Univariate, Bivariate and multivariate analysis using IRIS data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
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       "      <td>3</td>\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",
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       "      <td>4</td>\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",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>setosa</td>\n",
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       "      <td>5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\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",
       "      <td>7</td>\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",
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       "    <tr>\n",
       "      <td>8</td>\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",
       "      <td>9</td>\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",
       "      <td>10</td>\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",
       "      <td>11</td>\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",
       "      <td>12</td>\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",
       "      <td>13</td>\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",
       "      <td>14</td>\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",
       "      <td>15</td>\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",
       "      <td>16</td>\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",
       "      <td>17</td>\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",
       "      <td>18</td>\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",
       "      <td>19</td>\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",
       "      <td>20</td>\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",
       "      <td>21</td>\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",
       "      <td>22</td>\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",
       "      <td>23</td>\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",
       "      <td>24</td>\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",
       "      <td>25</td>\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",
       "      <td>26</td>\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",
       "      <td>27</td>\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",
       "      <td>28</td>\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",
       "      <td>29</td>\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",
       "      <td>30</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\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",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\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",
       "      <td>38</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>5.1</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",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>4.4</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",
       "      <td>43</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>5.3</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",
       "      <td>49</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width species\n",
       "0            5.1          3.5           1.4          0.2  setosa\n",
       "1            4.9          3.0           1.4          0.2  setosa\n",
       "2            4.7          3.2           1.3          0.2  setosa\n",
       "3            4.6          3.1           1.5          0.2  setosa\n",
       "4            5.0          3.6           1.4          0.2  setosa\n",
       "5            5.4          3.9           1.7          0.4  setosa\n",
       "6            4.6          3.4           1.4          0.3  setosa\n",
       "7            5.0          3.4           1.5          0.2  setosa\n",
       "8            4.4          2.9           1.4          0.2  setosa\n",
       "9            4.9          3.1           1.5          0.1  setosa\n",
       "10           5.4          3.7           1.5          0.2  setosa\n",
       "11           4.8          3.4           1.6          0.2  setosa\n",
       "12           4.8          3.0           1.4          0.1  setosa\n",
       "13           4.3          3.0           1.1          0.1  setosa\n",
       "14           5.8          4.0           1.2          0.2  setosa\n",
       "15           5.7          4.4           1.5          0.4  setosa\n",
       "16           5.4          3.9           1.3          0.4  setosa\n",
       "17           5.1          3.5           1.4          0.3  setosa\n",
       "18           5.7          3.8           1.7          0.3  setosa\n",
       "19           5.1          3.8           1.5          0.3  setosa\n",
       "20           5.4          3.4           1.7          0.2  setosa\n",
       "21           5.1          3.7           1.5          0.4  setosa\n",
       "22           4.6          3.6           1.0          0.2  setosa\n",
       "23           5.1          3.3           1.7          0.5  setosa\n",
       "24           4.8          3.4           1.9          0.2  setosa\n",
       "25           5.0          3.0           1.6          0.2  setosa\n",
       "26           5.0          3.4           1.6          0.4  setosa\n",
       "27           5.2          3.5           1.5          0.2  setosa\n",
       "28           5.2          3.4           1.4          0.2  setosa\n",
       "29           4.7          3.2           1.6          0.2  setosa\n",
       "30           4.8          3.1           1.6          0.2  setosa\n",
       "31           5.4          3.4           1.5          0.4  setosa\n",
       "32           5.2          4.1           1.5          0.1  setosa\n",
       "33           5.5          4.2           1.4          0.2  setosa\n",
       "34           4.9          3.1           1.5          0.1  setosa\n",
       "35           5.0          3.2           1.2          0.2  setosa\n",
       "36           5.5          3.5           1.3          0.2  setosa\n",
       "37           4.9          3.1           1.5          0.1  setosa\n",
       "38           4.4          3.0           1.3          0.2  setosa\n",
       "39           5.1          3.4           1.5          0.2  setosa\n",
       "40           5.0          3.5           1.3          0.3  setosa\n",
       "41           4.5          2.3           1.3          0.3  setosa\n",
       "42           4.4          3.2           1.3          0.2  setosa\n",
       "43           5.0          3.5           1.6          0.6  setosa\n",
       "44           5.1          3.8           1.9          0.4  setosa\n",
       "45           4.8          3.0           1.4          0.3  setosa\n",
       "46           5.1          3.8           1.6          0.2  setosa\n",
       "47           4.6          3.2           1.4          0.2  setosa\n",
       "48           5.3          3.7           1.5          0.2  setosa\n",
       "49           5.0          3.3           1.4          0.2  setosa"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_setosa = df.loc[df['species'] == 'setosa']\n",
    "df_setosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</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",
       "      <td>50</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width     species\n",
       "50           7.0          3.2           4.7          1.4  versicolor\n",
       "51           6.4          3.2           4.5          1.5  versicolor\n",
       "52           6.9          3.1           4.9          1.5  versicolor\n",
       "53           5.5          2.3           4.0          1.3  versicolor\n",
       "54           6.5          2.8           4.6          1.5  versicolor\n",
       "55           5.7          2.8           4.5          1.3  versicolor\n",
       "56           6.3          3.3           4.7          1.6  versicolor\n",
       "57           4.9          2.4           3.3          1.0  versicolor\n",
       "58           6.6          2.9           4.6          1.3  versicolor\n",
       "59           5.2          2.7           3.9          1.4  versicolor\n",
       "60           5.0          2.0           3.5          1.0  versicolor\n",
       "61           5.9          3.0           4.2          1.5  versicolor\n",
       "62           6.0          2.2           4.0          1.0  versicolor\n",
       "63           6.1          2.9           4.7          1.4  versicolor\n",
       "64           5.6          2.9           3.6          1.3  versicolor\n",
       "65           6.7          3.1           4.4          1.4  versicolor\n",
       "66           5.6          3.0           4.5          1.5  versicolor\n",
       "67           5.8          2.7           4.1          1.0  versicolor\n",
       "68           6.2          2.2           4.5          1.5  versicolor\n",
       "69           5.6          2.5           3.9          1.1  versicolor\n",
       "70           5.9          3.2           4.8          1.8  versicolor\n",
       "71           6.1          2.8           4.0          1.3  versicolor\n",
       "72           6.3          2.5           4.9          1.5  versicolor\n",
       "73           6.1          2.8           4.7          1.2  versicolor\n",
       "74           6.4          2.9           4.3          1.3  versicolor\n",
       "75           6.6          3.0           4.4          1.4  versicolor\n",
       "76           6.8          2.8           4.8          1.4  versicolor\n",
       "77           6.7          3.0           5.0          1.7  versicolor\n",
       "78           6.0          2.9           4.5          1.5  versicolor\n",
       "79           5.7          2.6           3.5          1.0  versicolor\n",
       "80           5.5          2.4           3.8          1.1  versicolor\n",
       "81           5.5          2.4           3.7          1.0  versicolor\n",
       "82           5.8          2.7           3.9          1.2  versicolor\n",
       "83           6.0          2.7           5.1          1.6  versicolor\n",
       "84           5.4          3.0           4.5          1.5  versicolor\n",
       "85           6.0          3.4           4.5          1.6  versicolor\n",
       "86           6.7          3.1           4.7          1.5  versicolor\n",
       "87           6.3          2.3           4.4          1.3  versicolor\n",
       "88           5.6          3.0           4.1          1.3  versicolor\n",
       "89           5.5          2.5           4.0          1.3  versicolor\n",
       "90           5.5          2.6           4.4          1.2  versicolor\n",
       "91           6.1          3.0           4.6          1.4  versicolor\n",
       "92           5.8          2.6           4.0          1.2  versicolor\n",
       "93           5.0          2.3           3.3          1.0  versicolor\n",
       "94           5.6          2.7           4.2          1.3  versicolor\n",
       "95           5.7          3.0           4.2          1.2  versicolor\n",
       "96           5.7          2.9           4.2          1.3  versicolor\n",
       "97           6.2          2.9           4.3          1.3  versicolor\n",
       "98           5.1          2.5           3.0          1.1  versicolor\n",
       "99           5.7          2.8           4.1          1.3  versicolor"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_versicolor = df.loc[df['species'] == 'versicolor']\n",
    "df_versicolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "      <td>100</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\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",
       "      <td>102</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\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",
       "      <td>121</td>\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",
       "      <td>122</td>\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",
       "      <td>123</td>\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",
       "      <td>124</td>\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",
       "      <td>125</td>\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",
       "      <td>126</td>\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",
       "      <td>127</td>\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",
       "      <td>128</td>\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",
       "      <td>129</td>\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",
       "      <td>130</td>\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",
       "      <td>131</td>\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",
       "      <td>132</td>\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",
       "      <td>133</td>\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",
       "      <td>134</td>\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",
       "      <td>135</td>\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",
       "      <td>136</td>\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",
       "      <td>137</td>\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",
       "      <td>138</td>\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",
       "      <td>139</td>\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",
       "      <td>140</td>\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",
       "      <td>141</td>\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",
       "      <td>142</td>\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",
       "      <td>143</td>\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",
       "      <td>144</td>\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",
       "      <td>145</td>\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",
       "      <td>146</td>\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",
       "      <td>147</td>\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",
       "      <td>148</td>\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",
       "      <td>149</td>\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",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "100           6.3          3.3           6.0          2.5  virginica\n",
       "101           5.8          2.7           5.1          1.9  virginica\n",
       "102           7.1          3.0           5.9          2.1  virginica\n",
       "103           6.3          2.9           5.6          1.8  virginica\n",
       "104           6.5          3.0           5.8          2.2  virginica\n",
       "105           7.6          3.0           6.6          2.1  virginica\n",
       "106           4.9          2.5           4.5          1.7  virginica\n",
       "107           7.3          2.9           6.3          1.8  virginica\n",
       "108           6.7          2.5           5.8          1.8  virginica\n",
       "109           7.2          3.6           6.1          2.5  virginica\n",
       "110           6.5          3.2           5.1          2.0  virginica\n",
       "111           6.4          2.7           5.3          1.9  virginica\n",
       "112           6.8          3.0           5.5          2.1  virginica\n",
       "113           5.7          2.5           5.0          2.0  virginica\n",
       "114           5.8          2.8           5.1          2.4  virginica\n",
       "115           6.4          3.2           5.3          2.3  virginica\n",
       "116           6.5          3.0           5.5          1.8  virginica\n",
       "117           7.7          3.8           6.7          2.2  virginica\n",
       "118           7.7          2.6           6.9          2.3  virginica\n",
       "119           6.0          2.2           5.0          1.5  virginica\n",
       "120           6.9          3.2           5.7          2.3  virginica\n",
       "121           5.6          2.8           4.9          2.0  virginica\n",
       "122           7.7          2.8           6.7          2.0  virginica\n",
       "123           6.3          2.7           4.9          1.8  virginica\n",
       "124           6.7          3.3           5.7          2.1  virginica\n",
       "125           7.2          3.2           6.0          1.8  virginica\n",
       "126           6.2          2.8           4.8          1.8  virginica\n",
       "127           6.1          3.0           4.9          1.8  virginica\n",
       "128           6.4          2.8           5.6          2.1  virginica\n",
       "129           7.2          3.0           5.8          1.6  virginica\n",
       "130           7.4          2.8           6.1          1.9  virginica\n",
       "131           7.9          3.8           6.4          2.0  virginica\n",
       "132           6.4          2.8           5.6          2.2  virginica\n",
       "133           6.3          2.8           5.1          1.5  virginica\n",
       "134           6.1          2.6           5.6          1.4  virginica\n",
       "135           7.7          3.0           6.1          2.3  virginica\n",
       "136           6.3          3.4           5.6          2.4  virginica\n",
       "137           6.4          3.1           5.5          1.8  virginica\n",
       "138           6.0          3.0           4.8          1.8  virginica\n",
       "139           6.9          3.1           5.4          2.1  virginica\n",
       "140           6.7          3.1           5.6          2.4  virginica\n",
       "141           6.9          3.1           5.1          2.3  virginica\n",
       "142           5.8          2.7           5.1          1.9  virginica\n",
       "143           6.8          3.2           5.9          2.3  virginica\n",
       "144           6.7          3.3           5.7          2.5  virginica\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_virginica = df.loc[df['species'] == 'virginica']\n",
    "df_virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Petal length')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XSb6W5NKe7eM0J3ONZVzm5Nxpfbw/yfeSvKunZizmZcCxjMu8vDvJQ0m+kuS2JC/p2X5Ckk93c7Izydo5d1pVx/Q/4FFgxSzbfxz4LBDgEmDnQvd5iLFcBvzRQvdzgHFsBX6+Wz4eWD7GczLXWMZiTnr6vAT4K+CV4zovA4xl0c8LsAr4BrCsW/894Gd7an4B+K1ueSPw6bn2e8yfKQxgA/A7NeVuYHmSMxa6U0erJC8Hfgz4OEBVPVdV3+0pG4s5GXAs4+hy4OtV1XtVgbGYlx4zjWVcLAWWJVkKvBR4vGf7BqZ+MQG4Hbg8SWbboaEABfxpknuTbO6zfRXw2LT1PV3bYjTXWAAuTfK/k3w2yQ8fyc4N6B8Ae4H/nuS+JB9LcmJPzbjMySBjgcU/J702Arf1aR+XeZluprHAIp+XqvoW8CvAN4EngKer6k97ytqcVNV+4Gng1Nn2ayjAa6vqNcDVwL9O8mM92/ul6mL9Hu9cY/kyU6fJ/wT4L8AdR7qDA1gKvAb4zaq6APhb4IaemnGZk0HGMg5z0iQ5HvhJ4Pf7be7TthjnBZhzLIt+XpKczNSZwNnAmcCJSd7cW9bnprPOyTEfClX1ePfzSeAzwEU9JXuAs6atr+bQU7RFYa6xVNX3quqZbvlPgOOSrDjiHZ3dHmBPVe3s1m9n6oW1t2Yc5mTOsYzJnEx3NfDlqvp2n23jMi8HzTiWMZmX1wHfqKq9VfU88AfAP+upaXPSvcV0ErBvtp0e06GQ5MQkLzu4DFwBfKWnbBvwM903Ky5h6hTtiSPc1TkNMpYkrzj4fmKSi5ia/78+0n2dTVX9FfBYknO7psuBr/aUjcWcDDKWcZiTHtcx89stYzEv08w4ljGZl28ClyR5adfXy4Gv9dRsAzZ1y9cCX6juU+eZLB15N8fL6cBnurlfCvxuVX0uydsBquq3gD9h6lsVu4HvAz+3QH2dyyBjuRb4V0n2A88CG+d6gCyQfwN8sju9fwT4uTGdE5h7LOMyJyR5KbAe+JfT2sZyXgYYy6Kfl6rameR2pt7q2g/cB2xJcjMwWVXbmPqSwyeS7GbqDGHjXPv1MheSpOaYfvtIkvRChoIkqTEUJEmNoSBJagwFSVJjKOioleRAd4XLryT5/e5riLPV//sB9/tovz9kmql9GEnWJnnjtPWfTfLRUR5Dms5Q0NHs2ap6dVX9Y+A54O1z1A8UCkfYWuCNcxVJo2Io6FjxReAcgCRvTvKl7izivyZZkuQWpq42eX+ST3Z1d3QXF3xolgsM9tXvGF37M0k+1F1o7e4kp3ftr+rW70lyc5Jnul3dAvxot593d21nJvlckl1JfnkE943UGAo66nXXfLkaeDDJPwLewNTFA18NHADeVFU38HdnFm/qbvqWqroQmADemWTWq0tOO17fY3SbTwTu7i609r+At3XtHwE+UlX/lBdeL+gG4Itdv27t2l7d7f984A1Jpl9vSBrKsX6ZCx3dliW5v1v+IlN/8r8ZuBC4p7skyDLgyRlu/84kP9UtnwWsY7Dr31w+yzGeA/6oW76XqUstAFwKXNMt/y5Tl0SeyZ1V9TRAkq8Cr+SFl6yWDpuhoKPZs91v6k134bCtVfXe2W6Y5DKmrkJ5aVV9P8ldwEtmu830m89yjOenXUPnAIf3HPzBtOXD3YfUl28f6VhzJ3BtktMAkpyS5JXdtueTHNctnwQ81QXCDzH130uO4hgzuRv46W55+kXL/gZ42TyOLQ3FUNAxpaq+CryPqf+h7gFgB3Dwv4zcAjzQfdD8OWBpV/MBpl60R3GMmbwLuD7Jl7rap7v2B4D93QfT757x1tKIeJVUaRHo/obi2aqqJBuB66pqw0L3S8ce34uUFocLgY92n3l8F3jLAvdHxyjPFCRJjZ8pSJIaQ0GS1BgKkqTGUJAkNYaCJKn5/xp5qb0Fti0CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(df_setosa['sepal_length'], np.zeros_like(df_setosa['sepal_length']), 'o')\n",
    "plt.plot(df_virginica['sepal_length'], np.zeros_like(df_setosa['sepal_length']), 'o')\n",
    "#plt.plot(df_versicolor['sepal_length'], np.zeros_like(df_setosa['sepal_length']), 'o')\n",
    "plt.xlabel('Petal length')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sumee\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KMpKsNswVZajaUHLMbAKwCNib4L24FwGXADcC7wxX+5C7P2pmk4ErCN7qBPA5d7/PzHYFvkMwV6MDX3P3n5jZ40CXu28ysw8D5wCvAFYQdFsCuDpvux+4+7djfcJF1NLb8F3Acnf/S7whVabehiLSpBLpbWhmHwSOdvczw8cTgd8AV7n7P5rZR4FT3P1YM/sx8K/ufq+Z7QPc4e7/K2ysvou7fy7cx6vc/blc8iKocfgn4APuvt3M/hVYDjwEfMPd3xNuNymNK7VaSuXnAVeY2TPAL8OPe939uTgCExGRktYA3wwT0DJ3/6UF8+vdEC6/AchdDb0beLPtnH9vdzPbLRw/LTdY5G/5kcAc4Nfhtp3A08CtBL1uvwP0AXc29qlVp5aCjY8CmNlU4CTgX4CptexDRESic/c/mNkc4H3AxWaWSyD5t9JyX7cBh7n7iGouCzJSuVtvBix09/NHLTA7ADiKoG3gKcAn6noiEVRdrmRmHzazfwMWE2Ts7wL/O67ARESkuPAi4i/ufh1BPUJuuqpT8z7/Kvz6TuCzedseWGL8VQWHuQs4ycxeEy7fI3yP755Am7v/BLgg79iJquWq6TLgjwQv/P3M3R+PJaIU1NtSqdJ285euiWUCR7WACq1eVPNkk3Gr1Dqqb21fLBNZlttv2WNGOIdxPRepyizgUjMbBrYDZxNcWOxiZisILkxy78M9B/iXcEqrccAvgE8BC8Lx3wJDwNeAm3MHcPffmdl84E4zawuP8xlgG/DDcAxg1JVZEqou2AAws7cAbwfeBrwJeMTdPxJTbCU1smCjsN0SBOXlF39gVtmEUGm7+UvXcN3yJ0Zt9+FD94mUwOqNd8xZvQhuPQe2590JGd8Jx12eWgLb0ToqrwNHx7DT8/oggfWt7aPn/h4GhwZ3Lm/voOfwnkh/9MvtFyh9zBe31n0O43ouY0Bqk1HmVwmmFUOSarltuDtBqeXrgBnARCDzb/Gvt6VSpe1uWLGu6HalxqulFlChuy4c+UcXgsd3XZhOPFRuHdW7snfEH3uAwaFBelf2Rjtumf2WPWaEcxjXcxGpVi23De/N+/iuuz8ZT0jJqrelUqXthkpc0ZYar5ZaQIW2lPjxKzWegEqtozZs3VB8eYnxqo9bx343bN0Q6RzG9Vykfu4+I+0YklT1lZe7z3b3T7v7j4slrrBsMnNKtU6q1FKp0nbtVvzuQanxatUb75gzce/axhNQqkVUbnzKhCnFl5cYr/q4ZfZb9pgRzmFcz0WkWo1sjnZEA/eVmHpbKlXa7vRDphfdrtR4tdQCKnTkV4LXZ/KN7wzGU1KpdVT3wd10tHeMXN7eQffB0VqGlttv2WNGOIdxPReRarX8e7SqabdUz3a5ooxGVxvWG++YkysoaKJqw0qto3KFDI2u0Ktmv2WPWcc5jOu5iFSrpmrDsjsyW+nuidT7qz2UiDSp1KoNW00jbxvqmyYikkFmNi9843NmNPK2oWpkRUTqMONLfR8Cvk7wdqQngC8//o1jfpxgCPOA3wJPJXjMSCreNjSzWynT/8rdj290UJXotqGINKma70CFiesq4JV5w38BzoySwEpMm/Io8C1gV2ATQdI6ArgGWE/QPeMw4HCCtlPjgF8DZ7v7S2b2DeB44GXgTnf/gpkdB8wnmDblGeAMd/9zvXFXq5orL83jJSISn68zMnERPv46EOXq62jgKXc/BnZMm3I78H5332hmpwL/6O6fMLPPAl9w934z6yBIZkeGDYCvBc4OP58I/JW7u5lNCo9zL3BoOPZJ4IvAP0SIuyoVk5e7/zzuILIqSo/BStuecdWvuO+Pz+54fMQb9uD6Mw9r+HOQxoqtd+Hi0+nd8iAb2tuZMjRE98QDOeakGypvCCxYNo+bNvUzTPAi98l7djH/2GsixyQNs0+N49UaMW0K8BywP/DTcIqTdmCgyHb7AY+5+x/CxwsJehp+FxgEvm9mfeE+Ibiyu9HM9iK4+nosYtxVqaU91JvMbLGZ/c7M1uY+4gyumeV6DK7fvA0H1m/exvk3r2HpqvWRty1MXAD3/fFZzrjqV0X2Js0i1+9vYOsAjjOwdYCe+3voW9sXbb+LT6fnhdUMjBuHmzEwbhw9L6ymb/HpFbddsGweN27qZ9gMzBg248ZN/SxYNi9STNJQo5uglh+vSph85hAksYuBDwIPufuB4ccsd59bZNOitz7d/WXgrcBPgBOA/wwXfYeg69Is4O+AjmLbN1ot1YY/BL5HcK/zncC1wI/iCCoLovQYrLRtYeLKKTUuzSG23oVbHmSwbeSv6mBbG71bHqy47U2b+qGwq4tZMC7N4ssEr3Hl+0s4Xrci06YcAkw2s8PC5ePDZusALwC7hV//HphhZm8MH38E+LmZ7QpMdPfbgM8BualVJhK8XgbwsSgx16KW5NXp7ncRFHn8yd17gHfFE1bzi9JjUP0Jx6bYehe2t9c0nq9U5+zMd9QeQ8KijDOBPxEUx/2JiMUaoVnAf5vZg8D/Bb5CMJHwJWb2G+BBgsIMCF7juiJc14CPAzeZ2RqCH5crCJLbsnBqlZ8Dnw+37QnX/SVBEUgiaimVHwznb/l/4Yt764HXxBNW85s6qZP1RZJNNT0Go2wrzWvKhCkMbB39EkLk3oVDQwyMG/2rOmVoqMjaI7VRPFE18g2eEl2YqBpaGu/udwB3FFn09iLr/oTgdmDOXcBBBasNENw2LNz2P4D/qD/S+tTyM/w5ggqYcwjuo36EBC8Rm02UHoOVtj3iDXsU3a7UuDSH2HoXTjyQjuGRKahjeJjuiQeW2GKnk/fsgsK3w7gH4yIZVnN7qHBeL3f3F+IJqbJmeZ+Xqg2lkKoNW546DSWk6uRlZl0ERRu5F/W2AJ9w9wdiiq2kZkleIiIFlLwSUstrXj8APu3uvwQws7cRJLPZcQQmIiJSSi2veb2QS1wA7n4vQXmliIhIomq58vpvM/s34AaCcs5TgXvM7GAAd18ZQ3wiIiKj1JK8cqVNXy0YP5wgmY16z1fYI+sXwC7hsRa7+1cL1tmF4A3PcwiaOp7q7o/XEFdFlYojohRe1Gv+0jVlJ6osF1Ma8bJ6Uf0TPy47Fx64BnwIrB3mzINjvxV53333XFBy4kcoXzxRadu4nHnHmSzfsHzH40OnHMpVR10VPChzHqI81yjKnsOYjpnFmFqBmV0I/MLd/6vG7d5B0Dfx2IbG06jJKIvuPGigNcHdXzSz8QQNHLvdfXneOp8GZrv7p8zsNOBEdz+13H5rKdjItWLK72jROb6diz8wixMOmlZxeRzmL13DdctHd3758KH7sOCEWWVjAhKPl9WL4NZzYHvee9PGd8Jxl1dOMsvOhf6rR493/W2QwOrcd989F9Dz2BIG23a+Pt4x7PS8PvijnmvVlN/xoqO9g57De+CJ5WW3jUth4so5dMqhXLXXe0qeh75n19T9XKP84S57DiGWY2YxpgL1FWz0TBw1JQo9W5KcEgXY8Tfb3L1h72OvNXmZ2biwFVX59WqoNnwtwcmd6u7vNbM3A4e5e5G/TEW3fyVB8jrb3Vfkjd8B9Lj7r8xsHLABmOxlAqsleR3xjbuLviF42qRO7vvSuyouj8Mbzr+NoSJPr92MP178vrIxAYnHy7f3hy3rRo9PnA6f/235bb+2R3DFVcja4avP1r3vuT/Yn4H20X8n9hpy7vzEb5m7eG7RNwzvNWEveP6pstvGZdbCWSWXrXnWS56HuROp+7needKddcdb9hxCLMfMYkwFak9eQeIqOiVKvQksbMb7J3f/1/BxD0GNQhtwCsHdsCXu/lUzm0HQbf5nBNOhnAB8DegiuKv2A3f/tpldAyxz98Vm9tcEczhOAF4CjgS2E7QQ7CJoI3iuu/8sP3mZ2R4ExX8zw+d4lruvDuObCswANrn7hyo9x1oKNq4heLd2brbNPxC8cbksM2sPW448Dfw0P3GFpgHrYEfjxy3Aq4vs5ywz6zez/o0bN1YddKVWTGm0aiqWuPLHy8WUSmupLU/WNp6vWOLKH69z3xtK/OTmxsu1aqq0bSrKnIcozzWKsucwpmNW0owxNUC5KVHq9e8EdQk5pwAbgTcRdMk4EJhjZrluG/sB17r7QcCewDR33z9stvvD/B2b2SuAGwnuoh0AvJtgHrDPAITbnA4sDF86yvc1YJW7zybo3Xht3rI5BNO1VExcUFvy2tPdFxF2mwkTTcX+NO4+5O4HErTNf6uZ7V+wSrH/VEb9dXf3K929y927Jk+eXHXQpVou5cYrLY9De2Gj1ILxcjGlES8T965tPJ+V6L+XG69z31NK3NTIjZdqyTRlwpSK26aizHmI8lyjKHsOYzpmJc0YUwM0fEoUd18FvMbMpprZAQTTocwG5gKrgJXAXxEkMwiu0nL3tNcCM83sO2Z2NPB8we73Awbc/dfhsZ4P88HbCJu1u/vvCXo07luwbf46dwOvDucZA7jF3av+L7yW5LXVzF5NmFjM7FCCq6SquPtm4B6CCdLyPQlMD/c5jqBDccPap1dqxRSlzVO9Tj9ketnxcjGlES9HfiV4/SXf+M5gvJI588qP17nv7pkn0jE88n+cjmGne+aJwfIyrZoqbRuXQ6ccWnq8zHmI8lyjKHsOYzpmFmNqgFimRAEWEzTiPZXgSsyAi/OmRHlj3ss+W3MbuftzwAEEf68/A3y/YL9GkQsMqrtlWu5iZWuRZSXVUm14LnAL8AYzuw+YTHBiSjKzycB2d99sZp0El5eXFKx2C0GPxF+F+7u73OtdtcoVMZSqzqu0PA65qsJS1YbVxJRotWGucKKeasNcVWGpasM6950rrChVgZd7gb5o5VluWcLVhlcddVX5akMoeh6O4ZT6n2sE1ew36cq+ZoypAb5M8de8Ik2JQpCwriK4Dfg3BF3mLzKz68MiumkEr1ONYGZ7Av/j7j8xsz8SvGSU7/fAVDP7a3f/tZntRnDb8BfAGcDdZrYvwZXjIwSvo+Xk1rkofC1sk7s/byXuRpVTS8HGyQSveU0nmNTsEOCCcu/vMrPZBLNwthNc5S1y9wvDkst+d78lvCf6I4IOxs8Cp7l72Uku1R5KRJpUU1UbhlOabHL3d4aPu4FPhotfBD5M8PLPMnffP1znAILXuXJ35s5399uLFGx8B+gkSFzvJijSuILgtatyBRs/BF7P6IKNF939m1U/txqS12p3nx22hfo68M/Al939kGoP1ihKXiLSpNTbMCG1vOaVK844BrginMPlFY0PSUREpLxaktf6sD3UKcBtYWcMzWknIiKJqyX5nELwmtfRYeXgHsB5sUSVsKWr1nPEN+7m9V/q44hv3M3SVevTDmlsWb0oeDNyz6Tg8+pFtS2P47j1LiPo8jB38VxmL5zN3MVz6Vvb15jnEtd5SEnZ8yQSUaztoeLSyNe80mgP1VIqtX+K0nqq3uNCfctmn1K+FdOLW+t/LnGdh5TE1bIqA/SaV0JaPnml0R6qpVRq/xSl9VS9x4X6ln2+QiumdU/V/1ziOg8piatlVQYoeSWklvd5jUmptFtqJZXaP0VpPRXluHUuK9uCKMpzies8pCTDrZokI1q+4CKVdkutpFL7pyitp+o9br3LqNCKKcpzies8pCTDrZrGrLBV1OI6tvt+2Ii93DqfMrOP1h9d7Vo+eaXSbqmVVGr/FKX1VL3HrXcZFVoxRXkucZ2HlGS4VVMqZi2c9aFZC2c9PmvhrOHwc1XNaWvh7k+5+6iuSGFbvnLbfdLdf1dhnSvc/dpy6zRay982TKM9VEup1P4pSuupKMetc1lVrZjqeS5xnYeUxNWyaiwKE1V+e6jXAVfNWjiLNR9b0+gpUT7u7vub2TyC9+x2ABPM7N3AdwnaSD1GcGHzg7Cbxj0EHTL6zexFgqlQjiXorPF+d/9zfocMM3sjQaeNyQTvDz4Z+DPwH8CrgPHA/PC9wnVr+YINEZEGqrlgY9bCWY8TJKxCf1rzsTUz6grC7CDgMnf/m/Dx74BPAf+al7wWEEwE/KyZnQR8giApvQZ4GDizSPJy4Hh3v9XM/gl43t0XFCSvFcA33H1J2P6vDfgf4JVhH8M9geXAm6L0sW35Ky8RkZTFMiWKmb3GzKYSXAE9x+gu9T9199wMHhdtFKIAABNDSURBVG8DbgpnUN5gZj8rsev/AZaFXz8AvCd/Ydikd5q7LwnjGAzHxwNfD+cPGyaYx/G1BJMP10XJS0QkXU9Q/MqrUVOiTCHoMF8ofwqSaq8Yt+ddLQ0xOoeU2s8ZBEl0jrtvN7PHCW5Z1q3lCzZERFL2ZYIO6/kaNSXKaQQJrFKV4b3AB82szcxeC7yjngO6+/PAk2Z2AoCZ7WJmrySYp/HpMHG9k+LJuiZKXiIiKQqLMs4kmHnYw89n1luskePuDwG7AevdffQ7xkf6CcHEwL8F/g1YQQ2TDRf4CHCOma0G7ie48rse6DKzfoKrsN/Xue8dVLAhsetb21e+6mzZuSUnq6y4bb0iHLPc8gXLF3DTH25i2IdpszZO3vdk5h86f+dxVy+Kp6Iwrv3GJK7va2w/L9XLbIcNM9s1nKTy1cB/A0e4e9O+q1yveUmsCnvcDWwdoOf+HiAsp152LvRfvXMDH9rxuO/NR5bftl4Rjlnu+ax6ehU3PnLjjt0O+/COx/MPnT+6f+GWdcFjaGwfx0btNyYVfyaabL8tZJmZTSKY6uqiZk5coCsviVnFHndf2yNIHoWsnblv6YqnP16EY5Z7Pn/+y58Z9uFRy9qsjd989Dfp9HFswr6IcfU9bJJ+ipm98soaXXlJrCr2uCuWRMLx2PrjRThmueVO8X8EdyS0ZurjmKK4vq/qp9haVLAhsarY487aiy7H2uPrjxfhmOWWt1nxX6cd42n0cWxCcX1f1U+xtSh5Sawq9ribM6/4hnPmxdcfL8Ixyy0/ed+Ti+52x3gafRybUFzfV/VTbC3tPT09acdQsyuvvLLnrLPOSjsMqcK+r9qXabtO46FnHmLr9q3sNWEvvvTWL+18AX3fo+DFjTCwGvDgqqjrE3DstypvW3dQ9R+z3PK37/12nh18loeffRjHabM2TtnvlJ3Vhq99C0zaB556EF56IXhN6uhvRC+qiGu/MYnr+xrbz0ttvpbkwVqZCjZERBpHBRsJ0W1DERHJHCUvERHJHCUvERHJHL3Pq9Wk0UYowjEX3PBebnppHcME/2mdvMt05p9+e+zHLadcC6ImaE8k0hJUsNFKCtsIQVBSfdzl8SWwCMdccMN7ufGldWB5r4G7c2o1CSym51rYggiCcuyew3sASi5TAmsZKthIiJJXK0mjjVCEYx5wzf4M2+i/BW3u/GZehXhjeq7lWhABzdCeSNKl5JUQ3TZsJWm0EYpwzNFdAsuPN+q45dTTgkjtiUQaTwUbrSSNNkIRjlnqh7OqH9qYnmu5FkRqTySSHCWvVpJGG6EIxzx5l+lQeFvbPRiP8bjllGtBpPZEIsnRbcNWkitUSLLaMMIx559+O9RbbRjTc80VXpSrKFS1oUj8VLAhItI4KthIiG4biohI5ih5iYhI5ih5iYhI5ih5iYhI5ih5SWOsXhR0teiZFHxevagx20bZbxl9a/uYu3gusxfOZu7iufSt7WvIfiVd+r62DpXKS3SFfQS3rAseQ+XS9HLbQv37LaOwP+HA1gF67u8BUFl7hun72lpUKi/RRekjWG5bSLw/oXoQZleTfF9VKp8QXXlJdFH6CNazbQr9CaX56fvaWvSal0QXpY9guW1T6E8o2aXva2tR8pLoovQRLLdtCv0JJbv0fW0tum0o0UXpI1jNtin0J5Ts0fe1tahgQ0SkcVSwkRDdNhQRkcxR8hIRkcxR8hIRkcyJNXmZ2XQz+5mZPWxmD5nZqLIfM3uHmW0xswfDjxin9c2OutvcxNROKdJxK8SUtZY+ffdcwNwf7M/sa/Zn7g/2p++eC+I/ZsbOkUjc4q42fBn4B3dfaWa7AQ+Y2U/d/XcF6/3S3Y+NOZbMqLvNTZQ2TVFEaPGUtZY+ffdcQM9jSxhsD16XH2iHnseWAHDMOy6K55gZO0ciSYj1ysvdB9x9Zfj1C8DDwLQ4jzkW9K7s3fGHKmdwaJDelb3lN7zrwp1JImf7tmA8TuWOWyGmup9rSnrXLmGwbWRB2WCb0bt2SXzHzNg5EklCYq95mdkM4CBgRZHFh5nZb8zsdjN7S4ntzzKzfjPr37hxY4yRpq/uNjdR2jRFUe64FWLKWkufDSV+Y0qNN+SYGTtHIklIJHmZ2a7AT4DPufvzBYtXAq9z9wOA7wBLi+3D3a909y5375o8eXK8Aaes7jY3MbVTqihCi6estfSZMlzbeEOOmbFzJJKE2JOXmY0nSFzXu/vNhcvd/Xl3fzH8+jZgvJntGXdczazuNjcxtVOqKEKLp6y19OmeeSIdwyPf2N8x7HTPPDG+Y2bsHIkkIdaCDTMz4GrgYXf/Vol1pgB/dnc3s7cSJNRn4oyr2dXd5iZKm6YoIrR4ylpLn1xRRu/aJWxoC664umeeGFuxBmTvHIkkIdb2UGb2NuCXwBogd2Ply8A+AO5+hZl9FjiboDJxG3Cuu99fbr9qDyUiTUrtoRIS65WXu99LhW+mu38X+G6ccYiIyNiiDhsiIpI5Sl4iIpI5Sl4iIpI5moyyWa1elHzVYBTLzoUHrgEfAmuHOfPg2KIFpiIikSl5NaO0ehTWa9m50H/1zsc+tPOxEpiIxEC3DZtRWj0K6/XANbWNi4hEpOTVjNLqUVgvH6ptXEQkIiWvZpRWj8J6WXtt4yIiESl5NaO0ehTWa8682sZFRCJS8mpGs0+B4y6HidMBCz4fd3lzFmtAUJTR9bc7r7SsPXisYg0RiUmsvQ3jot6GItKk1NswIbryEhGRzFHyEhGRzFHyEhGRzFGHjQqWrlrPpXc8wlObtzF1UifnHbUfJxw0Ld2gmrF1VDPGFJdWeq4iTUrJq4ylq9Zz/s1r2LY9eLPt+s3bOP/mNQDpJbBmbB3VjDHFpZWeq0gT023DMi6945EdiStn2/YhLr3jkZQiojlbRzVjTHFppecq0sSUvMp4avO2msYT0Yyto5oxpri00nMVaWJKXmVMndRZ03gimrF1VDPGFJdWeq4iTUzJq4zzjtqPzvEj+/N1jm/nvKP2SykimrN1VDPGFJdWeq4iTUwFG2XkijKaqtowVxTQTNVuzRhTXFrpuYo0MbWHEhFpHLWHSohuG4qISOYoeYmISOYoeYmISOYoeYmISOao2lDGrL57LqB37RI2tMGUYeieeSLHvOOiyhuqd6FI01PykjGp754L6HlsCYPtQfHXQDv0PLYEoHwCU+9CkUzQbUMZk3rXLmGwbWTV8mCb0bt2SfkN1btQJBOUvGRM2lDiJ7vU+A7qXSiSCUpeMiZNGa5tfAf1LhTJBCUvGZO6Z55Ix/DI7jEdw073zBPLb6jehSKZoIINGZNyRRk1Vxuqd6FIJqi3oYhI46i3YUJ021BERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDJHyUtERDIn1uRlZtPN7Gdm9rCZPWRm3UXWMTO73MweNbPVZnZwnDGNCasXwbf3h55JwefVi9KOSEQkUXFPRvky8A/uvtLMdgMeMLOfuvvv8tZ5L/Cm8OMQ4HvhZylm9SK49RzYvi14vGVd8Bg0YaKItIxYr7zcfcDdV4ZfvwA8DEwrWO39wLUeWA5MMrO94owr0+66cGfiytm+LRgXEWkRib3mZWYzgIOAFQWLpgHr8h4/yegEh5mdZWb9Zta/cePGuMJsfluerG1cRGQMSiR5mdmuwE+Az7n784WLi2ziowbcr3T3Lnfvmjx5chxhZsPEvWsbFxEZg2JPXmY2niBxXe/uNxdZ5Ulget7jvYGn4o4rs478CozvHDk2vjMYFxFpEXFXGxpwNfCwu3+rxGq3AB8Nqw4PBba4+0CccWXa7FPguMth4nTAgs/HXa5iDRFpKXFXGx4BfARYY2YPhmNfBvYBcPcrgNuA9wGPAn8BPh5zTNk3+xQlKxFpabEmL3e/l+KvaeWv48Bn4oxDRETGFnXYEBGRzFHyEhGRzFHyEhGRzFHyEhGRzFHyEhGRzFHyEhGRzFHyEhGRzFHyEhGRzFHyEhGRzFHyEhGRzFHyEhGRzLGgtWC2mNlG4E8JH3ZPYFPCx6xEMVVHMVXWbPFANmPa5O5HJxVMK8tk8kqDmfW7e1faceRTTNVRTJU1WzygmKQ83TYUEZHMUfISEZHMUfKq3pVpB1CEYqqOYqqs2eIBxSRl6DUvERHJHF15iYhI5ih5iYhI5ih5FWFm7Wa2ysyWFVk2z8w2mtmD4ccnE4jncTNbEx6vv8hyM7PLzexRM1ttZgc3QUzvMLMteefpKwnENMnMFpvZ783sYTM7rGB5ouepingSPUdmtl/esR40s+fN7HMF6yR9jqqJKY2fpc+b2UNm9lszu8HMOgqW72JmN4bnaYWZzYg7JhlpXNoBNKlu4GFg9xLLb3T3zyYYD8A73b3UmyPfC7wp/DgE+F74Oc2YAH7p7scmEEdOL/Cf7n6Smb0CeGXB8qTPU6V4IMFz5O6PAAdC8A8asB5YUrBaoueoypggwfNkZtOAc4A3u/s2M1sEnAZck7fa3wLPufsbzew04BLg1CTik4CuvAqY2d7AMcD3046lBu8HrvXAcmCSme2VdlBJMrPdgbcDVwO4+/+4++aC1RI7T1XGk6YjgT+6e2GnmjR/lkrFlIZxQKeZjSP4p+OpguXvBxaGXy8GjjQzSzC+lqfkNdplwBeB4TLrfDC8pbLYzKYnEJMDd5rZA2Z2VpHl04B1eY+fDMfSjAngMDP7jZndbmZviTmemcBG4IfhLd/vm9mEgnWSPE/VxAPJnqN8pwE3FBlP42cpp1RMkOB5cvf1wDeBJ4ABYIu731mw2o7z5O4vA1uAV8cZl4yk5JXHzI4Fnnb3B8qsdisww91nA//Fzv++4nSEux9McEvnM2b29oLlxf7ji/s9EJViWgm8zt0PAL4DLI05nnHAwcD33P0gYCvwpYJ1kjxP1cST9DkCILyFeTxwU7HFRcZifz9NhZgSPU9m9iqCK6vXA1OBCWb24cLVimyq9x0lSMlrpCOA483sceDfgXeZ2XX5K7j7M+7+UvjwKmBO3EG5+1Ph56cJXg94a8EqTwL5V4B7M/o2R6Ixufvz7v5i+PVtwHgz2zPGkJ4EnnT3FeHjxQTJo3CdpM5TxXhSOEc57wVWuvufiyxL/GepUkwpnKd3A4+5+0Z33w7cDBxesM6O8xTeWpwIPBtjTFJAySuPu5/v7nu7+wyCWxh3u/uI/7gK7v8fT1DYERszm2Bmu+W+BuYCvy1Y7Rbgo2Gl2KEEtzkG0ozJzKbkXgMws7cS/Kw9E1dM7r4BWGdm+4VDRwK/K1gtsfNUTTxJn6M8p1P69lyiP0vVxJTCeXoCONTMXhke90hG/57fAnws/Pokgr8VuvJKkKoNq2BmFwL97n4LcI6ZHQ+8TPCf1ryYD/9aYEn4uzsO+LG7/6eZfQrA3a8AbgPeBzwK/AX4eBPEdBJwtpm9DGwDTkvgl/vvgevDW1BrgY+nfJ4qxZP4OTKzVwLvAf4ubyzNc1RNTImeJ3dfYWaLCW5XvgysAq4s+DtwNfAjM3uU4O/AaXHFI8WpPZSIiGSObhuKiEjmKHmJiEjmKHmJiEjmKHmJiEjmKHmJiEjmKHmJiEjmKHnJmBdOqTFqepu85fPM7LsxHHeemU3Ne/x4Qh00RMY8JS+R+Mwj6I0nIg2mDhvSFMI2U4sIeum1AxcRdHn4FrArsAmY5+4DZnYP8CBBP8XdgU+4+3+HrYMuAzoJOjF8PJwvqpY4JgNXAPuEQ59z9/vMrCccmxl+vszdLw+3uQA4g6DL+CbgAeBxoIugw8Y2IDcR5d+b2XHAeOBkd/99LfGJSEBXXtIsjgaecvcD3H1/4D8JOoif5O5zgB8A/5i3/gR3Pxz4dLgM4PfA28Mu7l8Bvl5HHL3At939r4EPMnJet78CjiJIml81s/Fm1hWudxDwAYKEhbsvBvqBM9z9QHffFu5jU9iN/3vAF+qIT0TQlZc0jzXAN83sEmAZ8BywP/DTsIdiO8HcSjk3ALj7L8xsdzObBOwGLDSzNxFMTzG+jjjeDbw5b17B3XNNiIG+cEaBl8zsaYIej28D/iOXnMzs1gr7vzn8/ABBshOROih5SVNw9z+Y2RyCprAXAz8FHnL3w0ptUuTxRcDP3P1EM5sB3FNHKG3AYXlXSgCEyeylvKEhgt+fWmfPze0jt72I1EG3DaUphFV5f3H36whmsT0EmGxmh4XLxxfMoHtqOP42gmk7thDMqbQ+XD6vzlDuBD6bF9eBFda/FzjOzDrMbFfgmLxlLxBcDYpIg+k/P2kWs4BLzWwY2A6cTTAdxeVmNpHgZ/Uy4KFw/efM7H7Cgo1w7J8IbhueC9xdZxznAP9iZqvDY/4C+FSpld3912Z2C/Ab4E8Er3NtCRdfA1xRULAhIg2gKVEkc8Jqwy+4e3/asQCY2a7u/mI4L9UvgLPcfWXacYmMZbryEonuSjN7M9ABLFTiEomfrrykZZjZx4HuguH73P0zacQjIvVT8hIRkcxRtaGIiGSOkpeIiGSOkpeIiGSOkpeIiGTO/wfbiSfS8UfLMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 444.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue='species', size=5).map(plt.scatter, 'sepal_length', 'sepal_width').add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multivariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sumee\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x148e27b2b88>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1524.75x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue='species', size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}