ1UNN3WYx0XoqEb74BGc65l4Coc67knPsz4A0tS5XIMqWm77IY6bwUCd98h06a8seu/J6ZfQKvc9iO1iVLZHna2JsinSnQk4rPTFPTdwmbzkuR8M03h+w9/nd/HZgENgM/3apEiSxXavoui5HOS5Hw1ez2wsy2OOfOLmB66qJuL2QpUms2WYx0Xoq03s10e/HXeAOKY2Z/5ZxTrpjITVLTd1mMdF6KhGuuIkureL2jlQkRERERWanmCshcwGsRERERaZK5iixf7XcAa0CqojNYA5xzrrulqRO5CaoTs3It1LHXOSYizVIzh8w5F3XOdTvnupxzMf/19HsFY7JoqefxlWuhjr3OMRFppnl3DCuylKjn8ZVroY69zjERaSYFZLIsqefxlWuhjr3OMRFpJgVksixt7E0xni3Omqaex1eGhTr2OsdEpJkUkMmypJ7HV66FOvY6x0SkmRSQybK0q7+H++/dTk8qzmA6S08qzv33blcLuBVgoY69zjERaaaaQyctdho6SVY6dbsgIrJ03MzQSSILSgHG/E13u9CTis/qdmGuXBrt49bTPhaReqnIUhYN9etUn0a6XdA+bj3tYxFphAIyWTTUr1N9Gul2Qfu49bSPRaQRCshk0VC/TvVppNsF7ePW0z4WkUa0LCAzs81m9ndmdtTMnjezf+FPX21mj5jZi/7zqop5PmxmL5nZcTN7S6vSJouT+nWqTyPdLmgft572sYg0opU5ZEXgt5xzu4AfAt5vZruBDwHfcM7dCnzDf4//2buA24F9wGfMLNrC9Mkio36d6tNItwvax62nfSwijViwbi/M7EvAH/iPNzjnBs2sH3jMOXebmX0YwDn3H/zvfw34qHPuUNAy1e3F8qPWaa2nfdx62sciUk3o3V6Y2TbgLuA7QJ9zbhDAD8rW+1/bCHy7Yrbz/jRZQXb19+jC1WLax62nfSwi9Wp5pX4z6wT+Cvg/nHNjtb5aZdoN2Xdmdr+ZHTazw8PDw81KpoiIiEhoWhqQmVkcLxj7C+fcF/3JQ35RJf7zJX/6eWBzxeybgAvXL9M594Bzbo9zbs+6detal3gRERGRBdLKVpYG/Alw1Dn3+xUfPQzs91/vB75UMf1dZpYws+3ArcB3W5U+ERERkcWilXXIXg+8B/i+mX3Pn/Z/AR8HHjKz9wJngXcAOOeeN7OHgBfwWmi+3zlXamH6RERERBaFlgVkzrknqF4vDOAfB8zze8DvtSpNIiIiIouReuoXERERCZkCMhEREZGQKSATERERCZkCMhEREZGQKSATERERCZkCMhEREZGQKSATERERCZkCMhEREZGQKSATERERCVkrh04SkWXi6GCag0eGGBjNsLE3xb47+tjV3xN2spYV7WORlU05ZCJS09HBNA88fop0pkB/T5J0psADj5/i6GA67KQtG9rHIqKATERqOnhkiJ5UnJ5UnIjZzOuDR4bCTtqyoX0sIgrIRKSmgdEMXcnZtRu6kjEGRjMhpWj50T4WEQVkIlLTxt4U49nirGnj2SIbe1MhpWj50T4WEVXqF5Ga9t3RxwOPnwK8XJvxbJF0psA7X7cp5JQFa6SCfJiV6pfiPl7M1EBCliJzzoWdhobt2bPHHT58eEHWVesHrh//ytXMY7+Yz6PFnLbrTVeQ70nFZwU399+7PTDNjczTinQvlX28mC2GYykSxMyecs7tqfqZArK51fqBA/rxr1DN/OPXRaR5PvXICdKZAj2p+My06fcfePPOps0ji5OOpSxmtQIy1SGbh1otoNQ6auVq5rHXedQ8jVSQV6X65UPHUpYq1SGbh4HRDP09yVnTKn/gtT6T5Wuu8yKsZa10G3tTN+SQzFVBvpF5ZHHa2Jvi9OUJLo7lGMsW6E7GuaU7wba1nWEnTaQm5ZDNQ60WUGodtXI189jrPGqefXf0kc4USGcKlJ2beb3vjr6mziOL086+Dp4+O0o6U6CzLUo6U+Dps6Ps7OsIO2kiNSkgm4daf9b6I1+5mnnsdR41z67+Hu6/dzs9qTiD6Sw9qficdfEamUcWpxNDk9y1uZfuVJzJfJnuVJy7NvdyYmgy7KSJ1KRK/fOkVpZSzUppZSmyVPz2F56lvydJxGxmWtk5BtNZPvmOV4eYMpHalfpVh2yedvX3BF4ca30my1szj73OI5Gbp/qAslQpIBO5CcrVap6gfal9LPVQJ7uyVKnIUqRB6juseYL25Zt2rePRo8MLso9XekC4nLZzOW2LLC/qGFakBdQBZfME7csXBsfY3d/d8n28GALCMOnmQmRhqGNYkRZQB5TNE7Qvh8ayC7KPgzrmPXDo7IrosFcdE4uETwGZSIPUd1jzBO3Lvu7kguzjsAPCsOnmQiR8CshEGqS+w5onaF/u37tlQfZx2AFh2HRzIRI+BWQiDVJnos0TtC/vu3PjguzjsAPCsOnmQiR8qtQvIoJaWa6U7RQJk1pZyk1Tj/QSlq88N8CBQ2cZGsvS151k/94t3HfnxrCTJSJSN7WylJsy3SQ+nSnQ35MknSnwwOOnODqYDnVZsvx95bkBPv7V44xlCqzvbGMsU+DjXz3OV54bCDtpIiJNpYBM5tTMJvFqXi/1OHDoLB2JmHe+RCL0pOJ0JGIcOHQ27KSJiDSVhk6SOQ2MZujvSc6a1miT+GYua7FbTkWzYW3L0FiW9Z1ts6Z1JaIMjWVbvm5ZupbTb09WDgVkMqdmDtbb7IF/F+sfb2XP55VFs0uxFWaY29LXnWQsU6AndS0zfzxXoq87WWOu2hbrObMQVsK2L6ffnqwsKrKUOTWzSXwzl3V0MM0nv3aCx45f4sjAKI8dv8Qnv3Zipj7a0cE0n3rkBL/9hWf51CMnFrSe2nIqmg1zW/bv3cJkzhvGp1wuk84UmMwV2b93S0PLW8l1GFfKti+n356sLArIZE7N7G+rmct68NAZTl2eBKA76eW4nbo8yYOHzoR+8VmKPZ8HBbBhbst9d27kQz9xG92pOJcm8nSn4nzoJ25ruJXlSr5Yz7XtYd7ANNNS/O2JgIosZZ529fc0Lbu/Wct65lyazkSUZDwKQDIexTnHM+fSrO26dvEBZp4PHhlakGKLZhfNtlqtYp6wt+W+Ozc2rZuLlVSH8Xq1tn05FfOFfb6KNKplOWRm9qdmdsnMjlRMW21mj5jZi/7zqorPPmxmL5nZcTN7S6vSJcuHw2HXTTN/eth3yUut5/NauSdLbVtqWclDBNXa9uWUc7iczldZWVpZZPnnwL7rpn0I+IZz7lbgG/57zGw38C7gdn+ez5hZtIVpk2Xgrs29TORKZAslnHNkCyUmciXu2twb+oV3qQ2rVCuAXWrbUstKvljX2vawb2CaaTmdr7KytKzI0jn3uJltu27y24A3+K8PAI8B/9Kf/nnnXA44ZWYvAXcDh1qVPln63rN3K4PpLFcn84xni7TFImxZ08579m4F4IHHTwHehWU861UMf+frNi1Y+ppZzNtqcxXzLKVtqWX6Yl3Z0vCdr9u0LLZtLrW2fbkV8y2X81VWloWuQ9bnnBsEcM4Nmtl6f/pG4NsV3zvvTxMJtKu/hw/uuy2wGf9KvfA2Yt8dfaEHsAtlJV+sg7Z9JR1/kcVqsVTqv74qEEDVQTbN7H7gfoAtWxpr+i7LR62L60q+8NZrV38Pb9q17oYxI7X/VoZGcg5XQp9mIgtpoQOyITPr93PH+oFL/vTzwOaK720CLlRbgHPuAeAB8AYXb2ViRVaKo4NpHj06zO7+bu7ZvprxbJFHjw6zY12nLrIrRD03MMupVabIYrHQAdnDwH7g4/7zlyqmf87Mfh/YANwKfHeB0yYrxGK9sw8zXZWt7ODmuwlpZFuauf06xq1df7PPFxFpbbcX/weX3wUAACAASURBVA2vUv5tZnbezN6LF4i92cxeBN7sv8c59zzwEPACcBB4v3Ou1Kq0ycoVdoexizVdzWxl18i2NHP7w96XizVdzVz/cmqVKbJYtLKV5c8FfPSPA77/e8DvtSo9srQt9zv7sNO1sTfF6csTXBzLMZYt0J2Mc0t3gm1rO+te1sEjQ5RKZV4YHGMiW6QzGeOWrkTNbWnm9oe9Lxdrupq5/uXWKlNkMVgslfpFAjWzvspi6Km9WnAZdrp29nXwxafP05GI0ZWIks4UuDCa4cdvr79/rucvpDl/NUMiHqEzESVXKHFiaIKpQnCm98BohngUvn1ybCYg3LGunYHRYuA8tZZVa1+GVWwYdrqaeY4tZKvMsIt5RRaKxrIMyXIZN24hNLMX8bA7jA0qNkpErenpquccOzE0yV2be+lOxZnMl+lOxblrcy8nhibrXu9YtgjmDWVlZt7QVuZPD5CIGt85OUK2UKIrESNbKPGdkyMkotUaYNdW6xiHWWwYdrqaee4vVOerYRfziiwk5ZCFQC2U6tPonX21O+uw+1s6eGSIcrnM0cGxWUWDjgjpTCEwXfXmEtR7jg2MZti6toPt664VUZadayj3pCcVIz2VJ1sokYhFyBXLOOfoSQX/3Thu7PvG/On1bvu+O/r45NdO8MxEjlyxRCIWZW1ngne+ZWfg/l+IYsNa516t4sTp55vNIQr73G/EQhXzht0IRQQUkIUi7LokS00j9VVqBSSNdBj7lecGbuijq5EBr18YTHP2yhTJeHQmJ+jYxXG2rGnnN9+8s2q6am0LVL9Y13uONbNO0O7+Htrj0Vn10bataa9ZHy1fcrxu+ypOXp6aqXe2q7+LS+O5hm5eys7rEcf8MG/6fdD+r1Wc2iy1+vr6kydOV73peP5CmrNXp+re/qBgoZG+5qotC+ATB49zdTJPvljmxaFxnjs/ygf33VZzefX+jhaiKL+RG+S55lGwJo1QQBaCsOsLLTWN3NnXCkg+8Oaddf05fuW5AT7+1eN0JGKs72xjLFPg4189DlB3UJbOFK8V4+EV6+WKZdKZYmA/UEHb8tlDZ8gUylUvCvWeY43mngTnQk6xq7971rJqjRc5HRDu3bGmYl8VGMtOsWlVe103LwePDLF1TQd3buqdtayDR4Zq7v+FEHSMgwLisWyx7u0PChbetGtd3X3NBS0rmy9y9soUnckYXckYuWKZs1em+OyhM/z7n7qz6rIa+R0tROOBRnJN58rRVAmINEJ1yEIQdj2mpaaR+ioDoxlyxSLfPnmFr79wkW+fvEKuWGwo6D1w6CylcpmBkSmeG0gzMDJFqVzmwKGzdS+rOxkjmy9y+vIkLw9PcPryJNl8ke5k8L1RUBcDz5wbDaxbV+851sg+PjqY5hMHj/PY8Us8P5DmseOX+MRB7wJb77KCBr7uScXq7l6hVpcM3ckYOGYNSI+j5v5fCM3c/qA6lwcOna27LmbQsp48M0JnIjqrnmBnIsoz50YDl3Xg0Fk6EjFvWZEIPak4HYlYzd/RQgwG/8JgmmMXx2fVXzx2cZwXatRTq3WONbPOq6wsyiELwVKsyxG2eodBSkSNQyev0pWMzaokvnfH6rrXfWp4gtGpPLFohHgkQrHsGB7PUSjVP1DE+q4ExwbHcTiccziDYtmbHiQol8CwwIvCe39kW93nWL37+LOHztTMJalnWUHFeQePDNXdHUetLjw29qa84tTx3EzR6NbV7bPqzoWh1vbXm0MUlDs6NJblnu2rb5g+V3BbbVmFkrthbDuvHmBwI4yhsSzrO9tmLysRZWgsGzjPQgwG30iuaa2cu6VYAqIi1sVBAVkIFuJPZqWrVUm8XplCCTMjFvGWGDOjVDYyDdQ7MiBXKjGVK1Esl4lFIrhE9cFcpwUF8Hdt7mE8W6x6UViIc+yZc6MzuSTgXchwrmYuSS3VAsKTwxN1d8dRqwuPHes6eeDxKXbXUZy6UIIC4noD66Bgoa87GXi+1LusLatSTOS838V0w42JXKnmDU9fd5KxTIGe1LWCmfFcib7uZOA80PoxabuTMcamCrMaocyVazpXA42l1EebGpktHgrIQqKBr1srqJJ4voFcrUQsQr5Yplh2RAzK7tr0eu8sX748yWS2OBMwlp1jMlvk5cvB3UsEBVdQ+2IddI41627YsLpzSeo13R3HdI5WdyrOzvWdnBia5L5G5rlz45K6GWoksA4KFvbv3cKjR4dvmF4ruAta1q+8YQdffPoClye8XMhELMr2tR28e+/WwGXt37tlps5YVyLKeK7EZK7Ib7zxFXXvl2a6fUNP3bmmcx2XpVQCokZmi4cCMlmWgiqJV961ztcr1ndx/sokY7kihZIjHjVWpeKs6kzUfWd59uoU0YgXyJTKEI144cvZq1M10xAUXNV7sW7m3fBdm3s4dPJqXbkk9WqkO4655llqN0P1pjeoNeV9d25kx7rOus6XWoHH9cuaK7Cfrrhfma7feOMrGmqt3EzTjVDqzTUNOi5LrQRkKRaxLlcKyGRZamY9vek7+02r2mfd2W9dnap5Z1ktJypXKJErlolGjIhBqewolR1tDXa7UO/Fupl3w+/eu5WLY7m6cknq1Ugru5U+rM/RwXTN1pT1HudagUe9y7rvzo2hB2DXa0UAtZSC/pX+e1lMFJDJstTMP9mgO/u/O345sFJ9UE5U1Iy2aAQzKDmIRSBqRsKvh9Vqzbwb3tXfw2+/ZWdLKwM3Elgvt0Yz9RYxN7sIaiVU+A47gApzHy+338tSZs41Us15cdizZ487fPhw2MmQBbAYLwqfeuTEDXeWle+rffbI84OMZ4u0xaLEo0ah5MgXS+ze0M3nf/mHQ03zB968s+Xrb8Ry6kX9ZkZcqLxY1ipi/u0vPEt/T5KIXavLV3aOwXSWT77j1XWnt971z7W8xXhcwtTsfdxoGnRcFoaZPeWc21P1MwVkstjV+sOC5gwr0+x0Tfe8fv1F8dGjQ/R1tvHi8CQTuSKdiRi3ruvg1VtW1wyImhWUAA3ty6D1N2sEg4UU1sWnkQtvIwF0rXn23dFX1zGu1mKw0QB+MQQei9FSvEmSxtUKyNQxrCx6QR0tfvbQmVAHHp6uPP3C4BgPP3uBFwbHeNOudezq7wnsmPWuzb2s6kzy5t238It7t/Hm3bewqjPJvjv6AgcDb2SA5aB5oHqnrUDgOoKW9cd//yIf/+pxxjKFWT2vf+W5gRbt8ZsX5mDVjXQYWqsD0iBBnanu7Ouo+xg/fyFd9/qbuf0rQSPHWJYn1SGTRS+o3tOjR69wz/Y1ddeVaVYOSa3K00H1MqrlRFV2YVGt9WMjA0/XO3TUpx45UXMd1YaWOXDoLN3Jynm8+7sDh87W3QIPGhsvtN55ml2/qp71N1J/b64K10Hrr9bK8sTQZM1jXO2zgdFM3X2XBRkYzRCPwrdPXjuPdqxrZ2B0YYatWqxUqV6mKSCTRa+RnuqDNLPbh7mCnlqNCuoJiIIu5LUGnq734l/r++lMnhMXx8kWypSc4+pEnovpDCOTBTZeP08iyrmrU3Xv40bGOWxknmY2aqh3/Y1ceGtVuA5a//mRKY5dnLzhRmE8W2BXf3fgtlfbLz2pGOlMoer6a6l209PM0TOWE1Wql2kKyGTRa6Sn+iDNzCEZGM0Qi8ALg2MzHUruWNvORM6746+nY9ZagUIjA0/Xe/Gv9f2jg2OMTBVIxSO0RY1S2TEyVQBXZjxXuqHndbPquS219nHlOIfePNdy24KCq0bmaWZuRL3rb+TCW6u18O88/ELg+n989y1153ZV2y+7+3tuqHfWaF932XyxaaNnLCeLvd8yVfhfOArI5mk5nZRht1qrd1lBHV16w+DUd4FrZg5JW9T4zsmrdCZjdCai5Aolnjw1wj017viDLlapeCTwYhl0Ia818PR7f2Qbn/zaCZ6ZyJErlkjEoqztTPDOt1SvJFwrWHj42QG/ccL0AyJmdCXjTPrBZ2X/bFtWperOuWxknMNG5mlmbkSt9Qed441ceIMC+6D1nxuZqrr/58rtCtovzerr7tjFsaaNnrHchN3tRhANq7SwVKl/HsKsCNxszawg3sj2N7r+6bpab331Bnb3d88MAVOtgnqtP4qgyvaN5JBUu7uvNoZmpaCKzQZVK2JXXsiv387d/T01t6Xst6CeHsqoXKNFddA6dvX3kIrHWNfZRjRq5EtlolFjXWcba7uSfOgnbqM7FefSRJ7uVJwP/cRt3LNjbd37uK87yXhudue4c41z2Mg8tbazXkHr70zEAs/xXf09fODNO/nkO15dtS5fM9bfk4pX3f+7a2x7M/dLUCV1w0jGY+zdsYY37+5j7441JOMx1ZVaxNQQY2Eph2weGi3mWoy5ao1sSzOL+Zq9/novas3MIcmVHDv7Ovj+wNhMFxY/sLGbXI07/qAcusF0sWouYGWds2rbWWuA461rOrhzU+/Md9OZQs39HLSO6SGS1nUmZoZIGs8WuXtzT9We13f4QXe1dAVpZJzDRsdGbFZuRND6f2Bjd1MbDtS7/v17t3Dsojc2aj25Xc3aL0HFwndt7mmoPpqER8MqLSwFZPMwV+ugufp7anVWbz2BXyM/sGb+KOeqdxU0z3gmz9dfGGMyV6IjEeUHNnQzkWsLnCdIM+trtEWNF4cmWduZYGNvilyxzItDk9yzIxE4T9DFKhG1msPd1Lst0/2gVWr0mNU7RFIj+7jWOIdB5/dCjY1Y7/prjeDQTLW2v1rry7B7fg9qYRz2TaoEUwvQhaWAbB4SUeOxE8MUS964g1cn8pwfmeINO9fVrBNU6y65mV0v1BP4hT02YCP1riazBb718hWSsSjt8Qj5QplvvXyFH33l2rrXD83LCWikyDLoYjXX+RIkaFuaecwaGSKpWeMcznV+N3NsxEZurKqt/8TQ5IJdxHas62TvjjUzad6xrnPOsSxbba6AfDEGYIuxNGMxUAvQhaWAbB6uTua5OpGDil7XcY6rk/nA4rTvnLrCm3b1zVrOXOMcNrvrhennyj+ZsMcGbCSIOT+SwTCiEcPMiEa8elHnRxrLcWjWn2+u5LhnxypODk/N5Jzu3tBVs8gy6GL1J0+cZk1n83JV9t3RV1el/sWq2f2GBWn0xqqaRn8vNzOsUmWa2xsM7pupmZXUWx0sqeJ6sMXeAnS5UUA2D0cvjhGLRig7L4csGjEikQhHL47RkYxXLRoyLLDVXLO7Xqi3j6padZWqaeaPspEgZixXZH1XnKHxPIVSmXg0Ql9XG2M1ijmDNPrnW+2iMJ0L9UM71sx8L50psL4rHrgcqH6xakXRQD2V+msJ84K1UHVYGrmxCtLI76WRfXzwyBClUnlW8f8tXQmeuTjOnZu666oWsFgtxLk31/+xcs9koSggm4eJXIm2WIRkPDozLVsoMZErsbE3xenLE1wcy83qxfyuzT2cuTLJ1ck8+WKZtliE1R1tfHDfbfzJE6cD61HVO2ZgvX1UPXjoDMeHxjl3NUO2WCKbL/Fn3zq9YMUZjQQx3YkYp69M0dEWIxrx+sG6NF5g25r2utffSDAcdFF40651M609bzbnsNlFAwePDNGViDGZL1EoORLxKF2J2JzbWW+v/40Ws8/3AtdooFprHdV+S0H1RGvdWNXSrK4iau3j5y+kOX81QyIemSn+PzE0wVg2z5OnRuqqFrBYLUQOaa2g/+hgmk8cPD7zP/7i0DjPnR/lg/tuWxFBmXIPF5YCsnlIxiJcGs8xmSsRixqJWIRiybGmM87Ovg7+8smzFEuOsiszPJbjzJVJ/tnrvYrQAM4vpJseaLotajx+4jKFUoli2XFlwrgwkuHOjd1VT/5X3dLBg98+V7VH8H139M36w5gO/HpSMbKF4g1B3zdfvEw6U/DrY0UplBzHLo7z6Udf5L+8p+p4p039UTZSlLZpVYpTVyYplR0Rg1LZ4XBsWtXY8C31NioIuiicGJqsO7cxSFBfa9PLCgowgqYHXaynCqW660rNdcGaXlYiajggX3KBgVa959JcgWq923JyeIJ/+zcvUCx7xeQvZ733t/Z1cmE0d0Mv8q+6pXNBWgY2khM4li2CMXOjmIxHyRXLFEqO5HX3N3NVC2hEs3OOgjpMbmS4pWYF/Z89dIazV6boTHp9/uWKZc5emeKzh87w73/qzoa3dalYqCoDYVssuaAKyOZwdDBNLGrEoxEcjkLJ+8PrSXn96fzDS1colcpkCiWKZYhFyrQT5cvPXuS121Zzx8ZrB3W624HRqTxXJv1gzTnMDChy4tIEt2/ormvMwI+9dfdMoFcZ+MXNqt4lD49nScWj5IolpgoQNe/x5JmRme29/sRs9o+y3qK0jmSc179iDd+/cK2V5Wu39tJx/VVnHhppVBB0sXxh0CsWrrfydFAQEVQRG6oHGNM5dNUCj6CL9cV0lk9+7QSX/YD4xaFxjgykuaU7UXW8ylq9/ieiNpOueBQOnbyKAa/bvopTwxN84PMX2bwmNdPbeyO5bbUC1UbqfT16dIhMoUwqHpnJbc0Uynx/IE0yFmVoLDNTLN6ZiNHb3sZ79m4NLH5spN5Xte9fn9MeNaNQLBOJGp965ETV5fakYqSn8mQLpZnuSJxzxKORuqsF1Lstzc45CVperlDk2MWJuoZbambQ/5sPPUtnIjrrd4RzPHNutO5tXIpWQrcXiykXUAHZHA4eGeLOjb08N5BmKl+kVHYYRntbjHfv3cqvPfi0XyxUplSGcgScg5OXJ7ljU3fVO7tjF8eJGjiMMl4AZTgupLNEI0bSL17KFkocuzgeOGbg0FiWg0eG6GiLMu7ndiXiUTraopy8PEmuWGIiXaRYLhOLRIhHjVIZsoUysagRAcoO8kVHsRx8Ys41Bl69+7Pe/rE29qY4VSixaVX7TK5WIhZtuDPXkak8Z69OUnJeMNqVjNfMPQgKSNKZIht7qw9dVOtCVi0g6utOcHUiy3dOXZkJOm9d1zHn4N793YmqQVRPKsbZKxOcuzo1c/y7klFikQiXxrNM5IoUS45Y1BhMZzh+MUJHInbDuTdVKPGbb97J73zpeU5fmSRbKJGMR9m2poNXru+cSdfLwxNE/Y4jjwyMMZEtMJ4rcXkyx6Wx3Ewxz8BoholsnkdemN1320SujT/++xc5cOjszL7ev3cL9+5czx/+7UucuTpFvuS4PJ7lP30tw7deusIz50Yplsok41FKZSrqUI0F1vs6PzJF1GAq7+VOxyJGLGKksyUK0TLOjGgkQtnBeKbAyeGJwOLHo4Np/vVfH5kp/k/GonzrpWH+3dvvmDkPrg+6g76/s6+DP3viZaYK3v+IA2IGb9q9PjC43d3fQ6FQ4sXhyZl9eeu6Dnra2xgc8bZ1IldkLFEgFTNevWV1zZzWoKK5atvS6E1aUNWLoOU9OTROvlhmMJ2Zqb/bFo3WHG7p4JGhwJuLeoN+7595Ni+30eoe2H76nKknpztsK6Hbi8WUC6iAbA4Doxm2ru2gMxnjpeFJLyBIROlOxdnV38PweJZMoUw0AtGI92PNFMpEDL5zcqTqnd1Ytkg0YpSdFxBFzAvKsvkSZnZDrkY0QtUxA/u6k4FFU1cmc7S3eUHadE5UNGL+eh0QmWnyWHZlOtqigX9kY9liQ/VogvZnvXdcO/s6+OLT5+lIxOhKRBnLFBgczfCWO/oC54Hqf37PDaQZzRRmBgEqOxjNFHhuIHikgKA76O5k8NBFQR48dIZjF8dmulCJRozLEzmePVckX4S2WGSma49nzqbJFsp0t8c5e2XqhmDp6mSe9FS+ahAVjxhXJ/OUnXeDUCqXKEyWiFiEYqlMPBahLRaZ6cYlYsbaLseF0Qz5Upm2aISuZIx0psjJ4QnOXJmkVIa2qBf8nLkySSZfJF9yJONRnHNgcHkiR75UJha9Nu4lMFPMM5Ut8PiJyzi83OF8sczjJy6zaVWS//n9C7RFo3QnokzlSvw/33iJvzh0hnOjOcy8YUUyZcfJKxkm8xdJxWOMZryiq02rkjPnfq5YCjxfDWMsWyBi3m+1WGJmH7XFY3QkrtUTncyVOFujJe+nH32RYxfHbyj+/92/eYH1PakbbmwupTNVv//pR19kLFNgMl8GrrVCLjl4+vRVVnelwCA9VZh19z79u+hKxtnQ4/Xa//LlKV6zpZtHjg7TFvW6iZnMlfjumRF2rO8IzAkIKpr79KMvkmyLNeUm7SvPDVQtLobg/4Wrk3kSUaNQMsy8QCgWgUvjucD1vDCYrvp7mSqUqn6/Vjch050im9lMLuRErsSG3kTdA9vPVRd1MeTQXG8ldHuxmHIBNXTSHKaH2lnXlZwZ8mP3hh5u3+D9UAr+3Wy5DMWy9+zwHvlCiYvpLCcvT3IxnSVfKOGAeAQm8yUy+RK5QplMvsRkvuSFTc5rMOCcI1sogYNtq9uZzHk/hHK5TDpTmOmRu7JoaiaYM8gUSkQiRixqYHg5YhHvTyXutxgtlsqU/SKOHes7eWEwzbGL42QLpVl/ZBELHtan0f1Zaa7g7sTQJK/Z0ktPKs5E3hsa5jVbejkxNBk4T9AQTS9dmiAWMdrbojOPWMQ4dTl4WdOt5npSs4eVuX1D7aGLqjl08gqT/jxtMe/nN5ktcnWq6B2feGTW87nRDOlMcebYVj5nCiXypTKXJ3KcvDw5EwylM14ubKkM+MEGDi93tOhd9HOFMpM57/wDyJcdQ2M5pvIlCkXHVL7E0FiOTK7AgUNnWdWR4JXrO3nF+k5eub6TVR0JTl2ZmkmPdxNh3o1FoURb1PyhcrzPOhNRnjk3yolLE2QKZbKFMrmiI1sokymUeWl4iqgZxXKZ0YyXqxs146wfjEXN63VmunR7eLxA3s/hSsQijEwVZs79zkQs8HztSkQp+TdC0wH5dElexKBYcjjnPUcq1lfNk2dGSMYis45ZMhbh6XOjM3fclcPNPH1utOr3nzwzwtPnRolFzT8fIR41IgbDk0USsQjdyRjjueLMsg4eGQr8XTx5Jk1fV5L2RJRCGdoTUfq6khx8/lLVdB08MsQz50Zniuamj2lnIsqTZ0aqzjN9k1bPuf+Zx06SKZSJRaAt5gVWmUKZzzx2MvB/wQySbTG2re3gFes62ba2g2RbzPvfCxD0e0lnqs9Ta3igd+/dyva1HQCMZb26hNvXdjAyVZwZ2D0S8YrIOxIxDhw6G5iuoPUcOHQ2cP1hC/rvCztQbKZmDqd3s5RDNod9d/TxW3/5PU5eniRfchjQ2x7j377NK5aI+GVd0//blf/fJecYzxVmckJ6knEujefoSMQZGssTiUxX9HeUy9CditHX3caLw5Oziq1+cMtqdvZ1VO2R+6+/d6FqPZJYxLgynqMtFiUeNXKFMuOZAl3JGBt6kxy7OEGu6EhEjVfd0sk929dw6OSVqjl0ZUdDlder5VA1csc1MJqhvSLnAryLTK07mKDcvnyhTFvcyyWc7hMtGoFiqVxzW4KKrerdlrFskUKpzGS+RNk5Ima0+ZXhI+YoltxM3aaIOXDQnYxx/uokZ69OUvRzn3pTcaJG1WPc15VgaCw7c25G7Nqw4CUHxYqT1OEFJdNvIhFwfhF6uQwXx3K0J0p0tkU4PzJFrlgmEYvQm4rNBHzZQolV7XHOj1wb1DubL1Eoe7ln50emaI9HiMdiDIxMzVr3dFFx2V8OGA5HsXStxzpzYNMb408r4zW2yeZLRPGK4advZG7pTQV2OxGNGh1t3u+kVPa2tyMeoVAq05OKkyuVZxrHtLfFWdedDCyaKpQcbbNPS8ygWHZVc06LZTerK8Pp7+dLzis+nTle5uU4+ls7/bvu9utMVt69b1nTwba1nTPLKzvHo8cusfuWLiKRa/fb5XKZFy6OB+boBhXNFUrVt2WugcqrOT8yRTIeIRb10hWLGkl/etD/wpZVKUamCjf8v/Wkgi9d3ckYY9fNM/07qqZWDklQp8i/8uDTdQ9sH7SeobEs92xffcP0xVJPq5l9yi1GiykXUAHZHB4/cYmXhie9O2d/2shUkc/83cvsWNdJRyJKwb+YO3et79hy2csF60pc66phMl9iMJ2l7ByJuPnFJd5FORH3ckxOXp6iOxlno18EcfLyFD/7us2BPZLv7u+hPR6d1e3GtjXtjOeKtEWMQtl5F1G/SDMaMYbG8mxa1T4z/t3QWJ6dfR08fyFd9Y/MnKu75+9aFSXr7aMpETX+/sQwhZKjWC5zdSLPwMgUP7ZzXeA8LwymOXI+TTpbmKkrdfZKnFgsQtS84o/pOmSG0d1RfwOBRvqbMldmsnAt+Cs5R8G/GCfj0ZmALR71ckU2rW6nLWqMTObBr9/kHIxM5knGY/S0x244xmWMMjZTzDItV/QjKLys8emAaDo1Ub/xynROVMFvrHJLd5KTlycAL5DN5I3RqTxdiRg7+zq5OJ5jIuvYvDpFtlCiWCqRKZTpSERJxiLkCl6u7r2v7OLE0BiGdzGeNv3b8hrF+I1cnFevEaAEOP8707/BqMHariSxSIR0puDlpMSjbFvTzra1nYEXkfa2OBt6jKlCeSa4bI9HyBTKFMuO7mR81riQd2zoDCya2rIqxekrU0TsWhCdLThWpeJVi0xXpeJ+dYbZ39+2pp2hsSwjU95NWixi5IqOsoNYhJncoDs2ds8sa/ruvVr9np5UvGoVh56AdG3sTZGIWtWiuS2rUlXnma7LVs+5H496rdMreg/y30cCf0sHjwzd0K3Q9DEOcvsG/z9xPDdT53Tr6na2r6s+z1z1pKqdS33dScYyharVSIIEraevO9m0KiFSv8XU+a0Csjn812+e9urBVOQyAJwcnuTgkSFesbaT759P4yrufM1B0cq4smMidy0nJAJM5b0/15hFiMSY9Vm+WGbvjjUzfz49qTi39XVyYmiS+wLS50X3U+zq754V3W/uTZHOFOmJR2b+YHOFMtlikfWdcY5fmpy5IN22voMTy3yqOQAAHn1JREFUQ5OBf2QXx3N1V3qc74Dg07lof/LEaTb2ptjZ56Wl8m50ZDLPqN9VR1s0QqHkmMwXGZnMB1aGPTU8ydBYDi+TwMgXywyN5ehtjzGZLwIRIgZetZ0y+/e+YlZ65lvpttbdY7V5iuXqzQdiUa8uW1s0SlciSqbgGM0U2P+qtTz87EUcUCqVZ+ocRiNGvlQCYhT9MrdiyRGPGN3JGKvb4wyOZimUSlUrQJe5NmrCdHDWFjXypXJF5Wkvt7QnGSNbLHsV4P1ixWLZceu6JBfHspy5MjVTSX3rmnZuXd/J4TMjTOSKTOW9rmI6EzFWdbTRFo0wVfZaKk+rTF/ZvwGYnjadxuvzL1e1e92VPDmeoyMR454dq0jEYnMWpU/XCVp73UDpP7ZzHYm48dDhAbKFMvGocdv6Th5+9iI46G2fLpq61sL5V96wg3/35aMUS45Cyauf15GI8s9ev7Xq4N7v/dFtPPD4adLZPKWS83Pr4vzKG3ZwfmSK//T1F8kWSjM5lmawbbV3Ub6tr5PVHYmZ4tfpu/dqjS32793Cg98+561/noOOA1XHK/2p12wI7Guv3pyTu7et4vETl/2gzws688Uy9+4MHgIt6P+t1jGenmf3POdpJIekkYHtg9azf++WpvVnKI1ZLLmA0Y9+9KNhp6FhDzzwwEfvv//+li3/6GCaP/3Waa/Srz9t+rlYdnSlYty1ZTXRCEwVvItldyrOD2zs5upkkbLzLnsO/K4tHJ2JGO1tMUam8sB0QOZfGCPGrv5uRqYKFIqOtliE9V0J0pkim1enePDbZ/mrp8/z/IUxettjrOtKsq4rydY1Kc6PZLiQzrKuK8E7X7eJbNERNa/O0ESuRHvCC67OXZ3i4lieRMyr8BoxY3g8T65Q4n33bueZc2k2r2rnVf1d9KTi5P26NBt7U/42eNpiES6ks/z47bdU3Xd/9fR51nS21ZxnOhcNYE1nG6cvT/LfnxogFY+waVU7Y9ki//DyFY4MpOlJxmb6uErEI6xujzM8kWdgNDsz//T3t65J8adPnCJfcjMNJ6aPoXNlNq1qZ2Qq7xdBO7asbudX3/BKLk/kZqVnenmlcokvPDVQdT3ruqrfER8dTPNrn32Kbxwb4vnBMZ46fZVvnhhmeDJ3rYiwQrkMG3oSXBrPM54rkS+W2dCT4BXruzn08mUyhdKs+kxevUVHJl9kNFtiyq+HmC8Uee221USAM1UqpEeZXcwe9+vzxCPGZMFR8vdTyXn7+jWbe7iaKWKuRCbvyJcdEYw17V6QNjyRZyJXJF/06qMNT+QYTGcpFMu0xaIzdchS8SjJtiilUpmrU4Ub0hWLwLrOOLmCowTEzFjbGfcDUTcraDPglp4kfT0ptq5pZ/OqFPkSM+f+dABd7feyoTfJY8cv8fLwBOdHMn6nxAnu3r6Kh548T8S81sj5QpmrU3kKJa8oeXg8TzpTIFMokYpFGMkU+Pc/dSffP3+VIwPjTOXLFEpl/tFta/jIT/5A1d/k1jUdPHN2hFzBYRGjsy3O1jXt/PRrvc+eODHMeLZI2d8fW1en+IN3v5Z9d9zC5YnCrGXt6u/hu6eu8PD3LlAqQ2y6ZWi2wD999Qa2rW3nu6dHOD+awQx+ce8WfvnHbq2arl39PazrShKLwJEL44xmCvS0t/Fzd2/ivjs3UiqXePToJZ4+O0KmUOKn7trAD79yHV95boCP/PXz/MHfvcRXj1ykvS3Czr7uG47ttB3rOnjy9FWGJ3KMZYs4HNvXdvCRn9wd+Nt77dZVvHbrqqppDjrGQf+JQfPs6u8J/H6QnX3d9HW3ceziBJfGc6zpTPDr/6j2wPZB6frhV66re/2ydH3sYx8b/OhHP/pAtc/M1aq1usjt2bPHHT58uGXL/9QjJ/j/vvkyU/nyDWMwRgz2bFvNx966+4am7JtXpxidKnBpLEeqLXqtv6N8iS1r2uloi/K9syNeUYxfzBkFulIxelIJv1sHv9ggW2RnX+dMS6fKO6halSsriwz/V3tnHiTHfd33z+vuOfdeYLG4FhcJQABBgSQoEhBpSRYpBpQsUU4oh4xoMyk5klMuW6aTctH5QyGSsEquuCIrihPLpSiWy7EU+YitUhRKNGUlOligRIk0CcAEwCVxEIu9sNjZY+55+aN7BrO70wMstNhZoN+namune45+r6fnN2/e773vr/453z8xSqGsC7rJ0gmXHzx5X6gO2fxUe3X7ifftWPRzqvd/6+h54q7Dng2drG5P8vzgeDANEKsp+U9mi3z9787Rk/IWTDNNZEvsWdexoO1/76Ze/vO3T1AOubT7OuLM5ss1OYh0wuWBXf2s7kg2tPnoUIa0Jw2PM3/appo5e+hz3+XltzKLut48x898eY5QqvhdmLvWdfLG2Ay5QhlHApkU/EC+FOLfjjVphi5mmSo0foAjl+rGBL+AvFJWGpU893d4pOIxxqcLc5cOC5oK/CnRIGsW1FgikPJcSqr+e5XwSLgOfZ1Jzk/OcPrCwg65tAc9bUnfb1Vc8c/D6FSeQoMItjcd48efeqChf2HXfiNhWA3Ou+cIU/kyqZjDTL7kF/5XlEJZiQcNMa5AW8IjW6ww0JvmQ3vX8tnnThJ3XVIxIVtUCuUyn7zvZj7x7u0L7PrMs8dDPxOjU7maPl79Z//ubb2hAqS/8Pnna5+X+tfzHGF7f8eix4uG2bZ3bq51ANa/Vr1YdX2G6MkHdzbtMmwkYv1bB3dedoxZzGstdkz8abTTlkOqYjmOs1JlN25ERORFVW2owm5Tlk04cm6S3nSM2UJ+wbSP64CDMhj80i5X/IxUuaKcncjS1x6ntz3ObKEUSFcIve1x1nYlGZrM4XkuLkqlojiO35EWlGzNQfEX1963pXdRU4Zh8+KHB8cpVUoNi8erz1tM8fpilxV6x5bu2uP9DkDlxVMX2be5m+lcyZe1yF3KoHQkPRKucHoiF3SF+gGF5zokPfj+6+O1WqNMtsRIJkeuVAkNxgDGpwq1qcxyxe92/c7xUQ7ctJrDgyOcmSjUHjvQEydb0KC2y8/UZLIlRqfyjM8UeO7Yeb/hI/hSeO7Yef7DR/byyiKDMSAo8PenxOKukNcKb4zN+O+TQqnux1Mz3bSTI7MLpvjqqczLtBWanKzhqRI39yWYyZdr56xUqdS6ifOFCuKIb1v1MlKYLpRrEjCT2RIxF9Z0JDg70ViuYLYEnUHAV9EKiF/L1igYA5gIsmxhPwYarfH4zKvDPD84Tk9bYsEX//HhKbpTHrMFv/7NcaSWSayonxUsVTTopFY296b8jjqFmUKJqZz/OY45wpeeP90wIGtWPH7k3OSiBUiHMznaYnObLbqSHmcmsty5yPHic8+d4Oi5DImYS3vCI19Sjp7L8JlnT3Dv9r4Fr9VMrDosIGumQbhY6YGrUdBfSr2py4mJLlWAsxyipStJGDXqWEAWwrGhSc5OZGlPxvEkPycb4TmwoStFBX/w7U7HFwzw47NF7tzUM6cea21Hgq197bw+Ok3MFRLepYL/fMkXqmyksP2D1y8sWu8KGgdXG3vTjE7lKJTqsk2JeOjUW/V1wooeP/Ps8YaDXNiyQseHZ2rdj6PTeVyB7nSckyMztCc9BkcyTGbLnBw56de39bfjOBIUpPuUFcqlCo1WOypW4NUmmmLg1yNVKlAf/o5O5XhhcHROMAYs2K4+q1BWjpybmhP4lAoVXj03xdP/+2jTgCiMQlmDurBLuI6Sji0UwmyW176aYzfj1PhMw3MG+Fm1BkGTzrtdKMNbE7MNp2urTOeKTBcuWb++Kx76WCVcZHc6V+TMxCyT2WKtQeJsKsZsscxwJocnypFzs3MWqge/Wafa5FAOglTB73LMFit+Nq1UZu9AJ+lEjPHpPPm6gaFcVoplpTydb9iZuaE7xfeODy+o37x3Rz9Hz2UYnsoxPlOsLRGWcAXPc0OV+tsTHm+OTfvHrihZx2+28Fyn4bJpzZYHe+HNCVwHfwWPggaahXBmIstrQ5O8ci5Ts/nW9Z1MZsPFqsNoFnTNL3gfncpx5FyGYlkb+l8v0wFXFsAu5TJMzYI7CF+6q9XryDZi/jGK5TKDo9P85ldf5oHda5c0W2aZuOaYDlkIz7w6zM5+vysnFXSLJTwhFXPYurqNZMyjM2hZ7pgnydCRcFEF13XYva6T+3atYfe6TlzX4eCeflIxjzUdCbygiNpzhTUdCZIxl4TnsX/bKh7YvZb921aR8LxaF049V9uF8/iBTZTKSkcyxtZVaTqSMUpl5fEDm5o+b9e6Lp543w5+9yN75xTlv3Ux2zBYPHLuktjih/auZ/e6Tv7m2CiHB8dqWmf9HQnyZWU4k2Mkk2VqNsfYTAlQ4o7f5PDSmUnOTIQP8o2o/1K/UgplOB2SvQkj7CjfO3lh0ccPI1v067RaSXGJIrxzk83P7/z37dxkc7//5PlTNf24qiTEG2MzHBvKMDLl1+rFXZeK+kKib47NEHOEU+N+Rjvm+sK4p8aztezefFcVvzvVEWHb6jQ39bUzNBmIlYbMGedLyqf/z2tkssU5nZkvnx7npbMZP5tavb7PZpjK5Yl7cD5TCFYC8TtOZ4r+lG/1C/3Y0NwfGj1pj1yxEnSn+hn2XLHia5u9MUG+WJ6zPFjcDc+r5op+ZlDVnzZX9a+9QqnCC6cmKJYrftd22d928bsK67mSLsOwcezgnv5aw8JwJsvhwQtM5UrsHehs6H8zBf0wEq5weHBijs7i4cEJEk3OS5ie4dGhydAfyc10zRZzjGNDk6Hj61JKYtQfY2w6x4unLvqyK0rotXc1NPPT8LGALIS3LmbZtKqNfZu76UjGiHsOXqB039OWYEd/O7es7/KDpQYD00BvOlRQ7/aBLkoVWN2eYNvqNla3JyhV/C6kRoKWjx/YtGTCrB94+waefHAnnakYI9MFOlOxpnUflyNskM3kSg0HpdMT2UvCk8kYG7tTuI7DVL7MmYt50jGHZMyXbvBrxdyQI0eH67fKcy4Vrjwl7zSbkwVSMeEnZyYbiplOByLLji/tHixNBmNBHVy1wUYDxVwJavMCDeWFtjhCW8JltuB/zufXkzaikWjo91+fIB13SXjOpes77vLMkRHOTmRr68qW9VJH91SQNWr0hX5hpsSG7iQx15e3ibnChu4khZKGBCvhJGNO0DSiNSXh4DsZB79pQBzBcxy/K1c1VKw6jPqga/44Vi9A+vLZSdqTHgdu6mVNR6qh/7cPdDGdL88R0Z7Ol7l9IDzb0ugcXO69DAuuJrPhwriLDaKaBXDLIVpaf4yTIzMkPAcRoSOwY6lEahcbqEaRFTdlKSIHgc/i17l/QVU/3Qo7qin01e1JfnZnHy+e8lPhnUmP3es6awPJjv620PbnsHqsxw5sbthi/mv3+XUnjaYGt/W1L5lOSpim2dUQ1srdlWq8rFBVNb6qdeY6Ql97go29KZ4fHKcr5S0QtJwqNF7yZCVzJV/aUSQWcyhdScqt7uS5+Fpk9dvd6QSKLsiIVFe7SMYdcsVKTVYmGXNQ/C7SvvYYY9NF8lrBE6GvPcbQVIF03CFX8qcdHfw60bLCzX3tTMwWmSmU2Bhz2bWug0JZiXkOxVJlTlbNwQ88G2XNSxWlPeEuuL6rAqvdKc/vDA4aOFy51LjR6AtdUXrScdZ1p2v7coUSk7nZ0MXFw6aMbu5r5+/OXJwzpRwL/O9K+TVl1bU/u1Ie2ZLy5IM7G4pVh3E5vafqeFmd2nTqOrTn+x82hj52YHPo8Qtl5R1bexgcm61N5VbfyzDCplk7k+HCuI0aFJoFUc2mcj9275ZrLlpaP4ZPZgskXId8WbllfeccW35aVtISRSuVFRWQiYgL/D7wPuAs8EMR+ZqqHl1uW+ov0t62BDv723lteJrOIKqfr8Oz2IGpkfpz/cDU6Dkrca69maBjo0FpoDfN2o5EQ9HGo0MZZvNl2hKXXj9bXHxY4zlQWupCqkWSijnMLtVc32KP7QnZsBbMq6A+uJyrl784PEfoaYvjzRZry3v5mSAJLd4HP7iqitJWV7ZY3Rbn1o1dHB68APPETFNxIVdUPBcc8ZcJyxWVgd44qbjHmZkCPW2xWv3mdKGCJ5At+Gt8gl+4X6r49WOe69DXkWAglmb/tlW163qgJ8WbYzO1HxblivpyH67TUJjVc/xOzPnXd/UzMpsv05nyAnkWKFUqJIMmj0Zf6LcPdDf0f2NPulb6UGUyW8ShHFrbdPe21SQ9Z0En8Y9OX6RUETrrlPFn8mW6Ut5V/bC7knHsSha0vtwY2ux1D8w7L/XHuVJbblnfXBh3MUFUM3+XQ7S0/hiOOCDCHZu6anXFS5WRi8JC5T8tKyogA+4CTqrqIICIfAV4CFj2gGz+B2HL6nZ+5T03NfwgXKuB6XphMZ2ZVRHERqKN6bjw2edOQp45MgK7+tMcG55d8PpbehO82UBC4ZcObOLbx4Yb3hdGwoF0wmEi+9MHUQ7wyF0b+aMfnK7VJlXDjbTndxQuxq5tfY3970zAVJ7awtsV/C/yR+8eYHBkmu+cWFjL5kFDeYvOBGQanK6OhIPn+BIjQG19RxHoSTmMzy48X20xmC3ODdoE2L+1h5/ZsdqXinBcupJCJlemrEpvyuVC1s+DVYVqAfrbPWaKSjJYHqpYVnKlMht7Uvzigc0MTea4MFNgKlci7jlsWpVmXXeCF09N4Adx/tE9V3nb2g4ATo3NUKoES2YFXZO9bXGmAm0s1J+ucxxYlfaYzpVQYPf6jjnCrDv62/jUXx9lplAkX6zgukJXOs5Dt63l2aNBh3Fd1vz9t67h2WOjC67vf3FgK0Dt2k94Qibr65Ht29S5QAy2Spj/D+/b0LDDORVzQgvED+7p5/SFWd7X1zHnOb9w5wa+8sOzoTZfC65UqHWxY+jVCMA2e07Y8RcbRF3OruX4rqgeo2pL3HOpqC5pRm4lLVG0UllRwrCHDh3aD6x56qmnvhZsbwV2PfXUU9+oPkZEPn7o0KHPHzp06OOxWGz9E088cc3s6etIcuCmVTxwy1oO3LSqaSeiMZerEUG8c8sqPAeODk2RyZXoSHp84l1b+cwj+3hjNMPg6CyqEHOED769n//5K/eSyeY5MTJNRSEdd3ls/wCf+uCt/NN7tvFXPz7Nxeylya4tvQnu372O14czc2QxEg588PaN/Mz2Nbw+PMlsXVYu4cCOtR1cnC7MmZrqTMBrT3+A//Q3J+bq0wGDn/4A797Zz2Q2z/HAtqTnsHtdB/tv7mM6nydTZ9fm3iTd6XhtCmS+XX/8sf1885W3GJu5dP+u/jTf/+0HyGTzHB+eplSBVJ3/H75jgJdOjXP6QrZWOzPQneD+W9Yxlc0zlbsU/Lx7ey/f/M33znk8QFfSpac9yaZVbWzvSzE2U6AcHOejdw/wpx+/h+++NsxQXSS3b6CTP/ild/CT0xPBigh+fdKONe38+5+/lYN71tfe45lCmfaky+2burl1oJdSpcTkbDEQRvXf461ruujvSDA+W2C2UCEVd7lzcw/tqTgfvXszO9e2U6pAMu7y9o3dPP7OzZwYmWFDV5LJXImKKp2pGPu39tKWjBPzXNZ1JRibKTBTKJOKu9y1pYeJbIl9m7rJ5P31NDuS/lJJ4rocuGlVQ/HZHf2dbOxJcj5TwHGEm/o6eOL+m/nn77q5oWjor9//tobX9yfevX3OtT9TKNORjHHbhi7W9bSFCoX2dSQb+h/2GfvRqYuhYs0fvXtzw+c8vG9TqM3XimbCrsv9uldry2K+O66Vv1fDtbRlJfnZSq4bYVgR+QjwD1T1l4PtXwTuUtVfa/T4ay0Ma9x4NBPnrP6CWyrhyKWyq5Ew5kpnqdrbr+a8NHvO/NvV7aNDGXav67xhzn8jbrRrzDCuR5oJw660LsuzwEDd9kbgXItsMW5ArrTTa35nbCvtuh4Jk0lZLFdzXpo9J+y+pexkXqncaNeYYdxorLQMmQccB+4D3gJ+CPwTVT3S6PGWITOuhpUqTrhS7Wo1V3Nemj1nsQvI30hEwUfDWMk0y5CtqIAMQETeD/wefnf7F1X16bDHWkBmGIZhGMb1wnW1lqWqfgP4xmUfaBiGYRiGcYOw0mrIDMMwDMMwIocFZIZhGIZhGC3GAjLDMAzDMIwWYwGZYRiGYRhGi7GAzDAMwzAMo8VYQGYYhmEYhtFiLCAzDMMwDMNoMStOGHYxiMgocOoavfxqYOwavfb1gPkfXf+j7DuY/1H2P8q+g/m/HP5vVtW+Rndc1wHZtUREfhSmphsFzP/o+h9l38H8j7L/UfYdzP9W+29TloZhGIZhGC3GAjLDMAzDMIwWYwFZOH/YagNajPkfXaLsO5j/UfY/yr6D+d9S/62GzDAMwzAMo8VYhswwDMMwDKPFWEA2DxE5KCKvichJEXmy1fYsByLyRREZEZFX6/b1isizInIi+N/TShuvFSIyICJ/KyLHROSIiHwy2B8V/5Mi8oKIvBz4fyjYHwn/AUTEFZGfiMjXg+0o+f6miLwiIi+JyI+CfVHyv1tE/lxE/j4YAw5ExX8R2Rm879W/jIj8RoT8fyIY814VkS8HY2FLfbeArA4RcYHfBx4EdgOPisju1lq1LPwRcHDevieB51R1O/BcsH0jUgL+paruAvYDvxq851HxPw+8V1X3ArcBB0VkP9HxH+CTwLG67Sj5DvCzqnpbXbt/lPz/LPCMqr4N2It/HUTCf1V9LXjfbwP2AbPA/yIC/ovIBuDXgTtVdQ/gAo/QYt8tIJvLXcBJVR1U1QLwFeChFtt0zVHV/wdcmLf7IeBLwe0vAR9eVqOWCVUdUtUfB7en8AfkDUTHf1XV6WAzFvwpEfFfRDYCHwC+ULc7Er43IRL+i0gn8C7gvwGoakFVLxIR/+dxH/C6qp4iOv57QEpEPCANnKPFvltANpcNwJm67bPBvijSr6pD4ActwJoW23PNEZEtwO3AYSLkfzBl9xIwAjyrqlHy//eA3wIqdfui4jv4wfe3RORFEfl4sC8q/m8DRoH/HkxZf0FE2oiO//U8Anw5uH3D+6+qbwG/C5wGhoBJVf0WLfbdArK5SIN91oYaAUSkHfgL4DdUNdNqe5YTVS0H0xYbgbtEZE+rbVoOROTngBFVfbHVtrSQe1T1DvwyjV8VkXe12qBlxAPuAP6rqt4OzHADTs9dDhGJAx8C/qzVtiwXQW3YQ8BWYD3QJiKPtdYqC8jmcxYYqNveiJ/GjCLDIrIOIPg/0mJ7rhkiEsMPxv6Hqv5lsDsy/lcJpmu+g19PGAX/7wE+JCJv4pcnvFdE/oRo+A6Aqp4L/o/g1w/dRXT8PwucDTLCAH+OH6BFxf8qDwI/VtXhYDsK/t8PvKGqo6paBP4SeCct9t0Csrn8ENguIluDXw2PAF9rsU2t4mvA48Htx4G/bqEt1wwREfwakmOq+h/r7oqK/30i0h3cTuEPVH9PBPxX1d9W1Y2qugX/s/5tVX2MCPgOICJtItJRvQ08ALxKRPxX1fPAGRHZGey6DzhKRPyv41EuTVdCNPw/DewXkXTwHXAffv1wS303Ydh5iMj78etKXOCLqvp0i0265ojIl4H34K90Pwz8G+CvgK8Cm/Av3o+o6vzC/+seEbkX+C7wCpfqiP41fh1ZFPx/O37xqov/A+2rqvpvRWQVEfC/ioi8B/hXqvpzUfFdRLbhZ8XAn777U1V9Oir+A4jIbfgNHXFgEPhnBJ8DouF/Gr9uepuqTgb7IvH+BxI//xi/0/4nwC8D7bTQdwvIDMMwDMMwWoxNWRqGYRiGYbQYC8gMwzAMwzBajAVkhmEYhmEYLcYCMsMwDMMwjBZjAZlhGIZhGEaLsYDMMIxIIiI/LyIqIm9rtS2GYRgWkBmGEVUeBb6HLwprGIbRUiwgMwwjcgRrl94DfIwgIBMRR0T+i4gcEZGvi8g3ROTh4L59IvJ/g0W4v1ldXsUwDGOpsIDMMIwo8mHgGVU9DlwQkTuAfwhsAW7FV+0+ALW1Tj8HPKyq+4AvAjf8Ch6GYSwvXqsNMAzDaAGP4i+RBv7C4o8CMeDPVLUCnBeRvw3u3wnsAZ71l73DBYaW11zDMG50LCAzDCNSBGv1vRfYIyKKH2Apl9Z1XPAU4IiqHlgmEw3DiCA2ZWkYRtR4GPhjVd2sqltUdQB4AxgD/lFQS9YPvCd4/GtAn4jUpjBF5JZWGG4Yxo2LBWSGYUSNR1mYDfsLYD1wFngV+DxwGJhU1QJ+EPc7IvIy8BLwzuUz1zCMKCCq2mobDMMwVgQi0q6q08G05gvAPap6vtV2GYZx42M1ZIZhGJf4uoh0A3Hg31kwZhjGcmEZMsMwDMMwjBZjNWSGYRiGYRgtxgIywzAMwzCMFmMBmWEYhmEYRouxgMwwDMMwDKPFWEBmGIZhGIbRYiwgMwzDMAzDaDH/H9xEwnOEnWiLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "ax.scatter(df[\"Age\"], df[\"Fare\"], alpha=0.5)\n",
    "\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Fare\")\n",
    "\n",
    "fig.suptitle(\"Age vs. Fare for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's fine if the relationship between Age and Fare is what interests you, but it doesn't give you any information about the relationship between these features and the target\n",
    "\n",
    "### Scatterplot with Color to Distinguish Categories\n",
    "\n",
    "This kind of plot could help you understand how the two features relate to the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "ax.scatter(df[df[\"Survived\"]==1][\"Age\"], df[df[\"Survived\"]==1][\"Fare\"], c=\"blue\", alpha=0.5)\n",
    "ax.scatter(df[df[\"Survived\"]==0][\"Age\"], df[df[\"Survived\"]==0][\"Fare\"], c=\"green\", alpha=0.5)\n",
    "\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Fare\")\n",
    "\n",
    "color_patches = [\n",
    "    Line2D([0], [0], marker='o', color='w', label='survived', markerfacecolor='b', markersize=10),\n",
    "    Line2D([0], [0], marker='o', color='w', label='did not survive', markerfacecolor='g', markersize=10)\n",
    "]\n",
    "ax.legend(handles=color_patches)\n",
    "\n",
    "fig.suptitle(\"Survival by Age and Fare for Titanic Passengers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Most of the time if your target is a categorical variable, the best EDA visualization isn't going to be a basic scatter plot.  Instead, consider:\n",
    "\n",
    "#### Numeric vs. Categorical (e.g. `Survived` vs. `Age`)\n",
    " - Multiple histograms\n",
    " - Multiple density estimate plots\n",
    " - Multiple box plots\n",
    "\n",
    "#### Categorical vs. Categorical (e.g. `Survived` vs. `Pclass`)\n",
    " - Grouped bar charts\n",
    " - Stacked bar charts\n",
    "\n",
    "#### Numeric vs. Numeric vs. Categorical (e.g. `Age` vs. `Fare` vs. `Survived`)\n",
    " - Color-coded scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (prework-labs)",
   "language": "python",
   "name": "prework-labs"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}