#!/usr/bin/env python3
"""
Einfache Eurojackpot Bereichsanalyse
"""

import pandas as pd
from collections import Counter

def analyze_ranges():
    """Analysiert Zahlenbereiche in Eurojackpot-Daten."""
    
    # Daten laden
    df = pd.read_csv("/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot/data/AlleEurojackpotzahlen.csv", sep=';')
    
    print(f"Anzahl Ziehungen: {len(df)}")
    
    # Alle Zahlen sammeln
    all_numbers = []
    for col in ['Z1', 'Z2', 'Z3', 'Z4', 'Z5']:
        all_numbers.extend(df[col].tolist())
    
    # Bereiche definieren
    ranges = {
        '1-10': list(range(1, 11)),
        '11-20': list(range(11, 21)),
        '21-30': list(range(21, 31)),
        '31-40': list(range(31, 41)),
        '41-50': list(range(41, 51))
    }
    
    print(f"\nGesamt gezogene Zahlen: {len(all_numbers)}")
    print("="*50)
    
    # Analyse pro Bereich
    for range_name, range_numbers in ranges.items():
        count = sum(1 for num in all_numbers if num in range_numbers)
        percentage = (count / len(all_numbers)) * 100
        
        print(f"{range_name:6}: {count:4} Zahlen ({percentage:5.1f}%)")
    
    print("="*50)
    
    # Häufigste Zahlen
    number_counts = Counter(all_numbers)
    print("\nHäufigste 10 Zahlen:")
    for num, count in number_counts.most_common(10):
        print(f"Zahl {num:2}: {count:3} mal")
    
    print("\nSeltenste 10 Zahlen:")
    for num, count in number_counts.most_common()[-10:]:
        print(f"Zahl {num:2}: {count:3} mal")

if __name__ == "__main__":
    analyze_ranges()