Data_Viz_Tutorial.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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [],
   "source": [
    "# Generate descriptive statistics of your DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example using a color gradient to display data values in the DataFrame\n",
    "# For max/min use highlight_max()/highlight_min()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col0 {\n",
       "            background-color:  #2f9858;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col1 {\n",
       "            background-color:  #e53592;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col2 {\n",
       "            background-color:  #cdece6;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col3 {\n",
       "            background-color:  #cda0cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col0 {\n",
       "            background-color:  #65c2a3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col1 {\n",
       "            background-color:  #eae5f1;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col2 {\n",
       "            background-color:  #cdece6;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col3 {\n",
       "            background-color:  #cda0cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col0 {\n",
       "            background-color:  #b8e4db;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col1 {\n",
       "            background-color:  #d0abd3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col2 {\n",
       "            background-color:  #f7fcfd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col3 {\n",
       "            background-color:  #cda0cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col0 {\n",
       "            background-color:  #d6f0ee;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col1 {\n",
       "            background-color:  #dcc9e2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col2 {\n",
       "            background-color:  #65c2a3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col3 {\n",
       "            background-color:  #cda0cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col0 {\n",
       "            background-color:  #48b27f;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col1 {\n",
       "            background-color:  #d81b6a;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col2 {\n",
       "            background-color:  #cdece6;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col3 {\n",
       "            background-color:  #cda0cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col0 {\n",
       "            background-color:  #00441b;\n",
       "            color:  #f1f1f1;\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col1 {\n",
       "            background-color:  #67001f;\n",
       "            color:  #f1f1f1;\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col2 {\n",
       "            background-color:  #00441b;\n",
       "            color:  #f1f1f1;\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col3 {\n",
       "            background-color:  #67001f;\n",
       "            color:  #f1f1f1;\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col0 {\n",
       "            background-color:  #d6f0ee;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col1 {\n",
       "            background-color:  #df64af;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col2 {\n",
       "            background-color:  #cdece6;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col3 {\n",
       "            background-color:  #df2179;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col0 {\n",
       "            background-color:  #48b27f;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col1 {\n",
       "            background-color:  #df64af;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col2 {\n",
       "            background-color:  #65c2a3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col3 {\n",
       "            background-color:  #cda0cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col0 {\n",
       "            background-color:  #f7fcfd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col1 {\n",
       "            background-color:  #f7f4f9;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col2 {\n",
       "            background-color:  #cdece6;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col3 {\n",
       "            background-color:  #cda0cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col0 {\n",
       "            background-color:  #65c2a3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col1 {\n",
       "            background-color:  #dcc9e2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col2 {\n",
       "            background-color:  #65c2a3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col3 {\n",
       "            background-color:  #f7f4f9;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col4 {\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_e33b4c42_a3ae_11ea_8465_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_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col0\" class=\"data row0 col0\" >5.100000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col1\" class=\"data row0 col1\" >3.500000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col2\" class=\"data row0 col2\" >1.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col3\" class=\"data row0 col3\" >0.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow0_col5\" class=\"data row0 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col0\" class=\"data row1 col0\" >4.900000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col1\" class=\"data row1 col1\" >3.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col2\" class=\"data row1 col2\" >1.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col3\" class=\"data row1 col3\" >0.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow1_col5\" class=\"data row1 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col0\" class=\"data row2 col0\" >4.700000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col1\" class=\"data row2 col1\" >3.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col2\" class=\"data row2 col2\" >1.300000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col3\" class=\"data row2 col3\" >0.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow2_col5\" class=\"data row2 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col0\" class=\"data row3 col0\" >4.600000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col1\" class=\"data row3 col1\" >3.100000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col2\" class=\"data row3 col2\" >1.500000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col3\" class=\"data row3 col3\" >0.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow3_col5\" class=\"data row3 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col0\" class=\"data row4 col0\" >5.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col1\" class=\"data row4 col1\" >3.600000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col2\" class=\"data row4 col2\" >1.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col3\" class=\"data row4 col3\" >0.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow4_col5\" class=\"data row4 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col0\" class=\"data row5 col0\" >5.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col1\" class=\"data row5 col1\" >3.900000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col2\" class=\"data row5 col2\" >1.700000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col3\" class=\"data row5 col3\" >0.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow5_col5\" class=\"data row5 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col0\" class=\"data row6 col0\" >4.600000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col1\" class=\"data row6 col1\" >3.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col2\" class=\"data row6 col2\" >1.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col3\" class=\"data row6 col3\" >0.300000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow6_col5\" class=\"data row6 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col0\" class=\"data row7 col0\" >5.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col1\" class=\"data row7 col1\" >3.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col2\" class=\"data row7 col2\" >1.500000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col3\" class=\"data row7 col3\" >0.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow7_col5\" class=\"data row7 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col0\" class=\"data row8 col0\" >4.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col1\" class=\"data row8 col1\" >2.900000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col2\" class=\"data row8 col2\" >1.400000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col3\" class=\"data row8 col3\" >0.200000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col4\" class=\"data row8 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow8_col5\" class=\"data row8 col5\" >setosa</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384dlevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col0\" class=\"data row9 col0\" >4.900000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col1\" class=\"data row9 col1\" >3.100000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col2\" class=\"data row9 col2\" >1.500000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col3\" class=\"data row9 col3\" >0.100000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col4\" class=\"data row9 col4\" >0.000000</td>\n",
       "                        <td id=\"T_e33b4c42_a3ae_11ea_8465_784f435d384drow9_col5\" class=\"data row9 col5\" >setosa</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a1a770ad0>"
      ]
     },
     "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",
    "\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 0x1a1a5ddad0>"
      ]
     },
     "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": [],
   "source": [
    "# create Pandas scatter plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1abaa990>"
      ]
     },
     "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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAQsElEQVR4nO3df6xfdX3H8ecLKv7AArrWxdAKGItYyTb0DnBmggFNYUm7LE7bSRyOWKfDJcpMcC5KMNuibnFj1kHdnD8yQdRMG1fXJQrBOeu4BCUUJOkqwg1MKgKiCFh974/vwfvN5d7e03u/9962n+cjucn3nM/nnPPuJ/e+vuf7Od9zmqpCknT4O2KpC5AkLQ4DX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+DilJXp/kP5e6DulQZODroJLkziTnztReVf9aVa+ew36vT/JokoeT/CjJTUkuTfLUA9hHJXnBgR77QC3WcdQeA1+HjCTL5rmLi6tqOfBc4BJgI7A9SeZdnHQIMPB10EpyYZKvJ/lQkh8Cl3Xr/qtrT9d2X5KHktyS5NTZ9ltVP6mq64H1wMuA3+n2d3qSbyR5MMm9ST6c5Kiu7YZu828n+XGS1yV5VpIvJdmb5IHu9aop9e/pPlV8N8nrh9r+KMnt3XY7kpww03HmPZBSx8DXwe4MYA/wHOAvp7S9GngFcDJwHPA64P6+O66qu4Bx4Le7VT8H3g6sYPBGcA7w1q7vK7o+v15Vz6yqzzD4+/kX4ATgecBPgQ8DJDkauAI4r/tU8VvAt7q23wX+HPg9YCXwNeDq/RxHGgkDXwe7e6rqH6pqX1X9dErbz4DlwClAqur2qrr3QPcPPBugqm6qqp3dse4ErgLOmmnDqrq/qj5fVY9U1cMM3pCG+/8CODXJ06vq3qra1a1/M/DXXb37gL8CfuOJs3xpoRj4OtjdPVNDVX2VwRn1FuD7SbYmOeYA93888EOAJCd30zL/l+RHDIJ4xUwbJnlGkquSfK/rfwNwXJIjq+onDD5x/DFwb5J/T3JKt+kJwN93U0cPdsdPV4u0YAx8Hez2+zjXqrqiql4KvJjB1M47++44yWrgpQymVAD+EfgOsKaqjmEw7bK/C7qXAC8Ezuj6PzEdk662HVX1KgYXib8DfLRrvxt4c1UdN/Tz9Kr67761S3Nh4OuQleQ3k5yR5CnAT4BHGczDz7bdM5KcBXwR+B9ge9e0HPgR8OPubPwtUzb9PvD8oeXlDObtH0zybOC9Q8f41STru7n8x4AfD9V2JfCuJC/u+h6b5Pf3cxxpJAx8HcqOYXDW/ADwPQYXbP9mP/0/nORhBoH6d8DngXVV9Yuu/c+APwAe7vY79YLpZcAnuqmY13b7eDrwA2An8B9DfY9g8AngHgZTNmcxeQH434D3A9d0U0G3Auft5zjSSMT/AEWS2uAZviQ1YtbAT/Kx7saWW2doT5Irkuzubnx5yejLlCTNV58z/I8D6/bTfh6wpvvZzOCbDpKkg8ysgV9VN9B9T3kGG4BP1sBOBt9Dfu6oCpQkjcZ8H0YFg5tFhm+OmejWPemOxySbGXwK4Oijj37pKaecMrWLJGk/brrpph9U1cq5bDuKwJ/uxpRpv/pTVVuBrQBjY2M1Pj4+gsNLUjuSfG+u247iWzoTwOqh5VUMvnssSTqIjCLwtwFv6L6tcybw0BweYCVJWmCzTukkuRo4G1iRZILB7eNPAaiqKxncln4+sBt4BHjjQhUrSZq7WQO/qjbN0l7An4ysIknSgvBOW0lqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqRG9Aj/JuiR3JNmd5NJp2p+X5LokNye5Jcn5oy9VkjQfswZ+kiOBLcB5wFpgU5K1U7r9BXBtVZ0GbAQ+MupCJUnz0+cM/3Rgd1XtqarHgWuADVP6FHBM9/pY4J7RlShJGoU+gX88cPfQ8kS3bthlwAVJJoDtwNum21GSzUnGk4zv3bt3DuVKkuaqT+BnmnU1ZXkT8PGqWgWcD3wqyZP2XVVbq2qsqsZWrlx54NVKkuasT+BPAKuHllfx5Cmbi4BrAarqG8DTgBWjKFCSNBp9Av9GYE2Sk5IcxeCi7LYpfe4CzgFI8iIGge+cjSQdRGYN/KraB1wM7ABuZ/BtnF1JLk+yvut2CfCmJN8GrgYurKqp0z6SpCW0rE+nqtrO4GLs8Lr3DL2+DXj5aEuTJI2Sd9pKUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJakSvwE+yLskdSXYnuXSGPq9NcluSXUk+PdoyJUnztWy2DkmOBLYArwImgBuTbKuq24b6rAHeBby8qh5I8pyFKliSNDd9zvBPB3ZX1Z6qehy4Btgwpc+bgC1V9QBAVd032jIlSfPVJ/CPB+4eWp7o1g07GTg5ydeT7EyybrodJdmcZDzJ+N69e+dWsSRpTvoEfqZZV1OWlwFrgLOBTcA/JTnuSRtVba2qsaoaW7ly5YHWKkmahz6BPwGsHlpeBdwzTZ8vVtXPquq7wB0M3gAkSQeJPoF/I7AmyUlJjgI2Atum9PkC8EqAJCsYTPHsGWWhkqT5mTXwq2ofcDGwA7gduLaqdiW5PMn6rtsO4P4ktwHXAe+sqvsXqmhJ0oFL1dTp+MUxNjZW4+PjS3JsSTpUJbmpqsbmsq132kpSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY3oFfhJ1iW5I8nuJJfup99rklSSsdGVKEkahVkDP8mRwBbgPGAtsCnJ2mn6LQf+FPjmqIuUJM1fnzP804HdVbWnqh4HrgE2TNPvfcAHgEdHWJ8kaUT6BP7xwN1DyxPdul9Kchqwuqq+tL8dJdmcZDzJ+N69ew+4WEnS3PUJ/Eyzrn7ZmBwBfAi4ZLYdVdXWqhqrqrGVK1f2r1KSNG99An8CWD20vAq4Z2h5OXAqcH2SO4EzgW1euJWkg0ufwL8RWJPkpCRHARuBbU80VtVDVbWiqk6sqhOBncD6qhpfkIolSXMya+BX1T7gYmAHcDtwbVXtSnJ5kvULXaAkaTSW9elUVduB7VPWvWeGvmfPvyxJ0qh5p60kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRvQK/CTrktyRZHeSS6dpf0eS25LckuQrSU4YfamSpPmYNfCTHAlsAc4D1gKbkqyd0u1mYKyqfg34HPCBURcqSZqfPmf4pwO7q2pPVT0OXANsGO5QVddV1SPd4k5g1WjLlCTNV5/APx64e2h5ols3k4uAL0/XkGRzkvEk43v37u1fpSRp3voEfqZZV9N2TC4AxoAPTtdeVVuraqyqxlauXNm/SknSvC3r0WcCWD20vAq4Z2qnJOcC7wbOqqrHRlOeJGlU+pzh3wisSXJSkqOAjcC24Q5JTgOuAtZX1X2jL1OSNF+zBn5V7QMuBnYAtwPXVtWuJJcnWd91+yDwTOCzSb6VZNsMu5MkLZE+UzpU1XZg+5R17xl6fe6I65IkjZh32kpSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY3oFfhJ1iW5I8nuJJdO0/7UJJ/p2r+Z5MRRFypJmp9ZAz/JkcAW4DxgLbApydop3S4CHqiqFwAfAt4/6kIlSfPT5wz/dGB3Ve2pqseBa4ANU/psAD7Rvf4ccE6SjK5MSdJ8LevR53jg7qHlCeCMmfpU1b4kDwG/AvxguFOSzcDmbvGxJLfOpejD0AqmjFXDHItJjsUkx2LSC+e6YZ/An+5MvebQh6raCmwFSDJeVWM9jn/YcywmORaTHItJjsWkJONz3bbPlM4EsHpoeRVwz0x9kiwDjgV+ONeiJEmj1yfwbwTWJDkpyVHARmDblD7bgD/sXr8G+GpVPekMX5K0dGad0unm5C8GdgBHAh+rql1JLgfGq2ob8M/Ap5LsZnBmv7HHsbfOo+7DjWMxybGY5FhMciwmzXks4om4JLXBO20lqREGviQ1YsED38cyTOoxFu9IcluSW5J8JckJS1HnYphtLIb6vSZJJTlsv5LXZyySvLb73diV5NOLXeNi6fE38rwk1yW5ufs7OX8p6lxoST6W5L6Z7lXKwBXdON2S5CW9dlxVC/bD4CLv/wLPB44Cvg2sndLnrcCV3euNwGcWsqal+uk5Fq8EntG9fkvLY9H1Ww7cAOwExpa67iX8vVgD3Aw8q1t+zlLXvYRjsRV4S/d6LXDnUte9QGPxCuAlwK0ztJ8PfJnBPVBnAt/ss9+FPsP3sQyTZh2Lqrquqh7pFncyuOfhcNTn9wLgfcAHgEcXs7hF1mcs3gRsqaoHAKrqvkWucbH0GYsCjuleH8uT7wk6LFTVDez/XqYNwCdrYCdwXJLnzrbfhQ786R7LcPxMfapqH/DEYxkON33GYthFDN7BD0ezjkWS04DVVfWlxSxsCfT5vTgZODnJ15PsTLJu0apbXH3G4jLggiQTwHbgbYtT2kHnQPME6PdohfkY2WMZDgO9/51JLgDGgLMWtKKls9+xSHIEg6euXrhYBS2hPr8XyxhM65zN4FPf15KcWlUPLnBti63PWGwCPl5Vf5vkZQzu/zm1qn6x8OUdVOaUmwt9hu9jGSb1GQuSnAu8G1hfVY8tUm2LbbaxWA6cClyf5E4Gc5TbDtMLt33/Rr5YVT+rqu8CdzB4Azjc9BmLi4BrAarqG8DTGDxYrTW98mSqhQ58H8swadax6KYxrmIQ9ofrPC3MMhZV9VBVraiqE6vqRAbXM9ZX1ZwfGnUQ6/M38gUGF/RJsoLBFM+eRa1ycfQZi7uAcwCSvIhB4O9d1CoPDtuAN3Tf1jkTeKiq7p1towWd0qmFeyzDIafnWHwQeCbw2e669V1VtX7Jil4gPceiCT3HYgfw6iS3AT8H3llV9y9d1Quj51hcAnw0ydsZTGFceDieICa5msEU3oruesV7gacAVNWVDK5fnA/sBh4B3thrv4fhWEmSpuGdtpLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNeL/AZ/Akg/p1B30AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a seaborn scatter plot\n",
    "\n",
    "plt.title('Iris Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd3zURf748ddszab3EGoQpAiEIsWCIKKip4cKqJwVPM9DT8HjLGf5IefpeZY7y6Fir3iAqIjlqx4CIkUhAUIR6QFCgIT03Wzf+f3x2dTdhE0PYZ6PRx7ufj6zn89sMPvemXnPjJBSoiiKoijtja6tK6AoiqIowagApSiKorRLKkApiqIo7ZIKUIqiKEq7pAKUoiiK0i6pAKUoiqK0SypAKUoDCSFuFEJ819b1UJSOTgUoRalFCJEthLi4rvNSygVSyksbcd1VQgiHEKJMCFEqhMgUQvxVCGFuwDWkEKJ3Q+/dUK11H0WpjwpQitIAQghDEy9xt5QyCkgF/gJMBb4WQogmV05ROhgVoBSlHkKIaUKItUKI54UQhcBc/7E1/vPCfy5PCFEihNgqhBh4sutKKW1SylXAROBc4Ar/9UYKIdYLIYqFEEeFEPOEECb/udX+l2cJIaxCiOuFEHFCiC+FEPlCiCL/46616r/f32o7IIS4sdq524QQO/2v+1YI0aOu+zT5F6kojaAClKKc3ChgP5AMPFnr3KXAGKAPEAtcDxSEemEp5SEgA7jAf8gL/BlIRAtc44G7/GXH+MsMllJGSikXof0NvwP0ALoDdmAegBAiAngJuNzfajsP2OI/dzXwMDAJSAJ+BP5bz30UpdWpAKUoJ5crpfyPlNIjpbTXOucGooB+gJBS7pRSHm3o9YF4ACllppTyJ/+9soHXgLF1vVBKWSCl/ERKWS6lLEMLoNXL+4CBQgiLlPKolHKH//gfgaf89fUA/wCGVLSiFKU9UAFKUU7ucF0npJQr0FosLwPHhRCvCyGiG3j9LkAhgBCij7+b7pgQohQtcCTW9UIhRLgQ4jUhxEF/+dVArBBCL6W0obXoZgBHhRBfCSH6+V/aA3jR35VY7L+/8NdFUdoFFaAU5eTqXfJfSvmSlPJsYABaV9/9oV5YCNENOButiw3gVeBX4EwpZTRaN1x9CRR/AfoCo/zlK7rnhL9u30opL0FLyvgVeMN//jDwRyllbLUfi5RyXah1V5SWpgKUojSBEGKEEGKUEMII2AAH2jjSyV4XLoQYC3wObAC+9p+KAkoBq7+1c2etlx4Hzqj2PApt3KlYCBEPPFbtHilCiIn+sSgnYK1Wt/nAQ0KIAf6yMUKIa+u5j6K0OhWgFKVpotFaJUXAQbQEiefqKT9PCFGGFgBeAD4BLpNS+vzn7wNuAMr8162doDAXeM/fNXed/xoW4ATwE/BNtbI6tBZWLloX3liqEi4+A54GFvq7BrcDl9dzH0VpdUJtWKgoiqK0R6oFpSiKorRLKkApiqIo7ZIKUIqiKEq7pAKUoiiK0i41deHLkxJC6NGWcjkipbyy1rlpwLPAEf+heVLKN+u7XmJiokxLS2uBmiqKoihtITMz84SUMqn28RYPUMAsYCdaOm4wi6SUd4d6sbS0NDIyMpqlYoqiKErbE0IcDHa8Rbv4/KsqXwHU2ypSFEVRlNpaegzqBeABtAUr6zLZv0XBEv+yLwGEEHcIITKEEBn5+fktUlFFURSlfWmxACWEuBLIk1Jm1lPsCyBNSpkOLAfeC1ZISvm6lHK4lHJ4UlJAN6WiKIrSAbVkC+p8YKIQIhtYCFwkhPiwegH/VgFO/9M30BbNVBRFUZSWC1BSyoeklF2llGlo21qvkFLeVL2MECK12tOJaMkUiqIoitIqWXw1CCEeBzKklMuAmUKIiYAHbTHLaa1dH0VRFKV9OuUWix0+fLhUaeaKoigdhxAiU0o5vPbxVm9BKUp7ccLq5L8bDlFS7ua20T3pHGtp6yopilKNClDKaanU7mbush18ufUoAN/9cpxP7jyPpChzG9dMUZQKai0+5bTk9PjYdqSk8vmhwnK8vlOru1tROjoVoJTTUqTZwE2jelQ+nzCgEyaD+nNQlPZEdfEppyWLSc91w7syrl8S5S4vXePCiY8wtXW1FEWpRgUo5bQVE24iJlwFJUVpr1SfhqIoitIuqQClKIqitEsqQCmKoijtkgpQiqIoSrukApSiKIrSLqkApSiKorRLKkApiqIo7ZIKUIqiKEq7pAKUoiiK0i6pAKUoiqK0S2qpI+WUIqUkv8yJy+vDYtKTEKG2x1CUjkoFKOWUcqTYzqRX1pFX5mRsnyT+fd1gEiJVkFKUjkh18SmnlIUbDpFX5gTgh935FNhcbVwjRVFaigpQyiklLTGy8rFeJ4g0q04ARemo1F+3ckq5qF8yD0zoy6ZDRUw/vyexFmNbV0lRlBaiApRySomPMPHHsWfg9PgIN6n/fRWlI1N/4copR6/TEW5SvdOK0tGpv3JFURSlXVItKKXJTpQ58UlJhNlAhEpaUBSlmagWlNIkhwvLuf71n7jwuVV8nJFDmcPd1lVSFKWDUAFKaTSb08Pfv/yFfflWyl1e5n6xg1K7ClCKojQPFaCUJpFtXQFFUTosFaCURoswG5hz5Vn0Sook3KTnsd+eRbSal6QoSjNRI9pKk3SLD2fhHecgVZKEoijNTH2aKE2WFKUWa1UUpfmpLj5FURSlXWrxACWE0AshNgshvgxyziyEWCSE2CuE+FkIkdbS9VEURVFODa3RgpoF7Kzj3O+BIillb+B54OlWqI+ihMzq9LB+XwH/b+k2Mg8WUu70tHWVFOW00aIBSgjRFbgCeLOOIlcB7/kfLwHGCyFES9ZJURqiwOrkhjd/4oOfDnHdaz9RrOZ5KUqraekW1AvAA4CvjvNdgMMAUkoPUAIk1C4khLhDCJEhhMjIz89vqboqSoD8MifSP9nL65OUqAClKK2mxQKUEOJKIE9KmVlfsSDHAuZ+Silfl1IOl1IOT0pKarY6KsrJ9EyM4Pze2nemi/snk6wyFhWl1bRkmvn5wEQhxG+AMCBaCPGhlPKmamVygG5AjhDCAMQAhS1YJ0VpkIRIM//53TA8Xh9GvY64CFNbV0lRThst1oKSUj4kpewqpUwDpgIragUngGXArf7HU/xl1Oo5SrsSH2EiOTpMBSdFaWWtPlFXCPE4kCGlXAa8BXwghNiL1nKa2tr1URRFUdqnVglQUspVwCr/4znVjjuAa1ujDsqpwefTGtA6nUrmVJTTnVrqSGk3jpU42HCgAL1Ox/AecaTEhLV1lRRFaUMqQCntwvFSB099vZPPs3IBuHFUd+69+EySolSQUpTTlVqLT2kXXB4fPx+oSuD8+UAhTndd0+cURTkdqACltAthRh0Th3SufH7V4M6Em/VtWCNFUdqa6uJT2oWkqDB+f35PJg7ujE4IEiNNxEeoSbGKcjpTAUppN1JiwlRihKIolVSAUhRF6eDcPjfFjmKcXicmvYlIYyThxvDGXcvrpthZjFd6MegMJIQl0FJrfKsApSiK0oEVO4pZuncpb25/kxJnCUadkd+e8VtmDptJgiVgbe56FTmKWLBzAR/t/Igydxndo7oze/hsRnYaSZQpqtnrrpIklKCklBwrcbBi53E2HyrieImjravU7AptTjYcKGTFr3nklznbujqK0uxcXhef7PmEf2X+ixJnCaC1pj7d+ymPrHmEYkdxyNcqdZby9IaneW3ra5S5ywA4VHaIe1fey4ajG2iJVepUC0oJ6liJgzsXbGLLYe1/4NmX9OGmUd2Jj+wYiQtWp4cX/reH9386CMDALtG8O30kiR3k/SkKQImzhDe3Bd+Ob23uWoqdxcSGxYZ0LavbytcHvg567l+Z/2Jw8mASLYmNrmswqgWlBGVzeSuDE8Bnm49Q7va2YY2aV7nTw6KMw5XPtx8ppdzVcd6fogDYPXasbmud5/cV7wv5WjnWHGTgbkgAHC47jMfX/LtNqwClBGUx6YkNN1Y+75MShbEDrY9n1OsY2CWm8nmMxYjZoP4clI7FrDejF3XPJ0wOTw75WglhdY9XRRoj671PY4lTbXeL4cOHy4yMjLauRodnc7rJKbIz/4f9xEcY+cMFZ9ApxtLW1WpWx0oczFu5h+JyN3++uA89EsIx6FWQagtut5ucnBwcjo431tmWfNJHsbMYhyfw96rX6UkMS0SvCy2weKWXAntB0JZSpCmSKGPUSbP5wsLC6Nq1K0ajscZxIUSmlHJ47fIqQCn1KnW4MeoEFlPHHK50ebx4fVqLUWk7Bw4cICoqioSElktZPl25vW4Olx3G7rFXHjPoDKRFp2HSmxr0+3Z5XRwqO4TTU5VUFGOOoVNEJwy6+j8jpJQUFBRQVlZGz549a5yrK0B1zE8dpdlEhxlPXugUZjKowNQeOBwO0tLSVHBqAUa9ke7R3fH4PDg8Dox6Iya9CYMwNPj3bdKbSItOw+Pz4PF5MOlN6ITupMEJQAhBQkIC+fn5Id9PBShFUdoFFZxajkFnwKAzEGZo+kotFddqjIb+G6sApdSpxO7G5vSg1wniwo1Nam0UWJ04PD5MelHnFho2p4cyhxsJxFpMqttNUU5zKkApQZXa3byxej/zVu7FYtSz4PZRDO4Wi74RmXwFVid3Lcjk5wNFdI2z8PEfzyU1tmbCRbnLw9fbjvLgJ1sRQjDvhqGM65tMmFEFKaXtzJ07l8jISO677762rsppSaUsKUFZnR7mrdwLgN3t5W9f7KDE7m7UtfbkWfn5QBEAOUV2Flebf1R5P4eHR5duxyfB65M88tl2Sh2Nu5+iKB2DClBKUDohaswLigs3YWjkPKjaiRbBVmsQghrzruLCjQjUmITSut5//33S09MZPHgwN998c41zb7zxBiNGjGDw4MFMnjyZ8vJyAD7++GMGDhzI4MGDGTNmDAA7duxg5MiRDBkyhPT0dPbs2dPq76UjUAFKCSo+wsSHvx/FwC7RjDkzkX9OHkS0pXEZfZ1jw5hzZX/6p0Zx06juXD6oU0CZhAgzH/5+FMO6xzK8Rxxv3TqCpCi17JDSenbs2MGTTz7JihUryMrK4sUXX6xxftKkSWzcuJGsrCz69+/PW2+9BcDjjz/Ot99+S1ZWFsuWLQNg/vz5zJo1iy1btpCRkUHXrl1b/f10BCcdgxJCDAcuADoDdmA7sFxKWVjvC5VTmsmgY1j3WN6/bSQGna7RwQkgNtzEDaN6cNWQLoQZ9USYA/+30+kEZ6ZE8eatIxBAXISpCbVXlIZbsWIFU6ZMITFRW08uPj6+xvnt27fz6KOPUlxcjNVqZcKECQCcf/75TJs2jeuuu45JkyYBcO655/Lkk0+Sk5PDpEmTOPPMM1v3zXQQdbaghBDThBCbgIcAC7ALyANGA/8TQrwnhOjeOtVU2oJeryM+wtyk4FQhzKgnIdIcNDhVFx9hUsFJaRNSynrToKdNm8a8efPYtm0bjz32WOWqF/Pnz+eJJ57g8OHDDBkyhIKCAm644QaWLVuGxWJhwoQJrFixorXeRodS36dFBHC+lNIe7KQQYghwJnCoJSqmKIrSmsaPH88111zDn//8ZxISEigsrNlJVFZWRkxiDDklObz7wbt079odn/RxYP8BRo0axahRo/jiiy84fPgwJSUlnHHGGcycOZP9+/ezdetWLrroojZ6Z6euOgOUlPLl+l4opdzS/NXpmHw+iRCtNxGxyObCJyWx4aY608JtTg92t5dwk57wDrqMkaI0xIABA3jkkUcYO3Yser2eoUOHkpaWBoDH52HWw7MYfd5oUrum0qd/H0ptpdg9du67/z727tmLlJLx48czePBg/vnPf/Lhhx9iNBrp1KkTc+bMads3d4o66Vp8QoiewD1AGtUCmpRyYovWrA6n2lp8+WUOXlm1D7Nex+1jzmjx/YZyisr586It2Jxe/jl5EGelRgcsgFpoc/Hqqr2s+DWfK9NTmXZ+GnHhqltNaTs7d+6kf//+bV2NoKSUFDgKOG47HnBOL/T0iu2FUd+xlwRrTsH+rZuyFt9S4C3gC8DXLDU8TdhdHp78+leWbj4CgMPj49Er+rfYitnF5S7u/3grG7O1OUc3v7WB/80eQ3KtlRuycop548cDALz4/R7G9k0irrsKUIoSjFd6KXIU1XnO7XOrANVCQglQDinlSy1ekw7IK8FabbJpxTI+LcUntUm1FRxuL8FuaK+1MV/t54qiVJFS1rlRH1DvOaVpQvkq/6IQ4jEhxLlCiGEVPy1esw4g0mzg8asGMubMRC7ql8wDl/XD2IL7DcWFG/nnpEEkRZoJN+mZd8PQoBl4o3rGM7ZPEnqd4DcDO9E/NarF6qQopzq9Tk+MKSboOSEEJp3qfWgpobSgBgE3AxdR1cUn/c+Vk+gca+E/NwxDJyCqhbeuEEKbS/T1rAuQSKLDjEHXskuINPP89UPw+HyY9Dpi1fiTotRJJ3TEh8VT6irF5XXVONc5onPIG/4pDRdKgLoGOENK6TppSSWomGaYRxQqvU6EtAJDvJprpCghM+qNpMWkYXVZKXOVYdAZiA+Lx6gzohNqQZ6WEkqAygJi0SbpKoqinJaMOiOx5lhizFp3nwpMLS+U33AK8KsQ4lshxLKKn5O9SAgRJoTYIITIEkLsEEL8LUiZaUKIfCHEFv/P7Y15E0rDeXw+rA43Xm/9iZknOw/aPC+nWyVaKB2fEAKd0PH+e++Tm5vb1tXp8EJpQT3WyGs7gYuklFYhhBFYI4T4PynlT7XKLZJS3t3IeyiNkF/mYOmWXH7cnc+EAZ24bGAnEoLMz8o+YeO11ftIijJz46gepEQHbjRYYHXy5poDHCywcfe4MzkzJbJFE0GKy11szC5kw4FCrh3ejZ6J4Rj1agzgdLN08xGe/XYXucV2OsdauH9CX64e2qXV7v/uu+8ycOBAOnfu3Gr3PB2FEqAOAUellA4AIYQFrVVVL6nNALb6nxr9Pyofsx3YcKCQJ7/aCcDqPSdIS4zg/N41A9TREjvXv76e46VOAAqsLh66vB+R1RI9nG4v/1mxl3fXZQPww658VvzlQlJimr6tdF225pTwh/czAVjw8yFW3HchnaJVgDqdLN18hIc+3VY5peJIsZ2HPt0G0KQgZbPZuO6668jJycHr9fL//t//o3fv3syePRur1UpiYiLvvvsua9euJSMjgxtvvBGLxcL69etZt24d9913Hx6PhxEjRvDqq69iNpv561//yrJlyzAYDFx66aU899xzfPHFFzzxxBO4XC4SEhJYsGABKSkn/Ug9LYXyVfdjak7Q9fqPnZQQQi+E2II2fvU/KeXPQYpNFkJsFUIsEUJ0q+M6dwghMoQQGfn5+aHcWqnH7uPWGs/35VkDynh9sjI4AezNswbMl3J6fOzJK6t8bnN5cXhatqtv59HSysflLi8ONYfrtPPst7tqzPcDbf7fs9/uatJ1v/nmGzp37kxWVhbbt2/nsssu45577mHJkiVkZmZy22238cgjjzBlyhSGDx/OggUL2LJlC0IIpk2bxqJFi9i2bRsej4dXX32VwsJCPvvsM3bs2MHWrVt59NFHARg9ejQ//fQTmzdvZurUqTzzzDNNqndHFkqAMlTP4PM/DikFTErplVIOAboCI4UQA2sV+QJIk1KmA8uB9+q4zutSyuFSyuFJSUmh3Fqpx1VDOhPpX1U8NtzIhf2SA8qY9DquG67tYWPQCWaM7UVsrcy/SLOBOy/sXbne3/m9Eiqv21KuSE8lyd8dOaZPElFhah3B001ucdD1q+s8HqpBgwaxfPlyHnzwQX788UcOHz7M9u3bueSSSxgyZAhPPPEEOTk5Aa/btWsXPXv2pE+fPgDceuutrF69mujoaMLCwrj99tv59NNPCQ8PByAnJ4cJEyYwaNAgnn32WXbs2NGkendkofx15wshJkoplwEIIa4CTjTkJlLKYiHEKuAytP2kKo4XVCv2BvB0Q66rNE73+HD+N3sMx0udpESbA5ZCAkiODuO+S/syY2wvzAYdURZDwNiSTicY1j2WNQ+Mw+byEhduDDqW1Zy6xFr4auZonB4f4SZ9i99PaX86x1o4EiQYdY61NOm6ffr0ITMzk6+//pqHHnqISy65hAEDBrB+/fp6X1fXeqYGg4ENGzbw/fffs3DhQubNm8eKFSu45557mD17NhMnTmTVqlXMnTu3SfXuyEIJUDOABUKIef7nOWgTd+slhEgC3P7gZAEuplYAEkKkSimP+p9OBHaGXHOl0Qx6HakxFlJj6v+DTo4OI7BtVVO4ydCqq6ELIUgOkqyhnD7un9C3xhgUgMWo5/4JfZt03dzcXOLj47npppuIjIzk9ddfJz8/n/Xr13PuuefidrvZvXs3AwYMICoqirIyrXu7X79+ZGdns3fvXnr37s0HH3zA2LFjsVqtlJeX85vf/IZzzjmH3r17A1BSUkKXLtpY2XvvBe00UvxO+skipdwHnCOEiERb/bzsZK/xSwXeE0Lo0boSF0spvxRCPA5k+FtkM4UQEwEPUAhMa8ybUJRTis8LTVx9wO11U+IsASA2LBaDLvBP2Sd9FDq0PY0MOgOx5tgm3bO9qEiEaO4svm3btnH//fej0+kwGo28+uqrGAwGZs6cSUlJCR6Ph3vvvZcBAwYwbdo0ZsyYUZkk8c4773DttddWJknMmDGDwsJCrrrqKhwOB1JKnn/+eQDmzp3LtddeS5cuXTjnnHM4cOBAk38nHVWd220IIW4CPpJSBp0II4ToBaRKKde0YP0CnGrbbTQ3t9eHXgh0dezzpLRTHhdYj8Om96HkEAz+HaQMgoiEBl+qwF7Agp0LWLZvGUIIJvWexPV9ryfeUrVFeYmzhB8O/8D8rfM5aj3K4OTBPDjiQXrG9CTM0P5aoO15uw2leTXXdhsJwGYhRCaQCeQDYUBvYCzaONRfm6vSHVWB1cn2IyUY9Dr6p0Y3eokhq8PN3nwbb685wFmdo5k0rEvQsaNQFNmcZBeUs35fAWP6JNEtPjzockx5ZQ7W7DlBQoSJ9K6xQbdiL7G7OFRQTm6Jg6HdYxtdJ4DjpQ5+3JOPXgjO753YsbryCvfBG+PA7R87yVoIQ2+BSx8HS1zol3EUMmvFLLJOZFUeeyXrFTblbeKZMc8QFxaHy+vii31f8PTGqh71zOOZ3PD1DXw68VN6xvRstrelKC2pvh11X/SPO10EnA+kA3a0caKbpZRqq/eTKC538bcvfmFZljbj/NZze3D/hL415hKF6niZk0mvrMUnYVlWLtknbDx6ZX8izQ2/1r58G1PmawO/z323i69mXhAQoE6UObnxjZ/Z409BnzX+TGZc2AtLtcVnpZSs/DWfexdpmyv3To7kv384J6S1AGs7VmLngSVbWb1Hy7/5zcBOzJ04oGMEKXsxfPNQVXCqsPl9uGB2gwLUMduxGsGpwk9Hf6LQUUhcWBwlzhJezXo1oIzH5+GVLa8w97y5RBgjGvw2FKW11Ztm7k8T/5+Ucq6U8o9SynullK+p4BQal8fHil+rljD83y/HKW/kkkCZB4vwVeuN/WF3PmV2T6OutWp31Vwyn4T1+wKTMrU5TlXzo5bvPE65s+b9HG4vX207Wvl8b55V24OqEXwS1u6rSupcs/cE7hCWWToleBxwbGvwc4UNG3/YW7S3znMHSw8C4PK6KHWVBi2zu2g3Do+jQfdUlLaiVjtsQSaDjnHV5hhd3D+F8CDbX4Ti7B5xVB92GtsniShL47LnLuyThPBfSyfgnDMSA8qYjTrOTI6sfH5x/xTCa81xCjPq+c2gTpXPeydHBt3eIxQ6oc2jqjC6d2KLLpnUqgxh0Ck9+Ln4hnW39Y7rXee5HtE9ADDpTUSbooOW6RPXp12OQSlKMHUmSbRXp1qSRKHNydacEgw6HWd1bvwYVJnDzb48K2+vzaZ/ahSTz+7aamNQ8REmBtczBnWwoJyjzTQGtXp3PnqdYHRHG4PK21lzDAoaPQY18/uZAd1856SeU2MMatGuRTyzseYKBQadod2OQakkidNHQ5IkVIA6xagsvlNUG2TxrTq8ivlb53PMeoz0pHT+OvKvKotPaXPNGqCEEGZgMpBGtaQKKeXjzVHZhjrdA5RyipMSpK/J86BcXhelzlIkkriwuDrnQeWVa2OgBmEgMTywK7ctuL1uCh2F6HV6Ei1anTpqgJozZw5jxozh4osvbtDrVq1axXPPPceXX37ZQjVrO82VZl7hc6AELdXceZKyiqLURwgQTV993aQ31RtwSpwl7MjfxqtbX+NY+TGGJA3h7iF3kRrZBZO+7XZTzivP48v9X7L84HIijBH8rt/vGJQ4qOEX2roYvn8cSnIgpiuMnwPp1zV/hUMgpURKiU4XOGb6+OOt8z3e4/FgMHS8dSlDeUddpZSXtXhNlFaVV+qg3OUlwqwnqY5xoxNWJwcLbESFGUmOMhMbrraJPxU4PU6+2vclT238Z+Wxb2zfsPLwShZfsZAz6km0aEl55XncufxOdhftrjz209GfuPmsm7ki4orQL7R1MXwxs2o8r+Sw9hyaFKQefPBBevTowV133QVoKz5ERUXh8/lYvHgxTqeTa665hr/97W9kZ2dz+eWXM27cONavX8/SpUt57LHHyMjIQAjBbbfdxp///GemTZvGlVdeyZQpU9i4cSOzZs3CZrNhNpv5/vvvMRqN3HnnnWRkZGAwGPj3v//NuHHjatSrsLCQ2267jf379xMeHs7rr79Oeno6c+fOJTc3l+zsbBITE/noo48a/d7bq1DSpNYJIRrxFUdpr46V2Lnq5bVc+Nwqrp2/nrzSwLTjAquT6e9sZPKr67n0+dUszjiMy9NB0r47uFJnCS9ufinguNPr5N+Zz1PmCnW1sua14eiGGsGpwoKdC/AFX7AmuO8fD5xT5rZrx5tg6tSpLFq0qPL54sWLSUpKYs+ePWzYsIEtW7aQmZnJ6tWrAW0V81tuuYXNmzdz4sQJjhw5wvbt29m2bRvTp0+vcW2Xy8X111/Piy++SFZWFsuXL8disfDyyy8D2jJL//3vf7n11ltxOGr+PT722GMMHTqUrVu38o9//INbbrml8lxmZiaffxLrhbYAACAASURBVP55hwxOUE+AEkJsE0JsBUYDm4QQu/z7NlUcV05R6/cXcLRE+yPILihnx9HAOTNlTg/bjpRUPn/jxwMU210B5ZT2x+ouo9xTHvRcRl5mm8yDcngdrD6yOug5n/Th8TVgTl9J4JYX9R4P0dChQ8nLyyM3N5esrCzi4uLYunUr3333HUOHDmXYsGH8+uuv7NmzB4AePXpwzjnnAHDGGWewf/9+7rnnHr755huio2um+e/atYvU1FRGjBgBQHR0NAaDgTVr1nDzzdra2/369aNHjx7s3l0ziFcvc9FFF1FQUEBJifa3OXHiRCyWpq3i3p7V18V3ZavVQmlVvZKq5jcJAd3iwgPKWIx6zAYdTn+rqVdSBKaOMi+pgzPr687SSwhLQCda/9/RKIykhNe9a2yD6hTTVevWC3a8iaZMmcKSJUs4duwYU6dOJTs7m4ceeog//vGPNcplZ2cTEVG1GkdcXBxZWVl8++23vPzyyyxevJi333678ryUEiECM29DyaIOVqbiWtXr0BHV+X+FlPKglPIg8ETF4+rHWq+KSnNLS4jgjVuGM/nsLrx/20hSogOXJoqxGPnoD+dw7hkJTBycyvPXDVFjUKeIcGMEw1MCEqIAmD7gVuLCQp931Vz0Oj1T+kzBIAK/E/eI7oG+IYkj4+eAsVarwWjRjjfR1KlTWbhwIUuWLGHKlClMmDCBt99+G6tVW1XlyJEj5OXlBbzuxIkT+Hw+Jk+ezN///nc2bdpU43y/fv3Izc1l48aNAJSVleHxeBgzZgwLFiwAYPfu3Rw6dIi+fWtuG1K9zKpVq0hMTAxooXVUoSRJDKj+xL99xtktUx2lNURbjFxyVgrj+yXXOZ8qzKjn7B5xzL9pGAa9jogW3ilXaT6xYbE8fcFT/HnVX9h6QuuN1ws9v+s3lYt7XNImLSiAOHMc/7noP8xZN4d8u7bc1oCEATw75lmsR6wneXU1FYkQLZDFN2DAAMrKyujSpQupqamkpqayc+dOzj33XAAiIyP58MMP0etrBtQjR44wffp0fD6tx+Gpp56qcd5kMrFo0SLuuece7HY7FouF5cuXc9dddzFjxgwGDRqEwWDg3XffxWyu+YVx7ty5TJ8+nfT0dMLDw0+rPaTq227jIeBhwAJUdGgLwAW8LqV8qFVqWIuaB6UooSlyFFHmLKHEWUxSeAqRpigiTZEnf2ELcnldFDoKKXeXY9QbMelMpESk1DkPqq6usbbkkz4Eot3V61TRLPOgpJRPAU8JIZ5qq2CkKKcEjwvshVB2TOtqssRB5Mn2Im55cWFxJ+3OK3EUUeQoZHPeZpLDU+gT14fE8OQaH75en5dCRyEHSg7g8DroG9eXaHM0FkPDB+dNehOdIjrVW8br8+KVXoocRbh9bmLMMVgMloDJyB6fB7vHjs1tw6w3E2mMxKAz1AwcUoLPo20SCdoEaX3DdwCouJ/NbaPUVYpRZ9QmSAsD+iZOulbqVmeAEkIM8z/8uNrjSlLKTbWPdSQFVidenyTKYsBibHz3lsvjpbjcDQLiw00Y6kg0OFZix+OTmAy6Jq1nFyqrw43d7cViNBAZprrvGs3thJwNsOhGcPizHpP6wU2fNMugfUsqshcyb/N/WLxnSeWxhLAE3r/sXbrHpAFaC2ZfyT5u+/a2yh18DToDz455lvM6n0e4MTDBpil80ofNbeNwWVUSRImzBIvBQvfo7pVByu11c7D0IE5v1doBeqGnZ0xPzAZ/F5mU4HFqe3F5/RmoehMk9AZDw7aECXa/AnsB3aK6EWmKbLNu046uvt/qv/w/LwM/A68Db/gfB06y6ECOlti55e0NTHhhNd9sP4bN2bhtLVweLxnZRYz/1w9c+vxqtueW4A2yhcTBAhtXv7yO0U+v5MElW4POS2pORTYXz367i6vmreWVVXspKlfp443mKISPrq0KTgD5v8Lnd2v7QLVjB0r21QhOAAWOAh5e+yhF9sLK57NWzKoMTqC1JO7/4f4WmU/l9Xk5Yj0ScNzusVPqKkVKWbmVffVgAeCV2msrU9Z9HijcXxWcQHtcuB+87pDrJKWkyFkUcD+AI9YjeH2N22JGObn6svjGSSnHAQeBYVLK4VLKs4GhQN2b0pzivD4f//l+DztySykqd/OXxVmUORoXoIrL3cxcuJkyp4ficjezF2VRWF7zD8Pt9fHi8j0c8wellbvyOVBga/L7qM/BwnLeW3+Q3BIHr6zax4kytYJVo+X9GjhpFODAKnAHn4vUHnh8Hpbs+Szouaz8LBwe7T2Vu8vJsQbOL/JID1vzm386pNvnrnPSbrGjGK/Uuv+qB8zq7B57VVq29EGQoILHoZ0LUX3380kfbl/owU5pmFDapf2klNsqnkgptwNDWq5KbUsgamSsGfU6Gj0WKqixP5LFpKd20pxOUGOXWoAwQ8v2aYcZav6zmwyqe6LR6uoqEnq0nKL2y1zHmnwCgc7/P3196d8tsaZffYkH1c81PUGhYa8PtV5K8wrlk2mnEOJNIcSFQoixQog30LZ975B0OsEdY3oxcXBnhnaL5cPfjyIuvHGDqokRZt66dQQDu0QztFssL98wjITImh9oep2Ou8f3ZniPOKItBm6/oCddYlt2ZninmDCeuHog5/VK4N/XDQ66z5MSovheEB5ky4yzrgZz22bM1cegM3B93+Bp2Rd0GY3FqNXdYrTQL75fQBmLwcJZCWc1f73qSTqID4vHoDNgEIY6kz9qjAcJHQQbIzNFaudCpBd64s3xwc/p9EHndinNI5TtNsKAO4Ex/kOrgVellG2yb3RrpZnbnB5cHh/RFiP6Juy9JKWk0OZCCIiPqHtgNr/MgdsrCTfpW2VCrMfro9ztJdyorzNxQwmBzwsndsPiW7T/CgF9fgO/faFdZPLVp9RRwpf7v+C5zH9XdlOdFX8WL417gZTI1MpyR6xHuOf7e9hTrC3xk2hJ5KVxL9Evvh/GRmbE1VaReiylxO6xc7D0YI2uvhhzDJ0iOlUmSXh8Ho5Yj2B1Vc2fMulNpEWn1ayTxwXFB6GinCkK4rpryRIhyM3NZebMmSxcvJAjZUewuqvupxM6ekT3wGKwBLSibr/9dmbPns1ZZ9UdxOfPn094eHiNtfVOB2rDQkVpbdY8bSxKZwBTBFhi27pGISl3l2N1lnK8PI9IUwQxphjig2zjUWAvoNxTjsfnIcoURZw5rlnTq6t/aPmkD6/PW3m/CGOE1nKqlmb+1f6veHHTixyzHSM5PJm7Bt/FxN4Tg+6LhddTNeak02n/Ro3g8Xlwe91Y3VaETxBjiUGv06sMvgZqlnlQQojFUsrrhBDbgIAoJqVMb47KKkqH0M5bS3URCO1DWwh0OoP2OIgESwIJNHz339ocHgelzmKOlOVgMVhItCQFBESd0KHT64jRxwS9xlf7v2Luurk4vFonzvHy4zy14SkMOgMTe08MfIE+tIBU13Yb77zzDtu3b+fD9z/kq6++wuFwYLPZWL58OXf/6W5++OEHevbsic/n47bbbmPKlClceOGFPPfccwwfPpzIyEhmzZrFl19+icVi4fPPPyclJYW5c+cSGRnJfffdx969e5kxYwb5+fno9Xo+/vhjUlJSuOqqqygqKsLtdvPEE09w1VVXNeC3feqrL/TP8v/3SuC3QX6UEEgpKbK5KLI1PZXb4faSX+ag+CRp4fllDo6VOPCo7TGUepQ6S1myZwmXf3I5N3x9A1d+diV3/u9OjtuOt8j9bG4by7O/44rPfsst307n2q+mMvXrG8gu3o8M/A5cpxc3vVgZnCo4vA5e3PQi7gakj9cWbLuNitXHK6xfv5733nuPFStW8Omnn5Kdnc22bdt48803Wb9+fdDr2mw2zjnnHLKyshgzZgxvvPFGQJkbb7yRP/3pT2RlZbFu3TpSU1MJCwvjs88+Y9OmTaxcuZK//OUvIS0u25HUl2Z+1P9wPGAKsmCschI+n2RPnpXb3t3IHR9kkH2i8enjpQ43n246wpT565m1cHOdc6UOFdiYuXALd3yQwbbcEpxuNUdDCe5g2UGe2fgMLl/VF55fCn/h4TUP15lW3RQnbMd5eO2jNYLL8fLj3LF8RuUadqE4ZjsW9Hi+PZ8SV0nD9paqJth2G927d69R5pJLLiE+XkuYWLNmDddeey06nY5OnToFbDRYwWQyceWV2uYQZ599NtnZ2TXOl5WVceTIEa655hoAwsLCCA8PR0rJww8/THp6OhdffDFHjhzh+PGW+fLQXoXSeZoGvCaE2CeEWCyEuEcI0WHTzJtTgc3FTW/+zObDxWzMLuL29zPIb+ScoyKbi4c/28bBgnJ+2H2Cf37zK45awedoiZ17F2Wxfl8BW3NKuPmtDRQ0Q8tN6XgcHgcf/PJB0HMbjm2ocz+ppvh079KgLaXj5ccbFFTqWiopwZJAoaOwYXtL1VKx3caiRYuYOnVqwPnq21uE2poxGo2VSRR6vR6Pp2b96rrOggULyM/PJzMzky1btpCSkhKwmWFHd9IAJaWcI6W8CBgIrAHuBzJbumIdgZSSEntVl0NxuavRTfRyV81gVGhz4fHVvJbPJ2t0/1mdngZ0nCinE6/01siAq60pH/LBSCkpcQZujFl5ntAD1N1D/oRZXzMj1qQ3MbXv1Ea3nirU3m6jPqNHj+aTTz7B5/Nx/PhxVq1a1ah7RkdH07VrV5YuXQqA0+mkvLyckpISkpOTMRqNrFy5koMHT7+Oq5MGKCHEo0KI/wO+A3oD9wHte5GxdiIyzMDfrx6IEGDQCZ64eiDRlsal5SZHmRnbJ0m7rtnAAxP6EVlrC4z4SBOP/fYszP6Jtw9e1jdgUq6iAEQYI5jYK0hCAdA1qivhhuZdY08IwcReVwQ9F6YPQ9+AuUS/7TWRB0Y8QKIlEYEg0ZLIHYPuYHSX0USZopqUVVd7u436TJ48ma5duzJw4ED++Mc/MmrUKGJigid2nMwHH3zASy+9RHp6Oueddx7Hjh3jxhtvJCMjg+HDh7NgwQL69Qucj9bRhTIPahPgAb4CfgB+aqs5UHDqpZnbnB5KHW4EghiLEYup8am5hTYnNqcXk0FHQkTwhWfLHG5K7R4kEotRHzAxWFEqFNoLuX/1/Ww4tqHymFlv5u0JbzMocVCzr5BQVH6CJ35+ku8OLa88JhA8NfpJejp71TtnqDa3102uLTdgHlSP6B4tssJFXaxWK5GRkRQUFDBy5EjWrl1Lp071r9Z+umv2eVBCiChgtP/nOuC4lHJ081S3YU61AKUo7Vmho5C9RXv5/tD3dI7szIS0CcSHxbfYh3xR+QmyS7NZtv9Lok3RTOp9NYkRKRzaeyjoflD1cXvduH1u7B47Zr0Jsz4scLuNFnbhhRdSXFyMy+XigQceYNq0aa1271NVs8yDqvbCgcAFwFhgOHAY+LF5qqootVSsAN7Uia5er7Z6gMEUuD14dR63NonT2LSWps1to9xlQwhBbFhc8AmjaJsIeqWXaFN0nUHA6ijG7inHqDMRG2TSbHOKD4tnZOpIRqaOrLdcob1q9XC90JMUnhQ8EHhc2iK5poig+y7FhSdi1Bm4a/CdCCDaFIPJGLi9TMVkXdC6B4P9Po16I0a9sVm2/PD6vPikDx8+dOiCTsCVUuKRHnxSK6MTukaPOzUXKSUenweXz4XX5yXMEIZO6AJ+XxXvzyu92jyzIGXao1Bq+DRa195LwEYpZUgTDfxLJK0GzP77LJFSPlarjBl4H20L+QLgeilldsi1V5rE7fVhbC/LHJUXQPZa2PC6tk7auXdD1xEQXv+Ge0GVHoWcjbDzc4jpDmdPg/AkMFf7IHPZoOQwrH9FC2TDfw/JZzXqfsesubyw6UVWHF5JuCGcG/vdwJQ+k4mzVE1sLXWWsiV/C69mvUqRo4hx3cYxfeB0ksOrJvh6fB6OlB3m+czn2ZK/lW5R3fjLsHs5M/ZMIsKiG/57aCZ55Xm8vf1tlu5ditPjZFz3ccw+ezbJlmRMBn+QlRLKjsLPr2m/++7nwojbIbrmOE5uWQ7PbHyGVTmrCTeEM7XPtdx01k01ynh8HvLK8yh2FiOlxGww0zmic+WHb3Pz+DwctR2l1J/EoRM6ksKTiDXHVn6Ie31ebG4bR21HKxNIokxRpEakNttyTw3lkz4cHgeHyg7V2PIj1hxLSkRKjWWhTthPUOgorEzSijRF0jmyM0Zd29Q9VC221JHQvl5FSCmtQggjWgbgLCnlT9XK3AWkSylnCCGmAtdIKa+v77pN7eIrsDrJOlxMUpSZ7gkRxARJWigud5F5sIiDBeVcPqgTqTEtu3hrKNweH4cKy/k48zD9OkVzXq8EkqMDv3kWWJ18tfUopQ431w7vRkqQMsXlLn7Ync/3O/O4Mj2VUWckBP09tBqXDda8AKufqXn80idh5B8atrlc2VFY/jfI+m/VsbBYuGMVxPesOnZwHbx7Rc1tFy59Akb8AYJ8o6/LifJ8bv6/WwK2pPhd3+uZdfafiTBG4JM+vjnwDQ/++GCNMj2je/LOZe+Q4A9kx6y5XL1sEjZ31Xw5gWDB5e8zKLltZnYcsx3jnxv+yfeHvq9xvGtkV9657J2qlO+yY/DaBdqSTxWiu8AfVkJUCqDNg5r69Y0cL685l+eqnlfyu8SbGHDWALw+L0dtRwPmYQkh6B3bu9m7Hr0+L7m23MrgVF23qG5Em7UvBnaPnf3F+wPKRBgj6BrVtU1aI26vm73Fe4NmLnaK6ER8mDZfq9BRGHTuWKQpkq6RXVt9R+CGdPG12NdnqakYwTT6f2pHw6uA9/yPlwDjRQt2IBeXu3jo023c9l4Gv523lp/2FQQt9832Y/z+vQwe//IXrnttPfllbT/3IN/qZPL8dcz/YT/3LtrC8p2BE/ZsTg/PfruLOct28Nx3u5n+zkYKrIHzrrYcLmbWwi0sy8rljg8y2ZdXd7pxq3CUwroge2D+8LS2lXpDuO01gxOAoxjWz6vqPrSdgG8eCtwTaNVTYC9q0O32FO0Oul/Sx3s+web/0Ct0FPLCphcCyhwoPUB2STagfRv+bPcnNYITgETy4uZ5lDb099BMPD4PKw6tCDieY81hT9GeqgPbP60ZnABKj8Ce7yqf7jixPSA4AXyZ/X+V3+x90hd0krCUkiJHUbOvpOCTvqDBCbSJvx6fB6/PS355ftAyNretyantjVXuKa/z3ifsJ/BIDx6ptZ6CsbqsbVb3ULVo/44QQi+E2ALkAf+TUv5cq0gXtDEtpJQeoAQCF/wSQtwhhMgQQmTk5wf/HyUULo+PjINVH0A/7g28lsfrY/3+qsB1uNCOx9v2s4nsbv/W8X6ZB4twe2rOjXK4vWTlVO3iuvNYKV5fYN235tT8APjlaN3zU1qFx6H91OYs1Rb6bAhb8D9GCvaCfxO+yp1Wa3PZau6+GoJDpcHnpnh8nsoxGyklR21Hg5bLLs2uLL+vjmvlWHNwBdt4rxWUucrqXIboUKm/a8nng2N1bF54bHvlw+ySA0GLeKW38h6+euZDOb3OBi2JFIr67uf2uZFSIpH1bkrYVh/yrnr+X/X4PFpzQNY/p+20DlBSSq+UcgjavKmR/oSL6oK1loItTPu6f0ff4UlJSY2uT4TZwN3jegMQZTZw8zlpAWUMeh3Tz0+rnEt05aBUzMbWbQIHE2HSM6Sbljhg0uv43cjuGGttbBgVZmT6+VXdWL8b0T3oZoSXD+xU+f7CTXouOLNlB+JPymiByJTA43E9G9a9BxCVCoYgXXS9LtK6+kDbIyjtgsAyMd3qT6gIYkjy0KDH48xxhBm0axl0BgYnDa7j9VrXnUlvYkzn84KWGZlyNhGmthmDijHHEKYP3uU5NGWo1j2k00H/Opbn7PebyofDOwVPxIg0RmqL1kJl8kHQcqaqcs2lvvtZDJbKhIIIY0TA+aX/XUr+sfx6N3UEmDZtGkuWLAn5eKjqSg55/fnXCTOEIYTg4MGDXHPBNUHLCSFqvPcXXniB999/v9H1qTBv3jzeeeedJl8H6l/N/AuCBIsKUsrgs/yCly0WQqwCLgO2VzuVA3QDcoQQBiAGaLG+jAizgSnDu3LZwE7odYKEOjbq658azQ/3j8Pu9hJjMRDfDjb06xRjYf5NZ1NocxITbiI6LPCfzmTQcdnATpxzRgIuj4/4CFPQvaW6x4ez4r4L2Z9vpXdSJIlRbTxXKjwJrpkPC67VWjegBaZr5kNEA7+QGMPhty/B53dVXavbKBh0bVXwCYuGy56Co5uhNNf/OgtMfgsamDWXEp7Cpd0vrjG3B+DhUQ8R6x8DiAuLY865c7jp65uwe6q2h7+699WV408A53cZTb/4fvxa+GvlsfiweGYMvhOLKfADsjWYdCbuGnIX/878d43j47uPrxzjAKDbSOg1HvZVG6vqdwWkDKh82jmiE2M6j2Z17poa13rg7NnovNoHpV6nJyU8JaDFadQbiTJFNXsKuV6nJzk8OWCMRghBSnhK5fhMQlhC5ZbzFZYuXMqIoSPabLsNs96MWW+ubKlXeOOFN/jH3H9UblFS1/hYkiWp8v15PB7efvttNm3a1OR63XbbbZx//vlMnz69ydeqb2TvuaZcWAiRBLj9wckCXIyWEVjdMuBWYD0wBVghW3i53ugwI9Fh9ScEmA16OsW0fauptk4xYXSKqX8AP6T3Z9TTJdbS4jv3hkyvh27nwKws2Pu9tl9Prwu1YNHQD6SIBOg9Hu7ZBMd3aC2qqBSI7lyzXFwP+MMqKNynde11GgiW+Dq3m6hLrCWeR895lKt6X83SfZ8TbYrixn43kBrZpUZ2V8/oniy7ehnfZn/LUdtRLk+7nB7RPYg1V6XTJ0QkM/+il8k8nsHq3HWcFdeHS9IuJTE8SOuylSSGJ3LFGVcwKHEQC3ctxOFxcMUZVzAsZRgp1esVkQSTXofiQ3BsG3QeAtFdtX8Pv7iIZP5+3t/IPJ7B0gNfEWWM5Nb+N9E1sgs52drYlE7oiDHHYDaYtXGUavtPtUTGWcX9cg/lcvVvr2bQsEHs2r6Lfn378cH7H4ABMjMzmT17NmXWMqJjo/n7f/5O1sYsdmbt5J7f34PFYmH9+vU8++yzfPHFF9jtds477zxee+21kANqxT2sViuJiYm8++67pKamcuGFFzJq1ChWrlxJcXExb731FhdccAHl5eVMmzaNnb/uJO3MNA5mH+SRpx/h+y+/x+lwcv6I8xkwYABPPvkk0id57sHnWLtuLYmdEnltwWt0je9aY9WNFStWMGzYMAwGLSQE2/7j8OHDPPbYY6SkpLBlyxYmTZrEoEGDePHFF7Hb7SxdupRevXoRHh5OWloaGzZsYOTI+qcvnJSUskV+gHRgM7AVrdU0x3/8cWCi/3EY8DGwF9gAnHGy65599tlSUdojh9shXV5Xs1zL5/U2y3UK7YUyz5Yny5xlTbqOx+uR+eX58rjtuHS6nU2ul9VZJh0ue+XzX375Jeg93V639Pl8Tb7fyRw4cEACctUPq6TH65HTp0+Xzz77rHS5XPLcc8+VeXl5UkopP/roI3nrtFul2+uWY8eOlRs3bqy8RkFBQWW9b7jxBvnJZ59It9ctb731Vvnxxx8H3LPieO17LFy4UE6fPl1KKeXYsWPl7NmzpZRSfvXVV3L8+PFSSimfffZZeccdd0gppdyStUXq9Xq57qd10uV1yYiIiBrvS6/Xy82bN0u31y0nT5ks333v3YDf6Zw5c+RLL71U+XzkyJHy008/lVJKabfbpc1mkytXrpQxMTEyNzdXOhwO2blzZzlnzhwppZQvvPCCnDVrVuXrn3jiCfncc88F/V0H+7cGMmSQz/tQJuqeCTwFnOUPKBWB7YyTBL6tQEAHvZRyTrXHDuDak9VBURrE64HyE1pGnjkKTJGBE399XrDlw+ENWguq5wUQHq91ETaSub7xMq8XbHmw6ysoyYGzrobY7to9q7PmQfYaxO5voPNQGHA1RHaq2ZJ027V5Y3uWay2+3uO11ma1+5c6S8nKz+KVrFc4aj3K0OShzBo2iy61WnbYi7UU8X0rtPp0GxF0PFCv05NoOUn3pzUP9v8AB9dCz7GQdn6dGzlGmCLrv5b/nq2pW7dujB0zFoCbbrqJl156icsuu4zt27dzySWXAOD1eklNTQ3abbZy5UqefvppSm2lFBUWkdwzmcEXDq6cJFuXXbt2Bb1HhUmTJgE1t+pYs2YNs2ZpW/YNTh9Menq6NnE5SCuzZ8+eDBmijXWOGD6Cw4cOB7Tsjh49Wpn6HWz7jwojRoyorFuvXr249NJLARg0aBArV66sLJecnMyvv1Z1VTdWKMn77wCPAc8D44DpBE9u6DDKHG5OWJ2cKHPRMymCRLWe3alDSji+HT64uiplfNg0uHgOhFdLEC06AG9eXFVGZ4AbP4a0sVqXY3MrzoY3xoHDn0G55nk4byZccB9Y/AuMWvPho+sgd7P2fOsiWPNvrSsypot2zOeDI5vgg6ugYnM+QxhM+wq6atNIPF4P3x/6njnrKr8LsvzQctbmruWTiZ/QLaqbdtBRqk2sXfWPqnrGdIPffxfYJXoy1uPavLIT/tTzzHe08aebl54yuw3X/tAWQiClZMCAAXVuRljB4XBw1113sXj5YhJTE3n5mZdxOp04PA7K3GX1BqiT3cNs1j5/qm/VIRswElLx+opr2O32gDIWi6VyK4/6rl39WjqdrvK5TqersY2Iw+HAYmn6EEIoHe4WKeX3aJN6D0op5wIXNfnO7ZSUkvX7Chj33A9c+9p6bn9vIyeCzCVS2ilbPiy+peZ8pk3vQv6uquf2Yvj6gZplfB74bIbW8mpujhL49pGq4FRh3UvaHK0Kh3+uCk4VrHlaObc/Db88H5beWRWcQEvR//xPWoADipxFQedd2T12Xst6DbvbXlWvH56qWajkMKx/uep+ofD5YMfSquBU4fgOrZV3ijh0cBylpQAAIABJREFU6FBlkPjvf//L6NGj6du3L/n5+ZXH3W43O3bsACAqKoqysjJA+0CWSCJjIym3lvO/L/5XdWGppevXFaTqu0ddRo8ezeLFiwH45Zdf2LZtW+U5o9GI292wnYX79+/P3r17gbq3/2iI3bt3M3Bg7aTthgslQDmEEDpgjxDibiHENcCp8ZWoEexuL4szDlc+33K4JGBjQKUd8zihOMh8oj3VPjA8Dm05ntqsx4PPx2oqtwOOBLkfQMG+qsd76/gwP7hOW44JtLXugr2//F/BP1fH5XNR6AieDLvtxLaqzQiLsrUWZ23ZP1bdLxQeB+xfGfzcvuU1g2k71r9/f9577z3S09MpLCzkzjvvxGQysWTJEh588EEGDx7MkCFDWLduHaClic+YMYMhQ4ZgNpu54dYbuGbMNcy8dSYDh9T8cHZ6nXUGqPruUZe77rqL/Px80tPTefrpp0lPT6/c6uOOO+4gPT2dG2+8MeT3fvnll7N69erK58G2/2iItWvXcvHFFzfoNcGE0sV3LxAOzAT+jtZ6urXJd26nwgx6xvZJYvlObVZ8t3gLZkP7y+hT6qA3ahlltlqTsLtVyybSGSGpb2CQCotp+LyrUBhMkNg3+CTi2G5Vj7sO17rGakvuXzU2pjdCRGLgtWK6gn8+jlFnJMIYEbAqBUBaTFrVZn8xdWzrljKwYfPB9GZIHQK7/i/wXJezgy4a2x7pdDrmz58fcHzIkCE1PrwrTJ48mcmTJ1c+n/v4XP7wwB8Cyv3/9s48POrq6uOfM1smGwmQQCCETVDDKosiahWqta64AVq1feuGWrVY6/u2xa3VFrG1VSrWSlVUsFigLhR3ca07smhYRAQlJCwBkpBlkslk7vvHnSyzJJnJTJKZcD/PM08y93fnzslN8jtz7z3nfP+w4A9kJGVgCVgPPPnkk22+R/NitFlZWY1nUE6nkyVLluB0Ovnmm2849dRTGTRoEAD33Xcf993XFDBdUNCU2XPrrbcGvQfAoEGD6N27N19//TXDhw9n+PDhvPWWfwWRoUOHMmXKlJC2TZkypfHaunXrGDlyJFlZ0edXhqOo+5nSJYsOAT9XSl2omtXT625YLMI5Y/vz9JXH8YcLRrH82slkd3WekCF8UrLgvIeh+QH7oJMgt1mZr9TecNafILCu2+l/gOR2FKdti+Se8MO5wTfq/HP9gySG/UCfATXHngyn/AocPgeV0ht+OM+/jwic+afGnLEMRwb/MyL4M6RFLNxwzA2kNQQoODNhTICseVIP3/tFkHdltcL4n2jH2Zz0HBh1UejXdENS7akt5kRlJWdhiTCFoTWqq6s56aSTGDt2LBdccAGPPPIIDkd0+Zrz5s1j9+7QFU8iYf/+/dxzzz1RjwPhCRZORAdKpPuayoErlVJdIvtu9KAMbeKu0udLxet1cEFGXvDNs65Gb+mtXaxLKo3/iV5RRCvz0aJNLqgogg8XQEUxjL4Yhp4SbFfFbvjsMR0Nl50P37tFF121Nbv51BzS+Uaf/UOvmo67RtuelN7YpaymjOe3Pc8TBU9QVlvGsMxh3DbpNkb0HuFfgaBqP+xeDxuehd7DYNyPdRSfNcLip14vVO6BTxZC0RoYeLyuEJ+eE1YuW6gCoomGUora+loKKwobyxBZLVb6p/Yn1Z7a6VGJ8UpMBQtF5AvgBqXU+77nJwF/U0qNiZ3J4WMclCGh8bh1vb+kVsKsPW4dSt6WlpXXd6bRwidzj9dDhbui8XlPZ8urw0O1h3Rlgmi3OD1uXfPQnhLR1l53cFANeLxaM0op1ai7FCpht96raxAK0inOy+P16Ag9AZt0rrBjc2IqWAhUNDgnAKXUf0WkorUXGAxdSXltOQdcB/hsz2f0S+vHiF4jyApVwuhQsV5Jeet0rlRqH7D531QPuA5QcKCAgpICjulzDEf3OtqvPFHE2Bz+q6FAqg9A1QHYswF6HaFXT+n+eUk1nhrKa8tZv289FrEwts9YMpMy/aQolFKU1ZTx2d7P2FG+g8n9JqNQ/uWJ0FpP7+56l/d3vU92cjaX5l9KVnIWGUkZNBtMRxMe3K4dZ998cPYMLUvS1s8XLvV1+r0afjcWq04FSADakt6o99ZT562jpLqEmvoanDYn2cnZ2C32DnFU9d56qj3V7K3eS62nFpvF1vg7jnfRwnCs+1REHgWWomvzXQy8IyLjAZRS0RdvMhhiRFlNGX/f8Hee2fJMY1t2cjaLz1xMbnpuU8fyInj+Wh2xBnp760dLoefQxu2tkuoSrnztysaK4wBH9jySR3/waNtJq+2hsgTeugfWPtXUNuBYuHiJ3ipDawBtKNnA9W9e31hhO8maxMIfLGRs9tjGG1xxZTGXvXwZB2p0Zf5HNjzC5H6TmXfyvEYnta96H9e+cS3byrY1vt2Kr1fwwJQHODH3xKZgioPbYdGZeksU9KruR//SooSxcEaBeGp1yHrzCuLp/fXZYZzfUNtCKYXL4+K7ZpXr3fVuDtUeYnDGYFItsa25qJSioq6CooqixjaP18Oeqj3U1NeQk5IT11uP4ZzaHQMciU7W/S2QD5wA/Jko6/UZDLFmb/VeP+cEWtfnvs/ua9ruqi6F9+9vck6g5Tiem9V4E3bXu3lm8zN+zglga+lWVm5b2aqEQbs5sM3fOYGONFy/VFeiAMpqy7jlnVv85B9q62u59d1bKa3VeV2V7kr+tOZPjc6pgY92f8S6vTrPqt5bz6vfvurnnEDLL9z90d2U1vhyxKoOwAvXNTkn0CubZT+JXKsrHLweXWkjUN6iolhX/0hwPMpDcVVxyGu7K3fH/O/K4/WwtypYgwsIKn4bj4QTxTe1lUe3Tdg1JCZvF4bOx3l/1/tNVZ/dFfDVy8Gditc15uxUuCuCVGQbeO271/zOdmJGQQvSC5teAJ/DqHBXcMgdrN9V4iqhuk7nN7k8Lt7f9X5QH4BV21dR66mltLaUdwrfCdnnQM2Bpqrr9bW6HFQgNWVQ3QEOSikdtBKK2o4/WXj11Vc56qijGDZsGPPmzWv7BRGilKKuhbyw1nKl2v1+qFadXk1H5P3FkDYdlIj0FZHHReQV3/MRInJVx5tmMEROn5TQOeSZzmbReWIJLeNhT24MT7eK1a/SeHM6qrI26S2UF0rNagyJby2IoeEMShD/n7cZ2SlaYsFhcbT48zUfC7HoM6CQndpft7BVWtrGCwi68Hg87N+/36/ETjTU19dzww038Morr7Bp0yaWLl3Kpk2bYjJ2A63pWYlIzPWu2iKet/cgvC2+J4HXgIb/nq3o5F2DIe44KfekkOJyV466sumGnNoXTv6/4BdPvKoxITbTmcmsMbNCvsesMbOacoliyZgZoYvVnvx/WsMKSLWlMrFvULATUwZMIdknkJjpzOSKkcFaPFaxcunRl2Kz2OiR1IOfjPhJSDMm5UzCYfE5KGcmHH9dcKdBJ/qFtccMiy107T6LzW9uNmzYwGmnnca0adM47bTT2LBhQ9Rv/emnnzJs2DCGDh2Kw+Hgkksu4cUXX4x63OZYxEKPFsQnezh6xFxbqiWxRdB/D42/5zglnNnIUkotA62NrLQ0e3xvXBoOW3o5e7H4zMWMzhoN6H/62eNnc+7Qc5silmx2GDgJLn9eK+3mTYJzHoQTbvLTLxqTPYY7jr+j0bH1dvZm7klzGd5zeMcYn5YDV76m7RELZA2HHz0L2Uc3dsl0ZvKnk//EeUech91iJ8maxIwjZ/DbE37bGHlns9g4Z+g5XDf2OlJs+qaem5bLI6c94qfhlJeex5xJcxodG8C4PuO456R7yE7xrTDtTjj+Z/CDu7W2ljMDjr0aZizyL74bK0QgubcuVtvw6d6RDllHNq6sPB4Ps2fPprKyErfbTWVlJbNnz6a+PrrbUlFREXl5TYnSAwYMoKioqJVXRI7VYiUnNQdngOpzsi3ZTyAxVtgsNvqn9Q9a8YsIA3sM7BZRfFUi0hufuq6IHI9O1jUY4g6bxcbwnsN5+NSHcde7GwXpHIFVI9L6wtAsHTLt9UJadlBliYykDC4cfiFT86ZSr+qxipWezp4d909tc0C/MXDJP3WulFgao/eak5WSxZxJc5g9fjYiQqo91c/JgHZkV4++mplHzqTOW4fD6qCXs5ffJ/Teyb05d+i5TM2bSllNGSn2FJKsSfRNDZDbSOkNx9/gqzqhtJOKpBRSpFhtegs22RcSL+K37VdWVobb7fZ7idvtprS0NKryOqFyQjsiV8hutTOoxyA8Xg/uejcOq6NV5dtocVgdDMkcgqvORbWnGofVQbo9HavF2mW5UOESzozcgla+PUJEPgCy0eq3BoM+1K4qAeXVZXI66lwiQnp6FdR7QRQtbhRYrHpV0AqlNaW4PC6KK4vJTc+lrKYsdE5VOFSW6DynmnKtveTMCD1fgdUlQpBiT/GvCBGCGk8NHq+HqroqbBYbtfW1QY5MofAqLyXVJfRM7klOarBDBLTTSO9EZV+xgDX07y0zMxOHw+HnpBwOBz17RlemasCAARQWNhWK3rVrF/37Ryg7EiYNDilwJdVR2C127El2eiSF3l6MV9p0UEqptSJyCnAUWgfqK6VUYpQnNnQsNeW60vYbd2onlT8Nvn9bSMG7TkMpXaX7heth50f60//3b4eRF0ZcxqikuoR5n87j9e9eb2ybdsQ0bh5/c9MWWLgcKoIl02Gf79DdlqS3FfPP7ZCznIOug/z+k9/zxne6inuSNYlfHfsrzhhyBukO/X7lteUs3rSYhV8sROkNEvLS83js9Mfon9YxN+ZYYLPZmD9/PrNnz8btduNwOJg/fz7WKHW8jj32WL7++mt27NhBbm4uzz77LP/85z9jZLWhPYQTxTcDrQm1ETgf+FdDkq7hMGf/17D0Eti/Vde+W/sUPHetzjPqKir3wVPnaOcEesWy6hda5C8CGvKEmjsngJXfrOSD4g8is8lVBi/e1OScQCejvviz0BXOo8Tj9bDi6xWNzgl0CPPdH9/tlxOz89BOHv3i0UbnBFBYUcjtH9xOeW187+KPHTuW1atXs3LlSlavXs3YsWOjHtNms7FgwQJ++MMfkp+fz8yZMxk5cmQMrDW0l3CCJO5QSlX4avD9EHgKeKRjzTLEPXXVWkgvkO1vQwiZh06jap9O9AzkowW6yGqYlFSX8MK2F0Jee+7r59hXvS98m+qqYftbwe1Kwcbnwx8nTMpqy/j31n+HvPbvr3W7x+th2dZlIft8tuezpjyoOMZqtZKVlRX1yqk5Z511Flu3buWbb77htttui9m4hvYRjoNqCI05G3hEKfUiEN+xiQaDwWBIeMJxUEW+WnwzgZdFJCnM1xm6M/YUOOHnwe1Dp4bO5eksUvuEFuKbfGNjLlE4ZKdkc/6w80Neu3D4hS0mBIfEngJDQxRdEYGRF4Q/TphkJmVy0ZGhdZguGq7bbRYbM4+cGbLPsTnHBgVTGAxdQTiOZiY6UfcMpVQZ0Av43w61ypAYNObpHKVF+cb/FC581F+Er7NJ6wP/s0oXMgUdEXfOA5Ab2bGp1WLljMFncPqg0xvbBOG8I87jxP4nRmZTciac9xD0GdHUZkuC8/4WVsRepNgsNqYPn+5nu9Pq5M7j7/QLIR/YYyDXjbnOr3pBXnoevz/x9/7VzA2GLqJNPah4w+hBxRlK6YN+VR9XYeZUH9SBCCJaZTdSAT4f+6v3U+2pZnfVbvqn9SfZmhxdmLnrALjaCDOPEeW15VTXVVNZV0lGUgbpjvSglVGFu4JKdyVbS7fSO7k3Oak5HVOpvQ26kx5UPKOU6vLcp1jrQRkMLSOik1w7k+rSJtG/lqTJY7SKy0rJAo+bgbYMcCRHJy+Rlt3mXDU4lPLactIcaaTaUtudu5KRlNHmSijdkU66I51+aa3ngxkSF6V0wdjKukqq6qpIsiY1akHFurRSrDEOypA4uKtg32Z4/Xad6zTkFDj1jtBnTm1R79ESEl8u19F9x1wKPfoFO7xDu+Hjv2nZi8EnwrHXhKzuEAvKast4euPTLNq4qLEC9WkDT+P242+PTiTRcFjjrnez49AO6pvJlZRUl5DXI49Ue2pcO6n4tcxgCKT0O3j8BzrHqWI3fPEsPH46VITWu2mVimL42/Hw5l3w3z/Dw8fC7vUBffbC46fpcPqdH8F79/uE+yIIMY+Aj4s/5h9f/sNPHuHNnW/yt/V/S4iw70SnsLCQqVOnkp+fz8iRI5k/f35XmxQ1Hq+HXZW7/JwT6AoiuyqC2+MN46AMiYG7WjuIQL2cQ0WwtyCysTx18NHD/rpDyguv3e6fOLvrk+CcqoPb/RNuY8TBmoMsKlgU8tqL37zYMfpTCYzL5WL58uXceeedLF++HJcregdus9n485//zObNm/n44495+OGHYy630dl4lbdFzSev8uKqj+8PPmaLz5AY1LuhBWXQiFc03jooLwxur9zrr9p6aHfo11fsiez9wjFJeSl3h67e0BFCdomMy+XisssuY9++fdTU1PDWW2+xdOlSnnnmGZKT2x8e369fP/r102dx6enp5OfnU1RUxIgRI9p4ZfzSVhCcWUEZDLHAmQFjLg59bdAJkY3lSIEJPw1uHz3Tv17fsNOC+4gl8vcLgzR7GicPODnktRG9RsS9bk9nsmrVqkbnBFBTU8O+fft46aWXYvYe3377LevWrWPSpEkxG7MrsIil1TOmBjmWeMU4KENiIAJHnQVHn9PUZrXDufPbF7GXOxFO/70W5LM6tMM64Qadn9RAajac/0iToqwzA6Y/qfWKYozT5uSqUVcFJQAnWZO464S76JXchbllccaXX37Z6JwaqKmpoaAgwq3eFqisrOSiiy7iwQcfpEePxKr+HYjVYm0xqTzdkR73irpmi8+QOKRmwbSHtGOpKtHRe86MlkPNWyOlFxx3rV41gXZCSQHjOHvoKuhHTIU6l64IkdzT34nFkL6pfXn27Gd57bvX+GT3JwzLGMaMo2Z0SV5SPDN69GjeeustPyfldDoZNWpU1GPX1dVx0UUXcdlll3HhhRdGPV5X06CHZrVY2Ve9j7r6OqxipVdyL3o5e3ULwUJDglHr0WcWyR0pKtdVpPTSj15Doh5KWe2U2ux48ZJpTwr9z2B3gr3zcoSyU7K59OhLuWj4Rdgt9qhvIOXV+6mrd5PqSCM5TrSADtYcpNZTi8ViIdWWSlrDCjVMzjnnHJYuXdq4zed0OunTpw9nn312VHYppbjqqqvIz8/nlltuiWqseMJmsZHhyNDS774jKavFGtfh5Q10mIMSkTzgaSAHLRe/UCk1P6DPFOBFYIev6Tml1N0dZVN3p9JdSWFFIU9tfAqXx8XMo2YyMmtko2R5xFTtB3el3gJzpEVUy67DqNynNaj2bYLhp2tHFSg97iqDyj3w9RvQcwjkHadLIDWjrKaMd3e9y+JNi6mpr+Hcoecy/cjpcZFvZBFL1LXwqlylbCvfzvwND1NcWcyxfcZz/djryUnrj6WLtnXq6uvYVraNOz+8ky0Ht2AVK1PzpvKbSb+JaJzk5GSeeeYZXnrpJQoKChg1ahRnn312VAESAB988AGLFy9m9OjRHHPMMQDMnTuXs846K6px4wERwS72tjvGGR1W6khE+gH9fIKH6cDnwPlKqU3N+kwBblVKndPCMEGYUkeh8Sovbxe+zc1v3+zXftWoq7hmzDX601MkVOzVWk/Fa3VgwPE/g+/9smvr7FXugyfP0jpUDRx3LUyd0xTc4K6CtU/Dq79u6tNrKFz5aqOQYnVdNQu/WMjjBY/7DT+h7wQemPIAPZ3RKbPGA2uKP+bKN2b5aT31TOrJinOW0SetYxKN22J31W6mPT+Nmnr/86P8XvncdcRdjBxhtJcOByIpddRhazyl1G6l1Frf9xXAZiC3o97vcOdgzUHu+/S+oPanNj5FpbsyssE8tfDhQ9o5gc4R+mgBlO2MgaVR8NUr/s4J4NNH/fOZag7p5NvmHNwOX/5b1w0EKusqeXrT00HDf773c/a7Yi8g2NmUVu3l/rUP+jkngNLaUl7ZvqpLbPJ6vTz39XNBzglg88HN1Kv4Dnc2dA2dsgkpIoOBccAnIS5PFpENIvKKiIT8CCUis0RkjYisKSkp6UBLE5d6bz27q4LzdjzKE3kVgrpq2PVpcPvuDe20LgZ4vVD4cehrzfOV6qq0gw2k8BOdSwXUeGqo89aFHKqwIkR+VILhUV6+PfRtyGsby7b6VaroLNxeN1tLt7Z4Pd7zcQxdQ4c7KBFJA/4N3KyUCpQ0XQsMUkqNBR4CQkqYKqUWKqUmKqUmZmd3cmHSBMFusTMsc1hQe7ItmZRI9ZmS0uHIM4LbB01up3UxwGIJbZPFCpnNavE50nRV9UCOPKMx+s5pc7a45XlE5hGxsLZLcVhsjOwdertscp8JXRK55bA6mNBnQovX4z2azNA1dKiDEhE72jk9o5R6LvC6UuqQUqrS9/3LgF1ETExtO+iV3IvfTv4tdov/Qeivjv0VPRwRBjdYbDD+xzDmEu0AknvCtAWNZzhdxqATYMiUpuci8MN50Lxit7MnXLhQ50g1MPgkv6TbDEcGv5jwi6Dhzx16bvsDSuKIjJRsfj3x1qDk3sE9BvO9vCldYpNFLJw55Ex6O4ODUE7JPSUhIsoMnU9HBkkI8BRwUCl1cwt9coC9SiklIscBK9ArqhaNMkESLVPrqeVgzUFe+/Y1qjxVnDn4TLJTskl3pLdvwJpDessMgeRe0UlNxIqq/bpQbOl3kDNaO8/A6EJ3NdSUQckWSMvREXwBwoCH3If4tvxbnt70NC6Pi+nDp3NMn2O6RYAEQG1dNSWuEhZvfJodlYWcnHM8Zww9m6zUrvuQoZSiuKqY+Wvn807hO6TYUrj4qIu5+OiL2btjr9GDOkyIJEiiIx3UScD7wJfoMHOAOcBAAKXU30XkRuB6wAO4gFuUUh+2Nq5xUIZY4vK48Cpv5FGOCUKdp4baOhfJjh5YrfFRNaCqrorqumpEhMykTGwWmxEsPIyIC8FCpdR/gValG5VSC4AFHWXDYYm7Ske1KQX2VEjuZtLdVft1VQd3lRYsdPaCpPbXE4s23yjesduc2G3OrjbDj1R7atQfCJRSbNy4kZ07dzJw4EBGjhwZM6XY+vp6Jk6cSG5uLqtWdU3Uo0FjTia7E9UHtIzERw/riLX8aXD2/bqmXHegYg988qjWZ/J69PbezMWQOyF+pOYNHc7evXu58cYb2bNnDyKCUoqcnBwWLFhA377Rb2HOnz+f/Px8Dh0KjOkydDbmZLI7UbwO3v8zeGp07tKmF+CTheBxd7VlsaG8CP77F+2cAFylsOzH+qvhsEApxY033sjOnTtxuVxUV1fjcrnYuXMnN910U5vyEm2xa9cuXnrpJa6++uoYWWyIBuOgugteL3y5Irj9q5egNrTOUMKx7Y3gNlcp1Boxv8OFjRs3smfPHurr/fOm6uvr2b17Nxs3boxq/Jtvvpk//vGPWCzm1hgPmN9Cd8Fi0VtdgWTnQ3c5Z+kbolq11d6+auaGhGTnzp0tnjWJCIWF7U+0XrVqFX369GHChJbztQydi3FQ3YkR50HfZgmaqVlw6h06mKA7kDsB+o/3bzvpF1oGw3BYMHDgwBa38ZRS5OXltXvsDz74gJUrVzJ48GAuueQS3nrrLS6//PJ2j2eIHhMk0Z1I6wM/flHLmde5oPewoCreCU2PfnDJP3WNwL2bYPgPID0HUru+Armhcxg5ciQ5OTns3LnTb5vParXSr18/Ro5sf8HZe++9l3vvvReAd955h/vvv58lS5ZEbbOh/RgH1d1Iy9aP7kqPftDjbDg6Ou0fQ2IiIixYsCAoiq9fv3489NBDMQs1N8QHxkEdrtRWNuUSJdIZjqcWXAehvg5sTh1Cb25KoXGVaz0vd6VWHnZmagHGBKdv374sW7aMjRs3UlhYSF5eXkzzoACmTJnClClTYjaeoX0YBxUFZdVu3B4vVqvQO7VjZMA7hLJCeOMOKPpc17c79S7o0b+rrWobVxkUrIA3f6eTkXsNhRlPQZ8RYDV/yn5U7YfVd8O6p3XStiMNzvsbDDu1W5xJigijRo2Kicy7IX4xQRLt5GBVLXev2sSke1dz9VNr2F8RQuIhHqncB0+dCxuf1/pOG56FZ2ZAZQLImBzcAS/9skn/6eB2eHoaVCe+hlPM+eZtWPtUowYW7kpY8VOTM2ZIKIyDaif7K908t7YIpWDdzjLe35YAN3jQBWBLd/i37S3wFYWNY5TSN9xAXKVwqLjz7Ylnasph3eLgduWFb//b+fYYDO3EOKh2kuKw+h19ZKUlyBZfqIrkIhAg0xF3iEBKC0osJszcH4tNnzmFIrl7VGs3HB4YB9VOMpMdLPzxRE45Mpvbzs5nVP8EKcrqSNM1+ppzzI8TI1Biwk/AHpB0nDPGhJkH4kiF7/0yuD2lN/Qf1/n2GAztxJwst5M0p41Tj+7D5KG9cDqs2BKlNEpKLzjnL3DMpbD9XRh+GvQbB8kJINSXlgOz3tOH/we3wbDTYfIN3acYbizpfQRc/hy8fps+uxv8PTjrj2auDAmFcVBRYLEIac443xoLRWo2HHWmfiQSNgdkHwnnPwIel97GsiXI1mpnk5SuI/ZyxoCq1yH5ifAhJAxcLhdLlixh2bJllJeXk5GRwcyZM7n88stJTo6urFdZWRlXX301BQUFiAhPPPEEkydPjpHlhkgxDsqQeDjTgXaqBHchZbVluNxV1NbXkupIo2dSL2ztDY+vOaTz2ECHjSe1MB/dLGnb5XJxxRVXsHPnTtxuXaW/tLSURYsWsXr1ahYtWhSVk5o9ezZnnHEGK1aswO12U11dHSvTDe3AOCiDoRPYX7WPOz68k/8WfwBAL2cv5p00l3F9J+CMVFCwYg+8/L+6Uj0CIy6AM+Z2r7JWLbBkyRI/59SA2+1m586dLFmyhGvv+B4UAAAO6ElEQVSuuaZdYx86dIj33nuPJ598EgCHw4HDESKoyNBpJMjBicGQuFTVVfHnz//S6JwADtYc5Gerb6S05kBkg7lK4YWfweaV4K3X2lgFy+GlW3V4eTdn2bJlQc6pAbfbzfLly9s99vbt28nOzuaKK65g3LhxXH311VRVxXn6RTfHOCiDoYOpclfw6revBbV7lIf3Ct+LbDB3FXyzOrh9y3+atvy6MeXlrTvhsrKydo/t8XhYu3Yt119/PevWrSM1NZV58+a1ezxD9BgHZTB0NArqVX3IS7XeCNWOW1KMVV796OZkZLSezpGZ2f5AkAEDBjBgwAAmTZoEwPTp01m7dm27xzNEj3FQBkMH47SncEL/E0JemzpgSmSD2ZND5zINPumwSFieOXNmi+dCDoeDGTNmtHvsnJwc8vLy+OqrrwBYvXo1I0aMaPd4hugxDspg6GB6JPXgruPvYFCPQY1tNrFx56Tb6ZkcYZJxahbMfNpfmLL/OLjgUZ3j1s25/PLLGThwYJCTcjgcDBw4MGqBwYceeojLLruMMWPGsH79eubMmRPVeIbokJbUKeOViRMnqjVr1nS1GQZDxOyvLqGkei9lNWUMzTyC9KQMUtq76qks8dVPFF05IrWFMlAJwubNm8nPzw+rb0Me1PLlyykrKyMzM5MZM2bEJA/K0PGE+l2LyOdKqYmBfU2YucHQSWSlZJOVEqO8pLRsoHvlOIVLcnIy11xzTbvDyQ2Jg9niMxgMBkNcYhyUwWAwGOIS46AMBoPBEJcYB2UwGAyGuMQESRgMhoSkqKiIkpISsrOzyc3N7WpzDB2AcVAGgyGh2LRpE3PnzmXHjh3Y7Xbq6uoYMmQIc+bMiTqx9oEHHuCxxx5DRBg9ejSLFi3C6YywmK8hZpgtPoPBkDBs2rSJWbNmsWXLFmpra6msrKS2tpYtW7Ywa9YsNm3a1O6xi4qK+Otf/8qaNWsoKCigvr6eZ599NobWGyLFOCiDwZAwzJ07l5qampDXampquPfee6Ma3+Px4HK58Hg8VFdX079//6jGM0RHhzkoEckTkbdFZLOIbBSR2SH6iIj8VUS2icgXIjK+o+wxNKOmAsqLoOhzOFR8WFTBNiQ+RUVF7Nixo9U+27dvp6ioqF3j5+bmcuuttzJw4ED69etHRkYGp59+ervGMsSGjlxBeYBfKqXygeOBG0QkcIP4TGC47zELeKQD7TEA1FbA+mfgwVHwj+/Dg6Nh04vGSRninpKSEux2e6t97HY7JSUl7Rq/tLSUF198kR07dlBcXExVVRVLlixp11iG2NBhDkoptVsptdb3fQWwGQgMtTkPeFppPgYyRaRfR9lkQIvavfbrJmkGrwf+M1tLiBsMcUx2djZ1dXWt9qmrqyM7u30loN58802GDBlCdnY2drudCy+8kA8//LBdYxliQ6ecQYnIYGAc8EnApVygsNnzXQQ7MURkloisEZE17f10ZPBRVhisKVTvBtfBrrHHYAiT3NxchgwZ0mqfoUOHtjvkfODAgXz88cdUV1ejlGL16tVhF7A1dAwd7qBEJA34N3CzUirwY7qEeElQeXWl1EKl1ESl1MT2fjoy+MjMAwn4tVsdkNz9pRoMic+cOXNaDPt2Op385je/affYkyZNYvr06YwfP57Ro0fj9XqZNWtWu8czRE+HOigRsaOd0zNKqedCdNkF5DV7PgAo7kibDnucGXDGfWCx6udWO5z7V3D26Fq7DIYwGDFiBAsXLiQ/P5+kpCTS0tJISkoiPz+fhQsXRp0H9bvf/Y4tW7ZQUFDA4sWLSUpKipHlhvbQYYm6IiLA48BmpdRfWui2ErhRRJ4FJgHlSqndHWWTAUhKh2N+BEefDZV7IT1HOy1HaldbZjCExYgRI1i8eLGpJHEY0JGVJE4Efgx8KSLrfW1zgIEASqm/Ay8DZwHbgGrgig60x9BAUrp+ZJh/akPikpubaxxTN6fDHJRS6r+EPmNq3kcBN3SUDQaDIXFQSqE3XgzdlUgV3E0lCYPB0OU4nU4OHDgQ8Q3MkDgopThw4EBEtQ1NsViDwdDlDBgwgF27drU7ydaQGDidTgYMGBB2f+OgDAZDl2O329vMcTIcfpgtPoPBYDDEJcZBGQwGgyEuMQ7KYDAYDHGJJFrUjIiUAN91tR0BZAH7u9qIdpCodoOxvaswtncN3d32QUqpoDp2Ceeg4hERWaOUmtjVdkRKotoNxvauwtjeNRyutpstPoPBYDDEJcZBGQwGgyEuMQ4qNizsagPaSaLaDcb2rsLY3jUclrabMyiDwWAwxCVmBWUwGAyGuMQ4KIPBYDDEJcZBRYCIWEVknYisCnHtpyJSIiLrfY+ru8LGUIjItyLypc+uNSGui4j8VUS2icgXIjK+K+wMRRi2TxGR8mbzfmdX2BkKEckUkRUiskVENovI5IDr8Tzvbdkel/MuIkc1s2m9iBwSkZsD+sTlvIdpe7zO+y9EZKOIFIjIUhFxBlxPEpF/+eb8ExEZHM64plhsZMwGNgMt6aP/Syl1YyfaEwlTlVItJcudCQz3PSYBj/i+xgut2Q7wvlLqnE6zJnzmA68qpaaLiANICbgez/Pelu0Qh/OulPoKOAb0B0qgCHg+oFtcznuYtkOczbuI5AI/B0YopVwisgy4BHiyWbergFKl1DARuQS4D7i4rbHNCipMRGQAcDbwWFfb0gGcBzytNB8DmSLSr6uNSmREpAdwMvA4gFLKrZQqC+gWl/Mepu2JwKnAN0qpwMozcTnvAbRke7xiA5JFxIb+MFMccP084Cnf9yuAUyUMdUrjoMLnQeD/AG8rfS7ybRmsEJG8TrIrHBTwuoh8LiKzQlzPBQqbPd/la4sH2rIdYLKIbBCRV0RkZGca1wpDgRJgkW9b+DERSQ3oE6/zHo7tEJ/z3pxLgKUh2uN13pvTku0QZ/OulCoC7gd2AruBcqXU6wHdGudcKeUByoHebY1tHFQYiMg5wD6l1OetdPsPMFgpNQZ4k6ZPC/HAiUqp8eitjRtE5OSA66E+ycRL/kFbtq9F1/EaCzwEvNDZBraADRgPPKKUGgdUAb8O6BOv8x6O7fE67wD4tiWnActDXQ7RFg/zDrRpe9zNu4j0RK+QhgD9gVQRuTywW4iXtjnnxkGFx4nANBH5FngW+L6ILGneQSl1QClV63v6D2BC55rYMkqpYt/Xfeg97eMCuuwCmq/4BhC8RO8S2rJdKXVIKVXp+/5lwC4iWZ1uaDC7gF1KqU98z1egb/qBfeJx3tu0PY7nvYEzgbVKqb0hrsXrvDfQou1xOu+nATuUUiVKqTrgOeCEgD6Nc+7bBswADrY1sHFQYaCU+o1SaoBSajB66f2WUsrvE0LAHvY0dDBFlyMiqSKS3vA9cDpQENBtJfATX3TT8egl+u5ONjWIcGwXkZyGvWwROQ79N32gs20NRCm1BygUkaN8TacCmwK6xeW8h2N7vM57M35Ey1tkcTnvzWjR9jid953A8SKS4rPtVILvfyuB//F9Px19D21zBWWi+KJARO4G1iilVgI/F5FpgAf9yeCnXWlbM/oCz/v+pm3AP5VSr4rIdQBKqb8DLwNnAduAauCKLrI1kHBsnw5cLyIewAVcEs4ffidxE/CMb8tmO3BFgsw7tG173M67iKQAPwCubdaWEPMehu1xN+9KqU9EZAV6+9EDrAMWBtwfHwcWi8g29P3xknDGNqWODAaDwRCXmC0+g8FgMMQlxkEZDAaDIS4xDspgMBgMcYlxUAaDwWCIS4yDMhgMBkNcYhyUwRAjfJWmQ1W6D9keg/c7X0RGNHv+johMDON1/WJhj4hki8ir0Y5jMLSEcVAGQ+JyPjCizV7B3IKudhIVSqkSYLeInBjtWAZDKIyDMhw2+CpTvOQrtFkgIhf72ieIyLu+grSvNVQF8a1IHhSRD339j/O1H+drW+f7elRr7xvChidE5DPf68/ztf9URJ4TkVdF5GsR+WOz11wlIlt99vxDRBaIyAnoiiV/Eq0LdISv+wwR+dTX/3stmHER8KpvbKuI3C9ac+sLEbnJ1/6tiMwVkY9EZI2IjPfNzTcNiaM+XgAuC/fnNxgiwVSSMBxOnAEUK6XOBhCRDBGxo4tunqeUKvE5rT8AV/pek6qUOkF0kdongFHAFuBkpZRHRE4D5qJv+uFwG7rMy5Uikgl8KiJv+q4dA4wDaoGvROQhoB64A10LrwJ4C9iglPpQRFYCq5RSK3w/D4BNKXWciJwF3IWuk9aIiAxB6/I01I2chS7yOc738/Rq1r1QKTVZRB5Aa/ucCDiBjcDffX3WAL8P82c3GCLCOCjD4cSXwP0ich/6xv6+iIxCO503fDd4K1oyoIGlAEqp90Skh8+ppANPichwdEVmewQ2nI4uPHyr77kTGOj7frVSqhxARDYBg4As4F2l1EFf+3LgyFbGf8739XNgcIjr/dBSGg2cBvzdJ4FAw/v4WOn7+iWQppSqACpEpEZEMn0aUfvQFawNhphjHJThsEEptVVEJqDrsN0rIq+jK6RvVEpNbullIZ7fA7ytlLpAtHT1OxGYIcBFPvXUpkaRSeiVUwP16P/PNkXdAmgYo+H1gbjQTrG5PS3VO2sYyxtgm7fZ2E7fmAZDzDFnUIbDBhHpD1QrpZagBdbGA18B2SIy2dfHLv4icA3nVCehq16Xo6UCinzXfxqhGa8BN/mqPiMi49ro/ylwioj0FC1T0HwrsQK9mouErfivrF4HrvONTcAWXzgcSXB1fIMhJhgHZTicGI0+81mPPgv6vVLKja4QfZ+IbADW469lUyoiH6LPXK7ytf0RvQL7AL0lGAn3oLcEvxCRAt/zFvGplc4FPkELYW5Cq5GC1ib7X1+wxREtDBE4XhXwjYgM8zU9hpZL+ML3818a4c8zFXgpwtcYDGFhqpkbDC0gIu8Atyql1nSxHWlKqUrfKud54Aml1PNRjHcBMEEpdXsMbHsPHWBSGu1YBkMgZgVlMMQ/v/Wt+gqAHUQp8+1zbt9Ga5SIZAN/Mc7J0FGYFZTBYDAY4hKzgjIYDAZDXGIclMFgMBjiEuOgDAaDwRCXGAdlMBgMhrjEOCiDwWAwxCX/D2nJM59r9V2eAAAAAElFTkSuQmCC",
      "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1aed72d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAARQElEQVR4nO3df6xfdX3H8ecLWqxiAaV1MbRCjUWsZBt6BzgzYQFNYUlZFqd0Eocj4nS4RJkJzkUJZlvULW7MblA3549MEDXTxtV1iUJwzjouQQk/JOkqwg1MrhUQxQLV9/74Huzd7b29p/d+771tP89HcpPvOZ/POefdT+593XM/50dTVUiSDn9HLHYBkqSFYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwNchJckbkvzHYtchHYoMfB1Uktyb5Nzp2qvqX6rqNbPY701Jdid5LMmPktya5IokzziAfVSSFx3osQ/UQh1H7THwdchIsmSOu7isqpYDzwcuBy4EtibJnIuTDgEGvg5aSS5O8vUkH07yQ+DKbt1/du3p2h5K8miS25OcOtN+q+onVXUTsAF4BfBb3f5OT/KNJI8keTDJR5Ic1bXd3G3+7SQ/TvL6JM9J8qUk40ke7j6vmlT/zu6viu8mecOEtj9Icne33bYkJ053nDkPpNQx8HWwOwPYCTwP+PNJba8BXgWcDBwHvB7Y1XfHVXUfMAr8RrfqZ8A7gBUMfhGcA7yt6/uqrs+vVNWzq+ozDH5+/hk4EXgB8FPgIwBJjgauBs7r/qr4deBbXdtvA38K/A6wEvgacN1+jiMNhYGvg90DVfV3VbWnqn46qe0pYDlwCpCquruqHjzQ/QPPBaiqW6tqe3ese4FrgbOm27CqdlXV56vq8ap6jMEvpIn9fw6cmuSZVfVgVd3ZrX8L8JddvXuAvwB+9emzfGm+GPg62N0/XUNVfZXBGfUm4PtJNic55gD3fwLwQ4AkJ3fTMv+b5EcMgnjFdBsmeVaSa5N8r+t/M3BckiOr6icM/uL4Q+DBJP+W5JRu0xOBv+2mjh7pjp+uFmneGPg62O33da5VdXVVvRx4KYOpnXf13XGS1cDLGUypAPwD8B1gbVUdw2DaZX8XdC8HXgyc0fV/ejomXW3bqurVDC4Sfwf4aNd+P/CWqjpuwtczq+q/+tYuzYaBr0NWkl9LckaSpcBPgN0M5uFn2u5ZSc4Cvgj8N7C1a1oO/Aj4cXc2/tZJm34feOGE5eUM5u0fSfJc4H0TjvFLSTZ0c/lPAD+eUNs1wLuTvLTre2yS393PcaShMPB1KDuGwVnzw8D3GFyw/av99P9IkscYBOrfAJ8H1lfVz7v2PwF+D3is2+/kC6ZXAp/opmJe1+3jmcAPgO3Av0/oewSDvwAeYDBlcxZ7LwD/K/AB4PpuKugO4Lz9HEcaivgfoEhSGzzDl6RGzBj4ST7WPdhyxzTtSXJ1kh3dgy8vG36ZkqS56nOG/3Fg/X7azwPWdl+XMrjTQZJ0kJkx8KvqZrr7lKdxAfDJGtjO4D7k5w+rQEnScMz1ZVQweFhk4sMxY926fZ54THIpg78COProo19+yimnTO4iSdqPW2+99QdVtXI22w4j8Kd6MGXKW3+qajOwGWBkZKRGR0eHcHhJakeS781222HcpTMGrJ6wvIrBvceSpIPIMAJ/C/DG7m6dM4FHZ/ECK0nSPJtxSifJdcDZwIokYwweH18KUFXXMHgs/XxgB/A48Kb5KlaSNHszBn5VbZyhvYA/GlpFktSIp556irGxMXbv3r1P27Jly1i1ahVLly4d2vGGcdFWkjQLY2NjLF++nJNOOomJ/9NmVbFr1y7GxsZYs2bN0I7nqxUkaZHs3r2b448//v+FPUASjj/++CnP/OfCwJekRTQ57GdaPxcGviQ1wsCXpEYY+JK0iKb7P0nm4/8qMfAlaZEsW7aMXbt27RPuT9+ls2zZsqEez9syJWmRrFq1irGxMcbHx/dpe/o+/GEy8CVpkSxdunSo99nPxCkdSWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjegV+EnWJ7knyY4kV0zR/oIkNya5LcntSc4ffqmSpLmYMfCTHAlsAs4D1gEbk6yb1O3PgBuq6jTgQuDvh12oJGlu+pzhnw7sqKqdVfUkcD1wwaQ+BRzTfT4WeGB4JUqShqFP4J8A3D9heaxbN9GVwEVJxoCtwNun2lGSS5OMJhkdHx+fRbmSpNnqE/iZYl1NWt4IfLyqVgHnA59Kss++q2pzVY1U1cjKlSsPvFpJ0qz1CfwxYPWE5VXsO2VzCXADQFV9A1gGrBhGgZKk4egT+LcAa5OsSXIUg4uyWyb1uQ84ByDJSxgEvnM2knQQmTHwq2oPcBmwDbibwd04dya5KsmGrtvlwJuTfBu4Dri4qiZP+0iSFtGSPp2qaiuDi7ET1713wue7gFcOtzRJ0jD5pK0kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRvQK/CTrk9yTZEeSK6bp87okdyW5M8mnh1umJGmulszUIcmRwCbg1cAYcEuSLVV114Q+a4F3A6+sqoeTPG++CpYkzU6fM/zTgR1VtbOqngSuBy6Y1OfNwKaqehigqh4abpmSpLnqE/gnAPdPWB7r1k10MnBykq8n2Z5k/VQ7SnJpktEko+Pj47OrWJI0K30CP1Osq0nLS4C1wNnARuAfkxy3z0ZVm6tqpKpGVq5ceaC1SpLmoE/gjwGrJyyvAh6Yos8Xq+qpqvoucA+DXwCSpINEn8C/BVibZE2So4ALgS2T+nwB+E2AJCsYTPHsHGahkqS5mTHwq2oPcBmwDbgbuKGq7kxyVZINXbdtwK4kdwE3Au+qql3zVbQk6cClavJ0/MIYGRmp0dHRRTm2JB2qktxaVSOz2dYnbSWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEb0Cvwk65Pck2RHkiv20++1SSrJyPBKlCQNw4yBn+RIYBNwHrAO2Jhk3RT9lgN/DHxz2EVKkuauzxn+6cCOqtpZVU8C1wMXTNHv/cAHgd1DrE+SNCR9Av8E4P4Jy2Pdul9Ichqwuqq+tL8dJbk0yWiS0fHx8QMuVpI0e30CP1Osq180JkcAHwYun2lHVbW5qkaqamTlypX9q5QkzVmfwB8DVk9YXgU8MGF5OXAqcFOSe4EzgS1euJWkg0ufwL8FWJtkTZKjgAuBLU83VtWjVbWiqk6qqpOA7cCGqhqdl4olSbMyY+BX1R7gMmAbcDdwQ1XdmeSqJBvmu0BJ0nAs6dOpqrYCWyete+80fc+ee1mSpGHzSVtJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjegV+EnWJ7knyY4kV0zR/s4kdyW5PclXkpw4/FIlSXMxY+AnORLYBJwHrAM2Jlk3qdttwEhV/TLwOeCDwy5UkjQ3fc7wTwd2VNXOqnoSuB64YGKHqrqxqh7vFrcDq4ZbpiRprvoE/gnA/ROWx7p107kE+PJUDUkuTTKaZHR8fLx/lZKkOesT+JliXU3ZMbkIGAE+NFV7VW2uqpGqGlm5cmX/KiVJc7akR58xYPWE5VXAA5M7JTkXeA9wVlU9MZzyJEnD0ucM/xZgbZI1SY4CLgS2TOyQ5DTgWmBDVT00/DIlSXM1Y+BX1R7gMmAbcDdwQ1XdmeSqJBu6bh8Cng18Nsm3kmyZZneSpEXSZ0qHqtoKbJ207r0TPp875LokSUPmk7aS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjegV+kvVJ7kmyI8kVU7Q/I8lnuvZvJjlp2IVKkuZmxsBPciSwCTgPWAdsTLJuUrdLgIer6kXAh4EPDLtQSdLc9DnDPx3YUVU7q+pJ4Hrggkl9LgA+0X3+HHBOkgyvTEnSXC3p0ecE4P4Jy2PAGdP1qao9SR4Fjgd+MLFTkkuBS7vFJ5LcMZuiD0MrmDRWDXMs9nIs9nIs9nrxbDfsE/hTnanXLPpQVZuBzQBJRqtqpMfxD3uOxV6OxV6OxV6OxV5JRme7bZ8pnTFg9YTlVcAD0/VJsgQ4FvjhbIuSJA1fn8C/BVibZE2So4ALgS2T+mwBfr/7/Frgq1W1zxm+JGnxzDil083JXwZsA44EPlZVdya5Chitqi3APwGfSrKDwZn9hT2OvXkOdR9uHIu9HIu9HIu9HIu9Zj0W8URcktrgk7aS1AgDX5IaMe+B72sZ9uoxFu9McleS25N8JcmJi1HnQphpLCb0e22SSnLY3pLXZyySvK773rgzyacXusaF0uNn5AVJbkxyW/dzcv5i1DnfknwsyUPTPauUgau7cbo9yct67biq5u2LwUXe/wFeCBwFfBtYN6nP24Brus8XAp+Zz5oW66vnWPwm8Kzu81tbHouu33LgZmA7MLLYdS/i98Va4DbgOd3y8xa77kUci83AW7vP64B7F7vueRqLVwEvA+6Ypv184MsMnoE6E/hmn/3O9xm+r2XYa8axqKobq+rxbnE7g2ceDkd9vi8A3g98ENi9kMUtsD5j8WZgU1U9DFBVDy1wjQulz1gUcEz3+Vj2fSbosFBVN7P/Z5kuAD5ZA9uB45I8f6b9znfgT/VahhOm61NVe4CnX8twuOkzFhNdwuA3+OFoxrFIchqwuqq+tJCFLYI+3xcnAycn+XqS7UnWL1h1C6vPWFwJXJRkDNgKvH1hSjvoHGieAP1erTAXQ3stw2Gg978zyUXACHDWvFa0ePY7FkmOYPDW1YsXqqBF1Of7YgmDaZ2zGfzV97Ukp1bVI/Nc20LrMxYbgY9X1V8neQWD539Oraqfz395B5VZ5eZ8n+H7Woa9+owFSc4F3gNsqKonFqi2hTbTWCwHTgVuSnIvgznKLYfphdu+PyNfrKqnquq7wD0MfgEcbvqMxSXADQBV9Q1gGYMXq7WmV55MNt+B72sZ9ppxLLppjGsZhP3hOk8LM4xFVT1aVSuq6qSqOonB9YwNVTXrl0YdxPr8jHyBwQV9kqxgMMWzc0GrXBh9xuI+4ByAJC9hEPjjC1rlwWEL8Mbubp0zgUer6sGZNprXKZ2av9cyHHJ6jsWHgGcDn+2uW99XVRsWreh50nMsmtBzLLYBr0lyF/Az4F1VtWvxqp4fPcficuCjSd7BYArj4sPxBDHJdQym8FZ01yveBywFqKprGFy/OB/YATwOvKnXfg/DsZIkTcEnbSWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJasT/AT1qxb2fMn0DAAAAAElFTkSuQmCC",
      "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",
    "\n",
    "# create figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "# plot each column\n",
    "\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": [],
   "source": [
    "# pandas line chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "Line-chart with seaborn calls method sns.lineplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seaborn line chart \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",
    "\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": [],
   "source": [
    "# histogram using Pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1af78550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1ae42750>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1af72c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1af83890>]],\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",
    "\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",
    "\n",
    "plt.ylabel('Distribution')\n",
    "plt.title('Wine Alcohol')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotnine\n",
    "Use **geom_histogram**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like seaborn, plotnine can be extended to display the probability density using **geom_density()**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (7008179717)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(wine_df)\n",
    "    + aes(x='alcohol', fill='class')\n",
    "    + geom_density(adjust=1/4, alpha=0.5)\n",
    "    + labs(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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 25,
     "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",
    "\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 occurrences using **value_counts** and sort using **sort_index**."
   ]
  },
  {
   "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": [
    "\n",
    "plt.title('Wine Classes')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '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.barh(color=['red', 'blue', 'green'])\n",
    "plt.title('Wine Classes')\n",
    "plt.ylabel('Class')\n",
    "plt.xlabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Alcohol')"
      ]
     },
     "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": [
    "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wine Class')"
      ]
     },
     "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": [
    "\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cm')"
      ]
     },
     "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": [
    "# 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",
    "\n",
    "\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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cm')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAATXklEQVR4nO3df5Bl5V3n8feHISSEn8nOxEoxE0AdxJFCSVpAXQP5ITVg1aBWEmETFaVCTJa4pWyqcN2KKSx1DZuKxqBhdNkklgnB+COz7OhYFUkRMRNpJEFmCLXjQKALlAnhV0KATPjuH+eMfdN0P3O7Z0530/N+VXXNPec855zvPNXdnz7Puee5qSokSZrLYUtdgCRpeTMoJElNBoUkqcmgkCQ1GRSSpCaDQpLUZFDokJDkzUn+dqnrkJ6PDAqtCEnuTfL6ubZX1Z9W1XkLOO5nkjyV5Ikkjye5LcmVSV44j2NUku+e77nna7HOo0OPQaEVL8nhB3iIy6vqGODlwBXARcDWJDng4qTnAYNCK06SS5LckuT9Sb4KvKdf9/f99vTbHkryWJI7kpy2v+NW1der6jPAJuCHgB/vj3dmks8leTTJg0k+mOSIftvN/e5fTPK1JD+d5CVJbkyyJ8kj/eu1M+rf3V/F3JPkzSPbfiHJXf1+25KcONd5DrgjpZ5BoZXqLGA38DLgN2dsOw94NXAKcDzw08DD4x64qu4DJoEf7Vd9C/hlYDVdgLwOeEff9tV9m++vqqOr6hN0P3f/GzgReAXwDeCDAEmOAj4AnN9fxfww8IV+208A/w34KWAN8Fng443zSAeFQaGV6oGq+v2q2ltV35ix7ZvAMcCpQKrqrqp6cL7HB14KUFW3VdX2/lz3AtcC58y1Y1U9XFV/XlVPVtUTdEE22v5Z4LQkR1bVg1W1o1//NuC3+3r3Ar8F/MC+qwppKAaFVqr759pQVX9H9xf8NcC/Jdmc5Nh5Hv8E4KsASU7ph4/+NcnjdL/AV8+1Y5IXJ7k2yZf79jcDxydZVVVfp7vC+UXgwST/N8mp/a4nAr/XD3E92p8/fS3SYAwKrVTNaZGr6gNV9Srg++iGoN417oGTrANeRTf0A/CHwJeA9VV1LN3wUOtG9xXA9wBn9e33DRulr21bVf0Y3c3zLwF/1G+/H3hbVR0/8nVkVf3DuLVLC2FQ6JCT5AeTnJXkBcDXgafo7jPsb78XJzkH+BTwj8DWftMxwOPA1/q//t8+Y9d/A75zZPkYuvsSjyZ5KfDrI+f4jiSb+nsVTwNfG6ntQ8CvJvm+vu1xSd7YOI90UBgUOhQdS/dX+iPAl+luZP/PRvsPJnmC7hfx7wJ/Dmysqmf77f8V+E/AE/1xZ95Ifg/wkX7I6E39MY4EvgJsB/5mpO1hdFccD9ANLZ3D9I3xvwR+B7i+H7K6Ezi/cR7poIgfXCRJavGKQpLUNFhQJLmuf6Dpzjm2J8kHkuzqH3h65VC1SJIWbsgrig8DGxvbzwfW91+X0b1zRJK0zAwWFFV1M/37zOdwIfDR6mynex/5y4eqR5K0MAc6WdqBOIFvfyhqql/3nCdkk1xGd9XBUUcd9apTTz11ZhNJUsNtt932lapas5B9lzIoZnsgada3YFXVZmAzwMTERE1OTg5ZlyStOEm+vNB9l/JdT1PAupHltXTvHZckLSNLGRRbgJ/t3/10NvDYAiZmkyQNbLChpyQfB84FVieZopum4AUAVfUhuukPLgB2AU8CPz9ULZKkhRssKKrq4v1sL+A/D3V+SdLB4ZPZkqQmg0KS1GRQSJKaDApJUpNBIUlqMigkSU0GhSSpyaCQJDUZFJKkJoNCktRkUEiSmgwKSVKTQSFJajIoJElNBoUkqcmgkCQ1GRSSpCaDQpLUZFBIkpoMCklSk0EhSWoyKCRJTQaFJKnJoJAkNRkUkqQmg0KS1GRQSJKaDApJUpNBIUlqMigkSU0GhSSpyaCQJDUZFJKkJoNCktRkUEiSmgYNiiQbk9ydZFeSK2fZ/ookNyW5PckdSS4Ysh5J0vwNFhRJVgHXAOcDG4CLk2yY0ey/AzdU1RnARcAfDFWPJGlhhryiOBPYVVW7q+oZ4HrgwhltCji2f30c8MCA9UiSFmDIoDgBuH9keapfN+o9wFuSTAFbgXfOdqAklyWZTDK5Z8+eIWqVJM1hyKDILOtqxvLFwIerai1wAfAnSZ5TU1VtrqqJqppYs2bNAKVKkuYyZFBMAetGltfy3KGlS4EbAKrqc8CLgNUD1iRJmqchg+JWYH2Sk5McQXezesuMNvcBrwNI8r10QeHYkiQtI4MFRVXtBS4HtgF30b27aUeSq5Js6ptdAbw1yReBjwOXVNXM4SlJ0hI6fMiDV9VWupvUo+vePfJ6J/AjQ9YgSTowPpktSWoyKCRJTQaFJKnJoJAkNRkUkqQmg0KS1GRQSJKaDApJUpNBIUlqMigkSU0GhSSpyaCQJDUZFJKkJoNCktRkUEiSmgwKSVKTQSFJajIoJElNBoUkqcmgkCQ1GRSSpCaDQpLUZFBIkpoMCklSk0EhSWoyKCRJTQaFJKnJoJAkNRkUkqQmg0KS1GRQSJKaDApJUpNBIUlqMigkSU2DBkWSjUnuTrIryZVztHlTkp1JdiT52JD1SJLm7/ChDpxkFXAN8GPAFHBrki1VtXOkzXrgV4EfqapHkrxsqHokSQsz5BXFmcCuqtpdVc8A1wMXzmjzVuCaqnoEoKoeGrAeSdICDBkUJwD3jyxP9etGnQKckuSWJNuTbJztQEkuSzKZZHLPnj0DlStJms2QQZFZ1tWM5cOB9cC5wMXAHyc5/jk7VW2uqomqmlizZs1BL1SSNLchg2IKWDeyvBZ4YJY2n6qqb1bVPcDddMEhSVomhgyKW4H1SU5OcgRwEbBlRpu/Al4DkGQ13VDU7gFrkiTN02BBUVV7gcuBbcBdwA1VtSPJVUk29c22AQ8n2QncBLyrqh4eqiZJ0vylauZtg+VtYmKiJicnl7oMSXpeSXJbVU0sZF+fzJYkNRkUkqQmg0KS1GRQSJKaDApJUpNBIUlqMigkSU0GhSSpyaCQJDUZFJKkJoNCktQ09kehJjkdOGl0n6r6iwFqkiQtI2MFRZLrgNOBHcCz/eoCDApJWuHGvaI4u6o2DFqJJGlZGvcexeeSGBSSdAga94riI3Rh8a/A03Sfh11VdfpglUmSloVxg+I64GeAf2b6HoUk6RAwblDcV1UzP+9aknQIGDcovpTkY8D/oRt6Anx7rCQdCsYNiiPpAuK8kXW+PVaSDgHjBsVhwH+pqkcBkrwEeN9gVUmSlo1x3x57+r6QAKiqR4AzhilJkrScjBsUh/VXEQAkeSnzmP5DkvT8Ne4v+/cB/5Dkk3T3Jt4E/OZgVUmSlo2xgqKqPppkEngt3cN2P1VVOwetTJK0LIw9fNQHg+EgSYcYP49CktRkUEiSmgwKSVKTQSFJajIoJElNBoUkqcmgkCQ1GRSSpCaDQpLUNGhQJNmY5O4ku5Jc2Wj3hiSVZGLIeiRJ8zdYUCRZBVwDnA9sAC5OsmGWdscAvwR8fqhaJEkLN+QVxZnArqraXVXPANcDF87S7jeA9wJPDViLJGmBhgyKE4D7R5an+nX/LskZwLqqurF1oCSXJZlMMrlnz56DX6kkaU5DBkVmWVf/vjE5DHg/cMX+DlRVm6tqoqom1qxZcxBLlCTtz5BBMQWsG1leCzwwsnwMcBrwmST3AmcDW7yhLUnLy5BBcSuwPsnJSY4ALgK27NtYVY9V1eqqOqmqTgK2A5uqanLAmiRJ8zRYUFTVXuByYBtwF3BDVe1IclWSTUOdV5J0cI39CXcLUVVbga0z1r17jrbnDlmLJGlhfDJbktRkUEiSmgwKSVKTQSFJajIoJElNBoUkqcmgkCQ1GRSSpCaDQpLUZFBIkpoMCklSk0EhSWoyKCRJTQaFJKnJoJAkNRkUkqQmg0KS1GRQSJKaDApJUpNBIUlqMigkSU0GhSSpyaCQJDUZFJKkJoNCktRkUEiSmgwKSVKTQSFJajIoJElNBoUkqcmgkCQ1GRSSpCaDQpLUZFBIkpoGDYokG5PcnWRXkitn2f4rSXYmuSPJp5OcOGQ9kqT5GywokqwCrgHOBzYAFyfZMKPZ7cBEVZ0OfBJ471D1SJIWZsgrijOBXVW1u6qeAa4HLhxtUFU3VdWT/eJ2YO2A9UiSFmDIoDgBuH9keapfN5dLgb+ebUOSy5JMJpncs2fPQSxRkrQ/QwZFZllXszZM3gJMAFfPtr2qNlfVRFVNrFmz5iCWKEnan8MHPPYUsG5keS3wwMxGSV4P/BpwTlU9PWA9kqQFGPKK4lZgfZKTkxwBXARsGW2Q5AzgWmBTVT00YC2SpAUaLCiqai9wObANuAu4oap2JLkqyaa+2dXA0cCfJflCki1zHE6StESGHHqiqrYCW2ese/fI69cPeX5J0oHzyWxJUpNBIUlqMigkSU0GhSSpyaCQJDUZFJKkJoNCktRkUEiSmgwKSVKTQSFJajIoJElNBoUkqcmgkCQ1GRSSpCaDQpLUZFBIkpoMCklSk0EhSWoyKCRJTQaFJKnJoJAkNRkUkqQmg0KS1GRQSJKaDApJUpNBIUlqMigkSU0GhSSpyaCQJDUZFJKkJoNCktRkUEiSmgwKSVKTQSFJajIoJElNgwZFko1J7k6yK8mVs2x/YZJP9Ns/n+SkIeuRJM3fYEGRZBVwDXA+sAG4OMmGGc0uBR6pqu8G3g/8zlD1SJIWZsgrijOBXVW1u6qeAa4HLpzR5kLgI/3rTwKvS5IBa5IkzdPhAx77BOD+keUp4Ky52lTV3iSPAf8B+MpooySXAZf1i08nuXOQip9/VjOjrw5h9sU0+2KafTHtexa645BBMduVQS2gDVW1GdgMkGSyqiYOvLznP/timn0xzb6YZl9MSzK50H2HHHqaAtaNLK8FHpirTZLDgeOArw5YkyRpnoYMiluB9UlOTnIEcBGwZUabLcDP9a/fAPxdVT3nikKStHQGG3rq7zlcDmwDVgHXVdWOJFcBk1W1BfhfwJ8k2UV3JXHRGIfePFTNz0P2xTT7Ypp9Mc2+mLbgvoh/wEuSWnwyW5LUZFBIkpqWbVA4/ce0MfriV5LsTHJHkk8nOXEp6lwM++uLkXZvSFJJVuxbI8fpiyRv6r83diT52GLXuFjG+Bl5RZKbktze/5xcsBR1Di3JdUkemutZs3Q+0PfTHUleOdaBq2rZfdHd/P4X4DuBI4AvAhtmtHkH8KH+9UXAJ5a67iXsi9cAL+5fv/1Q7ou+3THAzcB2YGKp617C74v1wO3AS/rlly113UvYF5uBt/evNwD3LnXdA/XFq4FXAnfOsf0C4K/pnmE7G/j8OMddrlcUTv8xbb99UVU3VdWT/eJ2umdWVqJxvi8AfgN4L/DUYha3yMbpi7cC11TVIwBV9dAi17hYxumLAo7tXx/Hc5/pWhGq6mbaz6JdCHy0OtuB45O8fH/HXa5BMdv0HyfM1aaq9gL7pv9Yacbpi1GX0v3FsBLtty+SnAGsq6obF7OwJTDO98UpwClJbkmyPcnGRatucY3TF+8B3pJkCtgKvHNxSlt25vv7BBh2Co8DcdCm/1gBxv5/JnkLMAGcM2hFS6fZF0kOo5uF+JLFKmgJjfN9cTjd8NO5dFeZn01yWlU9OnBti22cvrgY+HBVvS/JD9E9v3VaVT07fHnLyoJ+by7XKwqn/5g2Tl+Q5PXArwGbqurpRaptse2vL44BTgM+k+ReujHYLSv0hva4PyOfqqpvVtU9wN10wbHSjNMXlwI3AFTV54AX0U0YeKgZ6/fJTMs1KJz+Y9p++6IfbrmWLiRW6jg07KcvquqxqlpdVSdV1Ul092s2VdWCJ0Nbxsb5Gfkrujc6kGQ13VDU7kWtcnGM0xf3Aa8DSPK9dEGxZ1GrXB62AD/bv/vpbOCxqnpwfzsty6GnGm76j+edMfviauBo4M/6+/n3VdWmJSt6IGP2xSFhzL7YBpyXZCfwLeBdVfXw0lU9jDH74grgj5L8Mt1QyyUr8Q/LJB+nG2pc3d+P+XXgBQBV9SG6+zMXALuAJ4GfH+u4K7CvJEkH0XIdepIkLRMGhSSpyaCQJDUZFJKkJoNCktRkUOiQl+RbSb4w8nXSAo5xfJJ3HPzqpKXn22N1yEvytao6+gCPcRJwY1WdNs/9VlXVtw7k3NLQvKKQZpFkVZKrk9zaz9v/tn790f1nfvxTkn9Osm+W0v8BfFd/RXJ1knOT3DhyvA8muaR/fW+Sdyf5e+CNSb4ryd8kuS3JZ5Ocutj/X6llWT6ZLS2yI5N8oX99T1X9JN3cQI9V1Q8meSFwS5K/pZt58yer6vF+WoztSbYAVwKnVdUPACQ5dz/nfKqq/mPf9tPAL1bV/0tyFvAHwGsP9n9SWiiDQoJv7PsFP+I84PQkb+iXj6ObUG8K+K0krwaepZui+TsWcM5PQHeFAvww09OvALxwAceTBmNQSLML8M6q2vZtK7vhozXAq6rqm/0stS+aZf+9fPvQ7sw2X+//PQx4dJagkpYN71FIs9sGvD3JCwCSnJLkKLori4f6kHgNsO/zyZ+gm+Z8ny8DG9J9tvtx9DOXzlRVjwP3JHljf54k+f5h/kvSwhgU0uz+GNgJ/FP/QfXX0l2B/ykwkWQSeDPwJYB+VtZbktyZ5Oqqup/u8w/u6Pe5vXGuNwOXJvkisIPZP95VWjK+PVaS1OQVhSSpyaCQJDUZFJKkJoNCktRkUEiSmgwKSVKTQSFJavr/1y+DDxhEXKkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\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": 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": [
    "\n",
    "plt.title('Iris Dataset')\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('cm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris Dataset')"
      ]
     },
     "execution_count": 42,
     "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",
    "\n",
    "plt.title('Iris Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris Dataset')"
      ]
     },
     "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": [
    "# 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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPMklEQVR4nO3db4idaX2H8etrQiptVyvNCJI/m1iyxbCVrj2kFqFqXUt2C0mhIgksdWFr0DZaqpSmKIvEdytVKKTUFEQraIz7QqcSCagrWjE2s+y6miyx0/gnQ6Q76rp9IZpN++uLOcrx5GTOM5tzZjJ3rg8EzvM895753TuTa0+eM7NJVSFJWv+et9YDSJImw6BLUiMMuiQ1wqBLUiMMuiQ1YuNafeDNmzfXjh071urDS9K69Oijj/6gqmZGXVuzoO/YsYO5ubm1+vCStC4l+e71rnnLRZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIa0SnoSfYmuZBkPsmREde3J3kkyWNJnkhy7+RHlSQtZ2zQk2wAjgH3ALuBg0l2Dy17N3Cyqu4CDgD/NOlBJUnL6/IKfQ8wX1UXq+oKcALYP7SmgBf0H78QuDy5ESVJXXQJ+hbg0sDxQv/coPcA9yVZAE4Bbxv1REkOJZlLMre4uPgcxpUkXU+XoGfEuRo6Pgh8uKq2AvcCH01yzXNX1fGq6lVVb2Zm5N+gJEl6jroEfQHYNnC8lWtvqTwAnASoqq8Czwc2T2JASVI3XYJ+FtiVZGeSTSy96Tk7tOZ7wOsAkryMpaB7T0WSVtHYoFfVVeAwcBp4kqXvZjmX5GiSff1l7wTenOTrwMeB+6tq+LaMJGmKNnZZVFWnWHqzc/DcgwOPzwOvmuxokqSV8CdFJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRnYKeZG+SC0nmkxwZcf0DSR7v//pWkh9PflRJ0nI2jluQZANwDHg9sACcTTJbVed/vqaq/mZg/duAu6YwqyRpGV1eoe8B5qvqYlVdAU4A+5dZfxD4+CSGkyR11yXoW4BLA8cL/XPXSHI7sBP4wnWuH0oyl2RucXFxpbNKkpbRJegZca6us/YA8HBV/e+oi1V1vKp6VdWbmZnpOqMkqYMuQV8Atg0cbwUuX2ftAbzdIklrokvQzwK7kuxMsomlaM8OL0ry28CLgK9OdkRJUhdjg15VV4HDwGngSeBkVZ1LcjTJvoGlB4ETVXW92zGSpCka+22LAFV1Cjg1dO7BoeP3TG4sSdJK+ZOiktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktSITkFPsjfJhSTzSY5cZ80bk5xPci7JxyY7piRpnI3jFiTZABwDXg8sAGeTzFbV+YE1u4C/B15VVU8nefG0BpYkjdblFfoeYL6qLlbVFeAEsH9ozZuBY1X1NEBVPTXZMSVJ43QJ+hbg0sDxQv/coDuAO5J8JcmZJHsnNaAkqZuxt1yAjDhXI55nF/AaYCvw5SR3VtWPf+mJkkPAIYDt27eveFhJ0vV1eYW+AGwbON4KXB6x5tNV9WxVfRu4wFLgf0lVHa+qXlX1ZmZmnuvMkqQRugT9LLAryc4km4ADwOzQmk8BrwVIspmlWzAXJzmoJGl5Y4NeVVeBw8Bp4EngZFWdS3I0yb7+stPAD5OcBx4B/raqfjitoSVJ10rV8O3w1dHr9Wpubm5NPrYkrVdJHq2q3qhr/qSoJDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIzoFPcneJBeSzCc5MuL6/UkWkzze//UXkx9VkrScjeMWJNkAHANeDywAZ5PMVtX5oaWfqKrDU5hRktRBl1foe4D5qrpYVVeAE8D+6Y4lSVqpLkHfAlwaOF7onxv2Z0meSPJwkm2jnijJoSRzSeYWFxefw7iSpOvpEvSMOFdDx/8G7KiqlwOfAz4y6omq6nhV9aqqNzMzs7JJJUnL6hL0BWDwFfdW4PLggqr6YVX9rH/4L8DvTWY8SVJXXYJ+FtiVZGeSTcABYHZwQZKXDBzuA56c3IiSpC7GfpdLVV1Nchg4DWwAPlRV55IcBeaqahZ4e5J9wFXgR8D9U5xZkjRCqoZvh6+OXq9Xc3Nza/KxJWm9SvJoVfVGXfMnRSWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEZ2CnmRvkgtJ5pMcWWbdG5JUkpF/I7UkaXrGBj3JBuAYcA+wGziYZPeIdbcBbwe+NukhJUnjdXmFvgeYr6qLVXUFOAHsH7HuvcBDwE8nOJ8kqaMuQd8CXBo4Xuif+4UkdwHbquozE5xNkrQCXYKeEefqFxeT5wEfAN459omSQ0nmkswtLi52n1KSNFaXoC8A2waOtwKXB45vA+4EvpjkO8ArgdlRb4xW1fGq6lVVb2Zm5rlPLUm6RpegnwV2JdmZZBNwAJj9+cWqeqaqNlfVjqraAZwB9lXV3FQmliSNNDboVXUVOAycBp4ETlbVuSRHk+yb9oCSpG42dllUVaeAU0PnHrzO2tfc+FiSpJXyJ0UlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIa0SnoSfYmuZBkPsmREdffkuQbSR5P8u9Jdk9+VEnScsYGPckG4BhwD7AbODgi2B+rqt+pqt8FHgLeP/FJJUnL6vIKfQ8wX1UXq+oKcALYP7igqv5n4PDXgJrciJKkLjZ2WLMFuDRwvAD8/vCiJH8FvAPYBPzRqCdKcgg4BLB9+/aVzipJWkaXV+gZce6aV+BVdayqfgv4O+Ddo56oqo5XVa+qejMzMyubVJK0rC5BXwC2DRxvBS4vs/4E8Kc3MpQkaeW6BP0ssCvJziSbgAPA7OCCJLsGDv8E+M/JjShJ6mLsPfSquprkMHAa2AB8qKrOJTkKzFXVLHA4yd3As8DTwJumObQk6Vpd3hSlqk4Bp4bOPTjw+K8nPJckaYX8SVFJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJakSnoCfZm+RCkvkkR0Zcf0eS80meSPL5JLdPflRJ0nLGBj3JBuAYcA+wGziYZPfQsseAXlW9HHgYeGjSg0qSltflFfoeYL6qLlbVFeAEsH9wQVU9UlU/6R+eAbZOdkxJ0jhdgr4FuDRwvNA/dz0PAJ8ddSHJoSRzSeYWFxe7TylJGqtL0DPiXI1cmNwH9ID3jbpeVcerqldVvZmZme5TSpLG2thhzQKwbeB4K3B5eFGSu4F3Aa+uqp9NZjxJUlddXqGfBXYl2ZlkE3AAmB1ckOQu4IPAvqp6avJjSpLGGRv0qroKHAZOA08CJ6vqXJKjSfb1l70P+HXgk0keTzJ7naeTJE1Jl1suVNUp4NTQuQcHHt894bkkSSvkT4pKUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1IlUj/66K6X/gZBH47nP8xzcDP5jgOOuBe741uOdbw43s+faqGvk3BK1Z0G9Ekrmq6q31HKvJPd8a3POtYVp79paLJDXCoEtSI9Zr0I+v9QBrwD3fGtzzrWEqe16X99AlSddar6/QJUlDDLokNeKmDnqSvUkuJJlPcmTE9V9J8on+9a8l2bH6U05Whz2/I8n5JE8k+XyS29dizkkat+eBdW9IUknW/be4ddlzkjf2P9fnknxstWectA5f29uTPJLksf7X971rMeekJPlQkqeSfPM615PkH/v/Pp5I8oob/qBVdVP+AjYA/wW8FNgEfB3YPbTmL4F/7j8+AHxiredehT2/FvjV/uO33gp77q+7DfgScAborfXcq/B53gU8Bryof/zitZ57FfZ8HHhr//Fu4DtrPfcN7vkPgVcA37zO9XuBzwIBXgl87UY/5s38Cn0PMF9VF6vqCnAC2D+0Zj/wkf7jh4HXJckqzjhpY/dcVY9U1U/6h2eAras846R1+TwDvBd4CPjpag43JV32/GbgWFU9DVBVT63yjJPWZc8FvKD/+IXA5VWcb+Kq6kvAj5ZZsh/411pyBviNJC+5kY95Mwd9C3Bp4Hihf27kmqq6CjwD/OaqTDcdXfY86AGW/gu/no3dc5K7gG1V9ZnVHGyKunye7wDuSPKVJGeS7F216aajy57fA9yXZAE4BbxtdUZbMyv9/T7WxhsaZ7pGvdIe/h7LLmvWk877SXIf0ANePdWJpm/ZPSd5HvAB4P7VGmgVdPk8b2TptstrWPpT2JeT3FlVP57ybNPSZc8HgQ9X1T8k+QPgo/09/9/0x1sTE+/XzfwKfQHYNnC8lWv/CPaLNUk2svTHtOX+iHOz67JnktwNvAvYV1U/W6XZpmXcnm8D7gS+mOQ7LN1rnF3nb4x2/dr+dFU9W1XfBi6wFPj1qsueHwBOAlTVV4Hns/Q/sWpVp9/vK3EzB/0ssCvJziSbWHrTc3ZozSzwpv7jNwBfqP67DevU2D33bz98kKWYr/f7qjBmz1X1TFVtrqodVbWDpfcN9lXV3NqMOxFdvrY/xdIb4CTZzNItmIurOuVkddnz94DXASR5GUtBX1zVKVfXLPDn/e92eSXwTFV9/4aeca3fCR7zLvG9wLdYenf8Xf1zR1n6DQ1Ln/BPAvPAfwAvXeuZV2HPnwP+G3i8/2t2rWee9p6H1n6Rdf5dLh0/zwHeD5wHvgEcWOuZV2HPu4GvsPQdMI8Df7zWM9/gfj8OfB94lqVX4w8AbwHeMvA5Ptb/9/GNSXxd+6P/ktSIm/mWiyRpBQy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSI/4fwBdFs6tFYgUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 37,
   "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",
    "\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a mask so you don't show redundant information\n"
   ]
  },
  {
   "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 most easily accomplished 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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create FacetGrid\n",
    "\n",
    "# use map function with a histogram"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "#img = pd.plotting.scatter_matrix()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py37",
   "language": "python",
   "name": "py37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}