How to sum certain values in a pandas column DataFrame in a specific date range

Question

I have a large DataFrame that looks something like this: df =

    UPC   Unit_Sales  Price   Price_Change  Date 
 0   22          15    1.99         NaN     2017-10-10
 1   22          7     2.19         True    2017-10-12
 2   22          6     2.19         NaN     2017-10-13
 3   22          7     1.99         True    2017-10-16
 4   22          4     1.99         NaN     2017-10-17
 5   35          15    3.99         NaN     2017-10-09
 6   35          17    3.99         NaN     2017-10-11
 7   35          5     4.29         True    2017-10-13
 8   35          8     4.29         NaN     2017-10-15
 9   35          2     4.29         NaN     2017-10-15

Basically I am trying to record how the sales of a product(UPC) reacted once the price changed for the following 7 days. I want to create a new column ['Reaction'] which records the sum of the unit sales from the day of price change, and 7 days forward. Keep in mind, sometimes a UPC has more than 2 price changes, so I want a different sum for each price change. So I want to see this:

    UPC   Unit_Sales  Price   Price_Change  Date        Reaction
 0   22          15    1.99         NaN     2017-10-10      NaN
 1   22          7     2.19         True    2017-10-12      13   
 2   22          6     2.19         NaN     2017-10-13      NaN
 3   22          7     1.99         True    2017-10-16      11
 4   22          4     1.99         NaN     2017-10-19      NaN
 5   35          15    3.99         NaN     2017-10-09      NaN
 6   35          17    3.99         NaN     2017-10-11      NaN
 7   35          5     4.29         True    2017-10-13       15
 8   35          8     4.29         NaN     2017-10-15      NaN
 9   35          2     4.29         NaN     2017-10-18      NaN

What is difficult is how the dates are set up in my data. Sometimes (like for UPC 35) the dates don't range past 7 days. So I would want it to default to the next nearest date, or however many dates there are (if there are less than 7 days).

Here's what I've tried: I set the date to a datetime and I'm thinking of counting days by .days method. This is how I'm thinking of setting a code up (rough draft):

  x = df.loc[df['Price_Change'] == 'True']
  for x in df: 
       df['Reaction'] = sum(df.Unit_Sales[1day :8days])

Is there an easier way to do this, maybe without a for loop?

Answer 1

You just need ffill with groupby

df.loc[df.Price_Change==True,'Reaction']=df.groupby('UPC').apply(lambda x : (x['Price_Change'].ffill()*x['Unit_Sales']).sum()).values
df
Out[807]: 
   UPC  Unit_Sales  Price Price_Change        Date  Reaction
0   22          15   1.99          NaN  2017-10-10       NaN
1   22           7   2.19         True  2017-10-12      24.0
2   22           6   2.19          NaN  2017-10-13       NaN
3   22           7   2.19          NaN  2017-10-16       NaN
4   22           4   2.19          NaN  2017-10-17       NaN
5   35          15   3.99          NaN  2017-10-09       NaN
6   35          17   3.99          NaN  2017-10-11       NaN
7   35           5   4.29         True  2017-10-13      15.0
8   35           8   4.29          NaN  2017-10-15       NaN
9   35           2   4.29          NaN  2017-10-15       NaN

Update

df['New']=df.groupby('UPC').apply(lambda x : x['Price_Change']==True).cumsum().values

v1=df.groupby(['UPC','New']).apply(lambda x : (x['Price_Change'].ffill()*x['Unit_Sales']).sum())

df=df.merge(v1.reset_index())

df[0]=df[0].mask(df['Price_Change']!=True)
df
Out[927]: 
   UPC  Unit_Sales  Price Price_Change        Date  New     0
0   22          15   1.99          NaN  2017-10-10    0   NaN
1   22           7   2.19         True  2017-10-12    1  13.0
2   22           6   2.19          NaN  2017-10-13    1   NaN
3   22           7   1.99         True  2017-10-16    2  11.0
4   22           4   1.99          NaN  2017-10-17    2   NaN
5   35          15   3.99          NaN  2017-10-09    2   NaN
6   35          17   3.99          NaN  2017-10-11    2   NaN
7   35           5   4.29         True  2017-10-13    3  15.0
8   35           8   4.29          NaN  2017-10-15    3   NaN
9   35           2   4.29          NaN  2017-10-15    3   NaN