ass_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "131f4a76-e636-41bc-8248-4840bdf2ea11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04829499-e7e7-4cdd-a762-8c42296ec71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>CC</th>\n",
       "      <th>DS</th>\n",
       "      <th>CS</th>\n",
       "      <th>ANN</th>\n",
       "      <th>Percentile</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>84.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>77</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joey</td>\n",
       "      <td>72.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>46</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>56.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monica</td>\n",
       "      <td>91.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naruto</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>75</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Phoebe</td>\n",
       "      <td>65.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>78</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gauravi</td>\n",
       "      <td>48.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ishita</td>\n",
       "      <td>82.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>91</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Puja</td>\n",
       "      <td>42.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>38</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jayashree</td>\n",
       "      <td>78.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>50</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ross</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rachel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>67</td>\n",
       "      <td>70.250000</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Apeksha</td>\n",
       "      <td>55.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>87.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>81.250000</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>36</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Nidhi</td>\n",
       "      <td>69.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>25</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sahil</td>\n",
       "      <td>35.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>45</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nikhil</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.0</td>\n",
       "      <td>89</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>56</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name    CC    DS    CS  ANN  Percentile  Attendance\n",
       "0        Josh  84.0  89.0  68.0   77   79.500000        23.0\n",
       "1        Joey  72.0  45.0  89.0   46   63.000000        80.0\n",
       "2    Chandler  56.0  60.0  56.0   64   59.000000        55.0\n",
       "3      Monica  91.0  78.0   NaN   57   75.333333        54.0\n",
       "4      Naruto  30.0  23.0  25.0   75   38.250000         NaN\n",
       "5      Phoebe  65.0  94.0  63.0   78   75.000000         NaN\n",
       "6     Gauravi  48.0  37.0  76.0   81   60.500000        40.0\n",
       "7      Ishita  82.0  81.0  88.0   91   85.500000        49.0\n",
       "8        Puja  42.0  68.0  59.0   38   51.750000        68.0\n",
       "9   Jayashree  78.0  52.0  94.0   50   68.500000        45.0\n",
       "10       Ross  93.0  77.0  58.0   54   70.500000        76.0\n",
       "11     Rachel  63.0  95.0  56.0   67   70.250000        93.0\n",
       "12    Apeksha  55.0  12.0   NaN   86   51.000000        28.0\n",
       "13      Jenny  87.0  63.0  97.0   78   81.250000        91.0\n",
       "14        NaN  11.0  88.0  83.0   36   54.500000         NaN\n",
       "15      Nidhi  69.0  36.0  79.0   25   52.250000        59.0\n",
       "16      Sahil  35.0  70.0  95.0   45   61.250000        82.0\n",
       "17     Nikhil  99.0   NaN  92.0   89   93.333333        76.0\n",
       "18      Rohan   NaN  50.0  93.0   56   66.333333        91.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('academic.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a20a6513-1a47-4ea7-8252-88d4a501e920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          1\n",
       "CC            1\n",
       "DS            1\n",
       "CS            2\n",
       "ANN           0\n",
       "Percentile    0\n",
       "Attendance    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "858d53e1-4e84-46c5-b931-e88a8c768b69",
   "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>Name</th>\n",
       "      <th>CC</th>\n",
       "      <th>DS</th>\n",
       "      <th>CS</th>\n",
       "      <th>ANN</th>\n",
       "      <th>Percentile</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>84.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>77</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>23.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joey</td>\n",
       "      <td>72.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>46</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>80.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>56.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>55.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monica</td>\n",
       "      <td>91.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>54.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naruto</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>75</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>63.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Phoebe</td>\n",
       "      <td>65.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>78</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>63.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gauravi</td>\n",
       "      <td>48.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>40.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ishita</td>\n",
       "      <td>82.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>91</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>49.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Puja</td>\n",
       "      <td>42.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>38</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>68.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jayashree</td>\n",
       "      <td>78.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>50</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>45.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ross</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>76.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rachel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>67</td>\n",
       "      <td>70.250000</td>\n",
       "      <td>93.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Apeksha</td>\n",
       "      <td>55.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>28.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>87.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>81.250000</td>\n",
       "      <td>91.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Missing</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>36</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>63.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Nidhi</td>\n",
       "      <td>69.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>25</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>59.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sahil</td>\n",
       "      <td>35.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>45</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>82.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nikhil</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.0</td>\n",
       "      <td>89</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>76.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>56</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>91.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name    CC    DS    CS  ANN  Percentile  Attendance\n",
       "0        Josh  84.0  89.0  68.0   77   79.500000      23.000\n",
       "1        Joey  72.0  45.0  89.0   46   63.000000      80.000\n",
       "2    Chandler  56.0  60.0  56.0   64   59.000000      55.000\n",
       "3      Monica  91.0  78.0   0.0   57   75.333333      54.000\n",
       "4      Naruto  30.0  23.0  25.0   75   38.250000      63.125\n",
       "5      Phoebe  65.0  94.0  63.0   78   75.000000      63.125\n",
       "6     Gauravi  48.0  37.0  76.0   81   60.500000      40.000\n",
       "7      Ishita  82.0  81.0  88.0   91   85.500000      49.000\n",
       "8        Puja  42.0  68.0  59.0   38   51.750000      68.000\n",
       "9   Jayashree  78.0  52.0  94.0   50   68.500000      45.000\n",
       "10       Ross  93.0  77.0  58.0   54   70.500000      76.000\n",
       "11     Rachel  63.0  95.0  56.0   67   70.250000      93.000\n",
       "12    Apeksha  55.0  12.0   0.0   86   51.000000      28.000\n",
       "13      Jenny  87.0  63.0  97.0   78   81.250000      91.000\n",
       "14    Missing  11.0  88.0  83.0   36   54.500000      63.125\n",
       "15      Nidhi  69.0  36.0  79.0   25   52.250000      59.000\n",
       "16      Sahil  35.0  70.0  95.0   45   61.250000      82.000\n",
       "17     Nikhil  99.0   NaN  92.0   89   93.333333      76.000\n",
       "18      Rohan   NaN  50.0  93.0   56   66.333333      91.000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'] = df['Name'].fillna('Missing')\n",
    "\n",
    "df['Attendance'] = df['Attendance'].fillna(df['Attendance'].mean())\n",
    "df['CS'] = df['CS'].fillna(0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50887d43-3480-4e0d-b387-5abe6af36de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.017463\n",
       "1     0.848466\n",
       "2     0.408521\n",
       "3     0.458800\n",
       "4     0.000000\n",
       "5     0.000000\n",
       "6     1.162713\n",
       "7     0.710197\n",
       "8     0.245112\n",
       "9     0.911315\n",
       "10    0.647348\n",
       "11    1.502099\n",
       "12    1.766066\n",
       "13    1.401540\n",
       "14    0.000000\n",
       "15    0.207403\n",
       "16    0.949025\n",
       "17    0.647348\n",
       "18    1.401540\n",
       "Name: Attendance, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z= np.abs(stats.zscore(df['Attendance']))\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97836c2a-1dd3-419f-b1bf-c0459e80345a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0], dtype=int64),)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier = np.where(z>2)\n",
    "outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "66972752-2c44-4023-9e87-d10c15d241c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>CC</th>\n",
       "      <th>DS</th>\n",
       "      <th>CS</th>\n",
       "      <th>ANN</th>\n",
       "      <th>Percentile</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joey</td>\n",
       "      <td>72.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>46</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>80.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>56.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>55.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monica</td>\n",
       "      <td>91.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>54.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naruto</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>75</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>63.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Phoebe</td>\n",
       "      <td>65.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>78</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>63.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gauravi</td>\n",
       "      <td>48.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>40.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ishita</td>\n",
       "      <td>82.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>91</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>49.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Puja</td>\n",
       "      <td>42.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>38</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>68.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jayashree</td>\n",
       "      <td>78.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>50</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>45.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ross</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>76.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rachel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>67</td>\n",
       "      <td>70.250000</td>\n",
       "      <td>93.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Apeksha</td>\n",
       "      <td>55.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>28.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>87.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>81.250000</td>\n",
       "      <td>91.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Missing</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>36</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>63.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Nidhi</td>\n",
       "      <td>69.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>25</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>59.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sahil</td>\n",
       "      <td>35.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>45</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>82.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nikhil</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.0</td>\n",
       "      <td>89</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>76.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>56</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>91.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name    CC    DS    CS  ANN  Percentile  Attendance\n",
       "1        Joey  72.0  45.0  89.0   46   63.000000      80.000\n",
       "2    Chandler  56.0  60.0  56.0   64   59.000000      55.000\n",
       "3      Monica  91.0  78.0   0.0   57   75.333333      54.000\n",
       "4      Naruto  30.0  23.0  25.0   75   38.250000      63.125\n",
       "5      Phoebe  65.0  94.0  63.0   78   75.000000      63.125\n",
       "6     Gauravi  48.0  37.0  76.0   81   60.500000      40.000\n",
       "7      Ishita  82.0  81.0  88.0   91   85.500000      49.000\n",
       "8        Puja  42.0  68.0  59.0   38   51.750000      68.000\n",
       "9   Jayashree  78.0  52.0  94.0   50   68.500000      45.000\n",
       "10       Ross  93.0  77.0  58.0   54   70.500000      76.000\n",
       "11     Rachel  63.0  95.0  56.0   67   70.250000      93.000\n",
       "12    Apeksha  55.0  12.0   0.0   86   51.000000      28.000\n",
       "13      Jenny  87.0  63.0  97.0   78   81.250000      91.000\n",
       "14    Missing  11.0  88.0  83.0   36   54.500000      63.125\n",
       "15      Nidhi  69.0  36.0  79.0   25   52.250000      59.000\n",
       "16      Sahil  35.0  70.0  95.0   45   61.250000      82.000\n",
       "17     Nikhil  99.0   NaN  92.0   89   93.333333      76.000\n",
       "18      Rohan   NaN  50.0  93.0   56   66.333333      91.000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(outlier[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fd4cbc41-e1c3-4466-83bb-80c5c45465d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>CC</th>\n",
       "      <th>DS</th>\n",
       "      <th>CS</th>\n",
       "      <th>ANN</th>\n",
       "      <th>Percentile</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joey</td>\n",
       "      <td>72.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>46</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>4.382027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>56.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>4.007333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monica</td>\n",
       "      <td>91.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naruto</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>75</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>4.145117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Phoebe</td>\n",
       "      <td>65.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>78</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4.145117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gauravi</td>\n",
       "      <td>48.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ishita</td>\n",
       "      <td>82.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>91</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>3.891820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Puja</td>\n",
       "      <td>42.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>38</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>4.219508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jayashree</td>\n",
       "      <td>78.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>50</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>3.806662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ross</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>4.330733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rachel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>67</td>\n",
       "      <td>70.250000</td>\n",
       "      <td>4.532599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Apeksha</td>\n",
       "      <td>55.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>3.332205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>87.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>81.250000</td>\n",
       "      <td>4.510860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Missing</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>36</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>4.145117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Nidhi</td>\n",
       "      <td>69.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>25</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>4.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sahil</td>\n",
       "      <td>35.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>45</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>4.406719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nikhil</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.0</td>\n",
       "      <td>89</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>4.330733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>56</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>4.510860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name    CC    DS    CS  ANN  Percentile  Attendance\n",
       "1        Joey  72.0  45.0  89.0   46   63.000000    4.382027\n",
       "2    Chandler  56.0  60.0  56.0   64   59.000000    4.007333\n",
       "3      Monica  91.0  78.0   0.0   57   75.333333    3.988984\n",
       "4      Naruto  30.0  23.0  25.0   75   38.250000    4.145117\n",
       "5      Phoebe  65.0  94.0  63.0   78   75.000000    4.145117\n",
       "6     Gauravi  48.0  37.0  76.0   81   60.500000    3.688879\n",
       "7      Ishita  82.0  81.0  88.0   91   85.500000    3.891820\n",
       "8        Puja  42.0  68.0  59.0   38   51.750000    4.219508\n",
       "9   Jayashree  78.0  52.0  94.0   50   68.500000    3.806662\n",
       "10       Ross  93.0  77.0  58.0   54   70.500000    4.330733\n",
       "11     Rachel  63.0  95.0  56.0   67   70.250000    4.532599\n",
       "12    Apeksha  55.0  12.0   0.0   86   51.000000    3.332205\n",
       "13      Jenny  87.0  63.0  97.0   78   81.250000    4.510860\n",
       "14    Missing  11.0  88.0  83.0   36   54.500000    4.145117\n",
       "15      Nidhi  69.0  36.0  79.0   25   52.250000    4.077537\n",
       "16      Sahil  35.0  70.0  95.0   45   61.250000    4.406719\n",
       "17     Nikhil  99.0   NaN  92.0   89   93.333333    4.330733\n",
       "18      Rohan   NaN  50.0  93.0   56   66.333333    4.510860"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Attendance'] = np.log(df['Attendance'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2267e206-d115-43f6-a697-333f7954f242",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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