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2025-03-01 01:50:06 +01:00
commit 50920532bf
53 changed files with 3138 additions and 0 deletions

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First Name,Last Name,Sex,Group,Grader,Tutorial 1,Tutorial 2,Extended Applications,Numpy & MatPlotLib,SciPy,Monte Carlo,Pandas & Seaborn,Folium,Statistical Test Methods,Data Analysis
Abdalaziz,Abunjaila,Male,DiKum,30%,30.5,15,18,28,17,17,17,22,0,18
Marleen,Adolphi,Female,MeWi6,30%,29.5,15,18,32,19,20,17,24,23,0
Sarina,Apel,Female,MeWi1,30%,28.5,15,18,32,20,20,21,24,20,23
Skofiare,Berisha,Female,DiKum,30%,29.5,13,18,34,20,17,20,26,16,0
Aurela,Brahimi,Female,MeWi2,30%,17.5,15,15.5,26,16,17,19,16,0,0
Cam Thu,Do,Female,MeWi3,30%,31,15,18,34,19,20,21.5,22,12,0
Nova,Eib,Female,MeWi4,30%,31,15,15,34,20,20,21,27,19,21
Lena,Fricke,Female,MeWi4,30%,0,0,0,0,0,0,0,0,0,0
Nele,Grundke,Female,MeWi6,30%,23.5,13,16,28,20,17,21,18,22,11
Anna,Grünewald,Female,MeWi3,30%,12,14,16,29,16,15,19,9,0,0
Yannik,Haupt,Male,NoGroup,30%,18,6,14,21,13,2,9,0,0,0
Janna,Heiny,Female,MeWi1,30%,30,15,18,33,18,20,22,25,24,30
Milena,Krieger,Female,MeWi1,30%,30,15,18,33,20,20,21.5,26,20,22
Julia,Limbach,Female,MeWi6,30%,27.5,12,18,29,11,19,17.5,26,24,28
Viktoria,Litza,Female,MeWi5,30%,21.5,15,18,27,13,20,22,21,21,30
Leonie,Manthey,Female,MeWi1,30%,28.5,14,18,29,20,10,18,23,16,28
Izabel,Mike,Female,MeWi2,30%,29.5,15,15,35,11,15,19,21,21,27
Lea,Noglik,Female,MeWi5,30%,22.5,15,17,34,13,10,20,21,19,6
Donika,Nuhiu,Female,MeWi5,30%,31,13.5,18,35,14,10,17,18,19,8
Julia,Renner,Female,MeWi4,30%,27.5,10,14,32,20,17,11,20,24,14
Fabian,Rothberger,Male,MeWi3,30%,30.5,15,18,34,17,17,19,22,18,30
Natascha,Rott,Female,MeWi1,30%,29.5,12,18,32,19,20,21,26,23,26
Isabel,Rudolf,Female,MeWi4,30%,27.5,9,17,34,16,19,19,21,16,14
Melina,Sablotny,Female,MeWi6,30%,31,15,18,33,20,20,21,19,11,28
Alea,Schleier,Female,DiKum,30%,27,14,18,34,16,18,21.5,22,15,22
Flemming,Schur,Male,MeWi3,30%,29.5,15,17,34,19,20,19,22,18,27
Marie,Seeger,Female,DiKum,30%,27.5,15,18,32,14,9,17,22,9,25
Lucy,Thiele,Female,MeWi6,30%,27.5,15,18,27,20,17,19,18,22,25
Lara,Troschke,Female,MeWi2,30%,28.5,14,17,28,13,19,21,25,12,24
Inga-Brit,Turschner,Female,MeWi2,30%,25.5,14,18,34,20,16,19,22,17,30
Alea,Unger,Female,MeWi5,30%,30,12,18,31,20,20,21,22,15,21.5
Marie,Wallbaum,Female,MeWi5,30%,28.5,14,18,34,17,20,19,24,12,22
Katharina,Walz,Female,MeWi4,30%,31,15,18,31,19,19,17,24,17,14.5
Xiaowei,Wang,Male,NoGroup,30%,30.5,14,18,26,19,17,0,0,0,0
Lilly-Lu,Warnken,Female,DiKum,30%,30,15,18,30,14,17,19,14,16,24
1 First Name Last Name Sex Group Grader Tutorial 1 Tutorial 2 Extended Applications Numpy & MatPlotLib SciPy Monte Carlo Pandas & Seaborn Folium Statistical Test Methods Data Analysis
2 Abdalaziz Abunjaila Male DiKum 30% 30.5 15 18 28 17 17 17 22 0 18
3 Marleen Adolphi Female MeWi6 30% 29.5 15 18 32 19 20 17 24 23 0
4 Sarina Apel Female MeWi1 30% 28.5 15 18 32 20 20 21 24 20 23
5 Skofiare Berisha Female DiKum 30% 29.5 13 18 34 20 17 20 26 16 0
6 Aurela Brahimi Female MeWi2 30% 17.5 15 15.5 26 16 17 19 16 0 0
7 Cam Thu Do Female MeWi3 30% 31 15 18 34 19 20 21.5 22 12 0
8 Nova Eib Female MeWi4 30% 31 15 15 34 20 20 21 27 19 21
9 Lena Fricke Female MeWi4 30% 0 0 0 0 0 0 0 0 0 0
10 Nele Grundke Female MeWi6 30% 23.5 13 16 28 20 17 21 18 22 11
11 Anna Grünewald Female MeWi3 30% 12 14 16 29 16 15 19 9 0 0
12 Yannik Haupt Male NoGroup 30% 18 6 14 21 13 2 9 0 0 0
13 Janna Heiny Female MeWi1 30% 30 15 18 33 18 20 22 25 24 30
14 Milena Krieger Female MeWi1 30% 30 15 18 33 20 20 21.5 26 20 22
15 Julia Limbach Female MeWi6 30% 27.5 12 18 29 11 19 17.5 26 24 28
16 Viktoria Litza Female MeWi5 30% 21.5 15 18 27 13 20 22 21 21 30
17 Leonie Manthey Female MeWi1 30% 28.5 14 18 29 20 10 18 23 16 28
18 Izabel Mike Female MeWi2 30% 29.5 15 15 35 11 15 19 21 21 27
19 Lea Noglik Female MeWi5 30% 22.5 15 17 34 13 10 20 21 19 6
20 Donika Nuhiu Female MeWi5 30% 31 13.5 18 35 14 10 17 18 19 8
21 Julia Renner Female MeWi4 30% 27.5 10 14 32 20 17 11 20 24 14
22 Fabian Rothberger Male MeWi3 30% 30.5 15 18 34 17 17 19 22 18 30
23 Natascha Rott Female MeWi1 30% 29.5 12 18 32 19 20 21 26 23 26
24 Isabel Rudolf Female MeWi4 30% 27.5 9 17 34 16 19 19 21 16 14
25 Melina Sablotny Female MeWi6 30% 31 15 18 33 20 20 21 19 11 28
26 Alea Schleier Female DiKum 30% 27 14 18 34 16 18 21.5 22 15 22
27 Flemming Schur Male MeWi3 30% 29.5 15 17 34 19 20 19 22 18 27
28 Marie Seeger Female DiKum 30% 27.5 15 18 32 14 9 17 22 9 25
29 Lucy Thiele Female MeWi6 30% 27.5 15 18 27 20 17 19 18 22 25
30 Lara Troschke Female MeWi2 30% 28.5 14 17 28 13 19 21 25 12 24
31 Inga-Brit Turschner Female MeWi2 30% 25.5 14 18 34 20 16 19 22 17 30
32 Alea Unger Female MeWi5 30% 30 12 18 31 20 20 21 22 15 21.5
33 Marie Wallbaum Female MeWi5 30% 28.5 14 18 34 17 20 19 24 12 22
34 Katharina Walz Female MeWi4 30% 31 15 18 31 19 19 17 24 17 14.5
35 Xiaowei Wang Male NoGroup 30% 30.5 14 18 26 19 17 0 0 0 0
36 Lilly-Lu Warnken Female DiKum 30% 30 15 18 30 14 17 19 14 16 24

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import pandas as pd
import pprint
import sys
sys.path.append('../learnlytics/')
from dbmodel import *
df = pd.read_csv("Student_list.csv")
df = df.dropna()
courses = {
'Tutorial 1': 31,
'Tutorial 2': 15,
'Extended Applications': 18,
'Numpy & MatPlotLib': 35,
'SciPy': 20,
'Monte Carlo': 20,
'Pandas & Seaborn': 22,
'Folium': 27,
'Statistical Test Methods': 24,
'Data Analysis': 30
}
groups = {
"NoGroup": "No Project",
"MeWi1": "Covid-19",
"MeWi2": "Covid-19",
"MeWi3": "Discovery of Handwashing",
"MeWi4": "Uber Trips",
"MeWi5": "Extramarital Affairs",
"MeWi6": "Hochschulstatistik",
"DiKum": "Facebook Data"
}
print(df)
init_db('WiSe_24_25.db')
# Create Class
clas = Class.create(name='WiSe 24/25')
#print(clas.id)
# Create Courses
for k, v in courses.items():
Lecture.create(title=k, points=v, class_id=clas.id)
#print(l.title, l.points, l.class_id, l.id)
for k, v in groups.items():
Group.create(name=k, project=v, has_passed=True, class_id=clas.id)
for index, row in df.iterrows():
s = Student.create(
prename=row["First Name"],
surname=row["Last Name"],
sex=row["Sex"],
class_id=clas.id,
group_id=Group.select().where(Group.name == row["Group"]),
grader=row["Grader"],
)
for title, points in list(row.to_dict().items())[5:]:
Submission.create(
student_id=s.id,
lecture_id=Lecture.select().where(Lecture.title == title),
class_id=clas.id,
points=points
)

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