Count days till a specific column meets specific criteria in Pandas

My Dataframe looks like this

And I have to count the days in each line the number of days left until the event occurs for the specific serialnumber.
I want the column 'Days_to_event', like this:

Use a custom groupby.transform with mask/bfill/shift:

df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)

m = df['Event occured'].eq(1)
next_event = (df['Date'].where(m).groupby(df['Serialnr'])
              .transform(lambda g: g.bfill().shift(-1))
             )

df['Days_to_event'] = df['Date'].rsub(next_event).dt.days

Output:

    Serialnr       Date  Event occured  Days_to_event
0          1 2022-01-10              0           33.0
1          1 2022-02-05              0            7.0
2          1 2022-02-12              1           82.0
3          1 2022-04-20              0           15.0
4          1 2022-04-27              0            8.0
5          1 2022-05-01              0            4.0
6          1 2022-05-05              1            NaN
7          2 2022-01-10              0           33.0
8          2 2022-02-05              0            7.0
9          2 2022-02-12              1           82.0
10         2 2022-04-20              0           15.0
11         2 2022-04-27              0            8.0
12         2 2022-05-01              0            4.0
13         2 2022-05-05              1            NaN

handling the last event

If you want the last reference to be the max date instead of NaNs:

df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)

m = df['Event occured'].eq(1)
next_event = (df.groupby(df['Serialnr'])['Date']
              .transform(lambda g: g.where(m).bfill().shift(-1).fillna(g.max()))
             )

df['Days_to_event'] = df['Date'].rsub(next_event).dt.days

Output:

    Serialnr       Date  Event occured  Days_to_event
0          1 2022-01-10              0             33
1          1 2022-02-05              0              7
2          1 2022-02-12              1             82
3          1 2022-04-20              0             15
4          1 2022-04-27              0              8
5          1 2022-05-01              0              4
6          1 2022-05-05              1              2
7          1 2022-05-07              0              0
8          2 2022-01-10              0             33
9          2 2022-02-05              0              7
10         2 2022-02-12              1             82
11         2 2022-04-20              0             15
12         2 2022-04-27              0              8
13         2 2022-05-01              0              4
14         2 2022-05-05              1              0