Adding a total per level-2 index in a multiindex pandas dataframe

I have a dataframe:

df_full = pd.DataFrame.from_dict({('group', ''): {0: 'A',
  1: 'A',
  2: 'A',
  3: 'A',
  4: 'A',
  5: 'A',
  6: 'A',
  7: 'B',
  8: 'B',
  9: 'B',
  10: 'B',
  11: 'B',
  12: 'B',
  13: 'B'},
 ('category', ''): {0: 'Books',
  1: 'Candy',
  2: 'Pencil',
  3: 'Table',
  4: 'PC',
  5: 'Printer',
  6: 'Lamp',
  7: 'Books',
  8: 'Candy',
  9: 'Pencil',
  10: 'Table',
  11: 'PC',
  12: 'Printer',
  13: 'Lamp'},
 (pd.Timestamp('2021-06-28 00:00:00'),
  'Sales_1'): {0: 9.937449997200002, 1: 30.71300000639998, 2: 58.81199999639999, 3: 25.661999978399994, 4: 3.657999996, 5: 12.0879999972, 6: 61.16600000040001, 7: 6.319439989199998, 8: 12.333119997600003, 9: 24.0544100028, 10: 24.384659998799997, 11: 1.9992000012000002, 12: 0.324, 13: 40.69122000000001},
 (pd.Timestamp('2021-06-28 00:00:00'),
  'Sales_2'): {0: 21.890370397789923, 1: 28.300470581874837, 2: 53.52039700062155, 3: 52.425508769690694, 4: 6.384936971649232, 5: 6.807138946302334, 6: 52.172, 7: 5.916852561, 8: 5.810764652, 9: 12.1243325, 10: 17.88071596, 11: 0.913782413, 12: 0.869207661, 13: 20.9447844},
 (pd.Timestamp('2021-06-28 00:00:00'), 'last_week_sales'): {0: np.nan,
  1: np.nan,
  2: np.nan,
  3: np.nan,
  4: np.nan,
  5: np.nan,
  6: np.nan,
  7: np.nan,
  8: np.nan,
  9: np.nan,
  10: np.nan,
  11: np.nan,
  12: np.nan,
  13: np.nan},
 (pd.Timestamp('2021-06-28 00:00:00'), 'total_orders'): {0: 86.0,
  1: 66.0,
  2: 188.0,
  3: 556.0,
  4: 12.0,
  5: 4.0,
  6: 56.0,
  7: 90.0,
  8: 26.0,
  9: 49.0,
  10: 250.0,
  11: 7.0,
  12: 2.0,
  13: 44.0},
 (pd.Timestamp('2021-06-28 00:00:00'), 'total_sales'): {0: 4390.11,
  1: 24825.059999999998,
  2: 48592.39999999998,
  3: 60629.77,
  4: 831.22,
  5: 1545.71,
  6: 34584.99,
  7: 5641.54,
  8: 6798.75,
  9: 13290.13,
  10: 42692.68000000001,
  11: 947.65,
  12: 329.0,
  13: 29889.65},
 (pd.Timestamp('2021-07-05 00:00:00'),
  'Sales_1'): {0: 13.690399997999998, 1: 38.723000005199985, 2: 72.4443400032, 3: 36.75802000560001, 4: 5.691999996, 5: 7.206999998399999, 6: 66.55265999039996, 7: 6.4613199911999954, 8: 12.845630001599998, 9: 26.032340003999998, 10: 30.1634600016, 11: 1.0203399996, 12: 1.4089999991999997, 13: 43.67116000320002},
 (pd.Timestamp('2021-07-05 00:00:00'),
  'Sales_2'): {0: 22.874363860953647, 1: 29.5726042895728, 2: 55.926190956481534, 3: 54.7820864335212, 4: 6.671946105284065, 5: 7.113126469779095, 6: 54.517, 7: 6.194107518, 8: 6.083562133, 9: 12.69221484, 10: 18.71872129, 11: 0.956574175, 12: 0.910216433, 13: 21.92632044},
 (pd.Timestamp('2021-07-05 00:00:00'), 'last_week_sales'): {0: 4390.11,
  1: 24825.059999999998,
  2: 48592.39999999998,
  3: 60629.77,
  4: 831.22,
  5: 1545.71,
  6: 34584.99,
  7: 5641.54,
  8: 6798.75,
  9: 13290.13,
  10: 42692.68000000001,
  11: 947.65,
  12: 329.0,
  13: 29889.65},
 (pd.Timestamp('2021-07-05 00:00:00'), 'total_orders'): {0: 109.0,
  1: 48.0,
  2: 174.0,
  3: 587.0,
  4: 13.0,
  5: 5.0,
  6: 43.0,
  7: 62.0,
  8: 13.0,
  9: 37.0,
  10: 196.0,
  11: 8.0,
  12: 1.0,
  13: 33.0},
 (pd.Timestamp('2021-07-05 00:00:00'), 'total_sales'): {0: 3453.02,
  1: 17868.730000000003,
  2: 44707.82999999999,
  3: 60558.97999999999,
  4: 1261.0,
  5: 1914.6000000000001,
  6: 24146.09,
  7: 6201.489999999999,
  8: 5513.960000000001,
  9: 9645.87,
  10: 25086.785,
  11: 663.0,
  12: 448.61,
  13: 26332.7}}).set_index(['group','category'])

I am trying to get a total for each column per category. So in this df example adding 2 lines below Lamp denoting the totals of each column. Red lines indicate the desired totals placement:

What I've tried:

df_out['total'] = df_out.sum(level=1).loc[:, (slice(None), 'total_sales')]

But get:

ValueError: Wrong number of items passed 4, placement implies 1

I also checked this question but could not apply it to my self.