What is the most efficient way to fill missing values in this data frame?

t = df.groupby('ID')['year'].agg(['min','max']).reset_index()
t['missing'] = t.transform(lambda x: [y for y in range(x['min'], x['max']+1) if y not in x.values], axis=1)
t = t[['ID','missing']].explode('missing').dropna()
t['number'] = 0
t.columns = ['ID','year','number']
pd.concat([df,t]).sort_values(by=['ID','year'])

Output

    ID  year    number
0   A   2017    1
0   A   2018    0
1   A   2019    1
2   B   2017    1
3   B   2018    1
4   C   2016    1
2   C   2017    0
2   C   2018    0
5   C   2019    1

A slightly faster approach rather than using explode is to use pd.Series constructor. And you can use .iloc if years are already sorted from earliest to latest.

idx = df.groupby('ID')['year'].apply(lambda x: pd.Series(np.arange(x.iloc[0], x.iloc[-1]+1))).reset_index()
df.set_index(['ID','year']).reindex(pd.MultiIndex.from_arrays([idx['ID'], idx['year']]), fill_value=0).reset_index()

Output:

  ID  year  number
0  A  2017       1
1  A  2018       0
2  A  2019       1
3  B  2017       1
4  B  2018       1
5  C  2016       1
6  C  2017       0
7  C  2018       0
8  C  2019       1

Here is another approach with reindex

u = df.groupby('ID')['year'].apply(lambda x: range(x.min(),x.max()+1)).explode()

out = (df.set_index(['ID','year']).reindex(u.reset_index().to_numpy(),fill_value=0)
         .reset_index())

---

  ID  year  number
0  A  2017       1
1  A  2018       0
2  A  2019       1
3  B  2017       1
4  B  2018       1
5  C  2016       1
6  C  2017       0
7  C  2018       0
8  C  2019       1