updates = get_df_from_sheets('1LUI4DqbjcDrstcS_U_XeFDZqHWErpOHGvbNagZRXzig', 'bog')
    # Filtrar las columnas especificadas
updates = updates.filter(['Bodega', 'Trip', 'Parada', 'Problema', 'Solución', 'Comentario', 'Usuario', 'Hora'])
updates['Parada'] = updates['Parada'].astype(str)
updates = updates[updates['Trip'].str.count('-') == 2]
updates['Parada'] = updates['Parada'].replace('\.0', '', regex=True)
updates['Bodega'] = updates['Trip'].astype(str) + '-' + updates['Parada'].astype(str)
updates = updates.dropna(subset=['Problema'])
updates = updates.dropna(subset=['Usuario'])
condition = updates['Bodega'].str.contains('FS')
updates.loc[condition, 'Bodega'] = updates.loc[condition, 'Bodega'].str.split('FS').str[0] + 'FS'
updates = updates.applymap(lambda x: x.strip() if isinstance(x, str) else x)
updates['Parada'] = updates['Parada'].str.replace('^FS.*', 'FS', regex=True)

clear_notes = get_df_from_sheets('1LUI4DqbjcDrstcS_U_XeFDZqHWErpOHGvbNagZRXzig', 'temp_notes')
clear_notes = clear_notes[clear_notes['Bodega'].str.count('-') == 3]
clear_notes['Parada'] = clear_notes['Parada'].astype(str)
clear_notes['Parada'] = clear_notes['Parada'].replace('\.0', '', regex=True)
clear_notes = clear_notes.applymap(lambda x: x.strip() if isinstance(x, str) else x)
clear_notes['Parada'] = clear_notes['Parada'].str.replace('^FS.*', 'FS', regex=True)
condition = clear_notes['Bodega'].str.contains('FS')
clear_notes.loc[condition, 'Bodega'] = clear_notes.loc[condition, 'Bodega'].str.split('FS').str[0] + 'FS'
updates = pd.concat([updates, clear_notes])
updates = updates.drop_duplicates()

df_blanks = pd.DataFrame(
        np.full(clear_notes.shape, None),
        columns=[None for col in updates.columns])
sheets.print_in_sheets(df_blanks, '1LUI4DqbjcDrstcS_U_XeFDZqHWErpOHGvbNagZRXzig', 'temp_notes')
sheets.print_in_sheets(updates, '1LUI4DqbjcDrstcS_U_XeFDZqHWErpOHGvbNagZRXzig', 'temp_notes')