通过比较两列来跟踪 pandas 的状态反转 [英] track state reversal in Pandas by comparing two columns

问题描述

这是我之前的问题的后续内容:

This is a follow-on to my previous question: detecting value crossing between columns in Pandas

假设我有两列df['a']和df['b']，为简化起见，在此分别称为'a'和'b'

Let's assume I have two columns, df['a'] and df['b'], called 'a' and 'b' here to simplify

在上一个问题中，我试图检测以下位置的索引:a [i-1]< b [i]和a [i]> = b [i]

In the previous question, I was trying to detect indices where: a[i - 1] < b[i] and a[i] >= b[i]

现在，我正在尝试跟踪两个更改:

now, I am trying to track two changes:

a[i - 1] < b[i] and a[i] >= b[i]

和

a[i - 1] >= b[i] and a[i] < b[i]

并创建一个基于最后发生的更改设置值的列.

and create a column with a value set based on the LAST change that occurred.

这是一些伪代码:

state = 0
result = []
for i in myIndex:
    if a[i - 1] < b[i] and a[i] >= b[i]:
        state = 1
    elif a[i - 1] >= b[i] and a[i] < b[i]:
        state = 0

result.append(state)

在Pandas中是否有惯用的(非循环)方式来实现这一目标?

is there an idiomatic (non-looping) way to achieve this in Pandas?

我知道这个问题引起了一些混乱，所以我没有正确说出它.此处的示例:

I realize this question has caused some confusion, so I didn't word it properly. An example here:

假设我有两列用于跟踪的两个条件的输出:

Let's assume I have two columns for the outputs of the two conditions I'm tracking:

 cond_A    cond_B
 false     false
 false     true
 false     false
 false     false
 true      false
 false     false

然后输出应该是(如果我们将0和1设置为输出值):

then the output should be (if we set 0 and 1 as output values):

cond_A    cond_B    output
false     false     0
false     true      1
false     false     1
false     false     1
true      false     0
false     false     0

因此，如果两个条件都为假，则我们将重用最后一个状态，如果条件为真，则将设置状态.

so, if both conditions are false, we reuse the last state, if a condition is true, we set the state.

推荐答案

这是RS触发器的等式:

this is the equation of a RS flip flop: https://en.wikipedia.org/wiki/Flip-flop_(electronics)

使用给出的示例作为数据框:

using the example given as a dataframe:

print(df)

   cond_A  cond_B
0   False   False
1   False    True
2   False   False
3   False   False
4    True   False
5   False   False

---

更新

df['state']=df.any(axis=1).where(df['cond_B'].cumsum().ge(1),0).cumsum()%2
print(df)


   cond_A  cond_B  state
0   False   False      0
1   False    True      1
2   False   False      1
3   False   False      1
4    True   False      0
5   False   False      0

---

如果您有两个独立的系列

---

If you have two independent series

(cond_A | cond_B).where(cond_B.cumsum().ge(1),0).cumsum()%2

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