I can add a new column c that is a sum of the last two values in b as shown below...
df['c'] = df.b.rolling(window = 2).sum().shift()
df
    a   b     c
0   1   3   NaN
1   1   0   NaN
2   0   6   3.0
3   1   0   6.0
4   0   0   6.0
5   1   7   0.0
6   0   0   7.0
7   0   7   7.0
8   1   4   7.0
9   1   2   11.0
...however, what if I want to group by a first? E.g. I can do this:
df['c'] = df.groupby(['a'])['b'].shift(1) + df.groupby(['a'])['b'].shift(2)
Is there a more elegant way for summing a large number of shifts (1, 2, ...n) on a group?
f = lambda x: x.rolling(2).sum().shift()
df['c'] = df.groupby('a').b.apply(f)
df

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