From b8c420621b71b35a177fde4d7f76544b12a717f9 Mon Sep 17 00:00:00 2001 From: TheOriginalGraLargeShrimpakaReaper Date: Fri, 18 Oct 2024 10:40:27 +0200 Subject: [PATCH] edit corelation.py and add hr_data_cleaned.csv --- code/corelation.py | 47 ++++++- code/corelation_old5.py | 144 +++++++++++++++++++++ data/final/gramic_sleep_hr_correlation.png | Bin 0 -> 39156 bytes data/final/weekly_hr_sleep.png | Bin 0 -> 43004 bytes data/sandbox/combined_data.csv | 92 +++++++------ data/sandbox/sleep_data_clean.csv | 104 +++++++-------- data/sandbox/sleep_gramic.csv | 53 ++++++++ 7 files changed, 345 insertions(+), 95 deletions(-) create mode 100644 code/corelation_old5.py create mode 100644 data/final/gramic_sleep_hr_correlation.png create mode 100644 data/final/weekly_hr_sleep.png create mode 100644 data/sandbox/sleep_gramic.csv diff --git a/code/corelation.py b/code/corelation.py index 79bc956..a1f68c0 100644 --- a/code/corelation.py +++ b/code/corelation.py @@ -9,6 +9,18 @@ month_translation = { 'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec' } +# Funktion zur Umwandlung von '6h 11min' in numerische Stundenwerte +def convert_sleep_duration(sleep_duration_str): + print(sleep_duration_str) + hours = 0 + minutes = 0 + if 'h' in sleep_duration_str: + hours_part = sleep_duration_str.split('h')[0].strip() + hours = int(hours_part) + if 'min' in sleep_duration_str: + minutes_part = sleep_duration_str.split('h')[-1].replace('min', '').strip() + minutes = int(minutes_part) + return hours + (minutes / 60) # Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Jahr zu konvertieren def convert_to_week_and_year(date_range_str): @@ -85,10 +97,13 @@ def convert_to_week_and_year(date_range_str): # Datei Pfade hr_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv' -sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv' +sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_gramic.csv' hr_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/hr_data_clean.csv' sleep_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_data_clean.csv' combined_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/combined_data.csv' +graphic_corr_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/final/gramic_sleep_hr_correlation.png' +graphic_weekly_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/final/weekly_hr_sleep.png' + # Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm' hr_data = pd.read_csv(hr_data_path, sep=';') @@ -108,11 +123,15 @@ hr_data_clean = hr_data[['Woche', 'avg_hr']] hr_data_clean.to_csv(hr_clean_path, index=False) # Schritt 2: Lade die Schlafdaten (mit Komma separiert) -sleep_data = pd.read_csv(sleep_data_path, sep=',') +sleep_data = pd.read_csv(sleep_data_path, sep=';') # Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr) sleep_data['Woche'] = sleep_data['Datum'].apply(convert_to_week_and_year) +# Konvertiere die Spalte 'Durchschnittliche Dauer' in numerische Stunden +sleep_data['Durchschnittliche Dauer'] = sleep_data['Durchschnittliche Dauer'].apply(convert_sleep_duration) + + # Speichere nur die Spalten 'Woche' und 'Durchschnittliche Dauer' in einer neuen CSV-Datei sleep_data_clean = sleep_data[['Woche', 'Durchschnittliche Dauer']] sleep_data_clean.to_csv(sleep_clean_path, index=False) @@ -141,4 +160,28 @@ plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red', label=f'Trendlinie (Kor = {correlation:.2f})') plt.legend() +# Speichern der Grafik +plt.savefig(graphic_corr_path) +plt.show() + +# Schritt 6: Erstelle eine Grafik pro Kalenderwoche (Herzfrequenz und Schlafdaten) +plt.figure(figsize=(12, 6)) + +# Plot für die Herzfrequenz +plt.bar(combined_data['Woche'], combined_data['avg_hr'], width=0.4, label='Durchschnittliche Herzfrequenz', align='center') + +# Plot für die Schlafdauer (mit Verschiebung, um Überlappung zu vermeiden) +plt.bar(combined_data['Woche'], combined_data['Durchschnittliche Dauer'], width=0.4, label='Schlafdauer', align='edge') + +# Diagrammtitel und Achsenbeschriftungen +plt.title('Durchschnittliche Herzfrequenz und Schlafdauer pro Kalenderwoche') +plt.xlabel('Kalenderwoche') +plt.ylabel('Wert') + +# Legende und Rotation der x-Achse +plt.legend() +plt.xticks(rotation=45, ha='right') + +# Speichern der zweiten Grafik +plt.savefig(graphic_weekly_path) plt.show() diff --git a/code/corelation_old5.py b/code/corelation_old5.py new file mode 100644 index 0000000..79bc956 --- /dev/null +++ b/code/corelation_old5.py @@ -0,0 +1,144 @@ +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +from datetime import datetime + +# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch +month_translation = { + 'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Mrz': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun', + 'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec' +} + + +# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Jahr zu konvertieren +def convert_to_week_and_year(date_range_str): + # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas + date_range_str = date_range_str.replace(" - ", "-").replace(",", "") + + # Prüfen, ob das Format nur einen Tag und Monat enthält (z.B. 'Dez 28') + if "-" not in date_range_str and len(date_range_str.split(" ")) == 2: + # Nur ein Datum (Monat und Tag) + month_str, day_str = date_range_str.split(" ") + day = int(day_str.strip()) + year_str = str(datetime.now().year) # Verwende das aktuelle Jahr + + # Konvertiere den deutschen Monatsnamen in den englischen + if month_str in month_translation: + month_str = month_translation[month_str] + + # Konvertiere das Datum + start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y") + + # Berechne die Kalenderwoche und das Jahr + week_number = start_date.isocalendar()[1] + year = start_date.year + + return f"W{week_number}-{year}" + + # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023') + if date_range_str[-4:].isdigit(): + # Jahr am Ende des Datums + year_str = date_range_str[-4:] + date_range_str = date_range_str[:-5] # Entferne das Jahr aus der Datumsangabe + else: + # Kein Jahr am Ende -> nutze das aktuelle Jahr + year_str = str(datetime.now().year) + + # Splitte den Datumsbereich in Start- und Endteil + start_part, end_part = date_range_str.split("-") + + # Verarbeite den Startteil + start_parts = start_part.split(" ") + start_month_str = start_parts[0] + start_day = int(start_parts[1].strip()) # Nimm den Starttag und entferne etwaige Leerzeichen + + # Verarbeite den Endteil + end_parts = end_part.split(" ") + + # Verarbeite das Enddatum, basierend darauf, ob der Endteil das Jahr enthält + if len(end_parts) == 2: # Fall: 'Apr 4' + end_month_str = end_parts[0] + end_day = int(end_parts[1].strip()) + else: + # Fall: kein Monat -> nur Tag (z.B. '28' im Format 'Dez 22-28 2023') + end_month_str = start_month_str + end_day = int(end_parts[0].strip()) + + # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate + if start_month_str in month_translation: + start_month_str = month_translation[start_month_str] + if end_month_str in month_translation: + end_month_str = month_translation[end_month_str] + + try: + # Konvertiere das Startdatum in ein Datum + start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y") + except ValueError: + raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}") + + # Berechne die Kalenderwoche und das Jahr basierend auf dem Startdatum + week_number = start_date.isocalendar()[1] + year = start_date.year + + return f"W{week_number}-{year}" # Gib Kalenderwoche und Jahr im Format W-YYYY zurück + + +# Datei Pfade +hr_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv' +sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv' +hr_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/hr_data_clean.csv' +sleep_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_data_clean.csv' +combined_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/combined_data.csv' + +# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm' +hr_data = pd.read_csv(hr_data_path, sep=';') + +# Entferne 'bpm' und konvertiere die Werte in numerische Daten +hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float) +hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float) + +# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr) +hr_data['Woche'] = hr_data['Datum'].apply(convert_to_week_and_year) + +# Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch) +hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1) + +# Speichere nur die Spalten 'Woche' und 'avg_hr' in einer neuen CSV-Datei +hr_data_clean = hr_data[['Woche', 'avg_hr']] +hr_data_clean.to_csv(hr_clean_path, index=False) + +# Schritt 2: Lade die Schlafdaten (mit Komma separiert) +sleep_data = pd.read_csv(sleep_data_path, sep=',') + +# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr) +sleep_data['Woche'] = sleep_data['Datum'].apply(convert_to_week_and_year) + +# Speichere nur die Spalten 'Woche' und 'Durchschnittliche Dauer' in einer neuen CSV-Datei +sleep_data_clean = sleep_data[['Woche', 'Durchschnittliche Dauer']] +sleep_data_clean.to_csv(sleep_clean_path, index=False) + +# Schritt 3: Kombiniere die HR- und Schlafdaten basierend auf der 'Woche' +combined_data = pd.merge(hr_data_clean, sleep_data_clean, on='Woche', how='inner') + +# Speichere das kombinierte Dataset in einer neuen CSV-Datei +combined_data.to_csv(combined_data_path, index=False) + +# Schritt 4: Berechne die Korrelation zwischen avg_hr und der Schlafdauer +correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer']) +print(f"Die Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}") + +# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer +plt.figure(figsize=(10, 6)) +plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte') +plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer') +plt.xlabel('Durchschnittliche Herzfrequenz (bpm)') +plt.ylabel('Schlafdauer (Stunden)') +plt.grid(True) + +# Linie zur Visualisierung des Trends hinzufügen +m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1) +plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red', + label=f'Trendlinie (Kor = 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36min -W32-2024,105.0,5h 45min -W31-2024,103.5,7h 4min -W30-2024,107.0,6h 52min -W29-2024,102.5,6h 26min -W28-2024,108.5,7h 10min -W27-2024,106.5,7h 35min -W26-2024,111.5,6h 8min -W25-2024,102.5,6h 30min -W24-2024,107.5,6h 1min -W23-2024,101.5,6h 22min -W22-2024,104.5,6h 33min -W21-2024,107.0,6h 47min -W20-2024,103.5,5h 51min -W19-2024,109.0,6h 21min -W18-2024,107.5,5h 36min -W17-2024,110.0,6h 2min -W16-2024,98.0,6h 28min -W15-2024,99.0,6h 17min -W14-2024,97.5,6h 18min -W13-2024,101.0,6h 50min -W12-2024,100.0,6h 9min -W11-2024,101.5,6h 11min -W10-2024,99.0,6h 22min -W9-2024,102.0,6h 4min -W8-2024,106.0,7h 11min -W7-2024,101.5,6h 58min -W6-2024,104.0,8h 17min -W5-2024,99.5,6h 12min -W4-2024,100.5,7h 21min -W3-2024,99.0,6h 25min -W2-2024,97.0,6h 27min -W1-2024,97.5,6h 44min -W52-2024,93.5,54 +W40-2024,98.5,6.566666666666666 +W39-2024,105.0,6.383333333333334 +W38-2024,107.5,6.683333333333334 +W37-2024,105.0,6.3 +W36-2024,97.0,6.316666666666666 +W35-2024,102.5,7.016666666666667 +W34-2024,108.5,6.283333333333333 +W33-2024,103.0,6.6 +W32-2024,105.0,5.75 +W31-2024,103.5,7.066666666666666 +W30-2024,107.0,6.866666666666667 +W29-2024,102.5,6.433333333333334 +W28-2024,108.5,7.166666666666667 +W27-2024,106.5,7.583333333333333 +W26-2024,111.5,6.133333333333334 +W25-2024,102.5,6.5 +W24-2024,107.5,6.016666666666667 +W23-2024,101.5,6.366666666666666 +W22-2024,104.5,6.55 +W21-2024,107.0,6.783333333333333 +W20-2024,103.5,5.85 +W19-2024,109.0,6.35 +W18-2024,107.5,5.6 +W17-2024,110.0,6.033333333333333 +W16-2024,98.0,6.466666666666667 +W15-2024,99.0,6.283333333333333 +W14-2024,97.5,6.3 +W13-2024,101.0,6.833333333333333 +W12-2024,100.0,6.15 +W11-2024,101.5,6.183333333333334 +W10-2024,99.0,6.366666666666666 +W9-2024,102.0,6.066666666666666 +W8-2024,106.0,7.183333333333334 +W7-2024,101.5,6.966666666666667 +W6-2024,104.0,8.283333333333333 +W5-2024,99.5,6.2 +W4-2024,100.5,7.35 +W3-2024,99.0,6.416666666666667 +W2-2024,97.0,6.45 +W1-2024,97.5,6.733333333333333 +W52-2024,93.5,7.033333333333333 +W51-2023,88.0,7.266666666666667 +W50-2023,95.0,7.283333333333333 +W49-2023,99.0,7.883333333333333 +W48-2023,101.5,6.85 +W47-2023,103.5,7.183333333333334 +W46-2023,105.5,7.116666666666666 +W45-2023,104.0,7.083333333333333 +W44-2023,101.5,6.033333333333333 +W43-2023,95.0,6.65 +W42-2023,96.5,6.283333333333333 diff --git a/data/sandbox/sleep_data_clean.csv b/data/sandbox/sleep_data_clean.csv index 5416f58..2065d88 100644 --- a/data/sandbox/sleep_data_clean.csv +++ b/data/sandbox/sleep_data_clean.csv @@ -1,53 +1,53 @@ Woche,Durchschnittliche Dauer -W41-2024,6h 11min -W40-2024,6h 34min -W39-2024,6h 23min -W38-2024,6h 41min -W37-2024,6h 18min -W36-2024,6h 19min -W35-2024,7h 1min -W34-2024,6h 17min -W33-2024,6h 36min -W32-2024,5h 45min -W31-2024,7h 4min -W30-2024,6h 52min -W29-2024,6h 26min -W28-2024,7h 10min -W27-2024,7h 35min -W26-2024,6h 8min -W25-2024,6h 30min -W24-2024,6h 1min -W23-2024,6h 22min -W22-2024,6h 33min -W21-2024,6h 47min -W20-2024,5h 51min -W19-2024,6h 21min -W18-2024,5h 36min -W17-2024,6h 2min -W16-2024,6h 28min -W15-2024,6h 17min -W14-2024,6h 18min -W13-2024,6h 50min -W12-2024,6h 9min -W11-2024,6h 11min -W10-2024,6h 22min -W9-2024,6h 4min -W8-2024,7h 11min -W7-2024,6h 58min -W6-2024,8h 17min -W5-2024,6h 12min -W4-2024,7h 21min -W3-2024,6h 25min -W2-2024,6h 27min -W1-2024,6h 44min -W52-2024,54 -W51-2024,Schlecht -W50-2024,Schlecht -W49-2024,Schlecht -W48-2024,Schlecht -W47-2024,Schlecht -W46-2024,Schlecht -W45-2024,Schlecht -W44-2024,Schlecht -W43-2024,Schlecht -W42-2024,Schlecht +W41-2024,6.183333333333334 +W40-2024,6.566666666666666 +W39-2024,6.383333333333334 +W38-2024,6.683333333333334 +W37-2024,6.3 +W36-2024,6.316666666666666 +W35-2024,7.016666666666667 +W34-2024,6.283333333333333 +W33-2024,6.6 +W32-2024,5.75 +W31-2024,7.066666666666666 +W30-2024,6.866666666666667 +W29-2024,6.433333333333334 +W28-2024,7.166666666666667 +W27-2024,7.583333333333333 +W26-2024,6.133333333333334 +W25-2024,6.5 +W24-2024,6.016666666666667 +W23-2024,6.366666666666666 +W22-2024,6.55 +W21-2024,6.783333333333333 +W20-2024,5.85 +W19-2024,6.35 +W18-2024,5.6 +W17-2024,6.033333333333333 +W16-2024,6.466666666666667 +W15-2024,6.283333333333333 +W14-2024,6.3 +W13-2024,6.833333333333333 +W12-2024,6.15 +W11-2024,6.183333333333334 +W10-2024,6.366666666666666 +W9-2024,6.066666666666666 +W8-2024,7.183333333333334 +W7-2024,6.966666666666667 +W6-2024,8.283333333333333 +W5-2024,6.2 +W4-2024,7.35 +W3-2024,6.416666666666667 +W2-2024,6.45 +W1-2024,6.733333333333333 +W52-2024,7.033333333333333 +W51-2023,7.266666666666667 +W50-2023,7.283333333333333 +W49-2023,7.883333333333333 +W48-2023,6.85 +W47-2023,7.183333333333334 +W46-2023,7.116666666666666 +W45-2023,7.083333333333333 +W44-2023,6.033333333333333 +W43-2023,6.65 +W42-2023,6.283333333333333 diff --git a/data/sandbox/sleep_gramic.csv b/data/sandbox/sleep_gramic.csv new file mode 100644 index 0000000..74ff3b6 --- /dev/null +++ b/data/sandbox/sleep_gramic.csv @@ -0,0 +1,53 @@ +Datum;Ø Score;Ø Qualität;Durchschnittliche Dauer;Ø Schlafenszeit;Ø Aufstehzeit +Okt 10-16;46;Schlecht;6h 11min;1:28;7:50 +Okt 3-9;56;Schlecht;6h 34min;0:33;7:22 +Sep 26 - Okt 2;52;Schlecht;6h 23min;0:00;6:37 +Sep 19-25;59;Schlecht;6h 41min;0:27;7:16 +Sep 12-18;59;Schlecht;6h 18min;0:48;7:12 +Sep 5-11;55;Schlecht;6h 19min;0:56;7:22 +Aug 29 - Sep 4;66;Ausreichend;7h 1min;0:21;7:31 +Aug 22-28;57;Schlecht;6h 17min;0:35;6:59 +Aug 15-21;72;Ausreichend;6h 36min;0:38;7:25 +Aug 8-14;56;Schlecht;5h 45min;0:16;6:06 +Aug 1-7;64;Ausreichend;7h 4min;0:39;8:10 +Jul 25-31;61;Ausreichend;6h 52min;0:40;7:56 +Jul 18-24;54;Schlecht;6h 26min;2:16;9:19 +Jul 11-17;74;Ausreichend;7h 10min;0:06;7:26 +Jul 4-10;58;Schlecht;7h 35min;0:45;9:05 +Jun 27 - Jul 3;60;Ausreichend;6h 8min;1:22;7:42 +Jun 20-26;69;Ausreichend;6h 30min;0:19;6:58 +Jun 13-19;59;Schlecht;6h 1min;0:25;6:33 +Jun 6-12;60;Ausreichend;6h 22min;0:02;6:30 +Mai 30 - Jun 5;60;Ausreichend;6h 33min;0:34;7:15 +Mai 23-29;51;Schlecht;6h 47min;0:56;7:54 +Mai 16-22;50;Schlecht;5h 51min;1:13;7:08 +Mai 9-15;55;Schlecht;6h 21min;0:58;7:23 +Mai 2-8;40;Schlecht;5h 36min;0:39;6:19 +Apr 25 - Mai 1;55;Schlecht;6h 2min;1:18;7:26 +Apr 18-24;56;Schlecht;6h 28min;3:11;9:55 +Apr 11-17;56;Schlecht;6h 17min;1:23;7:51 +Apr 4-10;62;Ausreichend;6h 18min;0:39;7:10 +Mrz 28 - Apr 3;68;Ausreichend;6h 50min;1:45;8:51 +Mrz 21-27;60;Ausreichend;6h 9min;0:56;7:13 +Mrz 14-20;54;Schlecht;6h 11min;1:03;7:24 +Mrz 7-13;54;Schlecht;6h 22min;0:49;7:17 +Feb 29 - Mrz 6;50;Schlecht;6h 4min;0:56;7:09 +Feb 22-28;61;Ausreichend;7h 11min;0:08;7:25 +Feb 15-21;44;Schlecht;6h 58min;0:19;7:51 +Feb 8-14;50;Schlecht;8h 17min;0:09;8:47 +Feb 1-7;43;Schlecht;6h 12min;1:06;7:25 +Jan 25-31;57;Schlecht;7h 21min;1:17;8:40 +Jan 18-24;51;Schlecht;6h 25min;0:56;7:26 +Jan 11-17;50;Schlecht;6h 27min;1:36;8:12 +Jan 4-10;59;Schlecht;6h 44min;1:26;8:24 +Dez 28, 2023 - Jan 3, 2024;54;Schlecht;7h 2min +Dez 21-27, 2023;55;Schlecht;7h 16min;0:38 +Dez 14-20, 2023;56;Schlecht;7h 17min;0:31 +Dez 7-13, 2023;44;Schlecht;7h 53min;23:46 +Nov 30 - Dez 6, 2023;48;Schlecht;6h 51min;0:33 +Nov 23-29, 2023;48;Schlecht;7h 11min;0:21 +Nov 16-22, 2023;53;Schlecht;7h 7min;0:23 +Nov 9-15, 2023;47;Schlecht;7h 5min;0:19 +Nov 2-8, 2023;50;Schlecht;6h 2min;0:00 +Okt 26 - Nov 1, 2023;59;Schlecht;6h 39min;0:14 +Okt 19-25, 2023;48;Schlecht;6h 17min;0:46 \ No newline at end of file