我有一個如下所示的 pd 資料框：
| ID | 專案 ID | date_cod | 日期 | 年 | 月 | p50 | p90 |
|---|---|---|---|---|---|---|---|
| 1 | DCLT | 30-03-2022 | 01-01-2022 | 2022 | 1 | 5313.79 | 4571.03 |
| 2 | DLCT | 01-02-2022 | 2022 | 2 | 2350.25 | 1880.70 | |
| 3 | DLCT | 01-03-2022 | 2022 | 3 | 2450.25 | 1763.90 | |
| 4 | DLCT | 01-01-2023 | 2023 | 1 | 2180.25 | 1280.70 | |
| 5 | DLCT | 01-02-2023 | 2023 | 2 | 4871.03 | 5224.03 | |
| 6 | 瘋狂 | 01-01-2023 | 01-01-2022 | 2022 | 1 | 4575.03 | 1280.70 |
| 7 | 瘋狂 | 01-02-2022 | 2022 | 2 | 4331.03 | 5718.03 | |
| 8 | 瘋狂 | 01-03-2022 | 2022 | 3 | 4331.03 | 1235.75 | |
| 9 | 瘋狂 | 01-04-2023 | 2023 | 4 | 1224.00 | 1280.70 | |
| 10 | 瘋狂 | 01-05-2023 | 2023 | 5 | 1480.70 | 1330.70 | |
| 11 | 佩爾斯 | 01-03-2024 | 01-01-2024 | 2024 | 1 | 1280.70 | 1280.70 |
| 12 | 佩爾斯 | 01-05-2024 | 2024 | 5 | 1200.70 | 1235.75 |
基于date_cod,
對于每個（DCLT、MADD 、PEYRS projet_id），我想洗掉在.p50p90datedate_cod
輸出 df 應如下所示。
| ID | 專案 ID | date_cod | 日期 | 年 | 月 | p50 | p90 |
|---|---|---|---|---|---|---|---|
| 1 | DCLT | 30-03-2022 | 01-01-2022 | 2022 | 1 | ||
| 2 | DLCT | 01-02-2022 | 2022 | 2 | |||
| 3 | DLCT | 01-03-2022 | 2022 | 3 | |||
| 4 | DLCT | 01-01-2023 | 2023 | 1 | 2180.25 | 1280.70 | |
| 5 | DLCT | 01-02-2023 | 2023 | 2 | 4871.03 | 5224.03 | |
| 6 | 瘋狂 | 01-01-2023 | 01-01-2022 | 2022 | 1 | ||
| 7 | 瘋狂 | 01-02-2022 | 2022 | 2 | |||
| 8 | 瘋狂 | 01-03-2022 | 2022 | 3 | |||
| 9 | 瘋狂 | 01-04-2023 | 2023 | 4 | 1224.00 | 1280.70 | |
| 10 | 瘋狂 | 01-05-2023 | 2023 | 5 | 1480.70 | 1330.70 | |
| 11 | 佩爾斯 | 01-03-2024 | 01-01-2024 | 2024 | 1 | ||
| 12 | 佩爾斯 | 01-05-2024 | 2024 | 5 | 1200.70 | 1235.75 |
uj5u.com熱心網友回復：
像這樣的東西應該作業：
#Make sure dates are in to_datetime
df['date_cod']=pd.to_datetime(df['date_cod'])
df['date']=pd.to_datetime(df['date'])
#Condition:date column is less (in total seconds) than first date for each projet_id's first date_cod value
cond=((df['date']-df.groupby('projet_id')['date_cod'].transform('first')).dt.total_seconds())<0
import numpy as np
#replace by '' where condition defined above is true
df['p50']=np.where(cond,'',df['p50'])
df['p90']=np.where(cond,'',df['p90'])
df
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