This will be a very efficient method
In [39]: df = DataFrame(np.random.randn(10,2))
In [41]: start=3
In [42]: stop=4
In [43]: df.iloc[(max(df.index.get_loc(start)-2,0)):min(df.index.get_loc(stop)+2,len(df))]
Out[43]:
0 1
1 0.348326 1.413770
2 1.898784 0.053780
3 0.825941 -1.986920
4 0.075956 -0.324657
5 -2.736800 -0.075813
[5 rows x 2 columns]
If you want essentially a function of arbitrary indexers, just create a list
of the ones you want and pass to .iloc
In [18]: index_wanted = [71, 102, 103, 179, 505, 506, 607]
In [19]: from itertools import chain
In [20]: df = DataFrame(np.random.randn(1000,2))
You prob want unique ones
f = lambda i: [ i-2, i-1, i, i+1, i+2 ]
In [21]: indexers = Index(list(chain(*[ f(i) for i in [71, 102, 103, 179, 505, 506, 607] ]))).unique()
In [22]: df.iloc[indexers]
Out[22]:
0 1
69 0.792996 0.264597
70 1.084315 -0.620006
71 -0.030432 1.219576
72 -0.767855 0.765041
73 -0.637771 -0.103378
100 -1.087505 1.698133
101 1.007143 2.594046
102 -0.307440 0.308360
103 0.944429 -0.411742
104 1.332445 -0.149350
105 0.165213 1.125668
177 0.409580 -0.375709
178 -1.757021 -0.266762
179 0.736809 -1.286848
180 1.856241 0.176931
181 -0.492590 0.083519
503 -0.651788 0.717922
504 -1.612517 -1.729867
505 -1.786807 -0.066421
506 1.423571 0.768161
507 0.186871 1.162447
508 1.233441 -0.028261
605 -0.060117 -1.459827
606 -0.541765 -0.350981
607 -1.166172 -0.026404
608 -0.045338 1.641864
609 -0.337748 0.955940
[27 rows x 2 columns]