How to use Pandas rolling_* functions on a forward-looking basis

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Question :

How to use Pandas rolling_* functions on a forward-looking basis

Suppose I have a time series:

In[138] rng = pd.date_range('1/10/2011', periods=10, freq='D')
In[139] ts = pd.Series(randn(len(rng)), index=rng)
In[140]
Out[140]:
2011-01-10    0
2011-01-11    1
2011-01-12    2
2011-01-13    3
2011-01-14    4
2011-01-15    5
2011-01-16    6
2011-01-17    7
2011-01-18    8
2011-01-19    9
Freq: D, dtype: int64

If I use one of the rolling_* functions, for instance rolling_sum, I can get the behavior I want for backward looking rolling calculations:

In [157]: pd.rolling_sum(ts, window=3, min_periods=0)
Out[157]: 
2011-01-10     0
2011-01-11     1
2011-01-12     3
2011-01-13     6
2011-01-14     9
2011-01-15    12
2011-01-16    15
2011-01-17    18
2011-01-18    21
2011-01-19    24
Freq: D, dtype: float64

But what if I want to do a forward-looking sum? I’ve tried something like this:

In [161]: pd.rolling_sum(ts.shift(-2, freq='D'), window=3, min_periods=0)
Out[161]: 
2011-01-08     0
2011-01-09     1
2011-01-10     3
2011-01-11     6
2011-01-12     9
2011-01-13    12
2011-01-14    15
2011-01-15    18
2011-01-16    21
2011-01-17    24
Freq: D, dtype: float64

But that’s not exactly the behavior I want. What I am looking for as an output is:

2011-01-10    3
2011-01-11    6
2011-01-12    9
2011-01-13    12
2011-01-14    15
2011-01-15    18
2011-01-16    21
2011-01-17    24
2011-01-18    17
2011-01-19    9

ie – I want the sum of the “current” day plus the next two days. My current solution is not sufficient because I care about what happens at the edges. I know I could solve this manually by setting up two additional columns that are shifted by 1 and 2 days respectively and then summing the three columns, but there’s got to be a more elegant solution.

Answer #1:

Why not just do it on the reversed Series (and reverse the answer):

In [11]: pd.rolling_sum(ts[::-1], window=3, min_periods=0)[::-1]
Out[11]:
2011-01-10     3
2011-01-11     6
2011-01-12     9
2011-01-13    12
2011-01-14    15
2011-01-15    18
2011-01-16    21
2011-01-17    24
2011-01-18    17
2011-01-19     9
Freq: D, dtype: float64
Answered By: Andy Hayden

Answer #2:

I struggled with this then found an easy way using shift.

If you want a rolling sum for the next 10 periods, try:

df['NewCol'] = df['OtherCol'].shift(-10).rolling(10, min_periods = 0).sum()

We use shift so that “OtherCol” shows up 10 rows ahead of where it normally would be, then we do a rolling sum over the previous 10 rows. Because we shifted, the previous 10 rows are actually the future 10 rows of the unshifted column. 🙂

Answer #3:

Pandas recently added a new feature which enables you to implement forward looking rolling. You have to upgrade to pandas 1.1.0 to get the new feature.

indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=3)
ts.rolling(window=indexer, min_periods=1).sum()
Answered By: ort

Answer #4:

Maybe you can try bottleneck module. When ts is large, bottleneck is much faster than pandas

import bottleneck as bn
result = bn.move_sum(ts[::-1], window=3, min_count=1)[::-1]

And bottleneck has other rolling functions, such as move_max, move_argmin, move_rank.

Answered By: Tom

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