import pandas as pd

df = pd.read_excel(r'C:\Users\Claudio\AppData\Local\Temp\dre_copia.xlsx', sheet_name='Planilha1')
date_cols = [c for c in df.columns if hasattr(c, 'year') and 2021 <= c.year <= 2025]

# All unique Conta values with totals
for conta in df['Conta'].unique():
    if pd.isna(conta): continue
    mask = df['Conta'].astype(str).str.strip().str.upper() == str(conta).strip().upper()
    total = df.loc[mask, date_cols].apply(pd.to_numeric, errors='coerce').fillna(0).sum().sum()
    print(f"{str(conta).strip()!r:45s} Total={total:>15,.0f}")

print()
# Check rows 83-120
print("=== LINHAS CHAVE ===")
for i in [83, 87, 88, 91, 118, 119]:
    row = df.iloc[i]
    total = row[date_cols].apply(pd.to_numeric, errors='coerce').fillna(0).sum()
    conta = str(row['Conta'])
    sub = str(row['SubConta'])
    print(f"Row {i}: Conta={conta!r:25s} Sub={sub!r:30s} Total={total:,.0f}")

print()
# Trib specifically
print("=== TRIBUTARIO ===")
for idx, row in df.iterrows():
    conta_str = str(row['Conta']).strip()
    if 'TRIBUT' in conta_str.upper() or 'DAS' in str(row['SubConta']).upper():
        total = row[date_cols].apply(pd.to_numeric, errors='coerce').fillna(0).sum()
        print(f"Row {idx}: {conta_str!r:30s} {str(row['SubConta']).strip()!r:30s} {total:,.0f}")