Python DataFrame结合了两个布尔列

Question

我有一个数据框。我正在向前和向后的缩写。后来，进行比较并产生布尔输出。接下来，我想执行逻辑和这些结果，并产生一个结果。

代码：

xdf = pd.DataFrame({'data':range(0,6)},index=pd.date_range('2022-06-03 00:00:00', '2022-06-03 00:00:25', freq='5s'))

# perform 1 row backward substraction
bs = xdf['data'].diff(1).abs().le(1)

# perform 1 row forward substraction
fs= xdf['data'].diff(-1).abs().le(1)


bs = 
2022-06-03 00:00:00    False
2022-06-03 00:00:05     True
2022-06-03 00:00:10     True
2022-06-03 00:00:15     True
2022-06-03 00:00:20     True
2022-06-03 00:00:25     True
Freq: 5S, Name: data, dtype: bool

fs = 

2022-06-03 00:00:00     True
2022-06-03 00:00:05     True
2022-06-03 00:00:10     True
2022-06-03 00:00:15     True
2022-06-03 00:00:20     True
2022-06-03 00:00:25    False

目前和预期输出：

xdf['validation'] = np.logical_and(sa,sb)
2022-06-03 00:00:00    False
2022-06-03 00:00:05     True
2022-06-03 00:00:10     True
2022-06-03 00:00:15     True
2022-06-03 00:00:20     True
2022-06-03 00:00:25    False
Freq: 5S, Name: data, dtype: bool

输出是正确的，这是我期望的。我的问题是，有什么方法可以在单个行码中计算上述所有（正向缩写和向后缩）？

Answer 1

也许您可以尝试循环[1，-1]并使用np.logical_and.reduce

xdf['validation'] = np.logical_and.reduce([xdf['data'].diff(x).abs().le(1) for x in [1,-1]])

print(xdf)

                     data  validation
2022-06-03 00:00:00     0       False
2022-06-03 00:00:05     1        True
2022-06-03 00:00:10     2        True
2022-06-03 00:00:15     3        True
2022-06-03 00:00:20     4        True
2022-06-03 00:00:25     5       False

Answer 2

iiuc，您可以使用滚动最大值，然后检查最大目标是否≤您的目标：

xdf['validation'] = xdf['data'].diff(-1).abs().rolling(2).max().le(1)

输出：

                     data  validation
2022-06-03 00:00:00     0       False
2022-06-03 00:00:05     1        True
2022-06-03 00:00:10     2        True
2022-06-03 00:00:15     3        True
2022-06-03 00:00:20     4        True
2022-06-03 00:00:25     5       False