Python 缺失值的插值不是 NA

首先将id的值转换为索引，因此可以使用GroupBy.apply中的lambda函数，以及Series.reindex和Series.interpolate:

f = lambda x: x.reindex(range(x.index.min(), x.index.max() + 1)).interpolate()
df = df.set_index('id').groupby('timestamp')['strength'].apply(f).reset_index()

print (df)
        timestamp  id  strength
0   1383260400000  10  0.007802
1   1383260400000  11  0.005010
2   1383260400000  12  0.007624
3   1383260400000  13  0.010238
4   1383260400000  14  0.012852
5   1383260400000  15  0.015466
6   1383260400000  16  0.018080
7   1383260400000  17  0.020693
8   1383260400000  18  0.023307
9   1383260400000  19  0.025921
10  1383260400000  20  0.028535
11  1383260400000  21  0.031149
12  1383260400000  22  0.033763
13  1383260400000  23  0.036377
14  1383260400000  24  0.038991
15  1383260400000  25  0.041605
16  1383260400000  26  0.044218
17  1383260400000  27  0.046832
18  1383260400000  28  0.049446
19  1383260400000  29  0.052060
20  1383260400000  30  0.054674
21  1383260400000  31  0.057288
22  1383260400000  32  0.059902
23  1383260400000  33  0.062516
24  1383260400000  34  0.065129
25  1383260400000  35  0.067743
26  1383260400000  36  0.070357
27  1383260400000  37  0.072971
28  1383260400000  38  0.075585
29  1383260400000  39  0.078199
30  1383260400000  40  0.080813
31  1383260400000  41  0.083427
32  1383260400000  42  0.086041
33  1383260400000  43  0.088654
34  1383260400000  44  0.091268
35  1383260400000  45  0.093882
36  1383260400000  46  0.096496
37  1383260400000  47  0.099110
38  1383260400000  48  0.163029