Iterating pandas dataframe, checking values and creating some of them

You can use pivot for reshaping – you get NaN in missing values by column time, then unstack with reset_index and sort_values:

import pandas as pd

df = pd.DataFrame({'date': {0: 20100201, 1: 20100201, 2: 20100201, 3: 20100201, 4: 20100202, 5: 20100202, 6: 20100202, 7: 20100202, 8: 20100203, 9: 20100203, 10: 20100204}, 
                   'time': {0: 0, 1: 6, 2: 12, 3: 18, 4: 0, 5: 6, 6: 12, 7: 18, 8: 0, 9: 18, 10: 6},
                   'value': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 11, 10: 12}})

print (df)
        date  time  value
0   20100201     0      1
1   20100201     6      2
2   20100201    12      3
3   20100201    18      4
4   20100202     0      5
5   20100202     6      6
6   20100202    12      7
7   20100202    18      8
8   20100203     0      9
9   20100203    18     11
10  20100204     6     12

print (df.pivot(index='date', columns="time", values="value")
         .unstack()
         .reset_index(name="value")
         .sort_values('date'))

    time      date  value
0      0  20100201    1.0
4      6  20100201    2.0
8     12  20100201    3.0
12    18  20100201    4.0
1      0  20100202    5.0
5      6  20100202    6.0
9     12  20100202    7.0
13    18  20100202    8.0
2      0  20100203    9.0
6      6  20100203    NaN
10    12  20100203    NaN
14    18  20100203   11.0
3      0  20100204    NaN
7      6  20100204   12.0
11    12  20100204    NaN
15    18  20100204    NaN

Maybe you can reset_index again, if you need nice index like:

print (df.pivot(index='date', columns="time", values="value")
         .unstack()
         .reset_index(name="value")
         .sort_values('date')
         .reset_index(drop=True))

    time      date  value
0      0  20100201    1.0
1      6  20100201    2.0
2     12  20100201    3.0
3     18  20100201    4.0
4      0  20100202    5.0
5      6  20100202    6.0
6     12  20100202    7.0
7     18  20100202    8.0
8      0  20100203    9.0
9      6  20100203    NaN
10    12  20100203    NaN
11    18  20100203   11.0
12     0  20100204    NaN
13     6  20100204   12.0
14    12  20100204    NaN
15    18  20100204    NaN