Data_Viz_Tutorial_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization in Python\n",
    "Take data and turn it into something colorful, graphical and meaningful :) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro\n",
    "Data visualization allows data scientists to graphically represent data to extract and understand trends, outliers, patterns and further insights in the data.\n",
    "\n",
    "Python has many **many** graphing libraries with different features and it can be daunting to know which library to use.  This intro tutorial will focus on a few popular plotting libraries:\n",
    "* **Pandas** - built on Matplotlib and easy to use with Pandas DataFrames\n",
    "* **Matplotlib** - massive library with lots of flexibility (stackoverflow will be your friend!)\n",
    "* **Seaborn** - statistical visualization with default themes and beautiful styles\n",
    "\n",
    "For interactive plots and possibly a feature tutorial\n",
    "* Plotly\n",
    "* D3\n",
    "* Bokeh\n",
    "\n",
    "\n",
    "This tutorial compares Matplotlib, Pandas, and Seaborn for the following visualizations:\n",
    "* Scatter Plots \n",
    "* Line Charts \n",
    "* Histograms \n",
    "* Bar Charts \n",
    "* Box Plots \n",
    "* Pie Charts \n",
    "* Heatmaps \n",
    "* Faceting\n",
    "* Pairplots\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup \n",
    "Personally, I prefer to use a Conda environment and/or Docker container (if you are interested in knowing more about these setups let me know).  The README includes the specific packages you will need for this tutorial.  Please make sure you can run the following line without any errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_wine, load_iris\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data \n",
    "Scikit-learn includes out of the box [datasets](https://scikit-learn.org/stable/datasets/index.html#]) which are great for practicing your data visualization skills.  We will use the classic wine and iris datasets - imported above.\n",
    "\n",
    "Since most of the plotting libraries play very nicely with Pandas DataFrames, we will format the *wine* and *iris* dataset with Pandas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>class_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>class_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>class_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>class_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>class_0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  target    class  \n",
       "0                          3.92   1065.0     0.0  class_0  \n",
       "1                          3.40   1050.0     0.0  class_0  \n",
       "2                          3.17   1185.0     0.0  class_0  \n",
       "3                          3.45   1480.0     0.0  class_0  \n",
       "4                          2.93    735.0     0.0  class_0  "
      ]
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     "metadata": {},
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   "source": [
    "# Load the wine data set into a Pandas dataframe and view the first 5 rows\n",
    "wine = load_wine()\n",
    "wine_df = pd.DataFrame(data= np.c_[wine.data, wine.target],\n",
    "                 columns= list(wine.feature_names) + ['target'])\n",
    "# Add class label\n",
    "wine_df['class'] = pd.Categorical.from_codes(wine.target, wine.target_names)\n",
    "\n",
    "# Show the top 5 rows of Dataframe\n",
    "wine_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>setosa</td>\n",
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      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target   class  \n",
       "0     0.0  setosa  \n",
       "1     0.0  setosa  \n",
       "2     0.0  setosa  \n",
       "3     0.0  setosa  \n",
       "4     0.0  setosa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the iris data set into a Pandas dataframe and view the first 5 rows\n",
    "iris = load_iris()\n",
    "iris_df =  pd.DataFrame(data= np.c_[iris.data, iris.target],\n",
    "                 columns= list(iris.feature_names) + ['target'])\n",
    "\n",
    "# Add class label\n",
    "iris_df['class'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
    "\n",
    "# Show the top 5 rows of Dataframe\n",
    "iris_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas DataFrame Descriptive Statistics\n",
    "Don't forget to understand the statistics of your data!  You can also stylize your dataframe like you might in a csv.  There are ways to add visual impact to quantitative data.\n",
    "\n",
    "If you need a brief statistical refresher especially in the context of Python please see [Python Statistics Fundamentals: How to Describe Your Data](https://realpython.com/python-statistics/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)      target  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate descriptive statistics of your DataFrame\n",
    "iris_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "            background-color:  #55b98f;\n",
       "            color:  #000000;\n",
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       "            background-color:  #74c8ae;\n",
       "            color:  #000000;\n",
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       "            background-color:  #7dccb5;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384d\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >sepal length (cm)</th>        <th class=\"col_heading level0 col1\" >sepal width (cm)</th>        <th class=\"col_heading level0 col2\" >petal length (cm)</th>        <th class=\"col_heading level0 col3\" >petal width (cm)</th>        <th class=\"col_heading level0 col4\" >target</th>        <th class=\"col_heading level0 col5\" >class</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow0_col0\" class=\"data row0 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow0_col1\" class=\"data row0 col1\" >3.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow0_col2\" class=\"data row0 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow0_col3\" class=\"data row0 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow0_col5\" class=\"data row0 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow1_col0\" class=\"data row1 col0\" >4.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow1_col1\" class=\"data row1 col1\" >3.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow1_col2\" class=\"data row1 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow1_col3\" class=\"data row1 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow1_col5\" class=\"data row1 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow2_col0\" class=\"data row2 col0\" >4.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow2_col1\" class=\"data row2 col1\" >3.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow2_col2\" class=\"data row2 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow2_col3\" class=\"data row2 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow2_col5\" class=\"data row2 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow3_col0\" class=\"data row3 col0\" >4.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow3_col1\" class=\"data row3 col1\" >3.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow3_col2\" class=\"data row3 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow3_col3\" class=\"data row3 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow3_col5\" class=\"data row3 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow4_col0\" class=\"data row4 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow4_col1\" class=\"data row4 col1\" >3.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow4_col2\" class=\"data row4 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow4_col3\" class=\"data row4 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow4_col5\" class=\"data row4 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow5_col0\" class=\"data row5 col0\" >5.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow5_col1\" class=\"data row5 col1\" >3.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow5_col2\" class=\"data row5 col2\" >1.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow5_col3\" class=\"data row5 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow5_col5\" class=\"data row5 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow6_col0\" class=\"data row6 col0\" >4.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow6_col1\" class=\"data row6 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow6_col2\" class=\"data row6 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow6_col3\" class=\"data row6 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow6_col5\" class=\"data row6 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow7_col0\" class=\"data row7 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow7_col1\" class=\"data row7 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow7_col2\" class=\"data row7 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow7_col3\" class=\"data row7 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow7_col5\" class=\"data row7 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow8_col0\" class=\"data row8 col0\" >4.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow8_col1\" class=\"data row8 col1\" >2.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow8_col2\" class=\"data row8 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow8_col3\" class=\"data row8 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow8_col4\" class=\"data row8 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow8_col5\" class=\"data row8 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow9_col0\" class=\"data row9 col0\" >4.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow9_col1\" class=\"data row9 col1\" >3.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow9_col2\" class=\"data row9 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow9_col3\" class=\"data row9 col3\" >0.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow9_col4\" class=\"data row9 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow9_col5\" class=\"data row9 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow10_col0\" class=\"data row10 col0\" >5.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow10_col1\" class=\"data row10 col1\" >3.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow10_col2\" class=\"data row10 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow10_col3\" class=\"data row10 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow10_col4\" class=\"data row10 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow10_col5\" class=\"data row10 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow11_col0\" class=\"data row11 col0\" >4.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow11_col1\" class=\"data row11 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow11_col2\" class=\"data row11 col2\" >1.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow11_col3\" class=\"data row11 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow11_col4\" class=\"data row11 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow11_col5\" class=\"data row11 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow12_col0\" class=\"data row12 col0\" >4.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow12_col1\" class=\"data row12 col1\" >3.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow12_col2\" class=\"data row12 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow12_col3\" class=\"data row12 col3\" >0.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow12_col4\" class=\"data row12 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow12_col5\" class=\"data row12 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow13_col0\" class=\"data row13 col0\" >4.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow13_col1\" class=\"data row13 col1\" >3.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow13_col2\" class=\"data row13 col2\" >1.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow13_col3\" class=\"data row13 col3\" >0.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow13_col4\" class=\"data row13 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow13_col5\" class=\"data row13 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow14_col0\" class=\"data row14 col0\" >5.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow14_col1\" class=\"data row14 col1\" >4.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow14_col2\" class=\"data row14 col2\" >1.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow14_col3\" class=\"data row14 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow14_col4\" class=\"data row14 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow14_col5\" class=\"data row14 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow15_col0\" class=\"data row15 col0\" >5.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow15_col1\" class=\"data row15 col1\" >4.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow15_col2\" class=\"data row15 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow15_col3\" class=\"data row15 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow15_col4\" class=\"data row15 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow15_col5\" class=\"data row15 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow16_col0\" class=\"data row16 col0\" >5.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow16_col1\" class=\"data row16 col1\" >3.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow16_col2\" class=\"data row16 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow16_col3\" class=\"data row16 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow16_col4\" class=\"data row16 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow16_col5\" class=\"data row16 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow17_col0\" class=\"data row17 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow17_col1\" class=\"data row17 col1\" >3.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow17_col2\" class=\"data row17 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow17_col3\" class=\"data row17 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow17_col4\" class=\"data row17 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow17_col5\" class=\"data row17 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow18_col0\" class=\"data row18 col0\" >5.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow18_col1\" class=\"data row18 col1\" >3.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow18_col2\" class=\"data row18 col2\" >1.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow18_col3\" class=\"data row18 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow18_col4\" class=\"data row18 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow18_col5\" class=\"data row18 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow19_col0\" class=\"data row19 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow19_col1\" class=\"data row19 col1\" >3.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow19_col2\" class=\"data row19 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow19_col3\" class=\"data row19 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow19_col4\" class=\"data row19 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow19_col5\" class=\"data row19 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow20_col0\" class=\"data row20 col0\" >5.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow20_col1\" class=\"data row20 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow20_col2\" class=\"data row20 col2\" >1.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow20_col3\" class=\"data row20 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow20_col4\" class=\"data row20 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow20_col5\" class=\"data row20 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow21_col0\" class=\"data row21 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow21_col1\" class=\"data row21 col1\" >3.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow21_col2\" class=\"data row21 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow21_col3\" class=\"data row21 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow21_col4\" class=\"data row21 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow21_col5\" class=\"data row21 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow22_col0\" class=\"data row22 col0\" >4.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow22_col1\" class=\"data row22 col1\" >3.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow22_col2\" class=\"data row22 col2\" >1.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow22_col3\" class=\"data row22 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow22_col4\" class=\"data row22 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow22_col5\" class=\"data row22 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow23_col0\" class=\"data row23 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow23_col1\" class=\"data row23 col1\" >3.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow23_col2\" class=\"data row23 col2\" >1.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow23_col3\" class=\"data row23 col3\" >0.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow23_col4\" class=\"data row23 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow23_col5\" class=\"data row23 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow24_col0\" class=\"data row24 col0\" >4.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow24_col1\" class=\"data row24 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow24_col2\" class=\"data row24 col2\" >1.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow24_col3\" class=\"data row24 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow24_col4\" class=\"data row24 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow24_col5\" class=\"data row24 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow25_col0\" class=\"data row25 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow25_col1\" class=\"data row25 col1\" >3.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow25_col2\" class=\"data row25 col2\" >1.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow25_col3\" class=\"data row25 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow25_col4\" class=\"data row25 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow25_col5\" class=\"data row25 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow26_col0\" class=\"data row26 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow26_col1\" class=\"data row26 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow26_col2\" class=\"data row26 col2\" >1.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow26_col3\" class=\"data row26 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow26_col4\" class=\"data row26 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow26_col5\" class=\"data row26 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow27_col0\" class=\"data row27 col0\" >5.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow27_col1\" class=\"data row27 col1\" >3.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow27_col2\" class=\"data row27 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow27_col3\" class=\"data row27 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow27_col4\" class=\"data row27 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow27_col5\" class=\"data row27 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow28_col0\" class=\"data row28 col0\" >5.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow28_col1\" class=\"data row28 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow28_col2\" class=\"data row28 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow28_col3\" class=\"data row28 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow28_col4\" class=\"data row28 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow28_col5\" class=\"data row28 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow29_col0\" class=\"data row29 col0\" >4.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow29_col1\" class=\"data row29 col1\" >3.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow29_col2\" class=\"data row29 col2\" >1.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow29_col3\" class=\"data row29 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow29_col4\" class=\"data row29 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow29_col5\" class=\"data row29 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow30_col0\" class=\"data row30 col0\" >4.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow30_col1\" class=\"data row30 col1\" >3.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow30_col2\" class=\"data row30 col2\" >1.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow30_col3\" class=\"data row30 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow30_col4\" class=\"data row30 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow30_col5\" class=\"data row30 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow31_col0\" class=\"data row31 col0\" >5.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow31_col1\" class=\"data row31 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow31_col2\" class=\"data row31 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow31_col3\" class=\"data row31 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow31_col4\" class=\"data row31 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow31_col5\" class=\"data row31 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow32_col0\" class=\"data row32 col0\" >5.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow32_col1\" class=\"data row32 col1\" >4.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow32_col2\" class=\"data row32 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow32_col3\" class=\"data row32 col3\" >0.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow32_col4\" class=\"data row32 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow32_col5\" class=\"data row32 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow33_col0\" class=\"data row33 col0\" >5.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow33_col1\" class=\"data row33 col1\" >4.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow33_col2\" class=\"data row33 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow33_col3\" class=\"data row33 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow33_col4\" class=\"data row33 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow33_col5\" class=\"data row33 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow34_col0\" class=\"data row34 col0\" >4.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow34_col1\" class=\"data row34 col1\" >3.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow34_col2\" class=\"data row34 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow34_col3\" class=\"data row34 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow34_col4\" class=\"data row34 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow34_col5\" class=\"data row34 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow35_col0\" class=\"data row35 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow35_col1\" class=\"data row35 col1\" >3.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow35_col2\" class=\"data row35 col2\" >1.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow35_col3\" class=\"data row35 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow35_col4\" class=\"data row35 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow35_col5\" class=\"data row35 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow36_col0\" class=\"data row36 col0\" >5.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow36_col1\" class=\"data row36 col1\" >3.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow36_col2\" class=\"data row36 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow36_col3\" class=\"data row36 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow36_col4\" class=\"data row36 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow36_col5\" class=\"data row36 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow37_col0\" class=\"data row37 col0\" >4.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow37_col1\" class=\"data row37 col1\" >3.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow37_col2\" class=\"data row37 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow37_col3\" class=\"data row37 col3\" >0.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow37_col4\" class=\"data row37 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow37_col5\" class=\"data row37 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow38_col0\" class=\"data row38 col0\" >4.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow38_col1\" class=\"data row38 col1\" >3.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow38_col2\" class=\"data row38 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow38_col3\" class=\"data row38 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow38_col4\" class=\"data row38 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow38_col5\" class=\"data row38 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow39_col0\" class=\"data row39 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow39_col1\" class=\"data row39 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow39_col2\" class=\"data row39 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow39_col3\" class=\"data row39 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow39_col4\" class=\"data row39 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow39_col5\" class=\"data row39 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow40_col0\" class=\"data row40 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow40_col1\" class=\"data row40 col1\" >3.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow40_col2\" class=\"data row40 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow40_col3\" class=\"data row40 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow40_col4\" class=\"data row40 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow40_col5\" class=\"data row40 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow41_col0\" class=\"data row41 col0\" >4.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow41_col1\" class=\"data row41 col1\" >2.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow41_col2\" class=\"data row41 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow41_col3\" class=\"data row41 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow41_col4\" class=\"data row41 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow41_col5\" class=\"data row41 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow42_col0\" class=\"data row42 col0\" >4.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow42_col1\" class=\"data row42 col1\" >3.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow42_col2\" class=\"data row42 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow42_col3\" class=\"data row42 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow42_col4\" class=\"data row42 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow42_col5\" class=\"data row42 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow43_col0\" class=\"data row43 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow43_col1\" class=\"data row43 col1\" >3.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow43_col2\" class=\"data row43 col2\" >1.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow43_col3\" class=\"data row43 col3\" >0.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow43_col4\" class=\"data row43 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow43_col5\" class=\"data row43 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow44_col0\" class=\"data row44 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow44_col1\" class=\"data row44 col1\" >3.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow44_col2\" class=\"data row44 col2\" >1.900000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow44_col3\" class=\"data row44 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow44_col4\" class=\"data row44 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow44_col5\" class=\"data row44 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow45_col0\" class=\"data row45 col0\" >4.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow45_col1\" class=\"data row45 col1\" >3.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow45_col2\" class=\"data row45 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow45_col3\" class=\"data row45 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow45_col4\" class=\"data row45 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow45_col5\" class=\"data row45 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow46_col0\" class=\"data row46 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow46_col1\" class=\"data row46 col1\" >3.800000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow46_col2\" class=\"data row46 col2\" >1.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow46_col3\" class=\"data row46 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow46_col4\" class=\"data row46 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow46_col5\" class=\"data row46 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow47_col0\" class=\"data row47 col0\" >4.600000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow47_col1\" class=\"data row47 col1\" >3.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow47_col2\" class=\"data row47 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow47_col3\" class=\"data row47 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow47_col4\" class=\"data row47 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow47_col5\" class=\"data row47 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow48_col0\" class=\"data row48 col0\" >5.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow48_col1\" class=\"data row48 col1\" >3.700000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow48_col2\" class=\"data row48 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow48_col3\" class=\"data row48 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow48_col4\" class=\"data row48 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow48_col5\" class=\"data row48 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384dlevel0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow49_col0\" class=\"data row49 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow49_col1\" class=\"data row49 col1\" >3.300000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow49_col2\" class=\"data row49 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow49_col3\" class=\"data row49 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow49_col4\" class=\"data row49 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a077f1f6_a3ab_11ea_a98e_784f435d384drow49_col5\" class=\"data row49 col5\" >setosa</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a1b4eba50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example using a color gradient to display data values in the DataFrame\n",
    "# For max/min use highlight_max()/highlight_min()\n",
    "iris_df.head(50).style.background_gradient(subset=['sepal length (cm)', 'petal length (cm)'], cmap='BuGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }    #T_a084084c_a3ab_11ea_a98e_784f435d384drow8_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col0 {\n",
       "            background-color:  #65c2a3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col1 {\n",
       "            background-color:  #dcc9e2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col2 {\n",
       "            background-color:  #65c2a3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col3 {\n",
       "            background-color:  #f7f4f9;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col4 {\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_a084084c_a3ab_11ea_a98e_784f435d384d\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >sepal length (cm)</th>        <th class=\"col_heading level0 col1\" >sepal width (cm)</th>        <th class=\"col_heading level0 col2\" >petal length (cm)</th>        <th class=\"col_heading level0 col3\" >petal width (cm)</th>        <th class=\"col_heading level0 col4\" >target</th>        <th class=\"col_heading level0 col5\" >class</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow0_col0\" class=\"data row0 col0\" >5.100000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow0_col1\" class=\"data row0 col1\" >3.500000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow0_col2\" class=\"data row0 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow0_col3\" class=\"data row0 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow0_col5\" class=\"data row0 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow1_col0\" class=\"data row1 col0\" >4.900000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow1_col1\" class=\"data row1 col1\" >3.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow1_col2\" class=\"data row1 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow1_col3\" class=\"data row1 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow1_col5\" class=\"data row1 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow2_col0\" class=\"data row2 col0\" >4.700000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow2_col1\" class=\"data row2 col1\" >3.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow2_col2\" class=\"data row2 col2\" >1.300000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow2_col3\" class=\"data row2 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow2_col5\" class=\"data row2 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow3_col0\" class=\"data row3 col0\" >4.600000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow3_col1\" class=\"data row3 col1\" >3.100000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow3_col2\" class=\"data row3 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow3_col3\" class=\"data row3 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow3_col5\" class=\"data row3 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow4_col0\" class=\"data row4 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow4_col1\" class=\"data row4 col1\" >3.600000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow4_col2\" class=\"data row4 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow4_col3\" class=\"data row4 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow4_col5\" class=\"data row4 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow5_col0\" class=\"data row5 col0\" >5.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow5_col1\" class=\"data row5 col1\" >3.900000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow5_col2\" class=\"data row5 col2\" >1.700000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow5_col3\" class=\"data row5 col3\" >0.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow5_col5\" class=\"data row5 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow6_col0\" class=\"data row6 col0\" >4.600000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow6_col1\" class=\"data row6 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow6_col2\" class=\"data row6 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow6_col3\" class=\"data row6 col3\" >0.300000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow6_col5\" class=\"data row6 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow7_col0\" class=\"data row7 col0\" >5.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow7_col1\" class=\"data row7 col1\" >3.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow7_col2\" class=\"data row7 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow7_col3\" class=\"data row7 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow7_col5\" class=\"data row7 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow8_col0\" class=\"data row8 col0\" >4.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow8_col1\" class=\"data row8 col1\" >2.900000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow8_col2\" class=\"data row8 col2\" >1.400000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow8_col3\" class=\"data row8 col3\" >0.200000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow8_col4\" class=\"data row8 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow8_col5\" class=\"data row8 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a084084c_a3ab_11ea_a98e_784f435d384dlevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col0\" class=\"data row9 col0\" >4.900000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col1\" class=\"data row9 col1\" >3.100000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col2\" class=\"data row9 col2\" >1.500000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col3\" class=\"data row9 col3\" >0.100000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col4\" class=\"data row9 col4\" >0.000000</td>\n",
       "                        <td id=\"T_a084084c_a3ab_11ea_a98e_784f435d384drow9_col5\" class=\"data row9 col5\" >setosa</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a1b63b8d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head(10).style.background_gradient(subset=['sepal length (cm)', 'petal length (cm)'], cmap='BuGn').background_gradient(subset=['sepal width (cm)', 'petal width (cm)'], cmap='PuRd').highlight_max(color='yellow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter Plots\n",
    "The x-y plot or scatter plot represents the pairs of data from two datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Use **scatter** or use **plt.subplots** to stylize our plot with title and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'sepal width (cm)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# scatter the sepal_length against the sepal_width\n",
    "ax.scatter(iris_df['sepal length (cm)'], iris_df['sepal width (cm)'])\n",
    "# set a title and labels\n",
    "ax.set_title('Iris Dataset')\n",
    "ax.set_xlabel('sepal length (cm)')\n",
    "ax.set_ylabel('sepal width (cm)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot would have more meaning if the data points are colored by class.  In Matplotlib we can create a color dictionary and then assign a color for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1b806c50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create color dictionary\n",
    "colors = {'setosa':'r', 'versicolor':'g', 'virginica':'b'}\n",
    "# create a figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "# plot each data-point\n",
    "for c in np.unique(iris_df['class']):\n",
    "    ax.scatter(iris_df['sepal length (cm)'].loc[iris_df['class']==c], \n",
    "               iris_df['sepal width (cm)'].loc[iris_df['class']==c],\n",
    "               s=10*iris_df['petal length (cm)'].loc[iris_df['class']==c],\n",
    "               alpha=0.5,\n",
    "               color=colors[c],\n",
    "               label = c)\n",
    "\n",
    "# set a title and labels\n",
    "ax.set_title('Iris Dataset')\n",
    "ax.set_xlabel('sepal length (cm)')\n",
    "ax.set_ylabel('sepal width (cm)')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas \n",
    "To create a scatter plot in Pandas we can call **dataset.plot.scatter()**. The axes labels will automatically be created from the DataFrame column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b9558d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create Pandas scatter plot\n",
    "iris_df.plot.scatter(x='sepal length (cm)', \n",
    "                     y='sepal width (cm)', \n",
    "                     title='Iris Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b9c8550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add coloring to plot by class \n",
    "# Note here we use the numeric target and not the class names\n",
    "iris_df.plot.scatter(x='sepal length (cm)', \n",
    "                     y='sepal width (cm)', \n",
    "                     title='Iris Dataset', \n",
    "                     c='target',\n",
    "                     colormap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "Seaborn has a **.scatterplot** method to create a scatterplot similar to Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris Dataset')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a seaborn scatter plot\n",
    "sns.scatterplot(x='sepal length (cm)', \n",
    "                y='sepal width (cm)',\n",
    "                data=iris_df)\n",
    "plt.title('Iris Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add style to scatterplot by coloring by class\n",
    "sns.scatterplot(x='sepal length (cm)', \n",
    "                y='sepal width (cm)',\n",
    "                data=iris_df,\n",
    "                hue = 'class',\n",
    "                size='petal length (cm)')\n",
    "plt.title('Iris Dataset')\n",
    "#plt.legend(loc='upper center', bbox_to_anchor=(1.3, 1.0),\n",
    "#          fancybox=True, shadow=True, ncol=1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line Chart\n",
    "One of the most fundamental plots - a line chart to display a series of data points. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Call line-chart with the method **plot**.  Matplotlib will require a loop to plot multiple columns in one graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1bdeff90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get columns to plot\n",
    "columns = iris_df.columns.drop(['target', 'class'])\n",
    "# create x data\n",
    "x_data = range(0, iris_df.shape[0])\n",
    "# create figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "# plot each column\n",
    "for column in columns:\n",
    "    ax.plot(x_data, iris_df[column], label=column)\n",
    "# set title and legend\n",
    "ax.set_title('Iris Dataset')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas \n",
    "Create line-chart with **.plot.line()** without any loops :) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c1d9d10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pandas line chart\n",
    "iris_df.drop(['target', 'class'], axis=1).plot.line(title='Iris Dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "Line-chart with seaborn calls method sns.lineplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c2c2dd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aEITOwFKfTcnA46IovnpWZz6NAmbcCw0/3nO69K+RuFAxp3379mXbtm0kJydz6aWXUlRUxLvvvuundBs6Z32oHYsayPrwjTUVG4hMrR1j6htx6huXGow1vbjx6qpD/Hggn9V/G6Fs8/Wo61PUmQVSBUheWdMnF0VR9CPq0AYnE2ssCN8ltKrtLuUiUWZ1EuNzoZFRUGmjRZj/9hBZUQdoaqmyu2gZZiC7xPKHmFBszFJcB0VRTBdFMR3oDViAL5t9ZOcYFyrmVKfTkZiYyGeffcaAAQMYOnQoL730EkOHDq2z77Bhw5SlrTIyMhRvG0Cr1TZpkQKArl27Kkl5YWFhJCQksGLFCgDsdjsWS9MWBg3Gml7cyCm1cqyo2s+TLbME8qi9itpL1HJs6MkzUNTVDjduj0iESfaoTz+ZGKJT+5HnkcJq5eeS6sCxpPkVduLMBr9t8kWhNhG73B5sTg8twwzex3//JXpNnUwcBRwWRfF4cwymOXEhY06HDh1KixYtMJlMDB06lJycnIBEfdttt1FVVUVqaiovvviinxd8yy23kJqaqkwmNgbjxo1j7dq1yu+ffPIJCxYsIDU1lUGDBnHq1KlGHaek2kGF1cmGDRuUpcOCuPhQYXPiEaHYh+xKqx2oVdJdmlz1IVsQ5VYnJ8usnPSW6Z0sbzpR+7aPg1SJAYE9apmo48IMfuSZVVhT010fUQdS1GbZ+qhlo8gNNy3DZaL+/SvqpnrU1wJLAj0gCMItwC0ASUlJZzmsc4+xY8cyduzYOtvT09P9yMwXV199Nf/4xz/8tg0cONBvOaxnnnkGkKpE6rNBnnnmGWW/+Ph4Pxuibdu2ZGRkAJJV8Z///CfgMebPn8/8+fOV3+XnANx///0Bn9OmTRuio6PJzMykY8eOdOzYkdWrV/vtk5yc7Ddu3zAq+TUdPFXJwf176N69OzExTZ9sCuL8QG7lzq+w0cKrJkstTmJD9ZyqsCkt5L4dij8fLADAoFWdkaKWJytr6qglRR2o6kP2yGND9X7k6rtiSyCidro9FFU56lXU1bWJ2vt7Ky9R/xFK9BqtqAVB0AETgWWBHhdF8R1RFPuIotjHd12+IC4sXnjhBaXk8EzhEUUKCwqVi43HIzZbCH0QZw6526+gwq5sK7c6iArRoVEJSmei3elGq5ZU9uoDElH3bxdNXrmtycu/yYpaKc/T+ld92Jxu5QIhN7zEmvV+Kjczv4owrzoORNRFVdLrkS8+MkLqsT5kom5xGuujwub0e71VdtdFW8rXFOtjHLBDFMX85hrMxYQ1a9bQp0+fCz2Ms0bnzp0ZNmzYWR1DFEX6DhlBUhuphvVEqYUTpU3zt4NofiiKurJmUrDU4iQyRIteo/LrUEyOCQVgw+EiNCqBge2jsTjcCvE29ZyyotaoVeg1KkVR3/DBFp75Zj8gWR9atUCESetHrlmFVfRtKy2sHIio8ytkova3PkL1ga2PqlqKOlC7utPtYej8n3lzjdQnYHO6GfnSGuX3iw1NIepp1GN7BPHHhiyend4vusWnLTmIiwOiKCrkl++jqEstDiJMOvRataJsbU43USE6YkJ12Jwe2sWEkBQldfudbGLlh0zsESYdPx7/kZSPUjC0+FaZTMwqqOJYsTRZaHW6MWjVmA1aKr1q1uHycLzYQpdWZswGTT1ELY2ptvWhVgmYdOo65Xmymo8K0aFVCwE96uIqB+VWJx//egyX28PKvXkUVNo5VlRdZ9+LAY0iakEQTMClwBfNO5wgLjaIoojHe3vocHtwe6TAnd+79fHLoUK+2n3yQg/jnMHu8ijldwU+wf1lFqm9W6dWKdaHzenBoFWR6CXnDnGhivr09akdLg8v/3iIckv9Ktt3MjG/WrrZ1glmLHapI7DU4lBUt83pxqhVYzZocLpF7C4Px4urcXtEOsaZiQrRBSRq+fXUVtQgqeraFSayog7RazAbtAHL92Q7Jb/CzurfCvh0czZQE2AFsHx7juLhX2g0iqhFUbSIohgtimJ5cw8oiIsLvnxsd0llTwDuJnqZFxOcbg9/+2w3dy3Zyf/2Na7y5WJHhY9lUVApkZDHI1JmcRBp0qHX+lofbvQaNW18iLp1hFQfn+dT+bH5aDELfspskKzKLE7UKgGd2sOMbjPYfcNuol3jqHa4KKl2IIo11oPVISvqGm850zuR2CEutF6izq+woxLwa+SREWrQ1FHMsu0SqtdgDvA4QKGXqFUCvPi/g2w7Xgr4Nwj93w8HufWT7ezJKav39Z8v/GmyPoI4M/hOtjhcHuXL7vE03ETTHOc/V/hxfz5FVXbizHruW7pLCaM/W7gv4F2GbwOJbBVU2l14RIgwSR51jfUhKeokH6KWuv4EpVQPYG+upMt8VWZtyM0ur+x4hetXXs/zm5+nMnQJ1Xa3QobyZJ7N6VEUtbxdrvhIjg0hOkTnV1ooo6DSRqxZr5QZ+iJQ1Gm1j6IO1Qcm6iLvxezK9NZkFVShU6sY2jFGuUMQRZGSagcOl4dbP9nO4cIqCiptF+xvHCTqBvDhhx9y8uTpb49nzpwZMAGvvu1ni/MZeeqpQ9Rulnz4Dl8uXURzf2ZPldvo8tj37DpxbhXNp5uzaR1h5Mu5gzHpNcz7IkCmTBORkVtO18e/5/A5SqRrKsqtEhnFhxsUj1ounYsw6dD5TCbanJKiTo6VJhQ7tzSjUgm0DDf4WR97c8q9x2nY+gg3aukc1Zk+LfsQog1BrzZQ7XBRVCWdv8LqUpbhMujUmPXSxKOsqFtHGDHpNESadJTWo6hr+9MyQgMsxyUHMoXo1V5FHcj6kM4zd2QHVAKMS2lJu5gQ5aIkz8NckR5PqcXBqP/7hX7P/cRdS3bW+140J4JE3QAaS9TnG+cz8tSXjB1uD3anhyunzuDThf9udp/6aFE1dpeHQ6fOXcj9saJq1mcVMbVvIq0jjIzr0fKcKOp9J8txuDxsOlJ8DkbZdMiKukMLM8XVdpxuj3IbH2nSoteo/eqoDVoVf0lpxUez+tGlZRgArcKNfm3kexSiblhRhxk1XNH+Cu7udTf39r6XjprpWOxuRbU63NKdmNXp5kTIwzy+S8pfr7K72JdbTvd46fxRoTqvXeL/uZLqwuvaHlC/olYJeNW7NrCirrJj0qlpHxvKktkDeHxCNyJMOsqtTtweUbFgBneI4fPbBvHslT0YkBzFxsNF5+VOsjb+NER97NgxunTpwo033khqaiqTJ09WWqgDRZ0uX76cbdu2MX36dNLT07FarTz99NP07duXHj16cMsttzTpD9ZQnOqDDz5Iv3796NSpE+vWrQPAYrEwZcoUJfK0f//+bNu27bxHnn766WKumzCKyZcO5nDWYWwuNyZTCPEJSWzasrlRr/1kmZXPt+c0+r2SUe7NqWjo1rupWLIlG7VKYGpfKXw+PsJIhc111k0RuV6Cy8i9MNM4skfdMS4UUZSIqNRXUftNJkpesU6jYninmp6H1hFGJXOjpNqh/FzagKKusDoJMVrpvag3X2Z+yXt73+MQr3kVtd1vP5vTjVMopdIp3SHlllk5UlRNakI4ANEhOhxuT52/RUGlnbiwehS1oS5RV9ldhOg0CIKAuT7ro8quZIr0T44mOlRPhFEreepWp0LU0SE6useHM2NAG8anxlNqcZ5xeNXZ4IIR9U3f38SKrBXn9OfT4eDBg9xyyy3s2bOHsLAw3nzzTSXqdPny5Wzfvp1Zs2bxyCOPMHnyZPr06cPixYvZtWsXRqORO+64g61bt5KRkYHVauWbb75p1HnrO4cMl8vFli1bePXVV3nqqacAePPNN4mMjGTPnj089thjbN++HZAaWIxGI7t27VJyQTIzM5k7dy779u0jIiIiYOxqoMjTuXPnsnv3bjZu3Kik5+3evZvXXnuNvXv38sknn5B5KJNPv/mJaTNuZPHCf+NwST5j99R01q9b36jXv2xbDn9btttvhY/GQCaIsibW9tYHu8vNsu05jO4apzRDyNUOeWf55ZOfL6vQ8w15wq5jnGRnFFTYa6o11BU4tYexuaSSOLvLg15T96vfKtxAfoXkw8r+tFolNHihLLM6CTcYmNFtBu0j2qNX6zGoTVgcbn+itjmxOtz0V73BkjH/A1DuPnq0log60ptw55uh7XB5KKl20KIh6yOAopabYeq3PuzEhPon6kWGSJZMqcWhEHVkSM0+Kd5x7r0Af+M/TcwpQGJiIoMHDwZgxowZLFiwgMsuu6zBqFNf/Pzzz7z44otYLBZKSkro3r07l19++WnPe/DgwQbPcdVVVwH+caXr16/n7rvvBqBHjx4XLPL0klFStkdKSirr1v4CgEmnJiomlpO5dc8TCHJ5VKXNidGbV9wYyATR0K13U/C/ffmUVDv8wuflaofcMisdW5jP+NhyTsah/EpFsZ5PyIq6g5eo8ytsyvs386cJoIKW7vmKT60PML74CCMuj0hhpV25M0hPjGiwCabc6iTaGMN9ve8DIDU2lZxjffjQfkzxgaX9XFidbjLEF7lhVQHwDL8elohaJsBoL3EWV9tJipYmOgurAje7yJA9alEUleTJaoeLEG8ru9m7AK7v4wBFlQ7aeM8hQ45CLbM6lUnNaB+i7tLSjEYlsCe3nHEpgTmiuXDBiHrhZQvP+c+nQ6DYUlEUG4w6lWGz2bj99tvZtm0biYmJPPnkk0qE6OlwunPIsaJqtVqJFW2KrdKckac6nfRBNejUuNzS2Ew6NXa7Hb2X4PMrbFgdbtrGhAQ8phySU2FzERfW6JelTGL5Kqyzwaebj5MYZWSoTzh+K6Us7ezWD8wrs2HSqbE43Bw8VUlaYsRZHa+pqLA50alrKjnyK+2cqrAhCDAn9TYW7/0Bp1OrJOgFUtStveH/246XsCenjLbRJpKiTGw7XhLwnDmlFiqsTqrVGfRZdBMfj/uYgyUH+aHsI+yuv5JfYUOtEnB7RCpsTmxOD3YxF7wfv7xyG60jjErZnaKofS7MSrNLfURt0ODyiF7fXSLnKrtb6Vo0GzR4RGlyUFbZICnq3m0j/Y4lLydWZnEok5q+itqgVdO5pVm5iD26Yi95ZTben9k34NjOJf40HjVAdna2QpZLlixhyJAhDUadms1mKiuliSaZ5GJiYqiqqmpSNcfp4lQDYciQIXz22WcA7N+/n717ayoTzmfkqTxfqPN+sQWkSZrjR7Lo0rUbIH0JAsVayrCc4fp18pflXHjUWQVVbDpSwrV9k1D5lHm1MOtRCWdnfYiiSG6ZVVl9ZM8F8KkrbS7CjBqiQ2tez9e7TjK4fQwzuk2nq246dpdT8akDKf6BydF0axXGw1/sZeuxUlISIogwaSkLcKG0Otzc8vF2QnQarkzpwbQu04g1xmLQGAjRRIAgdRzKF45Kmwub083EyH+zYdoGjN7zy2oaIDpEIuNiHyVeUE9XogxzgDZyX+sj1FA3D8Tl9lBicdTJvfa1XoqrHWjVgnJ8GSmtw9mTU05RlZ2lW0+w+mDBObvjawh/KqLu2rUrH330EampqZSUlHDbbbc1GHU6c+ZM5syZQ3p6Onq9ntmzZ5OSksKVV15J376Nv4qeLk41EG6//XYKCwtJTU1l/vz5pKamEh4ufajPZ+SpXJ6nEgRUgoBOo0ajFti1bTNDRowEpA++u4GgJllRNzUXWPamm5o/EQhLtmSjUQlc0yfBb7tGraJFmEGZDDwTlFqc2F0e+rSJItKkJeMCeJgVVidhBi1qlUCsWc+KnbmcLLcxvX8SQ/4zhC32p7EL+UrDUiCiNmjV/Pv63mhUAiXVDlJbhxNh1FFpd+GsFVb08Bd7OHCqggXTejKsbQ/+1udvxJpiGdduHNPbPg2ihrxyK8neu6xyqxOry8amqlcZvnQ4oUaJOFMSaog6ymt9+Da91OR8BCbqQMFM/h61Vjn/Z9tOUGZxUGKRGnFia3vUPoq+tFoKs6p9F56SEE651cmrqw7hdIuIIoqF05z4U3nUKpWKt99+u872+qJOr776aq6++mrl92effZZnn322zn71LSDgu72+c/jGisbExCj+ssFgYNGiRRgMBg4fPsyoUaNo4w1FOp+Rp4WVdvLKrVxyyQi69ByASiWwe9cu2nfqQkRkNFDT6OHyeFhI1D8AACAASURBVNCp6hKA3CnW1FxgWamcraIWRZFv9+QxsktcQGXWKtzg15HXVMi1x/ERRnq0Dr8girrC5sLsvXWPMxvYm1tOrFnPqK5xTCyfSEaOhSPWKEVRB7I+ABKjTPxrei/uWrKTwR1iFNuj3GfllaIqOyt2neTWYclc0iWOt3a/xcKMhWyctpHNeZv56Nj/IWgm43GFkxwbwk+/QUmVA49gocB5AIAIg4rCCtFPUYfo1OjUKkp8/t6ZBZXSnEiIP6meqDxBlCGKdt4LwXvrjvDcpBRA8qh9rQ+AZ7/dz7rMIkrHdWFoR+nOp7aiNhs0qATptRZXOxTi9oU83sWbs+mZFEFmfhXrsoqa3bP+Uynq3xMsFgtDhgwhLS2NSZMm8dZbbyl+8ZniTCJPZUUtCAIJUSbiI4wUFxdz1wOPKI+5vETtdNejqO1npqjlqo9Si/OsalcPF1ZxqsLGiM5xAR+PjzCeURazjBqiNpCaEE6md0LxfEJS1BIpyRNvU/okoFWreKT/I7Q3DsfudjaoqGUMah/DtkcvpVt8WM0Emw95HveutdivnZR4lxabxnVdrkOj0qBT6wjTRSIb0a0jjOjUKvIrbYiuUG5O+ojN120mTC8Rni9RC4IgtZH7WB/rM4sYkBxdpytx2rfTGPDpAOzag8wZ3p7Fm7OVvI5qu7tmMtFL2OsyiwDJlpKrUWq3pKtUAuFGrbfqw65Mbvqic0szWrWAKMKNA9syIDma9d5jNyf+NETtG9D/e4DZbGbbtm3s3r2bPXv2MG7cuLM+5plEnnq8s+Uqn1vASy+9lMQ2bXF7RMnyEGWilkig2u7i5o+2KvvLLb1nqqgd3maJxkIURWYu3MK/fpY8eflLOrRj4EUP4iOMnDyDLGYZvoo6pXU4Lo/Ib+ewSac+vLYqk4c+l5Zrq7A5lUzoFmEGBAGu7ZvE3qK99P+0Pz+WPYZHf0R5H+tT1LURaZJL1mousie8S3jJVRMDWg3gnt73ANC3ZV/uSZmP6JImU2PMesKMGgoq7AjqajaWfcDYz8eiMxaQEGn0m6wD/PI+TpRYOFZsYUiAldHn9ZtHnCmOSH0kD4ztzLBOsTzxVQZZBVVKHTXUxK/2SopgdNc4MnyIunZ5nvR6dZRanJRanESF1J3A1GvUdGkZRqRJy2U9WjKkQzTZJRZloeDmwp+GqIM4M4iiFFxTG2qVgEcUcXtqvEuXV1HbXB5+OVSoEJ9F8agbT9SiKFJmcSrlUQ01XdTG9uOlrDlYyDtrj2BzutmQVUSbaJOSFlcb8eEGHC5PwJyJxiCv3IZOoyI6RKe0ZcvrETYnVv+Wz7d78xBFkQqrizCvHztrSDten9aTxCgT4fpwJnWYxNDIW3Fb21Bpc6IOOcgDW69gX3HDE9oAEUZ5gq2uok6IlN7Pu3++m+krpfmSgyUHmb/7dlQGSd3GhOoxG7QUVNpQ6YrYX/ELNpeNmwYl8+LkuiWnUSE6xfrYkBX4AptTmUPb8Lb8OPlHOkd1Rq0SeHlKGgICH248isPlUTzq9rGhPHF5N965oQ+92kRyvNiirNEYY65LxBEmLWUWB8VVdqK8F6naeOLybrw+rRcGrZohXhtlfVbzquogUQfRIDwe0U9Ny1ALUtmVy2cC0eklbbfbo8RYQs1Cp00h6iq7C5dHVDzI2jPr6zOL/IKIfPHpZqn7sNzq5KtdJ9l0pCSgKpOhlOid4YRibpmV+HADgiAEjAs9WxzIq+B4cd2c5OPeFbYLK+1eRV1DThNS4wFIMifx2IDHaGlsB4KHCpsTjXk/FlclR8qOnPbc8qK1vk1H2SUWWoYZFPtkdNJoJiRPAECr1hKuD0emlphQPWEGjdRIY03isZSlrJ+2nr90SWNQ+7p/E19FvS6riBZheqU2XMayQ8uY/u10xiwfw/wt85XzXNajJcu9HbAyUatUAjcNbkdMqJ7U1pLKX3OoAJ1GVaeiAyRFXVTpoMLmCqioAfq0jWKI9+LRPjaEVuEG1mcVnu6tPCsEiTqIBuGpR1GrVAJu0Z+oXW4R0WebTMwWr0cdKBe4Psg11G0Voq557qlyGzPe38ziTdkBnufgm715TOuXSHJMCC98/xtVdleDRO3b9HImyCu30SpcOobZoMVs0Jx1p6Mv7vh0B8+vPOC3rdzqVN6TfScrcLg8iqL2xes7X6fXol4sO/kQGnMGFVYX9oK/8PqQzxRybQiyNeF7ocwuqVbK7gAub38507pMAyA5PJn5QxbgsUnVNbGhesKMWoqqHAi6EjYWfc4VK65gQ+6GgOdrFxNCdomFnw7kszGriMEdYupUXlzf7XoWjFzAxPYTSYtNU7Zf1z9J8eBD9XU9+B6tpSL+jNwKYkP1dY4LEG7SKgsdRIUEVtS+EASBIR1i2JBV3KzJekGiDqJBeGp1dMlQCwIeD7i9dodGpcIpl+l5P6+VNicOn0D7pihqudJDVtS+lR9yPnCg1Tg+35GLw+Vhev82TOuXREm1A5VAQPUmQ2kjP8PKj5NlVuK9ZA8QH248q3I/X9icbo4WVVNYaffbfsLHWtnuzVKWJxN9kRabxk3db+K6pGdwVXajwupErc/j3/v/wdt76lZA1UaITo1WLfhZT9klFj8b6dLll/LsJqkaqthazN1rb0JjzkCnVhFm1BBm0OL2iKiN2fyU9xlGjRGdOvDE+Jzh7enaMozbFu2g1OKsY3uU28vZlLeJtLg07up1F2Pb1ixY3b9dFO1jpc9LSAC1HGHSKReYQP40SIpavhOsT1HXxhBvPGpz5rw0doWXCEEQlguC8JsgCAcEQRjYbCO6iHC2Maenw9tvvx0wdtQ3unTXrl2sXLlSeezJJ5/kpZdeOu2xRVFk5MiRVFRUNHlcvvCIIjddM5HS0lK/7SoVftaHQavC6RYVUgaJmH3zPU5H1DuzS7nijfWcKrcparGGqGuIQv5CHC/xJ2pRFFm8+Tg9kyLo2iqMq3snoFOrSEmIILwevxGk22295sxW4Xa5PeRX2IiPqCn7i4+ov9zP4fJw3bubGp2yd7SoGo9YN2rU1wPfke0lamPd1zg0YSj39bmPUF0ICNKaiJrwHWSU7GT7qe2nPb8gCIQbdYqitjnd5FfY/dqvp3WZxqD4QQDo1Doi9OGIooboUKkOWbZkXBVpvD54BcsuX0bfloH7EIw6Ne/c0FtpVBlc605oU94mHl73MEfLj/KPzf9g4oqJfmOd1i8JAFFVzbRvpnG0/Kjf8+Uqk9qleTIifT4ntUsC64M8xub0qRurqF8DvhdFsQuQBhw4zf5/CDR3zOmcOXO44YYbGtynNlE3FitXriQtLY2wsCb0bAeAR4Qrr7mWN99802+7WrE+PAgI6LVqXB6PEqUJEjH7diw2VJ6XX2Hj1k+2szunnC3HShQF3Tba2zDhq6i9RH2ixJ8MNx8t4UhhNdd5v6xRITr+eU0qD13WpcHXKAiCUvnRVORX2vGI+CnqVg2U+x0vrmbj4WKWbWtcmqAcrF+7llwm6o5xoez25nUHsj6u+foaHl73MAsPP4wucjMVNif2gnEsH/8N7419D4BF+xdx8/9uptweWBFGmrTKhUJW8rIyFUWRm7rfxMgkqfnJrDPzzph/o7Z1U8hQHpdKn8eO4p+Z+s1UPj9UNzhMRkKkiYUz+/LE5d3q1L2PThrNf8b/h+7R3enZoifj2vlXQ03rl8Q9ozuiD8khoziD7Ap/e0xusKmPqCN8aqcbS9QxoXq6tgpr1jK90xK1IAhhwDDgfQBRFB2iKF74tWmaiPMdc1pQUKCk1e3evRtBEMjOlj407du3x2Kx+Knj7du3k5aWxsCBA/nXv/4FgMPh4PHHH2fp0qWkp6ezdOlSQGopHzFiBMnJySxYsCDg+RcvXswVV1yh/P7xxx+TmppKWloa119/PSDdCdx2221ccsklJCcn88svvzBr1iy6du3KzJkzAemLOGbceJYs8V/XWO3NSXG5RdRqAa1amlyUPUKQPGm52UUlSCuOBILd5WbOou1UeXOEswqqFGKIC9Nj0qkVRS2KoqKoT5Zb/S4Mn27OxmzQKBNpAFekt2Zg++iA5/VFq1qh+fXB7nKz3Sf7QvaiZfsEJM+71OIMmBYoV0yszypsVDmgTNTlVqdf5+fxYguRYVY6tbYrnZ+ycvXFxPYTGdJ6CLM7P4OjtB8VVhdqYzbrT/7Mg2sfJKs0i3W569h8ajOb8jYFHINUsiZdKOQLhByaVGQtIv2TdD/ivX7l9Rijf1XsBVnpa0IPsPC312gZ0pIQnX8uTLG12G9yMy0xgpsGt/PbRxRFFu5biFatRaPScFnby7gt7Ta/9zFEr+Ge0Z0Y0+4Svp30LS1DWvodQ1HU5sAkHHEGihqkypTtx0ubnBDZWDRGUScDhcBCQRB2CoLwniAIddJ3BEG4RRCEbYIgbCssPP0M6PHrb/D7V/bFl37bbQcOYDtwoM5+tbcDynNPh/MZcxoXF4fNZqOiooJ169bRp08f1q1bx/Hjx4mLi8Nk8i8Vu+mmm1iwYIFfcJNOp+Ppp59m6tSp7Nq1i6lTpwLw22+/8b///Y8tW7bw1FNPBcz98I013bdvH8899xyrV69WYkxllJaWsnr1al555RUuv/xy7r33Xvbt28fevXvZtWsXHhEiI6Ow2+0UF9fcrst5GQ6XB41KQKOSPkrVDheyo11hcynNLrFmfUDrQxRFHluRwc7sMl6ekkab6BCyCiprspSNWj+iyCu3UVTlIC0xAlGUgoFAajv+PuMUV/dKaFJCn4ykKBNHCqtPuxjC6z9lMfntX5UMCnkCsrWvopYrPwLYHzLR5VfYFRJuCFneFWM8or91dKLEAq1eZ63tb8q22opaFEWmd53O+OTxhOvDELxVH7rodXy4/312Fuyk0FrIKyNe4eURL5Memx5wDBE+ilq+0MiKWqfWcVvabXSJrrlriTREEm0y06ml2Tsu6QLiKL6Ez8Z9x79G/YvL2l7md45H1j/CoxsebfC9yLfk88bON9iZL62y8suJX+izqA9ZZVl19v0g4wPGfzmeF7e+6Lc9JSEcs0FDp3qSEn27ESMbsMtqY0iHGBxuD5uPNk87eWOIWgP0At4SRbEnUA08VHsnURTfEUWxjyiKfWJjY2s/fFGgdszp+vXr/SJI09PTefbZZ8nJCXxb+vPPP9O/f39SUlJYvXr1aYOVBg0axIYNG1i7di3z5s1j7dq1rFu3jqFDh/rtV15eTllZGcOHDwdQFG99GD9+PHq9npiYGOLi4sjPz6+zT0lJCWaz9GFcvXo1kydPJiZG8tKioqKU/S6//HIEQSAlJYUWLVqQkpKCSqWie/fuHDt2DI8oohKkC4+vDaT2TjDavUStVUu/Wx1uNN6fK20updmlZZghoPWxaNNxPtuWw50jO3BZj1a0jw1VFLVZr0GjVvkRhZz3PMHbsisT3/LtJ3C4PVzXP6nB964+9E+OotzqZN/J+j19p9vD0m0nEEXJOwaURge5phhQKkAClftll1jQeC9y6xpxq5yVX0PmpX6VFxY6665nart7lW21Peq86jx6L+rN14e/5oNDz6GL+UlaweTUdL6Y+AU/TP4Bp8fJ3T/fjUljwiUGvuOJMGn9FHWITq3Ut4dqQ5mTNofu0d2V/ReMXMB3f72PB8Z0BmryNtTGY+wt2cqcH+fwr13/8jtHUlhSvb61jJYhLVl37TomtJeqVRLDEpnedToh2rqpjUfLjxJtiObvff/utz3MoGXzvFFMTIuv8xz5tYLUKKNRN77Wol+7KHQalVL7fa7RmKyPHCBHFEV5OY/lBCDqpqLNJ4HX7qu9vTH7RVw1qVHnPN8xp0OHDlVU9BVXXMH8+fMRBIEJE/zLompn5Z4OtWNN5WhUX2g0GjweDyqVqsHjy8dSqVR1Ik5dLpdU9aESsNlsGI01qlFu6XV5PKhVarTeD7VHFJWfK21O5bY8LszA7pxynG6P8viBvAqe+no/o7rEce/oToCUp/zLoQI6tTAT4S2P8lXUGbnlqFUCY7u35LmVB8gusSCKIku2nKBPm8h6ldLp4Dsh5BsU5ItV+/OV6ovsEgv9k6WutDiz3k/Fy+o6kJWSXWKhYwuz0ogza0i7OvvIcLk9HC2qplOLUA7lV1FqcdCWEJxuD7llVlq1O4xLiABiAVUdRa1T65idMptOkZ2Y0+1h/p6RSUWoE735MMcq2rA8cznfHf2OEE0I9665lys7XMm8/vPqjCPSpPPzqBOjTMrnaX3ueu5cfSdLJixRyPq+NffRJqwNd/eS8tRlS0YbsZ0P9n3PgFYDiNTXRIyKokjr0Na8sv0V7ki/A606sJJdmLGQliEtFV86OTyZe3vfi0f01Nn3mcHPUOGoIKMwA4vTgklbcyE16eqnPVlRR4foWLBjAfGh8UzuNLne/WUYtGr6tIls1MX3THDaS4YoiqeAE4IgdPZuGgXsb5bRNDPOd8zpsGHDWLRoER07dkSlUhEVFcXKlSsVVS8jIiKC8PBw1q+XVkyRV26pPYamoHPnzhw5Inl+o0aN4rPPPlOsi5KSwPnCgeARQUDk1KlTtG3bVtnu2wSjUakUlSj9LmDSqamyuRSPuqU3/cx3IdJtx0txeUSeuqK7YqV0iAvF6RbZk1OufGnCTVpltZI9ueV0jAslMcqIQasiu9jCkaJqjhZVc0XP1o1+XbURZzbQuYW5wcaFT7dk0yrcgEqoUfLHSyx1AuhbhEsXvPqsjzZRJgZ3iGbTkeI6qXS+OFFqxeH20LuNdAckN53klUmrsOy3fMPnRxah11vQqgWe3/Ikt62q8WzD9eHcnn47naM6E643g+CmwupCiFrJl5lfcqr6FN2iu7FkwhJeGv4SUzpNCTiOCG/JmtXh5niJxa+GOtGcyC2pt9DSVOMFR+gjCNXWNKnIFxBX/tUs/stinhz0JNd1rVm787297/HStpe4odsNuMXAHq8oinx75Fs259Us/+ZwO+j/aX8+yPjAb98CSwF3/HQHH+37iFtX3crB0oP1vsd1X6tXHIToeHfvu3x/7PtGP/fytHjSEiKapZ66sdr+TmCxIAh7gHSg6aurXgSoHXM6Z84c3IK62WJOZWKT8zWGDBlCREQEkZGRdfZduHAhc+fOZeDAgX7K9ZJLLmH//v1+k4mNwfjx45Vkvu7du/PII48wfPhw0tLSuO+++xp1DFGUGlj27trJgAEDlPUWAXzvCjVqAbVKUFSWRi14l0Cq8ahben1bX59VXvzUN8JS7kLLLrEoM/CR3ltveSIxNSEcQRBIijKRXWJRZtuH1ZPl0VgM6RjD1mOlAQOVjhdXsy6ziGv7JhEfYVSI+kStmmKQ8iBizfo6itrjEckusZAUbWJIh1iqHW52Ztc/Ly972H29AfdyiZxclvhErw+4v8/9tI2OwGzQEmmMZH3ueoVclh1cRu9PelNqK+Wjg6+hb/lfKm1OzKX3cn/f+5mTOoetp7ayIXcDyeHJ9ZKk7NWWWBycqHVhSgxLZG76XKKNNRO2jw98nL+m/FX5XbZkjOGZ7C7czeMbHufxDY8rjyeYE5jcaTL39r633vpqQRBYdvkyHupXczOvU+u4rst1pMSm+O1baisltyqX1JhUPhj7AZ0jO9c+XL0weteSjArRseeGPcxNn8uewj2Neu60fknMn5xaJ0DqXKBRMaeiKO4C+pzzs59n1I45Lbc4OJ5fSVLHrs0ScwoolR4A8+bNY968mlvLJ598Uvm5d+/e7N69u85jUVFRbN1aE3BUG/UFTd18883ccMMN3HzzzQDceOON3HjjjfWOu3Zo1YcffojbI7LvZDmfL/2U22+/3e+5vuH7Mklr1QIOl4hapZJWf/ap+pDJ2Lftu7jaTqRJq1ghgF+7sEwQkd7VoQ/kVVJS7VBWT5GJWswqIjHKSJvowCvMNBZDOsbw/vqjbDlawrBO/vMsK3aeRBBgat9EthwrJrvEgs3p5lSFjTZRdc8bH26os2pMQaUdh8tDYpSJgckSsW09VqKk0NVGZoF0J9W3rfS4vNKNfJFYV/AZvVumkNY6ht9UVu7ueTdHy48qnu3A+IFM6jiJcH04s3vcycZtW/GIIBr3k1fdEafbSYGlgDhTHPO3zudk1Uk+n1i3bE5WmYs2Hcfu8vj9jd7b8x7v7HmHbTO2ofZG3D6/+XmqndU8N+Q5oEZRCxFrWfLbIVJjazI+nB4no5NGE2WIotcnvVh42ULS4+pOai7av4jcqlwe6PuA3/Z7et+D0+M/99E5qjNfXiEVGOwu3E1mWaZfB2NDEASBttEmWkW5eXHriyw6sIjBrQfz9ujTNwc1J/7UnYlO7y1KbplNmfT6o6BVq1bMnj37rBpe5FS8rt26M2rUKL/H1IK/1QGg9VZ+aFQCoXp/RS1Hb/or6rqrbITqNUrVhLw0UoRJh0eEd9YeRqdWMa6HNJGYGGXieLGFTYeLG2wRbyz6t4tCpw48IbQus5CU1uG0DDdIF4hiCzmlVkQRkqKNdfaP91nRW4ZMsG2iTISbtIQbtcpSU4GQVVBFizA98RFGBKFGUWeXWNCpVWSU7OCf2/5J/x45fDyrH2M+H0PvuN4MbDUQi9OCw+3g8YGPoxJUROjNgAfwUBm6hF9O/EJabBqrJq+iY2RHbkm5hUf6PxJwHPKdzVtrDtOnTSSTetYsvtC7RW9uSbtFIWmQLJcIfc1SZLJHHV5+Gy8Nf4k7e97JnT3vBODXk78y+D+DqXRUMrPHTD9l7otjFcc4VX0KleBPWXeuvpOZ38/02/bOnnd46ldpkehnfn2Gt3c3jWSX3TqIq/uFsvzQcu7seSf/N/z/mvT85sCfZuGAQDGncimWViV4J3lClTIzkDrvTpZZaRlu8FN9vxdMmRLYc2wsZKK+cdbNdR5TqeoStUYtxaGqVTXWh8XhwqBVKSlsvksmSStB12086BAXSl65TSEImbC/2n2Sy9PilfrWNlEmJbZzSIezrzQy6TT0ahPB2swiHvbZXmlzsvNEGbcOSwakC0RxtYMDedJFMClAKl+rcCNrDhb6TeTKwUry/i3C9HWI2u0ReX7lAfbmlvNbXgWpCRGovTnJskedXWwhIcrIN5O+YclvS0iN60aIXuCSxEvIrsxm8H8GM6XTFD7a/xHLL19O56jOLMn8EGPCL1RnPUSydT7Tuw5GEARahLSQxhvaiuMVxwNOPMtzBS3DDLw5o5eyLBtAelx6nWqNuelz/X43atVoVAKiaQ8HS2LYcmoLewr38M6Yd2hhasGVHa5kSOshjEoahUhdf9fpcfLogEcDThqOaTMGq8v/gmhxWqh2SO/1s0OeJVwXeHK4PoSbtKSZurNl+hZEJG+82lnNtV2ubdJxziXOK/ucTfh7c8Dt/VAmRJlwuj1+E10AVoeLUoujyTnKfxTIf66AoUw+GdVq70UsMkSnLG8UZtB6qz6kbGCzsnZdzW1qUZU9YNRke29UqGJ9eKs/PCJM91lBXG66EAQY1Iimlsbg0m4tOZBXoZAwwOYjJbg9opKYJlsdsvJOCmR9RBiwOt1+rd8nSiyohJpFZOPMBmWpKRkv/3iQ99cfxeX20C0+TGmJjjBqlaaf7BILCVFa7v/lflqFtKJFSAu0ai2PDniUv6b8lSs7XMm4duN4etDTdIqUqmlu7HYj1tzrQHBg1+2l1OYfCfDtkW+Z+f1Mqpx1a7vbxYRwVa/WvHdjnzqdgveuuZdp307z2/be3veY/cNsAJb+tpSvj3yN2aChyvQV3x37jihDFPGhUnlcojmRh/s9jN1tp9eiXiz5zb+xCmDuqrk8ufHJOmoapECoKztc6cct9/S+hxeHS/XTkfpItuVvw+ZqWtfpprxNvLLjFZweJyuyVrAmZ02Tnn+ucd6I2mAwUFxcfFGRtccjkVCITo1aJdQhajmcxeE6v6t1XCzwXS8xEGRVLStqs16D2lmNwWCoUdR2Nya9OuAio0VVjoDhOB1beInaq5xlZd0hLlSZWIMaZZrSOrxOAP2Z4uperdFpVMpqISCV7Bm0Knq3ifQ777rMIkw6dcDXILeU+1Z+HC+xEB9hVO7O4sL0SuMMwLd78vjXz4eZ1i+Rz28bxH9uGcj4VMnmiTBJeRuiKEqKOlJLoaWQv6/9Oy9sfoFNeZsYvnQ45fZypnSawvb87UxInqCo43B9KILgRtBUkatexN6imsWSAUYmjeTfo/+NXl33wqnTqHh5Sjo9WtdVpuPbjWdKZ/87N7PWTLQxGqfbybObn+WR9Y8QZtTSwfEM9/W+j+u6XseTg54EYMZ3M7hvzX2EaEO4sduNdIvu5ncsURTpGdeTrlFd65wb4LODn9F7UW/K7NKkbKmtlOnfTlfS+XYX7mbe+nkcKT99pKsv9hfv5z+//QetSstbo9+64B71ebM+EhISyMnJoTFdi+cLJdUOHC4PqnIDpVUOCtweKn1agcutTiptLip1akrPERH8nmB3uimscuAp0aEPsHRTQYUNp1tEU2FQCMFgMJCQkEDovkwqbS5ltQ1ZUcvWh83ppsruCmh99IiXCEEmu1bh0ool1w9o43dbnhBpwqhV17vE1pkgwqRjfEorVuzM5eG/dMGk07Aus5B+7aLRa6T3QFbyuWVWurQ0B6xRl8n8fxmn6O59Pdm1KiZahBkorLLj8YgIAjz23wzSEyN4cmL3APaDlsIqO2UWJ5V2F8nRMTw/9DO+zPxSWelkZNJIog3RvLbjNb4+8jWTOk5SapK/PPwlpjbvUHngOfqpX2FU0iC/40cboim2FmN1WeutvAiEkUkj65D71C5TmdplKg63g/v73E9qbCovrLBiM/5CdmVL9hfvZ/H+xXxxxRdc1+U6IvQRaFQa7ul9D26PvyiyuqzckurvgfuiR0wP7ki/Q1HbVY4qv3S+gfEDWXHFCpLMgRuhKhwV7C/eT7+W/fwU+6wes7ipG9JddgAAIABJREFU+00IgsCximMsO7iMW9NuJcZ49nMhZ4LzRtRarZZ27eov7r8QmP3xNk6UWPj+np4s2nScR1dksPpvw5VVOm5fvJ2Ve0+RlhDOf+8YcoFHe/6x+rd8Zn+1jRVzB9M1MaLO4w+9sZ6jRdXseXJsncfMBi1Wp5TWZtKp0Wuksie56kNuHIkNQNRpiRH8eO8wpbqgVbiRH+4ZplgiMgxaNd/dPVQp/TtXuK5/El/uzOXr3ScZ1imWw4XVXNu35osebpQmAsutznpXjekeH8ZVPVuzYHUWKQkRXNqtBdnFFsZ0b6Hs08Ksx+kWKbU4EARp5e87LumgXBB8EWnScSi/SpmQDA2pZO5Pc5nZfSbtwtsRY4zhiYFPAPDIgEe4Pf12zLqa5p+JyRN5+3sQNFVUCllUO9P8mkCOlB/h+u+u5/WRrzMicUSj36txX4xjROII5dwAK7JWsGj/It4b8x7twtuxI38HL0yeyMSvb2VzXgsSzYkkRyTj9Di5rO1lSpXK5V9eTmpsqlItAjB/63w25G5g1TWrAp6/W3Q3OkZ0xOGRJloTwxKVsCmQQqJERG5bdRuTOk5ifPJ4v+d/e+Rbnt/8PA/2fZAZ3WYo2z/a9xFGjZEpnadQaivl6yNfc3n7yy8YUf/+ZsjOIapsLkXpybm3vlGF8pfifCyrVBvbj5fy1prDdbYfK6rmhe9+O20mxbmA1SFZP6Z6sjPMBm29wTXy+1pYaVeygc3eShCgZt26esJxOrbwV6odW5j9JjBltI0JaXCh1jOB1OEYyis/ZjJ38Q4AxZ+WISvmQBOJIJV5PX9VCqkJ4dy7dBezP95GcbXDj9jlksX8CnudicbaCDdJF4bj3s9iXLiaQkshT2x8gilfT2H+lvlcsUIK4QrRhpBgTvB7vlFrRKtRozLkcsC1kJwq/5iEduHteGv0W36lc43BrB6zGJk40m9bqDaU+NB4VmWvYu5Pc3l1x6totXY2TNvAtZ2v5dI2l/LyiJcpthYzcMlAVmStAOCaTtcwPGG437FGJY3yq8muDafbyYBPB/Dhvg8BeGvXW9zx0x3+r11tRK1SB5yMvKrjVWhUGpYe9O9RWJ+7Xgmp6tuyL79O+7XJ7825xJ+m6iMQpFtvbwVBdAiJUUbWZRZxw8C2iKLI8WILapUUml5hcwaMkWwuvLUmi59+K2DWkLZ+CuuLnbm8/cthpvZNVLKamwtyRYWxHiKcmBZPUbU94GMyUZ+qsClt3bJvDZI/DfXHTV5ICILAfZd24rWfsrA43Izr0ZLOtVrTk6JN7M0tr9OV6AuDVs3bM3rzwPLdnCixkJoQznCf+uw4b8lifqWNCm9FR33HizTpqLK7OOxtghmQ2IWR7T9jS94WcqpyMGqMGDV1ywRlrM1ZizP2X3iyHuAv4W/6ZXMAmDQmzDoz1Y5qogxS3fah0kMcLDlI+4j2dbxjgJNVJxmfPF7ZX8boNqMZ3WY0JypO8GDfBxnXbhwalYb/Zv2Xoa2Hkl2ZzcvbXub5oc/zt95/Uwjwhu431KmJTotNY2hr/2wcX2jVWu7seSc9YqT8dpPW5FcaKO/z9ui3sbgslNvLvUuFwb6ifewq3MWivywiROP/XXp3zLvKfJpKUPH27reJ1EcytcvUesfSnPhdKmq7y429ERN8bo+oNFwEQrXd5bcSxJAOsWw6XIzL7VH86Z7eW345fKfS5myWCVGX26NEJDrdHjYdKfGmw/mXHslf1EBr6J1rWL3vXX2KdUrfRG4f0SHgYzJRWxzSZKK0TUuV1/qoWQn64iNqgMt6tOK7u4fy/T3DeGtG7zpqXla+9VkfMuIjjCy+eQDf3zOMr+4YovjVgFJBUVBhUz5f9R1ProDZm1tOTKiew+UHuOOnO4gxxTCk9RBGtxnNXb3uqnccQ1oPIbz8LgSVjWLPblyeut+Lv/7vryw7tAyQJvEeXvcw89bP46vDXwU85ms7XmPSfyfVOdbG3I1M/moypyynGNJ6CN8e+Zb1uet5ceuLHCo9RKg2lOSIZIwaI1O7TKV9RHsAHvjlAaZ8XTMxKYoily6/lP/b1nAd8w3dbyDRnAjAjd1v5NkhdZvSBEFg1LJRfjXVP5/4mTd2vkGMIYaDpQcpskp30w63gyc3Psm2/G3KvltPbSWjOHBz2fnA75Ko71qyk7uW7Dztfu+uO8LIl36pl1gr7TXWB0j2R6Xdxe6cMsXukG95T5RYKKiw0fe5VXy1+9wvJvDcygOMf30dbo/IrhNlyqRbbdtFbis+cR7sGEVRn0FsqNnn7iPEG4Ljp6i9HnV0PUsiXezo4PXL28eEnmbP+iEr6oIKO9klFlqE6eu9KMqVL3tyymgTbcLqslJgKeDHYz8yatkoen3Sixe2vFDvufRqPVqVgCY0k82V/6ba6X+hFwSBN0a9wTWdrlG2PTHwCR7u9zD/z955h0dVbW38d9J7LwRCSEiAAKGHLlIFpEsR7GDBwvUqiIJdr16Ua0evBRVUFD5BQAQFKdKrVOkkBEJIQnqb9JnZ3x97zpTMTBoJgtf3efJM5swp+8ycs87aa73rXf/o/I+quwPgoQ4P8XzP53FysJyYuzu7E+YVxrz983hg4wO8dfAt8sry2DVlF/2b96draFfeHfAue9L20OO7HuSUSg2aIS2GWBxfL/Q80fWJGmPmHx75kOGrhlNQXsCIVSPYcMG2Psfs+NkMaTHE+P4fXf7BmnFryCvPY/b22UbDXFBewPbL27lcZAoPfTH0C17r+5rN/VbqKjmaebTaMV4tbrjQhxCCPedzauWJHUrO40phGVlF5YT4WCecNGVavMw86t4tA1EU2JWQQ0tD77WbYoJ4f3MCybklaMq1lFXq2Xgyg7Gd6y8AZAsnUgtIyipmx7ksjqaY9B/MDbJWpycpWxrqaxE3V2PU9kIf1cH8Aah61F6uTiRr5LizNeX4uDnZTJzdCBjTuSlRwZ5GBkh94OrkiL+HMxlFZVZiR1WhlnFnayro1yqYHmGdWT56OZeLLuPn6kdCfoJdPWmQ0/xs7w/Q5T7Ivc2+sApXAAS5BVFYIfnjP1/4mTb+bYhvEs/6i+stDCjA7tTdeLl4MTRyqNV+uoR04cNBH3LwykFKtCV0Cu5EYUUha8+vZXjUcC6WXmTuzrlMbD2RJ7o+gb+bpD0OixxGpc4U+tAKLaOjR+PtXL0i4uCIwTTzaoamUkP7wPY2zw0k51rlj6dr0vn8+OfcGXsn0b7RLB+1nChfSXYI9ghm6+1bLbY9lnWMJaeW8FLvl4yhE4Dcslzm7pjL3vS9/DL+F6Nn39C44Tzq5JwSisq01ZbeqlDDBLaMmk4vKK3U4eVq8vz8PV3o0MyXXYlZxm3aNfUhwNNFiv8YEo27z2c3uEKWerzv9l9iV2I2HcN9cXN2MAq1q+tUGprJmi9vaKw4mMKxlHxKKrW4ODnUS2TGtkftbCx4ydZU2Cx2uVHg7OhA1whrca26ItRHFr3YEncyh7mgfUSAB1svbeXxLY/j4+rD4BaDeaLrEwyPGm53+/ZB7YmomAWKnrSKozbpbv/5/T/M2z+PCl0F8/bPY+mZpey4vIN/7f0XmgrLQpgFRxbw9u+2e3cm5CUw8aeJJOQn0CWkC9svb+e/R//L/N/nk1mSiYeTB1G+UUT5RnFvu3uNtLhFJxbR9duuxuKUvWl76busLyeyqw85tA9sz4DmA/B08uSt/m/RI6yHzfXe2P8Gk9fJGPOVkitsSt5EUWURzo7OaCo1xuTh0cyjvLb3NaOnD1BcWczp3NPG8IiKn5N+Zm/6Xp7o+gSBbg1TdGULN5xHrfbLK6mQPFwvG92GQcax1Qx5ck4J8ZGWT1k1tOBZpa38TTFBLNyRRIiPG0Ferni4OBk0JYo5e6UIHzcn8ksqOZlWQMdwa8pafaA2DPV2c+K3MxkoisIj/VtSVqmzeMioYY8gL9dG86iFELy05iTdowKICvSolzcNWPwuKmvE283J2I4rS1NOUC27PP+VEeLjRkpuiV1xJxXmLaIiAjwo0ZZwpeQKJZUl3PLDLQD8d/B/uTn8ZpvbOzs44+LgiLPPUbZlnwQetlpnZjfZhMDF0YWfb/uZSn0lLg4ujIsZZyXOv2jYIrJKbNdEuDm64ePqw7z98yipLOG3lN9wd3Jn15RdeDh74OzgzLsD3mX6xul8dOQjlo5cCkDXkK483uVxYxl5S9+WzI6fTYRP9c0gSrQlDFw+ECcHJ1r5teL7Ud/b5LaPjh5N19CuxiKanVN2GsOiX538iivFVxjQfACpmlQ2JW/isc4mIbK+zfryy3jr3qV3xN5BbEAsYZ5hHM08Sp9mfazWaQjccB718cumsEB1XvXF7BKj12vLqKmG2nyKDjImrdULNp3KICJAZtEjAjz4/WIe2ZoKHhkgEx91FQjX6vTkaMrJ0ZRbeeNqeOPhm1sikN7+TTHBRAR4GpNMYGrLNLBNsFEwv6GRX1JJaaWOAxdyyDdwoOsD8+/VSM9zc0JTrkWvF4by8RszPt2QCPF25WxGkV1xJxUWHnWgByNbjmTF6BWEeoQyrf00+jbta0XJM8eVkiskOr2FriSKJ9p8YXMdP1c/8srzeHnPy5zJPUOIRwjuzu5sS9nGyRxTN6OPj37M9pTtRPpG2txPc5/mfDLkE94f8D6DIgax8JaFzOo2i3VJ69DqteSX5XPbmtvwcfXh3vam5s6dQzozLW4aLg7yXL1dvJnUepJFqMEWPJ09ean3S9wVexcdgzvabZLRKbgT3UK7UaYrY1XCKt4/9L5x3ed7Ps+iYVLXemTLkeyYssNKIOr1fa/z7alvje8PZxxm8YnFtA9sz2d/fMbTO562KthpKNx4hjq1wFiyXJPyGEgdCFuJN7Vc3Dz0AdCthT9uzg5UaPVG2cwWAR7GJqq3dWlGbBPvOrfcmbJwH91e30y31zfz5PeWiQc1jNE3Joh+rYLxcHGkaws/k4ynwSAnZmho4uNGu6Y+lFToyCmusDrO1UIteS6r1LM7MafeHrWbsyMuhlJp1VD7ujsjhIxPZxfZFmT6X0Ooj6tRU8WWZogKDxdHY7uzFgEerDi3gqe2PYWiKEyLm8ZrfV+jpW9Lu9uHeYbR0WEuQjiRXnbG5jo/nf+Jhzc9zM7LO0kvTgfAUXFk3v55bLm0BQCtXsu2lG1WJejmyC/LZ+SqkZzMOUm4dzhncs8wfdN03jzwJlq9FhdHFyJ9Iukd1pvBESZVxr1pe+m6pCvHsqTc75sH3mT8T+PtHsccI6NGMi5mHC/0st938VDGIYavHM7xrOMk5CVYsDqcHJz45cIvZJdms/T0Uj4++rHV9gl5CZTpTDZnf/p+lpxagpODEw/EPcB3I76zqUfSELihQh96veBEaiE9ogLYcz6HzELbHF6QhlpRoFO4nzEEYg7Vo/aq4lG7OjnSIyqQHeeyjDFDNckTHexJmK87/VoF8fWeZEordLViRPxxOZ+DyXncHh/O+axiDl207LBi7Owc4MH8CR1Iyy/D1clRZvcrdWRpygnxdiMxS0NMiJdxPJdySxrc2KWZ9fnL1pQTchVxZG83J3KKK/A0fEf9Wwfz+s+nWX4whcIy2+Xj/2swb5pQXTJRURT8PFwoKqsk2NuV4kvF5JXLxNjN38twx6G7D9kt/3Z2cMbZyRGXgN38lrGfZ7FWghsVPYruTbrTIagDaodiJwcn1o9fT7CH5H87Ko58P+p7YyWgLbg4upBRksHnxz9nUutJHM44TEF5AZsnbsbL2QtFUXjz5jeJ/zaenLIcpnecDkCkTyQzOs8gxENKAoyNGUu/cPscanO8ffBtVpxbwQs9X7DLdW7l34pXer9CC58WzOkxx+KzNE0a8/bPo6lnU87mnSVdk261/aJhi3B0cESn1+Ho4MijnR/lzrZ3ygePbyQ7Lu8grTiNPk0bPvxx3XjUpRU6PticwI5z9rVALuQUoynXMqStLMPNLLLvUSdkFhHu707rUK9qQx9ertaGtl+MqpJmMNSGzH6/VvJivalVMBU6PQfMDO6ao6lczLbNbV66/xLuzo68MKodg2JDSCsos5D7vJRbgperEwGeLoT5uluJ/6QYvOrETGmo1aII87BIcbmWL3YmVcsbBxlWWbz7gsVsZPWRy8ZZR7rBo440HKO+oQ8wPQTVHnWtQr3pHunPl7suANcvh7o65JblsiphVYOFnVQutT1xJ3P4ezgTYehXODVuqnGqfkfsHbQNaFutRkeFroJDFf9GW9yame1t85IdcOC+Dfex4eIGnB1MM83Lmst8c/IbtHotd/58JyvOrbAp3qTCw9mDXyf8yls3v0WwRzD3tr+Xz4d+zvbL242hhinrptDcu7lFMUuYVxgPdniQUA95f7fwacHQFtasElsYHjmcUI/QauPZAW4BRsP/zI5n2JayzfhZu8B2bJ64mYuFF+kS0oWFQxdabS8QTNswjQVHFvD1ya+Zu3OuRex+wWG5vDFQK0OtKMpFRVGOK4pyVFGUgzVvUXc4OyosO3CJLww3sS2cMCQSe0cH4uHiaCURaY7ETA0xwV60CPQkq6jcyoDZC30ADI9rQnSwJ/EGpba2YT60CfVmbGcpzdgjMgBFgSOXpEdTUqHlye+PsmBLgtW+isoqDTrKYfi4ORv1KlRGCkhDbd4wVIX6gLiUW0JaQRklFTpiQryMHa/NH0Dbz2Xx+s+nefqHP6o1Im/9epZX155i8e6LgAwfzfz+mNF4puaX4uyoMMZAP6wPh1qFGqc2T9je2TPCKNdZk2G6HrHo+CJe3vMyuWW17ztZHdSGChE2fv+quLlVMLe0k0bs02OfMm+/7Ij3XM/nWD56ebXbujm5cZPn8widO1fKEm2uoxM6wjzDjNKoKval72PBkQUUVxYT5BFUK3bDhJ8mcDDjIE4OTuSV5fHQxod4bZ+JhxzlG8X4VuNpE2Bqk1VQXkCXJV34/uz36PQ6Rq4aySfHPqnxWAA9wnqwedJmejftXe16k9ZO4u2Db3Mm9wxZpSan0MXRhVDPUDZe3Mie1D02t3VycCI2IJbm3s2p0FVQri234JC/N/A9FgxaUKvx1hV1CX0MFEI0TotdwMnRgSk9mvPBlgQu5ZTY5Kf+cbkAN2cHWoV4GWhNtj1qnV6QlF3Mza2DjeGLlNxS2jQx8TE15dJYVA19gKwO2/LUAON7X3dnfp1pyqa7uzjS3N/DGAdPyipGCKkTogqvCyHQC/jxSColFTruNOgoq0JDiZkaY0upS7klxgIKczQzdPZIzikhwMCQiAnxws3ZkVAfVwuKXqqhgvHnP9Jp39THZsXgT8fS+HT7eRwdFGOMXU2KnjckKtPzy2ji60b/1kEs2JJwVToa3oaHoHnX51vjwnh17SnySypvSHreba1uI9I3Ei+X+he6mEMNfdRU4QjwwihTGbemQkNBeUGtj+OgOODq5IJr8EZ2XCngfkZYrRPuHc7GiRutlk9tP5WWvi1JLkxmwcAFNT5QADSVGr4/+z0v9HrByM3+V59/GT8fFjmM2dtn069ZP6Ox9nL24rFOj9ExuCN6oefVPq8S42+78rW+eK7nczJeb0e347uR39lcrmJOjzkUlBdQqi3loY4PWXzWWBxquI5CHyD70SnAst8v2fz8eGoB7cJ8cHJ0INjb1RijfvvXszyy5JBxvct5JVRo9cQEW8ZzzaExtIiyR++rCTEhXkZDrfa2yywqJyFTgxCCsf/dTfRzv/DimpO0C/OhU7jMXLcI9MDZUTEyOMwbnlaFm7MjTXzcDA1cs4zHBSleb54kTSsoxdPFkdGdmvLWr2c5l2HZubygpJJnfjhG90h/ZgyI5kRaAXnFFcb9queSXlBKU193OoX74e3qhHc9vx+w7VG7OTsyoatkJ1xN/LuhUKGrIKUwpU7rv7r3VdYkrmmQ4wd7u+LooBhDTbXF7O6zmX/z/Dpts6vwLbRFcfyzo+2WW/agoPDZsc+Y//v8WhlpgGUjl/HV8K8AiPaN5q3+b1mEZhb+sRAHxcGCqeLo4Mj0jtNp7d+acl053Zt0J9Y/tk5jrQm9wnpxJPMIc3bMIb/MfmPh6nDT/91kpEReK9T2LhTARkVRBPCZEMIqgKMoynRgOkBERPW8R3sI83VnUGwoKw6mMHNIa4uWPzq94GRqARO7yR821MeNPwxUvV9PXiEtv9TozSZkSKMTHeJljDNX1cZQQx+e9ZzatwrxYleiLHxRE5dCSA81u6icPy4XMKFrOC0CPRjcNsR4gTs7OhAZ6Gk0jOYNT20hIsCDbWezyC2uYHzXZsa4bvMAD3YlmqZuafmlNPVzZ/bQ1qw9lsbRS/lGMSSQD5OySj2PDYzB192ZBb8lsisxm12JOSgKpBeUUVRWSVp+GT2iAnBydODz++IJvgpjWjVGreKfg1vRMdzXGML5s1BcWczr+15nc/Jm9t+1v1YZ+8UnFwNYTNmvBs6ODnxxbzztmvrUabvX971OsHswD3ey5kPbw7CgZ1mRtYnM8hTAPpWvKtyd3JkWN61O8qeqSBJIA/z0dtmUVpUZjQ2IpW+zvlb87FGrR9E5pDP9w/vz9I6nWTlmpVUo5mrwybFP+O70dzTzaoa7s306ZHVYNnKZlXhUY6O2hrqvECJNUZQQYJOiKGeEEBZtuw3GeyFAfHx8vTMtd/WMYPPpDDadyjB2twC4kK2huEJHB0ORSajBoy4u13I+S4NeQE6xbJaqeqsxIV74uEmvsCpFT1NeKXu51bMXYnSIFxVaPSm5JSRmaqSSnYBdCVkcTs7D192Zf98WZzN0EBPixdkr0uM1b3hqCxEBHuy/kEuHZr7Mu62DcXmLQA9WHi6nrFKHm7Mj6QVlhPm5E+7vgYuTg9HLV6GGSVoEeBAR4IG3mxOLdl8gW1POLe1C2XQqg3MZGq4UltHUT07He7W0HYs8nXMaPXorBbaqUNUGqz4Mfd2dG7wEvyoOpB8gxCPELtcXYOW5laxLWsfMbjPRCV2tDPWUNlMY2mIoMX4NNyUfGFv3xgeFFYXVquXZgquTK65B29h3JYzhMdXHcs2hKApjY8bWdYgWCHIPoltoN+N7IQQbLmxgVrdZFutNi5tGkHsQrf1b80rvVwj3qv0DpTYYEz2G7k26M7D5wHpT6cwfQtcKtRqpECLN8JoJrAZs12g2AG5uHUwzP3eWHki2WH7ckEjsYGgHFOoje9Ltv5CDWj+iGr3ETA0h3q74ujujKAoRgR42Qx+24tO1hRqCSMjUGBOXfWOC2JuUw8ZTV5jQNdxufDcmxIuLOcWyerIGHeIeUQE0D3Dns3u6WexPZX6oBjgtv4xmfm44Oii0DDJ57Cou5ZagKLIripOjA71bBnLkkpyRTO0TCcC+pBx0ekGYb/UGYMaWGbx38L1q1wGIa+ZLp3Dfej8MrwYPbHyAuTvnVrtOv/B+PNXtKW5pcQtFFUXVrqsi2COYJaeWMG3DtIYYZr3xn5v/w1PxT9Vpmz0Fn+BU2ZLHuz5W88oNjI0TN/J2f1PJub+bP238rWclE1tPpG1AWzZc3MC4mHEWzQ0aAtF+0Xx76ls+OvJRg+63sVHjHaQoiqeiKN7q/8BQoNH0/hwdFO7o0ZzdiTkWdLc/Lhfg7uxItEEsSVUe23w607iOSldTaWwqIgI8rLjUmnLtVcVf1f2fTi8kOUd2ML+pVRBllXoqdYI7e9oP/8SEeKEXsnqyasPTqpgU35wdTw80tqVSobJHEjM1lGt1ZGvKjQY2JsTLOKtQcSm3hKa+7sZwktooITrYk55RATg7Kmw3UCOb+VVvqKfETqnVNHhit/A/rTPO0hFLmddP6lbYQmJeIgXlBYyNGcuIVSNYe35trfY7Y8sMjmQeseoTeK0xc+tMoyRpbfFm/1cY3z6e7LKMRhqVfZjT/QCe7v40Hw7+0Gq9p7c/zdCVQ/n46MecL7BunHG1OJN7hoMZB40FPDcKauPqhAK7FEU5BhwAfhZC2NYRbCDcHt8cJwdJ11Nx/HIB7Zr6GL0zNVu+5XSGscvIpSp8YxURgR4kZRUT9/KvDH1vO+VaHZqySgst6rrCx82ZUB9XtpzOQKsXxIR40Ts6EEcHhZ5RARbHrwpz5seRlHyLhqe2YCuBEx3shaLIfVwpkOwX1Zi3CvHmcl6pUd8aVAqgyQDfZOCE92sVjJOjA1FBnhxOlnTDMD/7ra3KdeUUlBfw3qH3rqtGxeYo1ZaSV57H41se5+2DtoWDFp9czGObH8PdyZ3X+75e6/jrrG6zWDRsEcMjh1/zOKU58svz69xZu1xXzvJzyy0q8q43jGw5kpd6v8TqsasbNDatQk1sfjnsywbfd2OiRkslhEgCOl2DsRgR4uPGkLahrDh0mVlDW+Pk4MDJtEImdzfRX1TGQEZhOUPahnAitZBLuSVkFJajKdfSysxQ3tkjAiHgQnYxm05lcCG7uFpBp9oiJsSL3YlSYSsm2BsfN2fevb0TsU2qTwypRvbtjWe5kF3MnOF1z2y7uzgS7u9OQmYRqfmSmtfU0DswJsQLISTlTu0cnZxTwmCzWGhkoAdvju9AP0PHkZgQL84ZkrBVvXdzrElcwzenvuH+uPvRCz2Oyp8vU6oXeqasm0LnkM481/M5UopSmLFlBtG+tjuTADwV/xT3tLsHNyc3+oX3M5ZMVwchBM29m3M29yzTfp3G0hFL6RDcocbtGgOLhy+u8zbfnPqGJp5NuC3mtkYYUcNgUMSgmle6Cni5eDE80r7K4PWK64qeZ447e0aQW1zBhhNXOJ+lobRSR8dws+4YZqW3cc18pS5GTokxNhttZqhbBHry3Ii2zBwin9AJGZqrjlEDFtzn6BAZkhnbuZkFX9sW3Jylkb2QXcyIDk14pL99jYaajp+YqSE939KjVj12lRtdUqElW1NuQQFUFIUpPSKMYQ71XLxcnaxajp3NPUtKkaSwtQkm56H0AAAgAElEQVRow7S4aTzQ4QGbPegAskuz6yWkrhd6dlzeUWdPPb04ndO5p0nMl4Uc4V7hfHPrNywevphhkcO4VGhJ9yysKGTrpa3Gwo2PjnzE9I3TazxuqbaUcWvGcTTrKLO6zTKWVYPUwNh6aes1mWWUVJYwfeN0frv0W522u7fdvfQO691gxTp/49rhujXUN8UE0TLIk/nrz7DtrIxDq4lEkAZF9Yg7hvvS3CBglGhgO9gKPbQM9jSGCzTllVcVozY/RjM/dysKWk3o3iKA9k19eGtip1pzU20dPym72NiuS+3GHRnkgaODYtYNRn5eXVGF+mBraiPsMeXnKYxYJQskmnk1o1dYL/ou62tXmGfRiUXcs/6eOp/PoYxDzNgyw6gLXFu4O7kzo/MMnu3xLCDLvMu0ZXg6e3L/hvutOp+cyD7BK3tfMcZAJ7eZzNv93zbKa9qDk4MTb938FpPbTOaO2DssWBeZJZl8+sen7E7bXaex1weV+kqKtcXV6m3YQmZJJqsTV5NUkNRII/sbjYXr1lA7OCgsuKMLuSUVvLn+DB4ujrSsUr2nJhTjmvnSItCDK4VlnEgrxMfNiWAbOhJuzo5EBHiQmKVBU6a9qhg1QEyIt+G17lVqb0/qxJoZfa9qDDEGiuCBizkEebkYWSGuTo60CPAw8slVZok9CiDIuDZgk/Gx8JaFrBi9gnJdObesuIXNyZuZHT+bMM8wq3ULygto7d+ae9vdW2fvMtA9kFsjb7XbocMevJ29KdOW8cTWJwDYcmkL0zdNp0xXxoMdHuSedpYPjV5hvfj5tp+NHVFa+rbEw9nDShS+KrR6La39W9PMqxkjVo3g3UPvAjIksi99HzmlOVYtrhoDvq6+fDfiuzpP4Q9nyo7qPcN6Nsaw/kYj4ro11CAN8PwJHdELaN/Ux6rTSKi3G0183AjxdjPS27adzaJVqLddLzUm2IvEDI2MUV9t6MNgoOtjqB0clKumrakPit8v5FkZ2Ggz5oe5Op89qLMNWx71+gvr+f7s96xJXMMzPZ5hQqsJTGw9EW8XefxlZ5bxzPZnqNRXsvT0Uubtn8f0jtMtfgMhRI2Ge935dexK3UUr/1a1OHsT1pxfw5cnvqRdYDuEEIxsOZLFwxbj7ezNgOYDCHYPtuhQ8nPSz6QUpeDmJM81tyyXu3+5u0YmwKmcU4xdM5ajWUd5vMvj3Bp1KwBn887y8p6Xmd5xOsMih1W7D61ee9XhkZTCFB7e9HCdw0uDmg9iVMtRlFaW1rzy37iucF0bapAx3zfHd+CJwdYZ4H8MiuHl0TJZpE7rszXlNnUzVMSEeHE+S0OlTlx1MjHIy4UXR7XjrmqoeI0J9QFRodNbGdiYEC8uZhdTqdNzKbcEb1cniy4hVeHm7Mi/x3Xgnl6RVp9dLLzID+d+4LV9rzEschhtAtrQe2lvvj4llcJ0eh3rL66npLKEIS2G0Ma/DcNXDjdKRa49v5bu33UnpyzHat/m6BDUgaLKIpadWVaXr4G2gW15tNOjzLtpHoqikKZJw8nBCUVROJ59nNt+uo39V/Yb1//02Kf8mPij8X2IRwgfDvqQW1pUXxYc6RvJ/H7zaeXXilHRo4zaDm3827Bs5DIC3AIYuWqk3c4nIL+LMT+O4UrxlTqdozkq9BVoKjR2cwT2cDbvLOuS1v0do74Bcd0baoApPSKM3cDN0TcmiFs7yOl3C7NEWXUebnSIF1pDhUzV7i51haIoPHBTlFVI5lrB193ZWOJd1aNuZTjP5JwSo5ZITbHwO3tG2CxlXjRsEVsmbWFIxBBuW3Mbjoojc3rMoW/TvgAMaD6Ag3cfZGfqTjZc3MDcHnMZFzPOqO2w8eJGynX2lQ5V9G7am5EtRxplLlUIITiccdii8enpnNOUaqVnGOUTRdfQrgxYPoDjWcf5+NjHxrh0bEAs826aR8cgkwjP6rGrebbns8b3iqIQ5RtFUn71sVsHxYG2gW3xc/Xj8z8+59aVt3K56DIv7H4BP1c/mns3J8o3Ck2lxu4+gtyDuFh4kXcO2pYarQ2i/aL5buR3dA3tWqfttHopm9DEs0m9j/03/hzcEIa6Ngj0dDFqJ1dnqM1pe551TABej1BnD1WLVFRa3ld7Lkg1wlqos9nD3J1z2Z++n/va38cz3Z9BURQmtJpAhE8EFboKRqwawRfHv2Bd0jp+SfqFNgFtmNNjjrGVUZ9mfRjfajz+rtU3gx23ZhwuDi4MaTHEYvnBjIPct+E+VieuBqRGx+3rbmf+ASlK9NKel3hs82OMajkKbxdvnu/xPK/2eRWQ8p69wnqRqkkFpIH/5tQ3VrTCz459xvO7qxcrUr3hYm0xgyIG8WqfVynVlrL2/Fo0lRraBLTho8Ef2VVRyy7NpnNIZ57p/ozd3oa1wZHMIzyy6REjE6e2GBs9lm9u/aZa3eq/cX3ixrdUBiiKQkSAB2euFNXoUau42hj19YBWoV7sTcqxKlJpHerN9JtbsnCH9BJVHeP64Hz+edoGtGV09Gg6h8gE3D9++wdl2jK+HPYlr/V9jTYBbXis02OUaktJyEvg/l/v5z83/4d+4f0Y3XI07x96n2jfaIseeVVxZ+ydvHPoHfRCz+s3vQ5I4xYfGs+LvV7ktlaS/+vp7MmsbrOM2hG3Rt5K77DeTGg9ASEE21K2WWh8LDiygO0p29k+eTuHMw+z4PACJrex7ALyUMeHuFt7d7XfQ//w/gS6B+Ll7EVsQCyxAbFU6irZOHGj0Uv95OgnrDm/hvXj11vNYD4++jGbkzezedLmGhkm1aFCV0FhRSEKdWMLhXmFEeZlnQD+G9c/bnxLZYaIAA+Sc0qqLYFWKwozCsuvmp53PSDGSKuzPuc5w2M5nV7IzoRsmzKqtcWK0dalynfF3oVWaCnTltGjSQ9CPUNRFAUPZw8C3QMZGzPW2FLp1b2vUqItoW1gW7vH0Oq13N7mdjSVGqOiWmFFIePXjGdq3FS6hnRl1blVTI6dzPKzyzmde9qoxNY9rDueTp4M+H4AE1pPYNHxRUyNm8oTXSUL5J529zCh1QRAUvHGRo+10pP2dPYkpSiFCl2FXY/TxdGFTsGdLMR8nB2dLUIJsQGxlGpLKdeVG5OVKia1nkTH4I6sTFjJvP3z2H3Hbnxc6qaaB5K1oXbu/hv/G/jLhD4A7usTyZzhbXBwqN7TUKloRo/6zC+w/S1opA7CjYnhcU2Y2ieS9jZiy44OCh/e0YW7e0UwqB4KbSA5u7O2zbJiRPRt1pcOQR345cIvDF051ILaFuIRwtwec41SoGGeYTze5XGjN24Lp3JO0f277nQM7sh97e+Tx9ZVMi1uGn2a9mFrylbePvg2lfpKCsoLWH9hPfeul9758JXDmf/7fMbEjCEuMI7vR39v4TFH+UZRqa/kP7//hwc3Pmjz+LtTdzNjywyLrh9V8cHhD3h4U/WyogMjBjKz20yrmHxGcQY6oWNky5F0DOrIjM4z6s3+2HhxI49sfoSSSusWc3/jLwqVNtWQf926dRPXM15ec0K0mLNOJGYWyQUrpgnxso8Qyfv+3IFdhyipLBFjV48VK8+ttFj++R+fi7iv4sSp7FNi+dnlQq/XGz8rrSwVvZf2FouPLzYue/K3J8Ud6+6we5zUolSx8NhC8eKuF8Vta24TQghRXFEsSipLhBBC5Jfli+KKYuP6e1L3iDWJa4RerxdLTi4R+9Lkb5dbmis2XtwockpzjOuWactE5687i2E/DBNPbXtK6PQ6q+NnlWSJY5nHRJm2zO4Y/8j8Q2xP2W73cxV3/3y3eGzzYxbLvjrxlYj7Kk5klWQJIYSo0FUYz62uWHt+rbhj3R2iXFter+3/xvUJ4KCwY1Nv/Ll/PdCpuS8uTg4EGdpbkS7b03P0W4j4uxjAHO5O7vw47ker5f2a9cPP1Q9FURgcMdgiHuvq6MqY6DG0DpCUyklrJ+Hs4MyY6DF2j+Pr6svU9lP5NflXY+hhxbkVvH3wbXbfsRsPJw+WnVlGoHsgS08v5Z9d/0nPJj0prixmTMwYvJ29eW7nc6xNkip4Xwz9wljY4eroyhfDviDaNxo/Nz+bx/dw8qCwopD8snxCPW3H8z2dPY3hnOowofUEq+avI1uOJMI7giD3IEoqS+izrA//7PpP7o+7v9p9XSm+gpezl0WoZlTLUYxqOarGcfyNvxDsWfCr+bvePWqdTi/S8g3eTGm+9KZf9hHii6F/7sCuQ+SX5YuZW2eKPal7LJZrdVpxRXNF3LryVvGPzf+wu71OrxOzt80W686vE2XaMpverBBCvLn/TdHzu54WnvnxrOPiiz++EHq9Xuj1etF3WV/xyKZHxH3r7xOLji8ScV/FGT37LclbxE+JP4mPj3wsTuecFpoKTZ3OM7kgWcR9FSfWJK6xu86oVaPE7G2za7W/nNIckV2SbXz/W/JvIl2Tbnz/ydFPxOGMw9XuQ6fXidvX3i7ivooTz+983rj8u1PfiZlbZ9ZqHH/jxgHVeNR/qRh1beHgoJh4x6o37RcBWWdkP63rAWd+gU0v/dmjoEJXQVJ+kpWw/pm8Mwz5YQh9m/Y1xpTNMWntJF7Y9QIOigNv9X8LgSD+23gjTQ4gVZPKS7tfoqSyhAHNB/B4l8f57dJvDFo+iDRNGuFe4dzT7h4URUFRFNaPX88nQz7hq+FfMb7VeF7s9SI9mvRgdvxsYgNiGR09mkERg0gtSsXN0b5Uqy2EeYax5NYl9A/vb3edZ3s+y93tqmeGgBRNGrx8sLFwR1Oh4Z9b/8lP538yrjO943SifKIstnv34LvsvLyT7NJsHvz1QXan7mZ2/Gw6BnXEx9WUg6jQVVCi/Ts+/b+E/8nQhwXSjsjXjpNhx1tQnAVe9Uu8NeiY/u8O+f+QV6Geok0NgWCPYJuhj+bezXk6/mnCvMJoH2TdkmtY5DBCPUK5XHSZRzc/ysTWE5nReYaFkNGW5C2sTlzNnW3vJMYvhvjQeE7lnKJfeD8cFUdmbZ+FTq/j61tlBeSlwktM+XkK42LG8Vrf1xjSYgj5ZflMaj0JD2cP1iWt49mdspDl2L3H6nSezo7OlOnKSC9Ox9fV1+pzIQS+rr4086y5hZiHswcv9X7J2LLJ3cmdH0b/YLHf53Y9x7HMY6yfsN64/zXn15BXnkfbwLZU6isRCLo36W7VGXtq3FSmxk2t0/n9jRsc9lztq/m73kMfFsg4LcTvXwqRsFmGP5J2/LnjKcoU4p12QrzbXghN1p87FiGTfDO3zhTHMo9ZfbYleYuI+yqu2gTb5aLLYtbWWeJo5lFRqasUpZWlNtfp/3/9xUu7X7JYvvHiRrHx4kaL93FfxYmntj0lhJChiLiv4kTcV3GiuKJYnMk5I/61519i9+Xd9TrXgd8PtBqDiuKKYhH3VZxYdHxRrfZVoasQB68cFJoKjTiScURsT9luEdbZeXmn+OHsD7Xa15KTS8TdP99tfP/BoQ/E63tfr9W2f+PGAQ2RTFQUxRE4CKQKIf46mYyQWPmnyYSej4Cndan6NcWud6E4Ex7Y9OePBSjTlpGUn2RTFe6JrU8Q7B5ss9nnMzueIaUwhWWjlvHOgHco15UT/208j3V+jOkdpwOw+MRi1iWt41zeOSa0msCtUbeSUpTC1A1TmdtjLh2DOhLgblLSG9h8IMfuPWbkMT/Z7UkuF10GpBfbJqANgyMG4+RQv4niR4M/sqvc5+TgxAcDP6Clb+20w09kn2Dqhqn8+6Z/s+vyLo5lHePXib8aP+/btC/55fmU68pxdXQlTZPGohOLmNJmCjH+lo1zvVy8CHQPRAiBoiho9Vpj+fzf+B+BPQte9Q+YBSwF1tW07g3jUZfmC7HjHSFykv7skZjw7UQhPrlJiAu7hHg7VojU6hNOfyY+OvKRWHVulc3Pfkz4USw+vlhsT9kuxqweIy4VXBILjy0Uh64cMq7z4q4XxQO/PiA+OvKROJd7Tuj1epFfli9e2PWC2HV5V40ebHFFsdibttdIc8stzTV62PXByeyTVklTFZoKjTiVfcqCIlgd9Hq9WJ+0Xmh1WpFVkiUS8xItPj+QfkDEfRVnPN6RjCOi77K+4kD6gXqN/W/c+KAaj7q2Rjoc2AIM+ksZ6vPbZLgjYbN8n3tBiIu2b9RrhrMbhDixSohL++XYzm2seZtGREJugpi5daZIyE2w+uyfW/5Zo1E8eOWgmLl1psgqyRI6vU7kl+VbrbMvbZ9VCKW4olh8f+Z7cSbnjN19f3PyGxH3VZwYtWqUEEKyJObsmCN+SvyptqdngdnbZouRq0ba/Ew1rPvT9tdpn+svrBdxX8WJ5IJki+V5pXliycklIrUotcZ9bE/ZLiasmWBkjczcOlN8c/KbOo3jb1z/qM5Q13aO+D7wDGC3x5SiKNOB6QAREX+O7GedkWtQSws2tK3f9iYkbYOnzvxpQ6K1Qc84W7aVojTvzxsLUKwtJik/yab63U3hN9ktC//s2Gd8eeJL9t+536jJ8eTWJ0kuTGb12NXo9DpmbZvFba1uI8o3ir5N+xorF8f/NJ5uId14uNPD1TYRuDn8Zk7mnDSq+DkoDrzZ702769eEx7s8jk7Yrk6N9ovm/QHv11kru1NQJ/o07WPVCNfPzY+xMWONXdJ3XN7B7tTdPBX/lFUJu4eTB2FeYcZKxgpdBbobsIr2b9QfNdLzFEUZBWQKIQ5Vt54QYqEQIl4IER8cHFzdqlcPbYX8u1qUGPSRPQyx4OA2UJQOZQVXv+/6oKIEjnwHecngblCaK/lztYM7BXfix3E/2mR2TGo9iUc6PWJzuw5BHbgj9g6+PPEl438ajxCCsdFjubedLPvWVGpI0aSQX55PC58WfHrLp0bdiz5hfUjVpDJw+UAuFl60O7Zwr3DGRI+hS0iXqz9RpKE/m3fWKAdqDp1eR7h3uLFZQm0R5hXGZ7d8RrRftNVnk9ZO4q2DbwGQlJ/EhosbcHaw1gyPbxLPh4M+NAoqfTT4o79ZH/9jqA2Pui8wRlGUi8D/AYMURfm2UUdVEz6Kh8/6Xf1+SnLAxQucDZzbIINnnXX26vddH+Qnw5rH4PLv4O4HKFD65xrqY1nHmLVtFmmatDpt16dZH2Z2m0kTzya0DWiLoigMjBhIv/B+VOgq8HX1ZdWYVYyLGWe17ezus3mm+zM81/M5m+2+VFToK3h408P8e/+/63xetrArdRdPb3+a/PJ8q8+Wn1vOxLUTa6WrXVs80fUJxseMByTlbvvk7TY1w5MLk5m0dhJ70/aSW5bLfevvY1fqrgYbx9+4/lFj6EMI8SzwLICiKAOA2UKImln/jYn85IbZT3E2eASa3ocZxOXTj0HzHg1zjLqgQDIY8A0HB0dw8/3TPWpNhYbz+edtepnVYVPyJubsmMPKMSuN5c770/fz4MYH+XLol+jRs/zscp7r+RxB7pbslud3Pc+pnFMsHrbYgnddFe5O7nw/6nsivBsm1HZLi1voFtrNJo/6rti7aOnb0qjs1xAY0HwA2SVSzGrRiUU4KU42ZWDdHN1o4tEEV0dXKnQVOCgOOPxv1qr9z+LG/LVveU2+Xq0Raz0Mejxkeu/TDDyDTUUwlWWw8QX4efbVHae2MDfUAI/ugVtebdhj6PWw50MoMFUIcnCxKSZeBX2b9WXNuDVE+NTNGLbwacE97e5h/u/zeWrbU4CUAH06/mmaezenoLyAhLwEm5KiHYM6kpifyF2/3FXjcdoFtrOSLK0vnB2duVh4kdzSXM7knmHJqSUIITiedZy96XutGhpcLT479hlj1oxBp9dxNPMoR7Ns90AM9Qzlw8Ef0jW0K008m7B4+GL6NOvToGO5ahz59trOREtyYdd78nr+H0CdCKdCiG3AtkYZSW1xaT84GuJ42ecgolf999VhouV7RZEViqqXnbhZGjXf5jDy7fofp7YoTAXFAbwM+sa+NVfB1RmXD8iHT16yPKfKUlj3JLgHwJwLVqvvSdvDynMrebHXi3YFjWyhtX9rWndrzRfHv6BMWwZI4aWJrSeSWZLJsMhhdhvBTo6dTEu/lte8t19GcQazts3i7f5vs/zscg5cOcAdsXewOnE1m5M3MzhicIMeb2jkUGL8Y9CjZ8GgBXbX0ws9U9bJisyWfi1Z+MdCXuv7Gs28GuH6qA90WlgzA5w94fm6hcjqjV9mw4mV0CweohogDHqd48YrIf95FpQbdCeyztRsqIWwX4KdvFd6r35mrZOGmcU7k7bKi+/xw5BxSpaXtzRoQeh1MjzRkCi4DN5NwdHws+z9WC4bPq/hjpG8R772f0a+FhmarHa83ebqheWFJOYn1rkjyfn880xeN5k3+r1h0TT2hd0vcCb3DMMjh6MVWmZ1m2W17benvmX+7/PZfcfuOh3zatHCpwU/jP6BcO9wBkUMIl2Tzu7U3QyOGMzU9lMbvIVVa//WeDt7U6Yt45UDrzAscpjNFl0OioMxkakXenR6nc2k45+GYoOGt42iqEaDOusM63Ttjvkn4sYKfWgyIeMEdL0Xuj8EQdadyS1w7ldY2F8+8W1hyW1w4DPLZUJIb7M4B85vhci+4OQCv70OP8spPHo9/DANts2/+nMyR3h36DTF9D79KJxZ27DHSNoKIe1NeiaqoW411LRO2lGjQR8eNZw149bg71Z9v8OqCHAL4M7YO3l257N8eORD4/K72t7FnO5zyCvPI7/MOmkHGKv/Fhy272U2BlwcXUjVpLI1ZSuLji/Cw9mDNw68wZJTS+z2QbwanM8/z4jVI9icvJlDGYcsBKuq4t0B7zI6ejR9mvbh61u/rpXc6jVDkew2T8fJ1a/XkNBVSiKAW9075NyIuLE86qRt8jV6EDSrRQfmCo1MDKYfhfD4Kp8Vg7bURM1TUZQOH3SEPo9D7nnobugI0rQznP0FygrlxeETDtvmQUhbaGdfZ7lO6P6A5Xt3fyi1bczqhYoSuLQPdBWwZDzcs8p0kx3/QX6vigIbngW9Fh7cxObkzay/sJ43+72Js2PtvTh/N39mxc+iuLKYcK9w4/LOwZ1JKkji0U6P2jU2vZr2Yn6/+bTwaXFVp1sfvLj7Rcp15VTqK5nQegJLRy7lfP75Gju41wfNvZvzWt/X6NGkh7EfpD08vf1pfF19ifSJZHfabj4Z8kmDj+eq0Lwn9PnntTte/iV5fyduhpiGzR1cj7ixPOrzW6XxCuskPcHzv1W/fkh703ZVUWxoHWXO+gDwDgOvUEj5HTrdaboImnYBBFz5AwrTYNAL0KwbrH6kYZIoQkD6H6awDsi4cXmh9B6qoj5JlEt7pJF295cPIQBD/JhjS0GTIQts3P3kA06nJa88j8T8xDobKr3Q0/3b7gS5B1kYIU2lhvE/jWfwisH8dsn277c7dTdzds75U6Q8v7n1GzZN3MSOyTsIcg8iwC2A7k26N8qxPJw96BXWi3N553h+1/NcKrxkd90wzzBCPEJQFOX6CnuAdJrGfSIN57VCeaF8vVxtecdfBjeWob78O0T0kbHhw0tk6KKimrjYVyPka5INQ60Wu1QVPlIUCOssi15u+wSCDeGVMEO/v7Qj8PVo+PFRmGyQn9xfJXxSHxRnSW740WWmZR6Gqryq1YmVZbD4Vvkd1AXNe8GUZdB+PBRlyIdD5zvhrpXy87yLUgf77C+gK4fss0xqPYk149bUWejIQXFgTPQYPj72MeuS1hmX+7r68nzP52ni2QS9sP2wUTuI16fx69UioySDMT+OqdZoNiQe3vQwc3fO5cCVA8YqRVuYFT+L6R2nc1fbu6pNPNYLuUlwYhWU19PQasslA2O5/Q7zDY5JUvrWeB//xXFjhT5iR0JwrPxfNaDZ5wzebhWUFZp+xJQD8iJ0NaNxGasSA623DesECb9Kr93bwMDwCpbhjtPrICcR4u8HnzCIvMkUkrkaGKl5Zpl8tTqxNM8UUxYC1s2ElH3Qt45TTVcviB0hb0xtqXwYufuBf6T8PPcCZJvNDtKO8pPmPHvT9vJGvzfqfEqPd3mcUm0pwe6WlapDWgyhmVcz+oXbztY3927O8fuO1/l4DYHX9r5Gfnk+BRXXpjr1qfin8HHxqbbxL8B/j/6XIxlHaObdDC9nL57u/nTDDSJpm7ymHtsvlSTril9mw5ElUKX9WKPCIwACWkJJds3r/gVwfXnUaUfl9N8ebnkVOhsE9Y1VhOdsr5t3Ub52mwb6ShPbQYWTG0T0NhlicwQYpCyPVCnA7HqvKTTRcqB8bT0UfJrK+K89VJZJL3ydNcPBiKocapDju/0byzGe+lGGKfo+AXv/az3GxC3wdhvIOW+5XJMJKx+CjJOm/RVdgeX3wbY3AEV+Z1nn5MPQxQvSjpBTmsPFgov2x10NHt78MGW6MmPvQhXvHXqPx7Y8ZtG5/HrBl8O+ZOOEjfQO631NjtcpuBMbkzfy6t7q+fJBbkGEe4fj4uDS4OwT44ytvh2F1IS0rlzSPa8FVj4oHY6/Peo/ASsfkMm5yTYq1AtSoSxfGhEHR2lMFUdLD9AcqqHudIf0mgOq6AhH9YOoDba37TBJxnI73WG5fMAcebySHDlOkMlGNeFoC6oHfGEH9Jphf71CQ8bfx8xQ+zaz5lKfXitj6INfgfkt5INNnVDkJkk2SlmBjJsHmulLJG2H48uh16MyDg9QlAaZp+S5+IZDnsGjbtIRWg+HwGimxd3LtLhp9sddDcK9wtmYvJGT2ScttELm9phLr7BeVhWJ1wOaejW9psdbdmYZS04tIco3qtr1Jsc2IqNCLRzLOl2/7dWENMjkt7P9atIGQUUJHF8h/y/+21BfewS1se8hH/0Ots6D59LAxUNS5gKjZVjDFlRDHRJru7O4thwcXWxzrB2doJt1H0B0lZJkH9Xfcju9HgpSwN8GS+HA59ID7j8X2gyXHnnGKesxFVwGJ3dTXBrkBXn0O5lRD+soj5O0TSY4HRzk8VwVqIQAACAASURBVNTzBJl8jOwHZ9bJh5o5kswSsXotPJ0kj1V0RbI9ej4sS9aPr5APqoHPAdKQJOQl8FLvuntb0ztOx8nByaLfH4C3izejo0fXeX9/RQyNHEpsQKxRAdAe1p5fy2d/fGYsMx/fanzDDUL1qPMvScPnaSMcWB2Krshrp6xAXnc+9vVZGgRquCNugvyrDkLIe7bNCGk3blBcX6GP4NaSjWCL5ZB1RhammH/Zkf2kkdFWQHaC5fq6cvBrIS+gwjT4/QtLqtsvs+E9684k1UINe1Q14j/PgoUD4ORqWDHVFAYRQvZhjOoP/efIZWuflNO2qjKVHgGSQmj+ABA6OU41Bl5ZDG1HQ7ux8r1/pPSCVbj7wdiP5P/m5yqEZL5E3SxnI06u8masKJbZc+8mko4YM0TG+5t0lL9B2hGyCpJJKUqp2/dkwNcnv+ZUzqlG4SD/VRDuFc53p7/j0z8+rXY9fzd/on2j8XLxarzQB0D6kbptq6uUifDwHvJ+VK6BSVEZW3ETZd6qOqTslzP1jc83/rgaEdeZoY6V3l6udSkzWedMcWkVI9+R3M09C6Sinjm17ean4UlDvDs7QRarpB02fV6cI414XeARAK8UWD/FI3pLlbuVD8oQjVqxWJgm22rFjpIeMMgLq+CSNbWw31MwdZ3lMhcvcHAyKei5esPoD0wXp3+kLM7R6+HgItj6Brj6Qtf7LJNC2QkyzKHG1UGW/G4xxEW9w+T0N+ssTP0F2o6S3tXCAfzTuRmfD/28bt+TARklGVwsvEhJ5V+4Y/a+T+EVX1lFWg+kF6ezL30fy04vq3a9m5rdxAeDPuDbEd8aRa4aDM17yDZ0IPNEdUFJrpwJxo6U129wm5q3uVqooZqyAjj0VfVx8eY95T1kb6beEPi0H/w7zJRnagRcX4ZarTSsGnfW6yAnwfoiUBQZplCZINl2fgy1zDTNzFsoya77FM8eWg6Qrx5Bku725VDprTq7w4i3pdevInaUFH46uNhsLLm2k5GKIsMZ6oV5YYfJmwDwj5Izh6J0WbBy/jf5QBizwPKYKj0x2sxQpxyQlCyQMe/zv8GScSZ+tX8UuPryxeklvHPwnbp9H/s+hcUjua/9fYxsORI3J7e6bX8j4Y//k6/Hl9dr8zDPMN4b8B6rx66udr0T2Sfo8HUHOnzdgaOZBmO6+RVY9XC9jmuBm2bCrfMhtEPdudDeofB8uky063V114nf9T58O1GGImsLNfSRex7WPmEZI68KRYFuU+W9Xx8Ne10l/N9dMuxqC4XpsraisgRSG4/Tff0Z6pgh0pM0R36yLMywVzIeXIUBotPCW61g/0L53t1PJhPNvYWSHOuqxPrCOxRGvS8r/bybyErIK8elB97jIQgya1bq5AJd7oZz600KdnsWwFsxkh1SFR4B0qOuLIPvJsEOM3GotmPgsX3S8Kcfk9WTIOPW5rOSTlMkV1ql4YEcp18EPHlceh0BhmTWJwZVNgcH6PEgGbnnSE2po+bGppcgeRc3N+3Lm/3eNDaj/ctBWyFZNCCvrXqoObo4ujCkxRBCPUOrXc/L2Yto32hC3ENMD75d75keFFeDvIuSvvrIThjySt23VxQZQvtXAPxex9nXjrcgcROsf6b220T0hvGfQ5MO8n11CcXVj8h7obIYUg/WbWwAvz4vcz7b58sQYlWkm9mUtDqGjeqA6+sOcvWCu1eaPL+Mk/KJVVkm41/qD1MV/lHg4Czj2ACFl2XIwcmM1xnW2dJQV9WivlrET4PQ9maFMUfhjxVS+Kkqut4HQi+5pyDjx2EdTQ0MzOEeACV5kjetLbP0ir2CJWMj76L0hFQ++fJ7Yf0c03puvtCqSpmtd5j0TPwiZNzf3wbrYODzPO/XlXeP75Aee20x9HX5+lenTmWekuygno8CAi5sb7RDRfpG8uO4H9ly+xZiA2ItK1OvptuRELCgK+x6Vxpcvd46f2ILF3bIUOMfy+HzwSYjVlXyIOMU5FeT44ifJuPah76C37+s3Zj9W0gRMZUhZe86qyyV4wuMlsewVaFcHY58J7WAIvvB/RttG+q0I3Lfga3qHjaqA64vQw1yCpSbJJ/wi4bDl7fIYo+p6+zrezg6yR9DDX2oTIgAM+PTtIuMDRfnyAtRr7WuSmwI+IRJmdK0w7BhrjXPWR1Xz0eg3TjphaUfs4wfm6PDBMnV3jpPzjRaVGEH/Po8/CoZGsaHhJufifWRewF+fEzeMObwbiLj0Cq3Wy2uiTfTG3FwZEGbXnzSpLkppl6aV/2Np9eZHpCaTPvrNTSEuPZNFlRvqvsD4OpTd0NQBxRWFBpDH4UVhfJaBhi9QM7S6ovyQpm0dveXVM/5kbYrec2hyZR1AUsny3su7bB0Blx9rdlGn/SG96tJ2g99HV7Mlonurf+2bQyrImGTdBzU0KW9opeMk/LcovrDlKWySK0u2PaGnG3e86NkaTnYMJdpR2XurEUfabRrM/564Poz1JtfgY/7SO+kvFDGn74eXXMMK6KXTLaBadpvPtWPHgg3PyM9WQdHeC5Vvm8MNO0MZ36WF5AajqiKW+fLhF/CJkBYesrmiH9AVkKm7Iex/7WsrgQ4/ZOcOjp7mEJD7n6mTH5ukqT4Vb2BVM3rEwZPWVHgpVyZoDVDenkeV9qNkvKvQsC3E+QNaq90P/scrDVUTKryl40NXSUsGiZDQ9cSaUekgQqMkV5XQ1So2oG7k7sxhOTu6C5nmVE3y1nm1RgH9eHmHiBnWeUFNSfe1Cl+8m55fXmFynvK3c++iJitMZZrDA99BUa8Iw1ibXBwMex81zQjtudRq+Ns2hna3Fo32qBeL9lVPR+RjuDRZdZxaiHkMZp2kccoy2+47lNVUCOPWlEUN2AH4GpY/wchxMuNMhqQxkZbCqFx8M+jMum1ejq83xFmVyN+NPoD0/+552UoxMesWKRJB+vQia0nZEOgaRc4t8H0vz3o9fLcAJramS1knoZzGyUrpL11f0H8o+QUdOzHJh1rN7MbRjWWnlWU6mJHwq/PWhYC2dDXtiodH/icNIi/Pg+j37cejxp+gtpNoRsCaUflgwxkfsLxGpUH9Joh+bmKAkNfMzkKjQBnB2eO3XvMtCAkVuq2fNAJbnpS0ivrA5VR5BEgZ5ju/vaLyFSUFZr+P7HSNJNz97N0CFSarVcTeS1U/V0u7oJlk+HBLdbqltWhJEeO18ULOt9tzQZTkXZU5m98mknHZdubckZqrnYpBCxQnSlFiq11mChtg7k2fepBaaz7zbacwdz+Dbh4yuPcEyUfWo2A2liqcmCQEKIT0BkYrijKVbRVqQHGxOBZGSLoNBlGvQfjakF/0uvlxXH5oPSwqxqejFNSFjHld1g4EK6caPjxgyxb7zhZVk6GWnfvNsLBQQo7jfukGuMiZOXjQDs8UP9IeZzYEaZl7v7yhhHCFH7wqtIZ3r+F9ATNZx028Mb+N/j65NemBTFDZNXipX22N8g6ByjwXLp1XLyu0OtkMcWyO6unPhWYhWLMHxSNjeDWsp0byNCbl23Z1oaCGvoAZI7FyU3KI5gXPdUVJYaZl3uAfOBUV3SmouMkeDkfOhq009VKV3d/kxojmGZ1N8+2fX3nVZn5bnuzdkJjJdnyoaIoMO6/spDMFtKOyIeIooCLt7xm1TDghmdl6A9keKN5T1PMHSTtNeOkKRfQcqBMSF42K7BTFGjRW+aXfMLkrLiRqjJrNNRCQuXsOBv+GicQA6an49JJpi8p/n6IqaENUnYCzGsqS6zvWwcTvrBeZ+c78oc6vUbG1RpBYxiQLJDiLAhpV/MP13aUpPTZQ2h76P+0/W4yioO8cDPNyn/9IyVlUVchk6qOrjKGao6KYhlSqYH7mapJJaukSgjDp6l9SlTWGVOCsibodfYz5XodfNxLZtsv7pQUKXt82UIzwf26Zt6Lc0w3bF2QnQBbXpNceZAzmFUPS0dAReohO0yBP6oP5WWeqbk0etkU+Ha8/K3VUF9xjtxWRXmRvB9O/iidF1sQetlqTs3XBLe29KgvH7KW1NWWy3tn/GeSOaVqx9y9Gu4za3Th5gcP/SZzFrbCQnkXpVeshjDO/mIqDS8rMDFqqqI428TYKroiwy+2MHmJqeeooxNM+U6O5fNBsO9jmRBVFBi/UP61ukXmHYSQdQmf9ZffD0jJCcXRMg9xYiXsNlMyPP6DtDGNgFrN/RVFcVQU5SiQCWwSQuy3sc50RVEOKopyMCvrKmKTaoIgqn/dQhO+4fJpfvl3+aPYEltq0VtqLu/50BAaaURdByd3mWBobKjJRfMwQ/w0SbVycgVNlvT0qj6UVMpcDa3MPhr8EbO7V2nu691Eeuy2DGf2OTkr+uIWWYVZHXa+Iys6bWXLEzbJfbUcALd9Jm+go0tt76fgsrzhXbwt6VJVYavTz/J7ZE6krpri57fCzrdN03snN0mVUx8USdulQagqgVuYJuVst/4bmxACPu4Jb0VbFnABx+87LlUFhZBeb2CMDH2pHvWGufDZzSaN5l3vwfd3w4r7ZAzfPGShovVQmHnCpAsTHCuPW5ovE8hfDJJVvcbxp8O8ZibP8/5foZ8hIV31fnVykZrt+xdaGjQVuRfk+NVrs2kXk6H8v7skVVRTxZbotPLaU4376oel2JgtBEZbzmh9w6XxFjo5S+1yt+X6TbvIWUBxtrz2AqPNwom+8vNLZiyuY9/DMbNCJQfHmrtO1RO1soRCCJ0QojMQDvRQFMUqjSuEWCiEiBdCxAcHB1vvpC6YmyJpenWBs7vU7tj3MRyzwy3tdj/M+B0e3SsvTpXp0BgY/5mJptaY6DARnrkATexk1uOnWcbaVDi7w9xLMKR61bYXd7/IinMrLBdG9pPaJVVj0ELIZSFt5ZS8oBp2CJiMmi2P9tBiGe9rM0Imgnyb228UUXBZfh7Ry76nWlECiwzsmRXT5DS4OEfORCqKYNkddeumk7RVShSo+i7ObvJ6UpXk1JnKpSr0TPWh1MZO6bOiGPTBheQA22oQocmQSb/gNtKjzr8kHxjnt8gCqO/vlnrjA1+Q+xrxtmQ5VZVZsIX4B+D5KzLefHGXXGYed04/Kn9bP0NX+qAY0/+HvpYhRXUWkXpYcur9IkwG2Bx5Fy31cVQd+PSjchalHk9bLlUef5wBez+C3v8wORgeQbaTiafXyvBGVepiiz4wO0F+J1XR5R54NlWGCbPOWhfYhbazDK1lnTEV2wG0v01KPDQC6pRNE0LkI7uQ2wkKNRDcfEydxusCneEmbd7D9ucODnJqF9rOtsfdkHD1vjraVG2hKJZCTiCN0BvN4eJueUGr2iBV4eZbY4PeS4WXyC2tQnuL6AUDn7VmoCgKzNgnjb9niCk+nrzXYHSqGHb12OYl5idWSi87YaP0eByd5X5bDoALO20nKIVeJkXvWmHSOrH4XEgmSuph+RBJ3Cw5u56BMOs03L1KGruf/lHtd2GETivHUpWp49XEZKjV+GzkTZbrqLxbezUBQsjY/rB5stDik97y+zCfTajGIqi1zOPotZLTHdRGyt8WpUn+r4OD3JdK/VRDGpmn5cOqsgw2vSxLoFU4u5l+l+Q90nj2N2NHVTf+0lwZUlR/z9RDsPsDuW5JjvWD293P0uNVE+/r58rX+3+V4YjTa6W876k1cn+DXjR99552DPWZX+S1ZMuOuPvZDnuqYm+VZTJ+XjVJ2eNhqewphJxN5l+6NiXz1I71EQxUCiHyFUVxB4YADdzVtYFw90qZIKwqafq/Bic3SW0szYPD38iboVm3eu3q61u/tl6oLZc3oV+EpX62CkWRXskVg9bKhjmSK95unGXip++Tcgof0s5s7O4ygx47CnpMNy2PHig96oIU6wToHWbTT9WrN09e7f1Ixj4HvSi9nuQ9cGAhDJgrp7cxg6H3Y1LpsKKk5vh66iHphVflvnubGerCVNnBvqoEbvpR+WD5uCf8f3vnHR5F1f3x793NbjY9IY2EJIaaAAFpShGpCogUC1IsFFFRFAUVFfiB2F55rbzYOxakgyAq0gMI0ntCAiQhvWeT3WzfPb8/7tZkN5uENGA+z5Mn2Zk7M2cmO2fOnHvKU4nVH7KrHgJCOnJFrVfxqKfYgY4ldy2TfaHx/KF568NcwTz+F1/edpBjVcmgWP5mYvHr/vESD63rPY1b/lVcLPh1Mr8u2ceqR5PknjLXK/epfl1kgfy3Ws7XW0L/2g8DEpfxbS3WNwDM3OG4fVgX/lac9S//vsb04wpx73/4Odz7IQ8P3fs2n1yXePFz0lbw76R9gpv9RGJd+ONlfnwyVVfC9m+txZcAUJMp6tpY1BEA9jLGzgI4Bu6j3uZmm+ahw108VOlmx8tyw5RxP/HFP+q9qxf3vYg/0/50XKir5K3Aks0TR2o598n+OZ+Hi+nV3KKuLOKKU2JWfFXTnaP6AM8d5bPmFuJHc8t40s+Obz1d7gPmXag5SkWRD7zXlseNW7iyh79+dxnPQxwBHpUDAJ/0srkDBs4D5l+p3SRo2l4AjCtEe/wibIp68CvAjD8dIwcscbf+bbg1lnfGcXutku/bUn530Hx+Le54wVHhGDRAQAy/PhIvrqRL023H6XCXtUwtAP7QmrKa+6MB4EFzBmBBEreCqz4sFHlA0lau6Mqu8kYURr1Z/tO2cLyqWFyJFleJqpgr74hb+ZyQ/YS3s0lWDymPgOr2ED9nIuCd1jzcttc0oJ25fs2hT2yNMSyy21vVWiV/e6gpNNYVehUv/xB7Z/WILYMWSHyff9ctcxquQgMbmNpEfZwlop5E1J2IEojozaYQTOAasFg2Zel84qRqDHUtISJkVGRArq3iu/UK4srEEvnx1yvAT+O4lUomrjx8QvkrubqMz8q37sZvQnvOruO1SzLNc9M7/g/4wkVdZpGYK6uqiTalacDybsClXdynTSbHmg5/vMTTe8d/blN24V14+rFYyifkLOdU23rF3SfxqKKqCu62J3goqWV/eWeAL++wZRHqVTw/oL+5gUTVCJWr//BrVtWlkn+eT85axt/xPDDvHD8fIuCn8TwWeE8Nt6ZeY1Mu/hGAXyS37tVlPDTPntA4/r15cjfQcQSgzOf+ZHUZAHKtAL3sLGrAVqZBIuPb9H2aK7udS4B/lvMsyKpRR90m8GvbZTw/vx7mCb+ej3JXTi9ziWFLIEDgLfy7JfXl/mhFAVe0ZHKdbFYTlnO7/0tbcxALIgmfoE35i2dJj3zXsTlHI9KyGgcINAye/tyPaEmprxpDXUsYY9g0bpOzFWZ/bAH/nHeWNx0e9n+2yaFeU4Gej/AbqOsD/DVW4sXdEhYfaOJ/eYhgdD9g5t9mq7CGJJl/vwR2vc4tX4t/XJ7FrVMPsxVqyRAk4p+n/8GVe1V/+jMHuQVqb6ke/Ji/fTyxCzXSqq1jeQILUXbupa3P29wDRan8TUDqA0w1Z98d/bp6hMqVvdxtFV0lEkcWwON3s49zRU9km/9gzBb6FlND+7CDH/ECSM8d57IZteaKclr+ILMnpBNwdi13iVh6kxZd5BNlL6XyyURnyOze5ABbvDPAU7i9ArlVfeRrHjoKcm9EjPmYd1ayxKiPWc7fjCwPyfbD+HfO048/5INieSgd4NryrwnLNtnHq7v1RCLuliq6yBV0/9l13389aXkp5ALXjkjEb26LL7OeFrXaoMbcvXOxL2tf9ZV+rblFbTJyRRl9GxB7h+3L7elrm6y8ZxnPqvz3S+DT22zK2PK6qrSLlKjaesye0Dj+2m/f/7Jqr8l2Q7g8ltha/0h+c1XFK6h6mVsPGffLOotCyT3Fy3F+1pdPdjmLECnP4X0sS9OBk3a+fcvkX2WxLUQwsmd1izptH1e2VYtzBURxyzT3NJ8Ifa8ttxqrUrUOjD0hnbiVeW49cPUg0HEk9wlbsvzssWTX7fg/W7hZUQp3f4hEjr7gqsd4YrdtArXPTN45CLAZC2Gd+YQvGfl5uZtw95A6+rUtnY3sP4d05A+sjnfzjOA2fYAJ39ev04zFD73eSYcngH8Hi1N5Yk4jVsurimBR36g8f5pHTmx6st4ZcwaTAZmKTCh0iuor/Vrzm1ckBl676piRBnBr+48XeeZe1O38Jvbw5P7Gihz+6m2xvBT53Eosz7bVDndGdF8AjFuiFn+rtdekWcFbJvjS9vLIDq8gnjBUGyzbXtlbvYvPsW95yFjrbnxCU+LETVKRwwtkicyRBuEJ3AVkibbYPItPsD21lyvqC79xhe8VyCcx5Vd5Ea6qMGaLMfb05Q86eyv4oZX8oVz1rcEey6TXmTW8kP6Yj/kDobKk+oRb57H8gTD4NW6p+kfxh83HCdy94+p6Sr0dU8GdlTwAuHtDkWdLJmkoek/nLpUru/lEcX2QePG5AVfundA4/rax9Tk+GV4fP3g9EBT1jYpXIH8N7DWt3qGIflI/564PgIdAWiwrsaR6GJTIg4eXpf5tLoKVy1upAeb+kGarMagt96Ur8vmrckANbbuk3nwf9rGs5VlcGVpkCW4PtGrPrdtTvzjWdXBHaByfEEzb56ioiYAr+7jF5qzxsgXLdbb4yAOibCnZlonEuHv4ul7T+KSmzN92bq9l2UJMqxLZkxci0lbwxC17q7vr/e7PLbgDeKf5dP6wkcj4pJtJX/374RUITLR7IwjtxJtMkNH2P3TF7jd5xEbcaN6aLuJW537c+tYmqYlWbfnDfN+7/Pj2k9R1Ydj/uV5nP3loH0PdyAiujxuVxPf4k3/cirq3HDNTrC7GC3tewLH8Y9VXDpjDJ32OfQf8dF/1PpdeQTzl1qTn1p9IbFPC5Tk2t4flVdPSJq0m1wdQvRZFeY6jL5Ex4NmjvOiUttzWfac2MMat6it7HJs4lFzhNc5dlaK1YHEZZJuvV0AU9837hfMHlqrEFibpFWhT0hbEHs7D3gDuOyWzm8mdHM6QeNlcBhY/7LtRwIdx7juTPPANMGQB/7vdkJrHnljJz1VbAWyYwdPCm5Kej/HfjVTFDm1621xMFv99EyAo6huVwiSuRK+hYI/eqEeWMguVehclTbUKHqlQlOLEohZxSxewfaEt7okKc8p332d4l5q40dziey3TdXKOhdA4XhzHEt710MrqVq7Yw5bF2G5ILc7Uju4TuRvHEgMOcItz4k9czprw8OS+ZHv/+PDFwPDXuQuqdTdbISOAFyFaP53//fsLPCnIFe0G28ILXZXEdYflQWl5XbekYbvLyPQJ4W6fsFokioV1AQrO2x7EDdmcozb0fBR4ci+Pw28M/CNsbe4aKV3cGYLr40ZFFgiAgLWP8bof9SDCN8K16yNtHw8LA1wrQ09fQAnb66KnL5erPJu/Qt+zjC/vPrH2Qt39pmNKvMy/umWqU9lqadS1OUTbwcBLFx3LC3j6un+AWPCL4G8wg1+1FeQ6tIK/UUz+1TEEUK8CkreZ46cTa35V9/Tj1y4oFgjj8b3Kg//AKJcjYIybTtwWpm7hE2GWB2hgDHc3uetqXnyJ14evTdJUZA8e1WGJJ2+odne1hTHXDUYaCkuWaCOWta2KYFHfqFhiWq+h9GZmRSbm7p2LC8VOqpjZ19115avTlJvX21keTx8ERrzDixPln+MTY1olcOhTXqPCWW0Le+xT3kuu8EiMquVqpd7AbU9Wj9uuDSIRV9J6DZ/sNBp40lC2G/eAhV5TefLMrWbLWa/mSmvSKsfoBYC7MEx67h4oS3cfTnbH88CcU9biR1lPPIHcl1+ueRuHcxPzqAvLw+u+L4D2w6EXRyLnlVegPu2ioJV/JE+iGeuksFJVInpwP7ulTkhDNZBuJoxyObLnPA/1hQsgozla6bYnavZjNwKCor5RsViEzqITaonGqEGmIhMao5Omu/avwK5eAQfN5xEQYXYZXoHRfCLr5M/AlwP5zP9HnYEdi3iNEncVE/UaHiL37xfcvXF5p/NElXs/qLl8bE0YtDyBZNdSHjlz4gdb0oo7+s7i55Rhbggs8eLZhbFOQudi+vEStAfNSTK1iSBwcn1IV8+eiWHxwGObULEzERVbf4fqpItwM6kPL8/gqvCXPZZzsJR7bWqL2gkmtYvyuLWgfMsWKHbuRMaDE6DYY3an9Z/Nv9tNiKCob1QsE4j1KW5lplNQJ2watwm9w5288lqSGwDXFnXfWcCSYkeL+twG3nxXVcxlFEtsSt9Z3ZCqSGTc0s0/z8PoAmOcN+a9Fjw8eZW1EyuBNVP4pGjbwbXbtrKY+5svuHAZ2SPx4hEcheZ+ljWFJjoh+qsv4T/6Hpg0Th6kdUCXxR9CuowMt2NJr4di3z7XA1q146F/3SdyP3Fj9CWtA5WHDiGlZy8oDzh3/5FOB9XJUyBD9RK4RISytesgS0iAR+vWkK9d19jiukTwUd+oRN3Gf4d2rnlcDSSXJOOrs1/hhV4voG1AFWXImM0fW5dYUnkmr4LW6R7bRJNfa+479XcT8WEhpBOfsCpN46FpjdEAYtR/gfbDARBPU66aFOIKS6KLh6zmcRbaDeX+fs+A2h/DjPbyZQROmgyxv7/7wTXg07cv5GvWQpt2pcZxusxM5L/zDioT96PN8o/hP8pJEU3GbE1kqxakamKICJ6d+fdfffoMfO+8s9oY9ZkzuPrYVASMHw/Zrd3R6mHbW5j6+HHo0tIQ8c47kLSJhCTKTWhiIyJY1DcqXkFcgYZ3cT/WBWqDGpmKTOhdpQwHd+A+z9rWyABsVnPeGdtrsaXRbk0x1PaExvHkD21F/SMg3OEbylPgez5qS0muDRYfu6iWNlDvacD8NJ7SXgeICIXvf4DMadNQtmZtnbatit/dd6PVjBkQ+9Ws8Cv+/BOVifvh2bEjtGnOu6pUbP8bmoN/8ip0lcVQ7N4N1cmT1yRffTBptch8bCq0yckQh4RAX5DvdJzy0CFAJIK+sAAF7/wHlUftqg6KRPC58074j74HPv36QZ95FeXbqhc4idtcrAAAIABJREFUU585g4rtfzfWqQAQLOobF/9I4Kl917SLXuG9XEd9ALztUl2tWYuiVuTaXvUtir62HXfskw5q65JoKm6dxEvLVs1sdIU1uqRuk26k1cKrRw+oT5+GLrMercTsuDTwTgQ+NAHRX9Tcl1R9/jyksbFou3EDmFQKk1YLkactnZyMRij37gWrzEFEyFbg2DfIXhMJz/h4tPtt8zXJWFc0F5KgOn4chpISSKOjoc9y3nKu8tAheHXrhqhPPkXpDz9A5MUjdYgIsm7dEPPN19axJd9+B0NxcbUom4xJfOLYLzkJrJHa+wkWtYBLjucfx7y981BQWeB8QH2+lA6d4c2TU5bJyA61bIZr8XmPeLvO7oJGJzCGV7Zr5JroIpkMsWtWo83HHyFgvOvsS3ITRWOqrIRRLoehpBSZTzwJ9TknNUTMaM5fgCwhAUwqRcakyQ4RJ6rjx5E2fjwU+/ZBr+Ax7kSAOCQE3rfdBlN9JzxrATkpmeoRFoqQOc/Bu3dvhL/2KsIXLqg2xlhRAc258/C5YwDEvj4Imf0MxH485E6flYWU3n1Q8betZrbs1u7QXrniMDlpVNpyDIylVRpsNCCCohZwSaW+EhkVGTA1ZE0G/0gAjMcZW0Kc+j8LLC2vvZsmuh/w8mXekukmRZuWjszHH4fIxwewhI1VofTXX3F5yFDosnOcrgcAfT53CUhjY1F58CA0F5KcjjOUlMCQnw9ZAo/gEYeGQHuZ+7SJCDkvzwcMRkS8vgTBs58HADCZP2754XvI166FsqYJyGugYscOXOzcxXoeFjxCQxHy9NOQREZCEh0NQ0EBSO/owhP5+aHtb5sR+NBDAICSb77FlVH3wKhQQHPhAqDXQxptm+D26toVMBqhuWgrYWCqVMJ36FBEff4ZPIIbLxRRUNQCLhkcPRibx29GhG89qpC5Qizh3aC7T6r/PiQy7kNurC7y1wGGwgJUHjqMwg8/wtVHH3NqVVYeOAhDYSGy58xxGaKmz+U1xb179USr6dPh2cl5qKW4VSu037ULAeO49e7ZvgN0mZkgnQ76zEwY8vPR6vEZ8Bk4EGJ/P2DmLujHb0DZmrUgnQ6Vhw453e+1kreIP+w1yckOy0u++RYXu98K0uuh3L8fWU/Ngj7H8YGl2LkTxtJSSCL491vWlRsKmqRk+PTvj6gvPodnhw7W8bIE/gaoS8+wLhMHBiJqxf9gKCiA6kQtY+3rgaCoBVyyP3s/5u6dC6VO2bA7DuvMy52e29Cw+72JMJbzjuKyhK4wqVQwlpZCe+kSFHv2Wsd4de8Gv7vvAqnVMBQXO92PpE0kgp+eBWnbtgh/7VUHRU0GA+Sbf4OxvByVhw5Bc+4sPFpxV5Nn+3aA0QhdZibU53nCkVdCAkp/+QXp9z8AU/it0JYaULZqFUQBAag8dNjp8TVJSVD+84/L89Tn56PiT+f1Qkw6HZglrryKi8dQUgyxry+YRAJpdDQgkUBfWOgwpvDDD1G60lZ8StaVvy1ozp+H+uxZeLZtCya1ZW16hIej09EjCHzAVgQrY8IE5L/9Dgrf/wAVf213eR7XSm16JkYD+AlAawAmAF8T0f8aTSKBFkOFrgJXKxqhuM25jbzAkOe1hZXdzBgreNanrGtXlG/cBH1WFjIm876KcadPgUmlCH76aTDGQHo9yGCASaeDSOqYLi6JjETo88+DiUTIe30plImJ6LiPK/uKv/9G3oIFKO/bF0zmCX1WNvzv4dX/vHr2ROjcuRD5+UMWF4eQOc/Bs0MHaJK468RQWASDWTEGjB+Hsp9+hi47G9Iox1j59Ad4Wdf4C+fBxNUbLV999DHos7PNlrrj94VJJOj472FAr3dQqABgLC6BOIRHFXn16IH406cc9m+qrIQ4IBA+A23Nhz1atYJHZATU58+h+Msv4T/mXkS8/rrteIxBdfIk1KdOI2zeXOgLCqC9dBkB990HnzsGQORd/+Qyd9TGojYAeImIOgPoB+BZxlj9Y74ErhvGtBuDzeM3w1daQ53j+nDWHE7W1AV7biDEfv6Q3dodXubXccVunjUniYoCjEYo9yXi0h0Dob10CZqUVKT07oPKg9Ut1/w33sSVkTweWhIRAUN+vnWCTJt6CSI/P6iOHEFl4n54336bdTtpdDSCH58BkacUTCZDyKxZYFIpPMJ4yQJDYaFVUQdNnozWS1+H2Nfxe2SfUWlR8FXRZ/NoDe0Vxxhv0uuRPm4c5Bs2oGjFCpR8/4PDekNJidVnzMRilHz3vUNoncjHB23XrUWrRx9x2K71kiXwHzECJoWC+6SroDl7DiXffAOTSgWPoCDE/Pgj/EePRtQnnyDsxXlOz6EhqE3PxDwiOmn+WwEgGUAtMxMErmf+Sv8L8/bOa9jJRMBWxN5VSU8Bt/iPGom2a9fCMz4eUZ9+Al1mJphUilt+XAl9QSE0Fy7AKJdDEhkJz7axAMAnyKqgz8uzKjTPDrxutOrIvyAihM2bi44H9iP0hecRu24tIpYuddj2ypixyH/zLaTfdz8K/vMuAD6JBwCGoiKQTgtxaAg827VD0OTJuDRkKJK7JqDwgw8AAEwqRYfERIh8fGAoKqomGxEh8v33IOvSpZo1rdi5E9pLl+ERHALV8RNQ7t/vsD5o0kQETrIV+6r4fSsqtv9l/Zy3dCny33q72jF9+vZF5RHew9Pik7bH+/bb4Dd8GIwKBeS//QZxYAAkERFQ7t+PjCkPw6hw0mSjAahTHDVjLBZATwBHnKx7CsBTABATE1N1tcB1SLm2HJmKTIhYA09ljP+MpxeHNV3h9RuNkm+/hSYpGW0++hDSdu0Q+MD9CBg7BrkLF8GoqEDU8uXw7NiBR4UAuOXnn+DZvnoB/9ZLloA0fKLRZ8AASDu0R/4778D/6DEYiooQ+eEHCHnmGacySGNvgXLfPphUKutEnDQmBm2WL4dXjx7wHzkCIXNsDQKCH38cIl9ftJrOY8xLf/oZJq0GnY4fcxp/rL14EdLYtojduAGwS/Emkwm+w4Yh9KUX4Tt4EPTZWdXqnfgOGQKRnXKXREVDn5ll3V6x/W/4Dq4eg686cRJycwKRs+vl068ffPr1Q9Hnn6N4xScIX/AaZHFxIIMB6lOnoEtLg9etdSsFUBtqragZY74ANgKYS0QVVdcT0dcAvgaAPn36OOkFf3NwJO8IYvxiGjZSooHZl7UPvcN7w09ac5nGyfGTMTl+co1j6oVXINBjSsPv9yZCfeECtMk8TCxtNE/AiE+6AO2lSyj6/AtoLlyAdz9bk1yP0FAodu9B4IMPWJcREXRpV6yTaCJvb0R/9hmMFRXIeellyDp3rjGBI3zBAohkMoDI+kAQ+fjAs1NHkFaDos8/hyQ8HIEPcj906PNzQEQoWrEC4oAAyNet5xZ/hw4o/uxz3PLTjw5+3uIvv4L6zBlI28aCSaWI+eorGEpKkDnzCYQvWICQJ58EALSaOhVEBCICYwwmjQapffshdN48hMx6CgAgiY5C5ZEjICJokpJhlMvhc8eAaudkeeAEPfwwmMR5nZyUvv1gKi+H/7ixCJo6FQDg2Y7HzWsvX2kURV0rU4kxJgFX0quIqBbVZm5OiAhP7HgCL+x9oblFcUmxuhhz9szBsqPL3I7dkLoBCw8sbAKpBOqK/913I+gR7l9tNX06PMLDAcYQNGUK2v22GTnzXkSFnU9WuS8ReYsWQV9gi3wwlpUh+7k5UOy0dV2X3nILZF26wKt7d6vl6wqxvz8K3l0GZWIixAG2LkKZM59A8edfQL5hA1RHHbsDMcagSUpCyTffwqdfPwSMHQMmkUJz/ny18DZ9Xh58+veHR1Ar6K7wlPWytWuhTUmB2M/m7y7fsgWpvftYE06MJbxpgUewLRlKGh0Dkbc3TBUV1lBBn/7Vu7Z7BAWh0/FjaL1kscvz9hs+HJ5xcYh4803rg0wSFQUmkbitl1JfahP1wQB8ByCZiD5qFCluECr1lVg+ZDla+7rpgtGMhHiF4LsR36F9oJM+dlWQa+XIUbpOlhBoPvzuvttq8YW/9irCXn0FjDGIAwORt4RHKliSU/jf3N+quXAeknDeocQSQy2JdHz7Y2IxIt9/z206tO7qVSh27IBixw4ETbG9IXmEhcJQWAiffv3h3at6wa6gSZORnTgb3v37wf/uu2FSqxE8axafCLWj7bq1MGm10KWng7S8l2TloUOQdekCWRdbPIPIxwcmlQr6vHx4BAdDHBqKtps28oeX5ZiPPoJWjz0KAJDGRCNw4kSrP70qVSc9qxLxztsAkS00EADz8IDfiBGQhNW//ntN1MaivgPAYwCGMcZOm3/c9CS6+TCajJjw+wQcLzgOvVHvOu26mXnz8JtYm7IWZ4vOuh37RLcn8OM9P7od1xK4+thUyDc1bT2J5uTKvWOQ+5otLdpeqSp28LRnWbxtDkDWOR4QiaA5b2uykPUMb/3l0bq6m642NSssr/serR0Nk5hvvkH0N18j8j/vIHDChGrb+Q7iRa5y5vAMRpGXF8LmzUX+G28itf8ApPYfgOT4zshbvATMwwPSmBgYlUoes336DHwGOFrCFvkN+fzBYywqgj4/31q3AwBMCgVy5r+C5PjOMBQWIeLNN9yenysYYw5K2kKbDz9Aq2m1rPFSR9xa1ER0EMDNmwJWSwxkwIROExAsC8Zjfz2Gl3q/hOkJ05tbrGpojVrsuLoD/+T+g8NTDtd4Q/544UdkKbLwf/2atptFXTGpVFAdOwbV6dMOyQg3Mqbycpdxu223bIEmOclBUYm8vODZvj3kv/0G1YmTiHzvPQSMHg3m7c2VeD0QBwai9ZtvwKdvX4fl2suXUbZmDdSnzyDizTfh089xPfPwQMzKH2BSOWZL+gwYAM92tnK60nbtwcRiaLOzkTXzCYQvXIhWU6fC7y7HmjCWNwLLG4Ly0CHkL16CDnt2W6+RyM8P0raxCHp4CqTtGrcOS2NwXVbPy6zIBIFwi/8tTX7sf/P+RY/QHpBVqTecXJKMaV2nQSKSIMQrBN1D69mqvpF5Z+A7mN1jNgym6oXSq1KqKUV+pfPykC0Jkbc34s+fs7VKqgP63FwYlUrIXKROt0TIZIKxogKiAOcJQ7K4TpDFVT+fqM8+xdVp06HLyAATMYQvqF6oqK4ETaze71J19Cgqtv4OANUSUSz42E10Wgh56kmnY6WxsYBYDO3ly2j9xtJqxoU4KAhBjzxizaq0+KjFdrU3GGMInT3b/Qm1UK7LFPJ7N9+LsZvHNvpxMisycSTviLUec0ppCp7c8SR+OF8luN5kwIztM/DZqc8AAJ2DO2PX1V3QGrWNLmNd+PLMl5j21zT4S/2RXJqMLEVWjePn9Z6HT4d/2kTS1R9NSioypjyMzJkz66ys0+9/AOnjxjdqdbeGxqRUAkQQ+we4H2yHNCYGEW8sRdQnK1z6ZxsCj1Cbn9ajAXy2IqkU0uhoyNetQ868F6utZ4yh9eL/g/dtfQAAhuISiPz8HEqwXu9cl4p6zZg1WH3v6kY/zubLm3kUx54XQESIaxWHFUNXYGrXqQ7jCIQPh3yIe9vxMKkLxRew9PDSWvmBm5IQrxDEBsRCa9RifuJ8HMipuTv5F6e/wEcnWv78sfr0aWjOnYP6+Anoc3PrtG3gQ9yHqtixszFEaxxEYoQ8Pwfevevebdt30CB4dW/ctz175ewR1jAPBEtMtFc3530bs+c8j6uPPgaA1/lozEp2zcF1p6gvlFzA1fKr2HhpI/Zn73e/gRmlTlmr1317ZibMxLj241CoKkSmIhNvHn4TYd5hWHZ0GRKzEq3jVHoV2vi2sbpibo+4Hb+N/w3ZimyM3TwWR/Kq5QfhqzNfIaU0Bcfyj2HpoaUwmur+2l4XDCYDHuj4AN4Y8AaCZcHYMHYDHuz4IIwmI+btnYe9mXurbVOsLkaRqnrGmDsqtm9H3mLX4U0Njd48iQRUTzWuCV12DvzHjoMkKgrytbXrkqI6dQqZj8+sVuTIWc+9xkIk80TIU081usKtLxZFLfL1rVZbpL5ELnsX0V9/hYDx452uF/n6Qp/Hvwfhry1A1Ce16Jh+HXHdKer1Kevx9r9vY3/2fqSXp7sdn1ySjP3Z+zFs/TAkZie6HW+hXFuOrVe24oVeL2D92PXIVmRjW9o2EAhnis44hK0dzj2MCb9PQGYF77Th5eGF9oHtEewVjLHtx+L21rejVGMrKq42qPH9+e9xouAE1AY19mbtRW5l3SzBupKYnYjBawcjTZ4GxhjUBjV2Z+5GmbYMuzJ3Ib2i+rVc3H8x3r3z3TofK2fuPMjXb2gy5WXIzQMzTxrp6qCoy1b/ivQJExD08MPw6tHDbZF9AFD8vQOVhw4h+4W50OfkIG/xEpSuWoWMiZNgqqx0u31DoDp2DBcTujVqWc1rwbNDe8SdOY2448fcD67tPtu1g++gQfAIcd4st9X0aWjzIU9NV58906QPzibBktHTkD+9e/emxkJj0FBKaQoZTUbrslJ1KWVWZFo/K7QK2n11N1VoK2jMpjE0ZdsUWnZkGV0qveR0n0aTkc4XnXdYtjdzLyWsTKCTBSdpe/p2GrJ2COUocqzjiYj0Rj0RERVUFtDf6X+TWq92uv8/0/6k2365jS6WXLQuy1ZkU44ih0wmE8k1cqrUVdLai2urbVupq3TYrr6cLTxLiw8uJp1BR0RES/5ZQoPWDKJKXSUREeUqcq3nly5Pp3JtOb1/9H368vSX1n3ocnOpYudOqti5k7RZ2dbl+pIS0mbarr9RrSbt1atuZTIZDKROSnI/zmQi1dmzZNLprMvUKSlk1GiIiCjzmdmUPnkKpT00kYp/+MHt/ixcfXwmZTw21XqM2sic8cijlNK3H5Vt3kxpDzxIF3v1ppJffqGkzl0oa87zZDKZan38+lL+13ZKiosn9cWURj9WfUmKi6fUgXc22fEMFRWk/PcIGRRKunh7X8p7440mO3ZDAeA4udCpLV5R64w6+iXpFypVl1JWRRZ9f+57KlYVU3JJMj2+/XG6Ir9CmeWZ9PC2h+ly2WUiIvrx/I+UsDKB0uXpdLnsMqWU8i/0xZKLVFhZWO0YpwtPU8LKBErMSnRYnlmeSTqjji6WXKRBawZZ968z6Gjy75Pph3M/EBHRFfkVSpenuzyHYlUxvfPvO1SpqySTyUTLjiyj04WnHca8/s/rlLAygUwmE629uJbOFp4lIqJ/c/+l/qv6U4W2oubrZFbAFsXrTAZ7ilRFlFKaQn1+7kO/XfqN+q3qR4sPLia9UU/D1g2jyb9PpvmJ8+ntw29bt8l/7z1KiounpLh4ujzqHqtSSh08hJLi4smk05Hq9GnKf3cZZc97kVSnHc+xKmUbNlJS1wTSl5TUOK509WpKiosn+ZYtRERkKCuj1EGDqWLvXusYeyVORFS2fj0VfflVjfs1mUxkkMuJiCh36VJKHTykxvFERKVr1lLZps1kMpmo9NdfqWL3HiIiKv72W0qKi6eKPXvc7uNa0ZeWUuXRo2RUqRr9WPUlKS6e0idNbrLjVR49Sklx8VTw0ceUOniI2/99S6QmRd3iw/NOFpy0+oQP5/Hi43fdchf8pH4o05ZhfuJ8vHLbK9AatVDqlThbdBZTu05F99DuiPCNgKeYz/wWq4sxcdtEzEyYied7Pe9wjPhW8RhxywgEegZal604uQKDowcj2j8aca3isHHcRgTL+ASFRCxBjH8MWvvwQP/lJ5YjS5GFzeOdJ1wEewVjYd+FOFFwAh+d+Ajp8nR0COyAW0NtNQEW9VuEKfFTrG6RvhF9carwFEK9Q9E5uDPKNGU11uaY8fcMnCk6gyFRQ/DJ8E8c1uUp8zBi4wi8dcdbuK/DfQAAD+aBv9L/Qp/WfdA7vDfevuNthHmHoVxbjoV9FyLEKwRdWnWBh8gDJpUK6rPnEPbyy/AfMQKq48dR+P4HUB09xrPQ8vPhN2IEmESC8j/+QNlPPwMAvHr1qrHugS49DWCsxjq+hrIyFLz3PgBAczEFAeN4nK6hoACG/HyQyYSCZcvgd9ddMBQUoHzzZoS+9BLKt/4ObWoqgp98wmlygvLAQVQePGgtGiSJjOQlPhUKa9+8qpjUagSMudda18I+G6/V1KkoWbkS5Zs2w29oI3VGN2OUy0FGE5hM5n5wM9Hp+HEwqfNaGY2BV58+CBg/DiVffYWozz+D37BhTXbsJsGVBr+Wn/pa1JfLLtPDfzxMJwtOOiy/VHqJ1qesp7l75jq4ODLKM2jQmkG0MXUjmUwmSi1NpYSVCXQs75jT/e/K2EVl6jKHZSaTicZsGkPfn/ueilRFVjfEXevvolVJq2old3JJMh3NO+p23NbLW2nMpjGUWZFJWoPW5bg8ZR7lKHKo76q+9OmpT61yOkNr0JLBaKDPTn1GD2972Kn7pERdQt+d+46ultte7XMUOdTn5z60M2Ondf+LDy6mAb8OIL1RTwqtghJWJtCSf5ZQ7v/9HyV1TbC6O4xqNZX89DMZ5HLKX/ZfSuqaQIqDB6n4+x/o8r330tUZMyi5R0/K/89/HORQHj5MOYsWkcnIXUflf/1FSXHxVPgpP0ddQQFlPvus1QVhKC8nk9FIqtOnKX3iJCr44AMiIlKdPcct+y5dSV9URElx8VTy089U8vMvlBQXT5eGDqOkuHjKWbSIDBWObyJFX3xJ6RMnUeqgwXTx9r5kMhiIiEhfWEiatDSrbM6Qb9lCSfGdSXMlzel61dmzZFAoXW5PRGTUain7xZdIdfZsjeNqouCDDygpoVuTuFmuJ4xqNaUMHEhJcfGkueTczdmSwfViUYuYCL4SX4iZGNvTt0Nj1OB4/nHM7jEbEzpNwIMdH3QIdr/F/xZsvW8rAjx5PGmRqgizus9C7/DeTvffP7I/9mbtRdfgrogNiAXAJ/Zub307vDy8cPeGuzG963TMSJiBNfeuAcF1EcCV51di9cXV2P7gdih1SrQNaOtyrIWx7cdiROwIq5XvCoulvn7MekT4RuDz058jMTsRa8fwyIT8ynwcyz+Gy/LLOFd8Dq/e9ipmdZ+F2T1mQ21Q42LpRcS3smWblapL8WjnRyEV22bgI3wiMKHTBLQL5FlajDFE+ETgvUHvwUPkAR+JD56+9Wl0C+kG3dXvEDB2LKRRvAy5SCZD4MSHoNixA9qUi/AbPhyac+dRtHw5ACDw/vthKCqGLivbejxdRgZy5r2IoEcfsVq4vkOHQtKmjbXgjjIxEcpdu6G/ehUxP/6IrKdmwbNDB0Quexexa9dY9+XVLQFh8+ej8P33oS8ogO/QofDs1AniwAD4Dh8O5e7dCHvtVQRPnw6T1hbLTno91Od4Yoxnhw7wHTLE2vVDHBAA1fHjII0Gss6dnf5fNBcugHl6QhoT7XS9V7dukG/cBJDJaeo0wOsoV/zxB9RnzqDDrvqFBBrLKyAOCKhVmvfNhEgmQ9t161C04hOHZJcbAlca/Fp+rtVHrTPoaOzmsdRtZTfq/2t/ylfmX9P+LJSpy6jbym709ZmvrctUepufb3Xyapr4+0Qat3lcjRYvEdHuq7vprcNvkVwjp4SVCQ77bGi2Xt5Kbx9+m3RG7odddmQZ9V3Vl1aeX0lL/llCfX7uQ8uOLCMionl759GwtcOoTF1GR/OOUpm6jO5cfSctOrCoQWXSXL5MSXHxVPzNN2TS60lfWGj1X6uTkih7/nzKemEuEREZFEq6PPpeutizF+UsXEjKf/4ho0ZDSV26UlJcPF0Zfx8RERV8+BEld7+VkromUOYzs7nPd9cuIiJS7N9PV2fMIKNaTVnPPUepA++kpLh4Uuzf7yBX7uuvU1JcPBnKyijruecoY9p0IiJSnTpFlSdPkslotFrR9pj0ekpO6Eb5773n8pwN5eVuLeGrTz5JqUOGkkmvJ9Xp06Q8coRPhJqtX31pKaX07UdJcfGky8lx2FaXk0NGtW1CWl9URJUnHd8uiYiyXphLl+8ZXaMcAtcfuB4nE/OUeaQz6NwqzLqSWppKBqPtRn1m5zM0a8cs6+cT+SdoXcq6Wu9PZ9DRkdwjDi6ZxsBkMpFar6YcRQ7pjXrrBKnOqKO/0v+yRoacLTxLR/OO0rG8Y5SwMoEOZh+kvZl7rZOTdT6u0Ug5r7xKin37qq27NGIEZT79jON4g6HaK3nhJ59SUnxnUh46RBd796HcpUtJffEijwwYPISSu99qVZ5GpdLqYlGds0XilP/5p/UhkDp0KGXOepq7OYYNp7QHHiSjkrsckuLi6cp99xMRUf67yyg5oRtp09MpZeBAujJmjFMlbeHK2HGUOetp59fBYKDS1atJm5VV4/Wq2LOHsufPJ5NWS5eGDbc+vPL/8y5pr14l+bZtXJ47BpLiwEHb/rVafj7D7yKTTkfajAy6eHtfSr1zEP/f20V4pD04gTIeebRGOQSuP65LRd1YZJZn0oqTK0ip4zf2+pT1Tv267pBr5DR07VD634n/0fH84y5D8xqKR/54hBJWJlCfn/tQammq2/Hl2nI6knuE5Br5NR3XUF7OLefvf6i+Ti4nfVGR0+0qT5ygzGefJX1xMZX++ivlvLaAiIhyl7xORV99TeV//MF9y7/8QsXffksGhYJyFy9xGSmizcyk/HeXkTo5mZLi4qnoiy9IvmULZc56mit688NBl5dnjYZQ7N/Pj7FqFaVNeIg0qTVft6y5c+nS3SOcrrO8QZRt2lzjPkwmk9VSrjx5kpSH/+U+/rh4Sp/yMI9yKSoik15PJpPJ6hM3GY2U/18eVZP31tukLyykzKefIcXBg9aoF00a943rCwtJdfZcjXIIXH/UpKhblI+6KcitzMW3575F/4j+iPGPwei2o+EtqXv3YH+pP4ZED0G2IhvTt0/Hnw/8iWg/577LhmBU7CgMjxkOlUFVq1rS/lJ/3B5x+zUf11jBm/lU7VkHwKFYfFVMSiWUu3ZDN20aAidPtkZJm6dsAAASpUlEQVRIRLyxFABQtOITQCTivlwiqM+ehXzdOvgM6O80UkQaHY2wV1+B2pzkIW3fHj59+6Jg2X8hiYiwFXC3K7np3ZvPVRjLyhC7bq1bn65nu/ZQbP8bJq22Wp0I9blzAACvhOoNT+1hjEESGcmP37OnWY5e8B06FIo9eyCJiIBHSAh02dnIemoWwl6cB7+77kLl4cO8/KfJhNKVK+E7dAiiv/gcAO8/mP/2O7wjSnQUJJGR8BsypEY5BG4sbjpF3Su8Fw5MPgBfiS8e3Pog1AY1tt631WGirTYwxrCk/xIUqgpxX4f7rBOAjcWjXR5t1P27wigvBwCIA+tWAMizQwcETpkM1YkTyJ73Im756Sd4tmsL1clTyH3lFbSaOhVhL84D6fVIve12+N9zD3yHDoVXt24u95nx0ESQVoPAyZMg69wF8o2bYCwtdVnXQeTtjeBnnoYkPLxWE29Bjz6CVlMfc1rMR/HXdohDQ+pVIpNJJPAbNgx+w4ZZsx8lrVvDVFmJsrXr4HfXXShe8QkIhNhVq2DSaqwV4ADeRstv+HCUrVoFIkLAmDGCor7JuOkUtUQkwT8F/+BgzkEsu3MZDCZDnZW0hY9PfIzvz3+PdWPWQSJqupjRJsVogCQyEuKgVu7H2iGJjETE668jb+lSmFQqa8SIR3Ar6LOzYVQqEPLMM7wrSUgwmKcnoj/6sMZ9eoSFQZeWZu2GbbFuJW3auNwm7IXat0UTeXpCvnkzvLrfClnXLjAplRD7+/M48qQLCHzwQWuUSH2xRLwwDw9EvPkGPMLDQQYDiAi+Awfy5a+/Xm27oEkTofj7b+vfAjcZrnwilh8A3wMoBHDe3Vi6DnzURETfnfuOevzUgzQGzTXtJzErkRJWJtBDWx9qIMluLNIenEDpk6dQxe7d1mUmk4ku9uzFs8g+/IiIiNQXLtDlkaMo7623Xe2KiMzxw3HxlP7wI3xfOh0ZK51nYtYHo0ZDSZ27UMHy5ZQ5+1m62Ku31a9t1GrJoFA02LEsqM6dp/IdO4jIdaw8Efdhq86cEWKnb2BQg4+6NkWZVgIY1XiPiqbn0c6P4ud7fnYbz+yOQVGD8Nv437Cw743bAFZ58B/kLlgIo1JZ521NajXUp045NBFljFlLVkpjY/kyqRS6jAyIvGrOtJO25755k9lvziSSGrMa64rI0xPefW9H6bffQbl7N5hUCm16Oq5Om47KAwfc9tKrD8WffYacOc8jd8HCGt0zTCSCV/fuQuz0TYpbRU1E+wGUuht3PSEVS5EQ4ryubV0oqCzAfVvuw8Gcgw0gVctEk5yE8s2b6/XKr8/OdvhtIeSZZ+B3zyj4Dh0CACj5gTdisC847wzvXr3gO3w4Ql+cV2dZakvksv8CYjEC7r8fHRP3wSMkFKojR4BGUpBBkycBACrMbg0BAWc0mI+aMfYUgKcAICYmpqF226IJ9Q7F8Jjh6NO6T3OL0mh4tm+PgPvuq1ddiTb/W46KrVsh7dDBYXnQpIkOftaQWbOgz8mF/9gxNe5PGhOD6M8at+OMJDwMsWvXQBobCyaVgvR6+I0aBd9BgxrleD4DB8Lv7rsRcP/N0etRoH4w7hpxM4ixWADbiKhWZmifPn3o+PHj1yaZgICAwE0EY+wEETm1+q67xgECTUvuwkXInPlEc4shIHBTc9OF5wnUDX1uLkjbspr0CgjcbLi1qBljqwEcBhDHGMtmjM1sfLEEWgrGinKnWYkCAgJNh1uLmoimuBsjcONiKq+AqMpkoICAQNMiuD4EasRv1Eh4dujY3GIICNzUCIpaoEbC589vbhEEBG56hKgPAZcY5XKkDrgD8k3Oe0EKCAg0DYKivkkgIl5tzpx+XRuM5eUwlpYCIiFtWUCgOREU9U2CNjkZeYsWIefFl2q9jTg4GG0++hDefW5rRMkEBATcIfiobxIkUVHwGTwIIU8+WfuNTCZ4xsXBIyy08QQTEBBwi2BRNwBkMiH9gQdRvnVrc4viEt3VTLRetAgAoDp5qlbbVB48iLR7x0CfmdmYogkICLhBsKgbAE1SMjRJSc0tRo3kv/EGRN7e0OflQdK6NW75+Se321j82SIh4UVAoFm5Li1q7ZUr0KSmNug+dRkZKP3xR8g3bQbpdG7HK/bshbGct6mCyQiPiAgo9x+ASaVyGKc+fRq6jAz3x8/MhPr06fqI7hbS6aBNSYFX924IfOghqI4dg/bKFbfbGcvN/RJr6I0oICDQ+Fx3ippMJqTdOwbZs59t0P1qUlJR8O4ylHz1FQxyeY1j1RcuIPeVV6zKTtalCyLeegsV27ZBZVc10KRSIWPyFGTPmeP2+FdGjETG5Ckgo/HaTsQJmkuXQHo9ZF27IvCB+8G8vJC3eInbB5KxvBzM09NpD0EBAYGm47pT1JX//AMACJ03F+pz52F0o1TdQUTIW7wY4gB/dDp6BG03bQRMJlTs3OlyG/nadSC9HsayMhjKypA64A5oL18Ck0pR+c8h67iKP/8EALQ29/hzKYPBAHFoCABAc+HCNZ2PMzTn+T5lCQnwCAlB5Dtvc/kVihq3Cxg/HpHvvdfg8ggICNSN605Rl61dC3FwMHz69UPmjBnIeenla7JCVUeOQL5+AwwFBRD7+0Pk44Oijz9Gztx5SL3zTqTeeSdyXnoZAHchGJVKlG/bBkmbNsh+9jkUvP0OTBUV8GzXDl69e6Hy8GE7WdfBs2MHKHbuQsF777uUwVBQgHZbtgAAKg8dcjmuNqjPnsWVe0Yj9c47cXnkSKiOHYP/vfci5qcfIYmKAgD4jx6N2DWrYVKpULHddWcRkczT2kBWQECg+biuFLVJq4U2+SICH7gfHsHBCHv1Feizs2EoLqn3PsvWroU4IAB+I0dal4UvXIjgGdPhN3QY/IYOg9ett6Ly3yO4PHIUij/5BKRSIfLd/0Davj0q/vgDTCKBd58+8BkwAGQ0wqRWw1BWBmNZGQInToJRLod8zRoYlZXVz0mlQtp996Pku+8Q+d9l8B89ut7nAgDKfYkwaTXwGzoMMBHkW7ZAuXcvPIKCHPrtMbEYRR8vR878+VCdPOl0X3lLXkfOK69ekzwCAgLXTouK+jCUlUGZmOiwzKdfPzBPTygTEyFpHYH2f2+31kcOeughBIwdC5NSieKvv4FHWChkcXGQde5s22dJCZQHDkAaHQ3v3r0dj1dcDMXOXWj1yCMQ2bWaEgcEIOzllx3GWuoyK3bvQdgrr0DWvTti16zG1cemckvc2xvBM2cieMYMKHbuhCQiAu13/A0YDNBcugRZ53hozp6BvrDQuk+RTAYwEUwKBfyGDoWsa1eojh2DR0gITFotKg8cAERiBIy5F+pz56C9cgWS1hHw6de32nko9x+AODAAoc/PQatpUyEOCIChpAQiHx+k9OiJVtOnI/w1R6Xb+vUl0OflAUYjFHvtJkcBSFq3hrGiApLw8Lr8CwUEBBqBFqWo9dnZyHttgcOyqC+/gEdwMPJeWwCP8HCEL1wI/5EjrOtFMhnyFi9Bxe+/A+CdqWN++hGy+HiYlErosmz7bLN8OfxH2SxnMhoh69QJgZMmuZVNEhmJ6K++hEd4OCRhvAmr2M8PkjZt4NP3dn5skQhFn36GslWrAKkEEW+9Bb8hQ+DVtSu8unZF3pLXIV+3zrpPcWgIOh04AOlvm+EZF4fKQ4eQ9dQstFm+HMWffw5taipEfn4IGHMvyn//HWU//Qzf4cPh3fd2aFNS4BkXB9JooLt6FXkLFwISCdpt+Q2e7doBADyCg6HLzgEA+NxxR7VzEgcE4JZfV4ExhrQHHoA2Kdm6znfoUMSu+qVWETACAgKNS616JtaV+vZMNGm1MNhZnADgERICiETW5R7h4RBJpQ5jyGi0WrxZz8yG/6iR0OflQ3XiBNpu2ghTRQVyX3kVmkuXELt6NWRxnSDftBk+/frCo3VrMFH9PUAmlQpMJrPuw6ishLGMN22XRESAediehYbSUpgqbe4PJhJB0qaNbV8aDVL7DwCp1YBIhDYffQRZQgKkUW1gKCuDSamESCZD+datKFz+PwSMHwf16dOI+fZbkF4PkY8PPFq1qi5jZSVEPj41noc+Px+k11s/i2QyeIQKGYkCAk1FTT0Ta9vcdhSA/wEQA/iWiJbVNL45m9sa5XIYy8uR/sCDCH7ySYQ8PQsAoC8oRMbkyQh97jmQyYj8xUsQ9PAUtF6ypFnkdIVRWYmiFf+DJDwcwTOdN9MxyuVInzgJ+sxM+I0ciTbLP3bwPwsICFx/XJOiZoyJAaQCuBtANoBjAKYQkctUvJbQhVyfkwOPyEgHBWZSqSDy9oZi715UHjqMsBfnQeTl1YxSuoaIalS+2rR0yDduQOjs2W6tZQEBgZbPtSrq/gCWEtFI8+cFAEBE77rapiUoagEBAYHriZoUdW2cs20AZNl9zjYvq3qQpxhjxxljx4uKiuonqYCAgIBANWqjqJ29f1czw4noayLqQ0R9QoVJKAEBAYEGozaKOhtAtN3nKAC5jSOOgICAgEBVaqOojwHoyBhryxiTApgMoOUWXhYQEBC4wXCb8EJEBsbYcwD+Bg/P+56IGr5ykICAgICAU2qVmUhEfwL4s5FlERAQEBBwwnVVlElAQEDgZkRQ1AICAgItnEap9cEYKwJwtZ6bhwAobkBxGgNBxmunpcsHCDI2FIKMteMWInIa29woivpaYIwdd5Wd01IQZLx2Wrp8gCBjQyHIeO0Irg8BAQGBFo6gqAUEBARaOC1RUX/d3ALUAkHGa6elywcIMjYUgozXSIvzUQsICAgIONISLWoBAQEBATsERS0gICDQwmkxipoxNooxlsIYu8wYe6255QEAxlg0Y2wvYyyZMXaBMfaCeXkrxthOxtgl8++gFiCrmDF2ijG2zfy5LWPsiFnGteaCWs0pXyBjbANj7KL5evZvadeRMTbP/H8+zxhbzRiTNfd1ZIx9zxgrZIydt1vm9LoxzgrzPXSWMdarGWV83/y/PssY28wYC7Rbt8AsYwpjbKTzvTaufHbrXmaMEWMsxPy5Wa6hO1qEoja3+/oMwD0AugCYwhjr0rxSAQAMAF4ios4A+gF41izXawB2E1FHALvNn5ubFwAk233+L4CPzTKWAXDegLHp+B+A7UQUD+BWcFlbzHVkjLUB8DyAPkSUAF6AbDKa/zquBDCqyjJX1+0eAB3NP08B+KIZZdwJIIGIuoO38lsAAOb7ZzKAruZtPjff/00tHxhj0eAtBjPtFjfXNawZImr2HwD9Afxt93kBgAXNLZcTObeA/2NTAESYl0UASGlmuaLAb9hhALaBN3soBuDh7Po2g3z+ANJhnry2W95iriNsnYxagRcr2wZgZEu4jgBiAZx3d90AfAXez7TauKaWscq6+wGsMv/tcG+DV+Xs3xzyAdgAbjRkAAhp7mtY00+LsKhRy3ZfzQljLBZATwBHAIQTUR4AmH+HNZ9kAIDlAF4BYDJ/DgYgJyKD+XNzX892AIoA/GB2z3zLGPNBC7qORJQD4ANw6yoPQDmAE2hZ19GCq+vWUu+jxwH8Zf67RcjIGBsHIIeIzlRZ1SLkq0pLUdS1avfVXDDGfAFsBDCXiCqaWx57GGNjABQS0Qn7xU6GNuf19ADQC8AXRNQTQCVahrvIitnPOx5AWwCRAHzAX4Or0mK+l05oaf93MMYWgbsQV1kWORnWpDIyxrwBLAKwxNlqJ8ua/X/eUhR1i233xRiTgCvpVUS0yby4gDEWYV4fAaCwueQDcAeAcYyxDABrwN0fywEEMsYs9cab+3pmA8gmoiPmzxvAFXdLuo53AUgnoiIi0gPYBGAAWtZ1tODqurWo+4gxNg3AGACPkNmPgJYhY3vwB/IZ830TBeAkY6x1C5GvGi1FUbfIdl+MMQbgOwDJRPSR3aqtAKaZ/54G7rtuFohoARFFEVEs+HXbQ0SPANgLYIJ5WHPLmA8gizEWZ140HEASWtB1BHd59GOMeZv/7xYZW8x1tMPVddsKYKo5cqEfgHKLi6SpYYyNAvAqgHFEpLJbtRXAZMaYJ2OsLfik3dGmlI2IzhFRGBHFmu+bbAC9zN/TFnMNHWhuJ7md0340+OzwFQCLmlses0wDwV97zgI4bf4ZDe4D3g3gkvl3q+aW1SzvEADbzH+3A78BLgNYD8CzmWXrAeC4+Vr+BiCopV1HAG8AuAjgPICfAXg293UEsBrcZ64HVygzXV038Nf2z8z30DnwCJbmkvEyuK/Xct98aTd+kVnGFAD3NId8VdZnwDaZ2CzX0N2PkEIuICAg0MJpKa4PAQEBAQEXCIpaQEBAoIUjKGoBAQGBFo6gqAUEBARaOIKiFhAQEGjhCIpaQEBAoIUjKGoBAQGBFs7/A+iHDnN0MTQeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn line chart \n",
    "sns.lineplot(data=iris_df.drop(['target', 'class'], axis=1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms\n",
    "Histograms are useful for a large number of unique values in a dataset.  The values are sorted into intervals, called bins.  From histograms we can understand the distribution (aka frequency) of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Use method **hist**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "# plot histogram\n",
    "ax.hist(wine_df['alcohol'])\n",
    "# set title and labels\n",
    "ax.set_title('Wine Alcohol')\n",
    "ax.set_xlabel('Alcohol')\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    "Create Pandas hist with **plot.hist** - no parameters required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c7c8990>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram using Pandas\n",
    "wine_df['alcohol'].plot.hist(title='Wine Alcohol')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1c893090>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c8e6b10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1c98cd10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c9c4850>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Also easily create mulitple plots \n",
    "iris_df.drop(['target', 'class'], axis=1).plot.hist(subplots=True, layout=(2,2), figsize=(10, 10), bins=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn \n",
    "Seaborn uses method **sns.distplot** with many additional parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wine Alcohol')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns histogram\n",
    "sns.distplot(wine_df['alcohol'], bins=10, kde=False)\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Wine Alcohol')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wine Alcohol')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns histogram with kernel density estimate\n",
    "sns.distplot(wine_df['alcohol'], bins=10, kde=True)\n",
    "plt.ylabel('Distribution')\n",
    "plt.title('Wine Alcohol')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Charts\n",
    "Bar charts illustrate data corresponding to given labels or discrete numeric values (like pies charts).  These charts are good when there is low cardinality (not a lot of categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Use the **bar** method, with the caveat that you need to manually calculate the frequencies of the categories you are interested in which can be done using **value_counts**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and axis \n",
    "fig, ax = plt.subplots() \n",
    "# count the occurrence of each class \n",
    "data = wine_df['class'].value_counts() \n",
    "# get x and y data \n",
    "alcohol = data.index \n",
    "frequency = data.values \n",
    "# create bar chart \n",
    "ax.bar(alcohol, frequency, color=['red', 'blue', 'green']) \n",
    "# set title and labels \n",
    "ax.set_title('Wine Classes') \n",
    "ax.set_xlabel('Class') \n",
    "ax.set_ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "Use **plot.bar()** method, but like with matplotlib this method requires that we need to count the occurences using **value_counts** and sort using **sort_index**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df['class'].value_counts().sort_index().plot.bar(color=['red', 'blue', 'green'])\n",
    "plt.title('Wine Classes')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Count')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df['class'].value_counts().sort_index().plot.barh(color=['red', 'blue', 'green'])\n",
    "plt.title('Wine Classes')\n",
    "plt.ylabel('Class')\n",
    "plt.xlabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Alcohol')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df.groupby(\"class\").alcohol.mean().sort_values(ascending=False)[:5].plot.bar(color=['red', 'blue', 'green'])\n",
    "plt.title('Wine Classes with the Highest Alcohol (on Average)')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Average Alcohol')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "Use **.countplot** to create a bar-chart, no need to do any data manipulation :) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wine Class')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(wine_df['class'])\n",
    "plt.title('Wine Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box Plots\n",
    "Box plots (and violin plots) are excellent for visualizing descriptive statistics of a dataset since they show the range, interquartile range, median, mode, outliers and all quartiles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib \n",
    "Use the method **boxplot()**.  In order to display multiple columns in one figure we need to construct an array of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cm')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get columns to plot\n",
    "columns = iris_df.columns.drop(['target', 'class'])\n",
    "data = []\n",
    "for column in columns:\n",
    "    data.append(iris_df[column])\n",
    "# create figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "# plot each column\n",
    "ax.boxplot(data)\n",
    "# set title and legend\n",
    "ax.set_title('Iris Dataset')\n",
    "ax.set_xticklabels(columns, rotation=45)\n",
    "ax.set_xlabel('Feature')\n",
    "ax.set_ylabel('cm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    "Use **.boxplot()** to create boxplot from Pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cm')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_df.drop(['target', 'class'], axis=1).boxplot(grid=False, rot=45, fontsize=9)\n",
    "plt.title('Iris Dataset')\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('cm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "Just need **sns.boxplot()** method to create amazing boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cm')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=iris_df.drop(['target', 'class'], axis=1))\n",
    "plt.title('Iris Dataset')\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('cm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris Dataset')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots are also useful for viewing the different statistics of a feature broken down by class\n",
    "sns.boxplot(x=\"class\", y=\"sepal length (cm)\", data=iris_df)\n",
    "plt.title('Iris Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris Dataset')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alternative to box plot is a violin plot showing \n",
    "# the kernel density estimation underlying the distribution\n",
    "sns.violinplot(x=\"class\", y=\"sepal length (cm)\", data=iris_df)\n",
    "plt.title('Iris Dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pie Charts\n",
    "Represent data with a small number of labels and given relative frequencies.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Use **plt.pie** method to create a pie chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#first create data arrays \n",
    "data = wine_df['class'].value_counts()\n",
    "labels = data.index.tolist()\n",
    "sizes = data.values\n",
    "colors = ['yellowgreen', 'lightcoral', 'lightskyblue']\n",
    "explode = (0.1, 0, 0)  # explode 1st slice\n",
    "\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, \n",
    "        autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    "Use the method **.plot.pie** on a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b62eb10>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dczYuIdSb1Afg7VSqr+0MMVb3rTZUUW5jzPcBROQ24CJjzGrvv3cBfhxsvA4tsDBm7K1KJGwsmdUoltoYtJaj5aPaig1gjFkFHOx/pG4UKmsAPbLrs00iOscdHCtb7lrKnfC21gB4l37a+qCmu+Y+Wp1IrELEyl0xGkQ4d8u382PgWRF5APez9unAVYGk6t4C3PtzKx8sSafeBXbp9oWqp8JdbmPM7SLyPDAe98DaqcYYWwvv6ZbbRwsyGZ3jDla4yw3uUktAGFbS1INqPlqQSW+0nSHmXrYxaPTuOOLScvtoUTrdfopT+We5k3PKNgaOarkXYmeOPZbeTqf0Di7BmWFr4GiWu1DZCLxpO0Zc6Bx3oLTcPaDL8/pko8gnbGeIMS13D/zRdoA4qCQSFUSabeeIKQPMsjV4lMv9B9sB4mBJOqVLGwfnb07OsTbNGN1yFyqLcQ+sqV54LZNe3f2rVA9Z2yWHKJfbpVvvXlqg13EHScvdC4/ZDhB1i9I2btTaMB61OXjUy/0koGdX9cJbKZ3jDsgsJ+e8YTNAtMtdqKwHptuOEWUrkzrHHZAHbAeIdrldumveCzrHHZj7u39JsLTcDWxNQnSOOxiznZyzxHaI6Je7UJkPvG47RhSVda3yoFjfJYc4lNv1oO0AUfRaWue4A6Ll9tHPbQeIolczGZ1p8N/LTs4JxWIi8Sh3obIAeMJ2jKhZnEnrZbP+u892gDbxKLdLt941Wqpz3H7bDNxsO0SbOJX7ISzcjynKViYTeqTcX792ck5oDlL2qNwiUhCRS/0O026Mz4rIqyKyUETy3X5DobIFuKnb16m/26hrlfvtetsBthfKLbeIJIGfAv8IHAicISIHVvGtP8HCrVKjaK3IB0ZEz07zz3NOznnedojtVVVuEfmqiLwsIi+JyB3tnpsgIrO85x4Ukb7e46eJyFzv8eneYyNEZKaIzPHeb99OhjwCWGiMWWyM2QzcC3yh26CFyjvAndX8TI2unE7rddz+usZ2gPa6LbeIjAAuB8YbY0YDF7V7yUPGmMO95+YD53iPTwFO8h7/vPfYROB6Y8wY4DA6v4fSEHZcI22p91g1rkUXT+yWXsftq/nAb22HaK+aLfd44AFjzAoAY0z7W+geJCJPiYgDnAmM8B5/BrhVRCYASe+x54DJInIZsJcxZkMnY3a01G51hXXPWCtV9doGtiCT7uz/vardD52cE7oNSjXlFrou1q3AJGPMSOD7QB8AY8xE4LvAUGCOiAw0xtyNuxXfAEwVkfGdvOdS7/va7AG8XUXWNsUaXtuQFuoct1/eAO62HaIj1ZT7ceB0ERkIf78B4Pb6A8tEJI275cZ73XBjzAxjzBRgBTBURIYBi40xNwAPA6M6GXMWsK+I7C0iGeDL3uurU6g8QwiuygmzpalUk+0MMfE9J+dssR2iI9Xcn3ueiFwFTBORbcCLQHm7l1yBu5zM64CDW3aAa7wDZoL7D8RLQB44S0S2AO8AV3Yy5lYRmQRMxd2l/5UxZl6NP9slwOeAnWr8voawMpnUOe7emwHcZjtEZ8SYGO+dFZonY+9OpKE2Kjt0pfnoXpiqngGOdHKOtaWLuxPKeW4fXYveEfQj1oms1WL32q1hLjZYLreIDPTmvNt/DfRlgEJlMx+dumt4OsfdaxXcj5ihVtMtfP1mjHkfGBPoIIXKHyg0P8yHc+0Nb2Emvcp2hoi70sk5ob+OIe675W0uRldJ/Tud4+6Vv+Ge5hx6jVHuQmUJ8CPbMcJiYVrnuHvh4rBOfbXXGOV2FdlxCq9hvZnWOe4eusnJOVNth6hW45S7UNmAe257w2+13k8md7adIYLmARfaDlGLxik3QKEyFfcU2Ya2QWSw7QwRswH4kpNzInWsorHK7boSeMR2CFs2iKw3IoNs54iYi5ycU+sZktY1XrkLFQP8K7DIdhQbyunUMtsZIuY+J+f80naInmi8cgMUKquBU4D1tqPUm85x12QxcK7tED3VmOUGKFQcYILtGPW2IJ1puH/QemgL8GUn56yxHaSnGrfcAIXK3YRsUbug6XXcVbso7OeOd6exy+26FHjKdoh6eTOdytjOEAFXOjnnRtshekvLXahsBU4HGuJA0wqd4+7OjU7O+Z7tEH7QckPbqqknA7E/2LRe78fdlfuBSbZD+EXL3aZQmY27GOT7tqMEZaPIBp3j7tSfgbOcnNNqO4hftNzbK1TmAP8AvGc7ShDeSKX0Ou6OPQ+c4uScWN3QQsvdnjtFNg53jbdYeS2Tbr8stYJXgX90cs5a20H8puXuSKHyCjAWeMt2FD8tyOgcdzvzgROdnLPCdpAgaLk7497zeyzuutSx8FomHZvPkz6YARzr5JzO7noTeVrurhQqi3ALXracxBdvplJp2xlCYipwgpNzYv0xRcvdnUKljFvwyF9osiKlc9zA7cA/OTlnne0gQYv3uuV+KjTvhjsPeqztKD01Ojt0eWvjXsttgClOzvmB7SD1olvuarknuowHbrAdpSc2CRtbYVfbOSzZCJzRSMUGLXdtCpUtFCoXAWcRsctF30illyHS0d1T424RcLyTc+7ryTeLSEFELvU50/bvP1REnhCR+SIyT0R8W2dfy90ThcpdwKeJ0N1MFjbmHPctwJiQX921FbjEGPMp4Cjg6yJyoB9vrOXuqULlZeAQ3L9Aobcgk47UnkYvrQROc3LO2bWenCIiXxWRl0XkJRG5o91zE0RklvfcgyLS13v8NBGZ6z0+3XtshIjM9O6g87J3U8yPMMYsM8bM9n7/Ae7c+5Ae/MwfoeXujUJlLYXK2bhXla22HacrDTTH/RdglJNzHqj1G0VkBHA5MN4YM5qP3orqIWPM4d5z84FzvMenACd5j7fd2WYicL0xZgxwGO4957sbPwscjDsH32tabj8UKvcDo4HptqN05o1UOu5z3JuBb+OecdbTMwvHAw8YY1YAGGPaf5Q5SESeEhEH9170I7zHnwFuFZEJuLecBngOmCwilwF7GWO6XDlVRPoBDwIXG2N8Wf1Fy+2XQuUN3HPSc4TwtNUVyWT/7l8VWXOAo5ycc42Tc3oztyt0va79rcAkY8xI3CWy+wAYYyYC3wWGAnNEZKAx5m7crfgGYKqIjO90UJE0brHvMsY81Iv8O9By+6lQMRQqtwP7AQVCdER9XULiOA32NvDvwKFOznnRh/d7HDi97S6z8tHbHPcHlnllPLPtQREZboyZYYyZAqwAhorIMGCxMeYG4GFgVEcDijuDcTMw3xhznQ8/w4fvrSexBKjQ/EngauCruFsFKzbD5kOzQ9MxmgpbD1wDXOP3mWYikgO+BWwDXsQ99XitMeZaETkfd9f/dcAB+htj/k1EHgL2xf0zfhz3xpN53CnTLbhXGH6lg918RORY3GW+HKDtuMhkY8yjvf5ZtNx1UGg+FLgOON7G8IvSqde/uMcn97Ixts8M7umjl/fic3XD0HLXU6H5VNy7jQ6v57BTd+o7+9LBgw6p55gBeBK4xMk5s20HiYqU7QANpVB5iELzI8D5wH9Qp5IvSKejepFEK/B74AYn5/zFdpje8D7HP97BUycYYwJZ2ku33LYUmgU4ETgP+AIB/kN74eBB057Yqe/YoN4/AKtxDzL91Mk5S2yHiSotdxgUmncHzsa9A4rvn42/OGS3ZxdlMkf7/b4BmI97Yc4djXBJZtC03GFSaE4An8Xdmrfw4QkRvXLMnkOcNcnkSD/eKwCbgT/gbqX/ZDtMnGi5w6rQvAfwNeBLwAG9eavR2aHvtIrs5ksuf2zCXQ3lAeBhJ+dULOeJJS13FLjz5eO9rxOAPav91i2w5ZDs0CQitk9Yegd4DHgUeMzJOR9YzhN7Wu4oKjQPxy15W+E7PftsSTr1xuf3+GTV/xj4qAzMxl0T/I/A7F6eGqpqpOWOOveo+0G4ZT8M90yp/YCPA/yp78de/OYndj04wAQGWIhb5NnAC7hFjv2tmcJO57mjrlAxuKcuOjs+3jwI2G9FMjkE2B/3GuG2r12BNO6ff/tft999/wB41/ta3sHvlwFzo3wP6zjTLbfawcjbRibw/tGP2+11Go2WW6mYsn0EVSkVEC23UjGl5VYqprTcSsWUllupmNJyKxVTWm6lYkrLrVRMabmViiktt1IxpeVWKqa03ErFlJZbqZjScisVU1pupWJKy61UTGm5lYopLbdSMaXlViqmtNxKxZSWW6mY0nIrFVNabqViSsutVExpuZWKKS23UjGl5VYqpv4fMOxSR2sPPMIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df['class'].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn \n",
    "Sorry but seaborn does not have a piechart method :( "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmaps\n",
    "Heatmaps can visually represent a matrix.  The colors represent the numbers or elements of the matrix.  They are used for showing covariance and correlation matrices. Pandas **.corr()** allows you to easily create a correlation matrix or you can use numpy **np.corrcoef** method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Use Pandas correlation **.corr()** to easily get the correlation of features inside a dataframe and results in a correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get correlation matrix\n",
    "corr = iris_df.corr()\n",
    "fig, ax = plt.subplots()\n",
    "# create heatmap\n",
    "im = ax.imshow(corr.values)\n",
    "\n",
    "# set labels\n",
    "ax.set_xticks(np.arange(len(corr.columns)))\n",
    "ax.set_yticks(np.arange(len(corr.columns)))\n",
    "ax.set_xticklabels(corr.columns)\n",
    "ax.set_yticklabels(corr.columns)\n",
    "\n",
    "# Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(corr.columns)):\n",
    "    for j in range(len(corr.columns)):\n",
    "        text = ax.text(j, i, np.around(corr.iloc[i, j], decimals=2),\n",
    "                       ha=\"center\", va=\"center\", color=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas \n",
    "Although Pandas lets you quickly calculate the correlation matrix, you will need Matplotlib or Seaborn to plot the heatmap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "The best and easiest way to create a heatmap!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1de11590>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(iris_df.corr(), annot=True, \n",
    "            cmap=sns.diverging_palette(10, 220, sep=80, n=7),\n",
    "            vmin = -0.5,\n",
    "            vmax = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a mask so you don't show redundant information\n",
    "mask = np.zeros_like(iris_df.corr())\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    f, ax = plt.subplots(figsize=(7, 5))\n",
    "    ax = sns.heatmap(corr, mask=mask, vmax=1, square=True,\n",
    "                     annot=True,\n",
    "                     cmap=sns.diverging_palette(10, 220, sep=80, n=7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Faceting \n",
    "Faceting allows you to break the data variables up across multiple subplots and\n",
    "combine into a single feature.  This will allow you to quickly explore and\n",
    "visualize your datasets.  Faceting is primarily used in Seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "Faceting in Seaborn uses a FacetGrid,  You first define the **FacetGrid** and pass the data and column which you want to use to split your data.   Then use the **map** function on the FacetGrid object to make the multiple subplots with the different slices of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L0u9i0+/vz/0tSZsmjxxKkiSpZXEoSZKklsWhJEmSWhaHkiRJalkcSpIkqTVwcZhkXZKrk6xJsrrH8iT5aJLrk1yV5OmD9ilJkqTRGNatbJ5bVbfPsOxFwE7N8Ezgk81PSZIkTZhxnFY+EDi5Oi4Gtk6ybAz9SpIkaY6GURwWcG6Sy5Ks7LF8W+DGrun1zbz7SbIyyeokq2+77bYhhCUtTuaK1D/zRZq7YRSHe1fV0+mcPn5TkudMW54e69QGM6pOqKoVVbVi6dKlQwhLWpzMFal/5os0dwMXh1V1c/PzVuA0YM9pTdYD23dNbwfcPGi/kiRJGr6BisMkD0+y1dQ4sC+wdlqz04FXNd9afhZwZ1XdMki/kiRJGo1Bv638OOC0JFPbOqWqvpnkDQBVdTxwFrA/cD1wD/DqAfuUJEnSiAxUHFbVDcBTe8w/vmu8gDcN0o8kSZLGwyekSJIkqWVxKEmSpJbFoSRJklrDenyeJN3P8mPO7KvduuMOGHEk0uTpNz/AHNH4eeRQkiRJLYtDSZIktSwOJUmS1LI4lCRJUsviUJIkSS2LQ0mSJLUsDiVJktSyOJQkSVLLm2BLWlBzuRnwQvBm3pI2N/M+cphk+yTfSXJtkmuSvKVHm32S3JlkTTMcO1i4kiRJGqVBjhzeB7ytqi5PshVwWZLzquoH09pdWFUvHqAfSZIkjcm8jxxW1S1VdXkz/ivgWmDbYQUmSZKk8RvKF1KSLAeeBlzSY/FeSa5McnaSXYfRnyRJkkZj4OIwyZbA14CjququaYsvB55YVU8FPgZ8Y5btrEyyOsnq2267bdCwpEXLXJH6Z75IczdQcZhkCzqF4Req6uvTl1fVXVV1dzN+FrBFkm16bauqTqiqFVW1YunSpYOEJS1q5orUP/NFmrtBvq0c4DPAtVX14RnaPL5pR5I9m/5+Pt8+JUmSNFqDfFt5b+Aw4Ooka5p57wB2AKiq44GDgDcmuQ+4FzikqmqAPiVJkjRCmcRabcWKFbV69eqFDmOTMOk3EFbfN0fOfLY9zFzxvTQe3ix7KBY8XwZlvg1mLnk01329CHN0zvni4/MkSZLUsjiUJElSy+JQkiRJLYtDSZIktSwOJUmS1LI4lCRJUsviUJIkSS2LQ0mSJLUGeULKojPITUkHuWmmN0PVKPn+kkbPPBsv9/doeeRQkiRJLYtDSZIktSwOJUmS1LI4lCRJUsviUJIkSa2BisMk+yW5Lsn1SY7psfzBSU5tll+SZPkg/UmSJGm05l0cJlkCfAJ4EbALcGiSXaY1ey3wi6p6MvB3wAfm258kSZJGb5Ajh3sC11fVDVX1G+BLwIHT2hwIfLYZ/yrw/CQZoE9JkiSNUKpqfismBwH7VdXrmunDgGdW1ZFdbdY2bdY30z9p2tzeY3srgZXN5M7AdbN0vw2wwTYWkPHMznhmtw3ww6rar5/Gc8yVqe1P2us1npkZz8ymYrndfFkwkxTPJMUCkxtP3/kyZZAnpPQ6Aji90uynTWdm1QnACX11nKyuqhX9tB0H45md8cyuiafvxJ1LrnRtf9Jer/HMwHhmNp9YzJfhmqR4JikWWFzxDHJaeT2wfdf0dsDNM7VJ8kDgkcAdA/QpSZKkERqkOFwF7JTkSUkeBBwCnD6tzenA4c34QcD/q/mex5YkSdLIzfu0clXdl+RI4BxgCXBiVV2T5L3A6qo6HfgM8Lkk19M5YnjIMIJmDqcIxsR4Zmc8sxt1PJvb650r45ndJMUzjlgm6fWC8cxmkmKBRRTPvL+QIkmSpMXHJ6RIkiSpZXEoSZKk1sQWh0m2T/KdJNcmuSbJW3q0SZKPNo/nuyrJ0xc4nn2S3JlkTTMcO8J4HpLk0iRXNvG8p0ebsT2+sM94jkhyW9f+ed2o4mn6W5LkiiRn9Fg29kc7biSegfaN+bLReCYmXyYxV5o+zZf7txlLvpgrQ4nHfBl2vlTVRA7AMuDpzfhWwI+AXaa12R84m879FJ8FXLLA8ewDnDGm/RNgy2Z8C+AS4FnT2vw5cHwzfghw6gLHcwTw8TG+h94KnNLrdzLOfdNnPAPtG/NlKO/PsbwnJjFXmj7Nl/u3GUu+mCtDicd8GXK+TOyRw6q6paoub8Z/BVwLbDut2YHAydVxMbB1kmULGM/YNK/57mZyi2aY/u2isT2+sM94xibJdsABwKdnaDLWRzv2Ec9AzJeNxjMx+TJpuQLmCwuYL+bKUOIZq80hXya2OOzWHJJ9Gp1PDN22BW7sml7PGJJqlngA9moOf5+dZNcRx7EkyRrgVuC8qppx/1TVfcCdwGMWMB6AlzenaL6aZPsey4flI8DRwO9mWD7WfdNHPDCkfWO+zBjHxOTLhOUKmC8TkS/myrzjAfNlqPky8cVhki2BrwFHVdVd0xf3WGWknyg2Es/lwBOr6qnAx4BvjDKWqvptVe1B5+k0eybZbXq4vVZbwHj+AVheVbsD3+L3n6yGKsmLgVur6rLZmvWYN5J902c8Q9k35svMJilfJiVXwHxhQvLFXBkoHvNlyPky0cVhki3oJMsXqurrPZr08wi/scVTVXdNHf6uqrOALZJsM6p4uvr9JXA+MP35vAvy+MKZ4qmqn1fVr5vJTwHPGFEIewMvSbIO+BLwvCSfn9ZmnPtmo/EMY9+YL/2ZpHyZgFwB82XB88VcGSwe82UE+VJjvIBzLgOdyvtk4COztDmA+18wfOkCx/N4fn9j8T2Bf56aHkE8S4Gtm/GHAhcCL57W5k3c/6LYL49w//QTz7Ku8ZcBF4/hfbQPvS/QHdu+6TOegfaN+TKU9+dY3hOTmitNX+bL79uMJV/MlaHEY77MHs+c98+8H583BnsDhwFXN9caALwD2AGgqo4HzqLzjbLrgXuAVy9wPAcBb0xyH3AvcEg1v40RWAZ8NskSOkeAv1xVZ2Q8jy+cbzxvTvIS4L4mniNGGM8GFnDf9BPPoPvGfJndJOXLxOcKmC+ML1/MlcHjMV9mj2fO+8fH50mSJKk10dccSpIkabwsDiVJktSyOJQkSVLL4lCSJEkti0NJkiS1LA4XiST7JDljHus9IclXZ1h2fpIVzfg7uuYvT7K2z+0fleRVc42rx3aOTDLKW69oM2GuSP0zXzZPFoebuaq6uaoO6qPpOzbe5P6aO8O/BjhlzoFt6ETgzUPYjjQv5orUP/Nl02ZxOCZJHp7kzHQenL42ycHN/Gck+W6Sy5Kck2RZM//8JB9JclHTfs9m/p7NvCuanztvpN+zkuzejF+R5Nhm/H1JXtf9SS3JQ5N8KZ2Hc59K5270JDkOeGiSNUm+0Gx6SZJPJbkmyblJHtqj++cBl1fnweMkeXKSbzX74PIkOzafSr+b5MtJfpTkuCSvSHJpkquT7AhQVfcA66b2gxYvc8VcUf/MF/NlJMbxSBeHAng58Kmu6UcCWwAXAUubeQcDJzbj50+1B54DrG3GHwE8sBl/AfC1mv2xOcfQeZTPI4BVwDnN/O8AOwPLu7b91q7+d6dzN/UVzfTdXdtc3izbo5n+MvDKHn2/B/iLrulLgJc14w8BHtbE/Us6d8F/MHAT8J6mzVvoeqQU8E7gbQv9u3QY7WCumCsO/Q/mi/kyimGSH5+32FwNfCjJB+gk2oVJdgN2A85LArAEuKVrnS8CVNUFSR6RZGtgKzqPEtoJKDp/BGZzIZ1D5j8FzgRemORhwPKqui7J8q62zwE+2vR5VZKrZtnuT6tq6lFPl9FJ6umWAdcCJNkK2LaqTmu2/2/NfIBVVXVLM/0T4Nxm/auB53Zt71bgKRt5vdr0mSvmivpnvpgvQ2dxOCZV9aMkz6DzrM6/TnIucBpwTVXtNdNqPabfB3ynql7WJN/5G+l6FbACuAE4D9gG+DM6SddPnzP5ddf4b2lOE0xzL51PcdB5uHw/2/pd1/TvuP979CHNNrWImSvmivpnvpgvo+A1h2OS5AnAPVX1eeBDwNOB64ClSfZq2myRZNeu1aauHXk2cGdV3UnnlMFNzfIjNtZvVf0GuBH4U+BiOp/23t78nO4C4BVNn7vROfw/5d+TbOyT5HTXAk9u4rgLWJ/kpc32H9x8ypyLPwD6+iabNl3mirmi/pkv5ssoWByOzx8ClyZZQ+f6hvc3yXUQ8IEkVwJrgD/qWucXSS4Cjgde28z7GzqfDr9H51RBPy4EfladC28vBLajdwJ/EtiyOeR/NHBp17ITgKu6Lhrux9l0TidMOQx4c7P9i4DHz2FbAHsD35rjOtr0mCvmivpnvpgvQ5fmYkxNmCTnA2+vqtULHcsgkpwGHF1VPx5wO08D3lpVhw0nMi0W5soG2zFXNCPzZYPtmC89eORQo3YMnYuHB7UN8FdD2I40qcwVqX/mywh55FCSJEktjxxKkiSpZXEoSZKklsWhJEmSWhaHkiRJalkcSpIkqfX/AcU8jvva4O8GAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(iris_df.drop(['target'], axis=1), col='class')\n",
    "g = g.map(plt.hist, 'sepal width (cm)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pairwise Data Compairson\n",
    "Pandas and Seaborn both have methods to plot pairwise relationships in your dataset - which can be extremely useful - just be careful for large datasets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas \n",
    "Pandas has a **scatter_matrix** which makes it easy to \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juliarossi/.conda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "img = pd.plotting.scatter_matrix(iris_df.drop(['target', 'class'], axis=1), alpha=1, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn \n",
    "Seaborn also has a very useful **pairplot** method that automatically plots a grid of pairwise relationships in the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a1ea4a110>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris_df.drop(['target', 'class'], axis=1))"
   ]
  },
  {
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