问题描述
在 R 中，您可以使用指定的窗口计算滚动平均值，该窗口每次可以移动指定的量.
In R you can compute a rolling mean with a specified window that can shift by a specified amount each time.
不过，也许我只是没有在任何地方找到它，但您似乎无法在 Pandas 或其他 Python 库中找到它?
However maybe I just haven't found it anywhere but it doesn't seem like you can do it in pandas or some other Python library?
有没有人知道解决这个问题的方法?我会给你一个例子来说明我的意思:
Does anyone know of a way around this? I'll give you an example of what I mean:
这里我们有两周一次的数据，我计算的是两个月移动平均线，该移动平均线移动了 1 个月，即 2 行.
Here we have bi-weekly data, and I am computing the two month moving average that shifts by 1 month which is 2 rows.
所以在 R 我会做这样的事情:two_month__movavg=rollapply(mydata,4,mean,by = 2,na.pad = FALSE) Python 中没有等价物吗?
So in R I would do something like: two_month__movavg=rollapply(mydata,4,mean,by = 2,na.pad = FALSE) Is there no equivalent in Python?
编辑 1:
DATE A DEMAND ... AA DEMAND A Price
0 2006/01/01 00:30:00 8013.27833 ... 5657.67500 20.03
1 2006/01/01 01:00:00 7726.89167 ... 5460.39500 18.66
2 2006/01/01 01:30:00 7372.85833 ... 5766.02500 20.38
3 2006/01/01 02:00:00 7071.83333 ... 5503.25167 18.59
4 2006/01/01 02:30:00 6865.44000 ... 5214.01500 17.53
推荐答案
你可以再次使用rolling，只需要一点点工作和你分配索引
You can using rolling again, just need a little bit work with you assign index
这里 by = 2
by = 2
df.loc[df.index[np.arange(len(df))%by==1],'New']=df.Price.rolling(window=4).mean()
df
Price New
0 63 NaN
1 92 NaN
2 92 NaN
3 5 63.00
4 90 NaN
5 3 47.50
6 81 NaN
7 98 68.00
8 100 NaN
9 58 84.25
10 38 NaN
11 15 52.75
12 75 NaN
13 19 36.75
这篇关于是否可以使用步长大于 1 的 pandas.DataFrame.rolling ?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！