BATCH 16¶

Importation of libraries¶

[35]:

import pandas as pd
import seaborn as sns
import numpy as np
import re
sns.set(rc={'figure.figsize':(13.2,14.27)})

Reading the data¶

lib_transaction
exam_cell
subject_code

[36]:

lib_trans=pd.read_excel('issue_records.xlsx')
exam_cell = pd.read_excel('exam_cell_data.xlsx',index_col=0)
sub_code = pd.read_csv('mech.csv')

This function Represents Assigning the semester details to the `lib_tans`..¶

[37]:

def assign_semester_lib(lib_data):
    lib_data=lib_data[lib_data['year'].isin([16])]
    lib_data.set_index(lib_data['issue_dt'],inplace=True)
    lib_sem1 = lib_data['2016-06-22':'2016-12-17']
    lib_sem1['semester'] = 1
    lib_sem2 = lib_data['2016-12-17':'2017-06-25']
    lib_sem2['semester'] = 2
    lib_sem3 = lib_data['2017-06-25':'2017-12-12']
    lib_sem3['semester'] = 3
    lib_sem4 = lib_data['2017-12-12':'2018-06-17']
    lib_sem4['semester'] = 4
    lib_sem5= lib_data['2018-06-17':'2018-12-12']
    lib_sem5['semester'] = 5
    lib_sem6 = lib_data['2018-12-12':'2019-06-19']
    lib_sem6['semester'] = 6
    lib_sem7 = lib_data['2019-06-19':'2019-12-02']
    lib_sem7['semester'] = 7
    lib_frames = [lib_sem1,lib_sem2,lib_sem3,lib_sem4,lib_sem5,lib_sem6,lib_sem7]
    lib_transaction = pd.concat(lib_frames)
    lib_transaction['book_id'] = lib_transaction.groupby('title').ngroup()
    lib_transaction.drop(columns=['Unnamed: 0'],inplace=True)
    return lib_transaction

the function is assigned to the `lib` variable…¶

[38]:

lib = assign_semester_lib(lib_data=lib_trans)

[39]:

lib

[39]:

	id	level	dept	year	section	access_no	title	issue_dt	due_dt	return_dt	doc	semester	book_id
issue_dt
2016-06-22	ANFRAsTt	B	ME	16	0	35715	ELUNTHIRU VIZHITHIRU , PART 1	2016-06-22	2016-07-06	2016-07-06	BOOK	1	175
2016-07-25	ANFRAsTt	B	ME	16	0	34361	TRANSFORMS AND PARTIAL DIFFERENTIAL EQUATIONS	2016-07-25	2016-08-01	2016-08-03	BOOK	1	732
2016-07-27	ANFRAsTt	B	ME	16	0	28278	TEXTBOOK OF FLUID MECHANICS AND HYDRAULIC MACH...	2016-07-27	2016-08-10	2016-08-03	BOOK	1	676
2016-08-05	ANFRAsTt	B	ME	16	0	g20478	TAMILAR PALAKKAVALAKANKALUM NAMPIKKAIKALUM	2016-08-05	2016-08-19	2016-08-24	BOOK	1	644
2016-08-10	ANFRAsTt	B	ME	16	0	25081	VECTOR MECHANICS FOR ENGIEERS: STATICS AND DYN...	2016-08-10	2016-08-24	2016-08-27	BOOK	1	758
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2019-10-25	NxAp7RV1	B	ME	16	0	42485	DESIGN DATA : DATA BOOK OF ENGINEERS	2019-10-25	2019-11-08	2020-01-02	BOOK	7	132
2019-07-26	JY91ui4o	B	ME	16	0	28261	TEXTBOOK OF ENGINEERING MATERIALS AND METALLURGY	2019-07-26	2019-08-09	2019-10-24	BOOK	7	669
2019-11-04	6OgZwIyl	B	ME	16	1	30496	AUTOMATIC CONTROL SYSTEMS	2019-11-04	2019-11-18	2019-11-19	BOOK	7	50
2019-11-04	6OgZwIyl	B	ME	16	1	32298	MODERN CONTROL SYSTEMS	2019-11-04	2019-11-18	2019-11-19	BOOK	7	468
2019-11-04	6OgZwIyl	B	ME	16	1	41463	SIGNALS AND SYSTEMS	2019-11-04	2019-11-18	2019-11-19	BOOK	7	599

3552 rows × 13 columns

The `exam_cell_clean` function has done to add `subject_code` & `course_title` to `exam_cell` data.¶

[40]:

def exam_cell_clean(data,sub_code):
    data=data[data['year'].isin([16])]
    li = ['FNE', 'GDH','HND1','HND2','BEC','RDS', 'RLD','SPO', 'QIZ', 'CCC','PHO','FLM','SMT']
    data = data.loc[10973:]
    data = data[~data['subjectcode'].isin(li)]
    data = data[~data['subjectcode'].str.startswith('14')]
    sub_code.rename(columns={'Course code':'subjectcode','title':'course_title'},inplace=True)
    exam_cell_merged = pd.merge(data,sub_code,how='inner',on='subjectcode')
    # exam_cell_merged.drop(columns=['Unnamed: 0'],inplace=True)
    return exam_cell_merged

the function is assigned to the `exam` variable…¶

[41]:

exam = exam_cell_clean(exam_cell,sub_code)

[42]:

exam

[42]:

	id	section	level	dept	year	semester	subjectcode	ciamark	esemark	total	credit	grade	course_title
0	fnEGB3Z1	1	B	ME	16	1	16CYT11	37.0	50.0	87.0	3	A+	Applied Chemistry
1	o0vzCHHX	0	B	ME	16	1	16CYT11	38.0	49.0	87.0	3	A+	Applied Chemistry
2	5Kt1kauF	1	B	ME	16	1	16CYT11	36.0	50.0	86.0	3	A+	Applied Chemistry
3	BXOH59ME	0	B	ME	16	1	16CYT11	36.0	36.0	72.0	3	A	Applied Chemistry
4	xy7RRKHn	1	B	ME	16	1	16CYT11	32.0	43.0	75.0	3	A	Applied Chemistry
...	...	...	...	...	...	...	...	...	...	...	...	...	...
4912	kIHecAxf	0	B	ME	16	5	16MEE28	31.0	49.0	80.0	3	A+	Computer Integrated Manufacturing
4913	wDXZ5IJv	0	B	ME	16	5	16MEE28	18.0	33.0	51.0	3	B	Computer Integrated Manufacturing
4914	t8HSkDNx	0	B	ME	16	5	16MEE28	23.0	53.0	76.0	3	A	Computer Integrated Manufacturing
4915	ROaOVNOC	1	B	ME	16	5	16MEE28	27.0	44.0	71.0	3	A	Computer Integrated Manufacturing
4916	ut7RLLw1	0	B	ME	16	5	16MEE28	25.0	43.0	68.0	3	B+	Computer Integrated Manufacturing

4917 rows × 13 columns

This function helps in assigning the subject_code in the `lib_transaction` data..¶

[43]:

def assign_subjectcode(data,sub_code,exam_data):
    data.set_index(data['issue_dt'],inplace=True)
    chemistry = data[data['title'].str.contains('CHEMISTRY')==True]
    chemistry['subjectcode'] = '16CYT11'
    physics = data[data['title'].str.contains('PHYSICS')==True]
    physics['subjectcode'] = '16PHT11'
    maths1 = data[data['title'].str.contains('ENGINEERING MATHEMATICS')==True]
    maths1['subjectcode'] = '16MAT11'
    maths2 = data[data['title'].str.contains('ENGINEERING MATHEMATICS II')==True]
    maths2['subjectcode'] = '16MAT21'
    Communication_skills1 = data[data['title'].str.contains('COMMUNICATION')==True]
    Communication_skills1['subjectcode'] = '16ENT11'
    Communication_skills2 = data[data['title'].str.contains('COMMUNICATION')==True]
    Communication_skills2['subjectcode'] = '16ENT21'
    material_science = data[data['title'].str.contains('MATERIAL SCIENCE')==True]
    material_science['subjectcode'] =  '16PHT21'
    engineering_mechanics = data[data['title'].str.contains('ENGINEERING MECHANICS')          ==True]
    engineering_mechanics['subjectcode'] = '16GET21'
    metrology = data[data['title'].str.contains('METROLOGY')==True]
    metrology['subjectcode'] = '16GET22'
    tpde = data[data['title'].str.contains('DIFFERENTIAL')==True]
    tpde['subjectcode'] = '16MAT31'
    thermodynamics = data[data['title'].str.contains('THERMODYNAMICS')==True]
    thermodynamics['subjectcode'] = '16AUT31'
    fluid_mech = data[data['title'].str.contains('FLUID MECHANICS')==True]
    fluid_mech['subjectcode'] = '16AUT32'
    theory_machines1 = data[data['title'].str.contains('THEORY OF MACHINES')==True]
    theory_machines1['subjectcode'] = '16MET33'
    theory_machines2 = data[data['title'].str.contains('THEORY OF MACHINES II')==True]
    theory_machines2['subjectcode'] = '16MET43'
    metallurgy = data[data['title'].str.contains('METALLURGY')==True]
    metallurgy['subjectcode'] = '16MET31'
    mfj = data[data['title'].str.contains('MANUFACTURING PROCESS')==True]
    mfj['subjectcode'] = '16MET32'
    m4 = data[data['title'].str.contains('NUMERICAL METHODS')==True]
    m4['subjectcode'] = '16MAT41'
    c_prog = data[data['title'].str.contains('PROGRAMMING')==True]
    c_prog['subjectcode'] = '16CST46'
    som = data[data['title'].str.contains('STRENGTH OF MATERIALS')==True]
    som['subjectcode'] = '16MET41'
    mcp = data[data['title'].str.contains('METAL CUTTING')==True]
    mcp['subjectcode'] = '16MET42'
    edc = data[data['title'].str.contains('ELECTRICAL DRIVES')==True]
    edc['subjectcode'] = '16EET45'
    dom = data[data['title'].str.contains('DESIGN OF MACHINE ELEMENTS')==True]
    dom['subjectcode'] = '16MET51'
    design_data = data[data['title'].str.contains('DESIGN DATA')==True]
    design_data['subjectcode'] = '16MET51-16MET61'
    thermal = data[data['title'].str.contains('THERMAL')==True]
    thermal['subjectcode'] = '16MET52'
    auto = data[data['title'].str.contains('AUTOMOBILE')==True]
    auto['subjectcode'] = '16MET54'
    micro_cont = data[data['title'].str.contains('MICROCONTROLLER')==True]
    micro_cont['subjectcode'] = '16ECT56'
    fea = data[data['title'].str.contains('FINITE')==True]
    fea['subjectcode'] = '16MET61'
    transmission = data[data['title'].str.contains('DESIGN OF TRANSMISSION')==True]
    transmission['subjectcode'] = '16MET62'
    hmt = data[data['title'].str.contains('HEAT')==True]
    hmt['subjectcode'] = '16MET63'
    powerplant = data[data['title'].str.contains('POWER PLANT')==True]
    powerplant['subjectcode'] = '16MET64'
    mechatronics = data[data['title'].str.contains('MECHATRONICS')==True]
    mechatronics['subjectcode'] = '16MET71'
    evs = data[data['title'].str.contains('ENVIRONMENTAL')==True]
    evs['subjectcode'] = '16CET73'
    frames = [chemistry,physics,material_science,engineering_mechanics,metrology,tpde,maths1,maths2,Communication_skills1,Communication_skills2,theory_machines1,theory_machines2,      thermodynamics,fluid_mech,metallurgy,mfj,m4,c_prog,som,mcp,edc,dom,thermal,auto,        micro_cont,fea,transmission,hmt,powerplant,mechatronics,evs]
    new_data = pd.concat(frames)
    sub_code.rename(columns={'Course code':'subjectcode','title':'course_title'},           inplace=True)
    lib_merged = pd.merge(new_data,sub_code,on='subjectcode',how='inner')
    design_data['subjectcode'] = '16MET51-16MET61'
    design_data['course_title'] = 'Design data Book'
    # frames2 = [maths,theory_machines,Communication_skills,design_data]
    # lib_merged = lib_merged.append(frames2)
    lis = exam_data['id']
    lib_merged = lib_merged[lib_merged['id'].isin(lis)]
    return lib_merged

the function is assigned to the `new_lib` variable…¶

[44]:

new_lib = assign_subjectcode(data=lib,sub_code=sub_code,exam_data=exam)

[45]:

new_lib

[45]:

	id	level	dept	year	section	access_no	title	issue_dt	due_dt	return_dt	doc	semester	book_id	subjectcode	course_title
2	y6mAMTLY	B	ME	16	0	40403	ENGINEERING CHEMISTRY 1	2016-08-13	2016-08-27	2016-09-08	BOOK	1	181	16CYT11	Applied Chemistry
3	y6mAMTLY	B	ME	16	0	38594	CHEMISTRY IN ENGINEERING AND TECHNOLOGY : SYST...	2016-09-23	2016-10-07	2016-09-28	BOOK	1	95	16CYT11	Applied Chemistry
4	y6mAMTLY	B	ME	16	0	10737	APPLIED CHEMISTRY	2016-09-28	2016-10-12	2016-10-13	BOOK	1	33	16CYT11	Applied Chemistry
5	y6mAMTLY	B	ME	16	0	4997	APPLIED CHEMISTRY	2016-10-20	2016-11-03	2016-11-02	BOOK	1	33	16CYT11	Applied Chemistry
6	y6mAMTLY	B	ME	16	0	40404	ENGINEERING CHEMISTRY 1	2016-11-02	2016-11-16	2016-11-16	BOOK	1	181	16CYT11	Applied Chemistry
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2214	LvsGkET2	B	ME	16	0	3627	POWER PLANT ENGINEERING : STEAM AND NUCLEAR	2019-05-11	2019-05-25	2019-05-15	BOOK	6	527	16MET64	Power Plant Engineering
2224	W0N7CvUi	B	ME	16	1	17598	MECHATRONICS: ELECTRONIC CONTROL SYSTEMS IN ME...	2018-10-25	2018-11-08	2018-11-16	BOOK	5	454	16MET71	'Mechatronics
2225	zfhyFOND	B	ME	16	1	32120	MECHATRONICS : A MULTIDISCIPLINARY APPROACH	2019-06-21	2019-07-05	2019-07-16	BOOK	7	452	16MET71	'Mechatronics
2226	U8jBXao0	B	ME	16	0	17599	MECHATRONICS: ELECTRONIC CONTROL SYSTEMS IN ME...	2019-11-06	2019-11-20	2019-11-08	BOOK	7	454	16MET71	'Mechatronics
2227	jFJBTnTl	B	ME	16	0	25587	SEWAGE DISPOSAL AND AIR POLLUTION ENGINEERING ...	2019-03-25	2019-04-08	2019-03-26	BOOK	6	596	16CET73	Environmental Studies

1827 rows × 15 columns

This function is used to calculate mean_score for `exam` in class and in eah semester…¶

And also Library usage by `section` and `semster`¶

[46]:

def mean_score(lib_data,exam_data):
    class_mean_per_sem = pd.DataFrame()
    class_mean_per_sem['mean_score_sec'] = exam_data.groupby(['section','semester'])['total'].mean()
    class_mean_per_sem['library_id_frequency'] = lib_data.groupby(['section','semester'])['id'].count()
    class_mean_per_sem.reset_index(inplace=True)
    course_mean_per_sem = pd.DataFrame()
    course_mean_per_sem['course_mean_score'] = exam_data.groupby(['section','semester','subjectcode'])['total'].mean()
    course_mean_per_sem['library_freq_course_by_id'] = lib_data.groupby(['section','semester','subjectcode'])['id'].count()
    course_mean_per_sem.reset_index(inplace=True)
    return class_mean_per_sem,course_mean_per_sem

So both `class` and `course` mean score is calculated and stored in the below variables…¶

[47]:

class_mean_score,course_mean_score = mean_score(lib_data=new_lib,exam_data=exam)

[48]:

class_mean_score

[48]:

	section	semester	mean_score_sec	library_id_frequency
0	0	1	66.278146	171
1	0	2	66.442623	124
2	0	3	64.624672	141
3	0	4	67.142061	171
4	0	5	70.127877	77
5	0	6	67.888889	36
6	0	7	70.390698	22
7	1	1	64.158055	456
8	1	2	61.273556	147
9	1	3	61.951220	162
10	1	4	64.386189	175
11	1	5	68.876289	71
12	1	6	67.984375	60
13	1	7	70.382353	14

[49]:

course_mean_score

[49]:

	section	semester	subjectcode	course_mean_score	library_freq_course_by_id
0	0	1	16CYT11	64.189655	93.0
1	0	1	16ENT11	65.310345	1.0
2	0	1	16GET11	69.350877	NaN
3	0	1	16MAT11	68.132353	16.0
4	0	1	16PHT11	64.245902	55.0
...	...	...	...	...	...
93	1	7	16MEE10	79.769231	NaN
94	1	7	16MEE11	63.050000	NaN
95	1	7	16MEE18	70.896552	NaN
96	1	7	16MEE56	74.833333	NaN
97	1	7	16MET71	71.509091	1.0

98 rows × 5 columns

As there are no transaction in certain semester and in section, we are replacing the null values with ‘0’:¶

[50]:

course_mean_score.fillna(0,inplace=True)

[51]:

course_mean_score

[51]:

	section	semester	subjectcode	course_mean_score	library_freq_course_by_id
0	0	1	16CYT11	64.189655	93.0
1	0	1	16ENT11	65.310345	1.0
2	0	1	16GET11	69.350877	0.0
3	0	1	16MAT11	68.132353	16.0
4	0	1	16PHT11	64.245902	55.0
...	...	...	...	...	...
93	1	7	16MEE10	79.769231	0.0
94	1	7	16MEE11	63.050000	0.0
95	1	7	16MEE18	70.896552	0.0
96	1	7	16MEE56	74.833333	0.0
97	1	7	16MET71	71.509091	1.0

98 rows × 5 columns

subject_code¶

[52]:

sub_code

[52]:

	subjectcode	course_title
0	16ENT11	Communication Skills - I
1	16MAT11	Engineering Mathematics - I
2	16PHT11	Applied Physics
3	16CYT11	Applied Chemistry
4	16GET11	introduction to Engineering
...	...	...
87	16MEE54	Spark Ignition Engines
88	16MEE56	Battery System for Electric Vehicles
89	160EXXX	Total Quality Management
90	160EXXX	Industrial Robotics
91	160EXXX	Automation Systems

92 rows × 2 columns

Adding subject_code and title in the `lib_transaction`¶

[53]:

course_mean_score_merged = pd.merge(sub_code,course_mean_score,on='subjectcode',how='inner')

[54]:

course_mean_score_merged

[54]:

	subjectcode	course_title	section	semester	course_mean_score	library_freq_course_by_id
0	16ENT11	Communication Skills - I	0	1	65.310345	1.0
1	16ENT11	Communication Skills - I	1	1	62.433333	0.0
2	16MAT11	Engineering Mathematics - I	0	1	68.132353	16.0
3	16MAT11	Engineering Mathematics - I	1	1	62.658537	9.0
4	16PHT11	Applied Physics	0	1	64.245902	55.0
...	...	...	...	...	...	...
93	16MEE34	Manufacturing Systems Engineering	1	6	74.615385	0.0
94	16MEE42	Industrial Safety Management	0	6	69.857143	0.0
95	16MEE42	Industrial Safety Management	1	6	68.176471	0.0
96	16MEE56	Battery System for Electric Vehicles	0	7	73.400000	0.0
97	16MEE56	Battery System for Electric Vehicles	1	7	74.833333	0.0

98 rows × 6 columns

Assigning variables for each semester¶

[55]:

sem1 = exam[exam['semester']==1]
sem2 = exam[exam['semester']==2]
sem3 = exam[exam['semester']==3]
sem4 = exam[exam['semester']==4]
sem5 = exam[exam['semester']==5]
sem6 = exam[exam['semester']==6]
sem7 = exam[exam['semester']==7]

Importing `matplotlib` library¶

[56]:

import matplotlib.pyplot as plt

[57]:

def plotting(exam_cell,class_mean,course_mean,sem):
    sns.set(rc={'figure.figsize':(20.2,18.27)})
    plot = sns.boxplot(x='course_title',y='total',hue='section',data=
    sem)
    plot.axhline(50)
    return plot

The below is the correlation plot for `course_mean_score` & `library_freq_course_by_id`¶

[58]:

correlation = course_mean_score_merged[['course_mean_score','library_freq_course_by_id']]

[59]:

ax = sns.heatmap(correlation.corr(method='pearson'),annot=True)
ax.figure.savefig("correlation.png")

Correlation based on the Semester and the Section¶

[60]:

sns.heatmap(course_mean_score_merged.corr(method='pearson'),annot=True)

[60]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f95810e2ac0>

Ploting of `boxenplot` for each `semester` and `section` is hued…¶

`sem1` boxplot for both `section`¶

[61]:

plotting(exam_cell=exam,class_mean=class_mean_score,course_mean=course_mean_score_merged,sem=sem1)

[61]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9585391940>

`sem2` boxplot for both `section`¶

[62]:

plotting(exam_cell=exam,class_mean=class_mean_score,course_mean=course_mean_score_merged,sem=sem2)

[62]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9584616490>

`sem3` boxplot for both `section`¶

[63]:

plotting(exam_cell=exam,class_mean=class_mean_score,course_mean=course_mean_score_merged,sem=sem3)

[63]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f95849a52b0>

`sem4` boxplot for both `section`¶

[64]:

plotting(exam_cell=exam,class_mean=class_mean_score,course_mean=course_mean_score_merged,sem=sem4)

[64]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f95846343d0>

`sem5` boxplot for both `section`¶

[65]:

plotting(exam_cell=exam,class_mean=class_mean_score,course_mean=course_mean_score_merged,sem=sem5)

[65]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9584f80790>

`sem6` boxplot for both `section`¶

[66]:

plotting(exam_cell=exam,class_mean=class_mean_score,course_mean=course_mean_score_merged,sem=sem6)

[66]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9584e538e0>

`sem7` boxplot for both `section`¶

[67]:

plotting(exam_cell=exam,class_mean=class_mean_score,course_mean=course_mean_score_merged,sem=sem7)

[67]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9584c62940>

BATCH 16¶

Importation of libraries¶

Reading the data¶

This function Represents Assigning the semester details to the lib_tans..¶

the function is assigned to the lib variable…¶

The exam_cell_clean function has done to add subject_code & course_title to exam_cell data.¶

the function is assigned to the exam variable…¶

This function helps in assigning the subject_code in the lib_transaction data..¶

the function is assigned to the new_lib variable…¶

This function is used to calculate mean_score for exam in class and in eah semester…¶

And also Library usage by section and semster¶

So both class and course mean score is calculated and stored in the below variables…¶

As there are no transaction in certain semester and in section, we are replacing the null values with ‘0’:¶

subject_code¶

Adding subject_code and title in the lib_transaction¶

Assigning variables for each semester¶

Importing matplotlib library¶

The below is the correlation plot for course_mean_score & library_freq_course_by_id¶

Correlation based on the Semester and the Section¶

Ploting of boxenplot for each semester and section is hued…¶

sem1 boxplot for both section¶

sem2 boxplot for both section¶

sem3 boxplot for both section¶

sem4 boxplot for both section¶

sem5 boxplot for both section¶

sem6 boxplot for both section¶

sem7 boxplot for both section¶

This function Represents Assigning the semester details to the `lib_tans`..¶

the function is assigned to the `lib` variable…¶

The `exam_cell_clean` function has done to add `subject_code` & `course_title` to `exam_cell` data.¶

the function is assigned to the `exam` variable…¶

This function helps in assigning the subject_code in the `lib_transaction` data..¶

the function is assigned to the `new_lib` variable…¶

This function is used to calculate mean_score for `exam` in class and in eah semester…¶

And also Library usage by `section` and `semster`¶

So both `class` and `course` mean score is calculated and stored in the below variables…¶

Adding subject_code and title in the `lib_transaction`¶

Importing `matplotlib` library¶

The below is the correlation plot for `course_mean_score` & `library_freq_course_by_id`¶

Ploting of `boxenplot` for each `semester` and `section` is hued…¶

`sem1` boxplot for both `section`¶

`sem2` boxplot for both `section`¶

`sem3` boxplot for both `section`¶

`sem4` boxplot for both `section`¶

`sem5` boxplot for both `section`¶

`sem6` boxplot for both `section`¶

`sem7` boxplot for both `section`¶