Null value treatment & Statistical functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "27ccb020",
   "metadata": {},
   "source": [
    "# Null value treatment & Statistical functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9bbdce64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7f6aae",
   "metadata": {},
   "source": [
    "Reading a csv file using pandas in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "644f31fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Salaries.csv\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d24bf511",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
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       "      <td>6</td>\n",
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       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
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       "      <td>B</td>\n",
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       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
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       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "..        ...        ...  ...      ...     ...     ...\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0122188",
   "metadata": {},
   "source": [
    "Reading an excel file using pandas in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e5e3f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"mtcars.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "00095036",
   "metadata": {},
   "outputs": [
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       "      <td>105</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>245</td>\n",
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       "      <td>3.570</td>\n",
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       "      <td>62</td>\n",
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       "      <td>95</td>\n",
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       "      <td>123</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.30</td>\n",
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       "      <td>123</td>\n",
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       "      <td>180</td>\n",
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       "      <td>4.070</td>\n",
       "      <td>17.40</td>\n",
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       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
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       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>205</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.250</td>\n",
       "      <td>17.98</td>\n",
       "      <td>0</td>\n",
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       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>215</td>\n",
       "      <td>3.00</td>\n",
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       "      <td>17.82</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>230</td>\n",
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       "      <td>17.42</td>\n",
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       "      <td>32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>78.7</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.200</td>\n",
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       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.7</td>\n",
       "      <td>52</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.615</td>\n",
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       "      <th>19</th>\n",
       "      <td>33</td>\n",
       "      <td>9.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>65</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.835</td>\n",
       "      <td>19.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.1</td>\n",
       "      <td>97</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.465</td>\n",
       "      <td>20.01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>21</th>\n",
       "      <td>15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.520</td>\n",
       "      <td>16.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <th>22</th>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.435</td>\n",
       "      <td>17.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <th>23</th>\n",
       "      <td>13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>245</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.840</td>\n",
       "      <td>15.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <th>24</th>\n",
       "      <td>19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>175</td>\n",
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       "      <td>17.05</td>\n",
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       "      <th>25</th>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.935</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.3</td>\n",
       "      <td>91</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.140</td>\n",
       "      <td>16.70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>95.1</td>\n",
       "      <td>113</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.513</td>\n",
       "      <td>16.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>264</td>\n",
       "      <td>4.22</td>\n",
       "      <td>3.170</td>\n",
       "      <td>14.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19</td>\n",
       "      <td>7.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.770</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.0</td>\n",
       "      <td>335</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  Unnamed: 1   disp   hp  drat     wt   qsec  vs  am  gear  carb\n",
       "0    21         NaN  160.0  110  3.90  2.620  16.46   0   0     4     4\n",
       "1    21         NaN  160.0  110  3.90  2.875  17.02   0   0     4     4\n",
       "2    22         8.0  108.0   93  3.85  2.320  18.61   1   1     4     1\n",
       "3    21         4.0  258.0  110  3.08  3.215  19.44   1   1     3     1\n",
       "4    18         7.0  360.0  175  3.15  3.440  17.02   0   0     3     2\n",
       "5    18         1.0  225.0  105  2.76  3.460  20.22   1   1     3     1\n",
       "6    14         3.0  360.0  245  3.21  3.570  15.84   0   0     3     4\n",
       "7    24         4.0  146.7   62  3.69  3.190  20.00   1   0     4     2\n",
       "8    22         8.0  140.8   95  3.92  3.150  22.90   1   0     4     2\n",
       "9    19         2.0  167.6  123  3.92  3.440  18.30   1   0     4     4\n",
       "10   17         8.0  167.6  123  3.92  3.440  18.90   1   0     4     4\n",
       "11   16         4.0  275.8  180  3.07  4.070  17.40   0   0     3     3\n",
       "12   17         3.0  275.8  180  3.07  3.730  17.60   0   0     3     3\n",
       "13   15         2.0  275.8  180  3.07  3.780  18.00   0   0     3     3\n",
       "14   10         4.0  472.0  205  2.93  5.250  17.98   0   0     3     4\n",
       "15   10         4.0  460.0  215  3.00  5.424  17.82   0   0     3     4\n",
       "16   14         7.0  440.0  230  3.23  5.345  17.42   0   0     3     4\n",
       "17   32         4.0   78.7   66  4.08  2.200  19.47   1   1     4     1\n",
       "18   30         4.0   75.7   52  4.93  1.615  18.52   1   1     4     2\n",
       "19   33         9.0   71.1   65  4.22  1.835  19.90   1   1     4     1\n",
       "20   21         5.0  120.1   97  3.70  2.465  20.01   1   0     3     1\n",
       "21   15         5.0  318.0  150  2.76  3.520  16.87   0   0     3     2\n",
       "22   15         2.0  304.0  150  3.15  3.435  17.30   0   0     3     2\n",
       "23   13         3.0  350.0  245  3.73  3.840  15.41   0   0     3     4\n",
       "24   19         2.0  400.0  175  3.08  3.845  17.05   0   0     3     2\n",
       "25   27         3.0   79.0   66  4.08  1.935  18.90   1   1     4     1\n",
       "26   26         NaN  120.3   91  4.43  2.140  16.70   0   1     5     2\n",
       "27   30         4.0   95.1  113  3.77  1.513  16.90   1   1     5     2\n",
       "28   15         8.0  351.0  264  4.22  3.170  14.50   0   1     5     4\n",
       "29   19         7.0  145.0  175  3.62  2.770  15.50   0   1     5     6\n",
       "30   15         NaN  301.0  335  3.54  3.570  14.60   0   1     5     8\n",
       "31   21         4.0  121.0  109  4.11  2.780  18.60   1   1     4     2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9ed7bd",
   "metadata": {},
   "source": [
    "Reading an excel file using pandas in python and speciying index, header & encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f006c17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas as a library tends to create an index column by default while reading a file.\n",
    "# By specifying index_col =0 we are specifying the pandas to use 0th column (i.e Sr no.) as the index column\n",
    "# By specifying header =0 we are specifying the pandas to use the default header if we use header = None, it will demote the header\n",
    "# By specifying encoding=\"utf-8\" we are specifying the pandas to eliminate any error in case of special characters.\n",
    "\n",
    "data = pd.read_excel(\"mtcars.xlsx\",index_col=0,header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8993b1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>105</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.460</td>\n",
       "      <td>20.22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>245</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.570</td>\n",
       "      <td>15.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.0</td>\n",
       "      <td>146.7</td>\n",
       "      <td>62</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.190</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.0</td>\n",
       "      <td>140.8</td>\n",
       "      <td>95</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.150</td>\n",
       "      <td>22.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>167.6</td>\n",
       "      <td>123</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.0</td>\n",
       "      <td>167.6</td>\n",
       "      <td>123</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.0</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.070</td>\n",
       "      <td>17.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.0</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.730</td>\n",
       "      <td>17.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.780</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>205</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.250</td>\n",
       "      <td>17.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>215</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.424</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>230</td>\n",
       "      <td>3.23</td>\n",
       "      <td>5.345</td>\n",
       "      <td>17.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.0</td>\n",
       "      <td>78.7</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.200</td>\n",
       "      <td>19.47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>75.7</td>\n",
       "      <td>52</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.615</td>\n",
       "      <td>18.52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>65</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.835</td>\n",
       "      <td>19.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.0</td>\n",
       "      <td>120.1</td>\n",
       "      <td>97</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.465</td>\n",
       "      <td>20.01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.520</td>\n",
       "      <td>16.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.435</td>\n",
       "      <td>17.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>245</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.840</td>\n",
       "      <td>15.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.845</td>\n",
       "      <td>17.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.935</td>\n",
       "      <td>18.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>120.3</td>\n",
       "      <td>91</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.140</td>\n",
       "      <td>16.70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>95.1</td>\n",
       "      <td>113</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.513</td>\n",
       "      <td>16.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>264</td>\n",
       "      <td>4.22</td>\n",
       "      <td>3.170</td>\n",
       "      <td>14.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.770</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>301.0</td>\n",
       "      <td>335</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 1   disp   hp  drat     wt   qsec  vs  am  gear  carb\n",
       "mpg                                                                \n",
       "21          NaN  160.0  110  3.90  2.620  16.46   0   0     4     4\n",
       "21          NaN  160.0  110  3.90  2.875  17.02   0   0     4     4\n",
       "22          8.0  108.0   93  3.85  2.320  18.61   1   1     4     1\n",
       "21          4.0  258.0  110  3.08  3.215  19.44   1   1     3     1\n",
       "18          7.0  360.0  175  3.15  3.440  17.02   0   0     3     2\n",
       "18          1.0  225.0  105  2.76  3.460  20.22   1   1     3     1\n",
       "14          3.0  360.0  245  3.21  3.570  15.84   0   0     3     4\n",
       "24          4.0  146.7   62  3.69  3.190  20.00   1   0     4     2\n",
       "22          8.0  140.8   95  3.92  3.150  22.90   1   0     4     2\n",
       "19          2.0  167.6  123  3.92  3.440  18.30   1   0     4     4\n",
       "17          8.0  167.6  123  3.92  3.440  18.90   1   0     4     4\n",
       "16          4.0  275.8  180  3.07  4.070  17.40   0   0     3     3\n",
       "17          3.0  275.8  180  3.07  3.730  17.60   0   0     3     3\n",
       "15          2.0  275.8  180  3.07  3.780  18.00   0   0     3     3\n",
       "10          4.0  472.0  205  2.93  5.250  17.98   0   0     3     4\n",
       "10          4.0  460.0  215  3.00  5.424  17.82   0   0     3     4\n",
       "14          7.0  440.0  230  3.23  5.345  17.42   0   0     3     4\n",
       "32          4.0   78.7   66  4.08  2.200  19.47   1   1     4     1\n",
       "30          4.0   75.7   52  4.93  1.615  18.52   1   1     4     2\n",
       "33          9.0   71.1   65  4.22  1.835  19.90   1   1     4     1\n",
       "21          5.0  120.1   97  3.70  2.465  20.01   1   0     3     1\n",
       "15          5.0  318.0  150  2.76  3.520  16.87   0   0     3     2\n",
       "15          2.0  304.0  150  3.15  3.435  17.30   0   0     3     2\n",
       "13          3.0  350.0  245  3.73  3.840  15.41   0   0     3     4\n",
       "19          2.0  400.0  175  3.08  3.845  17.05   0   0     3     2\n",
       "27          3.0   79.0   66  4.08  1.935  18.90   1   1     4     1\n",
       "26          NaN  120.3   91  4.43  2.140  16.70   0   1     5     2\n",
       "30          4.0   95.1  113  3.77  1.513  16.90   1   1     5     2\n",
       "15          8.0  351.0  264  4.22  3.170  14.50   0   1     5     4\n",
       "19          7.0  145.0  175  3.62  2.770  15.50   0   1     5     6\n",
       "15          NaN  301.0  335  3.54  3.570  14.60   0   1     5     8\n",
       "21          4.0  121.0  109  4.11  2.780  18.60   1   1     4     2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5286cecf",
   "metadata": {},
   "source": [
    "Describing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a34f5e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.607143</td>\n",
       "      <td>230.721875</td>\n",
       "      <td>146.687500</td>\n",
       "      <td>3.596563</td>\n",
       "      <td>3.217250</td>\n",
       "      <td>17.848750</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>3.687500</td>\n",
       "      <td>2.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.249927</td>\n",
       "      <td>123.938694</td>\n",
       "      <td>68.562868</td>\n",
       "      <td>0.534679</td>\n",
       "      <td>0.978457</td>\n",
       "      <td>1.786943</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.498991</td>\n",
       "      <td>0.737804</td>\n",
       "      <td>1.6152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>1.513000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.825000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>2.581250</td>\n",
       "      <td>16.892500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>196.300000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>3.695000</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>17.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>5.424000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 1        disp          hp       drat         wt       qsec  \\\n",
       "count   28.000000   32.000000   32.000000  32.000000  32.000000  32.000000   \n",
       "mean     4.607143  230.721875  146.687500   3.596563   3.217250  17.848750   \n",
       "std      2.249927  123.938694   68.562868   0.534679   0.978457   1.786943   \n",
       "min      1.000000   71.100000   52.000000   2.760000   1.513000  14.500000   \n",
       "25%      3.000000  120.825000   96.500000   3.080000   2.581250  16.892500   \n",
       "50%      4.000000  196.300000  123.000000   3.695000   3.325000  17.710000   \n",
       "75%      7.000000  326.000000  180.000000   3.920000   3.610000  18.900000   \n",
       "max      9.000000  472.000000  335.000000   4.930000   5.424000  22.900000   \n",
       "\n",
       "              vs         am       gear     carb  \n",
       "count  32.000000  32.000000  32.000000  32.0000  \n",
       "mean    0.437500   0.406250   3.687500   2.8125  \n",
       "std     0.504016   0.498991   0.737804   1.6152  \n",
       "min     0.000000   0.000000   3.000000   1.0000  \n",
       "25%     0.000000   0.000000   3.000000   2.0000  \n",
       "50%     0.000000   0.000000   4.000000   2.0000  \n",
       "75%     1.000000   1.000000   4.000000   4.0000  \n",
       "max     1.000000   1.000000   5.000000   8.0000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()      # Gives statistical desrciption of data (For e.g. Mean,Median, Mode...)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac2285a7",
   "metadata": {},
   "source": [
    "Information about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b2d1081a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 32 entries, 21 to 21\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 1  28 non-null     float64\n",
      " 1   disp        32 non-null     float64\n",
      " 2   hp          32 non-null     int64  \n",
      " 3   drat        32 non-null     float64\n",
      " 4   wt          32 non-null     float64\n",
      " 5   qsec        32 non-null     float64\n",
      " 6   vs          32 non-null     int64  \n",
      " 7   am          32 non-null     int64  \n",
      " 8   gear        32 non-null     int64  \n",
      " 9   carb        32 non-null     int64  \n",
      "dtypes: float64(5), int64(5)\n",
      "memory usage: 2.8 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d85dcf",
   "metadata": {},
   "source": [
    "Fetching the column headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7a3502d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 1', 'disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear',\n",
       "       'carb'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7562eab",
   "metadata": {},
   "source": [
    "Fetching the size of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16981d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73654b02",
   "metadata": {},
   "source": [
    "Describing the type of the column header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "507da3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 1    float64\n",
       "disp          float64\n",
       "hp              int64\n",
       "drat          float64\n",
       "wt            float64\n",
       "qsec          float64\n",
       "vs              int64\n",
       "am              int64\n",
       "gear            int64\n",
       "carb            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bce2e3",
   "metadata": {},
   "source": [
    "Task: Make multiple lists segregating dtypes of the column headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "23d4b69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data =pd.read_csv(\"Salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5f0bdb0b",
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "..        ...        ...  ...      ...     ...     ...\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "877904c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "object_list=[]\n",
    "int_list=[]\n",
    "for i in data.columns:\n",
    "    if data[i].dtype == \"int64\":\n",
    "        int_list.append(i)\n",
    "    else:\n",
    "        object_list.append(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61cff4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['phd', 'service', 'salary']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d778a99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rank', 'discipline', 'sex']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c76470",
   "metadata": {},
   "source": [
    "Fetching the top number of rows in a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "48f821b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000\n",
       "2  Prof          A   23       20  Male  110515\n",
       "3  Prof          A   40       31  Male  131205\n",
       "4  Prof          B   20       18  Male  104800"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be5daf1",
   "metadata": {},
   "source": [
    "Fetching the bottom number of rows from a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "699143a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service     sex  salary\n",
       "76  Prof          A   28       14  Female  109954\n",
       "77  Prof          A   23       15  Female  109646"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca167b5",
   "metadata": {},
   "source": [
    "Fetching a column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4044bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"phd\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5ac7a0",
   "metadata": {},
   "source": [
    "Statistics: Mean, Median & Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "07b0b965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.705128205128204"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean: Retruns the average value of the column\n",
    "\n",
    "data[\"phd\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eb5fba2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median : Returns the central value from the dataset\n",
    "\n",
    "data[\"phd\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d43d8d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "Name: phd, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mode : Returns the repeated value (highly frequent value) from the dataset\n",
    "\n",
    "data[\"phd\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a2f6baa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specifying the index of the mode we want. \n",
    "# Say in the above case we got 2 values 0 and 4. If we want 0 write index as [0] or if want 4 write index as [1]\n",
    "\n",
    "data[\"phd\"].mode()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6a528d",
   "metadata": {},
   "source": [
    "Null value treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9e0efa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The most important thing in Data preprocessing is to treat the null values before implementing the data on Machine \n",
    "# Learning algorithms. Because one can't apply data with missing values on Machine Learning algorithms.\n",
    "# To show an example for the same we have removed some data from the \"Salaries.csv\" file and updated it to \n",
    "# \"Salaries_new.csv\". So this new file now has null entries as well.\n",
    "\n",
    "data_new= pd.read_excel(\"Salaries_new.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "77f0d7f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "    <tr>\n",
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       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline   phd  service     sex  salary\n",
       "0        Prof          B  56.0     49.0    Male  186960\n",
       "1        Prof          A  12.0      6.0    Male   93000\n",
       "2        Prof          A  23.0     20.0    Male  110515\n",
       "3        Prof          A  40.0     31.0    Male  131205\n",
       "4        Prof          B  20.0     18.0    Male  104800\n",
       "..        ...        ...   ...      ...     ...     ...\n",
       "73       Prof          B  18.0     10.0  Female  105450\n",
       "74  AssocProf          B  19.0      6.0  Female  104542\n",
       "75       Prof          B  17.0     17.0  Female  124312\n",
       "76       Prof          A  28.0     14.0  Female  109954\n",
       "77       Prof          A  23.0     15.0  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8623021d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rank  discipline    phd  service    sex  salary\n",
       "0   False       False  False    False  False   False\n",
       "1   False       False  False    False  False   False\n",
       "2   False       False  False    False  False   False\n",
       "3   False       False  False    False  False   False\n",
       "4   False       False  False    False  False   False\n",
       "..    ...         ...    ...      ...    ...     ...\n",
       "73  False       False  False    False  False   False\n",
       "74  False       False  False    False  False   False\n",
       "75  False       False  False    False  False   False\n",
       "76  False       False  False    False  False   False\n",
       "77  False       False  False    False  False   False\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isnull()\n",
    "# isnull() function substitutes 'True' in the place where we've null values and 'False' in the place of non-null values\n",
    "\n",
    "data_new.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f78dee5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          0\n",
       "discipline    0\n",
       "phd           6\n",
       "service       3\n",
       "sex           0\n",
       "salary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isnull().sum()\n",
    "# isnull().sum() returns the total number of null values in a particular column\n",
    "\n",
    "data_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "15384885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     56.0\n",
       "1     12.0\n",
       "2     23.0\n",
       "3     40.0\n",
       "4     20.0\n",
       "      ... \n",
       "73    18.0\n",
       "74    19.0\n",
       "75    17.0\n",
       "76    28.0\n",
       "77    23.0\n",
       "Name: phd, Length: 78, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fillna()\n",
    "# fillna() is used to fill the null values with the value specified inside it's argument.\n",
    "\n",
    "# Looking at the o/p above we have got null values for the column phd and service. Let us try to replace the null values \n",
    "# the phd column with it's mean. The query would look like\n",
    "\n",
    "data_new[\"phd\"].fillna(data_new[\"phd\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d383554e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now adding an extra argument inplace = True, so inplace will substitute the null values with the specified value \n",
    "# (i.e. mean in this example) not only in phd column, but also in the original dataset. \n",
    "# So if we fetch the original datset back, the eliminated null values would be reflected in the table.\n",
    "\n",
    "data_new[\"phd\"].fillna(data_new[\"phd\"].mean(),inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3e85fdc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</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>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline   phd  service     sex  salary\n",
       "0        Prof          B  56.0     49.0    Male  186960\n",
       "1        Prof          A  12.0      6.0    Male   93000\n",
       "2        Prof          A  23.0     20.0    Male  110515\n",
       "3        Prof          A  40.0     31.0    Male  131205\n",
       "4        Prof          B  20.0     18.0    Male  104800\n",
       "..        ...        ...   ...      ...     ...     ...\n",
       "73       Prof          B  18.0     10.0  Female  105450\n",
       "74  AssocProf          B  19.0      6.0  Female  104542\n",
       "75       Prof          B  17.0     17.0  Female  124312\n",
       "76       Prof          A  28.0     14.0  Female  109954\n",
       "77       Prof          A  23.0     15.0  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "77f0dd7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          0\n",
       "discipline    0\n",
       "phd           0\n",
       "service       3\n",
       "sex           0\n",
       "salary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the null values for phd column now\n",
    "\n",
    "data_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e89380e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As one can clearly see, the null values of phd column are eliminated (since, sum of phd null values =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "aa109163",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similary treating the null values of service column by it's mode\n",
    "\n",
    "data_new[\"service\"].fillna(data_new[\"service\"].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "afca0e8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          0\n",
       "discipline    0\n",
       "phd           0\n",
       "service       0\n",
       "sex           0\n",
       "salary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3cc7f8f",
   "metadata": {},
   "source": [
    "Unique value treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84136271",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28149dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"adult_new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f75da112",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
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       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>104626</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
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       "      <td>184454</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>6418</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>279724</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
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       "      <td>0</td>\n",
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       "      <td>&gt;50K</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>27409</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27410</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
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       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27411</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27412</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27413</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27414 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt     education  educational-num  \\\n",
       "0       28         Local-gov  336951    Assoc-acdm               12   \n",
       "1       44           Private  160323  Some-college               10   \n",
       "2       63  Self-emp-not-inc  104626   Prof-school               15   \n",
       "3       65           Private  184454       HS-grad                9   \n",
       "4       48           Private  279724       HS-grad                9   \n",
       "...    ...               ...     ...           ...              ...   \n",
       "27409   27           Private  257302    Assoc-acdm               12   \n",
       "27410   40           Private  154374       HS-grad                9   \n",
       "27411   58           Private  151910       HS-grad                9   \n",
       "27412   22           Private  201490       HS-grad                9   \n",
       "27413   52      Self-emp-inc  287927       HS-grad                9   \n",
       "\n",
       "           marital-status         occupation relationship   race  gender  \\\n",
       "0      Married-civ-spouse    Protective-serv      Husband  White    Male   \n",
       "1      Married-civ-spouse  Machine-op-inspct      Husband  Black    Male   \n",
       "2      Married-civ-spouse     Prof-specialty      Husband  White    Male   \n",
       "3      Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "4      Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "...                   ...                ...          ...    ...     ...   \n",
       "27409  Married-civ-spouse       Tech-support         Wife  White  Female   \n",
       "27410  Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "27411             Widowed       Adm-clerical    Unmarried  White  Female   \n",
       "27412       Never-married       Adm-clerical    Own-child  White    Male   \n",
       "27413  Married-civ-spouse    Exec-managerial         Wife  White  Female   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country income  \n",
       "0                 0             0              40  United-States   >50K  \n",
       "1              7688             0              40  United-States   >50K  \n",
       "2              3103             0              32  United-States   >50K  \n",
       "3              6418             0              40  United-States   >50K  \n",
       "4              3103             0              48  United-States   >50K  \n",
       "...             ...           ...             ...            ...    ...  \n",
       "27409             0             0              38  United-States  <=50K  \n",
       "27410             0             0              40  United-States   >50K  \n",
       "27411             0             0              40  United-States  <=50K  \n",
       "27412             0             0              20  United-States  <=50K  \n",
       "27413         15024             0              40  United-States   >50K  \n",
       "\n",
       "[27414 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f83c643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0\n",
       "workclass          0\n",
       "fnlwgt             0\n",
       "education          0\n",
       "educational-num    0\n",
       "marital-status     0\n",
       "occupation         0\n",
       "relationship       0\n",
       "race               0\n",
       "gender             0\n",
       "capital-gain       0\n",
       "capital-loss       0\n",
       "hours-per-week     0\n",
       "native-country     0\n",
       "income             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d467b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Local-gov', 'Private', 'Self-emp-not-inc', 'State-gov',\n",
       "       'Self-emp-inc', '?', 'Federal-gov', 'Without-pay', 'Never-worked'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# At times, even if we see zero null values in the data, we do have some redundant values. \n",
    "# This can be checked by using unique function.\n",
    "# Let us check unique values for the coulmn \"workcalss\"\n",
    "\n",
    "df[\"workclass\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a998732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique value for coulmn: age\n",
      "\n",
      "[28 44 63 65 48 43 40 34 45 46 36 22 42 41 55 33 59 29 27 31 58 54 37 56\n",
      " 39 51 50 52 47 24 64 53 35 32 30 68 57 66 49 38 61 73 26 23 25 62 90 60\n",
      " 67 69 72 85 71 77 70 19 21 74 75 20 81 76 80 88 78 79 84 17 18 82 83 86\n",
      " 87]\n",
      "\n",
      "Unique value for coulmn: workclass\n",
      "\n",
      "['Local-gov' 'Private' 'Self-emp-not-inc' 'State-gov' 'Self-emp-inc' '?'\n",
      " 'Federal-gov' 'Without-pay' 'Never-worked']\n",
      "\n",
      "Unique value for coulmn: fnlwgt\n",
      "\n",
      "[336951 160323 104626 ...  84661 310152 257302]\n",
      "\n",
      "Unique value for coulmn: education\n",
      "\n",
      "['Assoc-acdm' 'Some-college' 'Prof-school' 'HS-grad' 'Masters' 'Doctorate'\n",
      " 'Bachelors' 'Assoc-voc' '9th' '10th' '7th-8th' '11th' '5th-6th' '1st-4th'\n",
      " '12th' 'Preschool']\n",
      "\n",
      "Unique value for coulmn: educational-num\n",
      "\n",
      "[12 10 15  9 14 16 13 11  5  6  4  7  3  2  8  1]\n",
      "\n",
      "Unique value for coulmn: marital-status\n",
      "\n",
      "['Married-civ-spouse' 'Never-married' 'Divorced' 'Separated'\n",
      " 'Married-AF-spouse' 'Widowed' 'Married-spouse-absent']\n",
      "\n",
      "Unique value for coulmn: occupation\n",
      "\n",
      "['Protective-serv' 'Machine-op-inspct' 'Prof-specialty' 'Exec-managerial'\n",
      " 'Tech-support' 'Craft-repair' 'Sales' 'Other-service' '?'\n",
      " 'Transport-moving' 'Adm-clerical' 'Handlers-cleaners' 'Farming-fishing'\n",
      " 'Priv-house-serv' 'Armed-Forces']\n",
      "\n",
      "Unique value for coulmn: relationship\n",
      "\n",
      "['Husband' 'Wife' 'Not-in-family' 'Unmarried' 'Own-child' 'Other-relative']\n",
      "\n",
      "Unique value for coulmn: race\n",
      "\n",
      "['White' 'Black' 'Asian-Pac-Islander' 'Other' 'Amer-Indian-Eskimo']\n",
      "\n",
      "Unique value for coulmn: gender\n",
      "\n",
      "['Male' 'Female']\n",
      "\n",
      "Unique value for coulmn: capital-gain\n",
      "\n",
      "[    0  7688  3103  6418  7298 14084  5178 15024 99999 27828  4934 10605\n",
      "  8614 13550 25236  4787  4386 10520 20051 14344 15020  9386 11678  7262\n",
      "  6514  7430 15831  9562  6097  5556 25124  4687  6612  7896  5455  3325\n",
      "  5721  7443  2036  3137  2176  4064  6360  1848  2174  6849  2885  4416\n",
      "  2354  5013  3781  3908  3464  1506   594  3411  1797  4650  1173  2907\n",
      "  2635  4865  2407  4508  2463  2829  3273  1151  4101  6723  2009  2202\n",
      "  2346  2414  6497 34095  1831  3418  2050  1055  3942  2580  2964  3432\n",
      "  3674  2290   914  2653   991  1639 18481  2062  3887  3818  6767  2329\n",
      "  2936  2597   114  2977 41310  2228  2961  1409 10566  2105  7978  1471\n",
      "  3471  2993  1086  2387  5060  3456]\n",
      "\n",
      "Unique value for coulmn: capital-loss\n",
      "\n",
      "[   0 2415 1887 1977 1485 1902 2444 1825 1848 3004 2559 2258 2377 1564\n",
      " 2547 2472 2174 2282 1911 2231 2246 2392 2824 1648  653 1092 3770 1590\n",
      " 1672 1408 1617 1579 1876 1974 1602 1651 1719 2001 1628 1741 2206 1755\n",
      " 3683 2205 1762 1594 1721 2339 1740 1669 2002 1668 1504 2129 4356  625\n",
      " 1980  880 1726 2267 2080 2051 1380  323 2457  213 2179 1138 2057 2603\n",
      " 1573  419  155 3900 2201 2042 1340 1944 1258 2238 2467 2163 2754 1411]\n",
      "\n",
      "Unique value for coulmn: hours-per-week\n",
      "\n",
      "[40 32 48 50 45 47 90 38 36 60 56 10 41 55 16 70 35 42 12 84 80 25 99 24\n",
      " 65 54 53 20 52 30 44 37 67 43 76 39  8 15 78 63  6 58  3 72 31 46 27  4\n",
      " 23 75 62 34 66 86  5 85 17 51 64  2 89 49 13 21  9 57 18 68  7 61  1 33\n",
      " 28 74 73 59 92 69 26 98 97 14 29 22 11 91 81 19 96 88 77 95]\n",
      "\n",
      "Unique value for coulmn: native-country\n",
      "\n",
      "['United-States' '?' 'Ireland' 'El-Salvador' 'Japan' 'Poland' 'England'\n",
      " 'Philippines' 'Germany' 'Thailand' 'Puerto-Rico' 'Portugal' 'China'\n",
      " 'Canada' 'India' 'Jamaica' 'Honduras' 'South' 'Ecuador' 'Hungary' 'Cuba'\n",
      " 'Mexico' 'Greece' 'Yugoslavia' 'Dominican-Republic' 'Nicaragua' 'Haiti'\n",
      " 'Italy' 'Columbia' 'Taiwan' 'Peru' 'France' 'Cambodia'\n",
      " 'Outlying-US(Guam-USVI-etc)' 'Hong' 'Iran' 'Vietnam' 'Guatemala'\n",
      " 'Trinadad&Tobago' 'Scotland' 'Laos' 'Holand-Netherlands']\n",
      "\n",
      "Unique value for coulmn: income\n",
      "\n",
      "['>50K' '<=50K']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Now let us check unique values for every column in the data df\n",
    "\n",
    "for i in df.columns:\n",
    "    print(f\"Unique value for coulmn: {i}\\n\\n{df[i].unique()}\\n\")   ## f string is used to make data dynamic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5176bef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As one can see from the output above, null values can also be present in the form of \"?\" in the dataset.\n",
    "# Generally such null values, are left unidentified using isnull fn.\n",
    "# To identify such null values, we use unique() fn & replace() fn\n",
    "\n",
    "df.replace(\"?\",np.nan, inplace= True) ## Here we're intentionally replacing the ? mark with null values, so that\n",
    "                                      ## it gets identified when we use isnull fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24c4047e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   0\n",
       "workclass          1283\n",
       "fnlwgt                0\n",
       "education             0\n",
       "educational-num       0\n",
       "marital-status        0\n",
       "occupation         1288\n",
       "relationship          0\n",
       "race                  0\n",
       "gender                0\n",
       "capital-gain          0\n",
       "capital-loss          0\n",
       "hours-per-week        0\n",
       "native-country      508\n",
       "income                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "87261670",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Task: Write a code treating null values with mode if the column is categorical (dtype: object)\n",
    "# and treating null values with mean if the column is numeric (dtype: int64 ordtype: int64 ) for the source \"data_new\"\n",
    "\n",
    "data_new= pd.read_excel(\"Salaries_new.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d192cfda",
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</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>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline   phd  service     sex  salary\n",
       "0        Prof          B  56.0     49.0    Male  186960\n",
       "1        Prof          A  12.0      6.0    Male   93000\n",
       "2        Prof          A  23.0     20.0    Male  110515\n",
       "3        Prof          A  40.0     31.0    Male  131205\n",
       "4        Prof          B  20.0     18.0    Male  104800\n",
       "..        ...        ...   ...      ...     ...     ...\n",
       "73       Prof          B  18.0     10.0  Female  105450\n",
       "74  AssocProf          B  19.0      6.0  Female  104542\n",
       "75       Prof          B  17.0     17.0  Female  124312\n",
       "76       Prof          A  28.0     14.0  Female  109954\n",
       "77       Prof          A  23.0     15.0  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e3d9074",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 78 entries, 0 to 77\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   rank        78 non-null     object \n",
      " 1   discipline  78 non-null     object \n",
      " 2   phd         72 non-null     float64\n",
      " 3   service     75 non-null     float64\n",
      " 4   sex         78 non-null     object \n",
      " 5   salary      78 non-null     int64  \n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 3.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "00326b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "90f1f3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Null_Value_Treatment(data_new):\n",
    "    for i in data_new.columns:\n",
    "        if data_new[i].dtypes == \"int64\" or data_new[i].dtypes == \"float64\":\n",
    "            data_new[i].fillna(data_new[i].mean(),inplace=True)\n",
    "        else:\n",
    "            data_new[i].fillna(data_new[i].mode()[0],inplace=True)\n",
    "    return data_new.isnull().sum()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "af0b6943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          0\n",
       "discipline    0\n",
       "phd           0\n",
       "service       0\n",
       "sex           0\n",
       "salary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Null_Value_Treatment(data_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bccf1182",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Task: Write a code treating null values with mode if the column is categorical (dtype: object)\n",
    "# and treating null values with mean if the column is numeric (dtype: int64 ordtype: int64 ) for the source \"df\"\n",
    "\n",
    "df=pd.read_csv(\"adult_new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d8db0b1a",
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>104626</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>Private</td>\n",
       "      <td>184454</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>6418</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>279724</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</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",
       "      <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",
       "      <th>27409</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27410</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27411</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27412</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27413</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27414 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt     education  educational-num  \\\n",
       "0       28         Local-gov  336951    Assoc-acdm               12   \n",
       "1       44           Private  160323  Some-college               10   \n",
       "2       63  Self-emp-not-inc  104626   Prof-school               15   \n",
       "3       65           Private  184454       HS-grad                9   \n",
       "4       48           Private  279724       HS-grad                9   \n",
       "...    ...               ...     ...           ...              ...   \n",
       "27409   27           Private  257302    Assoc-acdm               12   \n",
       "27410   40           Private  154374       HS-grad                9   \n",
       "27411   58           Private  151910       HS-grad                9   \n",
       "27412   22           Private  201490       HS-grad                9   \n",
       "27413   52      Self-emp-inc  287927       HS-grad                9   \n",
       "\n",
       "           marital-status         occupation relationship   race  gender  \\\n",
       "0      Married-civ-spouse    Protective-serv      Husband  White    Male   \n",
       "1      Married-civ-spouse  Machine-op-inspct      Husband  Black    Male   \n",
       "2      Married-civ-spouse     Prof-specialty      Husband  White    Male   \n",
       "3      Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "4      Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "...                   ...                ...          ...    ...     ...   \n",
       "27409  Married-civ-spouse       Tech-support         Wife  White  Female   \n",
       "27410  Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "27411             Widowed       Adm-clerical    Unmarried  White  Female   \n",
       "27412       Never-married       Adm-clerical    Own-child  White    Male   \n",
       "27413  Married-civ-spouse    Exec-managerial         Wife  White  Female   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country income  \n",
       "0                 0             0              40  United-States   >50K  \n",
       "1              7688             0              40  United-States   >50K  \n",
       "2              3103             0              32  United-States   >50K  \n",
       "3              6418             0              40  United-States   >50K  \n",
       "4              3103             0              48  United-States   >50K  \n",
       "...             ...           ...             ...            ...    ...  \n",
       "27409             0             0              38  United-States  <=50K  \n",
       "27410             0             0              40  United-States   >50K  \n",
       "27411             0             0              40  United-States  <=50K  \n",
       "27412             0             0              20  United-States  <=50K  \n",
       "27413         15024             0              40  United-States   >50K  \n",
       "\n",
       "[27414 rows x 15 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e6d3b688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "workclass          object\n",
       "fnlwgt              int64\n",
       "education          object\n",
       "educational-num     int64\n",
       "marital-status     object\n",
       "occupation         object\n",
       "relationship       object\n",
       "race               object\n",
       "gender             object\n",
       "capital-gain        int64\n",
       "capital-loss        int64\n",
       "hours-per-week      int64\n",
       "native-country     object\n",
       "income             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8a88b0b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e7f9d34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Null_Value_Treatment(df):\n",
    "    for i in df.columns:\n",
    "        if df[i].dtypes == \"int64\" or df[i].dtypes == \"float64\":\n",
    "            df[i].fillna(df[i].mean(),inplace=True)\n",
    "        else:\n",
    "            df[i].fillna(df[i].mode()[0],inplace=True)\n",
    "    return df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e5493397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0\n",
       "workclass          0\n",
       "fnlwgt             0\n",
       "education          0\n",
       "educational-num    0\n",
       "marital-status     0\n",
       "occupation         0\n",
       "relationship       0\n",
       "race               0\n",
       "gender             0\n",
       "capital-gain       0\n",
       "capital-loss       0\n",
       "hours-per-week     0\n",
       "native-country     0\n",
       "income             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Null_Value_Treatment(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd1a07da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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