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DerGrumpf
2025-01-24 12:33:03 +01:00
parent 6f3605c450
commit c382aac84d
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69
assets/Student_list.csv
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@@ -1,34 +1,35 @@
First Name,Last Name,Sex,Tutorial 1,Tutorial 2,Extended Applications,Numpy & MatPlotLib,SciPy,Monte Carlo,Pandas & Seaborn,Folium
Abdalaziz,Abunjaila,Male,30.5,15,18,28,17,17,17,22
Marleen,Adolphi,Female,29.5,15,18,32,19,0,17,24
Skofiare,Berisha,Female,29.5,13,18,34,20,17,20,26
Aurela,Brahimi,Female,17.5,15,15.5,26,16,17,19,16
Cam Thu,Do,Female,31,15,18,34,19,20,21.5,22
Nova,Eib,Female,31,15,15,34,20,20,21,27
Nele,Grundke,Female,23.5,13,16,28,20,17,21,18
Anna,Grünewald,Female,12,14,16,29,16,15,19,9
Yannik,Haupt,Male,18,6,14,21,13,2,9,0
Janna,Heiny,Female,30,15,18,33,18,20,22,25
Milena,Krieger,Female,30,15,18,33,20,20,21.5,26
Julia,Limbach,Female,27.5,12,18,29,11,19,17.5,26
Viktoria,Litza,Female,21.5,15,18,27,13,20,22,21
Manthey,Leonie,Female,28.5,14,18,29,20,10,18,23
Izabel,Mike,Female,29.5,15,15,35,11,4,19,21
Lea,Noglik,Female,22.5,15,17,34,13,10,20,0
Donika,Nuhiu,Female,31,13.5,18,35,14,10,17,18
Julia,Renner,Female,27.5,10,14,0,20,17,11,20
Fabian,Rothberger,Male,30.5,15,18,34,17,17,19,22
Natascha,Rott,Female,29.5,12,18,32,19,20,21,26
Isabel,Rudolf,Female,27.5,9,17,34,16,19,19,21
Melina,Sablotny,Female,31,15,18,33,20,20,21,19
Alea,Schleier,Female,27,14,18,34,16,18,21.5,22
Flemming,Schur,Male,29.5,15,17,34,19,20,19,22
Marie,Seeger,Female,27.5,15,18,32,14,9,17,22
Lucy,Thiele,Female,27.5,15,18,27,20,17,19,18
Lara,Troschke,Female,28.5,14,17,28,13,19,21,25
Inga-Brit,Turschner,Female,25.5,14,18,34,20,16,19,22
Alea,Unger,Female,30,12,18,31,20,20,21,22
Marie,Wallbaum,Female,28.5,14,18,34,17,20,19,24
Katharina,Walz,Female,31,15,18,31,19,19,17,24
Xiaowei,Wang,Male,30.5,14,18,26,19,17,0,0
Lilly-Lu,Warnken,Female,30,15,18,30,14,17,19,14
First Name,Last Name,Sex,Tutorial 1,Tutorial 2,Extended Applications,Numpy & MatPlotLib,SciPy,Monte Carlo,Pandas & Seaborn,Folium,Statistical Test Methods,Data Analysis
Abdalaziz,Abunjaila,Male,30.5,15,18,28,17,17,17,22,0,0
Marleen,Adolphi,Female,29.5,15,18,32,19,20,17,24,23,0
Sarina,Apel,Female,28.5,15,18,32,20,20,21,24,20,0
Skofiare,Berisha,Female,29.5,13,18,34,20,17,20,26,16,0
Aurela,Brahimi,Female,17.5,15,15.5,26,16,17,19,16,0,0
Cam Thu,Do,Female,31,15,18,34,19,20,21.5,22,12,0
Nova,Eib,Female,31,15,15,34,20,20,21,27,19,0
Nele,Grundke,Female,23.5,13,16,28,20,17,21,18,22,0
Anna,Grünewald,Female,12,14,16,29,16,15,19,9,0,0
Yannik,Haupt,Male,18,6,14,21,13,2,9,0,0,0
Janna,Heiny,Female,30,15,18,33,18,20,22,25,24,30
Milena,Krieger,Female,30,15,18,33,20,20,21.5,26,20,0
Julia,Limbach,Female,27.5,12,18,29,11,19,17.5,26,24,0
Viktoria,Litza,Female,21.5,15,18,27,13,20,22,21,21,0
Leonie,Manthey,Female,28.5,14,18,29,20,10,18,23,16,28
Izabel,Mike,Female,29.5,15,15,35,11,15,19,21,21,27
Lea,Noglik,Female,22.5,15,17,34,13,10,20,21,19,0
Donika,Nuhiu,Female,31,13.5,18,35,14,10,17,18,19,6
Julia,Renner,Female,27.5,10,14,32,20,17,11,20,24,0
Fabian,Rothberger,Male,30.5,15,18,34,17,17,19,22,18,0
Natascha,Rott,Female,29.5,12,18,32,19,20,21,26,23,0
Isabel,Rudolf,Female,27.5,9,17,34,16,19,19,21,16,0
Melina,Sablotny,Female,31,15,18,33,20,20,21,19,11,0
Alea,Schleier,Female,27,14,18,34,16,18,21.5,22,15,22
Flemming,Schur,Male,29.5,15,17,34,19,20,19,22,18,0
Marie,Seeger,Female,27.5,15,18,32,14,9,17,22,9,0
Lucy,Thiele,Female,27.5,15,18,27,20,17,19,18,22,0
Lara,Troschke,Female,28.5,14,17,28,13,19,21,25,12,0
Inga-Brit,Turschner,Female,25.5,14,18,34,20,16,19,22,17,0
Alea,Unger,Female,30,12,18,31,20,20,21,22,15,21.5
Marie,Wallbaum,Female,28.5,14,18,34,17,20,19,24,12,0
Katharina,Walz,Female,31,15,18,31,19,19,17,24,17,14.5
Xiaowei,Wang,Male,30.5,14,18,26,19,17,0,0,0,0
Lilly-Lu,Warnken,Female,30,15,18,30,14,17,19,14,16,0
1 First Name Last Name Sex Tutorial 1 Tutorial 2 Extended Applications Numpy & MatPlotLib SciPy Monte Carlo Pandas & Seaborn Folium Statistical Test Methods Data Analysis
2 Abdalaziz Abunjaila Male 30.5 15 18 28 17 17 17 22 0 0
3 Marleen Adolphi Female 29.5 15 18 32 19 0 20 17 24 23 0
4 Skofiare Sarina Berisha Apel Female 29.5 28.5 13 15 18 34 32 20 17 20 20 21 26 24 20 0
5 Aurela Skofiare Brahimi Berisha Female 17.5 29.5 15 13 15.5 18 26 34 16 20 17 19 20 16 26 16 0
6 Cam Thu Aurela Do Brahimi Female 31 17.5 15 18 15.5 34 26 19 16 20 17 21.5 19 22 16 0 0
7 Nova Cam Thu Eib Do Female 31 15 15 18 34 20 19 20 21 21.5 27 22 12 0
8 Nele Nova Grundke Eib Female 23.5 31 13 15 16 15 28 34 20 17 20 21 18 27 19 0
9 Anna Nele Grünewald Grundke Female 12 23.5 14 13 16 29 28 16 20 15 17 19 21 9 18 22 0
10 Yannik Anna Haupt Grünewald Male Female 18 12 6 14 14 16 21 29 13 16 2 15 9 19 0 9 0 0
11 Janna Yannik Heiny Haupt Female Male 30 18 15 6 18 14 33 21 18 13 20 2 22 9 25 0 0 0
12 Milena Janna Krieger Heiny Female 30 15 18 33 20 18 20 21.5 22 26 25 24 30
13 Julia Milena Limbach Krieger Female 27.5 30 12 15 18 29 33 11 20 19 20 17.5 21.5 26 20 0
14 Viktoria Julia Litza Limbach Female 21.5 27.5 15 12 18 27 29 13 11 20 19 22 17.5 21 26 24 0
15 Manthey Viktoria Leonie Litza Female 28.5 21.5 14 15 18 29 27 20 13 10 20 18 22 23 21 21 0
16 Izabel Leonie Mike Manthey Female 29.5 28.5 15 14 15 18 35 29 11 20 4 10 19 18 21 23 16 28
17 Lea Izabel Noglik Mike Female 22.5 29.5 15 17 15 34 35 13 11 10 15 20 19 0 21 21 27
18 Donika Lea Nuhiu Noglik Female 31 22.5 13.5 15 18 17 35 34 14 13 10 17 20 18 21 19 0
19 Julia Donika Renner Nuhiu Female 27.5 31 10 13.5 14 18 0 35 20 14 17 10 11 17 20 18 19 6
20 Fabian Julia Rothberger Renner Male Female 30.5 27.5 15 10 18 14 34 32 17 20 17 19 11 22 20 24 0
21 Natascha Fabian Rott Rothberger Female Male 29.5 30.5 12 15 18 32 34 19 17 20 17 21 19 26 22 18 0
22 Isabel Natascha Rudolf Rott Female 27.5 29.5 9 12 17 18 34 32 16 19 19 20 19 21 21 26 23 0
23 Melina Isabel Sablotny Rudolf Female 31 27.5 15 9 18 17 33 34 20 16 20 19 21 19 19 21 16 0
24 Alea Melina Schleier Sablotny Female 27 31 14 15 18 34 33 16 20 18 20 21.5 21 22 19 11 0
25 Flemming Alea Schur Schleier Male Female 29.5 27 15 14 17 18 34 19 16 20 18 19 21.5 22 15 22
26 Marie Flemming Seeger Schur Female Male 27.5 29.5 15 18 17 32 34 14 19 9 20 17 19 22 18 0
27 Lucy Marie Thiele Seeger Female 27.5 15 18 27 32 20 14 17 9 19 17 18 22 9 0
28 Lara Lucy Troschke Thiele Female 28.5 27.5 14 15 17 18 28 27 13 20 19 17 21 19 25 18 22 0
29 Inga-Brit Lara Turschner Troschke Female 25.5 28.5 14 18 17 34 28 20 13 16 19 19 21 22 25 12 0
30 Alea Inga-Brit Unger Turschner Female 30 25.5 12 14 18 31 34 20 20 16 21 19 22 17 0
31 Marie Alea Wallbaum Unger Female 28.5 30 14 12 18 34 31 17 20 20 19 21 24 22 15 21.5
32 Katharina Marie Walz Wallbaum Female 31 28.5 15 14 18 31 34 19 17 19 20 17 19 24 12 0
33 Xiaowei Katharina Wang Walz Male Female 30.5 31 14 15 18 26 31 19 17 19 0 17 0 24 17 14.5
34 Lilly-Lu Xiaowei Warnken Wang Female Male 30 30.5 15 14 18 30 26 14 19 17 19 0 14 0 0 0
35 Lilly-Lu Warnken Female 30 15 18 30 14 17 19 14 16 0

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assets/convert.py
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@@ -5,6 +5,7 @@ sys.path.append('..')
from model import *
df = pd.read_csv("Student_list.csv")
df = df.dropna()
courses = {
'Tutorial 1': 31,
'Tutorial 2': 15,
@@ -13,9 +14,17 @@ courses = {
'SciPy': 20,
'Monte Carlo': 20,
'Pandas & Seaborn': 22,
'Folium': 27
'Folium': 27,
'Statistical Test Methods': 24,
'Data Analysis': 30
}
print(df)
db.init("WiSe_24_25.db")
db.connect()
db.create_tables([Class, Student, Lecture, Submission])
# Create Class
clas = Class.create(name='WiSe 24/25')
#print(clas.id)
@@ -39,4 +48,4 @@ for index, row in df.iterrows():
lecture_id=Lecture.select().where(Lecture.title == title),
points=points
)

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