these could be implemented in cython, should be very straightforward as we already have much templated code
In [1]: np.random.seed(1234)
In [4]: G = 1000
In [5]: N = 10000
In [6]: df = pd.DataFrame({'A':np.random.randint(0,G,size=N),'B':np.random.randn(N)})
In [7]: %timeit df.groupby('A').cumsum()
1000 loops, best of 3: 1.27 ms per loop
In [8]: %timeit df.groupby('A').cummax()
1 loop, best of 3: 799 ms per loop
In [9]: %timeit df.groupby('A').cummin()
1 loop, best of 3: 796 ms per loop
In [10]: %timeit df.groupby('A').cumprod()
1000 loops, best of 3: 1.26 ms per loop
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