Python Dataframe使用窗口创建列表列的滚动聚合

我有一个df，它有一列列表

我想知道是否有一种方法可以为给定窗口创建“single_input_vector”列的滚动聚合。我查看了下面的SO链接，但它没有提供包含窗口的方法。在我的例子中，3个窗口所需的输出列是：

Row1: [[24.68, 164.93]] 
Row2: [[24.68, 164.93], [24.18, 164.89]]
Row3: [[24.68, 164.93], [24.18, 164.89], [23.99, 164.63]] 
Row4: [[24.18, 164.89], [23.99, 164.63], [24.14, 163.92]]

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诸如此类。

我想不出更有效的方法来实现这一点，因此，虽然这确实有效，但在海量数据集上可能存在性能限制

我们基本上使用滚动计数来创建一组开始：停止的切片索引

import pandas as pd
import numpy as np
# Get some time series data
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/timeseries.csv")
input_cols = ['A', 'B']
df['single_input_vector'] = df[input_cols].apply(tuple, axis=1).apply(list)


window = 3

df['len'] = df['A'].rolling(window=window).count()

df['vector_list'] = df.apply(lambda x: df['single_input_vector'][max(0,x.name-(window-1)):int(x.name)+1].values, axis=1)

import pandas as pd
import numpy as np
# Get some time series data
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/timeseries.csv")
input_cols = ['A', 'B']
df['single_input_vector'] = df[input_cols].apply(tuple, axis=1).apply(list)


window = 3

df['len'] = df['A'].rolling(window=window).count()

df['vector_list'] = df.apply(lambda x: df['single_input_vector'][max(0,x.name-(window-1)):int(x.name)+1].values, axis=1)