132 lines
3.1 KiB
Markdown
132 lines
3.1 KiB
Markdown
Arbeiten zusammen:
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- Jamie Beu
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- Julianne Kitzinger
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- Arian Temouri
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Fehlende Daten:
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- 59513b5256dc16e99c505915cdd84aa1
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- 6948c64227d75cd50901c246be88e264
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- a22ad77be0d478b5fe34d1167d4dbb3a
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- a96f7c881d27b739c38b81da2058a2fb
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- c6679ce22dfd5646f81d40a8dbb0236b
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Beispiele:
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>[!danger]
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>```C
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>void (*(*f[])())()
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>```
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>*defines **f** as an array of unspecified size of pointers to functions that return pointers to functions that return void*
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Congyu Ding
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Annemike Rörig
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```python
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def fakultaet_generator(n: int) -> int:
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"""
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Generiert mittels ChatGPT (26.11.2025)
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Prompt: "..."
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- Berechnung der Fakultät
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- Robuste Fehlerbehandlung
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- Benötigt für Berechnung XXX
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"""
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# Robuste Fehlerbehandlung bei Werten kleiner 0
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if n < 0:
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raise ValueError("Die Fakultät ist nur für nicht-negative Zahlen definiert.")
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# Berechnung der Fakultät mittels aufmultiplizieren
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ergebnis = 1
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for i in range(1, n + 1):
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ergebnis *= i
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yield ergebnis
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```
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Ideen:
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- Stock Market Simulation (Animal Crossing)
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- Gini Index
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938-828
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791-748
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```python
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# Binäre Variable: Vegan = 1, Nicht-Vegan = 0
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df['ist_vegan'] = (df['Ernährung'] == 'Vegan').astype(int)
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# Filter: mindestens 2 Instagram-Accounts UND mindestens einer privat
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h2_data = df[
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(df['Instagram_Anzahl'] >= 2) & (df['Instagram_Privat'] == 'Ja')
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]
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# Gruppen
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vegan_mit = h2_data['ist_vegan']
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vegan_ohne = df[
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(df['Instagram_Anzahl'] < 2) | (df['Instagram_Privat'] != 'Ja')
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]['ist_vegan']
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# Independent t-test
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t_stat, p_value = ttest_ind(vegan_mit, vegan_ohne)
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vegan_pct_mit = vegan_mit.mean() * 100
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vegan_pct_ohne = vegan_ohne.mean() * 100
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```
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$$\text{Birth Rate} = \frac{B}{P} * 1000$$
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# Projekte
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- [Social Media Addiction](https://www.kaggle.com/datasets/adilshamim8/social-media-addiction-vs-relationships)
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- [Movies & Shows](https://www.kaggle.com/datasets/shivamb/amazon-prime-movies-and-tv-shows)
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Annemike Rörig anschreiben
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result
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------------------------------------------
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Dania Arshad: 126.5 / 282 44.9%
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Jamie Beu: 240 / 282 85.1%
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Michael Biermann: 258.5 / 282 91.7%
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Antonia Lilli Elsner: 234 / 282 83.0%
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Lillian Fitzner: 248.5 / 282 88.1%
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Nicole Funke: 267.5 / 282 94.9%
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Julia Gebel: 269 / 282 95.4%
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Alexandra Geworsky: 232 / 282 82.3%
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Zoe Giese: 260.5 / 282 92.4%
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Jule Hansen: 258 / 282 91.5%
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Annelie Hartkopp: 167 / 282 59.2%
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Hermine Hesse: 177 / 282 62.8%
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Mirja Jordan: 228 / 282 80.9%
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Julianne Kitzinger: 243 / 282 86.2%
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Wenyu Liu: 161.5 / 282 57.3%
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Daniel Lock: 235.5 / 282 83.5%
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Tim Matschulla: 207.5 / 282 73.6%
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Franka Milbrandt: 264 / 282 93.6%
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Linda Plöger: 244 / 282 86.5%
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Adrian Reis: 179.5 / 282 63.7%
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Annemike Rörig: 194 / 282 68.8%
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Kenji Sato: 249.5 / 282 88.5%
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Helene Scheler: 192 / 282 68.1%
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Fiona Schmitz: 244 / 282 86.5%
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Marie-Christine Schmitz: 236 / 282 83.7%
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Jenna Seeger: 237.5 / 282 84.2%
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Jette Siever: 240.5 / 282 85.3%
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Josefine Sinkemat: 187 / 282 66.3%
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Agatha Stark: 194.5 / 282 69.0%
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Ye Sun: 234 / 282 83.0%
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Arian Temouri: 239 / 282 84.8%
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Alexander Trey: 259.5 / 282 92.0%
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Denis Weber: 230 / 282 81.6%
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Elina Winkler: 243 / 282 86.2%
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Fabian Zirkel: 265 / 282 94.0%
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