DataTalks_Deleting II.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deleting\n",
    "import dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n"
   ]
  },
  {
   "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",
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>14.23</th>\n",
       "      <th>1.71</th>\n",
       "      <th>2.43</th>\n",
       "      <th>15.6</th>\n",
       "      <th>127</th>\n",
       "      <th>2.8</th>\n",
       "      <th>3.06</th>\n",
       "      <th>.28</th>\n",
       "      <th>2.29</th>\n",
       "      <th>5.64</th>\n",
       "      <th>1.04</th>\n",
       "      <th>3.92</th>\n",
       "      <th>1065</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
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       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 14 columns</p>\n",
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       "     1  14.23  1.71  2.43  15.6  127   2.8  3.06   .28  2.29   5.64  1.04  \\\n",
       "0    1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
       "1    1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
       "2    1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
       "3    1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
       "4    1  14.20  1.76  2.45  15.2  112  3.27  3.39  0.34  1.97   6.75  1.05   \n",
       "..  ..    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
       "172  3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
       "173  3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
       "174  3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
       "175  3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
       "176  3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
       "\n",
       "     3.92  1065  \n",
       "0    3.40  1050  \n",
       "1    3.17  1185  \n",
       "2    3.45  1480  \n",
       "3    2.93   735  \n",
       "4    2.85  1450  \n",
       "..    ...   ...  \n",
       "172  1.74   740  \n",
       "173  1.56   750  \n",
       "174  1.56   835  \n",
       "175  1.62   840  \n",
       "176  1.60   560  \n",
       "\n",
       "[177 rows x 14 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delete the first, fourth, seventh, nineth, eleventh, thirteenth and fourteenth columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>127</th>\n",
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       "      <td>0</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
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       "      <td>2</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.24</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
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       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
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       "      <td>4</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
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       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
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       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
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       "<p>177 rows × 7 columns</p>\n",
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      "text/plain": [
       "     14.23  1.71  15.6  127  3.06  2.29   5.64\n",
       "0    13.20  1.78  11.2  100  2.76  1.28   4.38\n",
       "1    13.16  2.36  18.6  101  3.24  2.81   5.68\n",
       "2    14.37  1.95  16.8  113  3.49  2.18   7.80\n",
       "3    13.24  2.59  21.0  118  2.69  1.82   4.32\n",
       "4    14.20  1.76  15.2  112  3.39  1.97   6.75\n",
       "..     ...   ...   ...  ...   ...   ...    ...\n",
       "172  13.71  5.65  20.5   95  0.61  1.06   7.70\n",
       "173  13.40  3.91  23.0  102  0.75  1.41   7.30\n",
       "174  13.27  4.28  20.0  120  0.69  1.35  10.20\n",
       "175  13.17  2.59  20.0  120  0.68  1.46   9.30\n",
       "176  14.13  4.10  24.5   96  0.76  1.35   9.20\n",
       "\n",
       "[177 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(df.columns[[0, 3, 6, 8, 11, 12, 13]], axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
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       "      <td>0</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0    13.20        1.78               11.2        100        2.76   \n",
       "1    13.16        2.36               18.6        101        3.24   \n",
       "2    14.37        1.95               16.8        113        3.49   \n",
       "3    13.24        2.59               21.0        118        2.69   \n",
       "4    14.20        1.76               15.2        112        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['alcohol', 'malic_acid', 'alcalinity_of_ash', 'magnesium', 'flavanoids', 'proanthocyanins', 'hue'] \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the values of the first 3 rows from alcohol as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.82</td>\n",
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       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
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       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
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       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
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       "      <td>13.40</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
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       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0        NaN        1.78               11.2        100        2.76   \n",
       "1        NaN        2.36               18.6        101        3.24   \n",
       "2        NaN        1.95               16.8        113        3.49   \n",
       "3      13.24        2.59               21.0        118        2.69   \n",
       "4      14.20        1.76               15.2        112        3.39   \n",
       "..       ...         ...                ...        ...         ...   \n",
       "172    13.71        5.65               20.5         95        0.61   \n",
       "173    13.40        3.91               23.0        102        0.75   \n",
       "174    13.27        4.28               20.0        120        0.69   \n",
       "175    13.17        2.59               20.0        120        0.68   \n",
       "176    14.13        4.10               24.5         96        0.76   \n",
       "\n",
       "     proanthocyanins    hue  \n",
       "0               1.28   4.38  \n",
       "1               2.81   5.68  \n",
       "2               2.18   7.80  \n",
       "3               1.82   4.32  \n",
       "4               1.97   6.75  \n",
       "..               ...    ...  \n",
       "172             1.06   7.70  \n",
       "173             1.41   7.30  \n",
       "174             1.35  10.20  \n",
       "175             1.46   9.30  \n",
       "176             1.35   9.20  \n",
       "\n",
       "[177 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3, 0] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the value of the rows 3 and 4 of magnesium as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
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       "      <td>120.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.35</td>\n",
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       "      <td>175</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.68</td>\n",
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       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
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       "<p>177 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0        NaN        1.78               11.2      100.0        2.76   \n",
       "1        NaN        2.36               18.6      101.0        3.24   \n",
       "2        NaN        1.95               16.8        NaN        3.49   \n",
       "3      13.24        2.59               21.0        NaN        2.69   \n",
       "4      14.20        1.76               15.2      112.0        3.39   \n",
       "..       ...         ...                ...        ...         ...   \n",
       "172    13.71        5.65               20.5       95.0        0.61   \n",
       "173    13.40        3.91               23.0      102.0        0.75   \n",
       "174    13.27        4.28               20.0      120.0        0.69   \n",
       "175    13.17        2.59               20.0      120.0        0.68   \n",
       "176    14.13        4.10               24.5       96.0        0.76   \n",
       "\n",
       "     proanthocyanins    hue  \n",
       "0               1.28   4.38  \n",
       "1               2.81   5.68  \n",
       "2               2.18   7.80  \n",
       "3               1.82   4.32  \n",
       "4               1.97   6.75  \n",
       "..               ...    ...  \n",
       "172             1.06   7.70  \n",
       "173             1.41   7.30  \n",
       "174             1.35  10.20  \n",
       "175             1.46   9.30  \n",
       "176             1.35   9.20  \n",
       "\n",
       "[177 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:4, 3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fill the value of NaN with the number 10 in alcohol and 100 in magnesium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>174</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.35</td>\n",
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       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
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       "      <td>120.0</td>\n",
       "      <td>0.68</td>\n",
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       "<p>177 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      10.00        1.78               11.2      100.0        2.76   \n",
       "1      10.00        2.36               18.6      101.0        3.24   \n",
       "2      10.00        1.95               16.8      100.0        3.49   \n",
       "3      13.24        2.59               21.0      100.0        2.69   \n",
       "4      14.20        1.76               15.2      112.0        3.39   \n",
       "..       ...         ...                ...        ...         ...   \n",
       "172    13.71        5.65               20.5       95.0        0.61   \n",
       "173    13.40        3.91               23.0      102.0        0.75   \n",
       "174    13.27        4.28               20.0      120.0        0.69   \n",
       "175    13.17        2.59               20.0      120.0        0.68   \n",
       "176    14.13        4.10               24.5       96.0        0.76   \n",
       "\n",
       "     proanthocyanins    hue  \n",
       "0               1.28   4.38  \n",
       "1               2.81   5.68  \n",
       "2               2.18   7.80  \n",
       "3               1.82   4.32  \n",
       "4               1.97   6.75  \n",
       "..               ...    ...  \n",
       "172             1.06   7.70  \n",
       "173             1.41   7.30  \n",
       "174             1.35  10.20  \n",
       "175             1.46   9.30  \n",
       "176             1.35   9.20  \n",
       "\n",
       "[177 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.alcohol.fillna(10, inplace = True)\n",
    "df.magnesium.fillna(100, inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count the number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol              0\n",
       "malic_acid           0\n",
       "alcalinity_of_ash    0\n",
       "magnesium            0\n",
       "flavanoids           0\n",
       "proanthocyanins      0\n",
       "hue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create an array of 10 random numbers up until 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 6, 2, 8, 0, 1, 0, 8, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random = np.random.randint(10, size = 10)\n",
    "random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use random numbers you generated as an index and assign NaN value to each of cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.52</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.15</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.35</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      NaN        1.78               11.2      100.0        2.76   \n",
       "1      NaN        2.36               18.6      101.0        3.24   \n",
       "2      NaN        1.95               16.8      100.0        3.49   \n",
       "3      NaN        2.59               21.0      100.0        2.69   \n",
       "4    14.20        1.76               15.2      112.0        3.39   \n",
       "5    14.39        1.87               14.6       96.0        2.52   \n",
       "6      NaN        2.15               17.6      121.0        2.51   \n",
       "7    14.83        1.64               14.0       97.0        2.98   \n",
       "8      NaN        1.35               16.0       98.0        3.15   \n",
       "9    14.10        2.16               18.0      105.0        3.32   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  \n",
       "5             1.98  5.25  \n",
       "6             1.25  5.05  \n",
       "7             1.98  5.20  \n",
       "8             1.85  7.22  \n",
       "9             2.38  5.75  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.alcohol[random] = np.nan\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many missing values do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol              6\n",
       "malic_acid           0\n",
       "alcalinity_of_ash    0\n",
       "magnesium            0\n",
       "flavanoids           0\n",
       "proanthocyanins      0\n",
       "hue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delete the rows that contain missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.52</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "4      14.20        1.76               15.2      112.0        3.39   \n",
       "5      14.39        1.87               14.6       96.0        2.52   \n",
       "7      14.83        1.64               14.0       97.0        2.98   \n",
       "9      14.10        2.16               18.0      105.0        3.32   \n",
       "10     14.12        1.48               16.8       95.0        2.43   \n",
       "..       ...         ...                ...        ...         ...   \n",
       "172    13.71        5.65               20.5       95.0        0.61   \n",
       "173    13.40        3.91               23.0      102.0        0.75   \n",
       "174    13.27        4.28               20.0      120.0        0.69   \n",
       "175    13.17        2.59               20.0      120.0        0.68   \n",
       "176    14.13        4.10               24.5       96.0        0.76   \n",
       "\n",
       "     proanthocyanins    hue  \n",
       "4               1.97   6.75  \n",
       "5               1.98   5.25  \n",
       "7               1.98   5.20  \n",
       "9               2.38   5.75  \n",
       "10              1.57   5.00  \n",
       "..               ...    ...  \n",
       "172             1.06   7.70  \n",
       "173             1.41   7.30  \n",
       "174             1.35  10.20  \n",
       "175             1.46   9.30  \n",
       "176             1.35   9.20  \n",
       "\n",
       "[171 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(axis = 0, how = 'any')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print only the non-null values in alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      True\n",
       "5      True\n",
       "7      True\n",
       "9      True\n",
       "10     True\n",
       "       ... \n",
       "172    True\n",
       "173    True\n",
       "174    True\n",
       "175    True\n",
       "176    True\n",
       "Name: alcohol, Length: 171, dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df.alcohol.notnull()\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      14.20\n",
       "5      14.39\n",
       "7      14.83\n",
       "9      14.10\n",
       "10     14.12\n",
       "       ...  \n",
       "172    13.71\n",
       "173    13.40\n",
       "174    13.27\n",
       "175    13.17\n",
       "176    14.13\n",
       "Name: alcohol, Length: 171, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.alcohol[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reset the index, so it starts with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
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       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      14.20        1.76               15.2      112.0        3.39   \n",
       "1      14.39        1.87               14.6       96.0        2.52   \n",
       "2      14.83        1.64               14.0       97.0        2.98   \n",
       "3      14.10        2.16               18.0      105.0        3.32   \n",
       "4      14.12        1.48               16.8       95.0        2.43   \n",
       "..       ...         ...                ...        ...         ...   \n",
       "166    13.71        5.65               20.5       95.0        0.61   \n",
       "167    13.40        3.91               23.0      102.0        0.75   \n",
       "168    13.27        4.28               20.0      120.0        0.69   \n",
       "169    13.17        2.59               20.0      120.0        0.68   \n",
       "170    14.13        4.10               24.5       96.0        0.76   \n",
       "\n",
       "     proanthocyanins    hue  \n",
       "0               1.97   6.75  \n",
       "1               1.98   5.25  \n",
       "2               1.98   5.20  \n",
       "3               2.38   5.75  \n",
       "4               1.57   5.00  \n",
       "..               ...    ...  \n",
       "166             1.06   7.70  \n",
       "167             1.41   7.30  \n",
       "168             1.35  10.20  \n",
       "169             1.46   9.30  \n",
       "170             1.35   9.20  \n",
       "\n",
       "[171 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index(drop = True)\n",
    "df"
   ]
  }
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