pandas rolling max with groupby
It looks like you need cummax() instead of .rolling(N).max()
In [29]: df['new'] = df.groupby('id').value.cummax()In [30]: dfOut[30]: id value new0 1 3 31 1 6 62 1 3 63 2 2 24 2 1 2Timing (using brand new Pandas version 0.20.1):
In [3]: df = pd.concat([df] * 10**4, ignore_index=True)In [4]: df.shapeOut[4]: (50000, 2)In [5]: %timeit df.groupby('id').value.apply(lambda x: x.cummax())100 loops, best of 3: 15.8 ms per loopIn [6]: %timeit df.groupby('id').value.cummax()100 loops, best of 3: 4.09 ms per loop
Using apply will be a tiny bit faster:
# Using apply df['output'] = df.groupby('id').value.apply(lambda x: x.cummax())%timeit df['output'] = df.groupby('id').value.apply(lambda x: x.cummax())1000 loops, best of 3: 1.57 ms per loopOther method:
df['output'] = df.groupby('id').value.cummax()%timeit df['output'] = df.groupby('id').value.cummax()1000 loops, best of 3: 1.66 ms per loop