Edric Tam Section 9 Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This week's review (stuff relevant to this assignment)\n",
    "    - several more advanced python keywords:\n",
    "        - is\n",
    "        - None\n",
    "        - with\n",
    "    - brief overview of API, JSON, CSV and using request.get and file write. \n",
    "    - more fancy plotting\n",
    "        - seaborn\n",
    "        - various plottign: box, pair, reg\n",
    "    - Regression\n",
    "        - statsmodels\n",
    "        - how to use formula\n",
    "    - Statistical Testing\n",
    "        - scipy\n",
    "- Q&A\n",
    "     - answer any questions about scraping etv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install a conda package in the current Jupyter kernel\n",
    "import sys\n",
    "!conda install --yes --prefix {sys.prefix} rpy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from rpy2.robjects import r, pandas2ri\n",
    "pandas2ri.activate()\n",
    "df = r['iris']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>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>121</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species\n",
       "1             5.1          3.5           1.4          0.2     setosa\n",
       "2             4.9          3.0           1.4          0.2     setosa\n",
       "3             4.7          3.2           1.3          0.2     setosa\n",
       "4             4.6          3.1           1.5          0.2     setosa\n",
       "5             5.0          3.6           1.4          0.2     setosa\n",
       "6             5.4          3.9           1.7          0.4     setosa\n",
       "7             4.6          3.4           1.4          0.3     setosa\n",
       "8             5.0          3.4           1.5          0.2     setosa\n",
       "9             4.4          2.9           1.4          0.2     setosa\n",
       "10            4.9          3.1           1.5          0.1     setosa\n",
       "11            5.4          3.7           1.5          0.2     setosa\n",
       "12            4.8          3.4           1.6          0.2     setosa\n",
       "13            4.8          3.0           1.4          0.1     setosa\n",
       "14            4.3          3.0           1.1          0.1     setosa\n",
       "15            5.8          4.0           1.2          0.2     setosa\n",
       "16            5.7          4.4           1.5          0.4     setosa\n",
       "17            5.4          3.9           1.3          0.4     setosa\n",
       "18            5.1          3.5           1.4          0.3     setosa\n",
       "19            5.7          3.8           1.7          0.3     setosa\n",
       "20            5.1          3.8           1.5          0.3     setosa\n",
       "21            5.4          3.4           1.7          0.2     setosa\n",
       "22            5.1          3.7           1.5          0.4     setosa\n",
       "23            4.6          3.6           1.0          0.2     setosa\n",
       "24            5.1          3.3           1.7          0.5     setosa\n",
       "25            4.8          3.4           1.9          0.2     setosa\n",
       "26            5.0          3.0           1.6          0.2     setosa\n",
       "27            5.0          3.4           1.6          0.4     setosa\n",
       "28            5.2          3.5           1.5          0.2     setosa\n",
       "29            5.2          3.4           1.4          0.2     setosa\n",
       "30            4.7          3.2           1.6          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "121           6.9          3.2           5.7          2.3  virginica\n",
       "122           5.6          2.8           4.9          2.0  virginica\n",
       "123           7.7          2.8           6.7          2.0  virginica\n",
       "124           6.3          2.7           4.9          1.8  virginica\n",
       "125           6.7          3.3           5.7          2.1  virginica\n",
       "126           7.2          3.2           6.0          1.8  virginica\n",
       "127           6.2          2.8           4.8          1.8  virginica\n",
       "128           6.1          3.0           4.9          1.8  virginica\n",
       "129           6.4          2.8           5.6          2.1  virginica\n",
       "130           7.2          3.0           5.8          1.6  virginica\n",
       "131           7.4          2.8           6.1          1.9  virginica\n",
       "132           7.9          3.8           6.4          2.0  virginica\n",
       "133           6.4          2.8           5.6          2.2  virginica\n",
       "134           6.3          2.8           5.1          1.5  virginica\n",
       "135           6.1          2.6           5.6          1.4  virginica\n",
       "136           7.7          3.0           6.1          2.3  virginica\n",
       "137           6.3          3.4           5.6          2.4  virginica\n",
       "138           6.4          3.1           5.5          1.8  virginica\n",
       "139           6.0          3.0           4.8          1.8  virginica\n",
       "140           6.9          3.1           5.4          2.1  virginica\n",
       "141           6.7          3.1           5.6          2.4  virginica\n",
       "142           6.9          3.1           5.1          2.3  virginica\n",
       "143           5.8          2.7           5.1          1.9  virginica\n",
       "144           6.8          3.2           5.9          2.3  virginica\n",
       "145           6.7          3.3           5.7          2.5  virginica\n",
       "146           6.7          3.0           5.2          2.3  virginica\n",
       "147           6.3          2.5           5.0          1.9  virginica\n",
       "148           6.5          3.0           5.2          2.0  virginica\n",
       "149           6.2          3.4           5.4          2.3  virginica\n",
       "150           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1aa6aa90>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a199a3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## use default pandas df\n",
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1aaea780>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L3CHpiYi4t2L/EuDgiNgo6XTg58A2vXQpocwGOOigg+p4WTMzG4ia1ylExP0RcXNEvF6x7al6ZkaNiLXp50vAzcBxVfs3RMTGtLwQ2EXSxF7qmR8RrRHR2tTUVPNNmZnZwNR78Vq/SdpN0h49y8BpwCNVZfaTpLR8XIrn5aJiMjOzvhXZWTwJuDl95o8B/jkifiXpAoCImEc2pPVzkraQ9VmcW3GKyszMBllhSSEingaO7GX7vIrlK4Eri4rBzMz6p7DTR2ZmNvQ4KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVlu2N4op6gbfBep6JuHF2Uo3ZTczPo2bJNCR0cHS1c8xtbx+5QdSt30VjYX4OLfv1ByJPUbtemV2oXMbMgYtkkBYOv4fXjjMN/+uUhjH7ut7BDMrIHcp2BmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs1yhSUHSKkkrJC2T1N7Lfklqk9Qhabmko4uMx8zM+jYY1ymcHBHrtrNvBjA1PY4Hrko/zcysBGVfvHYWcF1EBHC/pL0l7R8Rz+9oxZ2dnYzatN4XVxVs1KaX6ezcUnYYZtYgRfcpBLBI0mJJs3vZPxl4rmK9M237NyTNltQuqb2rq6ugUM3MrOiWwkkRsVbSvsAdkp6IiHsr9quX58Q2GyLmA/MBWltbt9nfm+bmZl58c4ynuSjY2Mduo7l5v7LDMLMGKbSlEBFr08+XgJuB46qKdAIHVqw3A2uLjMnMrNq6deu46KKLePnll8sOpXSFJQVJu0nao2cZOA14pKrYLcAn0yikE4D1jehPMDPrjwULFrB8+XIWLFhQdiilK7KlMAn4raSHgQeBX0bEryRdIOmCVGYh8DTQAXwf+HyB8ZiZbWPdunXcfvvtRAS33377iG8tFNanEBFPA0f2sn1exXIAFxYVg1m1Im++VORNknwjo+IsWLCA7KMItm7dyoIFC7j44otLjqo8vqLZrEHGjRvHuHHjyg7D+umOO+5g8+bNAGzevJlFixaVHFG5yr5OwWxQ+du2VTv11FNZuHAhmzdvZpddduG0004rO6RSuaVgZiParFmzkLLR8aNGjWLWrFklR1QuJwUzG9EmTpzIjBkzkMSMGTOYMGFC2SGValifPhq16ZUhNc2F3tgAQIzds+RI6jdq0yuAL16zoW3WrFmsWrVqxLcSYBgnhZaWlrJD6LeVK18DYOq7h9KH7H5D8ndtVmnixInMnTu37DB2CsM2KQzFDsWemNva2kqOxMxGKvcpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWKzwpSBotaamkbaYrlXSepC5Jy9LjM0XHY2Zm2zcYE+J9EXgc2N580D+NiC8MQhxmZlZDoS0FSc3AfwCuKfJ1zMysMYo+ffRd4L8BW/soc7ak5ZJulHRgwfGYmVkfCksKks4AXoqIxX0UuxWYEhFHAL8BFmynrtmS2iW1d3V1FRCtmZlBsS2Fk4AzJa0CfgJ8WNI/VRaIiJcj4s20+n3gmN4qioj5EdEaEa1NTU0FhmxmNrIVlhQi4pKIaI6IKcC5wF0R8fHKMpL2r1g9k6xD2szMSjLot+OUdBnQHhG3AHMknQlsAV4BzhvseMzM7I8GJSlExN3A3Wn56xXbLwEuGYwYzMysNl/RbGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlhv0IanDQVtbGx0dHQ2vd+XKlQDMmTOn4XW3tLQUUq+ZDS9OCjuRcePGlR2CmY1wTgoD4G/cZjZcuU/BzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWU4RUXYM/SKpC1hddhwFmgisKzsIGzAfv6FruB+7gyOi5k3uh1xSGO4ktUdEa9lx2MD4+A1dPnYZnz4yM7Ock4KZmeWcFHY+88sOwHaIj9/Q5WOH+xTMzKyCWwpmZpZzUiiRpPMkHVB2HLZjJF0m6ZQBPG+apNuKiGkkknSApBsH8LxrJB1Wo8wFkj458OiGDp8+KpGku4GvRER72bFY3ySJ7P9lawPrnEZ2/M+os/yYiNjSqNcfKfx76x+3FBpM0m6SfinpYUmPSDpH0jGS7pG0WNKvJe0vaSbQCvxY0jJJ4yRNl7RU0gpJP5T0jlTn/5L0mKTlki5P2z4i6YFU/jeSJpX5vocKSd+W9PmK9Usl/YWkv5T0UPod/03aN0XS45K+BywBDpT0o3RcV0j6cir3o3Q8kXSspH9Nx/9BSXtIGivp2vScpZJO7iWufST9PL3+/ZKOqIhvvqRFwHWD8CsaEvo4jo+k9fMk3SDpVmCRpFGSvifpUUm3SVpYcczultSaljdK+tt0/O7v+b9K9X8lLbek/7mHJS2R9G5Ju0u6M62vkHTWoP9SGiUi/GjgAzgb+H7F+l7AvwJNaf0c4Idp+W6gNS2PBZ4DDknr1wFfAvYBnuSPrbq90893Vmz7DPD3Zb/3ofAA3g/cU7H+GPBJspEnIvuidBvwIWAKsBU4IZU9Brij4rk9x+JHwExgV+Bp4Ni0fU+yW97+BXBt2vYe4Nl0vKcBt6Xtc4FvpOUPA8vS8qXAYmBc2b+7nemxneP4IeCRtH4e0Ansk9ZnAgvT8d0PeBWYmfZV/h8G8JG0/HfA1yqOw1fS8gPAf0rLY4Hx6TjvmbZNBDp6/j+H2sP3aG68FcDlkr5N9uHyKnA4cEd2BoLRwPO9PO9Q4JmIeCqtLwAuBK4E3gCukfTLVCdAM/BTSfuTfRg9U8zbGV4iYqmkfVNfThPZ8TkCOA1YmortDkwl+/BeHRH3p+1PA++SNBf4JbCoqvpDgecj4qH0WhsAJH2Q7EOfiHhC0mrgkKrnfpDsCwURcZekCZL2SvtuiYjuHX/3w8d2juOzVcXuiIhX0vIHgRsiO/33gqR/2U7Vb/HH/7HFwKmVOyXtAUyOiJtTHG+k7bsA/1PSh8i+SEwGJgEv7MDbLIWTQoNFxFOSjgFOB74F3AE8GhEn1niqtlPfFknHAdOBc4EvkH2TnAv8Q0Tcks5NX9qYdzAi3Ej2zXE/4CdkLYJvRcTVlYUkTQFe71mPiFclHQn8e7KE/V+AT1U+heybZrVej20dZXrqer2XfbbtcaxW+Xur5xgAbI70dR94m20/I7dXz8fIktMxEbFZ0iqyVsSQ4z6FBkvfXDZFxD8BlwPHA02STkz7d5H03lT8NWCPtPwEMEVSS1r/BHCPpN2BvSJiIdnppKPS/r2ANWl5VpHvaRj6CVmCnUn2wfJr4FPpd42kyZL2rX6SpInAqIi4Cfhr4OiqIk8AB0g6NpXfQ9IY4F6yDw0kHQIcRHZKsFJlmWnAup6Whm1X9XHsy2+Bs1PfwiSyU3f9lo5Jp6T/CCDpHZLGk/0/vpQSwsnAwQOpf2fglkLjvQ/435K2ApuBzwFbgLZ0OmAM8F3gUbJz0fMkdQMnAucDN6QPkoeAeWR9Cr+QNJbsW8qX0+tcmsquAe4H/mRQ3t0wEBGPptMAayLieeB5SX8K/C6d4tsIfJzsm2KlycC1knq+TF1SVe9bks4B5koaB3QDpwDfIzvOK8j+Fs6LiDfTa/W4NNW9HNiEE31N1ccxtey25yay1vYjwFNk/QLrB/jSnwCulnQZ2f/4fwZ+DNwqqR1YRvYFYUjykFQzGxEk7R4RGyVNAB4EToqIIXfOv2huKZjZSHGbpL3JBmb8DyeE3rmlYGZmOXc0m5lZzknBzMxyTgpmZpZzUjADJP1VmhdnubK5qI5vYN0LUwen2U7Po49sxEsXFp4BHJ2uH5hINkKlISLi9EbVZVY0txTMYH+yK4jfBIiIdRGxVtKqNBvng+nRAiCpSdJNymZVfUjSSWn77vrjbKjLJZ2dtq9KiQZJH091LZN0taTR6bHN7KtmZXBSMMsmtjtQ0lNpeuU/q9i3ISKOI5uY8Ltp2xXAdyLiWLJJ7K5J2/8aWB8R74uII4C7Kl8kXTV9DtlFU0eRXTH9MbKpSyZHxOER8T7g2mLeplltPn1kI166yvUY4N8BJ5PNPvvVtPv6ip/fScunAIdVTFOxZ5pu4RSyuXh66n216qWmk02//VB67jjgJeBW+p591WzQOCmYARHxNtm8+nenOYp65h6qvLqzZ3kUcGL1dNbKPun7uhpUwIKIuGSbHX3Pvmo2aHz6yEY8SYdKmlqx6ShgdVo+p+Ln79LyIrIpzHuef9R2tr+z6qXuBGb2zMCq7G5rB9cx+6rZoHFLwSy7qc7cNGx0C9lds2aTjUh6h6QHyL5AfTSVnwP8Y5rRtGdq7AuAb6btj5D1F/wN8LOeF4mIxyR9jXR7SLIZNi8km011u7Ovmg0mz31kth3pRimtEbGu7FjMBotPH5mZWc4tBTMzy7mlYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOz3P8HA9pJ48GvG7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1aaf70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'Species', y = 'Sepal.Length', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a1b511390>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LTmLlhh3cevH0AX2y6SMFymUabbp83QWrgD8TSwazTlX7ch2fdu5y4AxgjIjsBL4DBAFU9S7g08C1IhIGuoFWVdWh/EcYY7yVaxprcpS/rz/C/p4wC1e0cfa0o1g2bybd/RGu+fmGlKDXoy/u5vdb3uKuy07mytMmEaryp6wwm16BltM02nT5ugvuAY4Abgb+LiLPishtIvIpETkq14mqOldVx6lqUFXHq+q9qnqXU8Giqj9W1RNU9SRV/bCqWlYvY0pUrgUJ41F+nwj9UU0EvL6z+mXefLcnMWU2Oej14cljeG7bPq55cCMigt/nY1RNEJ8410qrOEthQcShytdd8DvgdwAi4gdmEGud3gZMAiqjZ9oA0HTDIwUfu/0H53lYElNqCp3Gmr5wYbagVqbptW7cvxTlHcIlImNE5HxirdkfEvua/wRwlcdlM8aUiEKnsaYvXJgtqJVpeq0b9y9F+fpk/wq8B6wE1gD/ER8RYIypXOlBptqALxHgOnvaUVzQfDSjamL9p1HV2CwuJ1D10NWnsr+7n/rqAPs6+1g8t5mFSWkMb7tkOj9c88qgpsJmCrCV6jTadPmmSdwHfBi4GDgRmCYizwGbVLX0/4QYYwYt+yKIQX76uRb294R54NntA2ZpLW5tZsXaN1jyZDunNDVw68XTWbVpJ/M+0sSSuc001lfT1RvGJ8LtlzYPaoRAOU2jTZcvQcwtqnqBqrYAi4gl674aeFFE/jgM5TPGDLPss7iiicBWplla8e3xz/HZXNc+uJGOzn4m3/go0xb9F5+/fz1dfZGMAa5ckgNsgz23mAqa8Csik4FZwKnEWrZjgW0elssYUyT5gky5ZmnFA1rpx6VvL4eAlVtytmRF5Ncisht4FDiL2Eyvuao61rJnGVOZcgWZ4oGtfAGt5M+ZtpdDwMot+VqyPwWuVtW3h6MwxpjiyxZkqg346A1Hueuyk+kJRwculOj0yQZ84pzTTFThzstO5rnX3mbNVz/GlCPrnVSHhS6UXf7yjZNdnW2fiHxAVf/ufpGMMcWUKchUG/DR0dXP8udf5+KZE/jGwy9w1Ohqbv/XkzjqsJrYaIKqAPM+0sR1Z03lrfd6uPmRrc6iic3MPnYM1z64sexma7nhUP6c3OtaKYwxJSU9yNQdjrJg+SbmTBvHNx5+gee27WNV2y7294T57LLnaf7e40z59mM0f+9xPrvsefb3hFnVtssJmrXxbld/Wc7WcsOQK1lVtSk/xowQ8WBYesCr0ADYhIbQgGNGSvArX+CrIddruAppjCmueDAsPeBVaABsR0fXgGNGSvArX0s2njd2Q4bXIeWTNcaUj3gwbM2W3dx68fTEAoZrtuxmcWtzyoKGi1ubWbNld9ICh80cEQqWxaKHXsgX+HI9j6wxZvgdSi7W+LkNdcGMaQlrA74BQbKrTp/MdWdNTdwLKMvZWm4oePUxETmC2PIwNfFtqvq0F4UyxrjnUHKxZjo3Pl127qnHJK5R7499KU4sbJj2OWVfCS966IWCAl8i8gXgaWJJYr7r/FzkXbGMMW45lFysmc6NT5cdSSMEDkWhowsWAqcAr6vqx4nlld3rWamMMa45lFys2c6NjyoYKSMEDkWhlWyPqvYAiEi1qv4FOD7XCSJyn4jsEZEtWfaLiCwRkXYR2SwiJw+u6MaYQhxKLtZs58ZHFYyUEQKHotDOkZ0icjixZbsfF5F3gF15zvkZ8GPggSz7zyHWxzuVWOKZO52fxhgX5crFGolE6eqPUFcdcKa7+ukOp36++/JYkCu+COKFM8azatNOlsxtxgdEVUdcMGswCqpkVfVTzttFIvIH4DDgsTznPC0iTTkOuQB4wFk88b9F5HARGaequwspkzGmMNlysarGgloLVwzMP7DkyXYWnDmF1lkTU/fPbaYhVMUlLRNA4fP3rx+RU2UHo9DA18/j71X1j05Og/sO8d5HAzuSPu90thljXJYpF2tXfySx6GGmnLBzpo0buH95Gzvf6WZ/T5gFafssEJZZod0FJyR/cBZVnHmI98705y7jkuAiMh+YDzBx4sRDvK0xQ1Npz2H6ooeQOiU225TZ+BTZcl3YcLjlm1Z7o4gcAKaLyH4ROeB83gP85hDvvROYkPR5PFn6eVV1qaq2qGrL2LFjD/G2xgxNpT2H6YseQuqU2GxTZnd0dGXdZ4GwgQpZfmYUcJuqjlbVUc6rUVVvPMR7rwbmOaMMPgy8Z/2xxgyfUNCfc0pspimz8Smy6dNrR9pU2cEotLvgJhG5DJikqv8uIhOAcaq6NtsJIrIcOAMYIyI7ge8AQQBVvYvYagvnAu1AF3DlkP8rjDGD5vf7aKyrSkyRjY8muPK0SVx31tTE5/SAGcBVp0+mNuhLmV5rowsyK7SS/QkQBc4E/h046Gw7JdsJqjo31wWdUQVfLvD+xhgP+P0+RjlTYEfVBGM/A6mf6wPZp8iOqhm4z6Qq9DdzqqqeLCKbAFT1HRGp8rBcxhhTEQqd8dXvjChQABEZS6xla4wxJodCK9klwK+BI0XkZuAZ4PuelcoYYypEoTO+HhKRDcSWBRfgQlXd6mnJjDGmAuSsZEWkBrgGmAK8CNytquHhKJipHE03PFLwsdt/YEvHmcqSr7vgfqCFWAV7DvBDz0tkjDEVJF93wYdU9UQAEbkXyDou1hhjzED5WrL98TfWTWCMMYOXryV7kojsd94LUOt8FmLzCUZ7WjpjjClz+VartYnIxhhzCAodJ2uMMWYIrJI1xhgPWSVrjDEeskrWmCKKRpWDvWGi6vyMZlwcxJQxy09mTJFEo7GFDNNXkbXFCCuLtWSNKZKu/ggLlm+yxQgrnFWyxhRJqMpvixGOAJ5WsiJytoi8IiLtInJDhv2fE5G9ItLmvL7gZXmMKSVdfRFbjHAE8KySdZJ8/4RYYpkPAXNF5EMZDv2FqjY7r3u8Ko8xpSYU9LNk7owBCxX6BAuAVRAvA1+zgHZV3QYgIiuAC4CXPbynMWXD5xMa66pYNq+FULWfN/Z1cfMjW3lrf68FwCqIl5Xs0cCOpM87gVMzHHexiHwMeBX4H6q6I8MxpsQMJkesyc7nExD47LLneW7bvsT2Bcs3seyKFlugsAJ42Seb6U9w+neg3wJNqjodeIJY/tqBFxKZLyLrRWT93r17XS6mMYXx6jm0AFhl87KS3QlMSPo8HtiVfICq7lPVXufjMmBmpgup6lJVbVHVlrFjx3pSWGPy8eo5tABYZfPyu8g6YKqITALeBFqBzyQfICLjVHW38/F8YNjXDSuFpVHsq3fli0aVrv4IoSo/XX0Ravw+usMR6qoDqCoPfmEWOzq6ueOJV3lrfy93XXYyKERV6eqLEAr6rX+2THlWyapqWESuA9YAfuA+VX1JRL4HrFfV1cACETkfCAMdwOe8Ko8xxZI+s+tHc5uZeUwDC1e0JWZ63XrxdFa3vclN5/0jAb+P3kiUhQ9utJlgFcDTcbKq+qiqHqeqx6rqzc62f3MqWFT1RlU9QVVPUtWPq+pfvCyPMcWQPrNr9rFjWLiiLWWm1/UrNzNn2jgWLG9DgIXL22wmWIWwGV/GeCw9sDW6Npgx0DXlyHrWbe/Iut8CYeXJxod4xPpZTVw8sBUforW/uz/lM8QCXe17DnJKU0PW/V19ERvSVYasJWuMx9Jndj332tssbm1Omel168XTWbNlN7dePD3j/iVzZxAKWku2HNmfRWM85vMJR9QGWTpvZmJ0QV11IOVzqMrPladNIlTl54i6sdQG/Cy7ouX9/Ta6oGxZJVvhrNui+CKRKB1dfaxY+wYXzhjP9Ss3p4wqWLVpJ62zJrJi7RssebI9dTSBiHURlDnrLjDGY139ERauaGPOtHFcv3JzxlEF8f02mqDyWCVrjMfqqgMpoweSJW+fcmR9ynYbTVAZrJI1xmOdveGU0QPJkre37zmYst2m1VYG6+wxxmXJU2h7+iIEfcKdl51MbzjKsnkzCUeV0bVBDvaEqauOBbYe/MIsuvoitP/HOex8t5sx9VU2rbZCWEvWGBfFp9Beff96vvaLNjq6+tj69/30RaJsfL2Drr4I1z64keNueowv/nwDb77Tw0+f+VviZ0dXHzVBoasvwtUPrOe4mx7j6vvXs6+zzxJ5lymrZI1xUfIU2mvPmMI3H97MsWNHsXB5W87ptMkBsLrq4IDjLBBWvqySNcZFyVNo4wGt+ppAzumyyYGvdds7EoGy9OMsEFaerJI1xkXJuWHjAa2DPeGU6bLJ0gNfpzQ1JAJl6cdZIKw8WSVrjIuSp9De+VQ7t10yndf2HmBxa3Pe6bRrtuxmcWsznb39Nq22gtjoAmNc5PMJDaEgd18+k7rqAB2dvUweO4q6oJ/Tpo6lNuhn6bzYvs7ecMp02itPm0RtwE9PJEptwGfTaiuEVbLGuKw7HOWLP9+QkkVr9uRGls6byeX3rh2wfdH5J7Bo9UssnTeTQMBHfSD2BbPe7/y0abVlzboLjHFZtoURswW0kgNepvJYJWuMy7ItjJgtoJUc8DKVx9NKVkTOFpFXRKRdRG7IsL9aRH7h7H9eRJq8LI8xwyE9f2w8cFUb8GcNfC1ubbbAVoXy7PuJiPiBnwD/RGx58HUislpVX0467PPAO6o6RURagVuBS70qkzHDwecTGuuqMgauGuuqMge+gn78fvtiWYm87ASaBbSr6jYAEVkBXAAkV7IXAIuc978EfiwioqolOX/QcrOaQvl87+eBTQ5c+f0+RjmV6aiaoPPTKtdK5uX/3aOBHUmfdzrbMh6jqmHgPaAx/UIiMl9E1ovI+r1793pUXGNys+fQDIWXlWymQX3pLdRCjkFVl6pqi6q2jB071pXCGTNY9hyaofCykt0JTEj6PB7Yle0YEQkAhwEdGGNMhRCvuj+dSvNV4CzgTWAd8BlVfSnpmC8DJ6rqNU7g6yJV/dc8190LvJ60aQzwttvlHyblWvZil/ttVT27iPfP9Bx6qdi/72xGerkKeg49q2QBRORc4A7AD9ynqjeLyPeA9aq6WkRqgJ8DM4i1YFvjgbJB3GO9qra4XfbhUK5lL9dyl6tS/X1buQrj6RQTVX0UeDRt278lve8BLvGyDMYYU0w2dsQYYzxUCZXs0mIX4BCUa9nLtdzlqlR/31auAnjaJ2uMMSNdJbRkjTGmZFkla4wxHrJK1hhjPGSVrDHGeMgqWWOM8ZBVssYY4yGrZI0xxkNWyRpjjIeskjXGGA9ZJWuMMR6yStYYYzxklawxxnjIKlljjPGQVbLGGOMhq2SNMcZDZVfJnn322Ups2XB7jdxX0dlzaC8KVPRKVkSOF5G2pNd+EflqtuPffrsUF8c0I409h6ZQni6kWAhVfQVoBhARP7Hlw39d1EIZY4xLit6STXMW8JqqDtd69mYYRKPKwd4wUXV+Rgv+pmVM2Su1SrYVWJ6+UUTmi8h6EVm/d+/eIhTLDFU0quzr7OPq+9dz3E2PcfX969nX2VeWFa09h2YoSqaSFZEq4Hzg4fR9qrpUVVtUtWXs2LHDXzgzZF39ERYs38Rz2/YRjirPbdvHguWb6OqPFLtog2bPoRmKkqlkgXOAjar6VrELYtwTqvKzbntHyrZ12zsIVfmLVCJjhlcpVbJzydBVYMpbV1+EU5oaUrad0tRAV1/5tWSNGYqSqGRFJAT8E/CrYpfFZFdIACv9mNqAjyVzZzB7ciMBnzB7ciNL5s4gFLSWrBkZij6EC0BVu4DGYpfDZBcPYC1Yvol12zs4pamBJXNn0FhXhc8nOY9pCAVZdkULoSo/XX0RQkF/4hxjKl1JtGRN6csawOqLJFqunX3hjMd0h6PUVwfwicR+WgVrRhCrZE1Bsgawqv2J4VmhqoAFuYxJY5WsKUi2ANYb+7oSLdf2PQctyGVMGqtkTUFCQX+GAFYzdzzxauKYn/yhnVsvnm5BLmOSlETgy5Q+n09oCAVZOm8mddUBOnvDBH3CW/t7E8esfmEXU8bWJY6xIJcx1pI1BYpGlY6ufuY/sIHjbnqM+Q9s4EBvmLsuOzml5Tr31GOoq7IglzFx1pI1QKwS7eqPZB1mlTy6AHBGDrSxbF6LDc8yJgerZE1BY2BzjS7wSeyY+mp7nIxJZ90FpqAkLjZghTgEAAAeJUlEQVQ91pihsaaHKSiJS3x0QXpr10YOZNd0wyMFH7v9B+d5WBJTTFbJmkQrNd7fCu+3UuNdAD6f0FhXZf2vxgySdReYjGNg77rsZFBSksH4fDKk6bFDSSxTjkm9jcmkJFqyInI4cA8wjdgqkFep6nPFLdXIkd5K7emP0NkbZsGDG7MGwgp1KIllhnI/Y0pNqbRkFwO/V9V/AE4Ctha5PCNaJKosWN7mymoGBQXVKmj1BGPSFb2SFZHRwMeAewFUtU9V3y1uqUaW9HW43Ez0UlBQzVZPMBWsFLoLJgN7gZ+KyEnABmChqnYWt1gjR/pEg3iil1yBsLi8kxj6Iiw4cwpzpo1jypH1tO85yJotu1Ou1dkbzni/zt4wo2qCXv6nG+O5ordkiVX0JwN3quoMoBO4IfkAWyXUW+ktyUITvRSyEm1twEfrrIksWv0Sx3/7MRatfonWWROpDfhS7p9+v1svnl5yLVl7Ds1QlEIluxPYqarPO59/SazSTbBVQg9Nvsh9+kSD1S/sYtWmnSydN5NXbz6HZVe0ZAxCZepLXf7863T2vX+v7v4oC1ek9u8uXNFGdziauE53f5RVm3ay6PwTeOU/zmHR+SewatNOuvujlBJ7Ds1QFL27QFX/LiI7ROR4VX0FOAt4udjlqhSFRO5rAz4WtzazcEVb4pjWWRNjX/2d4VqZpLeAzz/pg1w4YzzzH9iQuM5DV59a0ESHuaceYxMdTEUqeiXr+ArwkIhUAduAK4tcnoqRObHLJpZd0ZKoPLvDUVasfYNF55+Q6DddsfYNrjp9MvX+7F920vtbD/T088Cz21Pu9ca+roImOqSnUbSJDqZSlEQlq6ptQEuxy1GJCo3uL3mynduf+GtiW8AnXHfW1JzXjve3JreAb714Ou17O1n9wi4A/vjqngGt5MWtzSl9svE0ijZO1lSikqhkzdDki+xDYVNmCzkmk0wt4Jd2vcv3LjiB/31pM+17DnJ4KJixlTz/Y5Pp6o/EWq5JCzBC5ta2MeWqFAJfZggKiexDtmVjUvs7432yycektzYzqQ36uHDG+MTIgTVbdjPzmAaufXBjYiSBT2Db253MueNpjv3Wo8y542kOCwXZ3xNOJAC3BRhNJbNmQpkqpK8VCkvscih9stev3Jwow5xp4xIjCd4vUxu3XHQiq9p2Jc67oPlorn1w45DG5RpTbqwlW6YGM0sqX2KXUJWfw0JBjhpdjQgcNbqaw0LBvC3JuurUFuiUI+szlmliYyillTy6NjikcbnGlCNrJpSpofajZtLXH+GcaeO4NikhzOLWZvr6I9RUZb9WV29qGbK2SHsjKS3p9BletgCjqWTWki1ThfS1Fqo/qhknDPTnSTfo88GPPtPMU984g9e+fy6jawLcmbaw4pK5M6gNpj5mtQH/gD7g9HG5VsGaSmEt2TLlZhLt9K/9EPuaX5enRVwd8PFOp3Ljr15MtIBvv/Qk7r2ihRqnTLUBX8bhWQ2hqgHjYv05+n+NKVf2VJexoSbRThf/+p4snqAlVxLtrr4I33j4hZQW8Nd+8QIR1USZusPRjGkMeyJRRtUE8YkwqiZoFaypWPZkG0LBgV/fF7c288xf9+YcHlZIC9jSGJqRzipZg9/vo7GuKpEQZum8mWx4vYNrH9qU2voMR1ISzfT051/BNlcr2ZiRwCpZA8Qq2vjX97rqAF9Z3pay/6jR1XT2hlMmP3T2hrkrQ6ArZaJDllZyrQ3PMiOEq4EvEbkIuBU4EhDnpao62s37mOwyTbUF8k6/TT6vszfMj+Y2M3nsqMTkhNE1gcSSNPD+RINl81pyT3Toj2Sc6HDlaZOQcNRWvjUVz+3RBf8L+BdVtTW6iiBzWsNmqvw+rsmxKGKm8xa3NrNi7Ruc92R77pSF1bFhV0DG8bl11YGsyWc+u+x5SwhjKp7b3QVvDaWCFZHtIvKiiLSJyHqXyzRiZF6QsI13uvoHRPeTE2t39Q08b+GKNuZ9pCmRRPut93qG1LearU92f/fAMtnCiaYSuVLJishFTlfBehH5hYjMjW9zthfi46rarKqW8nCIskXyJzSEBmwLVQUSfauh6sznjaoJJhK9BHzCkrmD71vNtrRMeqvXRhyYSuVWd8G/JL3vAv456bMCv3LpPiaHbAsSHujpTznulKYG2vccTLQisyXWTj5mwYo2ls2bmbFvdVSOMa7JS8vEz1u1aSfnNx89oEyWEKYyNd3wyKCO3/6D8zwqSXG40pJV1StV9Urgnvj7pG33FnIJ4L9EZIOIzE/faQvYFSZbq3FUTWDAtp/8oT1x3h1PvDqglZp+TLz1m2zb2515Z4XFl5ZJXkhx7qkTOSIULLuEMPYcmqFwu9nwI9IWQcyyLd1HVXWXiBwJPC4if1HVp+M7VXUpsBSgpaUl94T6Eay7L5Kx1XjVaZMSIwA6e8P89Jm/JVYuAJhzwlEE/T5uuehEJjSEEsvIJB9zSlMDB3rDLFr9UiJYddsl0+npjwyofJNlm/4LuDIleDjZc2iGwpVKVkRmAx8BxorI15J2jQbyNk9UdZfzc4+I/BqYBTyd+yyTPlwr4BMumjmebz68OaUi9IkQclqcdVUB5p56DM9t60gcc9rUsfz0mb8xZ9o4INbt8OmWCSnHLJ7bzP1//lvKEK5vPryZZfPyd6HHp/9C6giETNuMqTRuPd1VQL1zvVFJ2/cDn851oojUAT5VPeC8/2fgey6Vq2JlG671f7e+ldKS/eGaV7j90ubEeZlalvEVDq5f+X7lvKS1mWXzZhJyUg/WBn0sebI9pQzxIVzGmOxcqWRV9Y/AH0XkZ6r6+iBPPwr4tcTGWgaA/6Oqv3ejXOUs3/pdmVdGaOOO1mbe7Xo/0DV5TB2dvWFEJOVaya3IAz39KSscxANdS+fNTCR6OZglqGbBKmNyc/tfx49FJL2v6j1gPXC3qvakn6Cq24CTXC5HWcvcSk0drJ9puNZRo6vxCSn9potbmxHg6vvXZ71WQYlenPy16WUq9WCVMcXmdiW7DRgLLHc+Xwq8BRwHLAMud/l+FSnr+l3zWkBIBLAWnDmFOdPGJboGDg8FB0x9XbgitsZWrrXAsg396uwNM6omCLibv9aYkcTtGV8zVPUzqvpb53UZMEtVv0z+EQbGkTU9YLU/kaClNuCnddbElKFRPmd9rvTzMk9GSF6tNksSl0BqK9Wt/LXGjCRut2THishEVX0DQEQmAmOcfX0u36tiZVu/6419XYltB/vCBa0Me0pTA3v297Dmqx9LtHi37T1AZ284ZT2t9JUKagN+AnmWBDcj12AnGIxkbv8r+jrwjIj8QUSeAv4EfNMZNXC/y/eqWJnX72rmjideTRwzqiaYsbWbvjLs4rnNhKr8iRbvmi27mXlMA/Mf2JCSkNvnk5SVCqyCNcYdrrZkVfVREZkK/AOxNId/SQp23eHmvSqZzyc0hIIpLcugT5g8pi7RIj3Q05+xtfvWez0pQ7hU4ZoHNyaOmzNtXIYWcGofrTHGPV78q5oJNDnXni4iqOoDHtynYkWjmmHxwWbmfaQpsWz3gjOnsLi1mYUr2lKOufmRrSndBa99/9yUFu+UI+ttORhjhpHbSbt/DhwLtAHxvHUKWCU7CNnGwCaPEojnZ73zspMZVROkfc9BaoN+3trfm3KtHR2pyV/a9xy08a7GDCO3/1W1AB9SVZvXfQiyjS4Yf0RtSgDrzqfaGVUT5NhvPQrAd8//0IDW7eGhIEvmNrNgeWzbmi27M7SAbbyrMV5xu5LdAnwA2O3ydUeUTKMLFpw5hY7OvgEJWv7+XnfimN9veYtLWyYMGCXg80nqNNqAz8a7GjNM3K5kxwAvi8haIPG9VVXPd/k+FS0U9HPXZSfzTlc/ExpC7OjooqGuii/+fMOABC23/+tJBHySmN0V8PkIaxQAEcHnk4wJWuqdHLDWRWCMt9z+F7bI5euNWH2RKDf+6sVEqzXbGltHHVbDqzefk2i1vtOdHjCztbOMKSa3h3D9UUSOAaaq6hMiEqKAVIcjTWHJX1KHWe3v7h8wjXbNlt2Jqa+jaoIc7A1nno5rw7OMKRq3RxdcDcwHGoiNMjgauAs4K895fmJJZN5U1U+6WaZSM9TkL/VVAVpnTUwJWKWvsZV1Oq4NzzKmaNye1vNl4KPE8siiqn8FjizgvIXAiFhGPPOKsqkrtcYDX8mSp9Emryjbnee8+PAsY0xxuF3J9qpqIkeBiASIjZPNSkTGA+cB97hclpJUSGuzNuAbkLBldG3mabSZ0hGW29pZxlQytzvq/igi3wJqReSfgC8Bv81zzh3A/yR1RYWKlSmt4IIzp6QkbEFhxdo3UqbHvvVeT9Z0hMmJXiwdoTGlxe2W7A3AXuBF4IvAo6p6U7aDReSTwB5V3ZDropW0Smj6irJf+8RUWmdNTEnYEqr2s+TJdubc8TTHfutR5tzxNLf+/i8ZV5QNVflTEr0Alo7QI5X0HJrh4/bogiix5NzL4ttE5BeqemmWUz4KnC8i5wI1wGgRedDJQ5t83YpZJbS7P5qyomx8ZdjkVuuBnoGt3bf29xJVBqxEO+8jTSl9uzaSwDuV9Bya4TMc+exmZ9uhqjeq6nhVbQJagSfTK9hKEwr6mXvqMYnUg/XVAS6cMT4l+XZ9dWprd/bkRn54yUnUBHwpx7XOmsiqTW8mrm0jCYwpPdbkGWbpy7h09oYHLGK4o6Ob1W1vprRaV27YwRdOn5wyZfaZv+5l0W9fTlzbEr0YU3pc+dcoItmWlhEgWMg1VPUp4Ck3ylNOBBmwZMwdT7zKTef9YyKpS3wsbU1SEKuuKkBLUyOzJzdaohdjSphbTZ7/N8e+v7h0j4qQaTLCbZdMJ6qw+oVYHti39vdSVx3IOUrAFjY0pjy4Usmq6sfduM5IkClX7Dcf3swtF53Ioy/ufr/VGni/wox//c80HTc98YsxprR4/i9TRD6gqn/3+j7lIttkhImNIV69+ZysLdJCpuMaY0rPcIwuuHcY7lE2ck19zTW2tZDpuMaY0uN5Jauq53l9j3JS4x84ZXZxazM1/tz/Kyz5izHlya3RBQ259qtqR679I0l3ODJgyuyKtW9w5WmTGJVjGe5MqyXYkC1jSp9b/zo3EEsEk6lzUIHJLt2n7NVVB1jyZHtiIUSAgE+47qypOc+LJ39J75O1IVvGlDa3RhdMcuM6I0Fnbzhn8u1sbMiWMeXJ9e+ZInIEMJVYLgIAVPVpt+9TrmoD/szJtwP5W6SZ1uoyptI03fBIwcdu/0Hph3xcDXyJyBeAp4E1wHedn4vcvEe56w5HMiffDtsoAWMqkdujCxYCpwCvOxMUZhBLfWgcddWBvMm3jTGVw+1KtkdVewBEpFpV/wIc7/I9ylo8aXeyePJtY0zlcbuS3SkihwOrgMdF5DfALpfvUdZCQX/GcbI2SsCYyuR20u5POW8XicgfgMOAx3KdIyI1xPpxq53y/FJVv+NmuUqJ3++jsa4qJWVhKOjHn2cygjGmPLkd+Pp5/L2q/lFVVwP35TmtFzhTVU8CmoGzReTDbpar1Pj9PkbVBPGJMKomiIhwsDdMVDX2M2pJ942pFG5HW05I/iAifmBmrhNUVYGDzseg8xoxtYwlfjGmsrnSkhWRG0XkADBdRPaLyAHn8x7gNwWc7xeRNuf4x1X1eTfKVQ4s8Ysxlc2VSlZVb1HVUcBtqjpaVUc5r0ZVvbGA8yOq2gyMB2aJyLTk/ZW8SqglfikflfwcGu+4HW25SUQuE5H/B0BEJojIrEJPVtV3iS1Bc3ba9qWq2qKqLWPHjnW1wMWWK/WhKS2V/Bwa77hdyf6E2Oq0n3E+H3S2ZSUiY51hX4hILfAJRtCSNfHEL8lDuizxizGVw+3A16mqerKIbAJQ1XdEpCrPOeOA+50gmQ/4T1X9ncvlKlmW+MWYyuZ2JdvvVJYKsVYqEM11gqpuJjb9dsSyxC/GVC63uwuWAL8GjhSRm4FngO+7fA9jjCkbbs/4ekhENgBnEUvgfaGqbnXzHsYYU07cWn6mBrgGmAK8CNytqpbxxJgCVVoOVfM+t7oL7gdaiFWw5wA/dOm6xhhT1tzqLviQqp4IICL3Amtduq4xxpQ1t1qy/fE31k1gjDHvc6sle5KI7HfeC1DrfBZiOWBGu3QfY4wpK26tVmvTk4wxJgPLFG2MMR6y6UXGmLJVDkPfrCVrjDEeskrWGGM8ZJWsMcZ4qOiVrJPY+w8islVEXhKRhcUukzHGuKUUAl9h4OuqulFERgEbRORxVX252AUzxphDVfSWrKruVtWNzvsDwFbgaLeuH41q3uW2CznGGGOGohRasgki0kQsgffzadvnA/MBJk6cWPD1Cllu25bkNoUa6nNoRrait2TjRKQeWAl8VVX3J+8b6gJ2hSy3bUtym0LZQopmKEqikhWRILEK9iFV/ZVb1y1kuW1bktsY46WidxeIiAD3AltV9XY3rx1fbvu5bfsS2+LLbcfX0irkGGNGgsHMnjKFK4WW7EeBy4EzRaTNeZ3rxoVjy203py233YxPSAS5agO+vMdYIMwYM1RFb6qp6jPEUiJ6osrv45aLTmRCQ4gdHV0E/T7ueXobS55sd4JczdQG/Ylj9uzvIej38fmfrbdAmDHmkJVCS9YzXf0R9nX20lhfhQg01lfR0dnLnGnjkoJcbXT2RegNx1Yur63y88Cz2wcEwnrCERvmZYwZtKK3ZL1U4/dRXx1k/gMbEq3Sxa3NNISqEsccNboan8Ci1S8ljrn14um07+1k9Qu7Esd09oZZsLzNWrfGmEGpqEo2EonS1R+hrjpAZ29sFZwVa99g0fknMOXIetr3HGTF2je48rRJvPb9c2nfc5DRNQGWP596zKpNO/nyx6ckKtmvfuI4FixvSwTH4q3bZVe0WHDMGJNTxdQQkUiUfZ19LFzxfmvzwS/M4sIZ47l+5eaUVmqoys/x334s5zEfPLyGgE84pamBiY0hG+ZljBmSiqlku/ojA1qtXX0RVm3aOaCVeuVpk3jlP86hfc9BevqjXL9yc0or9fqVm1k6byav3nwOXX0RunptmJcxZmgqJvAVqvJz4YzxLFr9Esd/+zEWrX4p47YLZ4wnFPQnPnf1hTlqdHXKtdZt76CuOoBPhPrqAKEqP0vmzkgb5jWDUNBassaY3CqmGdbVFxnQIs207fqVm7n78pkpowtuuehEVrXtSlzrlKYGOnvDjKoJAuDzCY11VSy7ooVQlZ+uvgihoN+CXsaUkWItVVMxLdm66sCAftNM29Zt76C+JpDyeWJjKKWVuri1mdpAaivV54u1auOtW6tgjTGFqJiWbGdveEC/aaZtpzQ1sL+7P+VzZ2+YOy87mdG1QfZ39/Pca29z+nFHUh+omL9BxpgiqZhKNhT0s7i1OWV0QW2GbYtbm3nutbcTIwfiow2O/dZjiWsFfMKrN59TxP8aY0ylqJhK1u/30VhXxdJ5MxPjZLv7I2x4vSOllfq3tw8y+9gxidEFqzbtZN5HmlKuZSMHjDFuqahaRESIJfWKvQ8KzDymgWsf3JjSkn3g2e2J3AWLW5sTfbHJs7ls5IAxxg1Fr2RF5D7gk8AeVZ021OtkWuHgoatP5eW/7ePuy2dSXxPgYE+Yd7t6ufK0SVx31lQ6e8OEgn5ExEYOGGM8UfRKFvgZ8GPggUO5SPIKBxAbrrW/u5+7//g3ntu2PnHc7MmNLJ03E59IYogWkOgasC4CU0ksR2zxFT18rqpPAx15D8wj0woHbTveYXFrc97hWcYY45WKabZlWuGgqbE+Y4KYq06fbMOzjDHDoiwq2UJWCY2tgjAjpU92YmOIJU+2c/sTf00cF/AJ1501dVjKbSqLrVZrhqIsKllVXQosBWhpacmYLTvT1FdL7GLcVMhzaEy6ivrOnD711RK7GGOKrejNORFZDpwBjBGRncB3VPVeN65tiV2MMcVW9EpWVed6ef146xZseJYxZvhVVHeBMcaUGqtkjTHGQ6JaXkFSEdkLvJ60aQzwdpGKc6jKtezFLvfbqnp2Ee+f6Tn0UrF/39mM9HIV9ByWXSWbTkTWq2pLscsxFOVa9nItd7kq1d+3lasw1l1gjDEeskrWGGM8VAmV7NJiF+AQlGvZy7Xc5apUf99WrgKUfZ+sMcaUskpoyRpjTMkq60pWRM4WkVdEpF1Ebih2eQolIveJyB4R2VLssgyGiEwQkT+IyFYReUlEFha7TJWs1H/fIuIXkU0i8rtilyVORA4XkV+KyF+c39vsopepXLsLRMQPvAr8E7ATWAfMVdWXi1qwAojIx4CDwAOHsuTOcBORccA4Vd0oIqOADcCF5fA7L0el/vsWka8BLcBoVf1kscsDICL3A39S1XtEpAoIqeq7xSxTObdkZwHtqrpNVfuAFcAFRS5TQdxaDWK4qepuVd3ovD8AbAWOLm6pKlcp/75FZDxwHnBPscsSJyKjgY8B9wKoal+xK1go70r2aGBH0uedlMgDOBKISBMwA3i+uCUZGUrw930H8D+BaLELkmQysBf4qdONcY+I1BW7UOVcyWbKV1iefR9lRkTqgZXAV1V1f7HLU+lK7fctIvHVpTcUuyxpAsDJwJ2qOgPoBIoeqynnSnYnMCHp83hgV5HKMmKISJDYP/iHVPVXxS5PpSvR3/dHgfNFZDuxbrozReTB4hYJiNUJO1U13tr/JbFKt6jKuZJdB0wVkUlOB3crsLrIZapoIiLE+ru2qurtxS5PpSvV37eq3qiq41W1idi/uydV9bIiFwtV/TuwQ0SOdzadBRQ9SFi2layqhoHrgDXEAgL/qaovFbdUhXFWg3gOOF5EdorI54tdpgJ9FLicWMulzXmdW+xCVTD7fQ/eV4CHRGQz0Ax8v8jlKd8hXMYYUw7KtiVrjDHlwCpZY4zxkFWyxhjjIatkjTHGQ1bJGmOMh6ySdYGIRJzhNVtE5GERCeU5/lsFXne7iIxJ23aBiKxK+nyjiLQnff4XEVntvH9URA7PcN1FIvIN5/3nROSDue5pStdwPnvO9qdExLP1s0TkDBH5SNLnn4nIp72633CwStYd3ara7GTU6gOuyXN8QQ96Fs8CyenbZgP7ReRI5/NHgD8DqOq5BSTI+BzwwTzHmNI1nM/ecDiD2DNcMaySdd+fgCkAInKZiKx1Whp3O/k3fwDUOtseco5bJSIbnJyh83NdXFX3Au+JyBRn09HEpl3GH8yPEKuIU1ojInKTk3v3CeB4Z9uniaWqe8gpT61zja+IyEYReVFE/sGdX4sZBp4+e9k4175NRNaJyGYR+aKz/Qyn5RvP7/qQM4sNETnX2faMiCwRkd85SXCuAf6HU8bTnVt8TESeFZFtZdmqVVV7HeILOOj8DAC/Aa4F/hH4LRB09v1/wLzk45POb3B+1gJbgEbn83ZgTIb7/QyYR6yyXEFs+uD/cu7/DlCTfD4wE3gRCAGjgXbgG84xTwEtSdfeDnzFef8l4J5i/37tVVLPXsrz4mybD3zbeV8NrAcmEWuVvkcsr4iP2CzH04AaYhn0JjnnLAd+57xfFH82k571h53zP0QsvWnRf++DeQUwbqgVkTbn/Z+IzTefT6xyW+f88a4F9mQ5f4GIfMp5PwGYCuzLcb8/E2ux+ok9uGuBfyOWCu8VVe1JO/504Neq2gUQ77PNIZ6IZANwUZ5jTXEN97OXyT8D05NamYc51+kD1qrqTgCnnE3EEtZvU9W/Occvd8qczSpVjQIvi8hRgyxb0Vkl645uVW1O3uB8LbpfVW/MdaKInAF8Apitql0i8hSxv/S5PEtsjrYfWKaqB0SkhljL4c9ZzhnM/Ole52cEe0ZK3XA/exkvRezbz5oM1+9N2hR/njKlKc0l+RqDPbforE/WO/8X+HQ8ICUiDSJyjLOvX2Ip7CD2V/8d5yH/B+DDBVz7ZWLBqtOBTc62NmL9Wc9mOP5p4FMiUiuxZUz+JWnfAWDUIP67TOnz8tnLZA1wbfy6InKc5E6W/RdgstMHC3Bp0r6Kex6tkvWIxtZh+jbwXxLLCPQ4MM7ZvRTY7AQffg8EnGP+HfjvTNdzhmN90Lm2EsuQ/7aq9juHPEcsM/yASlZjS5j8glhFvJLY18q4nwF3pQW+TBnz8tlzPCKx7HE7ReRhYkvQvAxslNjioHeT4xuQqnYT6+//vYg8A7xFrO8WYn3Jn0oLfJU1y8JljBl2IlKvqgedro2fAH9V1f9d7HJ5wVqyxphiuNoJhL1ErNvi7iKXxzPWkjXGGA9ZS9YYYzxklawxxnjIKlljjPGQVbLGGOMhq2SNMcZDVskaY4yH/n+RxYEFiMDzuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b531a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, vars = ['Petal.Width', 'Petal.Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b86d748>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b8d25f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot('Petal.Width', 'Sepal.Length', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a1b8de7f0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b8fc908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, vars = ['Petal.Width', 'Sepal.Length', 'Sepal.Width'], kind = 'reg', hue = 'Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/statsmodels/statsmodels/\n",
    "from statsmodels.regression.linear_model import OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Petal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2.3</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1.8</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2.1</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.8</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1.8</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2.1</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.6</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1.9</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2.2</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1.5</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2.3</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.8</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1.8</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2.1</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2.3</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1.9</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2.3</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2.3</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2.3</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1.8</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Petal.Width  Petal.Length\n",
       "1            0.2           1.4\n",
       "2            0.2           1.4\n",
       "3            0.2           1.3\n",
       "4            0.2           1.5\n",
       "5            0.2           1.4\n",
       "6            0.4           1.7\n",
       "7            0.3           1.4\n",
       "8            0.2           1.5\n",
       "9            0.2           1.4\n",
       "10           0.1           1.5\n",
       "11           0.2           1.5\n",
       "12           0.2           1.6\n",
       "13           0.1           1.4\n",
       "14           0.1           1.1\n",
       "15           0.2           1.2\n",
       "16           0.4           1.5\n",
       "17           0.4           1.3\n",
       "18           0.3           1.4\n",
       "19           0.3           1.7\n",
       "20           0.3           1.5\n",
       "21           0.2           1.7\n",
       "22           0.4           1.5\n",
       "23           0.2           1.0\n",
       "24           0.5           1.7\n",
       "25           0.2           1.9\n",
       "26           0.2           1.6\n",
       "27           0.4           1.6\n",
       "28           0.2           1.5\n",
       "29           0.2           1.4\n",
       "30           0.2           1.6\n",
       "..           ...           ...\n",
       "121          2.3           5.7\n",
       "122          2.0           4.9\n",
       "123          2.0           6.7\n",
       "124          1.8           4.9\n",
       "125          2.1           5.7\n",
       "126          1.8           6.0\n",
       "127          1.8           4.8\n",
       "128          1.8           4.9\n",
       "129          2.1           5.6\n",
       "130          1.6           5.8\n",
       "131          1.9           6.1\n",
       "132          2.0           6.4\n",
       "133          2.2           5.6\n",
       "134          1.5           5.1\n",
       "135          1.4           5.6\n",
       "136          2.3           6.1\n",
       "137          2.4           5.6\n",
       "138          1.8           5.5\n",
       "139          1.8           4.8\n",
       "140          2.1           5.4\n",
       "141          2.4           5.6\n",
       "142          2.3           5.1\n",
       "143          1.9           5.1\n",
       "144          2.3           5.9\n",
       "145          2.5           5.7\n",
       "146          2.3           5.2\n",
       "147          1.9           5.0\n",
       "148          2.0           5.2\n",
       "149          2.3           5.4\n",
       "150          1.8           5.1\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Petal.Width', 'Petal.Length']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressionModel = OLS(df['Petal.Width'], df[['Sepal.Length', 'Sepal.Width']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelFit = regressionModel.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Petal.Width</td>   <th>  R-squared:         </th> <td>   0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.915</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   804.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 22 May 2018</td> <th>  Prob (F-statistic):</th> <td>3.12e-80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:47:58</td>     <th>  Log-Likelihood:    </th> <td> -79.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   150</td>      <th>  AIC:               </th> <td>   163.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   148</td>      <th>  BIC:               </th> <td>   169.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sepal.Length</th> <td>    0.5900</td> <td>    0.028</td> <td>   21.434</td> <td> 0.000</td> <td>    0.536</td> <td>    0.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sepal.Width</th>  <td>   -0.7308</td> <td>    0.053</td> <td>  -13.894</td> <td> 0.000</td> <td>   -0.835</td> <td>   -0.627</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.679</td> <th>  Durbin-Watson:     </th> <td>   1.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  14.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.762</td> <th>  Prob(JB):          </th> <td>0.000586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.240</td> <th>  Cond. No.          </th> <td>    11.6</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Petal.Width   R-squared:                       0.916\n",
       "Model:                            OLS   Adj. R-squared:                  0.915\n",
       "Method:                 Least Squares   F-statistic:                     804.3\n",
       "Date:                Tue, 22 May 2018   Prob (F-statistic):           3.12e-80\n",
       "Time:                        16:47:58   Log-Likelihood:                -79.864\n",
       "No. Observations:                 150   AIC:                             163.7\n",
       "Df Residuals:                     148   BIC:                             169.7\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Sepal.Length     0.5900      0.028     21.434      0.000       0.536       0.644\n",
       "Sepal.Width     -0.7308      0.053    -13.894      0.000      -0.835      -0.627\n",
       "==============================================================================\n",
       "Omnibus:                       13.679   Durbin-Watson:                   1.279\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.885\n",
       "Skew:                           0.762   Prob(JB):                     0.000586\n",
       "Kurtosis:                       3.240   Cond. No.                         11.6\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelFit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SL</th>\n",
       "      <th>SW</th>\n",
       "      <th>PL</th>\n",
       "      <th>PW</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>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>121</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SL   SW   PL   PW    Species\n",
       "1    5.1  3.5  1.4  0.2     setosa\n",
       "2    4.9  3.0  1.4  0.2     setosa\n",
       "3    4.7  3.2  1.3  0.2     setosa\n",
       "4    4.6  3.1  1.5  0.2     setosa\n",
       "5    5.0  3.6  1.4  0.2     setosa\n",
       "6    5.4  3.9  1.7  0.4     setosa\n",
       "7    4.6  3.4  1.4  0.3     setosa\n",
       "8    5.0  3.4  1.5  0.2     setosa\n",
       "9    4.4  2.9  1.4  0.2     setosa\n",
       "10   4.9  3.1  1.5  0.1     setosa\n",
       "11   5.4  3.7  1.5  0.2     setosa\n",
       "12   4.8  3.4  1.6  0.2     setosa\n",
       "13   4.8  3.0  1.4  0.1     setosa\n",
       "14   4.3  3.0  1.1  0.1     setosa\n",
       "15   5.8  4.0  1.2  0.2     setosa\n",
       "16   5.7  4.4  1.5  0.4     setosa\n",
       "17   5.4  3.9  1.3  0.4     setosa\n",
       "18   5.1  3.5  1.4  0.3     setosa\n",
       "19   5.7  3.8  1.7  0.3     setosa\n",
       "20   5.1  3.8  1.5  0.3     setosa\n",
       "21   5.4  3.4  1.7  0.2     setosa\n",
       "22   5.1  3.7  1.5  0.4     setosa\n",
       "23   4.6  3.6  1.0  0.2     setosa\n",
       "24   5.1  3.3  1.7  0.5     setosa\n",
       "25   4.8  3.4  1.9  0.2     setosa\n",
       "26   5.0  3.0  1.6  0.2     setosa\n",
       "27   5.0  3.4  1.6  0.4     setosa\n",
       "28   5.2  3.5  1.5  0.2     setosa\n",
       "29   5.2  3.4  1.4  0.2     setosa\n",
       "30   4.7  3.2  1.6  0.2     setosa\n",
       "..   ...  ...  ...  ...        ...\n",
       "121  6.9  3.2  5.7  2.3  virginica\n",
       "122  5.6  2.8  4.9  2.0  virginica\n",
       "123  7.7  2.8  6.7  2.0  virginica\n",
       "124  6.3  2.7  4.9  1.8  virginica\n",
       "125  6.7  3.3  5.7  2.1  virginica\n",
       "126  7.2  3.2  6.0  1.8  virginica\n",
       "127  6.2  2.8  4.8  1.8  virginica\n",
       "128  6.1  3.0  4.9  1.8  virginica\n",
       "129  6.4  2.8  5.6  2.1  virginica\n",
       "130  7.2  3.0  5.8  1.6  virginica\n",
       "131  7.4  2.8  6.1  1.9  virginica\n",
       "132  7.9  3.8  6.4  2.0  virginica\n",
       "133  6.4  2.8  5.6  2.2  virginica\n",
       "134  6.3  2.8  5.1  1.5  virginica\n",
       "135  6.1  2.6  5.6  1.4  virginica\n",
       "136  7.7  3.0  6.1  2.3  virginica\n",
       "137  6.3  3.4  5.6  2.4  virginica\n",
       "138  6.4  3.1  5.5  1.8  virginica\n",
       "139  6.0  3.0  4.8  1.8  virginica\n",
       "140  6.9  3.1  5.4  2.1  virginica\n",
       "141  6.7  3.1  5.6  2.4  virginica\n",
       "142  6.9  3.1  5.1  2.3  virginica\n",
       "143  5.8  2.7  5.1  1.9  virginica\n",
       "144  6.8  3.2  5.9  2.3  virginica\n",
       "145  6.7  3.3  5.7  2.5  virginica\n",
       "146  6.7  3.0  5.2  2.3  virginica\n",
       "147  6.3  2.5  5.0  1.9  virginica\n",
       "148  6.5  3.0  5.2  2.0  virginica\n",
       "149  6.2  3.4  5.4  2.3  virginica\n",
       "150  5.9  3.0  5.1  1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "df.columns = ['SL', 'SW', 'PL', 'PW', 'Species']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>SL</td>        <th>  R-squared:         </th> <td>   0.808</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   204.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 22 May 2018</td> <th>  Prob (F-statistic):</th> <td>4.51e-52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:50:41</td>     <th>  Log-Likelihood:    </th> <td> -60.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   150</td>      <th>  AIC:               </th> <td>   128.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   146</td>      <th>  BIC:               </th> <td>   140.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    4.5772</td> <td>    0.112</td> <td>   40.885</td> <td> 0.000</td> <td>    4.356</td> <td>    4.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PW</th>        <td>   -1.2393</td> <td>    0.219</td> <td>   -5.649</td> <td> 0.000</td> <td>   -1.673</td> <td>   -0.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PL</th>        <td>    0.4417</td> <td>    0.066</td> <td>    6.742</td> <td> 0.000</td> <td>    0.312</td> <td>    0.571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PW:PL</th>     <td>    0.1886</td> <td>    0.034</td> <td>    5.617</td> <td> 0.000</td> <td>    0.122</td> <td>    0.255</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.104</td> <th>  Durbin-Watson:     </th> <td>   1.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.576</td> <th>  Jarque-Bera (JB):  </th> <td>   1.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.174</td> <th>  Prob(JB):          </th> <td>   0.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.732</td> <th>  Cond. No.          </th> <td>    64.3</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     SL   R-squared:                       0.808\n",
       "Model:                            OLS   Adj. R-squared:                  0.804\n",
       "Method:                 Least Squares   F-statistic:                     204.5\n",
       "Date:                Tue, 22 May 2018   Prob (F-statistic):           4.51e-52\n",
       "Time:                        16:50:41   Log-Likelihood:                -60.348\n",
       "No. Observations:                 150   AIC:                             128.7\n",
       "Df Residuals:                     146   BIC:                             140.7\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      4.5772      0.112     40.885      0.000       4.356       4.798\n",
       "PW            -1.2393      0.219     -5.649      0.000      -1.673      -0.806\n",
       "PL             0.4417      0.066      6.742      0.000       0.312       0.571\n",
       "PW:PL          0.1886      0.034      5.617      0.000       0.122       0.255\n",
       "==============================================================================\n",
       "Omnibus:                        1.104   Durbin-Watson:                   1.831\n",
       "Prob(Omnibus):                  0.576   Jarque-Bera (JB):                1.209\n",
       "Skew:                           0.174   Prob(JB):                        0.546\n",
       "Kurtosis:                       2.732   Cond. No.                         64.3\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formulaFit = smf.ols('SL ~ PW*PL', df).fit()\n",
    "formulaFit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-36.463283934449116, pvalue=7.027919392009369e-112)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Testing\n",
    "\n",
    "## Feed in any two series and a test statistic and p value will be generated\n",
    "from scipy.stats import ttest_ind\n",
    "ttest_ind(df.SW, df.SL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "response = requests.get(\"https://www.google.com/finance/getprices?q=GOOGL&i=86400&p=1Y&f=d,c,v,o,h,l\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('sample1234.csv', 'w') as f:\n",
    "    f.write(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if data is None:\n",
    "    print(\"none\")"
   ]
  }
 ],
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