Fill np.nan with values based on other columns

I try to match the offer_id to the corresponding transaction. This is the dataset:

       time            event                          offer_id  amount
2077      0   offer received  f19421c1d4aa40978ebb69ca19b0e20d     NaN
15973     6     offer viewed  f19421c1d4aa40978ebb69ca19b0e20d     NaN
15974     6      transaction                               NaN    3.43
18470    12      transaction                               NaN    6.01
18471    12  offer completed  f19421c1d4aa40978ebb69ca19b0e20d     NaN
43417   108      transaction                               NaN   11.00
44532   114      transaction                               NaN    1.69
50587   150      transaction                               NaN    3.23
55277   168   offer received  9b98b8c7a33c4b65b9aebfe6a799e6d9     NaN
96598   258      transaction                               NaN    2.18

The rule is that when the offer is viewed, the transaction belongs to this offer id. If the offer is reveived, but not viewed, the transaction does not belong to the offer id. I hope the time variable makes it clear. This is the desired result:

       time            event                          offer_id  amount
2077      0   offer received  f19421c1d4aa40978ebb69ca19b0e20d     NaN
15973     6     offer viewed  f19421c1d4aa40978ebb69ca19b0e20d     NaN
15974     6      transaction  f19421c1d4aa40978ebb69ca19b0e20d    3.43
18470    12      transaction  f19421c1d4aa40978ebb69ca19b0e20d    6.01
18471    12  offer completed  f19421c1d4aa40978ebb69ca19b0e20d     NaN
43417   108      transaction                               NaN   11.00
44532   114      transaction                               NaN    1.69
50587   150      transaction                               NaN    3.23
55277   168   offer received  9b98b8c7a33c4b65b9aebfe6a799e6d9     NaN
96598   258      transaction                               NaN    2.18

Answer

Example code:

import pandas as pd
import numpy as np

d = {'time': [0, 6, 6, 12, 12, 108, 144, 150, 168, 258], 
     'event': ["offer received", "offer viewed", "transaction", "transaction", "offer completed", "transaction", "transaction", "transaction", "offer received", "transaction"], 
     'offer_id': ["f19421c1d4aa40978ebb69ca19b0e20d", "f19421c1d4aa40978ebb69ca19b0e20d", np.nan, np.nan, "f19421c1d4aa40978ebb69ca19b0e20d", np.nan, np.nan, np.nan, "9b98b8c7a33c4b65b9aebfe6a799e6d9", np.nan]}

df = pd.DataFrame(d)

print("Original data:n{}n".format(df))

is_offer_viewed = False
now_offer_id = np.nan
for index, row in df.iterrows():
    if row['event'] == "offer viewed":
        is_offer_viewed = True
        now_offer_id = row['offer_id']
        
    elif row['event'] == "transaction" and is_offer_viewed:
        df.at[index, 'offer_id'] = now_offer_id

    elif row['event'] == "offer completed":
        is_offer_viewed = False
        now_offer_id = np.nan

print("Processed data:n{}n".format(df))

Outputs:

Original data:
   time            event                          offer_id
0     0   offer received  f19421c1d4aa40978ebb69ca19b0e20d
1     6     offer viewed  f19421c1d4aa40978ebb69ca19b0e20d
2     6      transaction                               NaN
3    12      transaction                               NaN
4    12  offer completed  f19421c1d4aa40978ebb69ca19b0e20d
5   108      transaction                               NaN
6   144      transaction                               NaN
7   150      transaction                               NaN
8   168   offer received  9b98b8c7a33c4b65b9aebfe6a799e6d9
9   258      transaction                               NaN

Processed data:
   time            event                          offer_id
0     0   offer received  f19421c1d4aa40978ebb69ca19b0e20d
1     6     offer viewed  f19421c1d4aa40978ebb69ca19b0e20d
2     6      transaction  f19421c1d4aa40978ebb69ca19b0e20d
3    12      transaction  f19421c1d4aa40978ebb69ca19b0e20d
4    12  offer completed  f19421c1d4aa40978ebb69ca19b0e20d
5   108      transaction                               NaN
6   144      transaction                               NaN
7   150      transaction                               NaN
8   168   offer received  9b98b8c7a33c4b65b9aebfe6a799e6d9
9   258      transaction                               NaN