{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas using the standard naming convention (because I'm a cool data scientist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab the iris dataset in CSV format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  4600  100  4600    0     0  82142      0 --:--:-- --:--:-- --:--:-- 82142\n"
     ]
    }
   ],
   "source": [
    "!curl https://raw.githubusercontent.com/pydata/pandas/master/pandas/tests/data/iris.csv > ~/iris.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data into a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('~/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Name</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>Iris-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>Iris-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>Iris-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>Iris-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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLength  SepalWidth  PetalLength  PetalWidth            Name\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "5            5.4         3.9          1.7         0.4     Iris-setosa\n",
       "6            4.6         3.4          1.4         0.3     Iris-setosa\n",
       "7            5.0         3.4          1.5         0.2     Iris-setosa\n",
       "8            4.4         2.9          1.4         0.2     Iris-setosa\n",
       "9            4.9         3.1          1.5         0.1     Iris-setosa\n",
       "10           5.4         3.7          1.5         0.2     Iris-setosa\n",
       "11           4.8         3.4          1.6         0.2     Iris-setosa\n",
       "12           4.8         3.0          1.4         0.1     Iris-setosa\n",
       "13           4.3         3.0          1.1         0.1     Iris-setosa\n",
       "14           5.8         4.0          1.2         0.2     Iris-setosa\n",
       "15           5.7         4.4          1.5         0.4     Iris-setosa\n",
       "16           5.4         3.9          1.3         0.4     Iris-setosa\n",
       "17           5.1         3.5          1.4         0.3     Iris-setosa\n",
       "18           5.7         3.8          1.7         0.3     Iris-setosa\n",
       "19           5.1         3.8          1.5         0.3     Iris-setosa\n",
       "20           5.4         3.4          1.7         0.2     Iris-setosa\n",
       "21           5.1         3.7          1.5         0.4     Iris-setosa\n",
       "22           4.6         3.6          1.0         0.2     Iris-setosa\n",
       "23           5.1         3.3          1.7         0.5     Iris-setosa\n",
       "24           4.8         3.4          1.9         0.2     Iris-setosa\n",
       "25           5.0         3.0          1.6         0.2     Iris-setosa\n",
       "26           5.0         3.4          1.6         0.4     Iris-setosa\n",
       "27           5.2         3.5          1.5         0.2     Iris-setosa\n",
       "28           5.2         3.4          1.4         0.2     Iris-setosa\n",
       "29           4.7         3.2          1.6         0.2     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "120          6.9         3.2          5.7         2.3  Iris-virginica\n",
       "121          5.6         2.8          4.9         2.0  Iris-virginica\n",
       "122          7.7         2.8          6.7         2.0  Iris-virginica\n",
       "123          6.3         2.7          4.9         1.8  Iris-virginica\n",
       "124          6.7         3.3          5.7         2.1  Iris-virginica\n",
       "125          7.2         3.2          6.0         1.8  Iris-virginica\n",
       "126          6.2         2.8          4.8         1.8  Iris-virginica\n",
       "127          6.1         3.0          4.9         1.8  Iris-virginica\n",
       "128          6.4         2.8          5.6         2.1  Iris-virginica\n",
       "129          7.2         3.0          5.8         1.6  Iris-virginica\n",
       "130          7.4         2.8          6.1         1.9  Iris-virginica\n",
       "131          7.9         3.8          6.4         2.0  Iris-virginica\n",
       "132          6.4         2.8          5.6         2.2  Iris-virginica\n",
       "133          6.3         2.8          5.1         1.5  Iris-virginica\n",
       "134          6.1         2.6          5.6         1.4  Iris-virginica\n",
       "135          7.7         3.0          6.1         2.3  Iris-virginica\n",
       "136          6.3         3.4          5.6         2.4  Iris-virginica\n",
       "137          6.4         3.1          5.5         1.8  Iris-virginica\n",
       "138          6.0         3.0          4.8         1.8  Iris-virginica\n",
       "139          6.9         3.1          5.4         2.1  Iris-virginica\n",
       "140          6.7         3.1          5.6         2.4  Iris-virginica\n",
       "141          6.9         3.1          5.1         2.3  Iris-virginica\n",
       "142          5.8         2.7          5.1         1.9  Iris-virginica\n",
       "143          6.8         3.2          5.9         2.3  Iris-virginica\n",
       "144          6.7         3.3          5.7         2.5  Iris-virginica\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a few things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff5f962aeb8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SepalWidth'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff598630ef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PetalWidth'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Diff'] = df['SepalWidth'] - df['PetalWidth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff5f98f0b00>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Diff'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Name',\n",
       "       'Diff'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "classmap = {}\n",
    "for idx, name in enumerate(df['Name'].unique()):\n",
    "    classmap[name] = float(idx)\n",
    "df['Label'] = df['Name'].apply(lambda x: classmap[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff5b86294a8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = 'SepalLength', y = 'SepalWidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff5d846eb38>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = 'SepalLength', y = 'SepalWidth', c = 'Label', colormap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff5f994b940>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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koJEQqgADopGMRnJYy2wNrYvWHgKhEoaiKEoQXJpKGG2qsLCQNWvWUFJSghCCjIwMrrvuOr9zpJRs3LiRrKwsTCYT8+fPZ9CgQeEIT1HanKAaC39CJ4qplpk4GUM0f0FXXoiJdGqYRhR/I0IcxilHUc2NmHkfk9iFWw6ikluBztedU8dZLLwKQAU/RKNXC1e4ieYvGMQpHPJKapje9kEGSY3DaGN6vZ65c+cyaNAgqqurWbRoEaNHj6ZPnz6+c7Kysjh37hyrV68mNzeX9evXs3z58nCEpyhtzE28+CVGcQAAI7vxkEQEeQiXhlV8glu+gUF8jU44MbGdSPk+BnEcnahG8gkG8iiVS9v5fQRHRz4J4hcYxGkAjHI/dvkcGklNXCGJE49j4nOE0DDxORWyoMONy+jOI73Dkirj4+N9tYXIyEh69+6N3W73O2f37t1MnToVIQSpqalUVlZSXFwcjvAUpU0ZOIqBI75tvSghgmO+Lq86UUGEyEMnnLXbjtrkUQ2AEG7voDZqwh57a0Txd1+yADCI00TxdpPnC0qJ4Ijf78UsPmnzOIOlpjcPo/z8fI4dO8aQIUP89tvtdhITE33bNpsNu91OfLz/HP1btmxhy5YtAKxYscLvmkAYDIagrwk3FWNodJgY3cWIsgj8PvBFvQ8U4f/dTdQ7rtdFkBiXBKJ9llC9lN+lqEoAh/++yCgb5qgm7qOZ0JUavJPrXig3wkhibGDlhuvvO1RNUmvXrmXv3r1YrVZWrVrV4PjBgwd55plnSEry1sgmTJjA7NmzAcjOzmbjxo1omsaMGTO46aabQhJTS8KaMBwOB6tWrWLevHlERUX5HZNSNji//n8agIyMDDIyMnzbhYWFQcWQmJgY9DXhpmIMjY4TYxxWMQ4znyGEC7dMximHYxafoxPVeGQiNXJM7XY5HmmlRk7AJHahF8VoMpoqbSoVRaUtF9VGLuV3KfgfEsT/I0J4a1cumYq96n+QVU3fJ4ZJRIr3fb+XUucsnAGW21KMKSnN9xALVKjW9J42bRqZmZmsWbOmyXNGjBjBokWL/MvXNDZs2MCSJUuw2WwsXryY9PR0vyb+thK2hOF2u1m1ahVXXXUVEyZMaHDcZrP5/WUXFRU1qF0oSuckKJVP4mArelmAg6loJFEtdxEXVYC98lt46E+V/BKjzMHJZbgZhl5+jUnuxcVgXIxr7zcRNEkUdvkCJvkBADXMQLYwkrucBTjkFCLkcWq4okG34I7ApYWm80FaWhr5+flBX5eXl0dycjI9e/YEYPLkyezatavrJAwpJX/4wx/o3bs3119/faPnpKen89577zFlyhRyc3OJiopSCUPpQgQ1XO23x8V4ZGQinkrvFyU3I3Ez0nfcw2Cq6o2r6GwkkTho/P98U1yMx8X4Noqo9YJ5PlG3dlC/dSQQR44cYeHChcTHxzN37lz69u2L3W7HZrP5zrHZbOTmtu28XxeEJWEcPnyYrVu30q9fPxYuXAjAbbfd5qtRzJw5k7Fjx7J3714efvhhjEYj8+fPD0doiqIoQQmmSWrFihWXXM7AgQNZu3YtZrOZvXv3snLlSlavXh1w831bCEvCGD58OG+88Uaz5wghuPfee8MRjqJ0CEa+QFTno2ckHgZg5h1M7MDBNGrIbHC+ntOY2I6H3tQwGdqhe6ee45j4AjcDcHJF2MvvCMLVA6ruc95x48axYcMGysrKsNlsFBVdnIk4nM33aqS3orSDGJ7zPtytdpAgEvHIGCLEMYQAs/ycarmdMn7jOz+CLOLE/6IX+WjSRA1TKZWPhTVmI59iFc+jF4Vo0ky1/Dbl/CysMXQE4Rq4V1JSgtVqRQhBXl4emqYRExNDdHQ0Z8+eJT8/n4SEBHbs2MHDDz8clphUwlCUMBNUYBLb0Qlvn1O9KERHoa+nrRCSSD6nrE7Lg0X8Bb3wPiDViRqMchc68psZBBd60eJ19KKwNgYHZrZTIe8LyySFHYk7RAnj+eefJycnh/Lycu6//37mzJmD2+2dfn7mzJns3LmTzZs3o9frMRqNLFiwACEEer2eu+++m2XLlqFpGtOnT6dv374hiaklKmEoSti5ETRsh/ZX/7j/tsCDwBnSqFrSMGYN6qyv0V2EqklqwYIFzR7PzMwkM7Nh0yR4m6jGjQt/z7nuOymKorQTiRUXqb5FjjRpwSOtXHiWKSW4pX930mp5LR4ZW3tc4GJoizPPhppDTvMu3IR3gSYXqUisYY2hI1AjvRVFCSNBifwN0bxGtLGA0pp0argaq3ycCPJwym9RxhK/KxxchybjiORDPDKJCuYR7u97VXwfj0zEzHbcsjeVzKU9Hry3t66aDAKhEoaitAsDlcwjMiaRmhrvc4FSljVsiarDyWSccnKY4mtcDdOpkR1vBtlwUglDURRFCUiopgbpjFTCUJQ2IKWHsuqteGQZsZHXYtBFEcF+dJyjholAbINrBCXoOYeHFGQjx1vmxMQOJBE4mQQI9HyDwONborU+HfnosOOhf6PTdgjK0HMGtLQAY3Bj4BgSEx760hWbrNxqASVFUUJFSg8RrlkMjS5BANWel9DJPph0xwGJJAK7/D1uLs7YbOJDYsQf0WFHI5Ey+VOcTAyi1HJ6iNvQUQF4l1p1MQIj2YCGi2EUy6epu2Z2NK8QJd5BUIGHFErkb/FwcT4iIzuJFS94u/yW9sDEvdRwTZMRCKqIFwsx8DVgoIaJtWNFulbS6M5NUt03VSpKG7FXrMdmKkEnvLOYRxlcmHTHEEIiBOiEizjxqN81FvEqBnEenXBhEGeJERuCKjOexeioQNSWqcOOie3oRCU6UY2RbKL5i+98HcVEiX+jF3Z0wkmEOE6MWO13zxixAYM4i064EPIMFvFqszFYWIdRHEQnHOhEBSa2YmR3UO+jM1C9pBRFCRkDJxosd1GfDledLdnImIrgxljosPuV2WC5DQF6WXBxm3JEvcUqWorBe1zSVI1BJ0rrbTvRyeBnY+3oZBdNBoFQNQxFCTF9xO24tIsfKpr0vi6QEmrkqDpXCNz0rzMOQ+BmYFBlVnGz3weZlAK3vDhGwiMTqOY7F7dJwV1nHIcmjTjlZX73dDPId08JuOlHc81L1XIGnjplumVKkM1qnYOGCOjVFakahqKEmMX8LXIrfk5v81oEktPVE+gRM4d4uQSBmxo50tuFto4S+QQxrMbAOdyyL+U8GFSZ1cxGL+1E8m9AUCbvwcNgLLyKQKNK3oiLMXWuMFAinyaG59FRiVNeRiU/8LtnqfwVGnEYOIHBNIASx4+ajcHJlZTJaqLYjERPubwPDVuz13RGXbW5KRAqYShKG0iwXE917ToQ8THeCTQKZNPrWYOJchY2Ow6jJRX8iArp/6FeIp9u8nyNeErlk83cMYJyfgoSEi2J4Gh55bsarqVGXhtoyJ2SR/WSUhRFUQLRnZ9hqIShKAGrASII1aM/KSVVLlfLJyodimqSUhSlSYJq4sQS9JwEIqiUt1DNDa2656dnTrP2QBZOTZJgMvG/k6/CZm5+vWulY2hkwbtuo/s2xilKgGJYjUnswSDyMYjTRItX0dFye35TXJqHNfv3cqK8nHOVFeTYi1ix+/MQRqy0JdVLSlGUJl1YNMi3TTF6zqOReEn3K62poaJeU1SpM7xrWyiXrjs/9O6+71xRAuSSg5Hy4ncrDz1xc+krnMWbzcSbzH77ekd3r1XrOjMpA3t1RaqGoSgtqOA+dLKcCA4jMVIuf3SJkwN66YWOpyZOYeXeXdRISS9zJI9efkUII1bakuolpShKM/SUtXKMRH0DYq2smZZBYmIihYWX/jxECb9QJYy1a9eyd+9erFYrq1atanB827Zt/POf/wTAbDZz7733MmDAAAAefPBBzGYzOp0OvV7PihUrQhJTS1TCUBRFCUKoutVOmzaNzMxM1qxZ0+jxpKQknnjiCSwWC1lZWaxbt47ly5f7ji9dupTY2Euv6V6KoBLGmTNnOH78OA6H/6Rl11zT9JTHiqIoXUmonk+kpaWRn9/05IzDhg3z/Tx06FCKiopCU3ArBJww3n77bd566y369++PyWTyO6YShtKVeaTGyj27+KrYjlGn5/5RlzEuqaffOW8c+Yr3ThwHYGa/AdyaOjyoMgqqqvjt7p2U1tRgM5t5/IrJxNX7fxZqBr4iVryIwIGbgZTKR/EOTFSaowXRS2rRokW+nzMyMsjIyLikMj/88EPGjh3rt2/ZMu98ZNdee+0l3zdYASeM//73vyxfvpz+/fu3ZTyK0uH84cA+3j1+DE/tQ4wVe75g/YyZxBq9H+hfnD/LxkMHKXd5u8aeraxkYGwsE5JTmrxnfb/+fDsHirzPMvJK4YnPt/P81Lb8IuYgTizHIE4AYJBH0YiknJ+3YZldQzAVjFA8W/jyyy/56KOPeOqpp3z7fvOb35CQkEBpaSm//e1vSUlJIS0t0FURL13AqdJoNNK7d++2jEVROqSvS0t8yQIgv6qSE+Xlvu3Pzp7xJQuAcpeTz86eCfj+UkqKqqv99hXU2w41PefRcbGJQwjpSx5K86QUAb1C4ZtvvuGPf/wjCxcuJCYmxrc/ISEBAKvVyvjx48nLywtJeS1pNmFomuZ73XLLLfzpT3+iuLjYb7+maWEJVFHaS4/IKL/teJOZ5KiL+0bE2zDrLq6XbdbpGZEQ+LTeQgiiI4x++2Ii2rZpSMOGVq9rsEZ8m5bZZcgAX61UWFjIs88+y0MPPURKysXaqsPhoLr2C4XD4WD//v3069ev9QUGoNkmqdtuu63Bvg8++KDBvk2bNoUuIkXpYBaMuZzzVZWcLC8nQq/jtqEjSKyTRK7t15/9RQXsPOetVUzomcLMfgOCKuOXl49nxZ4vKHc6sRqNLEqfEMq30IDEQoW8FwuvADV4SKZMquaoQISq9vD888+Tk5NDeXk5999/P3PmzMHtdgMwc+ZM3nzzTSoqKli/fj2Ar/tsaWkpzz77LAAej4crr7ySMWPGNFlOKAkpm37mX1BQ0NQhPz169AhZQME6cybwqj/QKfq9qxhDI9Qx1ng8ROh06JpYf9VdW9s26AJ/KFo3RiklNR4PZkM4e7tLvEuxNv+AvSv8fdf9lt4agzcta/kk4OtbHgtJeR1Js/+ye/To4Xt99tlnftsXXp9/riZNU7oHk17fZLIAb6IIJlnUJ4QIc7IA75Krbdsbq8uRIrBXFxTwv+633norqP2KoihdkZpLqhlffvkl4H0AfuHnC86fP09kZMtz+Lc0BP7gwYM888wzJCUlATBhwgRmz54d0BtQlGBVuVz8Lms3hY5qBlvjeGDUmBZrBhl/fwOHxwPAfcO+RXJsDO+fOI5eCH408jJKahz89chXANyWOpzxPXv5XX+gIJ/Hdm7HrWmMSkzkmSljiRXPoyurwsJgKriDWF5CL87jln1q1/Q21g9D6Qi6aDIIRIsJ4/e//z0ATqfT9zN4q89xcXHcfffdLRbS0hB4gBEjRvgNclGUtrJox1b2FnhH2O7NP0+F08ni8RObPP/aOskC4OXDB4nS6aiqfWaRW1KMJsFe450B4WhpKc9MmUpqvLfro93h4OFtH+GqPX/H2dOUVbxMz9hT4IZosRszH6DnHEKAkb3oKKVUPtEWb19pJTX5YDMufMi/9NJLPPTQQ5dUSEtD4BUlXBxuN6cqKnzbGnCktKTZa6rrJIsLqup0Jy+sN1VOoaOa97457ksYn50740sWADZzNfERdcdAuNHLIi48HhFCYpDHAn5PSpipGkbLLjVZBOrIkSMsXLiQ+Ph45s6dS9++ja83sGXLFrZs2QJ4R1EmJga3iI3BYAj6mnBTMYZGYzFqUhIZEQF1xsVFGU2tei96IZBSotXZHpKU5LvnCKcDwcXPmQpXBE4tAr8ghB64uKiSwRBForXj/H476993W5CaqmE06oEHHgjoJnWbqi7FwIEDWbt2LWazmb1797Jy5UpWr17d6Ln152MJtqtfV+ge2BF05hhvHZrKxpyDlNQ4SIqM4v60kc2+l5+PuZxV2Xt82zogLSGRvNISInQ6JvRMptzl9E3tMcqWyMzkFN89BxnNDI9P4KtiOxLQpIlqOQuP/Bc6UY5HJlEpryNa/BMddjRslLlhCV2KAAAgAElEQVTuxNmBfr+d+e/7glB1q6WLLr8aiGYTxk9+8hPfz3l5eXzyySd85zvfoUePHhQUFPD+++8zderUVgcRVWfU7Lhx49iwYQNlZWVhn7pX6R6+O3AIV/bqw/mqSvrExGCJaP7h8veGpDKuR0825BxgcJyVO0eMwq1pHCsrxaTX0y8mFk1KjpeVATAgNrZB99uXZ3ybA4UFnK2sZFKvFKKNRork9SRY3RSVxCCJwiG/g55zeOiNJKaxUJSOQDVJNa7uZFYbNmzgscce881hAjB27FiWL1/Od7/73VYFUVJSgtVqRQhBXl4emqb5zZuiKKEWbzYTbza3fGKt/lYrT0260rdt0OkYGndxKg2dEAyyWpu9x6jEHoxKvDjIVcMGhkQktQP3iMNNXMAxKe1EJYyW2e12zPX+g5nNZux2e4vXtjQEfufOnWzevBm9Xo/RaGTBggWIZgZIKYqitJtO1Evqww8/DOi8QJeoCDhhpKen8/TTTzNr1iwSEhIoKiriH//4B5dffnmL1y5YsKDZ45mZmWRmZgYaiqL4KayuZv3B/bg1jR8MT2NAbPPf9APxQtYetp45RXJUFKuumt5gBPaRYjubcg9j1hu4b+RojpWWsGLPFwAsuvwKBlnjWPflfhweF7cMHe7rMRVOOoqw8CfARSW342FA2GPoijrToLxt27YFdF7IE8Z9993H3/72N15++WXsdjvx8fFMmjSJ73//+4HeQlFCrqSmhp9u/YBvaqcbzy4sYNWVV9O/FUlj0fZP+LR2evLz1VXM/u87vHP9TehqB/cdshfx2GfbyK+dMXR3/lnOVlb6ekn9dOuH9IqO5nRlJQBZBfksm3RVUDPYtpaghHjxMyJqpyw3yv0Uy6fxoNazabVO1Etq6dKlIb1fwAnDaDRyxx13cMcdd4Q0AEVpjfe+OepLFgDnqir565GvWjXb6578837bpc4aTlSU+2oum3K/8iULwJcYLtDq7cuvrub1I1/x5MQplxxTsCLZ7EsWAAZxjmj5BmUsDFsMXZXoRDWM+srLy8nKyqKkpIQbbrgBu92OlBKbLbAvM80mjJycHN+D7/rTgtQ1cuTIIEJWlNAx6Rv+Ezbp9Y2cGbj6z88EEFmnSSriEiYYvJRrWkNiQkqo+1akmmokNDppwsjJyWHVqlUMGjSIw4cPc8MNN3Du3DneeeedgGfZaDZhbNiwwTf3U1NjLYQQvPTSS0GGriihcd2AQbz/zXG+tHt7Gg2OtXJ32qhW3XP2kFRe+yrH18TUPzaWnlHRvuM/HnkZOXY735SX1Y7JsHGkuBin9F5hFDqGxSdw0F6IBvSPieXHIy9rVUzBqiaTSN4nQuYgBLjkICqYF9YYuqxO9NC7rldeeYUFCxYwatQo7rrrLgCGDBnC119/HfA9mk0YdScKbG4eKEVpLya9nheuvoYPTp7A5fEwo1+/FsdVtORHIy9jVEIP3jmWx4iEBH44wr8GnRgZxdppGXxw6huiDBFk9O1PmdPJ7w9kIYH5o8YSazTy4alvqHC5mNGnP1ZTuKcQN2GXz2HmI4R04uAaJJYwx9BFddIaRkFBAaNG+X+ZMhgMeBqZ+qYpLT7D+Oc//0laWhqDBg1C38qqvqK0BZNez3UDBob0npNSUpjUzMhgq8nEzYNTfdsJZjOPjZ/kd87MfqGNKXgmHKjehyHXSVel7tOnD9nZ2X6r8x04cCCo5V1bTBgHDhzgrbfeQkpJamoqI0aMIC0tjaFDhxLRxusOK4qidDidtElq7ty5PP3004wdOxan08m6devYs2cPCxcG3hGixYSxZMkSNE3j6NGjHDp0iEOHDvHuu+/icDgYPHgwaWlp3Hrrra16I4pS16mKcj49c4relhiu7NU76EGc1R4Xv9q+lVKXh/u/NYr0pJ68fPAAR8tKmD14GOOTk/nrkUNk5efz7X4DmNGvP/89dpSPT59kQnIvZg1J5XhZKTvPnWFAjJWJvVI4V1nBJ6dP0TMqiqm9+1LmdPLByeNEGYxk9OtHhE7VvruLUPWSammdICklGzduJCsrC5PJxPz58xk0aBAAH3/8MW+//TYAN998M9OmTWuxvNTUVFauXMm2bdswm80kJiayfPnygHtIQYDdanU6HUOGDGHIkCF897vfpbKyki1btvDvf/+bw4cPq4ShhMze/PP8dtdO8qurMOp0TO/Tj8evmNTyhbWcHg+Z/3gLT+3oqkc+/Rir0Uip0wnAZ2fPkGiOJN/h7Rb72bkz/P5ANuerq5DAjnNneOdoHqVOJ4WOasx6AxOTkzlcXMzZqkoMQse4Hl9zrrqSE+Xl6ID/fnOU566a3qrlWZVOJEQJo6V1grKysjh37hyrV68mNzeX9evXs3z5cioqKnjzzTdZsWIFAIsWLSI9PR2LpeVnVAkJCdxwww2Ul5cTExMT9JexgBJGeXk5hw4dIicnh0OHDlFSUsLQoUO58cYbGT58eFAFKkpz/vzVQfKrqwBwahqfnzvLuapKkuv0UmrOH/Zn+ZLFBReSBXibny8kC/D+3z9XW94FX5eV+n52eNxsP3txPQu31NhTcN5XhgbsK8jn0zOnmdan8Sn5FaUxLa0TtHv3bqZOnYoQgtTUVCorKykuLubgwYOMHj3alyBGjx5NdnY2V155ZZP3AqisrORPf/oTO3fuxO12YzAYmDhxInfddVdAyQYCSBiPPPIITqeTtLQ0hg8fzsyZM0M4TbCi+JP1Puw9UuLWAn/KWOVxhzqkBt8otXoxangTi9I9BNMkVXd8Q/2lGVpit9v91vew2WzY7XbsdrtfM1JCQkJAc/qtXbsWnU7H008/7Ztx/I033mDt2rX88pe/DCimFhNGfHw8J06coKCggMTERIqKirDZbJjC3k1Q6Q4y+w/k69ISSp1OBJAaH09KdODdQe/71mX85/gxv8/4CCFw1fmQN+t0OOokIbNe77cEq9VoxCMlFS4XOmCQNY78qgqKa2sqg2KtlLtcvprQEGscV6X0uaT3q3RCQUwNcqHZ6FLU//IEDQeVtrS/roMHD7Ju3TqMRm+38z59+vDggw/y4x//OOCYWkwYjz/+uN9D73fffZfVq1fTo0cPhg8fzogRIxg/fnzABSpKc64bMAirycQHJ74hKSqau9NGNlhbojm2yEj+cu11PPjJFlya5MaBg7lzxLf4xacfU+io5tv9BnJX2kge3f4J35SVcUXPXvxiXDq/3fUZ+wsLGRofz7KJV/Lx6VNsO3OKPpYY7hzxLbIL83n3+FHiTWbu/dZoTlSU8caRw5gNBu771miiVY/B7iNM4zBsNpvfglBFRUXEx8eTkJBATk6Ob7/dbvdbiqIpKSkp5Ofn06fPxS83hYWFQbUYtfqh93/+8x82bdoUcIGK0pIpvXozpVfvS75+gNXKf26Y5bcC2x+umel3zqqrpvtt/3qC/zxP1/TtxzV9L/ZPT09KJj0p2bedGpfAkiAexitdR7jmkkpPT+e9995jypQp5ObmEhUVRXx8PGPGjOGvf/0rFbVr0+/bt4/bb7+90XvUnd585MiRLFu2jKuuusr3f2Pbtm1BLYJ3SQ+9T5w4QXx8PKNGjQoosymKonQZIUoYLa0TNHbsWPbu3cvDDz+M0Whk/vz5AFgsFmbNmsXixYsBmD17dpMPretPb56cnExubi65ubm+7SNHjgQcs5CNNZTV8cgjj3D69GmSkpJ8g/bS0tJISkoKuJC2dObMmaDO7wprE3cEHSlGKSXHysrwSI1BVit6oeNURTmG6GgSNImxkRkKihzVnK+qoq8lhhhjw6lEKl0uTpSXkRgZSY/IqAbH69Ok5Fht76qBsdaAm9E60u+xOZ0hznCt6T342d8FdN7Xv3gkJOV1JC3WMGbNmkVaWhrx8fEtnaooYadJyWOfbSOrIB+PlAyLS6C3xcK2M6dwahr9LTH87qrpfnM5vXM0j1cOHaSkxkFSZBSL0ydwWY+LX4ByS+ws3bmDc1WVxBpN3Jo6jFtTRzQZg1vT+OX2T/iyyPthNdKWyDNTrlbjMrqozjy9+QVSSr+H6roA/602mzA0TWPSpEm+n5sSaGGKEmpbTn7DZ2fP4q6dKTa7MJ+DRQW+XlGHS4p5cd9e3/MGTUr+78ghXw+nU5UVrD2QzR/rPON4cV8WJyq8a2wUOqp5M+8INw4a6jfFeV1vf53L7vPnfFMM7T5/jrfyjnBLqhqj1CV1ogWU6rLb7WzYsIFDhw5RWW8Nl0CfQzebMG677baAbqIeeivt5XxVlS9ZXOCq18pa4XL5fnZ6PDg9/uc7630Zqqk3e2eNx0Oly9VkwjhfVek3H51WG5fSNXXWGsa6deswmUz8+te/ZunSpTz55JP87W9/Y+zYsQHfo9mEoda5UDq66X368o+jub4PaKvRiElv8NUgLBERTO19sRuh2WCgj8XiO65HkGqN87vntxISOVxc7EtEvaItJJjNTcbw7f4D+PDkCQpqR5AnmiPJHDAgZO9R6WA6acI4cuQIa9euxWw2I4RgwIABPPDAAyxZsiTgAYXNJowePXqEJFBFaSt9LDE8OWEKr+R8iYbkpkFDGRgby0v7s0CvZ3JSMtcNGOR3zYrJU3kuezeF1Q4GW63cP2qM3/GHLhtLpEFPjt2O1WTkkbHjm32InRqXwK/GT+D1I18BcGvqcFLjEkL/ZpUOobPWMHQ6nW+JiujoaMrKyoiMjAxolPgFAa/pDd65TXJycigrK/Pb/9BDDwVzG0UJqZG2RJ69aprfvqenXN1kr5moiIgGa1fUpROC+4JcIW98z16M79krqGuUTqqTJowhQ4aQlZXFFVdcwWWXXcZzzz2H0Whk8ODBAd8j4KfVf/vb31i3bh2aprFz504sFgv79u0jKqrlLoeKoihdhdACe3U0P/nJT3zj5ubNm8fIkSPp3bs3PXv2DPgeASeMjz76iCVLljBv3jwMBgPz5s3j0UcfpaCgIPjIlU7NpXlwBrGsY1twejx+kxK6Na3dY1KUjiw6Oto3wM9oNDJr1ixuvfVWPvroo4DvEXCTVGVlpW8pP4PBgNvtZsiQIX5zmihdm5SSlXt38fm5s+j1etJ7JLFw3Pig59RvDU1Klu36jOyCAoQQXN27DwLBx6dPIqXkssQeLLliUlDzTylKUDppk1Rj2mQ9DPAOIT958iR9+/alb9++bN68GYvFEvA86krnt/nEcTZ/cxyH5v0mv7mqitGJPcjsH761q/+Wd5gPT530rU/xj6/zkEhf19iPTp8kNTeBW9UYCKWNdNaH3qEQcMK45ZZbKC/3Dma64447eOGFF3A4HNxzzz1tFpzSsXxVbPclCwCH5uGrYntYE0ZucbEvWQDUaP7NUC5NI6+kOGzxKN1QJ0sYX375ZZPHLsxdFaiAE8a4ceN8Pw8ZMoQXX3wxqIKUzm9SrxTeP3Gcstp1IWKNRib3Cu9iWpOSU9h25hSVtf/QYyMi0KSkonY72mBgYrJa4EtpQ50sYfz+979v9njdRZpaEnDCuOuuu9i4cWOD/ffeey/r168PuECl87qiZy/uGjGS9745hiEighm9+nBFmLuSzujXn1OV5Xxy+hQ64KbBQwFv05SG5Orefcjo1z+sMSndS0fsAdWcptYMvxQBJwxPIz1Q3G53s3NMKV3P94cO4/tDh7Xr7KV3jhjJnSNG+u27fmDgfckVpTXUM4xm/PrXv0YIgcvlYunSpX7HioqKSE1NbbPgFEVROhyVMJp2zTXXAJCXl8f06RdXKRNCYLVaGTlyZFOX+qxdu5a9e/ditVpZtWpVg+NSSjZu3EhWVhYmk4n58+czaNCgRu6kKIrSzlTCaNq0adMAGDp0KL17X9qymdOmTSMzM7PJtrSsrCzOnTvH6tWryc3NZf369SxfvvySylI6lm2nT/GbXZ/h1jRijSZeycgkrs5EfvlVVdzzwXtUulxE6HQsn3wVl9dZClVKyXPZezhQVIhBCOaNGMmUlOb/HW4/c5pXDn0JOh0j4uLJ7DeA1fuycHjcDIy18qvxE4jQNVxUSVECoZqkApCSksKWLVvYvn075eXlPPvss+Tk5FBSUsLkyZObvTYtLY38/Pwmj+/evZupU6cihCA1NZXKykqKi4vVok2dnMPt5tefb/d1gy10VHPPB+/z1v/c6Dvn3g/fp7imBvBOM/7LT7fywc1zfMdf/eog/zr2te8ev8veTWp8fJOr4BVUV/G7rN2cr52NNtdexAcnT1Dq9JbxdWkJUQYDCy+/IvRvWOkeVMJo2aZNmzhw4ADXXXcdL7/8MgA2m40///nPLSaMltjtdr+uXTabDbvdrhJGJ3eivNRv+g7A98F9QUVtF90LXJqHIkc1NnMkADl2u9+4i/NVVRwutjeZMI6UFPuSBYBbSipcF8uQwDflZY1cqSiBCVUvqezsbDZu3IimacyYMYObbrrJ7/grr7zCwYMHAXA6nZSWlvLKK68A3nFxF2beSExM5NFHHw1NUC0IOGF88sknPP3008TGxvq60SYlJTVbcwhUY8uKNzVkfcuWLWzZsgWAFStWBNWHGLzTmgR7Tbh1lRgjY2PR63R+SSMqIsLvOrPBgLNO0jDo9AxN6e1bxXFwYiI7zp72falLiIxkTL8BJMb5r2FxwZgIA7a9kRRVV1+Mw2DwW0QpOdbaYX6/neHvGjpHnGGLMQQ1DE3T2LBhA0uWLMFms7F48WLS09Pp0+fi2i3z5s3z/fzuu+9y7Ngx37bRaGTlypWtDyRIAScMTdMw11tExuFwNNh3KWw2m18XzaKioiZrFxkZGX6LfQTbtbMrLGbfEQQa4w9SR/Da4Rw0KTHp9Tx31TS/6/530lX8/NOPcXo86HU6fjxqlN/8/POGDCO3IJ+jpaUYdDq+N3gIFre7ybKjgduGDufvX+eiCehviSGjb3/+fOggNR4PvaKjeXjkZR3m99sZ/q6hc8TZUowpKaEZ0BmKZxh5eXkkJyf7ZoqdPHkyu3bt8ksYdW3fvp05c+Y0eiycAk4YY8aM4dVXX+XOO+8EvLWCTZs2cfnll7c6iPT0dN577z2mTJlCbm4uUVFRqjmqi7h35GjuThtJhdtFrNHU4PhlPZLY8r05lDlrsBgiGqwPb9TrWXnlNJweDwadLqBJBW9NHc6cocOIS0igrNg7Tci3+w3AqWmY9Opht9JKQSSMRYsW+X6u+2XXbrdjs9l8x2w2G7m5uY3eo6CggPz8fL8eqS6Xi0WLFqHX67nxxhu54orwPJMLOGHceeedrFmzhnnz5uF2u/nhD3/I6NGjA1o86fnnnycnJ4fy8nLuv/9+5syZ45vDZObMmYwdO5a9e/fy8MMPYzQamT9//qW/I6XD0el0jSaLulo6bgzyg14nhN81QgiVLJTQCCJhrFixovFbBNEMv337diZOnOj3ZWrt2rUkJCRw/vx5nnrqKfr160dycnKj14dSiwmjpqaGt956i5MnTzJw4EDuuusuSkpKSExMJK6JduT6FixY0OxxIQT33ntvYBEriqK0o1A0SdlsNoqKinzbzTXD79ixo8EkrwkJ3iWAe/bsSVpaGsePHw9LwmhxAaUNGzawZ88eevfuzeeff84777zDkCFDAk4WiqIoXYmQgb2aM3jwYM6ePUt+fj5ut5sdO3aQnp7e4LwzZ85QWVnpN6NGRUUFrtpOHGVlZRw+fLjJZx+h1mINIzs7m6effpr4+HgyMzNZunQpd999dzhiUxRF6XhCUMPQ6/XcfffdLFu2DE3TmD59On379mXTpk0MHjzYlzw+/fRTJk+e7Ndcdfr0adatW4dOp0PTNG666aaOkzBqamp8VaXExESqqqpauEJRFKULC9HAvXHjxvktGwHe8RV1NdYzatiwYY1OsRQOLSYMj8fjtwCHpmkNFuQIZD4pRVGUrkBNDdIMq9XqtwCHxWLx2xZC8NJLL7VNdIqiKB2NShhNC+XiG4qiKJ1dZ1tAKZQCHoehKIqiqCYpRVEUJVAqYSiKoigBUQlDURRFCYRqklIURVECIrTumzFUwlAURQlG980XKmEoiqIEQzVJKYqiKIFRCUNRFEUJhKphKIqiKIFRCUNRFEUJhJoaRFEURQmIapJSFEVRAtPIetzdhUoYiqIoQVA1DEVRFCUwKmEoiqIogQjVQ+/s7Gw2btyIpmnMmDGDm266ye/4xx9/zF/+8hcSEhIAyMzMZMaMGb5jb7/9NgA333wz06ZNC01QLVAJQ1EUJQihSBiaprFhwwaWLFmCzWZj8eLFpKen06dPH7/zJk+ezD333OO3r6KigjfffJMVK1YAsGjRItLT07FYLK0PrAW6Ni9BURSlK5EysFcz8vLySE5OpmfPnhgMBiZPnsyuXbsCKj47O5vRo0djsViwWCyMHj2a7OzsULyzFqkaRgjZz5ZQfK6UlCE9iYwxt3c4iqK0gWAeei9atMj3c0ZGBhkZGQDY7XZsNpvvmM1mIzc3t8H1n3/+OYcOHaJXr17ceeedJCYmNrg2ISEBu91+Ce8keCphhMi76z7ivT9+SEVxFYl9Epi/9k76j+zT8oWKonQuQSSMC81GDW7RSA1ECOG3ffnllzNlyhQiIiLYvHkza9asYenSpY3er/61bUU1SYVATbWTLRu3UnK+DLfTzbmj+fzfk39v77AURWkDQgb2ao7NZqOoqMi3XVRURHx8vN85MTExREREAN7aydGjRwFvjaLutXa7vcG1bUUljBCoLnfgcrj89rlqXE2crShKZyY0GdCrOYMHD+bs2bPk5+fjdrvZsWMH6enpfucUFxf7ft69e7fvgfiYMWPYt28fFRUVVFRUsG/fPsaMGRP6N9oI1SQVAtYeMfToZ6O0oBwAfYSeweMGtG9QiqK0jRCMw9Dr9dx9990sW7YMTdOYPn06ffv2ZdOmTQwePJj09HTeffdddu/ejV6vx2KxMH/+fAAsFguzZs1i8eLFAMyePTssPaQAhGysMa0TOXPmTFDnJyYmUlhYGPI4KkuqeHXJm5TbKxg8pj/f+8V30OkurQLXVjGGkooxNDpDjNA54mwpxpSUlJCUc/V3VwZ03if/WhiS8joSVcMIkei4KB546YftHYaiKG1NremtKIqiBKT75ovwJYzWDIPvrKSU1FQ5MUUZw9btTVGUtqUmH2xjrRkG31kdzf6GDb/4K5Wl1Vjio7j/xR/SZ1iv9g5LUZRWaqkHVFcWlm61rRkG31ltfHQTp746S/HZEk7mnOFPC19v75AURQkFGeCrCwpLDaM1w+Dr27JlC1u2bAG8oygbO6c5BoMh6GuCJaXEWeX021dT6Qy43HDE2FoqxtDoDDFC54gzXDGKzt2xtFXCkjBCOQy+7nwsQNBd/cLVPTAm0cK5YwW+bWuSJeByu0IXxo5AxRg6nSHOcHWrpRuv6R2WJqnWDIPvrB76492MnDqMfmm9GT19BPPXzmvvkBRFCQEhZUCvrigsNYy6w+ATEhLYsWMHDz/8sN85xcXFviRSdxh8ZxWXFMvC/5vf3mEoihJqXTMXBCQsCaM1w+AVRVE6ku7cSyps4zDGjRvHuHHj/Pbdcsstvp9vv/12br/99nCFoyiKcmm6aHNTINRI7ya8t+4jdvx9NwCTb0on88fT/Y7/+Vdv8NFftiMlmKNNLHx9Pq8/9Q8clTX07J/InMe+yyuL3qC8qBJrUiz3vziXmISLE4S5nW7W//z/OHX4HEazgTuenMXgsf3D+h4VRQleqNb07oxUwmjE/o8P8c7qzVSWVAFQeMJOyrBkRk8bAcCx/Sf48NXtvvMdlTUsu/EFNM37L+lkzhm+2vk1FcWV3u1DZ1g7/888+vqDvmte+/VbfPaPPb720JcXvMaT7y3EFGkMx1tUFOVSdeMahloPoxH7PzrkSxYAlaVV7P8wx7e96z/7GlxzIVlc4Kh0+G0Xnyv12z6Td97v4VlJfimFJ8OzzKKiKK3QjQfuqYTRiMFj+mGMjPBtGyMjGDTmYnNR2uShDa6pP67EYPSvvEVbI/22rT1i/Y/HRROfbL3kmBVFCQ+haQG9uiLVJNWIiTddzpFdR9n/4SEQMGraCCZ973Lf8ZFXD2fQ2AEczToOgE6vY96KOby37iMclTXEJ8fxvZ9nsum371BZWkVMfDT3PHubXxnzVsyhrKCc/JNFGM0RfO+RTKJi/ZOKoigdUNfMBQFRCaMRQgjuXD4Ht8sDgCFC3+Ccpf/6GU6nk6qSauKSvDWDqbdOxOVwYax9DjHq6hE4q52+7bqirVEsfvMnOB0uIkwGNZutonQSXXVQXiBUwmhGY4miLqPRiDHpYjIQQjRIDo0lC7/j5ohmjyuK0sGohKEoiqIEJEQJo6U1gv7973/zwQcfoNfriY2N5YEHHqBHjx6Adwxbv379AO8cWo8++mhIYmqJShhN+NeL7/Puuk8A+M6PrkYi+Meqd5ESho4fwI9emMtrj7+Fs9rFyKuH8Z0fX+PXrFRT7eS1x9+i6JSdnoN6cPvSm4kwqV+3onR6IXiGEcgaQQMGDGDFihWYTCY2b97Ma6+9xs9+9jPA27qxcmVga4uHkvoEa8SOv+/mrZXvImunAHjz6f/6HT+88yiPXbMCR2UNAHl7jqMTOr/BfS/9eKOvK+7B7Ucot1fy0B/uCtM7UBSlrYSiB1TdNYIA3xpBdRPGyJEjfT8PHTqUbdu2tbrc1lLdahvx3h8/8iWLplxIFgA1VTUc2PqVb1tKybmvz188WcLpw2dDHqeiKO1AysBezWhsjSC7velxWB9++CFjxozxbbtcLhYtWsRjjz3GF1980fr3FCBVw2iErW8C33x5KqhrzNEm389CCCJM/g+zVXOUonQRQTzDWLRoke/numv5BLJG0AVbt27l6NGjPPHEE759a9euJSEhgfPnz/PUU0/Rr18/kpOTA47rUqlPsUY88OJcfnbFE1TYvVN7RMdH4apx4axyASB0gqtunYyumAUAAAk0SURBVEj25i9xVNXQc0Aic38zy+8eNz2SyaZl71BaUEZ8TyuzH/1u2N+HoihtIIgWqRUrVjS6P5A1ggD279/P3//+d5544gnfekEACQkJAPTs2ZO0tDSOHz+uEkZ7MZqNvLD3KfZ/6G1mGn3NcAwGA9s27aS8uJKrb5tMtDWSop/aqSiuoteQng26x15x/VjSpqRScNJOUj8b0XFR7fFWFEUJsVCMwwhkjaBjx47x8ssv86tf/Qqr9eIsEBUVFZhMJiIiIigrK+Pw4cPceOONrY4pECphNMFgMDBu5ki/fVfdMtFv29Y7AVvvhCbvYYmPxhIf3SbxKYrSTkKQMAJZI+i1117D4XDwu9/9DrjYffb06dOsW7cOnU6HpmncdNNNYVtwTiUMRVGUYHhCMzdIS2sEPf74441eN2zYMFatWhWSGILVbRLGzn/uYe/mLxmY1o9rfzS1wSjuHX/fzVvP/IcIUwQ/3XAP+z/O4fXfvAPArY/fQHWFg7+vfA+A5MFJ3PXMLfzv7BdBQo/+Nh5786f8fPITeFwa8SlWnvv8Sd5b9xHH9p8kdfwgZtx5ZYOHWnvfP8Dn/9pLQq84vvfz69Sob0XpDLrxSG8hG3tc34mcOXOmxXPeW/cR/3j+farLqhE6wehpI/jZn3/k+wDf9ubnbHjkry12pQ2G0Av0eh1upwdjZARX3zaJHzx18cH4J6/v5I1l73jXzBAwYtJQfvn6fHQ6HYmJiRQWFoYslragYgyNzhAjdI44W4oxJSUlJOV8Z8jCgM57Ny/8A+vaWrcYh7Hrv/uoLqsGQGqS41+epLy2BxTA23UG6YWK9EjcTu/khc5qFwc/PeJ3/LO3d/sWWELCiYOnKfimqP5tFEXpaDQZ2KsL6hZNUvWbgnQ6nV+TlMHQ/CSDoaDT+cdQv8u1zqDDoMZqKErHJ7vv/ObdooZx/YMzfIsTmS1mxn17lN/aEw/98S6/BGKMav2zhOj4KF9X2piEaKb/YLLf8Zse+Q623t5+18bICEZPH4EtpWE/bEVROhiPFtirC+oWX2nHZPz/7d1vTBNnHAfwr20x0hYYPYRqADfQLuIfFJqJNUSGBF14ISYatrmYCA7fKBNcohAScYSFqdOwmizGKVkWXKqLbIlzJjbMLaUjUhfmyGakmi52YWNwZoMho//2wlFbXOWg16PH/T5JE6594L7Hm+ee3/Pc05U4dHEhbn99B1l6HdKyFwV9vmRlKpo6atF+/Cpin4vFq/WlGHQOoaX8IwA+vHX+TcjmzUN98XvwuL3YfqgEmysL8Pa6d/D3n6Mo3rMRZXVbcaTkBH67N4ANO17CrsbtsH/vwL1bDujWZeCF1elB53xxXSYOX9qHH8w/YeESBtmFWQL+RwghMybuad+wSGLSO9BcmLyLBpSRH2LICIgjp2CT3qlVUzcC8JXzA17OF00kMcIghBDeiPseOyyS7TAGf2Vh+7IHmkWJ0JdkQyZ79nSOe9yN7z6/hdHhMRi25SJGqcC7W1vA/v4XSmu2oGhXvkDJCSGzioftzcVKkh2G4/YDGPeex+ADFvIYObK/uIWqsxUhd4t0uzw49vqHuHvzHnxeH663fvN4Cex/Nxqf1H2GX3qdqDj2moBXQQiZFRIeYUhildRkl9+/isEHj/ee97g8+NlyF847ob+vouf6j/7OAgD+cDzpLCZ8+2lXxPISQqIIrZKSFu+kh2rcbg/cLk/I9uNj7qkf7JPuTQchkuKj5zCk5eU3DIhPivMfP78qHWnLQ6+gWFu8EmlZTz5PSIl7qk3G2vSn3iOEzEH0pLe05G5ejVj1Algu3kT8wjhsO/jKU5sRBopVL8Bh0z5cPnEV/4yOY/OejVAsiMGRLcfhGndjVcFyHPx4r4BXQAiZNRKew5BkhwEAWRt0yNqg49xenajCrqYdQe+d7TvBdyxCSLSjVVKEEEI4oRFG5PX09KC1tRVerxebNm1CaWlp0OculwunT5/G/fv3ERcXhwMHDiA5OVmoeIQQwonPE3qBzFwnyKS31+vFuXPnUFdXh1OnTqGzsxNOpzOoTUdHB1QqFYxGI0pKStDW1iZENEIImR4JT3oL0mHY7XZotVqkpKRAoVDAYDCgu7s7qI3NZkNBQQEAIC8vD729vRD5NleEkLnI5+X2moMEKUmxLAuGYfzHDMOgr68vZBu5XA6lUonh4WHEx8cHtTObzTCbzQCA5uZmJCUlTSuLQqGY9u8IjTLygzLyRww5hcrI15ethVOmb29vR0dHB2QyGXbv3o01a9bwkmkqgnQY/zdSmLwNB5c2AFBUVISioiL/8XR30JwLu25GA8rIDzFkBMSRU6jdavkYPUyU6evr68EwDGpra6HX65GamupvE1im7+zsRFtbG6qrq+F0OmG1WnHy5Ek8fPgQjY2NaGlpmXI/PD4IUpJiGAZDQ0++fnRoaAiJiYkh23g8HoyOjkKtVgsRjxBCOPN5PJxezxJOmb67uxsGgwExMTFITk6GVquF3W6P1OUGEWSEkZmZif7+fgwMDECj0cBqtaKqKnhP+dzcXNy4cQM6nQ5dXV1YsWJFyM0AA83kroG3O40Iooz8oIz8EUNOITJe917i1O7Ro0c4evSo/ziwOhJOmZ5lWSxbtszfTqPRgGXZGV/PdAgywpDL5SgvL0dTUxOqq6uxfv16pKWlwWQywWazAQAKCwsxMjKC/fv348qVK9i5c2dEslRUVETk7/KJMvKDMvJHDDmjLWNsbCyam5v9r8BSejhl+tlcDCTYcxg5OTnIyckJeq+srMz/8/z581FTUxPxHEqlMuLnCBdl5Adl5I8Ycooh44TplOkZhgkq00/+XZZlodFoBMktuc0HVSrVbEeYEmXkB2XkjxhyiiHjhMAyvdvthtVqhV6vD2ozUaYHEFSm1+v1sFqtcLlcGBgYQH9/P5YuXSpIbnlDQ0ODIGeKIhkZGbMdYUqUkR+UkT9iyCmGjAAgk8mg1WphNBpx7do15OfnIy8vDyaTCWNjY1i8eDHS09NhsVhw4cIFOBwOVFZWQq1WIyEhASMjIzhz5gwsFgvKy8sFm1+a56On4wghhHAguZIUIYSQmaEOgxBCCCfUYRBCCOGEOgxCCCGcUIdBCCGEE+owCCGEcEIdBiGEEE7+BWAxP7UjmwpRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = 'PetalLength', y = 'PetalWidth', c = 'Label', colormap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
   "name": "conda-env-anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}