Online Coding Competition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95d00ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f86e2b2",
   "metadata": {},
   "source": [
    "# 2017 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "851d9b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2017 = pd.read_csv(\"data_2017.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "301d1e0e",
   "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>RegisterNo</th>\n",
       "      <th>Year</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Depart</th>\n",
       "      <th>PaperCode</th>\n",
       "      <th>Paper</th>\n",
       "      <th>PaperCredit</th>\n",
       "      <th>ExternalMark</th>\n",
       "      <th>ExternalMarkMax</th>\n",
       "      <th>InternalMark</th>\n",
       "      <th>...</th>\n",
       "      <th>Grade</th>\n",
       "      <th>GradePoint</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>FinalResult</th>\n",
       "      <th>ProgramCode</th>\n",
       "      <th>Program</th>\n",
       "      <th>Course</th>\n",
       "      <th>Coursecode</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38</td>\n",
       "      <td>2017</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>EN1CCT01</td>\n",
       "      <td>English - Fine - tune Your English</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>80</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>Passed</td>\n",
       "      <td>EN</td>\n",
       "      <td>English</td>\n",
       "      <td>CC</td>\n",
       "      <td>Common Course</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>2017</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>EN1CCT02</td>\n",
       "      <td>English-Pearls from the Deep</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>Passed</td>\n",
       "      <td>EN</td>\n",
       "      <td>English</td>\n",
       "      <td>CC</td>\n",
       "      <td>Common Course</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>2017</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>HN1CCT01</td>\n",
       "      <td>Hindi-Prose and One Act Plays</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>80</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>Passed</td>\n",
       "      <td>HN</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>CC</td>\n",
       "      <td>Common Course</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>2017</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>EC1CRT01</td>\n",
       "      <td>Perspectives and Methodology of Economics</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>80</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>Passed</td>\n",
       "      <td>EC</td>\n",
       "      <td>Economics</td>\n",
       "      <td>CR</td>\n",
       "      <td>Core Courses</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>2017</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>HY1CMT02</td>\n",
       "      <td>Social Formations in Pre Modern India</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>80</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>Passed</td>\n",
       "      <td>HY</td>\n",
       "      <td>History</td>\n",
       "      <td>CM</td>\n",
       "      <td>Complimentary</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegisterNo  Year Semester        Depart PaperCode  \\\n",
       "0          38  2017       S1  BA.Economics  EN1CCT01   \n",
       "1          38  2017       S1  BA.Economics  EN1CCT02   \n",
       "2          38  2017       S1  BA.Economics  HN1CCT01   \n",
       "3          38  2017       S1  BA.Economics  EC1CRT01   \n",
       "4          38  2017       S1  BA.Economics  HY1CMT02   \n",
       "\n",
       "                                       Paper  PaperCredit  ExternalMark  \\\n",
       "0         English - Fine - tune Your English            4            33   \n",
       "1               English-Pearls from the Deep            3            32   \n",
       "2              Hindi-Prose and One Act Plays            4            29   \n",
       "3  Perspectives and Methodology of Economics            4            43   \n",
       "4      Social Formations in Pre Modern India            4            29   \n",
       "\n",
       "   ExternalMarkMax  InternalMark  ...  Grade  GradePoint  CreditScore  \\\n",
       "0               80            14  ...      C           5           20   \n",
       "1               80            13  ...      C           5           15   \n",
       "2               80            19  ...      C           5           20   \n",
       "3               80            14  ...      B           6           24   \n",
       "4               80            15  ...      D           4           16   \n",
       "\n",
       "  FinalResult  ProgramCode    Program Course     Coursecode Mode    Type  \n",
       "0      Passed           EN    English     CC  Common Course    T  Theory  \n",
       "1      Passed           EN    English     CC  Common Course    T  Theory  \n",
       "2      Passed           HN      Hindi     CC  Common Course    T  Theory  \n",
       "3      Passed           EC  Economics     CR   Core Courses    T  Theory  \n",
       "4      Passed           HY    History     CM  Complimentary    T  Theory  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3597bf08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows:  2480\n",
      "Number of columns:  23\n"
     ]
    }
   ],
   "source": [
    "data_2017.shape\n",
    "print(\"Number of rows: \",data_2017.shape[0])\n",
    "print(\"Number of columns: \",data_2017.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8347c65b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RegisterNo         0\n",
       "Year               0\n",
       "Semester           0\n",
       "Depart             0\n",
       "PaperCode          0\n",
       "Paper              0\n",
       "PaperCredit        0\n",
       "ExternalMark       0\n",
       "ExternalMarkMax    0\n",
       "InternalMark       0\n",
       "InternalMarkMax    0\n",
       "TotalMark          0\n",
       "TotalMarkMax       0\n",
       "Grade              0\n",
       "GradePoint         0\n",
       "CreditScore        0\n",
       "FinalResult        0\n",
       "ProgramCode        0\n",
       "Program            0\n",
       "Course             0\n",
       "Coursecode         0\n",
       "Mode               0\n",
       "Type               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017.isnull().sum() # there is no missing value in this dataset of 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92569a47",
   "metadata": {},
   "source": [
    "# data 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55978591",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2018 =pd.read_csv(\"data_2018.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e104306a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegisterNo</th>\n",
       "      <th>Year</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Depart</th>\n",
       "      <th>PaperCode</th>\n",
       "      <th>Paper</th>\n",
       "      <th>PaperCredit</th>\n",
       "      <th>ExternalMark</th>\n",
       "      <th>ExternalMarkMax</th>\n",
       "      <th>InternalMark</th>\n",
       "      <th>...</th>\n",
       "      <th>Grade</th>\n",
       "      <th>GradePoint</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>FinalResult</th>\n",
       "      <th>ProgramCode</th>\n",
       "      <th>Program</th>\n",
       "      <th>Course</th>\n",
       "      <th>Coursecode</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>2018</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>EN1CCT01</td>\n",
       "      <td>English - Fine - tune Your English</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>80</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>B+</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>Passed</td>\n",
       "      <td>EN</td>\n",
       "      <td>English</td>\n",
       "      <td>CC</td>\n",
       "      <td>Common Course</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>2018</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>EN1CCT02</td>\n",
       "      <td>English-Pearls from the Deep</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>80</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>Passed</td>\n",
       "      <td>EN</td>\n",
       "      <td>English</td>\n",
       "      <td>CC</td>\n",
       "      <td>Common Course</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "      <td>2018</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>FR1CCT01</td>\n",
       "      <td>French -French Language &amp; Communicative Skills...</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>Passed</td>\n",
       "      <td>FR</td>\n",
       "      <td>French</td>\n",
       "      <td>CC</td>\n",
       "      <td>Common Course</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>2018</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>EC1CRT01</td>\n",
       "      <td>Perspectives and Methodology of Economics</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>Passed</td>\n",
       "      <td>EC</td>\n",
       "      <td>Economics</td>\n",
       "      <td>CR</td>\n",
       "      <td>Core Courses</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>2018</td>\n",
       "      <td>S1</td>\n",
       "      <td>BA.Economics</td>\n",
       "      <td>HY1CMT02</td>\n",
       "      <td>Social Formations in Pre Modern India</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>80</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>B+</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>Passed</td>\n",
       "      <td>HY</td>\n",
       "      <td>History</td>\n",
       "      <td>CM</td>\n",
       "      <td>Complimentary</td>\n",
       "      <td>T</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegisterNo  Year Semester        Depart PaperCode  \\\n",
       "0          58  2018       S1  BA.Economics  EN1CCT01   \n",
       "1          58  2018       S1  BA.Economics  EN1CCT02   \n",
       "2          58  2018       S1  BA.Economics  FR1CCT01   \n",
       "3          58  2018       S1  BA.Economics  EC1CRT01   \n",
       "4          58  2018       S1  BA.Economics  HY1CMT02   \n",
       "\n",
       "                                               Paper  PaperCredit  \\\n",
       "0                 English - Fine - tune Your English            4   \n",
       "1                       English-Pearls from the Deep            3   \n",
       "2  French -French Language & Communicative Skills...            4   \n",
       "3          Perspectives and Methodology of Economics            4   \n",
       "4              Social Formations in Pre Modern India            4   \n",
       "\n",
       "   ExternalMark  ExternalMarkMax  InternalMark  ...  Grade  GradePoint  \\\n",
       "0            52               80            18  ...     B+           7   \n",
       "1            44               80            19  ...      B           6   \n",
       "2            67               80            17  ...      A           8   \n",
       "3            65               80            18  ...      A           8   \n",
       "4            49               80            18  ...     B+           7   \n",
       "\n",
       "   CreditScore FinalResult  ProgramCode    Program Course     Coursecode Mode  \\\n",
       "0           28      Passed           EN    English     CC  Common Course    T   \n",
       "1           18      Passed           EN    English     CC  Common Course    T   \n",
       "2           32      Passed           FR     French     CC  Common Course    T   \n",
       "3           32      Passed           EC  Economics     CR   Core Courses    T   \n",
       "4           28      Passed           HY    History     CM  Complimentary    T   \n",
       "\n",
       "     Type  \n",
       "0  Theory  \n",
       "1  Theory  \n",
       "2  Theory  \n",
       "3  Theory  \n",
       "4  Theory  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acb7a92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows:  2480\n",
      "Number of columns:  23\n"
     ]
    }
   ],
   "source": [
    "data_2018.shape\n",
    "print(\"Number of rows: \",data_2017.shape[0])\n",
    "print(\"Number of columns: \",data_2017.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0e4c858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RegisterNo         0\n",
       "Year               0\n",
       "Semester           0\n",
       "Depart             0\n",
       "PaperCode          0\n",
       "Paper              0\n",
       "PaperCredit        0\n",
       "ExternalMark       0\n",
       "ExternalMarkMax    0\n",
       "InternalMark       0\n",
       "InternalMarkMax    0\n",
       "TotalMark          0\n",
       "TotalMarkMax       0\n",
       "Grade              0\n",
       "GradePoint         0\n",
       "CreditScore        0\n",
       "FinalResult        0\n",
       "ProgramCode        0\n",
       "Program            0\n",
       "Course             0\n",
       "Coursecode         0\n",
       "Mode               0\n",
       "Type               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018.isnull().sum() # there is no missing value in this dataset of 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5b0656b",
   "metadata": {},
   "source": [
    "# Comparision start from here between 2017 & 2018 data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47ccdae",
   "metadata": {},
   "source": [
    "# No. of Rows & Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "37f84ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2480, 23)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69a5afa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2383, 23)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6764dc62",
   "metadata": {},
   "source": [
    "# Information about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4ae0c97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2480 entries, 0 to 2479\n",
      "Data columns (total 23 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   RegisterNo       2480 non-null   int64 \n",
      " 1   Year             2480 non-null   int64 \n",
      " 2   Semester         2480 non-null   object\n",
      " 3   Depart           2480 non-null   object\n",
      " 4   PaperCode        2480 non-null   object\n",
      " 5   Paper            2480 non-null   object\n",
      " 6   PaperCredit      2480 non-null   int64 \n",
      " 7   ExternalMark     2480 non-null   int64 \n",
      " 8   ExternalMarkMax  2480 non-null   int64 \n",
      " 9   InternalMark     2480 non-null   int64 \n",
      " 10  InternalMarkMax  2480 non-null   int64 \n",
      " 11  TotalMark        2480 non-null   int64 \n",
      " 12  TotalMarkMax     2480 non-null   int64 \n",
      " 13  Grade            2480 non-null   object\n",
      " 14  GradePoint       2480 non-null   int64 \n",
      " 15  CreditScore      2480 non-null   int64 \n",
      " 16  FinalResult      2480 non-null   object\n",
      " 17  ProgramCode      2480 non-null   object\n",
      " 18  Program          2480 non-null   object\n",
      " 19  Course           2480 non-null   object\n",
      " 20  Coursecode       2480 non-null   object\n",
      " 21  Mode             2480 non-null   object\n",
      " 22  Type             2480 non-null   object\n",
      "dtypes: int64(11), object(12)\n",
      "memory usage: 445.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_2017.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07be7601",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2383 entries, 0 to 2382\n",
      "Data columns (total 23 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   RegisterNo       2383 non-null   int64 \n",
      " 1   Year             2383 non-null   int64 \n",
      " 2   Semester         2383 non-null   object\n",
      " 3   Depart           2383 non-null   object\n",
      " 4   PaperCode        2383 non-null   object\n",
      " 5   Paper            2383 non-null   object\n",
      " 6   PaperCredit      2383 non-null   int64 \n",
      " 7   ExternalMark     2383 non-null   int64 \n",
      " 8   ExternalMarkMax  2383 non-null   int64 \n",
      " 9   InternalMark     2383 non-null   int64 \n",
      " 10  InternalMarkMax  2383 non-null   int64 \n",
      " 11  TotalMark        2383 non-null   int64 \n",
      " 12  TotalMarkMax     2383 non-null   int64 \n",
      " 13  Grade            2383 non-null   object\n",
      " 14  GradePoint       2383 non-null   int64 \n",
      " 15  CreditScore      2383 non-null   int64 \n",
      " 16  FinalResult      2383 non-null   object\n",
      " 17  ProgramCode      2383 non-null   object\n",
      " 18  Program          2383 non-null   object\n",
      " 19  Course           2383 non-null   object\n",
      " 20  Coursecode       2383 non-null   object\n",
      " 21  Mode             2383 non-null   object\n",
      " 22  Type             2383 non-null   object\n",
      "dtypes: int64(11), object(12)\n",
      "memory usage: 428.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data_2018.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134d0056",
   "metadata": {},
   "source": [
    "# Department infomation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e4683b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BA.Economics', 'BA.History', 'BA.Sociology', 'Bsc.Botany',\n",
       "       'Bsc.Chemistry', 'BSc.FACS ', 'BSc.Maths', 'BSc.Physics',\n",
       "       'BSc.Zoology', 'BSc.Computer Science'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017[\"Depart\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "681c40c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BA.Economics', 'BA.History', 'BA.Sociology', 'Bsc.Botany',\n",
       "       'Bsc.Chemistry', 'BSc.FACS ', 'BSc.Maths', 'BSc.Physics',\n",
       "       'BSc.Zoology', 'BSc.Computer Science'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018[\"Depart\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e6cda3",
   "metadata": {},
   "source": [
    "# mean, max & min of ExternalMark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "16327d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017[\"ExternalMark\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c798cb69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018[\"ExternalMark\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4d97adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017[\"ExternalMark\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "940aba65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018[\"ExternalMark\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93fedab",
   "metadata": {},
   "source": [
    "# Only there is a difference in the average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "34e6bcce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.80564516129032"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017[\"ExternalMark\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "562c81e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.51867394041125"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018[\"ExternalMark\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3bda6e",
   "metadata": {},
   "source": [
    "# visualization based on ExternalMark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7b4f20a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ExternalMark', ylabel='Semester'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eRAt+zXvG7/d7LnaPGtM0aTQa3sDS2raS5NVqNarVqrdt369UKt62XKlQKVcoV4pUqxVq1eqGifnuQATxh8AfxPYFsQ1nYGhF7DrF50Ntr4MoX8jdd1cgD2PgsE3H373h+rzXSxiNIlIv4a8XoLYK9lrC457ePk48e5Jjx47xxBNP8MQTT3Q0KZhmZ6EFv2ZX4/f78fv9HVE/NJtNL7tqpVJZl2m1vbVWOa3mZGFdpFwo0ajX7/k54ndWEC31lC0+lKypwryBQSlE2aBsDGUhtumkGW7WUfadg1QoFKa3r5fBg89w8OBBBgcHOXLkCIcOHdLqmn2OFvwazSYEAgEvOOhBaTab3oBQLBbXDRCtAaVUKq1bnayl+W5i2RbKViil8Pl9+AwfgYCfSCTipfNtRZ220ghks1l6e3tJJpPao0azIVrwazQdJBAIbJsaTKPZDO3eoNFoNPsMLfg1Go1mn6EFv0aj0ewztODXaDSafYYW/BqNRrPP0IJfo9Fo9hla8Gs0Gs0+Qwt+jUaj2Wdowa/RaDT7DC34NRqNZp+hBb9Go9HsM3SuHk3HKZfLLC4usri4yNLSEqurqxQKBQqFgpcauZWczDRNLNNERPD5/fh8PiKRCLFYjGg06iUiy+Vy9Pb2cuDAAUKh0HZ/RY1mV6EFv+Y9o5RiaWmJ0dFRJiYmmJycZHJykpnpW8zOzlKuVDe8L+IXIgEI+xRhn40PG58ofAbYCpoKLCXM2z6qllAxhXLjzsIrXbksR44e5ejRYxw9epQTJ04wMDCAoSttaTQbogW/5r5QSjE9Pc3Vq1e5evUq169fY+jmTVYLa+UXgz6hO2rTFWpyNGuT67fJhBSpkE06aJMIKGIBhf8B5LJpw0pDWKkbzFUNZis+ZivTTFxd4Pyb38d0a43EY1GeevppnnnmWZ577jmeeuopwuHwQ3oKGs3uRgt+zV1RSjE2Nsa5c+e4dOkSb128wNLyCgB+AwYTFifjJgd7LQZiFn0xi0xIYXQoDbzfgHxYkQ9bHEtZQNM7Z9pwq+xjuOBjqFDnxrvf5fvf+z4K8Pt8nDhxnJOnXuDkyZM888wzuhiJZt8iD1Sz9BFz5swZ9eabb253N/YNpVKJ73//+7zxxht8/3vfZWFxCYBcBI6n6jyRNjmatDgYtx5o1v4oKTeF66s+ri0HuLoSYKTgw1JgGAZPPH6M50+e4rnnnuPpp58mm81ud3c1moeKiJxTSp2547gW/BqA2dlZvvWtb/Htb32Lty69hWXZxILC0+k6z+aaPJ016Y7Y936jHU7NhBurfq4u+7m6EmCo4PfUQ/19vTz19DM88cQTHD9+nGPHjuli45pdjRb8mjuYnJzk1Vdf5dVXv8716zcAOBBXnMrVOdXV4FjSwrfDZ/TvlYYFo0UfN1b93FjxM1wKstRmi85l0hx+7CiDg4P09fXR399PT08PuVyOdDqN36+1pZqdy2aCX/9q9xnj4+O8+uqrfP1rX2NoeBiAoymLnzxW50xXk77Y7p/V3w9BHzyRtngibcGhOlBmtSGMFnxMlHxMletM3VjiyqXzVJrrJ0kiQjIRIxFPkEgmicUThMNhwuEwwWCQQCCA33VJNQwDEUFEaJ9s2baNUmrT7e0Ts9Z7GIaBz+fzWiAQIBAIEAwGvT6Ew2HPDTYWi3m1ebX7q0YL/j2OUorR0VG+8Y1v8OrXv8bwyCgAj6ctfvrxOu/raZAP7/xV36MkFVQ8nzd5Pm+uO15uCnNVg6Wa4XkWFZpVys0lyvPCyrRBwzao20LDEiwFlsJTJdlKoRQIuP+AIeIZwg1xIyrdrcj6a1GgnA22WmumrTAt5/hWCAUDpJJJ0pkM6UyWbDbrFWnv6uoin8/T1dVFLpfTK5o9iv5f3YPYts3Vq1f59re/zTde/ToTk1MIjrD/mSfqvK+7QU4L+/smFlAcCVgcSVrb3ZU7UO4g07ChaQl1W6hbUDXFa2VTKDcNSs0axWaJwtIMC7MGQ00/K3WFddtizxAhn8vS09tHT28vfX199Pb20uvu9/T0EAgEtucLa94TWvDvEcrlMufOneONN97g9de+w+LSMobAiYzJJ483ONPdIBPSwn6vIgJ+cdxd8bfWBVvHVs6KZqlusFwXlmoGi3WDxVqNhclZLg37+VrVua5Fa2DoO3CAvr5++vr61rVcLofP53uo31PzcNCCf5fSbDa5evUq586d49ybb/LO5XewLJtIQHg2U+cTTzc5mW8SD2hhr7k3hkAiqEgELQ5t4shk2bBcN5ivGcxXW63G3Ngs37v+NstVtW648ft8dHfn6e3rp6enl56eHrq7u71tV1eXjqXYJrTg3yWsrKxw5coVLl++zKVLb3Hl3XepN5oIcDhp86cGGpzMNzmWMne8b71md+IzIB+xyUdsnszceb5pw0J1bWBYqBnMV6ssDt1i5F0/KzV1xzokFo04NoXuHvL5vJeHqd3ukE6nicfjiHQoKnAf0lHBLyKfBX4KsAAb+HngJeAMTsjl94CfV0o1N32TfYZpmszMzDAyMsLQ0BA3b97k2tUrzM7NA87M7HDS5mM9DY5nTJ7MmHpWr9kRBAzoi9mbeoaZNizVHeP4Ys1VKdVrLFVXWb4xyshlZ3CwNvg5B/w+UqkkqVSadCZLKpUimUySTCY9b6V4PE48HicWi3ktGo0SDAb1oHEbHRP8IvIh4OPAC0qpuojkgSDwu8DPuJf938DPAf+iU/3YiVQqFebm5pidnWVmZoapqSmmpqaYGB9jamqKpukYDwXoicHheIMfetzksaTFkYRJWK/TNLsQvwHdEfuugYAtW4PnNdUwWG0IhYZBsVmhUJhlddHHLctHqSGUmo6n1N3w+Qyi4TCRSIRINEo0GiMaixGJRIhGo0SjUW+/fcBoDSKtASUej+8ZL6dOfos+YEEpVQdQSi24x2+1LhCR7wEDHexDx7Btm3q9Tr1ep1KpeOmFS6USxWKRYrFIoVBgZWWFlZUVlpeXWFxYYHFxkUq1tu69AgZ0RxXd4SbPHLDpi1kciFkMxC3Cu9w29m+vRRgrPvovUTWdbJ5RvyLi35srokMJi79wfOPMp7uVNVuD4mD83jEltlr7vy43nW3VFC+ba80UqhbUzAo1S6jVhFpJKFhO1tea5VxbMxXNLThrhUMhEvEY8YQTuxGPr6022geJ9gGkNahEIhEvxmO7M8d2UvB/FficiFwH/hj4slLqG62TIhIA/gLw1zvVgVqtxmc/+1m+//3vd+oj7okh4BPwi8JvKEIGxMKKgAEBw9n6DUcwVU3h5qqPm6u7XNq3MVb0UbUe/Y88Eolw9uNneeWVV6iW9pZwbDFW9G3LoLoX8Iki6jOJtj0+Jz5CsBX0RC0+dqDhDSqtgaVs1qg0C1TKM5RXhSnLR9k0qDSFavNOG8b90tXVxb/8l/+Srq6u9/hOd6djgl8pVRKR08BHgI8BXxaRzyilvuRe8hvAN5VS39rofhH5NPBpgMHBwQfqQ71eZ2ho6IHufRgEDYVPnPTDPnEEvN+AgHssYHQui+V+5+zZs7z00ksAvPzyy9vcG81uQHAGBJ9APKAYjFsbCP71q4uyaVNpChXToGT5WK4J5fdgsZyfn6dSqTy077QZjyxXj4j8BPBJpdSPicjfA04B/71S6p7rue3M1WNZFqZp0mw2aTabNBoNGo0G9XqdWq1GrVajWq166p5yuUyxWKRUKrG6usrKyjIry0ssLy9TLN35HxoPCt1hi66ISW/Uoj9q0++mN97tah6Af/hmnKsrjz7IJxKJcPasO+Ov7s0Z/4l0k185U9rubuwYLBtPxeMFrllQM8VR87S2llBrO141hbptULN8VCyh2oSaeW+5GI2ESSTixOMJ4onkOlvA7aqeVmul0giFQgSDQUKhkJduw+/3P3QV0CPP1SMixwFbKXXDPXQSGBORnwN+FPjhrQj97aaVC+Vh5DdpNBosLS0xPz9/h3F3YnKC74/NYdvOI2kZdg/GGhxOWDyWNDmStHadB8+hxPZEuVZNk2/80Zfp9isi6d31zLbKdj3bR4GtoNgUCg1htW5QaLoG3oZQbBqUmuI0y0+5aVAxodrc2v9zMBAgGnGNvfEosVicvGvs3ci428pz1BLqiUSCWCy2q4PTOqnjjwOfF5E0YAI3cVQ3M8AY8LrrYvUflFK/2sF+7BiCwaAX8r4RzWaTqakpRkdHGR0dddw5b1zn+0PT3jW9McUTyQbH0ybHMyY9EZud7Km214yPmveGUlAxhSU3Orjl3rnScNw7Vxp+Vhs+VutqXZRwC0OEZCJOMpUi1Z3mYCp1hztnPB73PHRu99qJRCJ7xjPnvdBJHf854Ace5WfudgKBAIcPH+bw4cPrjheLRa5fv+4FcF146yLfnC4DkI/AU+k6z+SaPJs1SQT35uxWsztQyimN2QrkWqj6WKg5wVyLdT+LNeMONYqIkE4lyefz9Oa7eNoN3GoFcaXTaTKZDOl0mkQise0eMXsBnY9/F2LbNuPj41y4cIFz585x4fw5iqUyhsBjSYuTuQanuxsMxHb2akCzOyk3xUnX4EbozlUN5qs+5usB5qvc4RaZSsadlA29fXR3d69rrSygOtlbZ9CFWPYwlmVx7do13njjDd54/TWuXrsOQFcUTudrvK+7weMpS3sQae6J5RazX6470bWLrdl6zWC+5meh5rujLkE8FnUSs/UfuCNRW09PD5FIZJu+jUYL/n3E4uIir732Gt/61jc59+abNE2LdNgZBD7Q3eRExtSDwD5AKZzUzNZ6d8Ry06DQENd46kTGtnTrK/U7I2Ej4ZBjm+rr91IytzddnnLnogX/PqVcLvP666/zjW98gzdef416o0kyBO/LO0VYnkybe7684sOk6WaoXKk7KQWKTUeQtvy7GxbULaFpC6ZyZtCWEhTiFlER719oFVxxzgpOwF+rAEurCEv7GK3AE8ymDTaCpYSmMjBtwbSdz69bULfuns7A5zNIJ5Nkczly+a47CrG0smjqBGm7Fy34NVSrVd544w2+/vWv88brr1GrN0iG4HS+zpmuBk9ndWZPWDNQjhd9TJWdNlNxdNi3px5uEQwEiEYjRMJhQq6fdiAQxB8IeKUXbzdKtpdXtG3ba8q2UShn2/b3qZRy3sMtv+j3r5V2bJVdbPmG3630YiKR0Bkv9wla8GvWUavV+O53v8vXv/51Xn/tO1RrdSIB4flsnRe6GjyfM4ntspiBB6ViwtCqn+srfoYLfkZLQVbra989nUpycHCQ/v4D64qttzxOdB1bzU5FC37NptTrdc6fP883v/lNvvPtb7GyWsAQeDxl8my2yTO5Jo8l945xeKUuXF3xc23Zz7XVIBNFA4XjIz44eJDjJ57k+PHjHDt2jMOHD5NOp7e7yxrNA6EFv2ZLtOr1vvbaa7zx+mtcv3ETgGhAeCJVdwLH0iaHExbBXRK4uFgTrq34ubIc4OpKkOmyM4KFQyGeefYZnn32OZ599lmefPJJYrHYNvdWo3l4aMGveSBWVlY4d+4c58+f562LFxifmAQcI+RA3OZIoslg3OKg27Y7gMy0nayVQwU/Q6s+rq2GWHCDh6ORCM+ffJ6TJ0/x/PPP88QTT+goTs2eRgt+zUNheXmZd955h6tXr3L16hWuXb1KobiWKCwWFHojJj0Rk66wTS7slOrLhGxSQUU88HAykjYsmK8azFR8zFQNJko+xksBpsoGlpsBKptJ8+xzz/Pcc8/x/PPPc/To0V2dX0WjuV+04Nd0BKUUi4uLDA8PMzo6ysTEBJOTE0yMj7OwuOQlnWvhEycjaTxgE/NbRHyKsF8R9in8Aj7DSYurFFjKyY9es9aKbRRNPyt1g/JtQUS5TJqjjz/B0aNHOXHiBE899RTd3d3aa0Wzr3nk2Tk1+wMRIZ/Pk8/nef/737/unGmaLC4uMjMzw9LSEouLiywtLbG6ukqhUKBQKFApl1iuOimtzaaJaVmYpoVhCD7DwOfzOQm2kjFi8TjHMmtFuPv6+hgYGODAgQMkk8ltegIaze5DC35Nx/D7/fT09NDT07PdXdFoNG3ocB2NRqPZZ2jBr9FoNPsMLfg1Go1mn6EFv0aj0ewz7in4RcQnIr/0KDqj0Wg0ms5zT8GvlLKAP/MI+qLRaDSaR8BW3Tm/IyL/DPgyUG4dVEqd70ivNBqNRtMxtir4W0XTf7XtmAJ+6OF2R6PRaDSdZkuCXyn1sU53RKPRaDSPhi159YhIj4h8UUT+k/v6KRH5i53tmkaj0Wg6wVbdOb8E/Beg3319HfjFDvRHo9FoNB1mq4I/r5T6CmADKKVMwOpYrzQajUbTMbZq3C2LSA7HoIuIfBBY7VivNHsK27ZZXV1lfn6e5eVllpeXWVlZoVgsUigUKJVKVKtVr9UaNZqNJqZpYisbZTsFycUtMm64WTt9Ph8Bv1NkPBAIEAqF1hUcb20jkci6AuThcNjJ+BmNettoNEo8HicSiehUzpo9z1YF/98A/hA4KiLfAbqAT3SsV5pdhVKKpaUlbt26xdTUFLdu3WJmZobp6WmmZ6ZZWlzCNM07bxQwQgYEQPkVts92fpEGKENB0LmGlhxWbVsFYgmYOA7GNogtXsMCLFCWQpnKXaveGxEhGosSi8VIJBKkU2mSySTxeJxUKkUymSSVSpFOp0mlUl6LxWJ6wNDsGrYq+C8DHwWO4/wZXkOne9h3VCoVJiYmGBsbY2JiwtkfH2NycpJ6rb7uWiNmYEds7KgNR4EIqIiCME4LAX6w5ME1hor7KCJk4wwSlrtta9J0B5Cm04rNIsVGkdnyLLIsGKYBDbBrNpt9pOEziMfjJFNJMqkMyWSSRCJBIpEgHo8Ti8WIx+Pe6iISiXirj/aVSjAYxOfz6UFE01G2KvhfV0q9gDMAACAi54EXOtIrzbbRqqg1NjbG6Ogo4+Pjzv7YKEuLS2sXChhxAytmoQYUxEHFnS1RsHw7zARk4KwgNmCzAaR13GqZsxTOAFEHGs5W6uIMCnWblcYKq/VVJmYmMCYMpCmourviuA9EBJ/fh9/vX98Cfk+1FQwGCQXX1FmtFolEvBaLxbwBpzUIJRIJUqmUrjW8z7nr/76I9AIHgIiInGJt0Z0Eoh3um6aD2LbNzMyMJ+BHR0cZGR1hbGyMaqXqXScBQSUUdsKGPlAJ5fzvx3agcO80AgTc5nL7oNF6bbfrllqrDXdF4a02rDZ1leVe524t26JhN5xjbU0scVRbRff+lnrLctRbylSoptp0ZdIiEo2QSqXIZrNk0hlnm8mQyWS8CmetFo3qP/W9xr2G/R8FfhYYAP5P1gR/EfjlznVL87BoCfiRkRFGRkYYHR1leGSYsbExmo2md50RMbASFqrPEeyegA+z9r+ueTBaq40NVhz3pa7a6vWKNZVWa7BpgDSc1QkNKNfLlOtlpuenMaYMpC6bqrJC4RDpdJp8Lr9uUMhknAEjnU57r7VxfHdwV8GvlPpt4LdF5H9QSv3+I+qT5gFZWVlhaGiIoaEhhoeHGR4eZmR0ZJ3+3YgZWHELdcgV8ElnawX32ex9LyM4f9l+nIHbZaNBQ6HWVicKR41VB2ogVYE6jrdVrcrM3Ay+SR/UHNXWRvgDfsfOkc6QzWQ943fLGN4yjieTSW8/HA5v+F6azrFVRd+AiCRxZvr/Cke3/xml1Fc71jPNpjSbTcbGxjwhPzQ0xI2bN1hZXvGuMcIGVtLVv6e0gNdsAWHN+J7aWI3lDRI23gBBzbV11KFRb7BQW2CxtOgYxhuGY+dobL5SCQQCJJIJkqkk6WSadDrtDQobtXQ6rVcW75GtCv7/SSn1T0TkR4Fu4FPAbwFa8HeQlqF1eHiYoaEhbt68yY2bNxgfH8e2nD9A8QkqqbCTNgyCSjmC3gprAa/pIAYQcRub2zo8w7iNZxD3DOOu6qler1Nv1FmsLSIFwRi5txdVIBAglXZsFLlszlM7ZbNZcrkcuVyOfN5RTYVCoYf//Xc5WxX8raH1TwO/pZR6S/Rw+1ApFAqOgdXVxQ8PD3Nz6CalYsm7xoi5evjHFaRdIR9HO9Zqdj4Ga6sJl3sOFgrPPtFSQbVWFvV6nbn6HPMr8xhzro2iuvFAEYvH6O7upqe7h66uLrq7u+9o+21w2KrgPyciXwWOAH9XRBJsISRGRD4L/BRrPgs/D3waOIMzmFwHflYpVdr0TfYQ9Xqd6elpbt26xcTEBOPj44yPjzM6NsrqylogtOdJk3d84FXKEfRaTaPZVwhrRvG4c+iu6ieFM0hUWbNR1KBYLVKqlhi9Noq85Q4Qt5FMJent7aW3p5eenh5noHC3XV1dZLNZfD5f577rI0aUureXgIgYwElgWCm14qZvOKCUunSXez4E/Drwg0qpuojkcf4LS0qpgnvNrwNzSql/fLfPP3PmjHrzzTe3+p22hWq1yvLyMktLSywtLTE/P8/8/Dxzc3NeBOvy0jLtz9sIGdhx23GVbDO0EmXPetLIRUFWdvCXa3nB3Oa2uVtRaYU6eX+eQ3seC2dwqIBUBCruflXwVX2o8p2xF4ZhkM6k6e5yBoKWOqnl1ZROp8lkMl4Ut2HsjGW4iJxTSp25/fhWZ/wKeAr4OE4xlhjrFm0b0gcsKKXqAEqphds6JDgawh35q/zWt77F7Oyso3+s16nValSrVSqVCuVymVKpRKFUoLBaoFgo0mw273wTA4yogRWxHPfIHtYFOlmhu8/gd7yQfBBW3EjZHUokEuHs2bO88sorVFeq975hp7PC3vsNdRAraDkpRJIKdVR5A4JdtVmoLLC4vMi16WtITTb1bBIRYnEncC4ej5NMJNdFbLfnjWrlmWptb19V9PX18dRTTz3077lVwf8bOKqaH8IR/EXg94H33eWerwKfE5HrwB8DX1ZKfQNARH4Lx17wLvA3N7pZRD6NoxZicHBwi918OKysrPDZz352y9eroILcmmFVpV3de+i9pSTQPHrOnj3LSy+9BMDLL7+8zb3RbAsC+IC009rVS+tUSxaO7aEMUhBYBSkJqqQoFUvr7HPvhVdeeYVEIvFQ3qvFVgX/B5RSL4jIBQCl1LKIbBIA76CUKonIaeAjwMeAL4vIZ5RSX1JKfUpEfMDngZ/E8RC6/f4vAF8AR9Wz9a/03kmlUnzqU59ibGys1Rfq9boz26+UKZaKlMtlKqUKlmU53gmLIItrMysjZKAiCjtso6LKWSPF3Nl+jE3TB7SjTqr7DvDZ6RivGjC/3b3YnFdeeWXddteTBvsHt5ihTrOelkqo6qqEWvtVQWquq2r17ik5gqGgk/Qv6qTPiEQiRMLOrP92dVDLX6aViRbghRdeeOhCH7Yu+JuuoG6lZe5iC8ZdpZQFvAq8KiJvA5/EKeqCUsoSkS8Df5sNBP92IiJ86lOfuud1Simq1SqFQoHV1VWWlpZYXl5mcXGR+fl5FhYWmJ6ZZmZmhnKpvO5eI+zq9+Nt+v0EzqCwh1fmKr2zB7JKs8K//0//3kkiF9/u3rx3dvrz3jZawWot/b6r65eKIFXBqBobGoEj0Qi5XI6uwS5Px5/JZDw9fyvWoJWcb6fmRNpqr/4p8AdAt4j8GvATwK/c7QYROQ7YSqkb7qGTwLiIHFNK3XR1/D8GXH2gnu8ARMTT3fX29t712kqlwvT0NFNTU0xNTTE5OcnY+BhjY2OsjrZ59Pjb/PJTa+qje1pUdgl7cRWj2WFYeIFlrRl6+2zdV/VhV+w7pq6hcIju7m76jvV5Hj2t1tXVRT6f3zN5i7ZabP13ReQc8MM489E/q5S6co/b4sDnRSSNkzXkJvCXgT9wo4AFeAv4Kw/Y911FNBrl6NGjHD169I5zxWLRS5Q2Ojrq+fC3DwheLp10mx0hgaOL1Gj2Mpv589dYSy/hql42M7r6/X5y+RzdfY4/f0ug9/T0eEI+kUjsm2jgLblzAohIBjhI22ChlDrfoX6tYze4c3aC23Pv3Lh5g5GREcymW9TEAEkIVspavzqIsKfVRZpdTCuCty2KVxqyFtHb9trX9HkprzdTLIfCISejaDa3Lmq31Voz9VQqtW+EejvvyZ1TRP4XnCydQ6yvg/RDD6uDmjtJp9OcPn2a06dPe8dM02RqaoqbN2+uy9OzML7mLStBV12Ustfy9KTYkkFZo7kvWvUJ3Nl3K7K2fWYuDcFoGlAHu7G5adDv9xNPxEkmk2TyGS9nT/u2pUNv6dV1grcHY6sBXNeAZ5VSjc536U7264z/figWi15OHy8758jwutz6RtTNzJlcn7hNDwiadbTcFFsJ2Gqybt/zaKm5uf83IBAMeAnV0qn1hs+NMnQmk0mdeK0DvNcArndwvFrnHmanNA+PRCLB888/z/PPP+8dU0oxNzfn5f8ZGRlheGSY0dFRGjfXxnAj0jYg6Fz8e4tW1bAGa3n5622qlbaZuq/uplzeZFYei8fIZrPkB53kZ63CLa0Eae0z8Ugk8qi+oeYB2Krg/0fABRF5B+cnA4BS6sc70ivNQ0FEPOPVBz/4Qe+4bdvMzs7eUX1rdHSU6tAm1bcSbS6nOjHcg9OqxtVWhWtdc4vGe9mtbBzhfVslrnUVu1qVvCwQUzAswyvCYjc3z3AJjjBPpVLkenOeMG/pyturcWUyGQKBPZDDQgNsXfD/NvC/AW+zBf99zc7GMAz6+vro6+tbNyC019sdGxtzEsi5g8Ly2PLaG7TX20249XYTbmBalL0/KNyl9u46o2VTkKar326CaiiU/d5cWUUEv99PIBjwQv1DoRChWIhIOEI0GiUcDhOJRLwi77FYzKu3e3ue+53qZ67pLFv9X19QSv3TjvZEs+2ICPl8nnw+v86gDFAul9dlFJ2YmGB0bJSp8Ska9TbTj+Gmj45YqJhyBoIoqIhyvI1CODaF7VQhtZcmbDU3OZs0ZV25wvaShUbTQBpyVy8TESGecIqbJ7NJUsnUOgHsRW+6rb1QenvOllbz+/3e1u/376kMkZrt437SMv8j4A9Zr+p5JO6cmu0nFotx4sQJTpw4se64UoqFhQUmJyeZmpri1q1bTE1NMT0zzfT0NKsjq3e+meFELquAwg7YTlKsgHJ+jT7nvLeF9YNEa8K8kQqkpfZoqUpcFYhXjNxsK0a+BQLBgDdLTnY5QrxliGwZK28vKbiTMjNqNJuxVcF/yt1+sO2YdufUICJ0dXXR1dXFqVOn7jhfr9dZXFxkbm6OhYUFlpaWWFlZYXl5mWKxSKFQoFAsUKlWqBVr1Go1zKaJaZpb+myf3+fNiIPBoNfCcSf7YWs23ZpdRyIRLzNiK1tiNBr1ZuOtjIqxWIxgULs7afYmW43c/VinO6LZm4RCIfr7++nv77+v+5RSmKaJUgqlFLZtezNpwzDw+Xx6Zq3RPCBbDeDqAf5XoF8p9adE5CngQ0qpL3a0d5p9i4hoLxKNpkNsdcr0JeC/AK1p23XgFzvQH41Go9F0mK0K/rxS6iu4vgxKqZYHskaj0Wh2GVsV/GW3zm4rH/8HgQ3cNTQajUaz09mqV8/fwHHlPCoi3wG6cHLyazQajWaXcdcZv4i8T0R6XX/9jwK/jOPH/1Vg8hH0T6PRaDQPmXupen4TJ34R4AeAzwL/HFjGrYer0Wg0mt3FvVQ9PqXUkrv/k8AXlFK/D/y+iFzsaM80Go1G0xHuNeP3iUhrcPhh4Gtt53R2J41Go9mF3Et4/zvgGyKygFOu+FsAInIM7dWj0Wg0u5K7Cn6l1K+JyJ8AfcBX1Vq5LgP4hU53TqNpUa/XqdVqGIaBYRheFkuNRnP/3FNdo5R6Y4Nj1zvTHc1+pVqtOrn/3Uph09PTzExPMz83R7Fc3jBpWywSIZ1O093by6FDhxgcHOTYsWMcP35cV4DSaO6C1tNrHjm2bTM+Ps7bb7/N5cuXufruu4yOjWG7C8qACBkRkrbNYzhp/MOslQa2cdLll6tVStUqCzMzXH3rLaq2kyTfMAyOHjnCyRde4MyZM5w8eVIPBBpNG1sqtr7d6GLruxvLshgaGuLixYtcvHiRty5epFgqARA1DA7YNgdw9Ik9QAYw7rNSi0JRAm4BE24bF8FUCr/Px/MnT/Liiy/yAz/wA/edKVSj2a1sVmxdC37NQ8e2bYaHhzl//jwXLlzgrQsXKFUqAOQMg0O2zSHgEJAFpEPluJooxoEbwHUxmFfOiuCxI0f4bz76UV588UWeeOIJRHRFec3eRAt+TcdQSjEyMsKFCxe4cOECF8+fp+DO6HOGwWHb5ghwGEhtY83FRRRXgasIYygU0JXP8+GPfIQXX3yRkydP6uIrmj2FFvyah4ZSirGxMS5cuMD58+e5eOECq4UCAGnD4Igr6I8A6W0trrs5ZRTXgCvAkAhNpQgFg7xw+jTvf//7OXXqFEeOHNGrAc2uZjPBr427mnvSEvQXL1501Dfnz3uCPuXO6B/DEfQZW7G9ldS3RgzhBeAFoKEUI8CNRoNr3/0ur7/+OgCpZJJnn3uOp556iieffJKjR4+STqe3sdcazcNBC37NHSilmJiY8FQ3F86dY3nViddLtaluHEFvd0xH/6gIIhwHjgPYimVgBBguFLj62mt8+9vf9q5NJ5Mcfuwx+vv76enpoaenh3Q6TTqdJpFIeHV+A4EAhmEgIti2jWk6dYQbjYYXk1CtVte1SqVCtVr1rmk0GliWhWU5pS9aMQyt2sGRSMQr8p7JZMjn8+RyOXw+3zY8Rc1uQgt+DQC3bt3yjLHn33yTxeVlwBH0h2ybj7J3BP29yCBkcFYD2IoKjrfQLDBXKDB/8S2GL12i4LqPdoKACD4RfKzlVVE4rqymUjQ2UdEahkE+m2Xg4EEODAwwMDDA4cOHOXToEL29vbpOsQbQgn/fMjs76xhiL17k3Pe/z+z8PABxd0b/IvAYkN0Hgv5eRBGOAcfaD9oKEygCZaCCk9Ok6TYTR1C3FF8+t/mBgLsN4cQmBNv2W+cMBdzF/mbjpM2tup9fBgrAqm2zvLDA/OIi1956i3Lb4BQKBDh0+DCPHT3KkSNHvNbd3a1tGfsMLfj3AUopZmZmHB/6t97iwrlzTM/OAo4f/WHb5gzOjL5bC/ot43dXBplt+GwDIYwT2Lbh57sDRwWYb7Vmk9kbN3htaIj/3DYgREIhBg8d4vCRIwy4q4QDBw7Q19dHMpnUg8IeRAv+PYhpmoyMjPDOO+/w9ttvc/H8eRaWnOzaUVd1cxJH0PfY9n0HS2l2D1HEi5nwcNVXc26br9eZv36dN27eZPU29VU4FKK7u5uu7m7y+TzZbHadTSORSBCPx4nFYkSjUaLRKH6/Fis7Hf0/tMtRSjE7O8u1a9e4cuUK7777LteuXKFarwOQNAwGbZsP4vzxd2tBr8EZEA7jxFZ42IoGsOS2FWClXmdlYoK5yUmGRCgqhXUPF/CA308kHCYcDhOJRIjGYkTcQaHVYrEYsVjMGzwSiYRnqE6lUoTDYb3S6CBa8O8ims0mExMT3Lx5k6GhIW7evMm1K1e8YCmfCL3Ac0pxEEfQp7TqRnMfBHF+Q723n3BVRwpncCjj2Bdq7raOY3OoAU3TpFEqUS+VaLjHy0DDMLxrarbN3UzjoUDA8VbKZsnmcmQyGbLZLNlslkwmQ67tWDwe14PEfdJRwS8inwV+CrBw7FE/r5T6rnvu88CnlFLxTvZhN1Iul5mcnGRiYoLx8XHGxsYYGRpiYnISy12K+0ToBh5Xin6gH+hVCj/CbvCj1+xOBCGEY4y+b+y1lYLCMYK3Bo5Wq+Aay5tNygsLlBYWGBXhsgilTQaLgN9POpUim8uRzeU8VVR7a60kksmkHijooOAXkQ8BHwdeUErVRSSPm2BRRM4A6U599k6nVqsxPz/P7OwsMzMzzM7OcuvWLaZv3WJyYoIVNzgKHBGeNQzyrqdNN85sLK8UPi3kNbsUQTyPpuS9LnZXGzbO4FC6vZkmpcVFSouLjInwrhiUlL2pSsowDOKxGKlkklQ6TTKV8lRN7a39WCKRIBaL7Rl32E7O+PuABaVUHUAptQAgIj7gf8dZCfx3Hfz8jmNZFvV6nUqlQqVSoVwuUyqVKJVKFAoFVldXWV1dZXl5maWlJZYWFlhYWPASlrUQnFQHaTcCNndbC+ySaNj75T+imO7Qe9fc1vJ82Yv0AX96D/4uNsNAiAExnCyuG6IAZaNQ1GlbQbS1qm1TKRadNjXFkhhURKgqm/pd7BeGCPFYjLg7ICTcQSEej69rLftFJBIhGo16wXat4D6/37/tK45OCv6vAp8TkevAHwNfVkp9A/hrwB8qpaa3+8tvlVqtxl/6uZ9jbHz8vu8NiRATIW7bxICncWY4SSCFs+xJAr4NhPuaYNz5+ZQehGkc3XAniEQinD17lldeeYWZarVDn7K9TAPTe/S38SgJKpugcv4WW0FyWeB9rFdBVZWiWipRKZWoTU9zS4RhEUdddQ+bxVZ5/Ngx/vlv/AbhcGenKx0T/EqpkoicBj4CfAz4soj8U+BPAz94r/tF5NPApwEGBwc71c0tYZomt27duu/7ooZBEojZNnEgDiRYL/gT4KpsNA+Ts2fP8tJLLwHw8ssvb3NvNLuFVrBdKz4i3NZag0BrP6wUNaWoiRAxDAp3iajeKlO3bm1Ybe5h88iyc4rIT+AUb1/EWYUDDALDSqljm97IzsnOads2zWZzXb6VWq12h6qnWCxSLBZZXV1lZWWF5aUllhYXWV5ZodFsrntPAyctQtq2yeDMNLJAHkfNE9zDg8IXUYx26L3bZ/zVPTrjPwz8xT38+3gY2Kh1ap5WhPXt2ypQNQyqOCqfuwnwlo0gEY+TaDMYt6t5WnENLXVP2HVvDYVCXgsEAl7rlO3gkWfnFJHjgK2UuuEeOgn8plLqr7VdU7qX0N9JtBJkhUIhEonEfd+vlKJUKjE/P+8Zd2dnZ5menmb61i1Gbt3i/MrKunvShpC3bbpwDLs97ja0B/7g+zr43rVqlT9++WVS3EUfvMvp5PPbyTTcamu3t7K7rQBlw3BcTm21qTLM7/ORiMdJpVIkUikOtAnxlgdQsk2P39qPRqPbrqN/r3RSxx8HPi8iaZzUJTdxVTf7FRHxglUee+yxDa+pVCpMTU2tc+ccHRnh/NgY9UbDuy5nCD22TR9wAMedM7bLBoP9ZJjUbE4T5c2+W63c1lpCvWwYFIG6vbEoT8RiZDIZMtkshzIZMpnMOlfOljBvbSORyK4X4A9KJ3X854AfuMc12of/NqLRKI8//jiPP/74uuO2bTMzM8PQ0BDDw8PcvHmTG9eu8e7MjHdN1hD6bZsBHB1aH7h+/RrNw8V2vWbaA7harbFBaz9XF3EajsHUvIu2ORGLeUFcR90ArlYQV7btdTqdJhAIdPAb7y105O4uwTAM+vv76e/v5yMf+Yh3vFgscuPGjbWUDe+8wzsLCwD4RTig4BCKQziDQVgPBJp7UMepSbCMk7ZhFScLaZE1FUrlLiqUdoKBgJO6IRwm6uq+8+62pRffKGVDa6au8/50Bl16cQ+ysLDA5cuXefvtt3n70iWuX7+OZdsI0CfCYaU4gpPSIaIHgn2JQlGkLVEbsAAsGcYddQaCgQC5bJZ8d/ddk7S1GzQjkYjns67ZPnTN3X1MtVrl3XffdVIyX7jAu5cv0zRNbyA4ohSP4QwEe8ForFlDuYbQedaE/BwwZxhU2wR8Ihbj0JEjDA4OemmZe3t76e3tJZ1O71td+G5H19zdx0QiEU6fPs3p06cBqNfrXLlyxauf+9133+U7pokBHBC8geAge9ud9GFgtxkmW4VYGjjJqVpTKsFx220vxBJgfSGWADxQMj0bRQ3HAFrAUcus4GbYFGFR1gv4eCzGY489xsnHHltXjEXXEt5f6Bm/hlqtxuXLlzl//jznz53j6tWrWLaNT4QBBUdwVEMHgcA+HAiUq/OeYU0tsgKsumqRh/EXJEBQhKCIV4XLrxQGYLh/o7YICjBFaCDUUVQ3+HxDhK58FwODBzl48CCHDh3i0KFDHD58mFwup2fv+wit6tFsmUqlwqVLl7xC69dv3MBWCr8IA6594AgwwN4cCJooJnEKrk8AU7epRbrzefoPHKCnt5fu7m7PbbBVbD0cDuP3++9abL1arXrbVpH1VqvX616xddu2sSwL27bx+XybFltPp9N0dXU5RVO6urSHiwbQgl/zHiiVSrz99tueaujmzZt7aiBQKOaBG24bF6GpFIYIhw8f5ulnnuHEiRMcPXqUw4cPE41Gt7nHGs3W0IJf89AoFotcunSJixcvcv7cOW4ODaGUItCmGnoMJ7Bsp8YRWCjGgavAVcNgyZ3RHxoc5P0f+ACnTp3i+eeff6AIbY1mp6AFv6ZjFItF3nrrLW8gGBoe9gaCQXdF8BhOdPF2JqRroBgCrgDXDIOKbeP3+zl9+jQf/vCH+cAHPkBv7x21pzSaXYsW/JpHRqFQ4OLFi1y4cIHzb77JyNgY4BgvDyrFYRzX0QN03mtoCcVN4Bow4qpw4tEoH3rxRT7ykY/w/ve/X6tuNHsWLfg128bKygoXL150BoNz57yBwMCJIzigFAdwUkzkeXA7gY1iAZjEMcoOt6lw+np7efHDH+bFF1/k+eef14FFmn2BFvyaHUOhUODy5ctcunSJdy9f5urVq1RrTqZuA8gZBhnbJo1TsyACXp3XVqGMJmtJvFaABREWwEunG49Gef7UKc6cOcP73vc+Dh48qN0YNfsOLfg1OxbbthkfH2d4eJiRkRFGRka4NTXF7MwMxXL5rvcahkE+m+XwkSMMHjrEsWPHePrppzl48OCeqY+q0TwoOnJXs2MxDIPDhw9z+PDhO87VajWKxSLlcplKpeL5sgeDQTKZDPF4XAt4jeY+0YJfs6NpBUR1dXVtd1c0mj2DnippNBrNPkMLfo1Go9lnaMGv0Wg0+wwt+DUajWafoQW/RqPR7DO04NdoNJp9hhb8Go1Gs8/Qgl+j0Wj2GVrwazQazT5DR+5qNHfBtm2azSbNZtMrgaiU8soqttJHBAIBnTpCs2vQgl+zJzFNk1KpRLFYpFgsevulUsnbL5fLt7UKlXKZWq3m1L1t1LEsa8uf6ff7CYedWrixWJRkMkk8Hvfq4rZq42azWdLpNLlcjkwmQzAY7OCT0GjuRAt+zY7GsiyKxSIrKysUCgVWV1dZXV2lUChQKBQoFovednV17VitVr3r+xpiEAyECfhC+I0gfiNIwBckYKSJBAL4QgF8hh+f+DEMH4YYiBgIgojQymprKwtb2di2iWmbWHaTptWgudJgZnGFpj1Lw6pRb1YwreaGfYnH4mRzObq68uTzebLZLLlcbl3LZrNEo1GdWlrzUNCCX/PIqVQqLC0tsby87G3b28rKCkuLS6ysrlIqFdksdbghPkLBCEFfmIARIugPE/V1kU4dJJgNEfCHCfrCBP1hgr611wFfCJ/hf+RC1LSa1M0KtWaFulmm1qxQa5apNkvUCmVGl6a5dvkmlUYJ275zpeFkJM2Sz+XI5rJkMhmyWWd7e4vH43qQ0GyKFvyah4Jt26yurrK4uMjCwgKLi4teW1pacvYXFlleXqZWr234HqFAhHAgStAXIeSP0h3OcTDh7If8EYL+iLsNE/RH8BuBXSXc/L4Afl+KWCh11+uUUjSsGrVGyR0YytSaJW+gmJ8oMjU6R82sUGtUcMrT3PZZfj/plKNWyuWdFUNrJZHP59dtdTWy/Yf+H9fcE6UUy8vLzMzMMDs7y9zcHPPz8852bp65+XmWlhY31IcHA2EigTghX5RwIM1gup9wIEY4ECMUiBH2R519fwTD8G3Dt9t5iAghd5BLcfd01LayaZhVas2yu5KorO03yxTmKszfGqJmvk2tUb5j9SQipFNpurq76Onpobu7m+7ubnp6eryWzWa14XqPoQW/BnCModPT00xNTTE5OcnU1BS3bt1ianKKmdkZGo3Guuv9vgDRYIKwP040mCfXdYRIME4kECMciBMOOsLdbwS26RvtDwwxvIH0XtjKdgaGhqNeqjZKVJtFqo0ShZkis5NXqDa+R8Osr7vP5/PT1dVFb+/aYNAaILq7u+nq6tKqpV2GFvz7jGazyfj4OKOjo4yMjDA6Osro6BhTU1NYluldF/CHiIdSRAMpDmeeIxZKEQ0miYWSRIIJgr6w/kPfZRhiEAnEiQTiZOjZ9LqGWafaKFBuFKg0ClTqRSqNApM357lxdZhK/U67SzAYXGeIzmazpFIpz6MpHo8Ti8WIx+NecZ1wOEwgECAQ2F0qu72AFvx7FKUUs7OzDA0NMTw8zNDQEEM3h5icmvRUMiJCIpwhHsryeNcLJMJZ53U4Q8ivPUj2K0F/iKC/i1R0YzWTbVtUm2WqjSKVRtFbNVTrJWbHVpkYnnZVTTU2sj9shM/nx+czHA8qw0AEBAER73e4mZG/FU9hGAY+nw+/30/A7ycQDBKJOAOM42IbIxaLkUgkPPfaTCbjDViJRGLf/Oa14N8DVCoVRkZGHOHuCvibQ0NUKmuFyuPhNMlwjie630cqkicZyZEIZ/EZ+ieguT8Mw0cs5Kz+7oatbJpWnYZZpWnWaVpOM+0mpt3Esk0s28JWJrZtoVAo5QTIOSJ+a4OGc58ClONa27Cw6za1kkXZLmHaK9jKdbO16psOSMFgkO7uHnp7e+jr66O/v58DBw5w4MABBgYGiEQi9/uodiz6r34XUa1WGR8fZ2xsjNHRUYaHhxkeGmZmdsa7JuAPkQrn6Ys9Tqqri3Ski1Q0T8AX2saea/YjhhiekXon0RqQ6s2K6ylVWlvBlAsMX5ninUvvuh5Ta2SzOQ4dGmRw0GmHDh1icHCQ7u7uXWf87qjgF5HPAj8FWIAN/DzwV4CPAqvuZT+rlLrYyX7sJiqVCjMzM+sMrRMTE4yPjTO/MO9dZ4hBIpIlGc7zzIFjpCJ50tFuosHkvlmuajQPQvuAlIzkNr2uadUp1Vco1VYo1pYp1ZYYvzHL5Xeu0GiuuSQHg0EGBgY4dOgQBw8e9FYIAwMDpNPpHfn32DHBLyIfAj4OvKCUqotIHmjFpv9tpdTLnfrsnYZSinK5zOrqKisrK06AUsu3fXGR+fl5ZmfnmJubo1gsrLs3GAgTD2ZIhLt45sBxEuEsqUieeCi9r9wfL4x/jZXK3HZ3Y1OaVp2mWSfgD+2q1VU62s2pwR/a7m7sSAK+EJloD5noekO4Uoq6WaFYW6ZQW6RYXaK4uMSb0xd59dVX19kiwqEwvX299Pf3r/OEatkVMpkMiUTika8YOjnj7wMWlFJ1AKXUArAjR7/bKRQKfOc738GyrDsMSpZleUm7Go2Gk9OlXqdWq1Gr1ahUKlQqFcolJ/9LsVSiXC5h2/aGnxUORIkE44T9CbrDRzicSjneNMEUIwtvU6wtAVBxDWmzhbGOf/+dyEpljqZVv/eF20QkEuHsj5/llVdeYaW4cweo21mpzO3oAXW7uNuAKCKeC21XYmDdOcu2qDRW3RXCCuXGKuWVVd6du8m55gXqzTtTiYgYJBMJkskkiWSCRCJBLBYjGo0SjUb5kR/5EU6cOPFQv18nBf9Xgc+JyHXgj4EvK6W+4Z77NRH5HPAnwGdag0M7IvJp4NMAg4ODHezmnXzxi1/kD/7gDx7a+4X8EWKRNIlwlmQkRyqSIxXpIhyI47vLrH186cpD64Oms5w9e5aXXnoJgJdf3jeLWc1t+Ayf6x2X3fB802pQqi2zXJlluTLLamWeQm2J1cIqq4XVDe9ZWlric5/73EPtp2zmIvVQ3lzEB3wE+BiOfv8zwH8BZnDUPl8AhpRSv3q39zlz5ox68803O9bP25mZmeErX/kKtZqjx2s9I6XUHa5lpml6M/5qtUq1UqVcqVAul6lUyjSbGyfmAiESdKJWw/44kWCCaDBBLJh0fOZDKSIBHRTT4utXf4/54sR2d2NTIpEIZ886M/5q9e4J4nYSXYmDfOzEn9vubuwpTKvh2QZKdWfWX6kXqJpFKvUiDXPjlCXhcIR4PE4iHicWd1xPI5EIP/7jP86ZM2ceqC8ick4pdcfNHRX8t3XgJ4BPKqV+rO3YDwJ/Syn18bvd+6gF/8OkXq97GSQLhQIrKyteMrJWXpu5ufkN9ft+X4B4KE0s6KwWHGNulkQ4R9C/e/TIDwOt4+8MWsf/YNjKplIvUKwtOXr+2hKl2jKlxjKVemndtbFYjN7ePi/yOZ/Pe3mSWoFuqVSKQODhR7lvJvg7adw9DthKqRvuoZPAmIj0KaWmxZnK/lngnU71YScQCoUIhULk8/l7Xlur1ZidnWVmZsZJlzA1xdTUFBPjE9y4dW5dZG0slCQecgy9qWiX55u/V1MkaOGkedQopWiYVYr1ZUq1ZYq1ZUfA1519y177e0zEEwwODvLC4NMcPHiQgYEB+vv76e/vJ5FIbOO32JhO6vjjwOdFJA2YwE0cnf1XRKQLEOAi8Jc72IddRTgc5tChQxw6dOiOc61cOi0f/rGxMYaGhhgZe4vmrKNOEhESkSypkDMYpKNdpCLdRIP7JyJRo9kKSilMu+kmtHNapVFwnCjqBSrNVUr1VZpteYsMw6Cvt48nTxzz/PhbLpzpdHr7vswD8MhUPe+F3azq6TSmaXLr1q11qRlu3hxiZmbauyboD5OM5J3VQSRPKuKsEIL+8Db2XLMfUEph2aYXtduK3LXsJqZlYisTS1lO5K5SKGwvCnfLkkkpL3pXYWPbTnEcS5lOdLDVpGk3MK0Gpt1wC+NU183YW4RDYbq7uzkwcID+/n76+vo8n/y+vr6OqGM6ybbr+N8LWvDfP+Vy2Yns9QaDmwwPDVOprkUjRkJxEqEsyXDOy9OTCGeJBBMYsrsiETWPFidfT4lKo0itWaLSKLk1A5zZc8OqUjermwrYThIIBPD5/AQDAULhMKFQkGgkSjwRX5erJ5lMrqt21t3dveeyjD5yHb9me4nFYjz77LM8++yz3jGlFHNzcwwPD7tZOZ0MneNj16jMrQ0IhuEjEU4TDaSJhVJuSxILpoiGkjoz5x5HKUXTqruqjwLlemFNDdIoUG0WXQPm+kljIBAgk86Q7c+SzR4gnU57AraVnbPlqRIOhwmFQl6hekdY+7xka61i9nfrY/v51r3SltRNszla8O8jRMTLp/6hD33IO66UYmlpifHxcSYnJ702NXWLqekrVOfWuyd6ufgDcSKBhJuHP+5tw4E44UDsrjEKmu3BiTqtOvlpGiUqrcyaboBgzXU5bFq31V/w++nu6ubQ4V66u59bl5O/q6uLfD6/r7Jb7na04NcgIt5y99SpU+vOKaVYXV31vI3aK3DNzs4yPzfH5NzVDatvhQOt6lpRL9JxXfNHCbnVt7TAeHBapRrrbmnGVl3ftapcjvqlblWo1svYav3/lYiQSqXp6e2mu+cg3d3d9Pb2esK9p6eHTCaz6xKRaTZHC37NXRER0uk06XSa48ePb3iNbdteXMJGNXedersLTC1cxzTv1PeKGISDUae2rltvNxSIuom0ooQC7XV3nXq7e90G0VK3tHudVN1tvVmmZjplFlsVtWx1Z0oQwzBIJVNkslkG8oOe33jLhzyXy9HV1UUul9t1RkvNe0MLfs17xjAMT5DcDaUUpVKJ5eVllpaWvNYKaFvbX2FuZZRabfMI2KA/RDAQIWiECfhCBP0Rgr4wQX/rdXjttT/k7fuN4LauLmzbclUtZbdYeskT6C3jaN2qUG2UMK07o759Pj+ZdJpcT45srpdMJkM2myWdTnv7rW0ymdSzdM2GaMGveWSICImEk4RqK/mX6vW6l9G0UCis22+9bkVFr64WmC/eonSXhHitPrQGg4ARwm8ECfhC+I2As/UF8Rt+fEYAvy+AIT584sMw/AjiGQ9VmwuhbTsuiZZtOkVGrIZb9KNGw6w7W9vxcNkoSRdANBIlm8sxkO9aNyNvL2eYy+X2nNeJZnvQgl+zYwmFQp4BcasopahUKhSLxXWtVCp52ztasUS5ssxKpUK1Vr1LfqWtIWIQjUaIxxMkMnGSyR7S6TTJZHLdDL0lzLPZLOGwjqnQPDq04NfsKUTEcxvs7e19oPcwTZNGo0GtVvPSbzebTWzb9lJ1t7sdBgIBgsEgoVCISCRCMLi96iSN5l5owa/R3Ibf78fv9xONRre7KxpNR9CWH41Go9lnaMGv0Wg0+wwt+DUajWafoQW/RqPR7DO04NdoNJp9hhb8Go1Gs8/YFfn4RWQeGNvufuxA8sDCdndiB6Ofz+boZ3N39srzOaSU6rr94K4Q/JqNEZE3NyqyoHHQz2dz9LO5O3v9+WhVj0aj0ewztODXaDSafYYW/LubL2x3B3Y4+vlsjn42d2dPPx+t49doNJp9hp7xazQazT5DC36NRqPZZ2jBvwsQkYMi8nURuSIil0Xkr7vHsyLyX0XkhrvNbHdftxMR8YnIBRH5I/e1fj4uIpIWkZdF5Kr7O/qQfj5riMgvuX9b74jIvxOR8F5+Plrw7w5M4G8qpZ4EPgj8VRF5CvgM8CdKqceBP3Ff72f+OnCl7bV+Pmv8E+A/K6VOAM/jPCf9fAAROQC8BJxRSj0D+IA/xx5+Plrw7wKUUtNKqfPufhHnj/YA8GeA33Yv+23gz25LB3cAIjIAnAX+ddth/XwAEUkC/w3wRQClVEMptYJ+Pu34gYiI+IEocIs9/Hy04N9liMhh4BTwXaBHKTUNzuAAbL047d7j/wL+DtBeaV0/H4fHgHngt1xV2L8WkRj6+QCglJoC/g9gHJgGVpVSX2UPPx8t+HcRIhIHfh/4RaVUYbv7s1MQkY8Dc0qpc9vdlx2KH3gB+BdKqVNAmT2ktnivuLr7PwMcAfqBmIj8zPb2qrNowb9LEJEAjtD/XaXUf3APz4pIn3u+D5jbrv5tMy8CPy4io8DvAT8kIr+Dfj4tJoFJpdR33dcv4wwE+vk4/AgwopSaV0o1gf8A/AB7+Plowb8LEBHB0c9eUUr9etupPwQ+6e5/Evh/H3XfdgJKqb+rlBpQSh3GMcp9TSn1M+jnA4BSagaYEJHj7qEfBt5FP58W48AHRSTq/q39MI4dbc8+Hx25uwsQkQ8D3wLeZk2H/cs4ev6vAIM4P95PKKWWtqWTOwQR+UHgbymlPi4iOfTzAUBETuIYvoPAMPApnImffj6AiPwD4CdxPOguAD8HxNmjz0cLfo1Go9lnaFWPRqPR7DO04NdoNJp9hhb8Go1Gs8/Qgl+j0Wj2GVrwazQazT5DC37NrkZELBG52NbuGpEqIr/8iPr1syLyz9z9vy8iSkSOtZ3/JffYfRX0FpFX7/cejeZ2tODX7HaqSqmTbe0f3+P6+xb8IuJ7wL618zZOcFmLn8AJonrU/dBotODX7D1EJCUi11qRqm5+9b8kIv8YJwPjRRH5Xffcz4jI99xjv9kSriJSEpFfFZHvAh9yX/+aiLwlIm+ISI973Y+JyHfd5Gd/3Dq+Af8PTj4YROQxYBUncVqrz/9CRN50c8L/g7bjoyLyORH5NvCJtuOGiPy2iPzDh/fkNPsFLfg1u52WIG+1n1RKrQJ/DfiSiPw5IKOU+ldKqc+wtkL4aRF5Eida80Wl1EnAAn7afd8Y8I5S6gNKqW+7r99QSj0PfBP4S+513wY+6CY/+z2cDKEbUcBJm/AM8OeBL992/rNKqTPAc8BHReS5tnM1pdSHlVK/5772A78LXFdK/cr9PjCNxr/dHdBo3iNVV2ivQyn1X0XkE8A/xyk8shE/DJwGvu+kaCHCWiIuCycpXosG8Efu/jngv3X3B4Avu0m8gsDIXfr6ezjqnh91P/tTbef+RxH5NM7fZB/wFHDJPXf7IPGbwFeUUr92l8/SaDZFz/g1exIRMYAngSqQ3ewy4Lfb7APHlVJ/3z1XU0pZbdc21Vp+E4u1SdPngX+mlHoW+HkgfJdu/X/AXwDG29Nqi8gR4G8BP6yUeg545bb3Kd/2Pq8BHxORu32WRrMpWvBr9iq/hJNh8c8D/8ZNaw3QbNv/E+AnRKQbvBq9h+7zc1LAlLv/ybtdqJSqAv8zcPtMPYkj3FddG8GfusdnfhH4j8C/dytGaTT3hf7RaHY7ERG52Pb6PwP/Bie74vuVUkUR+SbwK8DfA74AXBKR866e/1eAr7orhCbwV4Gx+/j8v48jgKeAN3CKeWxKm56+/dhbInIBuIyTOfM79/pQpdSvi0gK+Lci8tNKKfte92g0LXR2To1Go9lnaFWPRqPR7DO04NdoNJp9hhb8Go1Gs8/Qgl+j0Wj2GVrwazQazT5DC36NRqPZZ2jBr9FoNPuM/x9GZ5LCmTyZVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.violinplot('ExternalMark','Semester',data=data_2017,orientation='v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f1a49242",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ExternalMark', ylabel='count'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"ExternalMark\",hue=\"Semester\",data=data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2f43c9b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ExternalMark', ylabel='count'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+4YQTaG5uxt0ZOHAgN9xwQ81jEwElApHCtdUM9V/+8pc6RCPyXro0JCJSckoEIiIlp0QgbXpk2PBCh1/k7wy4b3ovo2+K0ySdgxKBbHK6devG3LlzN6kdp7szd+5cunXrVu9QZBNU1c1iMxvr7ge3VybSGfTq1YsZM2bQ3Nxc71A6VLdu3ejVq1e9w5BN0FoTgZl1A94HdDez7am8IPZ+YNeCYxNZLw0NDey+++71DkNko9HeGcHpwDdJO/1xVBLBQuC64sISEZFaWWsicPergKvM7Ex3v6ZGMYmISA1VdY/A3a8xs/8F9M334+6/LCguERGpkWpvFt8KfAgYD6yKYgeUCERENnLVNjExGPiIb0rP44mICFD9ewQTgV2KDEREROqj2kTQHXjRzB4ws3uzT5GByaan6DeVRWT9VHtp6KIigxARkfqp9qmhR9qvtSYz2410M3kXYDXQ6O5XmdkOwB2kJ5CmASe4+1vrOnwREekYVV0aMrNFZrYwPkvNbJWZLWynt5XAOe7eHzgA+Hcz+whwHjDW3fsBY6NbRETqpNozgm3y3Wb2OWBIO/3MAmbF/4vMbBLwAeAYYERUGwM8DPzHOsQsIiIdaL1aH3X3e4CDqq1vZn2BjwJPATtHksiSxU7rE4OIiHSMal8oOy7X2YX0XkFV7xSY2dbA3cA33X2hmbXXS9bfKGAUQO/evavqR2pv+iUD6H3h8/UOQ0Q2QLVPDeV/8Xsl6SbvMe31ZGYNpCTwa3f/bRS/aWY93X2WmfUEZrfWr7s3Ao0AgwcP1otsIiIFqfYewWnrOmBLh/6/ACa5+89yX90LnAL8MP7+fl2HLSIiHafap4Z6mdnvzGy2mb1pZnebWXu/kDEU+BJwkJmNj88RpARwiJlNBQ6JbhERqZNqLw3dAvwG+EJ0nxxlh7TVg7s/TuX3C1rSL5uJiHQS1T411MPdb3H3lfEZDfQoMC5pQ5E/+N6R1JyEyMaj2kQwx8xONrOu8TkZmFtkYCIiUhvVJoIvAycAb5BeEjseWOcbyCIi0vlUe4/gUuCUrE2gaC/oJ6QEISIiG7Fqzwj2zTcM5+7zSG8Ki4jIRq7aRNDFzLbPOuKMoNqzCZHS0k1z2RhUuzP/KfCEmd1FalriBOCywqISEZGaqfbN4l+aWROpoTkDjnP3FwuNTEREaqLqyzux49fOX0RkE7NezVCLiMimQ4lARKTkSpEINpZmGURE6qEUiUBERNqmRCAiUnJKBCIiJadEICJSckoEGyk1XSAiHUWJQESk5ApLBGZ2c/zG8cRc2UVm9s8Wv2EsIiJ1VOQZwWjgsFbKr3D3gfG5v8Dxi4hIFQpLBO7+KDCvqOGLiEjHqMc9gq+b2YS4dLR9W5XMbJSZNZlZU3Nzcy3jkwJ05Nvdg879ZYcNS0RqnwiuBz4EDCT99vFP26ro7o3uPtjdB/fo0aNG4YmIlE9NE4G7v+nuq9x9NfBzYEgtxy8iIu9V00RgZj1znccCE9uqKyIitVHY7w6b2W3ACKC7mc0AvgeMMLOBpJ+7nAacXtT4RUSkOkU+NXSiu/d09wZ37+Xuv3D3L7n7AHff192PdvdZRY2/TLIbsfm3jVsrk/a1Nr9qMQ+HXjP03f91M1xqTW8Wi4iUnBKBiEjJKRGIiJScEoGISMkpEYiIlJwSQSfRkU0wbKhaPLXS1jjWZT4MvWboRvdUVD2Xc/7JpI507Tl/KGS4UjtKBCIiJadEICJSckoEIiIlp0QgIlJySgRSVx15A7NWN2I7MuZqb8yvrV7LeKZfMqDqGNd3WtQMxqZFiUBEpOSUCERESk6JQESk5JQIRERKTolANvgN3SLf7u2om5ItY1zbTdINeVM232928zorW5dpaevG99Brhq7z8Dryxm5++op6U1mSWr6xrUQgIlJySgQiIiVXWCIws5vNbLaZTcyV7WBmfzazqfF3+6LGLyIi1SnyjGA0cFiLsvOAse7eDxgb3SIiUkdF/nj9o8C8FsXHAGPi/zHA54oav4iIVKfW9wh2dvdZAPF3p7YqmtkoM2sys6bm5uaaBVgL69sUwoY+/dHRTTAU2czAhjwx0dZTTB35FEZ+HOs6H6p9yir/hFDRajGedR3Hus5XPcW0/jrtzWJ3b3T3we4+uEePHvUOR0Rkk1XrRPCmmfUEiL+zazx+ERFpodaJ4F7glPj/FOD3NR6/iIi0UOTjo7cBTwJ7mdkMM/tX4IfAIWY2FTgkukVEpI6KfGroRHfv6e4N7t7L3X/h7nPd/WB37xd/Wz5VtMnr6BustW4Xvr0bnR0RT61u+uWnpdob6WubvvVtaqNlUxTt1evIcXek/LxZn2Y1OqtaNvVQL532ZrGIiNSGEoGISMkpEYiIlJwSgYhIyZUqEdTzhlq1N5yKjrHljdj2buZ1xI2yIm4Ytrxx2to4Bp37y5r9oH29rOv0tbd+dfb5lS3njfkt4s5487lUiUBERN5LiUBEpOSUCERESk6JQESk5JQIRERKrnSJoB537Nt7wqGtp2qmXzKgUz0dsa6xdGT78x05H9p6Mqa1cXSm+V+U9n6/YUPXw6LnYWtPh9XjtwxaW9874xNCrSldIhARkTUpEYiIlJwSgYhIySkRiIiUnBJBOzaW9tTLcFOznjr6RmBnuInYEc1JtLzRPPSaoR3+mxVDrxnaaZs66Sj1Xh+UCERESm6zeozUzKYBi4BVwEp3H1yPOEREpE6JIHzK3efUcfwiIoIuDYmIlF69EoEDfzKzcWY2qrUKZjbKzJrMrKm5uXmDR9jRN1M39EbburzF2toNufUdR2vDq6Wi377syLeD1+XmYme5WZ+POb+c12V9reaG74aafskAHhk2fL1+5D6blmrWmw19G769t66rHW9H3agu6qZyvRLBUHf/GHA48O9mNqxlBXdvdPfB7j64R48etY9QRKQk6pII3H1m/J0N/A4YUo84RESkDonAzLYys22y/4FDgYm1jkNERJJ6PDW0M/A7M8vG/xt3/2Md4hAREeqQCNz9VWC/Wo9XRERaV/rHR6t5OiJ7wqE11T5lUCtFPVXQ3lMP9X5Ffn3Va37Je63L00AdpbXfMlibzvJ0WFvW92nG0icCEZGyUyIQESk5JQIRkZJTIhARKTklggJUe7OrVjfFNtYbudK51PtBiGpsSLMT7U1fEb/f0BE6YvtWIhARKTklAhGRklMiEBEpOSUCEZGS26gTQWs3b4p8S3Btbad39JukRb+ZuiG/C6Cbz7Kuqr1J2rLehtygrdX+YUN/e6C9ebO2t5/z487Guz5vP2/UiUBERDacEoGISMkpEYiIlJwSgYhIyW10iaC1Gysb+rZe/ubnuvxwd0fe2OpsOntzuyIbYkPeIs73W209qH6bamuY+eF19MMkG10iEBGRjqVEICJScnVJBGZ2mJlNMbOXzey8esQgIiJJzROBmXUFrgMOBz4CnGhmH6l1HCIiktTjjGAI8LK7v+ruy4HbgWPqEIeIiADm7rUdodnxwGHu/pXo/hKwv7t/vUW9UcCo6NwLmAJ0B+a0MtjWystW1tni6UxlnS2ezlTW2eLpTGWdLZ71Kevj7j1oj7vX9AN8Abgp1/0l4Joq+22qtrxsZZ0tns5U1tni6UxlnS2ezlTW2eLZ0GlZ26cel4ZmALvlunsBM+sQh4iIUJ97BE8D/cxsdzPbHPgicG8d4hAREWCzWo/Q3Vea2deBB4CuwM3u/kKVvTeuQ3nZyjpbPJ2prLPF05nKOls8namss8WzodPSpprfLBYRkc5FbxaLiJScEoGISNmt62NGtfoANwOzgYm5svHASmAJ8Cdg16i3CFgGvABcDrwKrIh606K/gTG8rHwI6emlvwPvAEuBiUB/4DFgcQxzMrBPfLcMcOAn0e/0KFsKTAC2A66KfpfG3/8TdR8CJgFvxDD2Bf6Ri2cG8C9R740Y7izgeuDNGN4SYG5M59PRvSTi+ATwPPB21J0C9ADG5ebPK8AuwNSo46Trid1iXEvjMwnYGfhnbhyvALvH/HouNx0fiHrZdLxBekHw71G+LOb75cD/jfiWAMujbreYt1n5zFg2H42yhcAfgD7AnyO+ZmB70qPILwCrYzndF+vJj6N7SUzXdsClsYzeif53jbpdI04nPX99UXS/AywAjoh6Z8W4F8W03EFar96Jz3jSOva36J4f0/HP3LTNB94P7BDdy2J4z8Y4ZueWy4tRtoDKsn8rpmV+brksIG0H00jLPz8t83PL5R3giKg3MzfMy3PxZTGNjzrvRNnbMS2fIK1/2Xw4GOhLWj+Xk9b3Q4FTIi4nreMHAleTlmXW7yEx7vlUtpWjYvruinnhwGGxTGbm6k6PYW4HPBsxLwN+Bfw2V2858BLwyVzcS4DvktaPbLpXkNaZ56lsKyuBn0b8SyKWxcBlueWU9ftT0rqczcOVpP3A+Phky+SKqLc86q0ivVA7nrT9L42yscD9ueEtj/LLSNv127n+HwZeprLO/T8q28rU+Lt9u/vbeu/w15IIhgEfY81EcFhWRtowbwC+ATwFvBB1dsr3GwvpQlLiODfKp8UM7EnakQwHtomFNCY+50VZM/Br4DjSi22PAa/FOL5GuuG+DTAPuAnoB3wsYjk3Fk4Wz27Ag7HyHAXcCHw7+n8p6o2KOjtG2TG54WXj+QXwJKmZjm1iJfo7aac/HGggJcObY+U7L8qmA7cCg2JaHol5dABwdExLA2mjuxXoGeNtiHn2W2DrmI4/kTaYzwD/FdPREMvigIjtQWCrKDscMGDrGOYVEfcBpBX/8Oh/MvBMxPogcB/wZeCJ+P83pET1I1LS3ou0IfyRSiI4NOL5TXz3I9IO+FtRNhG4IepeQkoW71BJBPdGvWx4n4plcXvEsFOUZ8N7hco61hhlT5HWsWXA0VH/y6SEdDlpp949ls2P4vt/AvtHf4Oj7A1g5/j/R/F5DeieS1A3xPLZl/QQxmtUEsGFLbarN0jLfYvc9jItN7xse1kCjIyyIyKmOcDlUfZvMR0Tgbuj7HzgSuCe+P/hWL7bxbwZlUvUV8Z8+kqUnU1aX8eQtptsOj4Yy6QpV3fzGOYDwIvAFlG2R/T/ldw69l+k9fnKKDsaeJx0IDWcdCCwgJSoLo/l0ZWUEK6nso5l09+HtH5tFvUWR7335w4sFgK/iu7dIs5VpAOFi0jrZtdYFn1I69eDwJZR9rEWy+xnEU+fmI+HR/9vkZJHU0zLnaR9wxPAedHvu+vX2j6d9tKQuz9K2unly/6YK9uKlGWHkhaiR53ZLfo9Abgtvp8e5V2Bme4+i7SgHnX3RaQj3RGklXdMlI0Dhrn7b919CmmBTiNtSP/t7iuj3lRgd3ef6u7PxLg3Iy2sLaLsCuAcUiLYnLQDIvqfRFqhPw380N3nRtk7ueEtJq0sj5FWtvdHv2/Ed/2AR0k71LdJK9gRpI2jgUhK7j4upsUiRnf3e919ZdRbAvSI+UOUNUS9xTEd/ydbLKQjljXqAacBP4z/G4B5niw2MyMdzWdHjStJO+qGmAcLSUe5/xXD/TMpeW1LSrYzgM+5+6SYzh1JO5/Mi6SN5aaY5l4x/M9GWVfAzawX8NXctBD1+ke9zLdi/jaSZsLs6Dcb3q6kdWxz0pHqTVQSagNpw8ym4/Ok5L44ysYAn4v/V5CSSl52lAjpbKNXzLPMVrnu7wPfafF9S9sAV7n7smxasi9iuWTbS1YX0nyfTTqT+Y8o+yNpp7onkLUKcDNpnuxHZdmtJJ2x9QN+HmVPkA7CDiTtuCCdGa4mHQztn5uOhaR1YvesrqemaVaTtv2z3H1ZlM2O/n8R03I8aee4HenAKZtf00k790dJZzUvkba7Y0jL42DStneQu0+KbWV7YIa7v+buf4pt5WDgddJ2uDCGfzBpH7Mouq8AfkdatjNyy+Fg4BV3fw04g7StfDLKsu09WyYnAy9FXSeto9l4VsUy+CvwPuAvpG1lTAwiv361rd5H/mv7kE47J7YouzZm6kTSpY/xpKOLd0hHOh/P9fsq8ZYdaeOeTto4V5BevYa0Uh4T9d+KBTg/N4zpwFu58f+NtON9f4s436FyJHIZaQWZEgv//aSN5qqouzL+XkRKKpNIO4beMT0Xk055lwIjcuM5gXSEme2ssulZCewd0/JqDGtsNi0xzMWko8m3SDvC8aSVaEzuSCar9zJp5cuOWlaTzox6kFaq5qi3gHTkeTGV09cJpI1mfPS7KuL8eG48U6M8OxLeO/pfHTHfT0rK55OOwL9F2gAGkRL1fdkyIV1GaAJOp3IEf1eu7psxLXeRdiSzSTuXHqSN57aol50RvBjz9JWIe/uYh43x3Vzg47lxnEVlfXmAlNxnk5Jpn1iGr5AOKO7MLZN/kM58xgFvR/9Z2SLgshZl42KenBxls2Ke/TOm5c0Y7zjSkWt30rJeFtP215iW7JLj26Qd4Mdz45gCTIvxzojhL4/hHB5xjSWtm3+LYawERkfZTTFv/x5li0gJ+oBc2bOkI/2LqFxmXE7aqR0eMb0Y9RaRtokbIu55Mf9/GcN8m7SevR3TdFpuPC+R1tMDSOvk4hjP28CHqWz3NwN3s+Z2fzMpueW3+1nE2VCu7GbS5aSTc9v9YtL604PKNn9zxJ2dcU6LaXmSyrZycSy/qcS24pUrI3OAr7fYjy0mrUcXkbalBaQrF98iHbDl43wr393qvrbeO/v1SAR9SUngP2PmTQRuib9DSCu1Rb25wDnR39Wko7G+MSMfjPIPk1but0mn/nNjBm9N2qiOo7LT2TpWmHNz8WxN2hj/RjyO26L8DlKmfop05DguFnh30nX490fZXbHCTCSdao4jHRVl07M1acUenZuek6LeT0inlh8mnTqOj/7mU1m5tyPdf1iQi/Fx0k50n1zZpTGefNl2pJ3Z9TEd20bZEtJ1451JO/jtqVxCmhgxbhfjmEHlceXrSTv5h0j3X7Jlsx1pJ/FGTMvTMQ2/JlZucokAOBL4b9Jp++lRfiTw31H3JtIGnC8bQUq8d5B2nttSSQT/m7QudSWdTWXXWOdGjCNinr2RG97vSTuuI0k7nM9HvXGxTD4ZyyS7r7Igpim7R7ETaWc6LFf2VyqXCrOy/4p+h7Xod1bE/ExMy06kHd5RpEtFXWP5vEm6fjw5pmWnGMfM3PBGR/cwUtL8PJVLRy9GnE/FtP2Nyv2M/aP/q6JsJZVLXLfFcsjKzicdaLxblpvm8TG8g6JsIWndPjTqHkDa2b4Q/XssRyNtuwtz47medMR/E+kAI7tUcj/pYOnDsWxXki4JZdv95qQd785UtvvNSQePh+a2ic1J+4z7qazXWb+XxecpKu3+TKeyzXeLsiupbPPXRtlhxDYfw7yRtNPfObfdj4y63yAllANjWuaTktrqMiWCPvH3j6S3kyfG96+QsvGHYiH3ivIFVBLERGBhlDeQjuS+RTrF+jvpyOihKOsZ3Vm9l6lcv20gHRFMA96XizGre2mMawCVo8R5Edd00ml+Nu4srgcihm/lpqcnaWeyqMX0ZP1aNj25GK6OcUyhcq3/x8DsXJ2HY0X7dnSfQjpK+X5Wlqv7MypHndPis4q0Q94lV+8K0s7pj8TZDPC9WHF7kC5FvRnT/j3S9dIFVFb8x0gb+DTSjvMd0g7LSWdZWdkC4AdUbrLNifKJUdZM2iHmy7JhLqFyyWVl/PWoP7OVekujOxv3sly9VVFvImmHk497eYt5eE0ry6RnxP7tFsulsZXlclkry+WKVpbL6laWy5WtLJeLWlkuP2tlmVwU82BabngnRp0VuWn5HGnnOC03HV8hJcRpuen4dFaWG97x0W926XUalQdDBuSG2Ze0I38wvsum5ZMxz6fnpuW4qLc6Ny2fBFbE/8eQtqv8dn9qlPUEpuTqzSO2+9z8nM+a2302vD6kg4jZEUd2A3k66WGNrF5fKvuw84E/tdiHbRbjeCQ3jgW5/r9Abh0jPWzy6xhffv2a0t6+ttPeI2iNmfXLdR5NOrq5h5QRMbM9qWTlTwDL3D27LjeTdEMF0nXCqXH97dekI8QrgQtIp6ErgQZ3/xlp5f096QhpEnGdL/r9H9JR/sfd/Z1cjFnd2RFjtrBvdPcdYhgfI23Ek2I8x0a9bUkZ/We56fkRaSN5xt1nxLgdWBT9HgT8w8z2iBjeRzpq+B9SsjjFzLYkbWxPmdl2MR+6kM6iZprZ50nXf0+I+TTTzD4Ww9sy5vc/gD3dvS/pFHUF6WbUti3qTYvxHhRlR1G5bn1MzJO5pB3CTNKOc3jU3Za0IQwhJfi/kI4+/0j6HYsvks4abnT3/3T3XvH9+cBf3H0f0s6nmbShZGWfiri/GMO/z927uvtmpOutS2JZDsrVe5W0fn2TtGP+Iukobzbpaamvkg4ExsY4sh3JF0lnAC+Y2e4xb7aOZXJXLJd/i/nxFVJimWpm2TX5LqSj2qlmdmwsly8SZylmNjCGuVXMz5eBD0Xc2WW2s0hnkfl6U2PcB0XZ0bFclsayeIl01D01t0y2ivn4OvCGme1lZl1iuT9B2tGdE3F/nXS0/rqZ7RVlQ0iXebIn6I4mbZvPAs25el8mbRdPAJ+JaVlM2pbm5IZ5LGmH/2zU/0L0fwJpu30tlsFk0hnRszF9J+fm91wz24mUzG6nst3fS7ppfRuV7Z6ol7+XchjpMtSFLbb7E6Pfo4Hn3H0n0gHl16hs85arl23z92RlLfZhn45lOZqKmaQzgdtIZ2urzKxP7BMOJt2LeTjip8V0tK3eR/1rORu4jXQEk91k+VfSyriCtPIuIa2Ad1B5vGs56aj3NtLKsirX7ydIWX0F6QjhTdIRpVN5BPRN0tGQU3mMbBFpBc6GvzqGMTlXlj3mdX8shCy+BaQNIxvmBNLp73LS0bDn+l1AutTlpKO57JG/66NsXkz/+Nzw8o/3/TCmNXuU7jHShjCBNR8f/QRpB5dNy2LSafmy3LTMIl3Pnk/liHgyacV8NoY5MeIcFrEtydU7KOJ8K8pmRtm+pAQwI/q/MMpeyk3LDNK192/E9C6OaduRdAlvBmkj2YG0IWXTPI842yHtGF+PvwtIG/ndMc5XYjl/ILeujaByj+BW0s79FdLOsCdpw/wVKREuoHLpYjTpCDq7N/EJ0mWTl2Paj6byCO0y0j0sI+0QsmWymHQG9sEYfn65PNrKcvk1lcdHl5IOOA4kJZ7nSOvbWzEP57Wot38su/xyOSn6mxfd50cs2SOJS2NeDiJdQlkaMb1EuhQ4nHRJZhkp+e5O2plm07E0ltv03HQsIF0SyY7qs2n7COnJmiYqj/p+KJbJVCpngv8T4x5M5bHQ+TG/B5LWjxmkHez2pMSQPXI5j7S8v03aP7xMWr+MdJaa3bAfS1rHRka9ZaT15oH4PrsfNp60ft1DSkQTSY87f4B0SXgu6eBmGmn9+k2u3r2k9WvbGP6LpEt82fp1K2k92Da3rh4c/T9POihpjLiWxXL4MZVtZWo2He3tb9XEhIhIyW1Ul4ZERKTjKRGIiJScEoGISMkpEYiIlJwSgYhIySkRyEbNzFaZ2fjc57x26n+3RnGdambXxv8XmZln73lE2dlRNngdh/vwuvYj0h4lAtnYLXH3gbnPD9upv86JwMy6rmdsec+TXgrLHE96brzWcYi8hxKBbHLMbFszm5K9tWpmt5nZv5nZD4Et48zh1/HdyWb29yi7MdvZmtliM7vEzJ4CDozuy8zsOTP7m5ntHPWOMrOnzOxZM3swK2/FPaS3ezGzD5JejGrOxXy9mTWZ2QtmdnGufJqZXWhmj1N5ixYz62JmY8zs+x0356SslAhkY5ft2LPPSHdfQGruYLSZfZH0wxw/d/fzqJxBnGRm/Ulvjg5194Gkt0VPiuFuRWq/an93fzy6/+bu+5He+M2aiHgcOMDdP0pqruA7bcS5kNRMwj6k5gTuaPH9+e4+mPSm9XAz2zf33VJ3/4S73x7dm5HeMH7J3S9Y1xkm0tJm9Q5AZAMtiZ34Gtz9z2b2BVL7RPu10e/BpKYTnk5NtbAllTZlVpGapcgsJ7VuCqkZiUPi/17AHWaWNUXxj7XEejvp8tBnYtyn5b47wcxGkbbJnqTmFibEdy2Txo3Ane5+2VrGJVI1nRHIJikaRutPal9mh7aqkX6PIbu/sJe7XxTfLXX3Vbm6K7zSHssqKgdR1wDXuvsAUlPY3dYS1h+ALwHTvfJDJkTDdN8GDnb3fUnNReeH83aL4TwBfMrM1jYukaopEcim6mxSQ2snAjebWUOUr8j9PxY4PlqixMx2MLM+6ziebUkNy0GlxcdWufsSUkuiLY/k30/a2S+IewyHtzPOX5AaOPy/ZqazetlgWolkY7elmY3Pdf+R1LLlV4Ah7r7IzB4lNTX8PVJrjRPM7Jm4T3AB8Kc4g1gB/DupKeNqXUTaIWc/TrT72irnrvPny54zs2dJLYe+SmoNdq2imfJtgVvN7CR3X70OMYusQa2PioiUnC4NiYiUnBKBiEjJKRGIiJScEoGISMkpEYiIlJwSgYhIySkRiIiU3P8H6+IpbMVFSPIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"ExternalMark\",hue=\"Semester\",data=data_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a41ed0a",
   "metadata": {},
   "source": [
    "# visualization based on ExternalMarkMax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a051de37",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ExternalMarkMax', ylabel='count'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"ExternalMarkMax\",hue=\"Semester\",data=data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "767e42e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ExternalMarkMax', ylabel='count'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"ExternalMarkMax\",hue=\"Semester\",data=data_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477726e0",
   "metadata": {},
   "source": [
    "# By this plot , we can make out that there is a progress in 2017 compare to 2018 in External Maximum mark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "662ce4c7",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8ddda66e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='InternalMark', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"InternalMark\",hue=\"Semester\",data=data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "969bb61a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='InternalMark', ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"InternalMark\",hue=\"Semester\",data=data_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2f5430",
   "metadata": {},
   "source": [
    "# There is a minor difference in Internal mark between both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8938bdab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='InternalMark', ylabel='Semester'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot('InternalMark','Semester',data=data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0a47c7e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='InternalMark', ylabel='Semester'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot('InternalMark','Semester',data=data_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b59c171d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='InternalMarkMax', ylabel='count'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"InternalMarkMax\",hue=\"Semester\",data=data_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "052c9058",
   "metadata": {},
   "source": [
    "# Total Mark "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "87affc39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TotalMark', ylabel='count'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"TotalMark\",hue=\"Depart\",data=data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ae178c78",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TotalMark', ylabel='count'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zzfLly6lVqxY1atQgODgYMC/CDR48GIBTp04RHBxMQEAAwcHBnD59Okm5Kclzb926lYCAABo0aMCIESOcGkpNmjRxOkAwL9bt2bMno02VKxCtJEHIpTgkMapUqUK/fv148803nfuSk91OTKdOnViyZAmBgYG8+uqr7Ny5E4DQ0FBef/11Vq1axa5du9i6dSuLFi3i4sWL9O/fn4ULF7J79+5kFVsHDx5Mz5492bNnD926dWPIkCFJ4vTs2ZP33nuPPXv24O/vz1tvvQVAnz59mDZtGps2bcLb29sZv1+/fs63sI8cOcKNGzcICAi4pba72xHHIAi5lFuV3S5TpgyHDx/mnXfewcvLi+DgYH799Ve2bt1K8+bNKV68OHny5KFbt26sXbuWzZs307RpU+caRNGiRZPkuWnTJrp27QpAjx49WL9+vdv+lOS5w8LCiIiIcK6ROPIAeO6551i6dCmxsbF8/vnnyU6LCe6IVpIgCG6y2+fOnXPKbgPExMRQoUIFXnzxxSTp8ubNy5NPPsmTTz5JyZIlWbRokXOKKDFaa+zaosd4Gj+1Bxzz58/P448/zg8//MA333zDtm3b0mVDbkRGDIIgZEh2e8eOHYSGhgLmCaU9e/ZQtmxZ6tWrx5o1a/jrr7+Ii4tj7ty5NGvWjAYNGrBmzRqnSN/ly5eT2NGwYUPmzZsHwJw5c2jcuLHb/pTkue+9914KFizoFOFz5OGgX79+DBkyhLp16yY7UhHckRGDINwGePJ4aWZzq7LbFy5coH///ty4cQOAoKAgBg8eTL58+XjnnXdo0aIFWmueeuop2rVrB8D06dPp0KED8fHxlChRghUrVriV8/HHH9O3b1/ef/99ihcvnqz0dkry3DNnzqR///74+fnRvHlzt0dsa9euTaFCheT7DZ6itb7t/2rXrq2FtFndpGmSbW93ezbd+WQkTXLM/6ZuqvsPvP1rppRzJ3LgwIGcNuGuIyIiwvn7nXfe0UOGDHGG//zzT12xYkUdFxeXE6ZlC8n1KWCbzsA1V6aSBEG4K1i2bBmBgYFUr16ddevW8cYbbwDw5ZdfUq9ePSZMmOB8tFZIHZlKEgThruD555/n+eefT7K9Z8+e9OzZMwcsunMR95nLyS5F1qmvLsmWcgRBuHXEMQiCIAhuiGMQBEEQ3BDHIAiCILghi8+CcBuQ2Ws9zdauSTNOWrLb6SUkJIRHHnmEypUro7XGz8+PWbNmUbly5RTT7Nq1i9DQUJ566qkMlytkPjJiEIRcikMraffu3bzzzjuMGjXqlvN8+OGHnXn26tWLiRMnphp/165d/Pjjj7dcrpC5iGMQBMFNdvvs2bM0bdrU7Z0ASF4y29M8o6Oj6dOnD/7+/tSsWZPffvuNmJgYxowZw/z58wkMDGT+/Pls2bKFhg0bUrNmTRo2bMjhw4cBI83doUMHWrVqRcWKFXnttdcA87bzK6+84izzs88+Y9iwYZnaNrkRmUoShFyKQxIjOjqas2fPsmrVKgC+/vprWrZsyejRo4mLiyMqKsopmb127VrKly+frM4RmO84BAYGEhERQVRUFL///jsAn3zyCQB79+7l0KFDPPHEExw5coTx48ezbds2pk6dChhnsnbtWvLkycPKlSv55z//ycKFCwEzuti5cyd58+alcuXKvPTSS3Tu3JmAgAD+/e9/4+Pjw6xZs/jvf/+b1U131yOOQRByKa5fcNu0aRM9e/Zk37591K1bl759+xIbG8szzzxDYGAgq1evTlMyGxKmksB84W3AgAEsX76c9evX89JLLwFQpUoVypYty5EjR5KkDw8Pp1evXhw9ehSlFLGxsc59wcHBTv2jqlWrcurUKR588EEeffRRli5dyiOPPEJsbCz+/v6Z1US5FplKEgTBTXa7adOmrF27lgceeIAePXrw5ZdfZkgyu23btqxduxZIXRbblTfffJMWLVqwb98+lixZQnR0tHNf3rx5nb+9vb25efMmkPAhnlmzZolIXiYhjkEQBDfZ7VOnTlGiRAn69+/PCy+8wI4dOzySzE7M+vXrefjhhwFo2rQpc+bMAcxX1E6fPk3lypUpWLAgERERzjTh4eE88MADAM6vrqVFvXr1+OOPP/j666/p0qVLeqotpECWTSUppT4H2gAXtNbV7baiwHygHBACdNJaX8kqGwThTsGTx0szm5Rkt1evXs3777+Pj48PBQoU4Msvv6R48eLJSmZv27bNKcMNCWsMWmvuuece5/Z//OMfDBw4EH9/f/LkycPs2bPJmzcvLVq04N133yUwMJBRo0bx2muv0atXLyZPnsyjjz7qcV06derErl273L5bLdwCGZFk9eQPaArUAva5bPs3MNL+Hgm850leIrvtGRmR3c4sqe7kcJXdnjJscZL9IrstZBatW7fWK1euzGkzcpQ7QnZba70WSDzebAd8YX9/ATyTVeULgnD3ExYWRqVKlfD19fXoEVrBM7L7qaSSWuuzAFrrs0qpEilFVEoNAAYAPPTQQ9lkniAIdxJFihRJ9ukm4da4bReftdbTtdZ1tNZ1ihcvntPmCIIg5Bqy2zGcV0qVArD/L2Rz+YIgCEIaZLdjWAz0sr97AT9kc/mCIAhCGmSZY1BKzQU2AZWVUmeUUi8A7wKPK6WOAo/bsCAIgnAbkWWLz1rrlN40kUcHBCERmf3p08H/93SacTJbdhvMy2svv/wyR44cwcfHB39/f6ZMmcJPP/3kpomUWYSGhjJkyBAWLFiQ7P6wsDC+/vpr/vGPf2RquXc7t+3isyAIWUtmy25HR0fTunVrBg0axLFjxzh48CCDBg3i4sWLmWRxUkqXLp2iUwDjGP7zn/8kuy8uLi6rzLrjEccgCEKmyG5//fXXNGjQgKefThittGjRgurVqwPm7j6xbDbAL7/8QoMGDahVqxbPPfcckZGRAJQrV45//vOfNGjQgDp16rBjxw5atmzJww8/zLRp0wDzcSBH/vv37ycoKIjAwEACAgI4evQoI0eOdL6NPWLECFavXk2LFi3o2rUr/v7+vPnmm3z00UdOW0aPHs3HH3+cmU17RyLqqoKQS8ls2e19+/ZRu3btFMtLTjbb19eXt99+m5UrV+Ln58d7773H5MmTGTNmDAAPPvggmzZt4pVXXqF3795s2LCB6OhoqlWrxsCBA93ynzZtGkOHDqVbt27ExMQQFxfHu+++y759+5yKr6tXr2bLli3s27eP8uXLExISQocOHRg6dCjx8fHMmzePLVu2ZFIL37mIYxCEXEpWyG6nRnKy2WFhYRw4cIBGjRoBEBMTQ4MGDZxp2rZtC4C/vz+RkZEULFiQggULki9fPsLCwtzyb9CgARMmTODMmTN06NCBihUrJmtHUFCQsx7lypXjvvvuY+fOnZw/f56aNWty3333pbtudxviGARBSFZ2e9myZfTo0YMRI0ZQpEiRNGW3q1Wrxpo1KYsBJiebrbXm8ccfZ+7cuamm8fLyckvv5eXllN120LVrV+rVq8eyZcto2bIlM2bMoEKFCkny9PPzcws7ZLvPnTtH3759U61jbkHWGARByBTZ7a5du7Jx40aWLVvm3LZ8+XL27t2bYrn169dnw4YNHDt2DICoqKgMS1ycOHGCChUqMGTIENq2bcuePXuSyHonR/v27Vm+fDlbt26lZcuWGSr7bkNGDIJwG+DJ46WZTWbLbvv6+rJ06VJefvllXn75ZXx8fAgICHBb3E1M8eLFmT17Nl26dOHGjRsAvP3221SqVCnd9Zk/fz5fffUVPj4+3H///YwZM4aiRYvSqFEjqlevzpNPPknr1q2TpLvnnnto0aIFRYoUwdvbO93l3pVkRJI1u/9EdtszRHb7zkFkt28f4uLidI0aNfSRI0dy2pRb4o6Q3RYEQbjdOXDgAH/7298IDg5OcbE6NyJTSYIg5FqqVq3KiRMnctqM2w4ZMQiCIAhuiGMQBEEQ3PDIMSilfvVkmyAIgnDnk+oag1IqH5AfKKaUuhdwvOFSCCidxbYJgiAIOUBai89/B17GOIHtJDiGq8AnWWeWIOQuJnTvmKn5jf4qZcVRBynJbkdFRdG/f3/27NmD1poiRYqwfPlyChQokGaeq1evpl27dk7JiWLFirFy5UoA2rVrx4ULF9i0aZNbmkmTJjFjxgzy5MmDt7c3r776Kj179mTp0qW8+eabxMfHExsby9ChQ/n73/+egdYQ0kuqjkFr/RHwkVLqJa31lGyySRCEbMBVK+nnn39m1KhRrFmzho8++oiSJUs631g+fPgwPj4+HufbpEkTli5d6rYtLCyMHTt2UKBAAU6ePOl0HNOmTWPFihVs2bKFQoUKER4ezqJFi4iNjWXAgAFs2bKFMmXKcOPGDUJCQjKl3kLaeLTGoLWeopRqqJTqqpTq6fjLauMEQcgeEstuP/DAA859lStXduoUffnllwQEBFCjRg169Ojhcf4LFy7k6aefpnPnzsybN8+5feLEifznP/+hUKFCABQuXJhevXoRERHBzZs3nYJ2DkVWIXvw6D0GpdT/gIeBXYDj6xYa+DJrzBIEIatJSXa7b9++PPHEEyxYsIDg4GB69epFxYoV2b9/PxMmTGDDhg0UK1YsWb0kgHXr1jmlNp577jlGjx7N3LlzGTt2LCVLlqRjx46MGjWKiIgIIiIiePjhh5PkUbRoUdq2bUvZsmUJDg6mTZs2dOnSBS8veZAyO/D0Bbc6QFX7ivUto5R6BeiHcS57gT5a6+jMyFsQBM9ISXY7MDCQEydO8Msvv7By5Urq1q3Lpk2bWLVqFR07dqRYsWJAytLbiaeSzp8/z7Fjx2jcuDFKKfLkycO+fft46KGHUlVsnTFjBnv37mXlypVMmjSJFStWMHv27Eyrv5AynrrffcD9mVGgUuoBYAhQR2tdHfAGOmdG3oIgZAxX2W2AAgUK0KFDB/7zn//QvXt3fvzxR7TWaUpvJ8f8+fO5cuUK5cuXp1y5coSEhDBv3jwKFSqEn59fqm8e+/v788orr7BixQoWLlyY4foJ6cNTx1AMOKCU+lkptdjxdwvl5gF8lVJ5MI/Dht5CXoIg3CKustsbNmzgypUrgPlwzoEDB5xTOt988w2XLl0CkpfeTo65c+eyfPlyQkJCCAkJYfv27c51hlGjRvHiiy9y9epVwKx1TJ8+ncjISFavXu3MY9euXZQtWzYTayykhqdTSeMyq0Ct9Z9KqUnAaeA68IvW+pfE8ZRSA4ABAA899FBmFX/XUnvEl0zOaSMykdPj/XloTMo6/pnNN98G0em5nPuk4+ivFnDhjzBKPFgEgAN//EXVB4ulOx/XPNIiJdnt48ePM7D/38FbER8fT+vWrXn22WdRSjF69GiaNWuGt7c3NWvWZPbs2SxevJht27Yxfvz4JGWEhIRw+vRp6tev79xWvnx5ChUqxO+//86gQYOIjIykbt26+Pj44OPjw6uvvorWmn//+9/8/e9/x9fXFz8/vxSnka6fvYpvqULpbapbziMzyr1d8cgxaK1T/ixTOrEvyrUDygNhwLdKqe5a668SlTkdmA5Qp06dTFnbEAQhgbi4uGS39+zZk+cefybZi16vXr3o1auX27a2bds6P8HZvHlzmjdv7txXrlw5/vzzzyT57Nixw/n7tdde47XXXksS58cff/SoHkLm46kkRoRS6qr9i1ZKxSmlrmawzMeAk1rri1rrWOA7oGEG8xIEQRAyGU9HDAVdw0qpZ4CgDJZ5GqivlMqPmUoKBrZlMC9BEAQhk8nQQ8Fa60XAoxlM+zuwANiBeVTVCztlJAiCIOQ8nr7g1sEl6IV5ryHD8/5a67HA2IymFwRBELIOT59Kcv1S+U0gBLOALAiCINxleLrG0CerDREEQRBuDzydSioDTAEaYaaQ1gNDtdZnstA2Qcg1HJxgdIou2bACDmYwr0vAI6PTXgJMTXa774v92H/0YIZkt1u0aMGMGTN44YUXANi5cye1atXi/fffZ/jw4SmmXbRoEZUqVaJq1aqAefR10qRJ1KlTx7OKC5mGp4vPs4DFmO8yPAAssdsEQbhDcWgl7d69m3feeYdRo0YB8NFHH1GieAn27t3Lvn37mDlzZrpkt/39/Zk/f74zPG/ePGrUqJFmukWLFnHgwIH0V0TIdDx1DMW11rO01jft32ygeBbaJQhCNpJYdrv0/aWc+9Iru/3QQw8RHR3N+fPn0VqzfPlynnzySef+zz77jLp161KjRg2effZZoqKi2LhxI4sXL2bEiBEEBgZy/PhxAL799luCgoKoVKkS69atA2D//v0EBQURGBhIQEAAx04cz5I2yc14uvj8l1KqOzDXhruQMOoVBOEOJFXZ7cce54dflmZIdhugY8eOfPvtt9SsWZNatWo5HQtAhw4d6N+/PwBvvPEGM2fO5KWXXqJt27a0adOGjh0TvmZ38+ZNtmzZwo8//shbb73FypUrmTZtGkOHDqVbt27ExMQQeeZKFrVQ7sVTx9AXmAp8gFlj2AjIgrQg3MGkJru9f/Nu1u3ZnCHZbYBOnTrx/PPPc+jQIbp06cLGjRud+/bt28cbb7xBWFgYkZGRtGzZMsV8OnQwT8rXrl3b+QW3Bg0aMGHCBM6cOUOHDh0oU6DkLbaEkBhPp5L+BfTSWhfXWpfAOIpxWWaVIAjZShLZbb9bk92+//778fHxYcWKFQQHB7vt6927N1OnTmXv3r2MHTuW6OiUP8XiGGl4e3tz8+ZNALp27crixYvx9fWlZcuWrF6faVJugsVTxxCgtXaO17TWl4GaWWPS3cs336ZPRSTxB+Iz64Px48aNSzNOarZOfXVJpthxq6xp2ixd8WuPSP6Dg67tenq8f5r5NJrSKF3l3gkkkd0OS5Dd3rZrW4Zkt8ePH897772Ht7e32/aIiAhKlSpFbGwsc+bMcW4vWLAgl/64kKatJ06coEKFCgwZMoS2bduy7+D+9FY3x7h+NqMSc9mLp1NJXkqpex3OQSlVNB1pBUFIg0dGP5qm7Pah84eoUrJKqvlkhex2/eb1MyS73bBh8tqY//rXv6hXrx5ly5bF39+fiIgIADp37ky/Pi8w7cvPWLBgQYp2z58/n6+++gofHx/uv/9+Rkx+2aP6Cp7j6cX9/4CNSqkFmDWGTsCELLNKEIQsx1PZ7UPnDzmnkNIru+3AdZQ6aNAgBg0alCROo0aN2LFmi7Nc1w/1FCtWzLnGMGrUKOejtXDn3IXfSXj65vOXSqltGOE8BXTQWssDx4IgCHchHk8HWUcgzkAQBOEuJ0Oy24IgCMLdizgGQRAEwQ1xDIIgCIIb4hgEQRAEN+RdBEG4DfDkpcPMzs9T2e28fnlZ8+saj2W327VrR4UKFYiOjqZz586MHTuW2bNns23bNqZOnXpL9erXrx/Dhg1zSnMLWYM4BkHIpbhqJf3888+MGjWKNWvWOGW35333DQA/bfgpXbLbTZo0YenSpVy7do3AwEDatGmTaTbPmDEj0/ISUiZHppKUUkWUUguUUoeUUgeVUg1ywg5BEAypyW6X/1v5dMluO/Dz86N27dpOCe3Q0FBatWpFxYoVee211wCYOXMmr7zyijPN53NmM2zYMK5du0br1q2pUaMG1atXd37foXnz5mzbtg2A5cuXU6tWLeo91sipx7RmzRoCAwMJDAykZs2azreqhfSRUyOGj4DlWuuOSql7gPw5ZIcg5Fo8ld1u3LoxVUpWSZfsNsClS5fYvHkzb775Jlu3bmXXrl3s3LmTvHnzUrlyZV566SU6d+5MQEAA//73v/Hx8eF/8+bw2awZLF++nNKlS7Ns2TIAwsPD3fK+ePEi/fv3Z+3atdyf7z6u5zUCe5MmTeKTTz6hUaNGREZGki9fvixoubufbB8xKKUKAU2BmQBa6xitdVh22yEIuR3HVNKhQ4dYvnw5PXv2RGvtlN0eMWIEly9fplOrThw8eNBj2e1169ZRs2ZNnnjiCUaOHEm1atUACA4OpnDhwuTLl4+qVaty6tQp/Pz8ePTRR1m6dCmHDh0i9mYs/v7++Pv7s3LlSl5//XXWrVtH4cKF3crYvHkzTZs2pXz58m62NGrUiGHDhvHxxx8TFhZGnjwyW54RcmIqqQJwEZillNqplJqhlPJLHEkpNUAptU0ptc0hBXw3k16l0OwmtcXMxLY3mtIoQ0qwmaUem1XUWPCzWzglBVpPFFpvJy78EZaq7PbTzz6dLtntJk2asHPnTrZv387AgQOd210/1uMqo92vXz9mz57NrFmz6PF8NwAqVarE9u3b8ff3Z9SoUU6BvtgbJk1KtowcOZIZM2Zw/fp16tevz6FDhzLYKrmbnHAMeYBawKda65rANWBk4kha6+la6zpa6zrFi8tXRAUhK0lNdvv4keMZkt32lHr16vHHH3/w9ddf0+kZc3MQGhpK/vz56d69O8OHD2fHjh1uaRo0aMCaNWs4efKkmy3Hjx/H39+f119/nTp16ohjyCA5Mc46A5zRWv9uwwtIxjEIQm5i1IDnCI97IFNktwvm8ex+z7HGcDM2Du88Xpkuu50eOnXqxK5du7i3iFkA37t3LyNGjMDLywsfHx8+/fRTt/jFixdn+vTpdOjQgbiYm5QsfT8rVqzgww8/5LfffsPb25uqVau6fWta8Jxsdwxa63NKqT+UUpW11oeBYEScTxCyHYfsduJvOGSF7Hbv3r3p3bu3M7x06VK3/evXr3d7Oqlly5bJfvLz+2+WOm198sknefLJJ7l+9qrT1ilTpnhQcyEtcurN55eAOUqpPUAgMDGH7BAEIQcJCwujUqVK+Pr6JvkEqJBz5MiSvdZ6F1AnJ8oWBOH2oUiRIhw5ciSnzRASIVpJgiAIghviGARBEAQ3xDEIgiAIbohjEARBENyQ98UF4Tbghw19zI/NCdv2bU4ab4+H+XV6bkuacRyy27ExN8mb755Mkd0ePXq0U98IICoqiuPHjxMeHu5RelcyS6pbSD/iGAQhl+LQSrrwRxg7D/yeKbLbEyZMYMKECc5wt27d6NSpU7qdgpCzyFSSIAhZIrv91VdfcezYMafO1uXLl3nmmWcICAigfv367NmzJ8n2Zm2CndtdOXXqFMHBwTR/wkhsnz59GjASGM3aBFO3bl3GjBnjdEA9evTghx9+cKbv1q0bixcvzmDr5D7EMQhCLsUhidGoRRD9+vXjzTffBIzs9uRPPqRBgwa88cYbhJwIAXDKbq9atYrdu3fz0UcfpZh3SEgII0eOZM6cOU6F07Fjx1KzZk327NnDxIkT6dmzZ5Ltb40c49zuyuDBg+nZsyerf9lAt27dGDJkCABDhw7lxRcGsXXrVkqXLu2M369fP2bNmgUYye6NGzfy1FNP3Xqj5RLEMeQQU19dkumfc0yJlFRAPWXqq0ucvxMrh9Ye8WWG8vRETTa1ch24KrI6VF1d6+uaR0bIaP0c5SZWZE0czkl8fX35/cc5bPhtS7Ky288P/EeGZLfj4uLo3r07//rXv/jb3/7m3L5+/Xqeftp8k+vRRx/l0qVLhIeHs379eufoo3njZs7tZ8PPOtNu2rSJrl27AmY0sH79euf2Dk8/A+DcD9CsWTOOHTvGhQsXmDt3Ls8++6zHEtyHzt/ewnvXz17N8jJkjUEQBDfZ7RIlSlDArwCPP92Wl3v1IDw6nB9//BEfHx+PZLfffvttSpUqRZ8+fdy2a62TxFVKpbg9NTyxo0ePHsyZM4d58+bx+eefpxlfSEBGDIIgZJrs9ubNm5k9ezbTp09Psq9p06YsWGDE81avXk2xYsUoVKgQTZs2Zc6cOQCs3bjOud2Vhg0bMm/ePADmzJlD48aNAahfvz6Llpm1BMd+B7179+bDDz8EcH4sSPAMGTEIwm1Au0azkpXddlUOTUl2OzQ01Dm/nl7Z7aDHnyUOn2Rlt2+g8VGkS3Z77NixREVF0aJFC7eyFi5cyLhx4+jWrSMBAQHkz5+fL774AjAfgerTpw8BAQHk88nr3O7Kxx9/TN++fXln4ruUKn2/c/3gww8/pOvzXZjy+ae0bt3a7UtvJUuW5JFHHuGZZ57xqD2EBMQxCEIuJS4ujhuh+90cEiTIbp/Iq6lWtHC6ZLd//jn1NZQ5cz6haNFH3LYVLVrU+QSRqyNs37m90xGWK1eOVatWJZEIf+CBB1iz9Ffyly7MvHnzqFMnQZszKiqKo0eP0qVLl3S0igDiGARBuIPZvn07Lw78B3grihQp4lxLWLlyJX379mXYsGFJvhctpI04BkEQ7liaNGnC7ys3OEcZDh577DHnuw5C+pHFZ0EQBMENcQyCIAiCG+IYBEEQBDdyzDEopbyVUjuVUkvTji0IgiBkFzm5+DwUOAgUSiuiINztBG08A5yB3102/p5czFMp5LDXLbS7Y8s0y/T29qZ6lYrc1HmSld3ecWg/93ipdMluA2zZsoXhw4dz/vx5lFI0btyYjz/+mPz583uUPrMICQlh48aNblIZGeHzzz/ngw8+QClFfHw8EyZMoF27dsnGDQ0NZciQISxYsOCWysxpcsQxKKXKAK2BCcCwnLBBEHI7vr6+bFmxkPC4B5KV3f5+xmdUK1o4XbLb58+f57nnnmPevHk0aNAArTULFy4kIiIiRxzD119/nS7HEBcX5xY+c+YMEyZMYMeOHRQuXJjIyEguXryYYvrSpUvf8U4Bcm4q6UPgNSA+h8oXBMGFzJLd/uSTT+jVqxcNGhixPKUUHTt2pGTJkly+fJnu3Qcnkd0eN24cvXr14oknnqBKkD/fffcdr732Gm2bt6VVq1bExsYC5iW3f00cS1BQEEFBQRw7dgyAAS8PcrsYO0Y2I0eOZN26dQQGBvLBBx8QFxfHiBEjqFu3LgEBAfz3v/8FjDxHq45t6Nq1K/7+7mKNFy5coGDBgs48CxQoQPny5QE4fvI4jz32GDVq1KBWrVocP36ckJAQqlevDpBqec2bN6djx45UqVKFbt26OfWitm7dSsOGDalRowZBQUFEREQkyWfG/7Je9ynbHYNSqg1wQWu9PY14A5RS25RS21Lz0NlFWgqlrkqpniiH3iopqY3eappGUxplxJxbxlFuRpRgU0qTXH09PTaJj+fBCavc9ruquqZFcgqt566eA+Dy5YMe55NRklPjvHz5oFMSo1GLIF544YUkstvdWj6eLtnt62evsm/fPkqUrZikvEPnDzF27FgCAh5JIrsN5rsKy5Yt45tZX9O9e3datGjB4tWL8fX1dfsiXIGCBdmyZQuDBw+m39//kWq93333XZo0acIvS1bzyiuvMHPmTAoXLszWrVvZunUrn332GSdPngRg287t9BrxOgcOHHDLo0aNGpQsWZLy5cvTp08flixJUOvtM7g/L774Irt372bW97MoVaqUW9rkygs5HcLJq5Hs3LmTDz/8kO2/bubEiRNs2LCBmJgYnn/+eT766CN2797NypUr8fX1TZLPrDlfOO3OKnJiKqkR0FYp9RSQDyiklPpKa93dNZLWejowHaBOnTpJ5RcFQbglXKeSNu9cS8+ePdm3b59TdnvOhl858vsmOrXqxO+bf/dYdjsl1q9fz8yZ7wHustsATz75JD4+PlR/pBpxcXG0atWKwxcO4+/vT0hIiDOP9m2NQ+7SpQtDhr6crvJ/+eUX9uzZ4xxdhIeHc/ToUe655x7q1KxNmbLlkqTx9vZm+fLlbN26lV9//ZVXXnmF7du38+qrrxJ67izt27cHIG++vEmmypIr79iJ4+DnQ1BQEGXKlOH62asEBgYSEhJC4cKFKVWqFHXr1gVwCgkmzifsyhWOHj3qHLlkBdnuGLTWo4BRAEqp5sDwxE5BEITspU6dOpkiu12tWjX2790NJJ1mSk1e2zFV5eXl5VaOl5cXN2/eTBLf9be3tzfx8fHOMmJiYpK1TWvNlClTaNnSfWF+9erVqa5/KKWc01ePP/44ffr0YdiwtJdGkyvv+tmrzNu61llfh/03b95Ea51s+ybOx1VPKquQ9xgEQeDYsWOZIrs9ePBgflgwn99/T3ik6quvvuLihYspym6nhx+WfAfA/PnzqVHLCOaVfbAs27ebmekffvjBuSZRsGBBIiIinGlbtmzJp59+6tx/5MgRrl27lmp5oaGh7NixwxnetWsXZcuWpVChQjxQqjSLFi0CIOZGDFFRUW5pky0vKuXyqlSpQmhoKFu3bgUgIiKCmzdvJsnn6PFjadp9q+SoVpLWejWwOidtEITbgS0NyySR3X7w2l/kKVzaTeW0SskqXL580KlQeuGPMG56R1G6dGmnUmpGZLfjddwty26PGjSckiVLMmnqdIYPH86FCxfw8vKiadOmVGtULUXZ7fQQExNDvXr1iI+PZ/zkTwDo060XnQd0JygoiODgYPz8/AAICAggT548tGjZmH79X2Do0KGEhIRQq1YttNYUL17ceWFPidjYWIYPH05oaCj58uWjePHiTJs2DYCZH09n6JuvMmbMGOJUHEu+X4KXV0Lb9+vXL0l5c6el/EXAe+65h/nz5/PSSy9x/fp1fH19WblyZZJ87itclMU/3tqXCdNCRPQEIZfiKrvtcC6QcdltxyJ3YO26rFu3zi3OofOHKFq0aLKy24k/cRsZGZnivj69+vHvye8AxnkClCxegs2bNzvjvPOO2e/j48Ovv/7qJtU9ceJEJk6c6JZn8+bNqVe5FidIOtVVtmxZVq1alWQ7wN8qPOzcd+j8ISqUrADAvn37ADMNlri862evEtS4CX3atnFumzp1qvN33bp13eriwDWf62ev4ltYppIEQRCEbEQcgyAIdwQhISHcV/S+nDYjVyCOQRByAI1O9ikdQcgImd2XxDEIQg7w57U/uXTpkjgH4ZbRWnPp0iXy5cuXaXnK4rMg5ABfHfmKxuUbc+nSWS5cUNwMO8d1Hc2lSPM8/bkrkdy4EYl32HUu+mi8zvty7uo59GXNtWtnOX/e5BNxJYo4FUN4eLgzj3xeXviEJVwkYsOj3cKAMw9HGkcermkSl5sWjnLOXYlERbqrFSRne2p5uKZxJeJKlFsbqciLydYvpTSplZvR+qZka1rlJM7DU5JLky9fPsqUKZOufFJDHIMg5ACRsZGUL1+erduep9NzWzg9vhNrwicy+P+eBqD7iC+Z/PtMSrQcy7DKsezu2JJ+U/qx4aUNfPNtLzo9twWAqa8u4a+C2xk3bpwzj+Cifjwy+lFnWQcnrHILA848HGkcebimSVxuWjjK6T7iS7a/39NtX3K2p5aHaxpXpr66xK2Ntr/fM9n6pZQmtXIzWt+UbE2rnMR5eEpG0qQXmUoSBEEQ3BDHkEEyIvaW3WSlIF5iUbnEYVcSi9klJyqXEsmJ2dVY8LPH6RPnAe62JtdGU1/N2MtDicvJijxcxfsyIqR4K33C0ec9PZ63KjSYEsm1UXr7xO1ITglYJoc4BkEQBMENcQyCIAiCG+IYBEEQBDfEMQiCIAhuiGMQBEEQ3BDHIAiCILghjkEQBEFwQxyDIAiC4IY4BkEQBMENcQyCIAiCG9nuGJRSDyqlflNKHVRK7VdKDc1uGwRBEISUyQl11ZvAq1rrHUqpgsB2pdQKrfWBHLBFEARBSES2jxi01me11jvs7wjgIPBAdtshCIIgJE+OrjEopcoBNYHfk9k3QCm1TSm17eLFi0nSZhZTX12SRMHTlcRhSJ9q5K2kSY7EypKpKYemhauiZUbUKROnSSuPzFTFTI8aZ3JkZrkpqWJmhtpqRqmx4Gc3Wz1VA6494stk+3xyeFK/1MpN6dzLDKVUR7lpKZZ6Wt+MKu7eKjlVbo45BqVUAWAh8LLW+mri/Vrr6VrrOlrrOsWLF89+AwVBEHIpOeIYlFI+GKcwR2v9XU7YIAiCICRPTjyVpICZwEGt9eTsLl8QBEFInZwYMTQCegCPKqV22b+ncsAOQRAEIRmy/XFVrfV6QGV3uYIgCIJnyJvPgiAIghviGARBEAQ3xDEIgiAIbohjEARBENwQxyAIgiC4IY5BEARBcEMcgyAIguCGOAZBEATBjTveMSRWb0xNKRUyT+XUlfQqIJ4e7+8WTk2xMzWVyJTKTU2dMrk06VFkFZInrX61pmmzJO2cFX0xp0isjJqZqsSOcyDxeZNcOZ4o3TrOgbRUXBOX68gjJVVi1/olttWTcz5xuYnJbOXb1LjjHYMgCIKQuYhjEARBENwQxyAIgiC4IY5BEARBcEMcgyAIguCGOAZBEATBDXEMgiAIghviGARBEAQ3xDEIgiAIbohjEARBENzIEceglGqllDqslDqmlBqZEzYIgiAIyZPtjkEp5Q18AjwJVAW6KKWqZrcdgiAIQvLkxIghCDimtT6htY4B5gHtcsAOQRAEIRmU1jp7C1SqI9BKa93PhnsA9bTWgxPFGwAMsMHKwGGgGPCXS7TE4eS2ZUWa3FbunWRrbiv3TrJV2ij7yy2rtS5OetFaZ+sf8BwwwyXcA5jiYdptqYU9iZMZaXJbuXeSrbmt3DvJVmmjnCs3vX85MZV0BnjQJVwGCM0BOwRBEIRkyAnHsBWoqJQqr5S6B+gMLM4BOwRBEIRkyJPdBWqtbyqlBgM/A97A51rr/R4mn55G2JM4mZEmt5WbkTRS7u2bJreVm5E0d1u56SLbF58FQRCE2xt581kQBEFwQxyDIAiC4M6tPNKUVX+Yp5Z+Aw4C+4Ghdttp4AYQbfetAy7a8FngMnAJiMU8/bQf2A2cs+nO2zyuAtdtmut2X5RNsx04Dlyz+R4BtgH7gEibf7jNJxrQdt8FlzJ+cMn7OnAC2Glt2Q+stOkuWFvP2X2HgT9tPhdsHmE2D0cdoq2t56xd3a2tV4ElQHn7+xqwAuhty9TAOtu+k2w+V4HvgfdtGeHAL0BpzPrPnzZdMeAtIMbG2QW0Af6wbbLfHo8ouz/E1mWz3RaGefLsqLUrytr6BxBh6+Vo01PWFm3TOI5vlLU13KVdw20ee61Nf9h0F62tUfZ/iD220dbWGJc8btiwq207gd/t/mhgPeBvj22MrXN7YJNNr4F/2DrfcElz2P6OtvlVARbYY6uBZ4EDmD4QDRyz6c7bfE7YPMLs/hgbZ4WL7dG2za4BcZj+ut+lXW/adjzp0q4X7F+0TXPdlnndhk9g+vQuEvrAWUyfi7FxYjDnyRkbduTnaNd4G+ewi21XbHxHnzhg7Y209v4KfE3CefUzMALzPP4Nm2avDUfb/I7YMvbZ46aBVbZNr9s4M22bhGL6vONccuw/Ze2PstsuAR/ato6y6fbZciIx/WsFMMfF1v/DXKcu2rxDgT0k9N8/gUP2bz/mGqFtnR22/glMs2WdJeEaEO2yP8TlOMZjzpkgoIltnxu2zIdcrqejbF0OAy1v18dVPeEm8KrW+hGgPvAi5oL3LuAHFAfux3SQh23YceE4CYyz4UEY+Y19mItdJDBOa10IKGHj/IK5oHS34ek2v6eAgpgF+gPAj8BETAPPt+lqYzr0FJu+MObAXQUWaq19gUKYkdlprXUNzAW1DuZEnA2MxnTMvwMvYy6wRW09/qG1LmLzOIXpTGeATjY8B5iBuUCttfVfhTmxf8N0usaYp74uuLRvARt3LebECrLhdcBSYAww1tp93aapb9txndY60LaPD7BWa10NmAsssnksxDiWPXbbYVunSOAprXV+W15x4AOtdV7gHdt+nYFAYI0tdxBQwKY5Yrc9aNv2TaAI0AJ42h6nOMzb9P+0aUIxJ+whoLC19ZxLHlMxF64zQEeb5hIQALSybT/XtuV6rfU91tYJwDIbby3Q1ob9bJqimGOez4a/s8fvd8wF7DTQFdOfRtk4W+yx3AvcB9TCXJyH23w+wfTR0kAHTP/sAdyL6VcXgc8wDnMCkN8evx/s8aqFuQj1xfTXQjbNbMwFuYANf4FxUE/bYx6PkbCZhumvF4GKQD/brheBupiLka/N5zrmvDiF6a+XMOd1IZvXOszN3mXgbeAnW7enMf1xDeALvIa5ufGzaR4Gvrbtsd62xVqbZynMhb4W8Ka1Zb09NsMw/WMtpm9WtPtXYM6Xe4EudttJTL8Lt+16CnNzdAVzDdiN6S9PAvVsno9jrlN9SThfy2D6YgF7vItae1pjzssLmP4zxh7L08CjwHBr6wnM9aWy3X8Q2GHtf9XGuWTb+Rtgvj2X1mD6LFZuqDNQDdOf/2NliVLltnQMWuuzWusd9ncEpkHyaq3/o7W+abcdAQppra/a8D5HcozXPAjkBR4D3tVaX7Jx/nDJdx/QAOOVfWyaspgOudZuu4bxxu0wB9kHc7FrgulACojWWv+CaU8fzIl9r7XHx/7dtOHJtrx4G/a2+zXQB+P8tN0WmiiPZjbtvTZcCrgH01mx5ZbFnGhgTvAWmBPtLIBSqgzmpP7MxjmK6XgzbNgPc0EZiLnwYsupiLlwOfJ4FjPyiLfhZi55OC4EDew2R/3/ZtsVzAmZF+NkHbbmB45qrQ+TwGqttaPtNtv2cuBn2wrgA8xFJDn6YPrADRuOt/VQ1tZrNp9CSqlCmJM1D8bpxWCcbXHA8Xb+N7YuE6ytXpgRxavW1nyYC/sFAJuHr23Hei521sGc6Ng0DTDH712tdaS1MwiYaW19DnPsz2D6foy10xfTxscxfbS2bc9gTJ9+VGt9EHMhvg6cs/21uU3zC+ZGKdiGY2x7fIBx4I4ROJh+cFxrfQpz8VzhCGutHTcfwdauaTafhpgLpZe1dYP9HwPUBL7CHPsfbTucwZxXeW36jTZfX2tLKaWUo95g+sSHmBuQm7YcH5c42tr6b5u/BiLt/vz22MUD99ltRTH9tyLmBusU5vypCczCXHgLAl5a690kjKjCtNZLbD87hblxLWr7xCnMdSkWc97Ms2WHArEuccIwzuUdzE1mmG3bm7bclhhnGmPrfhRzDSiBuSHGpq1jf7cD5mmtb2itT2JuRIJIi5yeNvJgWqkcxpMWSrQtCuhnw1NthzhgD/4Z23D/w1ws38LcpUUDzV3yOI85MR/BDNMcw+7tGG8diblTjMDciUYC72E64k0bdgzldrnsX4K5kzuH6XAXgZI2zxgbJ8SWH4+5gHxubT1nyzqNuYg48p2DmTo6aNM4psMOYu7ilmLuZDTmhF9q6xmDuVDswtxxLbDh5jZNKPCGbavrGGe5BXPH0dy282Lgv9a2qxiHeBRzp3nF1q+Hjb/R2vkzCcPh67Y+kZiOOQBzB6dt+2+32+Jcwo5pNdf9S0gYXsfY/acwF7PzNo5jau4Gph9E2PJDbfuvccn3sD0OJzF9xzFVFoM5qU9gHNtH1tbZmH70Heb4O8IXbXrX/WEYJ3jO1vu4/Ttg41yzx2QXCVNpf9n2dLTbDsy0w2zMjdBl27bfkzCl5eivKzGOaxig7bH/3G674hI+CtRJtH8JZgSxy5a/z4Y/snEuYe6Yx9n2/JOE/rrTtuEaoK7Ndxlwyv5+xNp5xbZJnK3rHFsPx9TfHBs/ysaPwYwihpIwfTPH2hZt2+8aCVMtUXZ/COacjbfH6Dgw0pZ7zbaxY4rIMSW0DXNzFU/C1NoJzHnwnG2XKGtrfsyo7isb9z7McT5s63ifjbML0xcHYs53xxTcQMzIb5Ot12XMubHXtnOY/T3B2huLubnNb+v+l63jWbvvBqZ/3ATa2TZ07QNTge4u186ZmJHxHTmVBIBSqgBmWuJlrfVVl20bMNMUM224AebO73+Yu4zzQBdMg5fBeNN4zJBtlku+OzGdaQimc3XCTOfcwHSeY0AFzAGNsHkFYTq7I1wQKG+nV8pgOpIv8JXW+n7M3cdVzEl1EXOnGYS5w2uOOeH2Yw5sGczdqONucyHmLqWMjfsbpiP3wnSOHZjO+Izd75jvdrRfGyBea73dbioKXHAJ17D1noDpML9iOnRVTAcGc/f1l227rpipg8vW7oKYC80aYLyNX9rmUQpz0emEuRiGYO5izmDu7qpjOm8tzJD8ReC6S/gBzDSTI/wWZqopUGtdyraJF+YE+wtzV/qita0F5s77ErDc1nGZPWZzMMeztk17D+ZEWomZzjuPcfyhmOP/NGbYD/Cp1rqmbWdvl3A8Zv3AEX7Y2jrC9oGZmIthBWCwjaMxI5NRJNwt32fz/RbjmP+wx+JTe2xCMNMkMZj+PRPjiGOApkB/e0y0fXm0rc0Ll/CFROHSmL73jW3TKvb3u8C/bJxrtv4zbN1rYy5Mjvj1MGsB39h8H8U4DuwxibdxHGt4WzBTNI1sO5QG/Kw+mg9m2niTrcsAGy5ty30IM/IpAqzG9IHLdpsfZtrGD9P/8tuyX8c41lmYqTwvzLlYGnM+nsTc2LyAGVHsxVxH/DBTyw/Y+oLpT7tJWDtYYY/jPszLuytsnBvWrgGY69PnGGfzMWaqdDemH04m4YbAsVb0N8z02f9sey2zeSrMteIYxinEYM69FhjH9aJSarttN8d1QJGUtN9RyOkRQSojBR/MXeewRNv2Yk6Q/InjYE7ICJdwORve4rLtOGZqYDjmIlDONrJjvwKuupT5McbBHMZ0trGYi9Rhu/8k8KH93QtzMo9KVJfJtqwL1vYrJIwK7rd5TrC2OkY0YzEdpjhmWB6JOVHDrY3rMHcX8Zi7UscClmMEEoW5iMfaMmMwF4BrNuxIG2XD5+zv45iOc9Pa6Fjk+iOZci7bsOPOxrGoetjud803xqU9xmEuPNeBUnbbJIzTcsQJ8aBdP0jUrmGJ2nWca7va8HBbx/tt2sl2m6NdJ9l6XbVlNMFcfONdbO2NcbgOO/ZiphMdth4Colz2N8H0QW3tDLHtGw/cb+O0s+3hsLUJxlnFutjal4QFfmXjLLPbfrH5VLL59MZMEZWyx6OdDa/GOOh21u5NmHOpnUseT1j7HAvSN22b9nKJU87u2+pSz+M2zg2gjN12zZb7HMaROdp1CuYCd93a2BNz4xNm96/GTDMedGnXs8Ahl/KmWBtv2ja9aNs00qVdv8BcwJfbdp2CGWU7+kC4LSuGhPe6euLeXydi+o7D1omYUchhF1s/x6wJOmw9AwxNJg+HrWG49FUb5xPMDYnD1omYhxoctl4D/mltLodxRhOtXYdJ6J+NMDdZYG48RrnY8TPQ4I4cMdj51JmYTjHZZdtPGC9fF9MYM4GzWuvJdv9c4KIjDebJkRuYk3iyUqoSpoH3YBr1EOZON4KEud72GM+NUio/ZuFmGWY65QXM3WtF4AellC/mLvy8UqoVprP8CZxSStWyefhi7uhPaK1LYEYbhzF3s09iDvJjmDuho8CjNk17a89fmIXDOGtzKGahC8zTRHutjT9j7g5/xqyBrMIMMT/QWpfD3LVv0lr7YUYDYcBKbRZbH7d5rMLczS/UWufBzBVfx4y4glzK+SfmTmaJDdfEOIBJwGWttUMNt7dNsxfYr5QqoZTyw1x4gjDOrZfd1gkzEsSG7wXOuLTrWeCAUirQJc4zmPntEpjFtUMu7RqBWWwrZbe3tOVew9wNBmLuIhtiTrxQG6ehPSaO9Z3HMHf/YZgFP7BPgimlKttwQSBcKdUPc3e6GLislHrC7g/GjBr/wjwVUg5z8TqHuTvEtuENzEXjUZsmBHOha2Pr8BDGaf2FWdMJtvncAOYqpbww04KrgVcw50MvzOJzFxt28Aqm77bVWkdhRhuO/ZUxfeg3zIXpDGak0t4lTnsSntjBnlv3YNZzTmqtHWsSNzHn1mlHney5WoGEheNedp8XcNOed2Du1IsopdrZdt0LFFZK+bvkkY+Ep/F+xBz3c8BVG6cOxiEvs+1aATMyc/SB65ipJA08YdN0Ay7Y/voQZqG/hm3XITbsWLjG5vUosML219E23x+VUhVd8lC2XZtaG/8Eummtz9k4HW1bLcL07Q4krCE8btN/inGG9TE3xt0x59pKzLnkhXF+q61ti4HOSqm8SqnyuKwVpkpOjwxSGC00tgdqDwnzsMPtthgSHtXTJDy+6AiHkfDInOMxzSskPHroyPeybWBNwpzjdcwI4LzN7wbmArjT5hVp84/AdPRYTMePxZzAsdi7GhIeJYy2ee4jwSF9QsLjd9GYTrLa7rtit12wnWKP3bba2uG4yzqHGRa/T8Jjo+9iOv12zAXwV8wQ2bHmcgNzIT9m8w+3bXvC2uh45PUBexya23YphhnWnrBxFmMuUitseAfmxFgO7HE5htttWeGYi7DjUdy/MB18HwmPVZ6y4ZOptOsBEh4zdDze6XgkeT/GMcW4tOtFW/YBu+28LeM45viH2jS7Xdr1LAlrPTdsu36AuRBfJeFxwKdsGsfc8WUSHuMMt8fO0QfCbds8jrkI7bHt2gbjyK7bOOtJON5XrV39bXudIeGhh0MkjBx22PTHbBu/i5niibX1/BVzgXWsDdwgod+fwRx/x7TGAVv+Eszo+xIJT9o9aNPut3GWWTvn2zbdgXHI0ZipXzAjkXASHtU+Y/O8Yeu3gYTHVa9ibky+J+G4XydhMTzWtrHjkVnXx1WPWRv+Z23dT8K59wfmZucQpg+E2WN82qbfjLmwf07CY6F/2XZx9IE/7N9h245nbLsedrH1iv2LIeGxYsdaiWPN4pK1dzcJjv+iS7kbrT0HbF4nbR4htu4f2Ha9Yo+ftuWPx4wMokh4PLuoy/V0tG3Hw8CTnlyDRRJDEARBcOO2nEoSBEEQcg5xDIIgCIIb4hgEQRAEN8QxCIIgCG6IYxAEQRDcEMcg3NUope5TSu2yf+eUUn+6hO9JFPdll2foU8tztVKqjv0dopRal2j/LqXUvuRTp5hnufSmEYSsQhyDcFejtb6ktQ7URrJkGuZZ8ED7F5Mo+suYZ+/TS0Gl1IMASqlH0pvYE7VLQchOxDEIuQ6lVLBSaqdSaq9S6nP7VugQzJu4vymlfrPxPlVKbVNK7VdKvZVKlt8Az9vfbm8Y25HAOqXUDvvX0G5vrpT6TSn1NeaFOlf7Klj76mZitQXBY8QxCLmNfBipkOe11v4YHapBWuuPMW9Ct9Bat7BxR2ut62BE0poppQJSyHMBRr4AjOjeEpd9F4DHtREDfB6jveUgyJZR1bHBymwsBPporbdmvJqCkHHEMQi5DW+Mls8RG/4Co12THJ2UUjswkg7VMEqnyXEZuKKU6kzCl+wc+ACfKaX2YrSsXPPYoo1GvoPiGF2j7lrrXZ5XSRAylzw5bYAgZDPX0o4CVnBsOOYbA1eUUrMxo42UmI/RwOqdaPsrGG2iGpgbsehUbAnHaPI0wuj9CEKOICMGIbeRDyinlHKomvYg4TOiERilVDCfZ7yGUU0tiRGIS43vMR+J+jnR9sIYBeB4W1ZqC80xGFXNnkqprmlXRRCyBhkxCLmNaIw09Lf2M45bMU8rgfkoy09KqbNa6xZKKYeq7gmsJHhKaPOp2PcAjHKzk/8AC5VSz2FkrFMdsWitr9kPLK1QSl3TWv+Q3goKwq0i6qqCIAiCGzKVJAiCILghjkEQBEFwQxyDIAiC4IY4BkEQBMENcQyCIAiCG+IYBEEQBDfEMQiCIAhu/D/FwDBGaGuYrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(\"TotalMark\",hue=\"Depart\",data=data_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "940f06f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='FinalResult'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x= \"FinalResult\",  data=data_2017,hue=\"Depart\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8db11969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='FinalResult'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x= \"FinalResult\",  data=data_2018,hue=\"Depart\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04976051",
   "metadata": {},
   "source": [
    "# Based Grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fed4928c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "Name: Grade, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017[\"Grade\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "53c3babb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    B+\n",
       "Name: Grade, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018[\"Grade\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b29db075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2636c5eda00>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.displot(data_2017['Grade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1daa5c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x263739684c0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.displot(data_2018['Grade'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9199cbee",
   "metadata": {},
   "source": [
    "# With the help of graph we can make out that 2017 has better performance than 2018 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdba824d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}