DataTalks_Deleting I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deleting\n",
    "import dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5.1</th>\n",
       "      <th>3.5</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.2</th>\n",
       "      <th>Iris-setosa</th>\n",
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       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "      <td>146</td>\n",
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       "      <td>Iris-virginica</td>\n",
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       "      <td>147</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     5.1  3.5  1.4  0.2     Iris-setosa\n",
       "0    4.9  3.0  1.4  0.2     Iris-setosa\n",
       "1    4.7  3.2  1.3  0.2     Iris-setosa\n",
       "2    4.6  3.1  1.5  0.2     Iris-setosa\n",
       "3    5.0  3.6  1.4  0.2     Iris-setosa\n",
       "4    5.4  3.9  1.7  0.4     Iris-setosa\n",
       "..   ...  ...  ...  ...             ...\n",
       "144  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "145  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "146  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "147  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "148  5.9  3.0  5.1  1.8  Iris-virginica\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create columns for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "      <td>6.2</td>\n",
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       "      <td>Iris-virginica</td>\n",
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       "      <td>148</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width           class\n",
       "0             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "1             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "2             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "3             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "4             5.4          3.9           1.7          0.4     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "144           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "145           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "146           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "147           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "148           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['sepal_length','sepal_width', 'petal_length', 'petal_width', 'class']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there any missing value in the dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "class           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>sepal_length</th>\n",
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       "      <th>petal_length</th>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0            4.9          3.0           1.4          0.2  Iris-setosa\n",
       "1            4.7          3.2           1.3          0.2  Iris-setosa\n",
       "2            4.6          3.1           1.5          0.2  Iris-setosa\n",
       "3            5.0          3.6           1.4          0.2  Iris-setosa\n",
       "4            5.4          3.9           1.7          0.4  Iris-setosa\n",
       "5            4.6          3.4           1.4          0.3  Iris-setosa\n",
       "6            5.0          3.4           1.5          0.2  Iris-setosa\n",
       "7            4.4          2.9           1.4          0.2  Iris-setosa\n",
       "8            4.9          3.1           1.5          0.1  Iris-setosa\n",
       "9            5.4          3.7           1.5          0.2  Iris-setosa\n",
       "10           4.8          3.4           NaN          0.2  Iris-setosa\n",
       "11           4.8          3.0           NaN          0.1  Iris-setosa\n",
       "12           4.3          3.0           NaN          0.1  Iris-setosa\n",
       "13           5.8          4.0           NaN          0.2  Iris-setosa\n",
       "14           5.7          4.4           NaN          0.4  Iris-setosa\n",
       "15           5.4          3.9           NaN          0.4  Iris-setosa\n",
       "16           5.1          3.5           NaN          0.3  Iris-setosa\n",
       "17           5.7          3.8           NaN          0.3  Iris-setosa\n",
       "18           5.1          3.8           NaN          0.3  Iris-setosa\n",
       "19           5.4          3.4           NaN          0.2  Iris-setosa\n",
       "20           5.1          3.7           NaN          0.4  Iris-setosa\n",
       "21           4.6          3.6           NaN          0.2  Iris-setosa\n",
       "22           5.1          3.3           NaN          0.5  Iris-setosa\n",
       "23           4.8          3.4           NaN          0.2  Iris-setosa\n",
       "24           5.0          3.0           NaN          0.2  Iris-setosa\n",
       "25           5.0          3.4           NaN          0.4  Iris-setosa\n",
       "26           5.2          3.5           NaN          0.2  Iris-setosa\n",
       "27           5.2          3.4           NaN          0.2  Iris-setosa\n",
       "28           4.7          3.2           NaN          0.2  Iris-setosa\n",
       "29           4.8          3.1           NaN          0.2  Iris-setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[10:30, 2:3] = np.nan\n",
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets substitute the NaN values to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
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       "      <td>4.9</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <td>4.7</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.6</td>\n",
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       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0            4.9          3.0           1.4          0.2  Iris-setosa\n",
       "1            4.7          3.2           1.3          0.2  Iris-setosa\n",
       "2            4.6          3.1           1.5          0.2  Iris-setosa\n",
       "3            5.0          3.6           1.4          0.2  Iris-setosa\n",
       "4            5.4          3.9           1.7          0.4  Iris-setosa\n",
       "5            4.6          3.4           1.4          0.3  Iris-setosa\n",
       "6            5.0          3.4           1.5          0.2  Iris-setosa\n",
       "7            4.4          2.9           1.4          0.2  Iris-setosa\n",
       "8            4.9          3.1           1.5          0.1  Iris-setosa\n",
       "9            5.4          3.7           1.5          0.2  Iris-setosa\n",
       "10           4.8          3.4           1.0          0.2  Iris-setosa\n",
       "11           4.8          3.0           1.0          0.1  Iris-setosa\n",
       "12           4.3          3.0           1.0          0.1  Iris-setosa\n",
       "13           5.8          4.0           1.0          0.2  Iris-setosa\n",
       "14           5.7          4.4           1.0          0.4  Iris-setosa\n",
       "15           5.4          3.9           1.0          0.4  Iris-setosa\n",
       "16           5.1          3.5           1.0          0.3  Iris-setosa\n",
       "17           5.7          3.8           1.0          0.3  Iris-setosa\n",
       "18           5.1          3.8           1.0          0.3  Iris-setosa\n",
       "19           5.4          3.4           1.0          0.2  Iris-setosa"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.petal_length.fillna(1, inplace = True)\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's delete the column class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.6</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>145</td>\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",
       "      <td>146</td>\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",
       "      <td>147</td>\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",
       "      <td>148</td>\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>149 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             4.9          3.0           1.4          0.2\n",
       "1             4.7          3.2           1.3          0.2\n",
       "2             4.6          3.1           1.5          0.2\n",
       "3             5.0          3.6           1.4          0.2\n",
       "4             5.4          3.9           1.7          0.4\n",
       "..            ...          ...           ...          ...\n",
       "144           6.7          3.0           5.2          2.3\n",
       "145           6.3          2.5           5.0          1.9\n",
       "146           6.5          3.0           5.2          2.0\n",
       "147           6.2          3.4           5.4          2.3\n",
       "148           5.9          3.0           5.1          1.8\n",
       "\n",
       "[149 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['class']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the first 3 rows as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\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",
       "      <td>147</td>\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",
       "      <td>148</td>\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>149 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             NaN          NaN           NaN          NaN\n",
       "1             NaN          NaN           NaN          NaN\n",
       "2             NaN          NaN           NaN          NaN\n",
       "3             5.0          3.6           1.4          0.2\n",
       "4             5.4          3.9           1.7          0.4\n",
       "..            ...          ...           ...          ...\n",
       "144           6.7          3.0           5.2          2.3\n",
       "145           6.3          2.5           5.0          1.9\n",
       "146           6.5          3.0           5.2          2.0\n",
       "147           6.2          3.4           5.4          2.3\n",
       "148           5.9          3.0           5.1          1.8\n",
       "\n",
       "[149 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3,:] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delete the rows that have NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.8</td>\n",
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       "</table>\n",
       "<p>146 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "3             5.0          3.6           1.4          0.2\n",
       "4             5.4          3.9           1.7          0.4\n",
       "5             4.6          3.4           1.4          0.3\n",
       "6             5.0          3.4           1.5          0.2\n",
       "7             4.4          2.9           1.4          0.2\n",
       "..            ...          ...           ...          ...\n",
       "144           6.7          3.0           5.2          2.3\n",
       "145           6.3          2.5           5.0          1.9\n",
       "146           6.5          3.0           5.2          2.0\n",
       "147           6.2          3.4           5.4          2.3\n",
       "148           5.9          3.0           5.1          1.8\n",
       "\n",
       "[146 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(how='any')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reset the index so it begins with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>146 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             5.0          3.6           1.4          0.2\n",
       "1             5.4          3.9           1.7          0.4\n",
       "2             4.6          3.4           1.4          0.3\n",
       "3             5.0          3.4           1.5          0.2\n",
       "4             4.4          2.9           1.4          0.2\n",
       "..            ...          ...           ...          ...\n",
       "141           6.7          3.0           5.2          2.3\n",
       "142           6.3          2.5           5.0          1.9\n",
       "143           6.5          3.0           5.2          2.0\n",
       "144           6.2          3.4           5.4          2.3\n",
       "145           5.9          3.0           5.1          1.8\n",
       "\n",
       "[146 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = df.reset_index(drop=True)\n",
    "df"
   ]
  }
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