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我有一个dataframe，我想创建一个带有事件标签的列。如果条件为true，则事件将获得一个数字。但是如果连续的值是事件，我想给出相同的事件标签。你知道吗？我尝试使用.apply和.rolling，但没有成功

df = pd.DataFrame({'Signal_1' : [0,0,0,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,1]})

    Signal_1  ExpectedColumn
0          0             NaN 
1          0             NaN
2          0             NaN
3          1               1
4          1               1
5          0             NaN
6          0             NaN
7          1               2
8          1               2
9          1               2
10         1               2
11         0             NaN
12         0             NaN
13         0             NaN
14         1               3
15         1               3
16         1               3
17         1               3
18         1               3

数据帧：

df = pd.DataFrame({'Signal_1' : [0,0,0,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,1]})

    Signal_1  ExpectedColumn
0          0             NaN 
1          0             NaN
2          0             NaN
3          1               1
4          1               1
5          0             NaN
6          0             NaN
7          1               2
8          1               2
9          1               2
10         1               2
11         0             NaN
12         0             NaN
13         0             NaN
14         1               3
15         1               3
16         1               3
17         1               3
18         1               3

---

这里有一个方法。首先创建倒计时标志，然后执行累计和。然后用NaN值更正它

df = pd.DataFrame({'Signal_1' : [0,0,0,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,1]})

    Signal_1  ExpectedColumn
0          0             NaN 
1          0             NaN
2          0             NaN
3          1               1
4          1               1
5          0             NaN
6          0             NaN
7          1               2
8          1               2
9          1               2
10         1               2
11         0             NaN
12         0             NaN
13         0             NaN
14         1               3
15         1               3
16         1               3
17         1               3
18         1               3

import pandas as pd
import numpy as np

df = pd.DataFrame({'Signal_1' : [0,0,0,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,1]})

# Only count up when the previous sample = 0, and the current sample = 1
df["shift"] = df["Signal_1"].shift(1)
df["countup"] = np.where((df["Signal_1"] == 1) & (df["shift"] == 0),1,0)

# Cumsum the countup flag and set to NaN when sample = 0
df["result"] = df["countup"].cumsum()
df["result"] = np.where(df["Signal_1"] == 0, np.NaN, df["result"] )