interpolation of missing values not NA

I want to interpolate (linear interpolation) data. but not indicated NA.

Here is my data.

timestamp	id	strength
1383260400000	10	0.007802251505435157
1383260400000	11	0.0050101566841440105
1383260400000	47	0.09910993935546883
1383260400000	48	0.16302926693340128

and expected data is :

timestamp	id	strength
1383260400000	10	0.007802251505435157
1383260400000	11	0.0050101566841440105
1383260400000	12	interpolated strength
1383260400000	13	interpolated strength
1383260400000	14	interpolated strength
1383260400000	15	interpolated strength
1383260400000	16	interpolated strength
1383260400000	17	interpolated strength
1383260400000	…	interpolated strength
1383260400000	47	0.09910993935546883
1383260400000	48	0.16302926693340128

and there are data with another timestamp.

timestamp	id	strength
1383261000000	73	0.00034018953748529387
1383261000000	80	0.015745603609017354
1383261000000	81	0.01332206498346922
1383261000000	101	0.003292329017912283
1383261000000	102	0.0028735259562922954
1383261000000	103	0.0033962211735905955

and the last id is 2025 and timestamp is 13833462000000

Answer

First convert values of id to index, so possible use lambda function in GroupBy.apply with Series.reindex and 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