如何在前瞻性的基础上使用Pandas rolling_ *函数 [英] How to use Pandas rolling_* functions on a forward-looking basis
问题描述
假设我有一个时间序列:
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
如果使用rolling_ *函数之一,例如rolling_sum,则可以得到想要的行为,用于向后看的滚动计算:
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
但是,如果我想做一个前瞻性的款项,该怎么办?我已经尝试过这样的事情:
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
但这并不是我想要的行为.我正在寻找的输出是:
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-我想要当前"天加上接下来的两天的总和.我目前的解决方案还不够,因为我担心边缘会发生什么.我知道我可以通过设置另外两个分别移动1天和2天的列,然后将三列相加来手动解决此问题,但是必须有一个更优雅的解决方案.
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.
推荐答案
为什么不只是在相反的系列上做(并反过来回答):
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
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