是否可以使用步长大于 1 的 pandas.DataFrame.rolling ？
use*_*831 11 python numpy r zoo pandas
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。
但是，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？
有谁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：
这里我们有双周数据，我正在计算两个月移动平均线，该移动平均线移动 1 个月，即 2 行。
所以在 RI 中会做类似的事情：two_month__movavg=rollapply(mydata,4,mean,by = 2,na.pad = FALSE)
Python 中没有等价物吗？
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
小智 7
如果数据量不是太大，这里有一个简单的方法：
by = 2
win = 4
start = 3 ## it is the index of your 1st valid value.
df.rolling(win).mean()[start::by] ## calculate all, choose what you need.
- 用 iloc 切片可能更安全，即。`df.rolling(win).mean().iloc[start::by]` 如果 DF 索引不是从 0 开始的数字序列。 (2认同)
所以，我知道自从提出这个问题以来已经很长时间了，因为我遇到了同样的问题，并且在处理长时间序列时，您确实希望避免对您不感兴趣的值进行不必要的计算。由于 Pandas 滚动方法没有实现step参数，因此我使用 numpy 编写了一个解决方法。
它基本上是这个链接中的解决方案和BENY提出的索引的结合。
def apply_rolling_data(data, col, function, window, step=1, labels=None):
"""Perform a rolling window analysis at the column `col` from `data`
Given a dataframe `data` with time series, call `function` at
sections of length `window` at the data of column `col`. Append
the results to `data` at a new columns with name `label`.
Parameters
----------
data : DataFrame
Data to be analyzed, the dataframe must stores time series
columnwise, i.e., each column represent a time series and each
row a time index
col : str
Name of the column from `data` to be analyzed
function : callable
Function to be called to calculate the rolling window
analysis, the function must receive as input an array or
pandas series. Its output must be either a number or a pandas
series
window : int
length of the window to perform the analysis
step : int
step to take between two consecutive windows
labels : str
Name of the column for the output, if None it defaults to
'MEASURE'. It is only used if `function` outputs a number, if
it outputs a Series then each index of the series is going to
be used as the names of their respective columns in the output
Returns
-------
data : DataFrame
Input dataframe with added columns with the result of the
analysis performed
"""
x = _strided_app(data[col].to_numpy(), window, step)
rolled = np.apply_along_axis(function, 1, x)
if labels is None:
labels = [f"metric_{i}" for i in range(rolled.shape[1])]
for col in labels:
data[col] = np.nan
data.loc[
data.index[
[False]*(window-1)
+ list(np.arange(len(data) - (window-1)) % step == 0)],
labels] = rolled
return data
def _strided_app(a, L, S): # Window len = L, Stride len/stepsize = S
"""returns an array that is strided
"""
nrows = ((a.size-L)//S)+1
n = a.strides[0]
return np.lib.stride_tricks.as_strided(
a, shape=(nrows, L), strides=(S*n, n))
您可以再次使用滚动，只需要一点点工作与您分配索引
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
现在，这对于一维数据数组来说有点大材小用，但您可以简化它并提取您需要的内容。由于 pandas 可以依赖 numpy，因此您可能需要检查它们的滚动/跨步功能是如何实现的。20 个连续数字的结果。7 天的窗口，跨步/滑动 2
z = np.arange(20)
z #array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
s = stride(z, (7,), (2,))
np.mean(s, axis=1) # array([ 3., 5., 7., 9., 11., 13., 15.])
这是我使用的代码，没有文档的主要部分。它源自 numpy 中 strided 函数的许多实现，可以在此站点上找到。有变体和化身，这只是另一个。
def stride(a, win=(3, 3), stepby=(1, 1)):
"""Provide a 2D sliding/moving view of an array.
There is no edge correction for outputs. Use the `pad_` function first."""
err = """Array shape, window and/or step size error.
Use win=(3,) with stepby=(1,) for 1D array
or win=(3,3) with stepby=(1,1) for 2D array
or win=(1,3,3) with stepby=(1,1,1) for 3D
---- a.ndim != len(win) != len(stepby) ----
"""
from numpy.lib.stride_tricks import as_strided
a_ndim = a.ndim
if isinstance(win, int):
win = (win,) * a_ndim
if isinstance(stepby, int):
stepby = (stepby,) * a_ndim
assert (a_ndim == len(win)) and (len(win) == len(stepby)), err
shp = np.array(a.shape) # array shape (r, c) or (d, r, c)
win_shp = np.array(win) # window (3, 3) or (1, 3, 3)
ss = np.array(stepby) # step by (1, 1) or (1, 1, 1)
newshape = tuple(((shp - win_shp) // ss) + 1) + tuple(win_shp)
newstrides = tuple(np.array(a.strides) * ss) + a.strides
a_s = as_strided(a, shape=newshape, strides=newstrides, subok=True).squeeze()
return a_s
我没有指出您可以创建一个输出，并将其作为一列附加到 pandas 中。回到上面使用的原始定义
nans = np.full_like(z, np.nan, dtype='float') # z is the 20 number sequence
means = np.mean(s, axis=1) # results from the strided mean
# assign the means to the output array skipping the first and last 3 and striding by 2
nans[3:-3:2] = means
nans # array([nan, nan, nan, 3., nan, 5., nan, 7., nan, 9., nan, 11., nan, 13., nan, 15., nan, nan, nan, nan])
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