问题
In pandas, we have pd.rolling_quantile(). And in numpy, we have np.percentile(), but I'm not sure how to do the rolling/moving version of it.
To explain what I meant by moving/rolling percentile/quantile:
Given array [1, 5, 7, 2, 4, 6, 9, 3, 8, 10], the moving quantile 0.5 (i.e. moving percentile 50%) with window size 3 is:
1
5 - 1 5 7 -> 0.5 quantile = 5
7 - 5 7 2 -> 5
2 - 7 2 4 -> 4
4 - 2 4 6 -> 4
6 - 4 6 9 -> 6
9 - 6 9 3 -> 6
3 - 9 3 8 -> 8
8 - 3 8 10 -> 8
10
So [5, 5, 4, 4, 6, 6, 8, 8] is the answer. To make the resulting series the same length as the input, some implementation inserts NaN or None, while pandas.rolling_quantile() allows to compute the first two quantile values by a smaller window.
回答1:
We could create the sliding windows with np.lib.stride_tricks.as_strided, implemented as a function as strided_app -
In [14]: a = np.array([1, 5, 7, 2, 4, 6, 9, 3, 8, 10]) # input array
In [15]: W = 3 # window length
In [16]: np.percentile(strided_app(a, W,1), 50, axis=-1)
Out[16]: array([ 5., 5., 4., 4., 6., 6., 8., 8.])
To make it of the same length as the input, we could pad NaNs with np.concatenate or easier with np.pad. Hence, for W=3, it would be -
In [39]: np.pad(_, 1, 'constant', constant_values=(np.nan)) #_ is previous one
Out[39]: array([ nan, 5., 5., 4., 4., 6., 6., 8., 8., nan])
回答2:
series = pd.Series([1, 5, 7, 2, 4, 6, 9, 3, 8, 10])
In [194]: series.rolling(window = 3, center = True).quantile(.5)
Out[194]:
0 nan
1 5.0000
2 5.0000
3 4.0000
4 4.0000
5 6.0000
6 6.0000
7 8.0000
8 8.0000
9 nan
dtype: float64
Center is False by default. So you need to manually set it to True for the quantile-calculation window to embrace the current index symmetrically.
来源：https://stackoverflow.com/questions/47585465/how-to-compute-moving-or-rolling-if-you-will-percentile-quantile-for-a-1d-arr