[FIXED] Group by ID and apply a condition on the value of one column

Issue

I am new to pandas library and I would appreciate your help. This is my input

ID	DATE	LABEL	STOCK
AA	01/26/2020	TRUE	100
AA	01/27/2020	FALSE	200
BB	01/28/2020	FALSE	300
BB	01/29/2020	TRUE	500
BB	01/30/2020	FALSE	100
CC	01/26/2020	TRUE	200
CC	01/27/2020	FALSE	300
CC	01/28/2020	FALSE	100
CC	01/29/2020	TRUE	400

I would like to look at the Label column and for the first "TRUE" label that is found for each ID I need to copy the value of stock in a new column. If an ID has more than one TRUE I need to consider only the first one.

Here is the output i want to obtain:

ID	DATE	LABEL	STOCK	NEW
AA	01/26/2020	TRUE	100	100
AA	01/27/2020	FALSE	200
BB	01/28/2020	FALSE	300
BB	01/29/2020	TRUE	500	500
BB	01/30/2020	FALSE	100
CC	01/26/2020	TRUE	200	200
CC	01/27/2020	FALSE	300
CC	01/28/2020	FALSE	100
CC	01/29/2020	TRUE	400

Thank you for your help!

Solution

Another version, using .groupby():

m = df.index.isin(
    df[df["LABEL"] == True].reset_index().groupby("ID").index.first()
)
df.loc[m, "NEW"] = df.loc[m, "STOCK"]
print(df)

Prints:

   ID        DATE  LABEL  STOCK    NEW
0  AA  01/26/2020   True    100  100.0
1  AA  01/27/2020  False    200    NaN
2  BB  01/28/2020  False    300    NaN
3  BB  01/29/2020   True    500  500.0
4  BB  01/30/2020  False    100    NaN
5  CC  01/26/2020   True    200  200.0
6  CC  01/27/2020  False    300    NaN
7  CC  01/28/2020  False    100    NaN
8  CC  01/29/2020   True    400    NaN

Answered By - Andrej Kesely