# Time series: Why does this data have seasonality on periods where observed values are zero

I'm a novice to time series and am using seasonal_decompose() to split a time series into three components: trend, seasonality, and residuals as below:

As shown in the observed value at the top, most of the values are 0 except for some periods in year 2020 and 2021. However, the seasonal plot shows the same recurring pattern of seasonality across "all" years, not for periods where the observed values are non-zero.

I'm confused as to:

1. why there is a seasonality in years 2016 - 2019 where observed values are 0
2. why the seasonality has the same recurring pattern across all years.

Here is the reproducible data for your reference:

import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose

bb_dict = {'YYYY-MM': {0: '2016-01',
1: '2016-02',
2: '2016-03',
3: '2016-04',
4: '2016-05',
5: '2016-06',
6: '2016-07',
7: '2016-08',
8: '2016-09',
9: '2016-10',
10: '2016-11',
11: '2016-12',
12: '2017-01',
13: '2017-02',
14: '2017-03',
15: '2017-04',
16: '2017-05',
17: '2017-06',
18: '2017-07',
19: '2017-08',
20: '2017-09',
21: '2017-10',
22: '2017-11',
23: '2017-12',
24: '2018-01',
25: '2018-02',
26: '2018-03',
27: '2018-04',
28: '2018-05',
29: '2018-06',
30: '2018-07',
31: '2018-08',
32: '2018-09',
33: '2018-10',
34: '2018-11',
35: '2018-12',
36: '2019-01',
37: '2019-02',
38: '2019-03',
39: '2019-04',
40: '2019-05',
41: '2019-06',
42: '2019-07',
43: '2019-08',
44: '2019-09',
45: '2019-10',
46: '2019-11',
47: '2019-12',
48: '2020-01',
49: '2020-02',
50: '2020-03',
51: '2020-04',
52: '2020-05',
53: '2020-06',
54: '2020-07',
55: '2020-08',
56: '2020-09',
57: '2020-10',
58: '2020-11',
59: '2020-12',
60: '2021-01',
61: '2021-02',
62: '2021-03',
63: '2021-04',
64: '2021-05',
65: '2021-06',
66: '2021-07',
67: '2021-08',
68: '2021-09',
69: '2021-10',
70: '2021-11',
71: '2021-12'},
'DE': {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
10: 0.0,
11: 0.0,
12: 0.0,
13: 0.0,
14: 0.0,
15: 0.0,
16: 0.0,
17: 0.0,
18: 0.0,
19: 0.0,
20: 0.0,
21: 0.0,
22: 0.0,
23: 0.0,
24: 0.0,
25: 0.0,
26: 0.0,
27: 0.0,
28: 0.0,
29: 0.0,
30: 0.0,
31: 0.0,
32: 0.0,
33: 0.0,
34: 0.0,
35: 0.0,
36: 0.0,
37: 0.0,
38: 0.0,
39: 0.0,
40: 0.0,
41: 0.0,
42: 0.0,
43: 0.0,
44: 0.0,
45: 0.0,
46: 0.0,
47: 0.0,
48: 0.0,
49: 0.0,
50: 0.0,
51: 0.0,
52: 0.0,
53: 0.0,
54: -10.31,
55: -13.85,
56: -1.79,
57: 0.0,
58: 0.0,
59: 0.0,
60: 0.0,
61: 0.0,
62: 0.0,
63: 0.0,
64: 0.0,
65: -35.82,
66: -35.52,
67: -12.02,
68: -15.44,
69: -13.17,
70: 0.0,
71: 0.0}}

bb = pd.DataFrame.from_dict(bb_dict)
bb['YYYY-MM'] = pd.to_datetime(bb['YYYY-MM'], format='%Y-%m')
bb = bb.set_index('YYYY-MM')

result = seasonal_decompose(bb['DE'], model = 'additive')
result.plot();