Alternative to pandas groupby with lambda and diff

Assume I have df below:

    ID  V
0   A   1
1   A   2
2   B   4
3   B   3

And the desired output is:

    V
0   NaN
1   1.0
2   NaN
3   -1.0

This can be done using groupby and lambda with diff:

df.groupby('ID').apply(lambda x: x.diff())

I am trying to come up with a solution that doesn’t rely on lambda as this quickly becomes very slow. Any ideas?

UPDATE

Performance comparison between (1) using groupby, lambda and diff, and, (2) only using groupby and diff:

1

3.67 ms ± 238 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

2

2.42 ms ± 20.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

>>> df
  ID  V
0  A  1
1  A  2
2  B  4
3  B  3
>>> df.groupby('ID').diff()
     V
0  NaN
1  1.0
2  NaN
3 -1.0
>>>

 df.groupby('ID')['V'].agg('diff')

0    NaN
1    1.0
2    NaN
3   -1.0