3_data_analysis_dse_pretest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Digital Skill Essential or known as DSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>First name</th>\n",
       "      <th>Email address</th>\n",
       "      <th>State</th>\n",
       "      <th>Started on</th>\n",
       "      <th>Completed</th>\n",
       "      <th>Time taken</th>\n",
       "      <th>Grade/100.00</th>\n",
       "      <th>Q. 1 /1.25</th>\n",
       "      <th>Q. 2 /1.25</th>\n",
       "      <th>...</th>\n",
       "      <th>Q. 71 /1.25</th>\n",
       "      <th>Q. 72 /1.25</th>\n",
       "      <th>Q. 73 /1.25</th>\n",
       "      <th>Q. 74 /1.25</th>\n",
       "      <th>Q. 75 /1.25</th>\n",
       "      <th>Q. 76 /1.25</th>\n",
       "      <th>Q. 77 /1.25</th>\n",
       "      <th>Q. 78 /1.25</th>\n",
       "      <th>Q. 79 /1.25</th>\n",
       "      <th>Q. 80 /1.25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ម៉ាលីស</td>\n",
       "      <td>វ៉ាន់</td>\n",
       "      <td>vmalispg1n31@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:03 AM</td>\n",
       "      <td>22 July 2022  7:23 AM</td>\n",
       "      <td>1 hour 20 mins</td>\n",
       "      <td>41.52</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>គឹមតៃ</td>\n",
       "      <td>ហៃ</td>\n",
       "      <td>hkimtaip22n72@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:04 AM</td>\n",
       "      <td>22 July 2022  6:38 AM</td>\n",
       "      <td>33 mins 29 secs</td>\n",
       "      <td>14.54</td>\n",
       "      <td>-</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</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>2</th>\n",
       "      <td>ប៊ុនលី</td>\n",
       "      <td>ទូច</td>\n",
       "      <td>tbunlypg2n84@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:08 AM</td>\n",
       "      <td>22 July 2022  7:22 AM</td>\n",
       "      <td>1 hour 13 mins</td>\n",
       "      <td>45.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Surname First name             Email address     State  \\\n",
       "0  ម៉ាលីស      វ៉ាន់   vmalispg1n31@era.gov.kh  Finished   \n",
       "1   គឹមតៃ         ហៃ  hkimtaip22n72@era.gov.kh  Finished   \n",
       "2  ប៊ុនលី        ទូច   tbunlypg2n84@era.gov.kh  Finished   \n",
       "\n",
       "              Started on              Completed       Time taken Grade/100.00  \\\n",
       "0  22 July 2022  6:03 AM  22 July 2022  7:23 AM   1 hour 20 mins        41.52   \n",
       "1  22 July 2022  6:04 AM  22 July 2022  6:38 AM  33 mins 29 secs        14.54   \n",
       "2  22 July 2022  6:08 AM  22 July 2022  7:22 AM   1 hour 13 mins        45.25   \n",
       "\n",
       "  Q. 1 /1.25 Q. 2 /1.25  ... Q. 71 /1.25 Q. 72 /1.25 Q. 73 /1.25 Q. 74 /1.25  \\\n",
       "0       1.25       0.00  ...        1.25        0.21        0.83        1.25   \n",
       "1          -       1.25  ...        0.00        0.00        0.42           -   \n",
       "2       1.25       0.00  ...        1.25        0.00        1.04        0.00   \n",
       "\n",
       "  Q. 75 /1.25 Q. 76 /1.25 Q. 77 /1.25 Q. 78 /1.25 Q. 79 /1.25 Q. 80 /1.25  \n",
       "0        0.63        0.75        0.42        0.42        0.00        0.00  \n",
       "1           -           -           -           -           -           -  \n",
       "2        0.63        0.50        0.83        1.25        1.25        0.00  \n",
       "\n",
       "[3 rows x 88 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./datasets/all_pretest_combined.csv')\n",
    "df.drop(columns=df.columns[0], axis=1, inplace=True) # drop the first column by name\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace - with None\n",
    "columns = df.columns\n",
    "for col in columns:\n",
    "    # print(col)\n",
    "    df[col] = df[col].replace(['-'], float(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['State'] == \"Finished\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Time taken</th>\n",
       "      <th>Grade/100.00</th>\n",
       "      <th>Q. 1 /1.25</th>\n",
       "      <th>Q. 2 /1.25</th>\n",
       "      <th>...</th>\n",
       "      <th>Q. 71 /1.25</th>\n",
       "      <th>Q. 72 /1.25</th>\n",
       "      <th>Q. 73 /1.25</th>\n",
       "      <th>Q. 74 /1.25</th>\n",
       "      <th>Q. 75 /1.25</th>\n",
       "      <th>Q. 76 /1.25</th>\n",
       "      <th>Q. 77 /1.25</th>\n",
       "      <th>Q. 78 /1.25</th>\n",
       "      <th>Q. 79 /1.25</th>\n",
       "      <th>Q. 80 /1.25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ម៉ាលីស</td>\n",
       "      <td>វ៉ាន់</td>\n",
       "      <td>vmalispg1n31@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:03 AM</td>\n",
       "      <td>22 July 2022  7:23 AM</td>\n",
       "      <td>1 hour 20 mins</td>\n",
       "      <td>41.52</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>គឹមតៃ</td>\n",
       "      <td>ហៃ</td>\n",
       "      <td>hkimtaip22n72@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:04 AM</td>\n",
       "      <td>22 July 2022  6:38 AM</td>\n",
       "      <td>33 mins 29 secs</td>\n",
       "      <td>14.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ប៊ុនលី</td>\n",
       "      <td>ទូច</td>\n",
       "      <td>tbunlypg2n84@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:08 AM</td>\n",
       "      <td>22 July 2022  7:22 AM</td>\n",
       "      <td>1 hour 13 mins</td>\n",
       "      <td>45.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ឈូក</td>\n",
       "      <td>ឈឿន​</td>\n",
       "      <td>cchhoukp22n15@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:11 AM</td>\n",
       "      <td>22 July 2022  7:30 AM</td>\n",
       "      <td>1 hour 18 mins</td>\n",
       "      <td>33.85</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>វណ្ណៈ</td>\n",
       "      <td>និន</td>\n",
       "      <td>nvannakp22n109@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:22 AM</td>\n",
       "      <td>22 July 2022  7:48 AM</td>\n",
       "      <td>1 hour 26 mins</td>\n",
       "      <td>10.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Surname First name              Email address     State  \\\n",
       "0  ម៉ាលីស      វ៉ាន់    vmalispg1n31@era.gov.kh  Finished   \n",
       "1   គឹមតៃ         ហៃ   hkimtaip22n72@era.gov.kh  Finished   \n",
       "2  ប៊ុនលី        ទូច    tbunlypg2n84@era.gov.kh  Finished   \n",
       "3     ឈូក       ឈឿន​   cchhoukp22n15@era.gov.kh  Finished   \n",
       "4   វណ្ណៈ        និន  nvannakp22n109@era.gov.kh  Finished   \n",
       "\n",
       "              Started on              Completed       Time taken Grade/100.00  \\\n",
       "0  22 July 2022  6:03 AM  22 July 2022  7:23 AM   1 hour 20 mins        41.52   \n",
       "1  22 July 2022  6:04 AM  22 July 2022  6:38 AM  33 mins 29 secs        14.54   \n",
       "2  22 July 2022  6:08 AM  22 July 2022  7:22 AM   1 hour 13 mins        45.25   \n",
       "3  22 July 2022  6:11 AM  22 July 2022  7:30 AM   1 hour 18 mins        33.85   \n",
       "4  22 July 2022  6:22 AM  22 July 2022  7:48 AM   1 hour 26 mins        10.38   \n",
       "\n",
       "  Q. 1 /1.25 Q. 2 /1.25  ... Q. 71 /1.25 Q. 72 /1.25 Q. 73 /1.25 Q. 74 /1.25  \\\n",
       "0       1.25       0.00  ...        1.25        0.21        0.83        1.25   \n",
       "1        0.0       1.25  ...        0.00        0.00        0.42         0.0   \n",
       "2       1.25       0.00  ...        1.25        0.00        1.04        0.00   \n",
       "3       0.50       1.25  ...        1.25        0.00        0.42         0.0   \n",
       "4       0.00       0.00  ...         0.0         0.0         0.0         0.0   \n",
       "\n",
       "  Q. 75 /1.25 Q. 76 /1.25 Q. 77 /1.25 Q. 78 /1.25 Q. 79 /1.25 Q. 80 /1.25  \n",
       "0        0.63        0.75        0.42        0.42        0.00        0.00  \n",
       "1         0.0         0.0         0.0         0.0         0.0         0.0  \n",
       "2        0.63        0.50        0.83        1.25        1.25        0.00  \n",
       "3        0.31        0.50        0.83        0.00        0.00        0.00  \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Grade/100.00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3064.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.300251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.060435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>52.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.420000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "       Grade/100.00\n",
       "count   3064.000000\n",
       "mean      41.300251\n",
       "std       17.060435\n",
       "min        0.000000\n",
       "25%       30.790000\n",
       "50%       40.980000\n",
       "75%       52.310000\n",
       "max       89.420000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Grade/100.00'] = df['Grade/100.00'].astype('float64')\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame without Duplicates:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>First name</th>\n",
       "      <th>Email address</th>\n",
       "      <th>State</th>\n",
       "      <th>Started on</th>\n",
       "      <th>Completed</th>\n",
       "      <th>Time taken</th>\n",
       "      <th>Grade/100.00</th>\n",
       "      <th>Q. 1 /1.25</th>\n",
       "      <th>Q. 2 /1.25</th>\n",
       "      <th>...</th>\n",
       "      <th>Q. 71 /1.25</th>\n",
       "      <th>Q. 72 /1.25</th>\n",
       "      <th>Q. 73 /1.25</th>\n",
       "      <th>Q. 74 /1.25</th>\n",
       "      <th>Q. 75 /1.25</th>\n",
       "      <th>Q. 76 /1.25</th>\n",
       "      <th>Q. 77 /1.25</th>\n",
       "      <th>Q. 78 /1.25</th>\n",
       "      <th>Q. 79 /1.25</th>\n",
       "      <th>Q. 80 /1.25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ម៉ាលីស</td>\n",
       "      <td>វ៉ាន់</td>\n",
       "      <td>vmalispg1n31@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:03 AM</td>\n",
       "      <td>22 July 2022  7:23 AM</td>\n",
       "      <td>1 hour 20 mins</td>\n",
       "      <td>41.52</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>គឹមតៃ</td>\n",
       "      <td>ហៃ</td>\n",
       "      <td>hkimtaip22n72@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:04 AM</td>\n",
       "      <td>22 July 2022  6:38 AM</td>\n",
       "      <td>33 mins 29 secs</td>\n",
       "      <td>14.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ប៊ុនលី</td>\n",
       "      <td>ទូច</td>\n",
       "      <td>tbunlypg2n84@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:08 AM</td>\n",
       "      <td>22 July 2022  7:22 AM</td>\n",
       "      <td>1 hour 13 mins</td>\n",
       "      <td>45.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ឈូក</td>\n",
       "      <td>ឈឿន​</td>\n",
       "      <td>cchhoukp22n15@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:11 AM</td>\n",
       "      <td>22 July 2022  7:30 AM</td>\n",
       "      <td>1 hour 18 mins</td>\n",
       "      <td>33.85</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>វណ្ណៈ</td>\n",
       "      <td>និន</td>\n",
       "      <td>nvannakp22n109@era.gov.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>22 July 2022  6:22 AM</td>\n",
       "      <td>22 July 2022  7:48 AM</td>\n",
       "      <td>1 hour 26 mins</td>\n",
       "      <td>10.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>CHANTREA</td>\n",
       "      <td>VONG</td>\n",
       "      <td>dse_22_4637@idg.edu.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>16 August 2022  4:18 PM</td>\n",
       "      <td>16 August 2022  5:38 PM</td>\n",
       "      <td>1 hour 20 mins</td>\n",
       "      <td>26.63</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3186</th>\n",
       "      <td>ជ</td>\n",
       "      <td>មករា</td>\n",
       "      <td>dse_22_4859@idg.edu.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>16 August 2022  4:31 PM</td>\n",
       "      <td>16 August 2022  5:07 PM</td>\n",
       "      <td>35 mins 24 secs</td>\n",
       "      <td>26.08</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3187</th>\n",
       "      <td>SAOVANNY</td>\n",
       "      <td>TITH</td>\n",
       "      <td>dse_22_4741@idg.edu.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>16 August 2022  5:27 PM</td>\n",
       "      <td>16 August 2022  6:47 PM</td>\n",
       "      <td>1 hour 20 mins</td>\n",
       "      <td>19.46</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>THY</td>\n",
       "      <td>CHUON</td>\n",
       "      <td>dse_22_4576@idg.edu.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>16 August 2022  5:43 PM</td>\n",
       "      <td>16 August 2022  6:24 PM</td>\n",
       "      <td>41 mins 3 secs</td>\n",
       "      <td>26.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3192</th>\n",
       "      <td>LEANGBOREY</td>\n",
       "      <td>PENH</td>\n",
       "      <td>dse_22_4747@idg.edu.kh</td>\n",
       "      <td>Finished</td>\n",
       "      <td>16 August 2022  8:04 PM</td>\n",
       "      <td>16 August 2022  9:19 PM</td>\n",
       "      <td>1 hour 15 mins</td>\n",
       "      <td>68.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3064 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Surname First name              Email address     State  \\\n",
       "0         ម៉ាលីស      វ៉ាន់    vmalispg1n31@era.gov.kh  Finished   \n",
       "1          គឹមតៃ         ហៃ   hkimtaip22n72@era.gov.kh  Finished   \n",
       "2         ប៊ុនលី        ទូច    tbunlypg2n84@era.gov.kh  Finished   \n",
       "3            ឈូក       ឈឿន​   cchhoukp22n15@era.gov.kh  Finished   \n",
       "4          វណ្ណៈ        និន  nvannakp22n109@era.gov.kh  Finished   \n",
       "...          ...        ...                        ...       ...   \n",
       "3185    CHANTREA       VONG     dse_22_4637@idg.edu.kh  Finished   \n",
       "3186           ជ       មករា     dse_22_4859@idg.edu.kh  Finished   \n",
       "3187    SAOVANNY       TITH     dse_22_4741@idg.edu.kh  Finished   \n",
       "3188         THY      CHUON     dse_22_4576@idg.edu.kh  Finished   \n",
       "3192  LEANGBOREY       PENH     dse_22_4747@idg.edu.kh  Finished   \n",
       "\n",
       "                   Started on                Completed       Time taken  \\\n",
       "0       22 July 2022  6:03 AM    22 July 2022  7:23 AM   1 hour 20 mins   \n",
       "1       22 July 2022  6:04 AM    22 July 2022  6:38 AM  33 mins 29 secs   \n",
       "2       22 July 2022  6:08 AM    22 July 2022  7:22 AM   1 hour 13 mins   \n",
       "3       22 July 2022  6:11 AM    22 July 2022  7:30 AM   1 hour 18 mins   \n",
       "4       22 July 2022  6:22 AM    22 July 2022  7:48 AM   1 hour 26 mins   \n",
       "...                       ...                      ...              ...   \n",
       "3185  16 August 2022  4:18 PM  16 August 2022  5:38 PM   1 hour 20 mins   \n",
       "3186  16 August 2022  4:31 PM  16 August 2022  5:07 PM  35 mins 24 secs   \n",
       "3187  16 August 2022  5:27 PM  16 August 2022  6:47 PM   1 hour 20 mins   \n",
       "3188  16 August 2022  5:43 PM  16 August 2022  6:24 PM   41 mins 3 secs   \n",
       "3192  16 August 2022  8:04 PM  16 August 2022  9:19 PM   1 hour 15 mins   \n",
       "\n",
       "      Grade/100.00 Q. 1 /1.25 Q. 2 /1.25  ... Q. 71 /1.25 Q. 72 /1.25  \\\n",
       "0            41.52       1.25       0.00  ...        1.25        0.21   \n",
       "1            14.54        0.0       1.25  ...        0.00        0.00   \n",
       "2            45.25       1.25       0.00  ...        1.25        0.00   \n",
       "3            33.85       0.50       1.25  ...        1.25        0.00   \n",
       "4            10.38       0.00       0.00  ...         0.0         0.0   \n",
       "...            ...        ...        ...  ...         ...         ...   \n",
       "3185         26.63       0.94       0.00  ...        0.00        0.00   \n",
       "3186         26.08       1.25       1.25  ...        1.25        0.00   \n",
       "3187         19.46       0.83       0.75  ...         0.0         0.0   \n",
       "3188         26.21       0.00       1.25  ...        1.25        0.21   \n",
       "3192         68.83       1.25       1.25  ...        1.25        0.42   \n",
       "\n",
       "     Q. 73 /1.25 Q. 74 /1.25 Q. 75 /1.25 Q. 76 /1.25 Q. 77 /1.25 Q. 78 /1.25  \\\n",
       "0           0.83        1.25        0.63        0.75        0.42        0.42   \n",
       "1           0.42         0.0         0.0         0.0         0.0         0.0   \n",
       "2           1.04        0.00        0.63        0.50        0.83        1.25   \n",
       "3           0.42         0.0        0.31        0.50        0.83        0.00   \n",
       "4            0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "3185         0.0         0.0         0.0         0.0         0.0        1.25   \n",
       "3186        0.00        0.00        0.83        1.04        1.25        0.00   \n",
       "3187         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3188        0.00        0.00        0.83        0.42        1.25        0.00   \n",
       "3192        0.75        1.25        0.83        1.25        0.00        0.00   \n",
       "\n",
       "     Q. 79 /1.25 Q. 80 /1.25  \n",
       "0           0.00        0.00  \n",
       "1            0.0         0.0  \n",
       "2           1.25        0.00  \n",
       "3           0.00        0.00  \n",
       "4            0.0         0.0  \n",
       "...          ...         ...  \n",
       "3185        0.00        0.00  \n",
       "3186         0.0        0.00  \n",
       "3187         0.0         0.0  \n",
       "3188         0.0        0.00  \n",
       "3192        1.25        0.00  \n",
       "\n",
       "[3064 rows x 88 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove duplicated rows\n",
    "df_no_duplicates = df.drop_duplicates(keep='first')\n",
    "print(\"DataFrame without Duplicates:\")\n",
    "\n",
    "df = df_no_duplicates\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Grade/100.00'] >= float(60), 'label'] = 'Passed'\n",
    "df.loc[df['Grade/100.00'] < float(60), 'label'] = 'Failed'\n",
    "\n",
    "df.to_csv('./datasets/pre_test_combined_with_label.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Started on'] = pd.to_datetime(df['Started on'])\n",
    "df['Completed'] = pd.to_datetime(df['Completed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Examinee: 3064\n",
      "Total Finished Examinee: 3064\n",
      "Total Examinee in Progress: 0\n",
      "Number of Passed Examinee: 429  | Passing rate: 0.1400130548302872\n",
      "Number of Failed Examinee: 2635  | Failing rate: 0.8599869451697127\n"
     ]
    }
   ],
   "source": [
    "num_of_examinee = len(df)\n",
    "num_finished_examinee = len(df[df['State'] == \"Finished\"])\n",
    "num_in_progress_examinee = len(df[df['State'] != \"Finished\"])\n",
    "num_failed = len(df[df['label'] == 'Failed'])\n",
    "num_passed = len(df[df['label'] == 'Passed'])\n",
    "\n",
    "\n",
    "print(\"Total Examinee:\", num_of_examinee)\n",
    "print(\"Total Finished Examinee:\", num_finished_examinee)\n",
    "print(\"Total Examinee in Progress:\", num_in_progress_examinee)\n",
    "print(\"Number of Passed Examinee:\", num_passed, \" | Passing rate:\", num_passed / (num_passed+num_failed))\n",
    "print(\"Number of Failed Examinee:\", num_failed, \" | Failing rate:\", num_failed / (num_passed+num_failed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Score of Passed Examinee:  68.61550116550117\n",
      "Average Score of Failed Examinee:  36.853100569259965\n"
     ]
    }
   ],
   "source": [
    "passed_df = df[df['label'] == 'Passed']\n",
    "print(\"Average Score of Passed Examinee: \", passed_df['Grade/100.00'].mean())\n",
    "\n",
    "failed_df = df[df['label'] == 'Failed']\n",
    "print(\"Average Score of Failed Examinee: \", failed_df['Grade/100.00'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "label = ['Passed', 'Failed']\n",
    "height = [num_passed, num_failed]\n",
    "bar_labels = ['blue', 'orange']\n",
    "bar_colors = ['tab:blue', 'tab:orange']\n",
    "\n",
    "ax.bar(label, height, label=bar_labels, color=bar_colors)\n",
    "\n",
    "for i in range(len(height)):\n",
    "    plt.text(i, i+height[i]+10, height[i], ha = 'center')\n",
    "\n",
    "ax.set_ylabel('Number of Examinee')\n",
    "plt.title(\"Pre-test Result\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passing Rate: 0.14\n",
      "Failed Rate: 0.86\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 7))\n",
    "\n",
    "passing_rate = format(num_passed / (num_passed+num_failed), '.2f')\n",
    "failed_rate = format(num_failed / (num_passed+num_failed), '.2f')\n",
    "\n",
    "print('Passing Rate:', passing_rate)\n",
    "print('Failed Rate:', failed_rate)\n",
    "\n",
    "# Data for the pie chart\n",
    "labels = 'Passing Rate', 'Failed Rate'\n",
    "sizes = [passing_rate, failed_rate]\n",
    "\n",
    "# Create the pie chart\n",
    "plt.pie(sizes, labels=labels, autopct='%1.0f%%')\n",
    "\n",
    "# Set the legend\n",
    "plt.legend(title=\"Rate\", loc='upper left')\n",
    "\n",
    "plt.title('Passing and Failed Rate of Pre-test')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_median_df = df\n",
    "\n",
    "def convert_list_to_integers(list):\n",
    "  new_list = []\n",
    "  for value in list:\n",
    "    try:\n",
    "      new_list.append(int(value))\n",
    "    except ValueError:\n",
    "      pass\n",
    "\n",
    "  return new_list\n",
    "\n",
    "plt.figure(figsize=(11,6))\n",
    "\n",
    "list = sorted(find_median_df['Grade/100.00'].round(0).tolist())\n",
    "counter = Counter(list)\n",
    "keys = counter.keys()\n",
    "values = counter.values()\n",
    "\n",
    "height = convert_list_to_integers(values) \n",
    "label = keys\n",
    "\n",
    "plt.bar(label, height)\n",
    "\n",
    "# for i in range(len(height)):\n",
    "#     plt.text(i, i+height[i], height[i], ha = 'center')\n",
    "\n",
    "plt.axvline(60, color='red')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Score\")\n",
    "plt.ylabel(\"Score Occurrence\")\n",
    "plt.title(\"Pretest Distribution Score\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x14af890a0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6zne+o71792rdunWqq6tTaWmpJCkyMlLBwcEqKCjQzp07NWzYMIWGhqqgoEDZ2dl66KGH1KlTJ3/9LAAAfMavQb17924NGzbM8/3ceeMJEyZozpw5evvttyVJN954o9dyW7Zs0dChQ+V0OrVq1SrNmTNHVVVVSk5OVnZ2ttf5ZwCBo7bOrWBnSKM1xtFGDuNutCa+S1cdPVzUaA0QKPwa1EOHDpUx5oLjjY1J0k033aQdO3b4ui0AfmLcdcpYkt9ozetTh+hfl25vtGZN1rBGx4FAElD3UQMAcLUhqAEAsBhBDQCAxQhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYlY/6xvwh6TuPVV84vhF62pqa1qhGwBXO4IaOE/xiePKWLTlonWvTx3SCt0AuNpx6BsAAIsR1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYLFmBXX37t31+eef15tfXl6u7t27X3ZTAADgK0HNWejo0aOqq6urN7+qqkonTpy47KYA4HLU1rkV7Ay5aF18l646erioFToCmu+Sgvrtt9/2fH733XcVHh7u+V5XV6dNmzYpKSnJZ80BQHMYd50yluRftG5N1rBW6Aa4PJcU1GPGjJEkORwOTZgwwWusXbt2SkpK0vPPP++z5gAAuNpdUlC73W5JUnJysnbt2qXOnTu3SFMAAOArzTpHfeTIEV/3AQAAGtCsoJakTZs2adOmTTp58qRnT/ucZcuWXXZjAACgmUE9d+5czZs3T4MGDVJcXJwcDoev+wIAAGpmUC9dulQrVqzQD37wA1/3AwAAvqFZDzyprq7Wrbfe6uteAADAeZoV1D/60Y+0cuVKX/cCAADO06xD32fPntVLL72kjRs3qn///mrXrp3X+AsvvOCT5gAAuNo1K6j379+vG2+8UZJ04MABrzEuLAMAwHeaFdRbtmzxdR8AAKABvOYSAACLNWuPetiwYY0e4t68eXOzGwIAAF9r1h71jTfeqAEDBnimvn37qrq6Wnv37lW/fv2avJ5t27bp3nvvVXx8vBwOh9auXes1bozRrFmzFBcXp/bt2ys9PV0ff/yxV82pU6c0fvx4hYWFKSIiQpMmTdLp06eb87MAALBOs/aof/WrXzU4f86cOZcUkpWVlRowYIAeeeQRZWRk1Bt/7rnn9Otf/1q/+93vlJycrJkzZ2rEiBH64IMPFBLy1btmx48fr5KSEm3YsEE1NTV6+OGHNWXKFG4fAwBcEZr9rO+GPPTQQxo8eLB++ctfNql+1KhRGjVqVINjxhgtXLhQTz/9tO6//35J0ssvv6yYmBitXbtW48aN04cffqj169dr165dGjRokCRp0aJFGj16tH75y18qPj7eNz8MAAA/8enFZAUFBZ493ct15MgRlZaWKj093TMvPDxcqampKigo8Py9iIgIT0hLUnp6utq0aaOdO3decN1VVVVyuVxeEwAANmrWHvX5h6mNMSopKdHu3bs1c+ZMnzRWWloqSYqJifGaHxMT4xkrLS1VdHS013hQUJAiIyM9NQ3Jy8vT3LlzfdIngMBVW+dWsLPxnYv4Ll119HBRK3UE1NesoA4PD/f63qZNG/Xq1Uvz5s3T3Xff7ZPGWlJubq5ycnI8310ulxISEvzYEQB/MO46ZSzJb7RmTdawVuoGaFizgnr58uW+7qOe2NhYSVJZWZni4uI888vKyjxPRYuNjdXJkye9lqutrdWpU6c8yzfE6XTK6XT6vmkAAHzsss5R79mzR6+88opeeeUV/fWvf/VVT5Kk5ORkxcbGatOmTZ55LpdLO3fuVFpamiQpLS1N5eXl2rNnj6dm8+bNcrvdSk1N9Wk/AAD4Q7P2qE+ePKlx48Zp69atioiIkCSVl5dr2LBhWrVqla699tomref06dMqKvr63M+RI0e0b98+RUZGKjExUTNmzNAvfvELXXfddZ7bs+Lj4zVmzBhJUp8+fTRy5EhNnjxZS5cuVU1NjaZNm6Zx48ZxxTcA4IrQrD3qrKwsffHFFzp48KBOnTqlU6dO6cCBA3K5XHrssceavJ7du3crJSVFKSkpkqScnBylpKRo1qxZkqQnnnhCWVlZmjJlim6++WadPn1a69ev97qy/NVXX1Xv3r01fPhwjR49WkOGDNFLL73UnJ8FAIB1mrVHvX79em3cuFF9+vTxzOvbt68WL158SReTDR06VMaYC447HA7NmzdP8+bNu2BNZGQkDzcBAFyxmrVH7Xa7672DWpLatWsnt9t92U0BAICvNCuo77zzTk2fPl3FxcWeeSdOnFB2draGDx/us+YAALjaNSuof/Ob38jlcikpKUk9evRQjx49lJycLJfLpUWLFvm6RwAArlrNOkedkJCgvXv3auPGjfroo48kfXUF9jcf9wkAAC7fJe1Rb968WX379pXL5ZLD4dBdd92lrKwsZWVl6eabb9a3vvUt/fnPf26pXgEAuOpcUlAvXLhQkydPVlhYWL2x8PBw/fjHP9YLL7zgs+YAALjaXVJQ/+1vf9PIkSMvOH733Xd7PSUMAABcnksK6rKysgZvyzonKChIn3322WU3BQAAvnJJQd2lSxcdOHDgguP79+/3eoEGAAC4PJcU1KNHj9bMmTN19uzZemNffvmlZs+erW9/+9s+aw4AgKvdJd2e9fTTT2vNmjW6/vrrNW3aNPXq1UuS9NFHH2nx4sWqq6vTz3/+8xZpFACAq9ElBXVMTIzee+89Pfroo8rNzfU8p9vhcGjEiBFavHixYmJiWqRRAACuRpf8wJNu3brpD3/4g/73f/9XRUVFMsbouuuuU6dOnVqiPwAArmrNejKZJHXq1Ek333yzL3sBAADnadazvgEAQOsgqAEAsBhBDQCAxQhqAAAsRlADAGAxghoAAIs1+/YsAMDXkrr3VPGJ443WxHfpqqOHi1qpI1wpCGoA8IHiE8eVsWhLozVrsoa1Uje4knDoGwAAixHUAABYjKAGAMBiBDUAABYjqAEAsBhXfcN6TbntRWrarS9NWVdNbc0l9QcALYmghvWactuL1LRbX5qyrtenDmlybwDQ0jj0DQCAxQhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLWR/USUlJcjgc9abMzExJ0tChQ+uNTZ061c9dAwDgG9a/PWvXrl2qq6vzfD9w4IDuuusuffe73/XMmzx5subNm+f53qFDh1bt8Zt8+UpGXJraOreCnSGN1vAKSwCBxvqgvvbaa72+z58/Xz169NAdd9zhmdehQwfFxsa2dmsN8uUrGXFpjLtOGUvyG63hFZYAAo31h76/qbq6Wq+88ooeeeQRORwOz/xXX31VnTt31g033KDc3FydOXPGj10CAOA71u9Rf9PatWtVXl6uiRMneuZ9//vfV7du3RQfH6/9+/frySefVGFhodasWXPB9VRVVamqqsrz3eVytWTbAAA0W0AF9W9/+1uNGjVK8fHxnnlTpkzxfO7Xr5/i4uI0fPhwHTp0SD169GhwPXl5eZo7d26L9wsAwOUKmEPfn3zyiTZu3Kgf/ehHjdalpqZKkoqKLnyhVm5urioqKjzTp59+6tNeAQDwlYDZo16+fLmio6N1zz33NFq3b98+SVJcXNwFa5xOp5xOpy/bAwCgRQREULvdbi1fvlwTJkxQUNDXLR86dEgrV67U6NGjFRUVpf379ys7O1u33367+vfv78eOAQDwjYAI6o0bN+rYsWN65JFHvOYHBwdr48aNWrhwoSorK5WQkKCxY8fq6aef9lOnAAD4VkAE9d133y1jTL35CQkJys9v/L5ZAAACWcBcTAYAwNWIoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABYjKAGAMBiBDUAABYjqAEAsBhBDQCAxQhqAAAsRlADAGCxgHgfNQD4S22dW8HOkHrzOxij8v//HNEpUjV1ta3aF64eBDUANMK465SxJL/efGfVl9JjwyVJ9z//R708/a7Wbg1XCQ59AwBgMYIaAACLEdQAAFiMoAYAwGJcTAa/SureU8UnjjdaU1Nb00rdAIB9CGr4VfGJ48pYtKXRmtenDmmlbgDAPhz6BgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGM/6BoBWUlvnVrAz5KJ18V266ujhokZrmvJCm6asB/YjqAGglRh3nTKW5F+0bk3WsIvWNOWFNk1ZD+zHoW8AACxGUAMAYDGCGgAAixHUAABYjKAGAMBiBDUAABazOqjnzJkjh8PhNfXu3dszfvbsWWVmZioqKkodO3bU2LFjVVZW5seOAQDwLauDWpK+9a1vqaSkxDNt377dM5adna133nlHq1evVn5+voqLi5WRkeHHbgEA8C3rH3gSFBSk2NjYevMrKir029/+VitXrtSdd94pSVq+fLn69OmjHTt26JZbbmntVgHAJ5ryBLOa2ppW6gb+Zn1Qf/zxx4qPj1dISIjS0tKUl5enxMRE7dmzRzU1NUpPT/fU9u7dW4mJiSooKGg0qKuqqlRVVeX57nK5WvQ3AMClaMoTzF6fOqSVuoG/WX3oOzU1VStWrND69ev14osv6siRI/qXf/kXffHFFyotLVVwcLAiIiK8lomJiVFpaWmj683Ly1N4eLhnSkhIaMFfAQBA81m9Rz1q1CjP5/79+ys1NVXdunXTG2+8ofbt2zd7vbm5ucrJyfF8d7lchDUAwEpW71GfLyIiQtdff72KiooUGxur6upqlZeXe9WUlZU1eE77m5xOp8LCwrwmAABsFFBBffr0aR06dEhxcXEaOHCg2rVrp02bNnnGCwsLdezYMaWlpfmxSwAAfMfqQ98//elPde+996pbt24qLi7W7Nmz1bZtWz344IMKDw/XpEmTlJOTo8jISIWFhSkrK0tpaWlc8Q0AuGJYHdTHjx/Xgw8+qM8//1zXXnuthgwZoh07dujaa6+VJP3qV79SmzZtNHbsWFVVVWnEiBFasmSJn7sGAMB3rA7qVatWNToeEhKixYsXa/Hixa3UEQAArSugzlEDAHC1IagBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGJWP/AEANB8tXVuBTtDLloX36Wrjh4uaoWO0BwENQBcoYy7ThlL8i9atyZrWCt0g+bi0DcAABYjqAEAsBhBDQCAxQhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYLEgfzcAAPCv2jq3gp0hjdbEd+mqo4eLWqkjfBNBDQBXOeOuU8aS/EZr1mQNa6VucD4OfQMAYDGCGgAAixHUAABYjHPUaBFJ3Xuq+MTxi9bV1Na0QjcAELgIarSI4hPHlbFoy0XrXp86pBW6AYDAxaFvAAAsRlADAGAxDn37SSA/YKAp55859wwAvkFQ+0kgP2CgKeefOfcMAL5h9aHvvLw83XzzzQoNDVV0dLTGjBmjwsJCr5qhQ4fK4XB4TVOnTvVTxwAA+JbVQZ2fn6/MzEzt2LFDGzZsUE1Nje6++25VVlZ61U2ePFklJSWe6bnnnvNTxwAA+JbVh77Xr1/v9X3FihWKjo7Wnj17dPvtt3vmd+jQQbGxsa3dHgAALc7qPerzVVRUSJIiIyO95r/66qvq3LmzbrjhBuXm5urMmTONrqeqqkoul8trAgDARlbvUX+T2+3WjBkzdNttt+mGG27wzP/+97+vbt26KT4+Xvv379eTTz6pwsJCrVmz5oLrysvL09y5c1ujbQC4IjTlThXJ3rtVAlnABHVmZqYOHDig7du3e82fMmWK53O/fv0UFxen4cOH69ChQ+rRo0eD68rNzVVOTo7nu8vlUkJCQss0DgBXgKbcqSLZe7dKIAuIoJ42bZrWrVunbdu2qWvXro3WpqamSpKKioouGNROp1NOp9PnfQIA4GtWB7UxRllZWXrzzTe1detWJScnX3SZffv2SZLi4uJauDsAAFqe1UGdmZmplStX6q233lJoaKhKS0slSeHh4Wrfvr0OHTqklStXavTo0YqKitL+/fuVnZ2t22+/Xf379/dz95ePc0IAAKuD+sUXX5T01UNNvmn58uWaOHGigoODtXHjRi1cuFCVlZVKSEjQ2LFj9fTTT/uhW9/jnBAAwOqgNsY0Op6QkKD8/IsHGQAAgSqg7qMGAOBqY/UeNQAgsATymwFtRVADAHwmkN8MaCsOfQMAYDH2qOElqXtPFZ843mhNTW1NK3UDACCo4aX4xHFlLNrSaM3rU4e0UjcAAA59AwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGLdnXSWacn+0xD3SAFoejxm9NAT1VaIp90dL3CMNoOXxmNFLw6FvAAAsRlADAGAxghoAAIsR1AAAWIyLya4ATbmCkqu5ASAwEdRXgKZcQcnV3AAQmAhqAIB1mnKkULo67rcmqAEA1mnKkULp6rjfmovJAACwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABYjKAGAMBi3EcNAAhYTXkwSqA/FIWgBgAErKY8GCXQH4rCoW8AACxGUAMAYDEOfQMArmiB/oIPghoAcEUL9Bd8cOgbAACLEdQAAFiMQ98AAMjee7IJagAAZO892Rz6BgDAYgQ1AAAWu2KCevHixUpKSlJISIhSU1P1/vvv+7slAAAu2xUR1K+//rpycnI0e/Zs7d27VwMGDNCIESN08uRJf7cGAMBluSKC+oUXXtDkyZP18MMPq2/fvlq6dKk6dOigZcuW+bs1AAAuS8AHdXV1tfbs2aP09HTPvDZt2ig9PV0FBQV+7AwAgMsX8Ldn/fOf/1RdXZ1iYmK85sfExOijjz5qcJmqqipVVVV5vldUVEiSXC7XZfdjjFHNl5VNKbx4XSCvi94Df1303miNo+pLnfs3RvWXldb05de/58t1Wdq7McYnWXFOaGioHA7HxfoKbCdOnDCSzHvvvec1//HHHzeDBw9ucJnZs2cbSUxMTExMTH6dKioqLppzAb9H3blzZ7Vt21ZlZWVe88vKyhQbG9vgMrm5ucrJyfF8d7vdOnXqlKKioi7+XzaNcLlcSkhI0KeffqqwsLBmrwdfY5v6FtvTt9ievnU1bs/Q0NCL1gR8UAcHB2vgwIHatGmTxowZI+mr4N20aZOmTZvW4DJOp1NOp9NrXkREhM96CgsLu2r+T9Za2Ka+xfb0Lbanb7E9vQV8UEtSTk6OJkyYoEGDBmnw4MFauHChKisr9fDDD/u7NQAALssVEdT/+q//qs8++0yzZs1SaWmpbrzxRq1fv77eBWYAAASaKyKoJWnatGkXPNTdWpxOp2bPnl3vsDqaj23qW2xP32J7+hbbs2EOY4zxdxMAAKBhAf/AEwAArmQENQAAFiOoAQCwGEENAIDFCGof4p3YzZOXl6ebb75ZoaGhio6O1pgxY1RYWOhVc/bsWWVmZioqKkodO3bU2LFj6z2NDg2bP3++HA6HZsyY4ZnH9rw0J06c0EMPPaSoqCi1b99e/fr10+7duz3jxhjNmjVLcXFxat++vdLT0/Xxxx/7sWO71dXVaebMmUpOTlb79u3Vo0cP/fu//7u+eW0z2/QbLvdZ2/jKqlWrTHBwsFm2bJk5ePCgmTx5somIiDBlZWX+bs16I0aMMMuXLzcHDhww+/btM6NHjzaJiYnm9OnTnpqpU6eahIQEs2nTJrN7925zyy23mFtvvdWPXQeG999/3yQlJZn+/fub6dOne+azPZvu1KlTplu3bmbixIlm586d5vDhw+bdd981RUVFnpr58+eb8PBws3btWvO3v/3N3HfffSY5Odl8+eWXfuzcXs8884yJiooy69atM0eOHDGrV682HTt2NP/5n//pqWGbfo2g9pHBgwebzMxMz/e6ujoTHx9v8vLy/NhVYDp58qSRZPLz840xxpSXl5t27dqZ1atXe2o+/PBDI8kUFBT4q03rffHFF+a6664zGzZsMHfccYcnqNmel+bJJ580Q4YMueC42+02sbGxZsGCBZ555eXlxul0mtdee601Wgw499xzj3nkkUe85mVkZJjx48cbY9im5+PQtw/wTmzfOvfa0cjISEnSnj17VFNT47V9e/furcTERLZvIzIzM3XPPfd4bTeJ7Xmp3n77bQ0aNEjf/e53FR0drZSUFP33f/+3Z/zIkSMqLS312p7h4eFKTU1le17Arbfeqk2bNukf//iHJOlvf/ubtm/frlGjRklim57vinkymT81553YaJjb7daMGTN022236YYbbpAklZaWKjg4uN6LU2JiYlRaWuqHLu23atUq7d27V7t27ao3xva8NIcPH9aLL76onJwcPfXUU9q1a5cee+wxBQcHa8KECZ5t1tA//2zPhv3sZz+Ty+VS79691bZtW9XV1emZZ57R+PHjJYlteh6CGlbJzMzUgQMHtH37dn+3ErA+/fRTTZ8+XRs2bFBISIi/2wl4brdbgwYN0rPPPitJSklJ0YEDB7R06VJNmDDBz90FpjfeeEOvvvqqVq5cqW9961vat2+fZsyYofj4eLZpAzj07QPNeSc26ps2bZrWrVunLVu2qGvXrp75sbGxqq6uVnl5uVc927dhe/bs0cmTJ3XTTTcpKChIQUFBys/P169//WsFBQUpJiaG7XkJ4uLi1LdvX695ffr00bFjxyTJs83457/pHn/8cf3sZz/TuHHj1K9fP/3gBz9Qdna28vLyJLFNz0dQ+8A334l9zrl3Yqelpfmxs8BgjNG0adP05ptvavPmzUpOTvYaHzhwoNq1a+e1fQsLC3Xs2DG2bwOGDx+uv//979q3b59nGjRokMaPH+/5zPZsuttuu63e7YL/+Mc/1K1bN0lScnKyYmNjvbany+XSzp072Z4XcObMGbVp4x0/bdu2ldvtlsQ2rcffV7NdKVatWmWcTqdZsWKF+eCDD8yUKVNMRESEKS0t9Xdr1nv00UdNeHi42bp1qykpKfFMZ86c8dRMnTrVJCYmms2bN5vdu3ebtLQ0k5aW5seuA8s3r/o2hu15Kd5//30TFBRknnnmGfPxxx+bV1991XTo0MG88sornpr58+ebiIgI89Zbb5n9+/eb+++//6q9lagpJkyYYLp06eK5PWvNmjWmc+fO5oknnvDUsE2/RlD70KJFi0xiYqIJDg42gwcPNjt27PB3SwFBUoPT8uXLPTVffvml+clPfmI6depkOnToYB544AFTUlLiv6YDzPlBzfa8NO+884654YYbjNPpNL179zYvvfSS17jb7TYzZ840MTExxul0muHDh5vCwkI/dWs/l8tlpk+fbhITE01ISIjp3r27+fnPf26qqqo8NWzTr/GaSwAALMY5agAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGrgKjZx4kSNGTPG320AaARBDViktLRU06dPV8+ePRUSEqKYmBjddtttevHFF3XmzBl/t+fxySefqH379jp9+rQOHjyosWPHKikpSQ6HQwsXLmxwmcWLFyspKUkhISFKTU3V+++/7zV+9uxZZWZmKioqSh07dtTYsWPrvT3pfMYYzZo1S3FxcWrfvr3S09P18ccfe9WcOnVK48ePV1hYmCIiIjRp0iSdPn36sn4/0JoIasAShw8fVkpKiv70pz/p2Wef1V//+lcVFBToiSee0Lp167Rx48YGl6upqWnlTqW33npLw4YNU8eOHXXmzBl1795d8+fPv+ArCF9//XXl5ORo9uzZ2rt3rwYMGKARI0bo5MmTnprs7Gy98847Wr16tfLz81VcXKyMjIxG+3juuef061//WkuXLtXOnTt1zTXXaMSIETp79qynZvz48Tp48KA2bNigdevWadu2bZoyZYpvNgTQGvz8rHEA/2/EiBGma9eu5vTp0w2Ou91uY8xXLzFZsmSJuffee02HDh3M7NmzTW1trXnkkUdMUlKSCQkJMddff71ZuHCh1/K1tbUmOzvbhIeHm8jISPP444+bH/7wh+b+++/31NTV1Zlnn33Ws57+/fub1atX1+vlzjvvNC+++GK9+d26dTO/+tWv6s0fPHiwyczM9Po78fHxJi8vzxhjTHl5uWnXrp3X3/rwww+NJFNQUHDB7REbG2sWLFjgmVdeXm6cTqd57bXXjDHGfPDBB0aS2bVrl6fmj3/8o3E4HObEiRMNrhewDXvUgAU+//xz/elPf1JmZqauueaaBmscDofn85w5c/TAAw/o73//ux555BG53W517dpVq1ev1gcffKBZs2bpqaee0htvvOFZ5vnnn9eKFSu0bNkybd++XadOndKbb77p9Tfy8vL08ssva+nSpTp48KCys7P10EMPKT8/31NTXl6u7du367777mvSb6uurtaePXuUnp7umdemTRulp6eroKBAkrRnzx7V1NR41fTu3VuJiYmemvMdOXJEpaWlXsuEh4crNTXVs0xBQYEiIiI0aNAgT016erratGmjnTt3Nql/wN+C/N0AAKmoqEjGGPXq1ctrfufOnT2HcTMzM/Uf//EfkqTvf//7evjhh71q586d6/mcnJysgoICvfHGG/re974nSVq4cKFyc3M9h5OXLl2qd99917NMVVWVnn32WW3cuFFpaWmSpO7du2v79u36r//6L91xxx2SpD/84Q/q37+/4uPjm/Tb/vnPf6qurk4xMTFe82NiYvTRRx9J+urcfHBwsCIiIurVlJaWNrjec/MbWu+5sdLSUkVHR3uNBwUFKTIy8oLrBWxDUAMWe//99+V2uzV+/HhVVVV55n9zD/GcxYsXa9myZTp27Ji+/PJLVVdX68Ybb5QkVVRUqKSkRKmpqZ76oKAgDRo0SOb/33RbVFSkM2fO6K677vJab3V1tVJSUjzf33rrrSbvTQO4fAQ1YIGePXvK4XCosLDQa3737t0lSe3bt/eaf/7h8VWrVumnP/2pnn/+eaWlpSk0NFQLFiy4pMO7566E/v3vf68uXbp4jTmdTklfhfb69ev11FNPNXm9nTt3Vtu2betdwV1WVua5+Cw2NlbV1dUqLy/32qv+Zs35zs0vKytTXFyc1zLn/gMlNjbW64I1SaqtrdWpU6cuuF7ANpyjBiwQFRWlu+66S7/5zW9UWVl5ycv/5S9/0a233qqf/OQnSklJUc+ePXXo0CHPeHh4uOLi4ryCu7a2Vnv27PF879u3r5xOp44dO6aePXt6TQkJCZKkrVu3qlOnThowYECTewsODtbAgQO1adMmzzy3261NmzZ5DrEPHDhQ7dq186opLCzUsWPHPDXnS05OVmxsrNcyLpdLO3fu9CyTlpam8vJyr9+5efNmud1ur6MLgM3YowYssWTJEt12220aNGiQ5syZo/79+6tNmzbatWuXPvroIw0cOPCCy1533XV6+eWX9e677yo5OVn/8z//o127dik5OdlTM336dM2fP1/XXXedevfurRdeeEHl5eWe8dDQUP30pz9Vdna23G63hgwZooqKCv3lL39RWFiYJkyYoLfffrveYe/q6mp98MEHns8nTpzQvn371LFjR/Xs2VOSlJOTowkTJmjQoEEaPHiwFi5cqMrKSs959vDwcE2aNEk5OTmKjIxUWFiYsrKylJaWpltuucXzt3r37q28vDw98MADcjgcmjFjhn7xi1/ouuuuU3JysmbOnKn4+HjPQ1z69OmjkSNHavLkyVq6dKlqamo0bdo0jRs3rsnn2AG/8/dl5wC+VlxcbKZNm2aSk5NNu3btTMeOHc3gwYPNggULTGVlpTHmq9uz3nzzTa/lzp49ayZOnGjCw8NNRESEefTRR83PfvYzM2DAAE9NTU2NmT59ugkLCzMREREmJyen3u1ZbrfbLFy40PTq1cu0a9fOXHvttWbEiBEmPz/fGGNMQkKC2bBhg9ffPnLkiJFUb7rjjju86hYtWmQSExNNcHCwGTx4sNmxY4fX+Jdffml+8pOfmE6dOpkOHTqYBx54wJSUlHjVSDLLly/36nfmzJkmJibGOJ1OM3z4cFNYWOi1zOeff24efPBB07FjRxMWFmYefvhh88UXX1zsfwrAGg5j/v9KEgBoxN69e3XnnXfqs88+U7t27fzdDnDV4Bw1gCapra3VokWLCGmglbFHDQCAxdijBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGL/ByW/yOwyD9Q0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution = df\n",
    "\n",
    "# plt.figure(figsize=(10,10))\n",
    "sns.displot(distribution, \n",
    "            x=\"Grade/100.00\", \n",
    "            bins=40)\n",
    "\n",
    "plt.axvline(60, color='red')\n",
    "\n",
    "# plt.savefig(\"Seaborn_histogram_with_displot.png\",\n",
    "#                     format='png',dpi=150)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}