How to create a Pandas Dataframe that uses previous rows like a trigger event?

Question

I tried this code but isn't doing what I wanted it to do. I'm try to get events to work on a pandas dataframe. If the Expiration Date equals the index date, then I want a value of 0. If the prediction column shows a 1 then I would like to get a 1 value. I would like it to check previous row if a 1 was already trigger that it stays 1 until the Expiration Date triggers a 0.

df['Forecast'] = np.select([df.ExpirationDate == df.index,
                         df.Predictions == 1,
                         df.Forecast.shift(1).eq(1)], [0, 1, 1])

I tried shift but it doesn't seem to work properly as it only does it once.

                 Open        High  ...  ExpirationDate  Predictions
Date                                ...                             
2017-09-18  249.610001  250.119995  ...      2017-10-20            0
2017-09-19  250.000000  250.070007  ...      2017-10-20            0
2017-09-20  250.070007  250.190002  ...      2017-10-20            0
2017-09-21  249.880005  249.979996  ...      2017-10-20            0
2017-09-22  249.050003  249.630005  ...      2017-10-20            0

Expected output

            Predictions  Forecast  
Date                               
2017-09-18            0         0  
2017-09-19            0         0  
2017-09-20            0         0  
2017-09-21            0         0  
2017-09-22            0         0  
2017-09-25            0         0  
2017-09-26            0         0  
2017-09-27            0         0  
2017-09-28            0         0  
2017-09-29            0         0  
2017-10-02            0         0  
2017-10-03            0         0  
2017-10-04            0         0  
2017-10-05            1         1  
2017-10-06            0         1  
2017-10-09            0         1  
2017-10-10            0         1  
2017-10-11            0         1  
2017-10-12            0         1  
2017-10-13            0         1  
2017-10-16            0         1  
2017-10-17            0         1  
2017-10-18            0         1  
2017-10-19            0         1  
2017-10-20            0         0

So, Forecast stays 1 after a 1 in predictions is present. Forecast would turn back to 0 when the expiration is present.

Answer 1

We can also use Series.cumsum + Series.clip:

df['Forecast']=(

df['Predictions'].cumsum()
                 .clip(0,1)
                 .where(df.index < df.ExpirationDate)
                 .fillna(0, downcast='infer')
)
print(df)

Output

            Predictions ExpirationDate  Forecast
Date                                            
2017-09-18            0     2017-10-20         0
2017-09-19            0     2017-10-20         0
2017-09-20            0     2017-10-20         0
2017-09-21            0     2017-10-20         0
2017-09-22            0     2017-10-20         0
2017-09-25            0     2017-10-20         0
2017-09-26            0     2017-10-20         0
2017-09-27            0     2017-10-20         0
2017-09-28            0     2017-10-20         0
2017-09-29            0     2017-10-20         0
2017-10-02            0     2017-10-20         0
2017-10-03            0     2017-10-20         0
2017-10-04            0     2017-10-20         0
2017-10-05            1     2017-10-20         1
2017-10-06            0     2017-10-20         1
2017-10-09            0     2017-10-20         1
2017-10-10            0     2017-10-20         1
2017-10-11            0     2017-10-20         1
2017-10-12            0     2017-10-20         1
2017-10-13            0     2017-10-20         1
2017-10-16            0     2017-10-20         1
2017-10-17            0     2017-10-20         1
2017-10-18            0     2017-10-20         1
2017-10-19            0     2017-10-20         1
2017-10-20            0     2017-10-20         0

Answer 2

We can use Series.where to mask, allowing for forward filling with fillna. Another where sets the values only prior to the expiration Date for each row and we set the remaining values back to 0.

df['Forecast'] = (df['Predictions'].where(df.Predictions.eq(1))
                       .ffill()
                       .where(df.index < df.ExpirationDate)
                       .fillna(0, downcast='infer'))

---

            Predictions ExpirationDate  Forecast
Date                                            
2017-09-18            0     2017-10-20         0
2017-09-19            0     2017-10-20         0
2017-09-20            0     2017-10-20         0
2017-09-21            0     2017-10-20         0
2017-09-22            0     2017-10-20         0
2017-09-25            0     2017-10-20         0
2017-09-26            0     2017-10-20         0
2017-09-27            0     2017-10-20         0
2017-09-28            0     2017-10-20         0
2017-09-29            0     2017-10-20         0
2017-10-02            0     2017-10-20         0
2017-10-03            0     2017-10-20         0
2017-10-04            0     2017-10-20         0
2017-10-05            1     2017-10-20         1
2017-10-06            0     2017-10-20         1
2017-10-09            0     2017-10-20         1
2017-10-10            0     2017-10-20         1
2017-10-11            0     2017-10-20         1
2017-10-12            0     2017-10-20         1
2017-10-13            0     2017-10-20         1
2017-10-16            0     2017-10-20         1
2017-10-17            0     2017-10-20         1
2017-10-18            0     2017-10-20         1
2017-10-19            0     2017-10-20         1
2017-10-20            0     2017-10-20         0