DerGrumpf/grapher
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Changed: Projects

Browse Source
This commit is contained in:
DerGrumpf
2025-01-31 14:34:57 +01:00
parent 4916a83a9c
commit 5d7b1d59c5
8 changed files with 343 additions and 411 deletions
Download Patch File Download Diff File Expand all files Collapse all files

72
assets/Student_list.csv
View File

@@ -1,35 +1,37 @@
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
First Name,Last Name,Sex,Group,Tutorial 1,Tutorial 2,Extended Applications,Numpy & MatPlotLib,SciPy,Monte Carlo,Pandas & Seaborn,Folium,Statistical Test Methods,Data Analysis
Abdalaziz,Abunjaila,Male,DiKum,30.5,15,18,28,17,17,17,22,0,18
Marleen,Adolphi,Female,MeWi6,29.5,15,18,32,19,20,17,24,23,0
Sarina,Apel,Female,MeWi1,28.5,15,18,32,20,20,21,24,20,0
Skofiare,Berisha,Female,DiKum,29.5,13,18,34,20,17,20,26,16,0
Aurela,Brahimi,Female,MeWi2,17.5,15,15.5,26,16,17,19,16,0,0
Cam Thu,Do,Female,MeWi3,31,15,18,34,19,20,21.5,22,12,0
Nova,Eib,Female,MeWi4,31,15,15,34,20,20,21,27,19,21
Lena,Fricke,Female,MeWi4,0,0,0,0,0,0,0,0,0,0
Nele,Grundke,Female,MeWi6,23.5,13,16,28,20,17,21,18,22,0
Anna,Grünewald,Female,MeWi3,12,14,16,29,16,15,19,9,0,0
Yannik,Haupt,Male,NoGroup,18,6,14,21,13,2,9,0,0,0
Janna,Heiny,Female,MeWi1,30,15,18,33,18,20,22,25,24,30
Milena,Krieger,Female,MeWi1,30,15,18,33,20,20,21.5,26,20,0
Julia,Limbach,Female,MeWi6,27.5,12,18,29,11,19,17.5,26,24,0
Viktoria,Litza,Female,MeWi5,21.5,15,18,27,13,20,22,21,21,0
Leonie,Manthey,Female,MeWi1,28.5,14,18,29,20,10,18,23,16,28
Izabel,Mike,Female,MeWi2,29.5,15,15,35,11,15,19,21,21,27
Lea,Noglik,Female,MeWi5,22.5,15,17,34,13,10,20,21,19,0
Donika,Nuhiu,Female,MeWi5,31,13.5,18,35,14,10,17,18,19,6
Julia,Renner,Female,MeWi4,27.5,10,14,32,20,17,11,20,24,0
Fabian,Rothberger,Male,MeWi3,30.5,15,18,34,17,17,19,22,18,0
Natascha,Rott,Female,MeWi1,29.5,12,18,32,19,20,21,26,23,0
Isabel,Rudolf,Female,MeWi4,27.5,9,17,34,16,19,19,21,16,0
Melina,Sablotny,Female,MeWi6,31,15,18,33,20,20,21,19,11,0
Alea,Schleier,Female,DiKum,27,14,18,34,16,18,21.5,22,15,22
Flemming,Schur,Male,MeWi3,29.5,15,17,34,19,20,19,22,18,0
Marie,Seeger,Female,DiKum,27.5,15,18,32,14,9,17,22,9,0
Lucy,Thiele,Female,MeWi6,27.5,15,18,27,20,17,19,18,22,0
Lara,Troschke,Female,MeWi2,28.5,14,17,28,13,19,21,25,12,0
Inga-Brit,Turschner,Female,MeWi2,25.5,14,18,34,20,16,19,22,17,0
Alea,Unger,Female,MeWi5,30,12,18,31,20,20,21,22,15,21.5
Marie,Wallbaum,Female,MeWi5,28.5,14,18,34,17,20,19,24,12,0
Katharina,Walz,Female,MeWi4,31,15,18,31,19,19,17,24,17,14.5
Xiaowei,Wang,Male,NoGroup,30.5,14,18,26,19,17,0,0,0,0
Lilly-Lu,Warnken,Female,DiKum,30,15,18,30,14,17,19,14,16,0
1 First Name Last Name Sex Group Tutorial 1 Tutorial 2 Extended Applications Numpy & MatPlotLib SciPy Monte Carlo Pandas & Seaborn Folium Statistical Test Methods Data Analysis
2 Abdalaziz Abunjaila Male DiKum 30.5 15 18 28 17 17 17 22 0 0 18
3 Marleen Adolphi Female MeWi6 29.5 15 18 32 19 20 17 24 23 0
4 Sarina Apel Female MeWi1 28.5 15 18 32 20 20 21 24 20 0
5 Skofiare Berisha Female DiKum 29.5 13 18 34 20 17 20 26 16 0
6 Aurela Brahimi Female MeWi2 17.5 15 15.5 26 16 17 19 16 0 0
7 Cam Thu Do Female MeWi3 31 15 18 34 19 20 21.5 22 12 0
8 Nova Eib Female MeWi4 31 15 15 34 20 20 21 27 19 0 21
9 Nele Lena Grundke Fricke Female MeWi4 23.5 0 13 0 16 0 28 0 20 0 17 0 21 0 18 0 22 0 0
10 Anna Nele Grünewald Grundke Female MeWi6 12 23.5 14 13 16 29 28 16 20 15 17 19 21 9 18 0 22 0
11 Yannik Anna Haupt Grünewald Male Female MeWi3 18 12 6 14 14 16 21 29 13 16 2 15 9 19 0 9 0 0
12 Janna Yannik Heiny Haupt Female Male NoGroup 30 18 15 6 18 14 33 21 18 13 20 2 22 9 25 0 24 0 30 0
13 Milena Janna Krieger Heiny Female MeWi1 30 15 18 33 20 18 20 21.5 22 26 25 20 24 0 30
14 Julia Milena Limbach Krieger Female MeWi1 27.5 30 12 15 18 29 33 11 20 19 20 17.5 21.5 26 24 20 0
15 Viktoria Julia Litza Limbach Female MeWi6 21.5 27.5 15 12 18 27 29 13 11 20 19 22 17.5 21 26 21 24 0
16 Leonie Viktoria Manthey Litza Female MeWi5 28.5 21.5 14 15 18 29 27 20 13 10 20 18 22 23 21 16 21 28 0
17 Izabel Leonie Mike Manthey Female MeWi1 29.5 28.5 15 14 15 18 35 29 11 20 15 10 19 18 21 23 21 16 27 28
18 Lea Izabel Noglik Mike Female MeWi2 22.5 29.5 15 17 15 34 35 13 11 10 15 20 19 21 19 21 0 27
19 Donika Lea Nuhiu Noglik Female MeWi5 31 22.5 13.5 15 18 17 35 34 14 13 10 17 20 18 21 19 6 0
20 Julia Donika Renner Nuhiu Female MeWi5 27.5 31 10 13.5 14 18 32 35 20 14 17 10 11 17 20 18 24 19 0 6
21 Fabian Julia Rothberger Renner Male Female MeWi4 30.5 27.5 15 10 18 14 34 32 17 20 17 19 11 22 20 18 24 0
22 Natascha Fabian Rott Rothberger Female Male MeWi3 29.5 30.5 12 15 18 32 34 19 17 20 17 21 19 26 22 23 18 0
23 Isabel Natascha Rudolf Rott Female MeWi1 27.5 29.5 9 12 17 18 34 32 16 19 19 20 19 21 21 26 16 23 0
24 Melina Isabel Sablotny Rudolf Female MeWi4 31 27.5 15 9 18 17 33 34 20 16 20 19 21 19 19 21 11 16 0
25 Alea Melina Schleier Sablotny Female MeWi6 27 31 14 15 18 34 33 16 20 18 20 21.5 21 22 19 15 11 22 0
26 Flemming Alea Schur Schleier Male Female DiKum 29.5 27 15 14 17 18 34 19 16 20 18 19 21.5 22 18 15 0 22
27 Marie Flemming Seeger Schur Female Male MeWi3 27.5 29.5 15 18 17 32 34 14 19 9 20 17 19 22 9 18 0
28 Lucy Marie Thiele Seeger Female DiKum 27.5 15 18 27 32 20 14 17 9 19 17 18 22 22 9 0
29 Lara Lucy Troschke Thiele Female MeWi6 28.5 27.5 14 15 17 18 28 27 13 20 19 17 21 19 25 18 12 22 0
30 Inga-Brit Lara Turschner Troschke Female MeWi2 25.5 28.5 14 18 17 34 28 20 13 16 19 19 21 22 25 17 12 0
31 Alea Inga-Brit Unger Turschner Female MeWi2 30 25.5 12 14 18 31 34 20 20 16 21 19 22 15 17 21.5 0
32 Marie Alea Wallbaum Unger Female MeWi5 28.5 30 14 12 18 34 31 17 20 20 19 21 24 22 12 15 0 21.5
33 Katharina Marie Walz Wallbaum Female MeWi5 31 28.5 15 14 18 31 34 19 17 19 20 17 19 24 17 12 14.5 0
34 Xiaowei Katharina Wang Walz Male Female MeWi4 30.5 31 14 15 18 26 31 19 17 19 0 17 0 24 0 17 0 14.5
35 Lilly-Lu Xiaowei Warnken Wang Female Male NoGroup 30 30.5 15 14 18 30 26 14 19 17 19 0 14 0 16 0 0
36 Lilly-Lu Warnken Female DiKum 30 15 18 30 14 17 19 14 16 0
37

BIN
assets/WiSe_24_25.db
View File

Binary file not shown.

21
assets/convert.py
View File

@@ -19,11 +19,22 @@ courses = {
'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)
db.init("WiSe_24_25.db")
db.connect()
db.create_tables([Class, Student, Lecture, Submission])
db.create_tables([Class, Student, Lecture, Submission, Group])
# Create Class
clas = Class.create(name='WiSe 24/25')
@@ -34,15 +45,19 @@ 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, 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
class_id=clas.id,
group_id=Group.select().where(Group.name == row["Group"])
)
for title, points in list(row.to_dict().items())[3:]:
for title, points in list(row.to_dict().items())[4:]:
Submission.create(
student_id=s.id,
lecture_id=Lecture.select().where(Lecture.title == title),