You can use itertools.product to generate all combinations, then construct a df from this, merge it with your original df along with fillna to fill missing count values with 0:
In [77]:
import itertools
all_person_ids = [0, 1, 2]
all_statuses = ['pass', 'fail']
all_years = [1980, 1981, 1982]
combined = [all_person_ids, all_statuses, all_years]
df1 = pd.DataFrame(columns = ['person_id', 'status', 'year'], data=list(itertools.product(*combined)))
df1
Out[77]:
person_id status year
0 0 pass 1980
1 0 pass 1981
2 0 pass 1982
3 0 fail 1980
4 0 fail 1981
5 0 fail 1982
6 1 pass 1980
7 1 pass 1981
8 1 pass 1982
9 1 fail 1980
10 1 fail 1981
11 1 fail 1982
12 2 pass 1980
13 2 pass 1981
14 2 pass 1982
15 2 fail 1980
16 2 fail 1981
17 2 fail 1982
In [82]:
df1 = df1.merge(df, how='left').fillna(0)
df1
Out[82]:
person_id status year count
0 0 pass 1980 4
1 0 pass 1981 0
2 0 pass 1982 0
3 0 fail 1980 0
4 0 fail 1981 0
5 0 fail 1982 1
6 1 pass 1980 0
7 1 pass 1981 2
8 1 pass 1982 0
9 1 fail 1980 0
10 1 fail 1981 0
11 1 fail 1982 0
12 2 pass 1980 0
13 2 pass 1981 0
14 2 pass 1982 0
15 2 fail 1980 0
16 2 fail 1981 0
17 2 fail 1982 0