向日期四分时回填和提前灌装大熊猫
向日期四分时回填和提前灌装大熊猫
提问于 2022-10-23 14:56:36
我试图根据到期日期填充季度到期日。我能够确定季度的到期日期，但类似于我如何做的每月到期。我希望季度日期回填和转发填补下一个季度到期。
我知道我可以使用熊猫以外的图书馆，但是在熊猫数据仓库里有办法做到这一点吗？
import pandas as pd
import pandas.tseries.offsets as offsets
import numpy as np
from datetime import date
expire = ['2022-10-24','2022-10-26','2022-10-28','2022-10-31','2022-11-02','2022-11-04','2022-11-07','2022-11-09','2022-11-11','2022-11-14',
'2022-11-16','2022-11-18','2022-11-21','2022-11-23','2022-11-25','2022-12-02','2022-12-16','2022-12-30','2023-01-20','2023-02-17','2023-03-17',
'2023-03-31','2023-06-16','2023-06-30','2023-09-15','2023-09-29','2023-12-15','2024-01-19','2024-06-21','2024-12-20','2025-01-17']
quarterlies = ['2022-12-30','2023-03-31','2023-06-30','2023-09-29','2023-12-29','2024-03-28','2024-06-28','2024-09-30','2024-12-31','2025-03-31',
'2025-06-30','2025-09-30','2025-12-31']
date_df = pd.DataFrame(expire)
date_df.columns = ["expiration_date"]
date_df = date_df.set_index(date_df["expiration_date"])
date_df["expiration_date"] = pd.to_datetime(date_df["expiration_date"], format="%Y-%m-%d")
date_df.index.name = "expiration_date_idx"
all_third_fridays = pd.DataFrame(pd.date_range(date_df["expiration_date"].min(),
date_df["expiration_date"].max()+pd.tseries.offsets.Day(30),
freq="WOM-3FRI"),
columns=["monthly_exp"])
date_df = pd.merge_asof(date_df, all_third_fridays, left_on="expiration_date", right_on="monthly_exp", direction="forward")
df_quarterly = pd.DataFrame(quarterlies)
df_quarterly.columns = ['quarterly_expire']
date_df['quarterly_expire'] = date_df['expiration_date'].isin(df_quarterly['quarterly_expire'])
date_df['quarterly_exp'] = date_df['expiration_date'] + offsets.QuarterEnd() - offsets.Week(weekday=4) #not accurate, so I had to use list
date_df.loc[(date_df['quarterly_expire'] == True), 'quarterly_expiration'] = date_df['expiration_date']
#date_df["quarterly_expiration"].bfill(axis =df_quarterly['quarterly_expiration']) series error
print(date_df)该填充将适用于所有的选项，除了那些突出显示在底部，他们需要转发填充到下一个四分卫日期。
但是，我得到了下面的错误，并且我很难理解为什么？这仍然不能解决黄色高光显示的全部问题。
ValueError: No axis named quarterly_expiration for object type Series这是我得到的输出：
expiration_date monthly_exp quarterly_expire quarterly_exp quarterly_expiration
0 2022-10-24 2022-11-18 False 2022-12-30 NaT
1 2022-10-26 2022-11-18 False 2022-12-30 NaT
2 2022-10-28 2022-11-18 False 2022-12-30 NaT
3 2022-10-31 2022-11-18 False 2022-12-30 NaT
4 2022-11-02 2022-11-18 False 2022-12-30 NaT
5 2022-11-04 2022-11-18 False 2022-12-30 NaT
6 2022-11-07 2022-11-18 False 2022-12-30 NaT
7 2022-11-09 2022-11-18 False 2022-12-30 NaT
8 2022-11-11 2022-11-18 False 2022-12-30 NaT
9 2022-11-14 2022-11-18 False 2022-12-30 NaT
10 2022-11-16 2022-11-18 False 2022-12-30 NaT
11 2022-11-18 2022-11-18 False 2022-12-30 NaT
12 2022-11-21 2022-12-16 False 2022-12-30 NaT
13 2022-11-23 2022-12-16 False 2022-12-30 NaT
14 2022-11-25 2022-12-16 False 2022-12-30 NaT
16 2022-12-16 2022-12-16 False 2022-12-30 NaT
17 2022-12-30 2023-01-20 True 2022-12-30 2022-12-30
18 2023-01-20 2023-01-20 False 2023-03-24 NaT
19 2023-02-17 2023-02-17 False 2023-03-24 NaT
20 2023-03-17 2023-03-17 False 2023-03-24 NaT
21 2023-03-31 2023-04-21 True 2023-06-23 2023-03-31
22 2023-06-16 2023-06-16 False 2023-06-23 NaT
23 2023-06-30 2023-07-21 True 2023-09-29 2023-06-30
24 2023-09-15 2023-09-15 False 2023-09-29 NaT
25 2023-09-29 2023-10-20 True 2023-09-29 2023-09-29
26 2023-12-15 2023-12-15 False 2023-12-29 NaT
27 2024-01-19 2024-01-19 False 2024-03-29 NaT
28 2024-06-21 2024-06-21 False 2024-06-28 NaT
29 2024-12-20 2024-12-20 False 2024-12-27 NaT
30 2025-01-17 2025-01-17 False 2025-03-28 NaT这是我拍摄的输出：
也许有更好的方法来处理这个我没有想到的问题。我愿意接受建议。
回答 1
Stack Overflow用户
回答已采纳
发布于 2022-10-23 15:26:28
BQuarterEnd偏移量应该可以满足您的需要。
date_df.assign(quarterly_expiration=lambda x: x['expiration_date'] + pd.tseries.offsets.BQuarterEnd())页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/74172141
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