用基於其他列的值填充 np.nan

Question

我嘗試將offer_id與相應的交易相匹配。 這是數據集：

       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

規則是，當查看報價時，交易屬於此報價 id。 如果報價已收到，但未查看，則交易不屬於報價 ID。 我希望time變量能說明問題。 這是期望的結果：

       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 1

示例代碼：

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))

輸出：

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