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DerGrumpf
2025-02-07 14:44:03 +01:00
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commit c320b27664
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73
assets/Student_list.csv
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@@ -1,37 +1,38 @@
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
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 Percent,30.5,15,18,28,17,17,17,22,0,18
Marleen,Adolphi,Female,MeWi6,30 Percent,29.5,15,18,32,19,20,17,24,23,0
Sarina,Apel,Female,MeWi1,30 Percent,28.5,15,18,32,20,20,21,24,20,23
Skofiare,Berisha,Female,DiKum,30 Percent,29.5,13,18,34,20,17,20,26,16,0
Aurela,Brahimi,Female,MeWi2,30 Percent,17.5,15,15.5,26,16,17,19,16,0,0
Cam Thu,Do,Female,MeWi3,30 Percent,31,15,18,34,19,20,21.5,22,12,0
Nova,Eib,Female,MeWi4,30 Percent,31,15,15,34,20,20,21,27,19,21
Lena,Fricke,Female,MeWi4,30 Percent,0,0,0,0,0,0,0,0,0,0
Nele,Grundke,Female,MeWi6,30 Percent,23.5,13,16,28,20,17,21,18,22,11
Anna,Grünewald,Female,MeWi3,30 Percent,12,14,16,29,16,15,19,9,0,0
Yannik,Haupt,Male,NoGroup,30 Percent,18,6,14,21,13,2,9,0,0,0
Janna,Heiny,Female,MeWi1,30 Percent,30,15,18,33,18,20,22,25,24,30
Milena,Krieger,Female,MeWi1,30 Percent,30,15,18,33,20,20,21.5,26,20,22
Julia,Limbach,Female,MeWi6,30 Percent,27.5,12,18,29,11,19,17.5,26,24,28
Viktoria,Litza,Female,MeWi5,30 Percent,21.5,15,18,27,13,20,22,21,21,30
Leonie,Manthey,Female,MeWi1,30 Percent,28.5,14,18,29,20,10,18,23,16,28
Izabel,Mike,Female,MeWi2,30 Percent,29.5,15,15,35,11,15,19,21,21,27
Lea,Noglik,Female,MeWi5,30 Percent,22.5,15,17,34,13,10,20,21,19,6
Donika,Nuhiu,Female,MeWi5,30 Percent,31,13.5,18,35,14,10,17,18,19,8
Julia,Renner,Female,MeWi4,30 Percent,27.5,10,14,32,20,17,11,20,24,14
Fabian,Rothberger,Male,MeWi3,30 Percent,30.5,15,18,34,17,17,19,22,18,30
Natascha,Rott,Female,MeWi1,30 Percent,29.5,12,18,32,19,20,21,26,23,26
Isabel,Rudolf,Female,MeWi4,30 Percent,27.5,9,17,34,16,19,19,21,16,14
Melina,Sablotny,Female,MeWi6,30 Percent,31,15,18,33,20,20,21,19,11,28
Alea,Schleier,Female,DiKum,30 Percent,27,14,18,34,16,18,21.5,22,15,22
Flemming,Schur,Male,MeWi3,30 Percent,29.5,15,17,34,19,20,19,22,18,27
Marie,Seeger,Female,DiKum,30 Percent,27.5,15,18,32,14,9,17,22,9,25
Lucy,Thiele,Female,MeWi6,30 Percent,27.5,15,18,27,20,17,19,18,22,25
Lara,Troschke,Female,MeWi2,30 Percent,28.5,14,17,28,13,19,21,25,12,24
Inga-Brit,Turschner,Female,MeWi2,30 Percent,25.5,14,18,34,20,16,19,22,17,30
Alea,Unger,Female,MeWi5,30 Percent,30,12,18,31,20,20,21,22,15,21.5
Marie,Wallbaum,Female,MeWi5,30 Percent,28.5,14,18,34,17,20,19,24,12,22
Katharina,Walz,Female,MeWi4,30 Percent,31,15,18,31,19,19,17,24,17,14.5
Xiaowei,Wang,Male,NoGroup,30 Percent,30.5,14,18,26,19,17,0,0,0,0
Lilly-Lu,Warnken,Female,DiKum,30 Percent,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 Percent 30.5 15 18 28 17 17 17 22 0 18
3 Marleen Adolphi Female MeWi6 30 Percent 29.5 15 18 32 19 20 17 24 23 0
4 Sarina Apel Female MeWi1 30 Percent 28.5 15 18 32 20 20 21 24 20 0 23
5 Skofiare Berisha Female DiKum 30 Percent 29.5 13 18 34 20 17 20 26 16 0
6 Aurela Brahimi Female MeWi2 30 Percent 17.5 15 15.5 26 16 17 19 16 0 0
7 Cam Thu Do Female MeWi3 30 Percent 31 15 18 34 19 20 21.5 22 12 0
8 Nova Eib Female MeWi4 30 Percent 31 15 15 34 20 20 21 27 19 21
9 Lena Fricke Female MeWi4 30 Percent 0 0 0 0 0 0 0 0 0 0
10 Nele Grundke Female MeWi6 30 Percent 23.5 13 16 28 20 17 21 18 22 0 11
11 Anna Grünewald Female MeWi3 30 Percent 12 14 16 29 16 15 19 9 0 0
12 Yannik Haupt Male NoGroup 30 Percent 18 6 14 21 13 2 9 0 0 0
13 Janna Heiny Female MeWi1 30 Percent 30 15 18 33 18 20 22 25 24 30
14 Milena Krieger Female MeWi1 30 Percent 30 15 18 33 20 20 21.5 26 20 0 22
15 Julia Limbach Female MeWi6 30 Percent 27.5 12 18 29 11 19 17.5 26 24 0 28
16 Viktoria Litza Female MeWi5 30 Percent 21.5 15 18 27 13 20 22 21 21 0 30
17 Leonie Manthey Female MeWi1 30 Percent 28.5 14 18 29 20 10 18 23 16 28
18 Izabel Mike Female MeWi2 30 Percent 29.5 15 15 35 11 15 19 21 21 27
19 Lea Noglik Female MeWi5 30 Percent 22.5 15 17 34 13 10 20 21 19 0 6
20 Donika Nuhiu Female MeWi5 30 Percent 31 13.5 18 35 14 10 17 18 19 6 8
21 Julia Renner Female MeWi4 30 Percent 27.5 10 14 32 20 17 11 20 24 0 14
22 Fabian Rothberger Male MeWi3 30 Percent 30.5 15 18 34 17 17 19 22 18 0 30
23 Natascha Rott Female MeWi1 30 Percent 29.5 12 18 32 19 20 21 26 23 0 26
24 Isabel Rudolf Female MeWi4 30 Percent 27.5 9 17 34 16 19 19 21 16 0 14
25 Melina Sablotny Female MeWi6 30 Percent 31 15 18 33 20 20 21 19 11 0 28
26 Alea Schleier Female DiKum 30 Percent 27 14 18 34 16 18 21.5 22 15 22
27 Flemming Schur Male MeWi3 30 Percent 29.5 15 17 34 19 20 19 22 18 0 27
28 Marie Seeger Female DiKum 30 Percent 27.5 15 18 32 14 9 17 22 9 0 25
29 Lucy Thiele Female MeWi6 30 Percent 27.5 15 18 27 20 17 19 18 22 0 25
30 Lara Troschke Female MeWi2 30 Percent 28.5 14 17 28 13 19 21 25 12 0 24
31 Inga-Brit Turschner Female MeWi2 30 Percent 25.5 14 18 34 20 16 19 22 17 0 30
32 Alea Unger Female MeWi5 30 Percent 30 12 18 31 20 20 21 22 15 21.5
33 Marie Wallbaum Female MeWi5 30 Percent 28.5 14 18 34 17 20 19 24 12 0 22
34 Katharina Walz Female MeWi4 30 Percent 31 15 18 31 19 19 17 24 17 14.5
35 Xiaowei Wang Male NoGroup 30 Percent 30.5 14 18 26 19 17 0 0 0 0
36 Lilly-Lu Warnken Female DiKum 30 Percent 30 15 18 30 14 17 19 14 16 0 24
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assets/convert.py
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@@ -54,10 +54,11 @@ for index, row in df.iterrows():
surname=row["Last Name"],
sex=row["Sex"],
class_id=clas.id,
group_id=Group.select().where(Group.name == row["Group"])
group_id=Group.select().where(Group.name == row["Group"]),
grader=row["Grader"]
)
for title, points in list(row.to_dict().items())[4:]:
for title, points in list(row.to_dict().items())[5:]:
Submission.create(
student_id=s.id,
lecture_id=Lecture.select().where(Lecture.title == title),