AI_botanist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
       "       'species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         setosa\n",
       "1         setosa\n",
       "2         setosa\n",
       "3         setosa\n",
       "4         setosa\n",
       "5         setosa\n",
       "6         setosa\n",
       "7         setosa\n",
       "8         setosa\n",
       "9         setosa\n",
       "10        setosa\n",
       "11        setosa\n",
       "12        setosa\n",
       "13        setosa\n",
       "14        setosa\n",
       "15        setosa\n",
       "16        setosa\n",
       "17        setosa\n",
       "18        setosa\n",
       "19        setosa\n",
       "20        setosa\n",
       "21        setosa\n",
       "22        setosa\n",
       "23        setosa\n",
       "24        setosa\n",
       "25        setosa\n",
       "26        setosa\n",
       "27        setosa\n",
       "28        setosa\n",
       "29        setosa\n",
       "         ...    \n",
       "120    virginica\n",
       "121    virginica\n",
       "122    virginica\n",
       "123    virginica\n",
       "124    virginica\n",
       "125    virginica\n",
       "126    virginica\n",
       "127    virginica\n",
       "128    virginica\n",
       "129    virginica\n",
       "130    virginica\n",
       "131    virginica\n",
       "132    virginica\n",
       "133    virginica\n",
       "134    virginica\n",
       "135    virginica\n",
       "136    virginica\n",
       "137    virginica\n",
       "138    virginica\n",
       "139    virginica\n",
       "140    virginica\n",
       "141    virginica\n",
       "142    virginica\n",
       "143    virginica\n",
       "144    virginica\n",
       "145    virginica\n",
       "146    virginica\n",
       "147    virginica\n",
       "148    virginica\n",
       "149    virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
       "       'species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>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",
       "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",
       "..            ...          ...           ...          ...        ...\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\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0</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>1</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>2</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>3</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>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>sepal_width</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width\n",
       "0           5.1          3.5\n",
       "1           4.9          3.0\n",
       "2           4.7          3.2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width\n",
       "0           5.1          3.5\n",
       "1           4.9          3.0\n",
       "2           4.7          3.2\n",
       "3           4.6          3.1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:3,'sepal_length':'sepal_width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
       "       'species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[['species']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>virginica</td>\n",
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       "      <th>147</th>\n",
       "      <td>virginica</td>\n",
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       "      <th>148</th>\n",
       "      <td>virginica</td>\n",
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       "      <th>149</th>\n",
       "      <td>virginica</td>\n",
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      "text/plain": [
       "       species\n",
       "145  virginica\n",
       "146  virginica\n",
       "147  virginica\n",
       "148  virginica\n",
       "149  virginica"
      ]
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     "execution_count": 34,
     "metadata": {},
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   ],
   "source": [
    "y.tail()"
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>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",
       "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",
       "..            ...          ...           ...          ...        ...\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\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['colour'] = colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
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       "      <td>red</td>\n",
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       "      <td>red</td>\n",
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       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
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       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>0.3</td>\n",
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       "      <td>red</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
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       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species colour\n",
       "0             5.1          3.5           1.4          0.2     setosa    red\n",
       "1             4.9          3.0           1.4          0.2     setosa    red\n",
       "2             4.7          3.2           1.3          0.2     setosa    red\n",
       "3             4.6          3.1           1.5          0.2     setosa    red\n",
       "4             5.0          3.6           1.4          0.2     setosa    red\n",
       "5             5.4          3.9           1.7          0.4     setosa    red\n",
       "6             4.6          3.4           1.4          0.3     setosa    red\n",
       "7             5.0          3.4           1.5          0.2     setosa    red\n",
       "8             4.4          2.9           1.4          0.2     setosa    red\n",
       "9             4.9          3.1           1.5          0.1     setosa    red\n",
       "10            5.4          3.7           1.5          0.2     setosa    red\n",
       "11            4.8          3.4           1.6          0.2     setosa    red\n",
       "12            4.8          3.0           1.4          0.1     setosa    red\n",
       "13            4.3          3.0           1.1          0.1     setosa    red\n",
       "14            5.8          4.0           1.2          0.2     setosa    red\n",
       "15            5.7          4.4           1.5          0.4     setosa    red\n",
       "16            5.4          3.9           1.3          0.4     setosa    red\n",
       "17            5.1          3.5           1.4          0.3     setosa    red\n",
       "18            5.7          3.8           1.7          0.3     setosa    red\n",
       "19            5.1          3.8           1.5          0.3     setosa    red\n",
       "20            5.4          3.4           1.7          0.2     setosa    red\n",
       "21            5.1          3.7           1.5          0.4     setosa    red\n",
       "22            4.6          3.6           1.0          0.2     setosa    red\n",
       "23            5.1          3.3           1.7          0.5     setosa    red\n",
       "24            4.8          3.4           1.9          0.2     setosa    red\n",
       "25            5.0          3.0           1.6          0.2     setosa    red\n",
       "26            5.0          3.4           1.6          0.4     setosa    red\n",
       "27            5.2          3.5           1.5          0.2     setosa    red\n",
       "28            5.2          3.4           1.4          0.2     setosa    red\n",
       "29            4.7          3.2           1.6          0.2     setosa    red\n",
       "..            ...          ...           ...          ...        ...    ...\n",
       "120           6.9          3.2           5.7          2.3  virginica   blue\n",
       "121           5.6          2.8           4.9          2.0  virginica   blue\n",
       "122           7.7          2.8           6.7          2.0  virginica   blue\n",
       "123           6.3          2.7           4.9          1.8  virginica   blue\n",
       "124           6.7          3.3           5.7          2.1  virginica   blue\n",
       "125           7.2          3.2           6.0          1.8  virginica   blue\n",
       "126           6.2          2.8           4.8          1.8  virginica   blue\n",
       "127           6.1          3.0           4.9          1.8  virginica   blue\n",
       "128           6.4          2.8           5.6          2.1  virginica   blue\n",
       "129           7.2          3.0           5.8          1.6  virginica   blue\n",
       "130           7.4          2.8           6.1          1.9  virginica   blue\n",
       "131           7.9          3.8           6.4          2.0  virginica   blue\n",
       "132           6.4          2.8           5.6          2.2  virginica   blue\n",
       "133           6.3          2.8           5.1          1.5  virginica   blue\n",
       "134           6.1          2.6           5.6          1.4  virginica   blue\n",
       "135           7.7          3.0           6.1          2.3  virginica   blue\n",
       "136           6.3          3.4           5.6          2.4  virginica   blue\n",
       "137           6.4          3.1           5.5          1.8  virginica   blue\n",
       "138           6.0          3.0           4.8          1.8  virginica   blue\n",
       "139           6.9          3.1           5.4          2.1  virginica   blue\n",
       "140           6.7          3.1           5.6          2.4  virginica   blue\n",
       "141           6.9          3.1           5.1          2.3  virginica   blue\n",
       "142           5.8          2.7           5.1          1.9  virginica   blue\n",
       "143           6.8          3.2           5.9          2.3  virginica   blue\n",
       "144           6.7          3.3           5.7          2.5  virginica   blue\n",
       "145           6.7          3.0           5.2          2.3  virginica   blue\n",
       "146           6.3          2.5           5.0          1.9  virginica   blue\n",
       "147           6.5          3.0           5.2          2.0  virginica   blue\n",
       "148           6.2          3.4           5.4          2.3  virginica   blue\n",
       "149           5.9          3.0           5.1          1.8  virginica   blue\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict = {'setosa':'red', 'versicolor':'green', 'virginica':'blue'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>first</th>\n",
       "      <th>second</th>\n",
       "      <th>third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first  second  third\n",
       "0      1      10    100\n",
       "1      2      20    200\n",
       "2      3      30    300\n",
       "3      4      40    400"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'first':[1,2,3,4],'second':[10,20,30,40],'third':[100,200,300,400]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'red'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['setosa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       red\n",
       "1       red\n",
       "2       red\n",
       "3       red\n",
       "4       red\n",
       "5       red\n",
       "6       red\n",
       "7       red\n",
       "8       red\n",
       "9       red\n",
       "10      red\n",
       "11      red\n",
       "12      red\n",
       "13      red\n",
       "14      red\n",
       "15      red\n",
       "16      red\n",
       "17      red\n",
       "18      red\n",
       "19      red\n",
       "20      red\n",
       "21      red\n",
       "22      red\n",
       "23      red\n",
       "24      red\n",
       "25      red\n",
       "26      red\n",
       "27      red\n",
       "28      red\n",
       "29      red\n",
       "       ... \n",
       "120    blue\n",
       "121    blue\n",
       "122    blue\n",
       "123    blue\n",
       "124    blue\n",
       "125    blue\n",
       "126    blue\n",
       "127    blue\n",
       "128    blue\n",
       "129    blue\n",
       "130    blue\n",
       "131    blue\n",
       "132    blue\n",
       "133    blue\n",
       "134    blue\n",
       "135    blue\n",
       "136    blue\n",
       "137    blue\n",
       "138    blue\n",
       "139    blue\n",
       "140    blue\n",
       "141    blue\n",
       "142    blue\n",
       "143    blue\n",
       "144    blue\n",
       "145    blue\n",
       "146    blue\n",
       "147    blue\n",
       "148    blue\n",
       "149    blue\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species'].map({'setosa':'red', 'versicolor':'green', 'virginica':'blue'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c322b70>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.plot.scatter(x = 'petal_length',\n",
    "               y = 'petal_width',\n",
    "               c = df['species'].map({'setosa':'red', 'versicolor':'green', 'virginica':'blue'}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b0f9828>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b3ac0f0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.cov(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species\n",
       "0  setosa\n",
       "1  setosa\n",
       "2  setosa\n",
       "3  setosa\n",
       "4  setosa"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "versicolor\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n",
      "virginica\n"
     ]
    }
   ],
   "source": [
    "for s in df['species']:\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 50 'red', 50 'blue', 50 'green'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours = []\n",
    "\n",
    "for item in df['species']:\n",
    "    if item == 'setosa':\n",
    "        #print('red')\n",
    "        colours.append('red')\n",
    "    elif item == 'versicolor':\n",
    "        #print('green')\n",
    "        colours.append('green')\n",
    "    else:\n",
    "        #print('blue')\n",
    "        colours.append('blue')\n",
    "\n",
    "len(colours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
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       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.2</td>\n",
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       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
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       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
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       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             5.1          3.5           1.4          0.2\n",
       "1             4.9          3.0           1.4          0.2\n",
       "2             4.7          3.2           1.3          0.2\n",
       "3             4.6          3.1           1.5          0.2\n",
       "4             5.0          3.6           1.4          0.2\n",
       "5             5.4          3.9           1.7          0.4\n",
       "6             4.6          3.4           1.4          0.3\n",
       "7             5.0          3.4           1.5          0.2\n",
       "8             4.4          2.9           1.4          0.2\n",
       "9             4.9          3.1           1.5          0.1\n",
       "10            5.4          3.7           1.5          0.2\n",
       "11            4.8          3.4           1.6          0.2\n",
       "12            4.8          3.0           1.4          0.1\n",
       "13            4.3          3.0           1.1          0.1\n",
       "14            5.8          4.0           1.2          0.2\n",
       "15            5.7          4.4           1.5          0.4\n",
       "16            5.4          3.9           1.3          0.4\n",
       "17            5.1          3.5           1.4          0.3\n",
       "18            5.7          3.8           1.7          0.3\n",
       "19            5.1          3.8           1.5          0.3\n",
       "20            5.4          3.4           1.7          0.2\n",
       "21            5.1          3.7           1.5          0.4\n",
       "22            4.6          3.6           1.0          0.2\n",
       "23            5.1          3.3           1.7          0.5\n",
       "24            4.8          3.4           1.9          0.2\n",
       "25            5.0          3.0           1.6          0.2\n",
       "26            5.0          3.4           1.6          0.4\n",
       "27            5.2          3.5           1.5          0.2\n",
       "28            5.2          3.4           1.4          0.2\n",
       "29            4.7          3.2           1.6          0.2\n",
       "..            ...          ...           ...          ...\n",
       "120           6.9          3.2           5.7          2.3\n",
       "121           5.6          2.8           4.9          2.0\n",
       "122           7.7          2.8           6.7          2.0\n",
       "123           6.3          2.7           4.9          1.8\n",
       "124           6.7          3.3           5.7          2.1\n",
       "125           7.2          3.2           6.0          1.8\n",
       "126           6.2          2.8           4.8          1.8\n",
       "127           6.1          3.0           4.9          1.8\n",
       "128           6.4          2.8           5.6          2.1\n",
       "129           7.2          3.0           5.8          1.6\n",
       "130           7.4          2.8           6.1          1.9\n",
       "131           7.9          3.8           6.4          2.0\n",
       "132           6.4          2.8           5.6          2.2\n",
       "133           6.3          2.8           5.1          1.5\n",
       "134           6.1          2.6           5.6          1.4\n",
       "135           7.7          3.0           6.1          2.3\n",
       "136           6.3          3.4           5.6          2.4\n",
       "137           6.4          3.1           5.5          1.8\n",
       "138           6.0          3.0           4.8          1.8\n",
       "139           6.9          3.1           5.4          2.1\n",
       "140           6.7          3.1           5.6          2.4\n",
       "141           6.9          3.1           5.1          2.3\n",
       "142           5.8          2.7           5.1          1.9\n",
       "143           6.8          3.2           5.9          2.3\n",
       "144           6.7          3.3           5.7          2.5\n",
       "145           6.7          3.0           5.2          2.3\n",
       "146           6.3          2.5           5.0          1.9\n",
       "147           6.5          3.0           5.2          2.0\n",
       "148           6.2          3.4           5.4          2.3\n",
       "149           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       species\n",
       "0       setosa\n",
       "1       setosa\n",
       "2       setosa\n",
       "3       setosa\n",
       "4       setosa\n",
       "5       setosa\n",
       "6       setosa\n",
       "7       setosa\n",
       "8       setosa\n",
       "9       setosa\n",
       "10      setosa\n",
       "11      setosa\n",
       "12      setosa\n",
       "13      setosa\n",
       "14      setosa\n",
       "15      setosa\n",
       "16      setosa\n",
       "17      setosa\n",
       "18      setosa\n",
       "19      setosa\n",
       "20      setosa\n",
       "21      setosa\n",
       "22      setosa\n",
       "23      setosa\n",
       "24      setosa\n",
       "25      setosa\n",
       "26      setosa\n",
       "27      setosa\n",
       "28      setosa\n",
       "29      setosa\n",
       "..         ...\n",
       "120  virginica\n",
       "121  virginica\n",
       "122  virginica\n",
       "123  virginica\n",
       "124  virginica\n",
       "125  virginica\n",
       "126  virginica\n",
       "127  virginica\n",
       "128  virginica\n",
       "129  virginica\n",
       "130  virginica\n",
       "131  virginica\n",
       "132  virginica\n",
       "133  virginica\n",
       "134  virginica\n",
       "135  virginica\n",
       "136  virginica\n",
       "137  virginica\n",
       "138  virginica\n",
       "139  virginica\n",
       "140  virginica\n",
       "141  virginica\n",
       "142  virginica\n",
       "143  virginica\n",
       "144  virginica\n",
       "145  virginica\n",
       "146  virginica\n",
       "147  virginica\n",
       "148  virginica\n",
       "149  virginica\n",
       "\n",
       "[150 rows x 1 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = 10, 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[     sepal_length  sepal_width  petal_length  petal_width\n",
       " 96            5.7          2.9           4.2          1.3\n",
       " 105           7.6          3.0           6.6          2.1\n",
       " 66            5.6          3.0           4.5          1.5\n",
       " 0             5.1          3.5           1.4          0.2\n",
       " 122           7.7          2.8           6.7          2.0\n",
       " 67            5.8          2.7           4.1          1.0\n",
       " 28            5.2          3.4           1.4          0.2\n",
       " 40            5.0          3.5           1.3          0.3\n",
       " 44            5.1          3.8           1.9          0.4\n",
       " 60            5.0          2.0           3.5          1.0\n",
       " 123           6.3          2.7           4.9          1.8\n",
       " 24            4.8          3.4           1.9          0.2\n",
       " 25            5.0          3.0           1.6          0.2\n",
       " 23            5.1          3.3           1.7          0.5\n",
       " 94            5.6          2.7           4.2          1.3\n",
       " 39            5.1          3.4           1.5          0.2\n",
       " 95            5.7          3.0           4.2          1.2\n",
       " 117           7.7          3.8           6.7          2.2\n",
       " 47            4.6          3.2           1.4          0.2\n",
       " 97            6.2          2.9           4.3          1.3\n",
       " 113           5.7          2.5           5.0          2.0\n",
       " 33            5.5          4.2           1.4          0.2\n",
       " 138           6.0          3.0           4.8          1.8\n",
       " 101           5.8          2.7           5.1          1.9\n",
       " 62            6.0          2.2           4.0          1.0\n",
       " 84            5.4          3.0           4.5          1.5\n",
       " 148           6.2          3.4           5.4          2.3\n",
       " 53            5.5          2.3           4.0          1.3\n",
       " 5             5.4          3.9           1.7          0.4\n",
       " 93            5.0          2.3           3.3          1.0\n",
       " ..            ...          ...           ...          ...\n",
       " 91            6.1          3.0           4.6          1.4\n",
       " 41            4.5          2.3           1.3          0.3\n",
       " 58            6.6          2.9           4.6          1.3\n",
       " 90            5.5          2.6           4.4          1.2\n",
       " 48            5.3          3.7           1.5          0.2\n",
       " 88            5.6          3.0           4.1          1.3\n",
       " 107           7.3          2.9           6.3          1.8\n",
       " 124           6.7          3.3           5.7          2.1\n",
       " 21            5.1          3.7           1.5          0.4\n",
       " 57            4.9          2.4           3.3          1.0\n",
       " 144           6.7          3.3           5.7          2.5\n",
       " 129           7.2          3.0           5.8          1.6\n",
       " 37            4.9          3.6           1.4          0.1\n",
       " 140           6.7          3.1           5.6          2.4\n",
       " 1             4.9          3.0           1.4          0.2\n",
       " 52            6.9          3.1           4.9          1.5\n",
       " 130           7.4          2.8           6.1          1.9\n",
       " 103           6.3          2.9           5.6          1.8\n",
       " 99            5.7          2.8           4.1          1.3\n",
       " 116           6.5          3.0           5.5          1.8\n",
       " 87            6.3          2.3           4.4          1.3\n",
       " 74            6.4          2.9           4.3          1.3\n",
       " 121           5.6          2.8           4.9          2.0\n",
       " 149           5.9          3.0           5.1          1.8\n",
       " 20            5.4          3.4           1.7          0.2\n",
       " 71            6.1          2.8           4.0          1.3\n",
       " 106           4.9          2.5           4.5          1.7\n",
       " 14            5.8          4.0           1.2          0.2\n",
       " 92            5.8          2.6           4.0          1.2\n",
       " 102           7.1          3.0           5.9          2.1\n",
       " \n",
       " [100 rows x 4 columns],\n",
       "      sepal_length  sepal_width  petal_length  petal_width\n",
       " 73            6.1          2.8           4.7          1.2\n",
       " 18            5.7          3.8           1.7          0.3\n",
       " 118           7.7          2.6           6.9          2.3\n",
       " 78            6.0          2.9           4.5          1.5\n",
       " 76            6.8          2.8           4.8          1.4\n",
       " 31            5.4          3.4           1.5          0.4\n",
       " 64            5.6          2.9           3.6          1.3\n",
       " 141           6.9          3.1           5.1          2.3\n",
       " 68            6.2          2.2           4.5          1.5\n",
       " 82            5.8          2.7           3.9          1.2\n",
       " 110           6.5          3.2           5.1          2.0\n",
       " 12            4.8          3.0           1.4          0.1\n",
       " 36            5.5          3.5           1.3          0.2\n",
       " 9             4.9          3.1           1.5          0.1\n",
       " 19            5.1          3.8           1.5          0.3\n",
       " 56            6.3          3.3           4.7          1.6\n",
       " 104           6.5          3.0           5.8          2.2\n",
       " 69            5.6          2.5           3.9          1.1\n",
       " 55            5.7          2.8           4.5          1.3\n",
       " 132           6.4          2.8           5.6          2.2\n",
       " 29            4.7          3.2           1.6          0.2\n",
       " 127           6.1          3.0           4.9          1.8\n",
       " 26            5.0          3.4           1.6          0.4\n",
       " 128           6.4          2.8           5.6          2.1\n",
       " 131           7.9          3.8           6.4          2.0\n",
       " 145           6.7          3.0           5.2          2.3\n",
       " 108           6.7          2.5           5.8          1.8\n",
       " 143           6.8          3.2           5.9          2.3\n",
       " 45            4.8          3.0           1.4          0.3\n",
       " 30            4.8          3.1           1.6          0.2\n",
       " 22            4.6          3.6           1.0          0.2\n",
       " 15            5.7          4.4           1.5          0.4\n",
       " 65            6.7          3.1           4.4          1.4\n",
       " 11            4.8          3.4           1.6          0.2\n",
       " 42            4.4          3.2           1.3          0.2\n",
       " 146           6.3          2.5           5.0          1.9\n",
       " 51            6.4          3.2           4.5          1.5\n",
       " 27            5.2          3.5           1.5          0.2\n",
       " 4             5.0          3.6           1.4          0.2\n",
       " 32            5.2          4.1           1.5          0.1\n",
       " 142           5.8          2.7           5.1          1.9\n",
       " 85            6.0          3.4           4.5          1.6\n",
       " 86            6.7          3.1           4.7          1.5\n",
       " 16            5.4          3.9           1.3          0.4\n",
       " 10            5.4          3.7           1.5          0.2\n",
       " 81            5.5          2.4           3.7          1.0\n",
       " 133           6.3          2.8           5.1          1.5\n",
       " 137           6.4          3.1           5.5          1.8\n",
       " 75            6.6          3.0           4.4          1.4\n",
       " 109           7.2          3.6           6.1          2.5,\n",
       "         species\n",
       " 96   versicolor\n",
       " 105   virginica\n",
       " 66   versicolor\n",
       " 0        setosa\n",
       " 122   virginica\n",
       " 67   versicolor\n",
       " 28       setosa\n",
       " 40       setosa\n",
       " 44       setosa\n",
       " 60   versicolor\n",
       " 123   virginica\n",
       " 24       setosa\n",
       " 25       setosa\n",
       " 23       setosa\n",
       " 94   versicolor\n",
       " 39       setosa\n",
       " 95   versicolor\n",
       " 117   virginica\n",
       " 47       setosa\n",
       " 97   versicolor\n",
       " 113   virginica\n",
       " 33       setosa\n",
       " 138   virginica\n",
       " 101   virginica\n",
       " 62   versicolor\n",
       " 84   versicolor\n",
       " 148   virginica\n",
       " 53   versicolor\n",
       " 5        setosa\n",
       " 93   versicolor\n",
       " ..          ...\n",
       " 91   versicolor\n",
       " 41       setosa\n",
       " 58   versicolor\n",
       " 90   versicolor\n",
       " 48       setosa\n",
       " 88   versicolor\n",
       " 107   virginica\n",
       " 124   virginica\n",
       " 21       setosa\n",
       " 57   versicolor\n",
       " 144   virginica\n",
       " 129   virginica\n",
       " 37       setosa\n",
       " 140   virginica\n",
       " 1        setosa\n",
       " 52   versicolor\n",
       " 130   virginica\n",
       " 103   virginica\n",
       " 99   versicolor\n",
       " 116   virginica\n",
       " 87   versicolor\n",
       " 74   versicolor\n",
       " 121   virginica\n",
       " 149   virginica\n",
       " 20       setosa\n",
       " 71   versicolor\n",
       " 106   virginica\n",
       " 14       setosa\n",
       " 92   versicolor\n",
       " 102   virginica\n",
       " \n",
       " [100 rows x 1 columns],\n",
       "         species\n",
       " 73   versicolor\n",
       " 18       setosa\n",
       " 118   virginica\n",
       " 78   versicolor\n",
       " 76   versicolor\n",
       " 31       setosa\n",
       " 64   versicolor\n",
       " 141   virginica\n",
       " 68   versicolor\n",
       " 82   versicolor\n",
       " 110   virginica\n",
       " 12       setosa\n",
       " 36       setosa\n",
       " 9        setosa\n",
       " 19       setosa\n",
       " 56   versicolor\n",
       " 104   virginica\n",
       " 69   versicolor\n",
       " 55   versicolor\n",
       " 132   virginica\n",
       " 29       setosa\n",
       " 127   virginica\n",
       " 26       setosa\n",
       " 128   virginica\n",
       " 131   virginica\n",
       " 145   virginica\n",
       " 108   virginica\n",
       " 143   virginica\n",
       " 45       setosa\n",
       " 30       setosa\n",
       " 22       setosa\n",
       " 15       setosa\n",
       " 65   versicolor\n",
       " 11       setosa\n",
       " 42       setosa\n",
       " 146   virginica\n",
       " 51   versicolor\n",
       " 27       setosa\n",
       " 4        setosa\n",
       " 32       setosa\n",
       " 142   virginica\n",
       " 85   versicolor\n",
       " 86   versicolor\n",
       " 16       setosa\n",
       " 10       setosa\n",
       " 81   versicolor\n",
       " 133   virginica\n",
       " 137   virginica\n",
       " 75   versicolor\n",
       " 109   virginica]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y['species'], test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 4)\n",
      "(50, 4)\n",
      "(100,)\n",
      "(50,)\n"
     ]
    }
   ],
   "source": [
    "for group in [X_train, X_test, y_train, y_test]:\n",
    "    print(group.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls = KNeighborsClassifier()\n",
    "cls.f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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