{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "55e66882", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt \n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "id": "fbd7e18d", "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv(\"Acadamic_Performance.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "cbe501e9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Email AddressNameRoll noPRN No.Mobile No.First year: Sem 1First year: Sem 2Second year: Sem 1Second year: Sem 2
0sejal.zambare19@pccoepune.orgSejal Zambare1272026841K82082177828.408.609.809.90
1rushikesh.thorat19@pccoepune.orgRushikesh Vilas Thorat1572026776F90212619258.148.149.329.82
2atharv.sontakke19@pccoepune.orgAtharv Sontakke2072026742M90098046296.616.619.149.14
3amisha.sherekar19@pccoepune.orgAmisha Sunil Sherekar2572026696D86982275487.207.308.909.20
4saurabh.sawardekar19@pccoepune.orgSaurabh Raju Sawardekar2872026682D77740728507.057.459.059.40
5priyanka.kizhekethottam19@pccoepune.orgPriyanka Sunil Kizhekethottam3072026604B88795288527.508.007.917.84
6janhavi.pimplikar19@pccoepune.orgJanhavi Pimplikar3472026588G70289250978.278.509.689.14
7lalit.shirsath19@pccoepune.orgLalit vilas shirsath3672026714F74980418278.689.009.599.66
8shishir.singh19@pccoepune.orgShishir Singh3872026730H93591467527.647.649.189.39
9vighnesh.pathrikar19@pccoepune.orgVighnesh Pathrikar3972026808H80076698559.099.369.919.95
10vedant.nerkar19@pccoepune.orgVedant Narendra Nerkar4272026501M90280081967.457.919.419.41
11twinkle.shirsath19@pccoepune.orgTwinkle Shirsath4672026715D93074526839.009.509.779.86
12akshay.siddannavar19@pccoepune.orgAkshay Ajay Siddannavar4872026725M98233436659.009.279.779.33
13avadhut.joshi19@pccoepune.orgAvadhut Joshi5272026290K83809958797.597.959.059.66
14ritik.bazaz19@pccoepune.orgRitik Bazaz5572026645K90867919526.366.598.508.59
15sumedha.zaware19@pccoepune.orgSumedha Zaware5772072651L83082616618.048.109.599.66
16rushikesh.markad19@pccoepune.orgRushikesh Karbhari Markad6072026443L96236141718.688.779.689.82
17tejaswini.zalki19@pccoepune.orgTejaswini Ashok Zalki6372026839H96896501648.718.919.869.84
18ashootosh.pawar18@pccoepune.orgAshootosh pawar6671910603G84593248116.256.896.907.20
19atharva.sarode19@pccoepune.orgAtharva Sachin Sarode7072026673E88056272678.329.009.559.48
20rizwan.sayyed19@pccoepune.orgRizwan Sayyed7572026646H93077817019.099.409.649.64
21siddhesh.vharamble19@pccoepune.orgSiddhesh Vharambale7872026807K97674174377.808.149.509.71
22riya.shah19@pccoepune.orgRiya Sameer Shah8072026685J95185978048.829.239.829.82
\n", "
" ], "text/plain": [ " Email Address Name \\\n", "0 sejal.zambare19@pccoepune.org Sejal Zambare \n", "1 rushikesh.thorat19@pccoepune.org Rushikesh Vilas Thorat \n", "2 atharv.sontakke19@pccoepune.org Atharv Sontakke \n", "3 amisha.sherekar19@pccoepune.org Amisha Sunil Sherekar \n", "4 saurabh.sawardekar19@pccoepune.org Saurabh Raju Sawardekar \n", "5 priyanka.kizhekethottam19@pccoepune.org Priyanka Sunil Kizhekethottam \n", "6 janhavi.pimplikar19@pccoepune.org Janhavi Pimplikar \n", "7 lalit.shirsath19@pccoepune.org Lalit vilas shirsath \n", "8 shishir.singh19@pccoepune.org Shishir Singh \n", "9 vighnesh.pathrikar19@pccoepune.org Vighnesh Pathrikar \n", "10 vedant.nerkar19@pccoepune.org Vedant Narendra Nerkar \n", "11 twinkle.shirsath19@pccoepune.org Twinkle Shirsath \n", "12 akshay.siddannavar19@pccoepune.org Akshay Ajay Siddannavar \n", "13 avadhut.joshi19@pccoepune.org Avadhut Joshi \n", "14 ritik.bazaz19@pccoepune.org Ritik Bazaz \n", "15 sumedha.zaware19@pccoepune.org Sumedha Zaware \n", "16 rushikesh.markad19@pccoepune.org Rushikesh Karbhari Markad \n", "17 tejaswini.zalki19@pccoepune.org Tejaswini Ashok Zalki \n", "18 ashootosh.pawar18@pccoepune.org Ashootosh pawar \n", "19 atharva.sarode19@pccoepune.org Atharva Sachin Sarode \n", "20 rizwan.sayyed19@pccoepune.org Rizwan Sayyed \n", "21 siddhesh.vharamble19@pccoepune.org Siddhesh Vharambale \n", "22 riya.shah19@pccoepune.org Riya Sameer Shah \n", "\n", " Roll no PRN No. Mobile No. First year: Sem 1 First year: Sem 2 \\\n", "0 12 72026841K 8208217782 8.40 8.60 \n", "1 15 72026776F 9021261925 8.14 8.14 \n", "2 20 72026742M 9009804629 6.61 6.61 \n", "3 25 72026696D 8698227548 7.20 7.30 \n", "4 28 72026682D 7774072850 7.05 7.45 \n", "5 30 72026604B 8879528852 7.50 8.00 \n", "6 34 72026588G 7028925097 8.27 8.50 \n", "7 36 72026714F 7498041827 8.68 9.00 \n", "8 38 72026730H 9359146752 7.64 7.64 \n", "9 39 72026808H 8007669855 9.09 9.36 \n", "10 42 72026501M 9028008196 7.45 7.91 \n", "11 46 72026715D 9307452683 9.00 9.50 \n", "12 48 72026725M 9823343665 9.00 9.27 \n", "13 52 72026290K 8380995879 7.59 7.95 \n", "14 55 72026645K 9086791952 6.36 6.59 \n", "15 57 72072651L 8308261661 8.04 8.10 \n", "16 60 72026443L 9623614171 8.68 8.77 \n", "17 63 72026839H 9689650164 8.71 8.91 \n", "18 66 71910603G 8459324811 6.25 6.89 \n", "19 70 72026673E 8805627267 8.32 9.00 \n", "20 75 72026646H 9307781701 9.09 9.40 \n", "21 78 72026807K 9767417437 7.80 8.14 \n", "22 80 72026685J 9518597804 8.82 9.23 \n", "\n", " Second year: Sem 1 Second year: Sem 2 \n", "0 9.80 9.90 \n", "1 9.32 9.82 \n", "2 9.14 9.14 \n", "3 8.90 9.20 \n", "4 9.05 9.40 \n", "5 7.91 7.84 \n", "6 9.68 9.14 \n", "7 9.59 9.66 \n", "8 9.18 9.39 \n", "9 9.91 9.95 \n", "10 9.41 9.41 \n", "11 9.77 9.86 \n", "12 9.77 9.33 \n", "13 9.05 9.66 \n", "14 8.50 8.59 \n", "15 9.59 9.66 \n", "16 9.68 9.82 \n", "17 9.86 9.84 \n", "18 6.90 7.20 \n", "19 9.55 9.48 \n", "20 9.64 9.64 \n", "21 9.50 9.71 \n", "22 9.82 9.82 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 4, "id": "9978271f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Email AddressNameRoll noPRN No.Mobile No.First year: Sem 1First year: Sem 2Second year: Sem 1Second year: Sem 2
0sejal.zambare19@pccoepune.orgSejal Zambare1272026841K82082177828.408.609.809.90
1rushikesh.thorat19@pccoepune.orgRushikesh Vilas Thorat1572026776F90212619258.148.149.329.82
2atharv.sontakke19@pccoepune.orgAtharv Sontakke2072026742M90098046296.616.619.149.14
3amisha.sherekar19@pccoepune.orgAmisha Sunil Sherekar2572026696D86982275487.207.308.909.20
4saurabh.sawardekar19@pccoepune.orgSaurabh Raju Sawardekar2872026682D77740728507.057.459.059.40
\n", "
" ], "text/plain": [ " Email Address Name Roll no \\\n", "0 sejal.zambare19@pccoepune.org Sejal Zambare 12 \n", "1 rushikesh.thorat19@pccoepune.org Rushikesh Vilas Thorat 15 \n", "2 atharv.sontakke19@pccoepune.org Atharv Sontakke 20 \n", "3 amisha.sherekar19@pccoepune.org Amisha Sunil Sherekar 25 \n", "4 saurabh.sawardekar19@pccoepune.org Saurabh Raju Sawardekar 28 \n", "\n", " PRN No. Mobile No. First year: Sem 1 First year: Sem 2 \\\n", "0 72026841K 8208217782 8.40 8.60 \n", "1 72026776F 9021261925 8.14 8.14 \n", "2 72026742M 9009804629 6.61 6.61 \n", "3 72026696D 8698227548 7.20 7.30 \n", "4 72026682D 7774072850 7.05 7.45 \n", "\n", " Second year: Sem 1 Second year: Sem 2 \n", "0 9.80 9.90 \n", "1 9.32 9.82 \n", "2 9.14 9.14 \n", "3 8.90 9.20 \n", "4 9.05 9.40 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "07f5aa76", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Email AddressNameRoll noPRN No.Mobile No.First year: Sem 1First year: Sem 2Second year: Sem 1Second year: Sem 2
18ashootosh.pawar18@pccoepune.orgAshootosh pawar6671910603G84593248116.256.896.907.20
19atharva.sarode19@pccoepune.orgAtharva Sachin Sarode7072026673E88056272678.329.009.559.48
20rizwan.sayyed19@pccoepune.orgRizwan Sayyed7572026646H93077817019.099.409.649.64
21siddhesh.vharamble19@pccoepune.orgSiddhesh Vharambale7872026807K97674174377.808.149.509.71
22riya.shah19@pccoepune.orgRiya Sameer Shah8072026685J95185978048.829.239.829.82
\n", "
" ], "text/plain": [ " Email Address Name Roll no \\\n", "18 ashootosh.pawar18@pccoepune.org Ashootosh pawar 66 \n", "19 atharva.sarode19@pccoepune.org Atharva Sachin Sarode 70 \n", "20 rizwan.sayyed19@pccoepune.org Rizwan Sayyed 75 \n", "21 siddhesh.vharamble19@pccoepune.org Siddhesh Vharambale 78 \n", "22 riya.shah19@pccoepune.org Riya Sameer Shah 80 \n", "\n", " PRN No. Mobile No. First year: Sem 1 First year: Sem 2 \\\n", "18 71910603G 8459324811 6.25 6.89 \n", "19 72026673E 8805627267 8.32 9.00 \n", "20 72026646H 9307781701 9.09 9.40 \n", "21 72026807K 9767417437 7.80 8.14 \n", "22 72026685J 9518597804 8.82 9.23 \n", "\n", " Second year: Sem 1 Second year: Sem 2 \n", "18 6.90 7.20 \n", "19 9.55 9.48 \n", "20 9.64 9.64 \n", "21 9.50 9.71 \n", "22 9.82 9.82 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail()" ] }, { "cell_type": "code", "execution_count": 6, "id": "a2799c68", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Roll noMobile No.First year: Sem 1First year: Sem 2Second year: Sem 1Second year: Sem 2
count23.0000002.300000e+0123.00000023.00000023.00000023.000000
mean46.4782618.808338e+097.9865228.2721749.2834789.367826
std20.1197997.518282e+080.8776710.8956150.7066670.672039
min12.0000007.028925e+096.2500006.5900006.9000007.200000
25%32.0000008.344629e+097.4750007.7750009.0950009.265000
50%46.0000009.009805e+098.1400008.1400009.5500009.640000
75%61.5000009.333464e+098.6950009.0000009.7250009.820000
max80.0000009.823344e+099.0900009.5000009.9100009.950000
\n", "
" ], "text/plain": [ " Roll no Mobile No. First year: Sem 1 First year: Sem 2 \\\n", "count 23.000000 2.300000e+01 23.000000 23.000000 \n", "mean 46.478261 8.808338e+09 7.986522 8.272174 \n", "std 20.119799 7.518282e+08 0.877671 0.895615 \n", "min 12.000000 7.028925e+09 6.250000 6.590000 \n", "25% 32.000000 8.344629e+09 7.475000 7.775000 \n", "50% 46.000000 9.009805e+09 8.140000 8.140000 \n", "75% 61.500000 9.333464e+09 8.695000 9.000000 \n", "max 80.000000 9.823344e+09 9.090000 9.500000 \n", "\n", " Second year: Sem 1 Second year: Sem 2 \n", "count 23.000000 23.000000 \n", "mean 9.283478 9.367826 \n", "std 0.706667 0.672039 \n", "min 6.900000 7.200000 \n", "25% 9.095000 9.265000 \n", "50% 9.550000 9.640000 \n", "75% 9.725000 9.820000 \n", "max 9.910000 9.950000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 7, "id": "59134925", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 23 entries, 0 to 22\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Email Address 23 non-null object \n", " 1 Name 23 non-null object \n", " 2 Roll no 23 non-null int64 \n", " 3 PRN No. 23 non-null object \n", " 4 Mobile No. 23 non-null int64 \n", " 5 First year: Sem 1 23 non-null float64\n", " 6 First year: Sem 2 23 non-null float64\n", " 7 Second year: Sem 1 23 non-null float64\n", " 8 Second year: Sem 2 23 non-null float64\n", "dtypes: float64(4), int64(2), object(3)\n", "memory usage: 1.7+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 8, "id": "558175dd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Email Address', 'Name', 'Roll no ', 'PRN No.', 'Mobile No.',\n", " 'First year: Sem 1', 'First year: Sem 2', 'Second year: Sem 1',\n", " 'Second year: Sem 2'],\n", " dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 9, "id": "a77b3463", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Email Address 0\n", "Name 0\n", "Roll no 0\n", "PRN No. 0\n", "Mobile No. 0\n", "First year: Sem 1 0\n", "First year: Sem 2 0\n", "Second year: Sem 1 0\n", "Second year: Sem 2 0\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 10, "id": "e04c3bee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(y=data['First year: Sem 1'])" ] }, { "cell_type": "code", "execution_count": 11, "id": "1a7fc0f5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(y=data['First year: Sem 2'])" ] }, { "cell_type": "code", "execution_count": 12, "id": "0b439108", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(y=data[\"Second year: Sem 1\"])" ] }, { "cell_type": "code", "execution_count": 13, "id": "085951df", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGKCAYAAADkAf55AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAnf0lEQVR4nO3df1RUdeL/8dc44gwmTB9MEAwUcaOkdtNyS/phhanoR92yzbItlSzbPB9/sFrgJ8ss/H34mNnJT7tlIqu2fVC3TnE0TSHyx8KCWZuZdkwUMcuMQYVBYL5/+HW2STTGZrwD9/k4Z87hvu+8Z18sxzOv7n3fey1ut9stAAAAE2ljdAAAAIBLjQIEAABMhwIEAABMhwIEAABMhwIEAABMhwIEAABMhwIEAABMhwIEAABMp63RAYJRY2OjDh8+rLCwMFksFqPjAACAZnC73aqurlZMTIzatLnwMR4KUBMOHz6s2NhYo2MAAICLcPDgQV155ZUXfA8FqAlhYWGSzvwfGB4ebnAaAADQHE6nU7GxsZ7v8QuhADXh7Gmv8PBwChAAAC1Mc5avsAgaAACYDgUIAACYDgUIAACYjqEFqLCwUEOHDlVMTIwsFovWrVvntd/tdmvmzJmKiYlRaGio7rjjDv3rX//62c/Ny8tTz549ZbPZ1LNnT61duzZAvwEAAGiJDC1AJ0+e1G9+8xstWbKkyf3z589Xdna2lixZouLiYnXu3Fl33323qqurz/uZ27Zt08iRI/Xwww/rk08+0cMPP6z7779fO3bsCNSvAQAAWhiL2+12Gx1COrNie+3atfrd734n6czRn5iYGE2ePFlPP/20JMnlcikqKkrz5s3T+PHjm/yckSNHyul0Kj8/3zM2aNAg/cd//IdWrVrVrCxOp1MOh0NVVVVcBQYAQAvhy/d30K4B2r9/v44cOaIBAwZ4xmw2m/r166etW7eed962bdu85kjSwIEDLzjH5XLJ6XR6vQAAQOsVtAXoyJEjkqSoqCiv8aioKM++883zdc6cOXPkcDg8L+4CDQBA6xa0Beisn97MyO12/+wNjnydk5mZqaqqKs/r4MGDFx8YAAAEvaC9E3Tnzp0lnTmiEx0d7Rk/evToOUd4fjrvp0d7fm6OzWaTzWb7hYkBAEBLEbRHgOLj49W5c2d98MEHnrG6ujoVFBQoOTn5vPP69u3rNUeSNmzYcME5AADAXAw9AnTixAnt27fPs71//37t3LlTERERiouL0+TJkzV79mz96le/0q9+9SvNnj1b7du316hRozxzHnnkEXXp0kVz5syRJE2aNEm333675s2bp+HDh+vvf/+7Nm7cqKKiokv++wHSmVOwtbW1RseAzvwtXC6XpDNHfpvzvCAEnt1u52+BS87QAlRSUqI777zTs52eni5JGj16tN5880099dRTqqmp0ZNPPqnjx4/rpptu0oYNG7ye8lpeXq42bf59ICs5OVmrV6/WM888oxkzZighIUFvvfWWbrrppkv3iwE/Ultbq9TUVKNjAEErPz9foaGhRseAyQTNfYCCCfcBgj/V1NRQgIALoADBX3z5/g7aRdBAa2G3271uzAnj1NbW6p577pEkrV27Vna73eBEkMTfAYagAAEBZrFY+K/bIGS32/m7ACYWtFeBAQAABAoFCAAAmA4FCAAAmA4FCAAAmA4FCAAAmA4FCAAAmA4FCAAAmA4FCAAAmA4FCAAAmA4FCAAAmA4FCAAAmA7PAmul3G63amtrjY4BBJUf/5vg3wfQNLvdLovFYnSMgKMAtVK1tbVKTU01OgYQtM4+FR6At/z8fFM8KJhTYAAAwHQ4AmQCJ65/UO42/KkBud1SY/2Zn9u0lUxwmB9oDktjvTrsXGV0jEuKb0UTcLdpK1lDjI4BBIl2RgcAgo7b6AAG4BQYAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwnbZGB0BguN3uf280nDYuCAAg+P3oe8Lr+6MVowC1Ui6Xy/Nz2CerDUwCAGhJXC6X2rdvb3SMgOMUGAAAMB2OALVSNpvN83P1bx6QrCEGpgEABLWG056zBT/+/mjNKECtlMVi+feGNYQCBABoFq/vj1aMU2AAAMB0KEAAAMB0KEAAAMB0KEAAAMB0KEAAAMB0KEAAAMB0KEAAAMB0KEAAAMB0KEAAAMB0KEAAAMB0gr4AVVdXa/LkyeratatCQ0OVnJys4uLi875/y5Ytslgs57y++OKLS5gaAAAEs6B/Fti4ceP02WefacWKFYqJiVFubq769++vzz//XF26dDnvvD179ig8PNyz3alTp0sRFwAAtABBfQSopqZGeXl5mj9/vm6//Xb16NFDM2fOVHx8vF599dULzo2MjFTnzp09L6vVeolSAwCAYBfUBai+vl4NDQ2y2+1e46GhoSoqKrrg3F69eik6OlopKSnavHnzBd/rcrnkdDq9XgAAoPUK6gIUFhamvn376oUXXtDhw4fV0NCg3Nxc7dixQ5WVlU3OiY6O1muvvaa8vDytWbNGiYmJSklJUWFh4Xn/d+bMmSOHw+F5xcbGBupXAgAAQcDidrvdRoe4kK+++kppaWkqLCyU1WpV7969ddVVV6m0tFSff/55sz5j6NChslgseuedd5rc73K55HK5PNtOp1OxsbGqqqryWkfUktTU1Cg1NVWSVN37YckaYnAiAEDQajitsNIVkqT8/HyFhoYaHOjiOJ1OORyOZn1/B/URIElKSEhQQUGBTpw4oYMHD+of//iHTp8+rfj4+GZ/xs0336y9e/eed7/NZlN4eLjXCwAAtF5BX4DOuuyyyxQdHa3jx49r/fr1Gj58eLPnlpWVKTo6OoDpAABASxL0l8GvX79ebrdbiYmJ2rdvn6ZNm6bExESNHTtWkpSZmamKigrl5ORIkhYtWqRu3bopKSlJdXV1ys3NVV5envLy8oz8NQAAQBAJ+gJUVVWlzMxMHTp0SBERERoxYoSysrIUEnJmTUtlZaXKy8s976+rq9PUqVNVUVGh0NBQJSUl6b333tPgwYON+hUAAECQCfpF0EbwZRFVsGIRNACg2VgEDQAA0PpRgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOm0NToAAs/SWC+30SGAYOB2S431Z35u01ayWIzNAwQJy9l/FyZCATKBDjtXGR0BAICgwikwAABgOhwBaqXsdrvy8/ONjgEEldraWt1zzz2SpLVr18putxucCAg+Zvl3QQFqpSwWi0JDQ42OAQQtu93OvxHAxDgFBgAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATMenAlRZWanc3Fy9//77qqur89p38uRJzZo1y6/hAAAAAqHZBai4uFg9e/bUhAkTdN999+naa6/Vv/71L8/+EydO6Pnnnw9ISAAAAH9qdgGaPn267r33Xh0/flzffPON7r77bvXr109lZWWBzAcAAOB3zX4a/D//+U+98soratOmjcLCwvTKK6+oa9euSklJ0fr16xUXFxfInAAAAH7T7AIkSbW1tV7bTz31lNq0aaMBAwbojTfe8GswAACAQGl2Abr22mu1detW/frXv/Yanzp1qtxutx588EG/hwMAAAiEZq8BeuSRR/Txxx83uW/atGmaNWsWp8EAAECLYHG73W6jQwQbp9Mph8OhqqoqhYeHGx0HgJ/U1NQoNTVVkpSfn6/Q0FCDEwHwJ1++v7kRIgAAMB0KEAAAMB0KEAAAMB0KEAAAMB0KEAAAMB2fboR41j/+8Q9t2bJFR48eVWNjo9e+7OxsvwQDAAAIFJ8L0OzZs/XMM88oMTFRUVFRslgsnn0//hkAACBY+XwK7KWXXtIbb7yh3bt3a8uWLdq8ebPn9eGHH/o9YHV1tSZPnqyuXbsqNDRUycnJKi4uvuCcgoIC3XDDDbLb7erevbuWLl3q91wAAKDl8rkAtWnTRrfccksgsjRp3Lhx+uCDD7RixQp9+umnGjBggPr376+Kioom379//34NHjxYt912m8rKyjR9+nRNnDhReXl5lywzAAAIbj4XoClTpuiVV14JRJZz1NTUKC8vT/Pnz9ftt9+uHj16aObMmYqPj9err77a5JylS5cqLi5OixYt0jXXXKNx48YpLS1NCxcuvCSZAQBA8PN5DdDUqVM1ZMgQJSQkqGfPngoJCfHav2bNGr+Fq6+vV0NDg+x2u9d4aGioioqKmpyzbds2DRgwwGts4MCBev3113X69Olz8kqSy+WSy+XybDudTj+kBwAAwcrnI0D/9V//pc2bN+uqq65Sx44d5XA4vF7+FBYWpr59++qFF17Q4cOH1dDQoNzcXO3YsUOVlZVNzjly5IiioqK8xqKiolRfX6/vvvuuyTlz5szx+h1iY2P9+nsAAIDg4vMRoJycHOXl5WnIkCGByHOOFStWKC0tTV26dJHValXv3r01atQolZaWnnfOT69GO/u81/NdpZaZman09HTPttPppAQBANCK+VyAIiIilJCQEIgsTUpISFBBQYFOnjwpp9Op6OhojRw5UvHx8U2+v3Pnzjpy5IjX2NGjR9W2bVt17NixyTk2m002m83v2QEAQHDy+RTYzJkz9dxzz+nUqVOByHNel112maKjo3X8+HGtX79ew4cPb/J9ffv21QcffOA1tmHDBt14441Nrv8BAADm4/MRoMWLF+urr75SVFSUunXrdk6puNCpqYuxfv16ud1uJSYmat++fZo2bZoSExM1duxYSWdOX1VUVCgnJ0eS9MQTT2jJkiVKT0/XY489pm3btun111/XqlWr/JoLAAC0XD4XoN/97ncBiHF+VVVVyszM1KFDhxQREaERI0YoKyvLU7wqKytVXl7ueX98fLzef/99z+X6MTExWrx4sUaMGHFJcwMAgOBlcZ9dIQwPp9Mph8OhqqoqhYeHGx0HgJ/U1NQoNTVVkpSfn6/Q0FCDEwHwJ1++vy/qafA//PCD/vKXvygzM1Pff/+9pDOnvs53d2YAAIBg4vMpsF27dql///5yOBz6+uuv9dhjjykiIkJr167VgQMHPGtxAAAAgpXPR4DS09M1ZswY7d271+sOzampqSosLPRrOAAAgEDwuQAVFxdr/Pjx54x36dLlnPvvAAAABCOfC5Ddbm/yWVl79uxRp06d/BIKAAAgkHwuQMOHD9esWbN0+vRpSWceL1FeXq6MjAwuNQcAAC2CzwVo4cKF+vbbbxUZGamamhr169dPPXr0UFhYmLKysgKREQAAwK98vgosPDxcRUVF+vDDD1VaWqrGxkb17t1b/fv3D0Q+AAAAv/O5AJ1111136a677vJnFgAAgEui2afAduzYofz8fK+xnJwcxcfHKzIyUo8//rhcLpffAwIAAPhbswvQzJkztWvXLs/2p59+qkcffVT9+/dXRkaG3n33Xc2ZMycgIQEAAPyp2QVo586dSklJ8WyvXr1aN910k/785z8rPT1dixcv1t/+9reAhAQAAPCnZheg48ePKyoqyrNdUFCgQYMGebb79OmjgwcP+jcdAABAADS7AEVFRWn//v2SpLq6OpWWlqpv376e/dXV1QoJCfF/QgAAAD9rdgEaNGiQMjIy9NFHHykzM1Pt27fXbbfd5tm/a9cuJSQkBCQkAACAPzX7MvgXX3xR9957r/r166cOHTpo+fLlateunWf/G2+8oQEDBgQkJAAAgD81uwB16tRJH330kaqqqtShQwdZrVav/W+//bY6dOjg94AAAAD+5vONEB0OR5PjERERvzgMAADApeDzs8AAAABaOgoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwHQoQAAAwnYsqQIWFhSopKfEaKykpUWFhoV9CAQAABFLbi5l0xx136Oqrr9bnn3/uGXv44Yf15ZdfqqGhwW/hAAAAAuGiCtD+/fsVEhLiNbZp0yadPn3aL6EAAAACyadTYPX19Xr++efVpk0bxcTEeO2LiYlR165d/RoOAAAgEHwqQG3bttWCBQs4zQUAAFo0nxdB9+/fX1u2bAlAFAAAgEvD5zVAqampyszM1GeffaYbbrhBl112mdf+YcOG+S0cAABAIPhcgP74xz9KkrKzs8/ZZ7FYOD0GAACCns8FqLGxMRA5AAAALhnuBA0AAEznou4DdPLkSRUUFKi8vFx1dXVe+yZOnOiXYAAAAIHicwEqKyvT4MGDderUKZ08eVIRERH67rvv1L59e0VGRlKAgJ9wu92qra01OgYkr78Df5PgYbfbZbFYjI4Bk/G5AE2ZMkVDhw7Vq6++qssvv1zbt29XSEiI/vCHP2jSpEmByAi0aLW1tUpNTTU6Bn7innvuMToC/r/8/HyFhoYaHQMm4/MaoJ07d+pPf/qTrFarrFarXC6XYmNjNX/+fE2fPj0QGQEAAPzK5yNAISEhnkOVUVFRKi8v1zXXXCOHw6Hy8nK/BwRaOrvdrvz8fKNjQGdOR7pcLkmSzWbjtEuQsNvtRkeACflcgHr16qWSkhJdddVVuvPOO/Xss8/qu+++04oVK3TdddcFIiPQolksFg7vB5H27dsbHQFAEPD5FNjs2bMVHR0tSXrhhRfUsWNH/fGPf9TRo0f12muv+T0gAACAv1ncbrfb6BDBxul0yuFwqKqqSuHh4UbHAQAAzeDL9/dF3Qixvr5eGzdu1P/+7/+qurpaknT48GGdOHHiYj4OAADgkvK5AB04cEDXXXedhg8frgkTJujbb7+VJM2fP19Tp071a7j6+no988wzio+PV2hoqLp3765Zs2Zd8HEcW7ZskcViOef1xRdf+DUbAABouXxeBD1p0iTdeOON+uSTT9SxY0fP+D333KNx48b5Ndy8efO0dOlSLV++XElJSSopKdHYsWPlcDh+9p5De/bs8Tr81alTJ79mAwAALZfPBaioqEgff/yx2rVr5zXetWtXVVRU+C2YJG3btk3Dhw/XkCFDJEndunXTqlWrVFJS8rNzIyMjdfnll/s1DwAAaB18PgXW2NiohoaGc8YPHTqksLAwv4Q669Zbb9WmTZv05ZdfSpI++eQTFRUVafDgwT87t1evXoqOjlZKSoo2b97s11wAAKBl8/kI0N13361FixZ5Lnm3WCw6ceKEnnvuuWYVE188/fTTqqqq0tVXXy2r1aqGhgZlZWXpwQcfPO+c6Ohovfbaa7rhhhvkcrm0YsUKpaSkaMuWLbr99tubnONyuTw3R5POrCIHAACtl8+XwR8+fFh33nmnrFar9u7dqxtvvFF79+7VFVdcocLCQkVGRvot3OrVqzVt2jQtWLBASUlJ2rlzpyZPnqzs7GyNHj262Z8zdOhQWSwWvfPOO03unzlzpp5//vlzxrkMHgCAlsOXy+Av6j5ANTU1WrVqlUpLS9XY2KjevXvroYce8vvdbmNjY5WRkaEJEyZ4xl588UXl5ub6dFVXVlaWcnNztXv37ib3N3UEKDY2lgIEAEAL4ksB8vkU2MmTJ3XZZZcpLS1NaWlpFx2yOU6dOqU2bbyXKVmt1gteBt+UsrIyz92rm2Kz2WSz2S4qIwAAaHl8LkBRUVG6//77lZaWpltvvTUQmTyGDh2qrKwsxcXFKSkpSWVlZcrOzvYqXpmZmaqoqFBOTo4kadGiRerWrZuSkpJUV1en3Nxc5eXlKS8vL6BZAQBAy+FzAVq1apXefPNNpaSkqGvXrkpLS9MjjzyimJgYv4d7+eWXNWPGDD355JM6evSoYmJiNH78eD377LOe91RWVno9hb6urk5Tp05VRUWFQkNDlZSUpPfee8/vC7QBAEDLddHPAjt27JhycnL05ptv6vPPP9fAgQOVlpamYcOGqW1bn3tVUOFZYAAAtDwBXwT9Uy+//LKmTZumuro6XXHFFXriiSeUkZGh9u3b/9KPNgQFCACAliegi6DPOnLkiHJycrRs2TKVl5frvvvu06OPPqrDhw9r7ty52r59uzZs2HCxHw8AABAwPhegNWvWaNmyZVq/fr169uypCRMm6A9/+IPXYyeuv/569erVy585AQAA/MbnAjR27Fg98MAD+vjjj9WnT58m39O9e3f993//9y8OBwAAEAg+rwE6depUi13b01ysAQIAoOXx5fvb54ehtvbyAwAAWj+fCxAAAEBLRwECAACmQwECAACmQwECAACm06zL4O+9995mf+CaNWsuOgwAAMCl0KwjQA6Hw/MKDw/Xpk2bVFJS4tn/z3/+U5s2bZLD4QhYUAAAAH9p1hGgZcuWeX5++umndf/992vp0qWyWq2SpIaGBj355JPcMwcAALQIPt8IsVOnTioqKlJiYqLX+J49e5ScnKxjx475NaARuBEiAAAtT0BvhFhfX6/du3efM7579241Njb6+nEAAACX3EU9CywtLU379u3TzTffLEnavn275s6dq7Fjx/o9IAAAgL/5XIAWLlyozp0763/+539UWVkpSYqOjtZTTz2lP/3pT34PCAAA4G8+rwH6MafTKUmtbp0Ma4AAAGh5fPn+9vkI0I9RDgAAQEvk8yLob775Rg8//LBiYmLUtm1bWa1WrxcAAECw8/kI0JgxY1ReXq4ZM2YoOjpaFoslELkAAAACxucCVFRUpI8++kjXX399AOIAAAAEns+nwGJjY/UL1k0DAAAYzucCtGjRImVkZOjrr78OQBwAAIDA8/kU2MiRI3Xq1CklJCSoffv2CgkJ8dr//fff+y0cAABAIPhcgBYtWhSAGAAAAJeOzwVo9OjRgcgBAABwyVzUjRAbGhq0bt067d69WxaLRT179tSwYcO4DxAAAGgRfC5A+/bt0+DBg1VRUaHExES53W59+eWXio2N1XvvvaeEhIRA5AQAAPAbn68CmzhxohISEnTw4EGVlpaqrKxM5eXlio+P18SJEwOREQAAwK98PgJUUFCg7du3KyIiwjPWsWNHzZ07V7fccotfwwEAAASCz0eAbDabqqurzxk/ceKE2rVr55dQAAAAgeRzAfrP//xPPf7449qxY4fcbrfcbre2b9+uJ554QsOGDQtERgAAAL/yuQAtXrxYCQkJ6tu3r+x2u+x2u2655Rb16NFDL730UiAyAgAA+JXPa4Auv/xy/f3vf9e+ffu0e/duud1u9ezZUz169AhEPgAAAL+7qPsASVKPHj0oPQAAoEXy+RTYfffdp7lz554zvmDBAv3+97/3SygAAIBA8rkAFRQUaMiQIeeMDxo0SIWFhX4JBQAAEEg+F6DzXe4eEhIip9Ppl1AAAACB5HMBuvbaa/XWW2+dM7569Wr17NnTL6EAAAACyedF0DNmzNCIESP01Vdf6a677pIkbdq0SatWrdLbb7/t94AAAAD+5nMBGjZsmNatW6fZs2fr//7v/xQaGqpf//rX2rhxo/r16xeIjAAAAH5lcbvdbqNDBBun0ymHw6GqqiqFh4cbHQcAADSDL9/fPq8BkqQffvhBf/nLXzR9+nR9//33kqTS0lJVVFRczMcBAABcUj6fAtu1a5f69+8vh8Ohr7/+WuPGjVNERITWrl2rAwcOKCcnJxA5AQAA/MbnI0Dp6ekaM2aM9u7dK7vd7hlPTU3lPkAAAKBF8LkAFRcXa/z48eeMd+nSRUeOHPFLKAAAgEDyuQDZ7fYmb3i4Z88ederUyS+hAAAAAsnnAjR8+HDNmjVLp0+fliRZLBaVl5crIyNDI0aM8HtAAAAAf/O5AC1cuFDffvutIiMjVVNTo379+qlHjx4KCwtTVlZWIDICAAD4lc9XgYWHh6uoqEgffvihSktL1djYqN69e6t///6ByAcAAOB3F3UfIEm66667NHXqVD311FMBKz/19fV65plnFB8fr9DQUHXv3l2zZs1SY2PjBecVFBTohhtukN1uV/fu3bV06dKA5AMAAC1TswvQjh07lJ+f7zWWk5Oj+Ph4RUZG6vHHH5fL5fJruHnz5mnp0qVasmSJdu/erfnz52vBggV6+eWXzztn//79Gjx4sG677TaVlZVp+vTpmjhxovLy8vyaDQAAtFzNPgU2c+ZM3XHHHUpNTZUkffrpp3r00Uc1ZswYXXPNNVqwYIFiYmI0c+ZMv4Xbtm2bhg8friFDhkiSunXrplWrVqmkpOS8c5YuXaq4uDgtWrRIknTNNdeopKRECxcuZJE2AACQ5MMRoJ07dyolJcWzvXr1at10003685//rPT0dC1evFh/+9vf/Bru1ltv1aZNm/Tll19Kkj755BMVFRVp8ODB552zbds2DRgwwGts4MCBKikp8Vy59lMul0tOp9PrBQAAWq9mHwE6fvy4oqKiPNsFBQUaNGiQZ7tPnz46ePCgX8M9/fTTqqqq0tVXXy2r1aqGhgZlZWXpwQcfPO+cI0eOeOWUpKioKNXX1+u7775TdHT0OXPmzJmj559/3q/ZAQBA8Gr2EaCoqCjt379fklRXV6fS0lL17dvXs7+6ulohISF+DffWW28pNzdXK1euVGlpqZYvX66FCxdq+fLlF5xnsVi8ts8+8P6n42dlZmaqqqrK8/J3kQMAAMGl2UeABg0apIyMDM2bN0/r1q1T+/btddttt3n279q1SwkJCX4NN23aNGVkZOiBBx6QJF133XU6cOCA5syZo9GjRzc5p3Pnzuc8kuPo0aNq27atOnbs2OQcm80mm83m1+wAACB4NbsAvfjii7r33nvVr18/dejQQcuXL1e7du08+994441z1t78UqdOnVKbNt4HqaxW6wUvg+/bt6/effddr7ENGzboxhtv9PsRKgAA0DI1uwB16tRJH330kaqqqtShQwdZrVav/W+//bY6dOjg13BDhw5VVlaW4uLilJSUpLKyMmVnZystLc3znszMTFVUVCgnJ0eS9MQTT2jJkiVKT0/XY489pm3btun111/XqlWr/JoNAAC0XD7fCdrhcDQ5HhER8YvD/NTLL7+sGTNm6Mknn9TRo0cVExOj8ePH69lnn/W8p7KyUuXl5Z7t+Ph4vf/++5oyZYpeeeUVxcTEaPHixVwCDwAAPCzusyuE4eF0OuVwOFRVVaXw8HCj4wAAgGbw5fv7oh+FAQAA0FJRgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgAAAgOlQgACYytatWzVy5Eht3brV6CgADEQBAmAatbW1ys7O1jfffKPs7GzV1tYaHQmAQShAAEzjr3/9q44dOyZJOnbsmFauXGlwIgBGoQABMIVDhw5p5cqVOvv0H7fbrZUrV+rQoUMGJwNgBAoQgFbP7XbrpZdeOu84j0QEzIcCBKDVKy8vV3FxsRoaGrzGGxoaVFxcrPLycoOSATAKBQhAqxcXF6c+ffrIarV6jVutVv32t79VXFycQckAGIUCBKDVs1gsmjRp0nnHLRaLAakAGIkCBMAUrrzySo0aNcpTdiwWi0aNGqUuXboYnAyAEShAAEzjoYceUseOHSVJV1xxhUaNGmVwIgBGoQABMA273a709HRFRUVpypQpstvtRkcCYJC2RgcAgEspOTlZycnJRscAYDCOAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANOhAAEAANMJ+gLUrVs3WSyWc14TJkxo8v1btmxp8v1ffPHFJU4OAACCVVujA/yc4uJiNTQ0eLY/++wz3X333fr9739/wXl79uxReHi4Z7tTp04BywgAAFqWoC9APy0uc+fOVUJCgvr163fBeZGRkbr88ssDmAwAALRUQX8K7Mfq6uqUm5urtLQ0WSyWC763V69eio6OVkpKijZv3nzB97pcLjmdTq8XAABovVpUAVq3bp1++OEHjRkz5rzviY6O1muvvaa8vDytWbNGiYmJSklJUWFh4XnnzJkzRw6Hw/OKjY0NQHoAABAsLG632210iOYaOHCg2rVrp3fffdeneUOHDpXFYtE777zT5H6XyyWXy+XZdjqdio2NVVVVldc6IgAAELycTqccDkezvr+Dfg3QWQcOHNDGjRu1Zs0an+fefPPNys3NPe9+m80mm832S+IBAIAWpMWcAlu2bJkiIyM1ZMgQn+eWlZUpOjo6AKkAAEBL1CKOADU2NmrZsmUaPXq02rb1jpyZmamKigrl5ORIkhYtWqRu3bopKSnJs2g6Ly9PeXl5RkQHAABBqEUUoI0bN6q8vFxpaWnn7KusrFR5eblnu66uTlOnTlVFRYVCQ0OVlJSk9957T4MHD76UkQEAQBBrUYugLxVfFlEBAIDg4Mv3d4tZAwQAAOAvFCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAAGA6FCAAprJ161aNHDlSW7duNToKAANRgACYRm1trbKzs/XNN98oOztbtbW1RkcCYBAKEADT+Otf/6pjx45Jko4dO6aVK1canAiAUShAAEzh0KFDWrlypdxutyTJ7XZr5cqVOnTokMHJABiBAgSg1XO73XrppZfOO362FAEwDwoQgFavvLxcxcXFamho8BpvaGhQcXGxysvLDUoGwCgUIACtXlxcnPr06SOr1eo1brVa9dvf/lZxcXEGJQNgFAoQgFbPYrFo0qRJ5x23WCwGpAJgJAoQAFO48sorNWrUKE/ZsVgsGjVqlLp06WJwMgBGoAABMI2HHnpIHTt2lCRdccUVGjVqlMGJABiFAgTANOx2u9LT0xUVFaUpU6bIbrcbHQmAQdoaHQAALqXk5GQlJycbHQOAwTgCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIcCBAAATIc7QTfB7XZLkpxOp8FJAABAc5393j77PX4hFKAmVFdXS5JiY2MNTgIAAHxVXV0th8NxwfdY3M2pSSbT2Niow4cPKywsTBaLxeg4APzI6XQqNjZWBw8eVHh4uNFxAPiR2+1WdXW1YmJi1KbNhVf5UIAAmIrT6ZTD4VBVVRUFCDAxFkEDAADToQABAADToQABMBWbzabnnntONpvN6CgADMQaIAAAYDocAQIAAKZDAQIAAKZDAQIAAKZDAQIAAKZDAQIAAKZDAQIAAKZDAQIAAKZDAQIAAKbz/wBMjS4uiPNF4wAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(y=data[\"Second year: Sem 2\"])" ] }, { "cell_type": "code", "execution_count": null, "id": "5e36b4cd", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }