我試圖簡單地將我定義的每個特定日期集之間的值相加。我遇到了麻煩，因為這不是日歷年資料，而是財政年度。希望對財政季度之間的價值求和。任何建議將不勝感激！
import pandas as pd
from datetime import date
#sample dataframe
begin_date = '2017-01-01'
values = [*range(2100)]
df = pd.DataFrame({'values': values,
'dates':pd.date_range(begin_date, periods=len(values))})
df
#defined dates
F2Q17 = date(2017, 4, 1)
F3Q17 = date(2017, 7, 1)
F4Q17 = date(2017, 9, 30)
F1Q18 = date(2017, 12, 30)
F2Q18 = date(2018, 3, 31)
F3Q18 = date(2018, 6, 30)
F4Q18 = date(2018, 9, 29)
F1Q19 = date(2018, 12, 29)
F2Q19 = date(2019, 3, 30)
F3Q19 = date(2019, 6, 29)
F4Q19 = date(2019, 9, 28)
F1Q20 = date(2019, 12, 28)
F2Q20 = date(2020, 3, 28)
F3Q20 = date(2020, 6, 27)
F4Q20 = date(2020, 9, 26)
F1Q21 = date(2020, 12, 26)
F2Q21 = date(2021, 3, 27)
F3Q21 = date(2021, 6, 26)
F4Q21 = date(2021, 9, 25)
F1Q22 = date(2021, 12, 25)
F2Q22 = date(2022, 3, 26)
F3Q22 = date(2022, 6, 25)
F4Q22 = date(2022, 9, 25)
uj5u.com熱心網友回復：
你可以試試：
group_dates = [date(2017, 4, 1),
date(2017, 7, 1),
date(2017, 9, 30),
date(2017, 12, 30),
date(2018, 3, 31),
date(2018, 6, 30),
date(2018, 9, 29),
date(2018, 12, 29),
date(2019, 3, 30),
date(2019, 6, 29),
date(2019, 9, 28),
date(2019, 12, 28),
date(2020, 3, 28),
date(2020, 6, 27),
date(2020, 9, 26),
date(2020, 12, 26),
date(2021, 3, 27),
date(2021, 6, 26),
date(2021, 9, 25),
date(2021, 12, 25),
date(2022, 3, 26),
date(2022, 6, 25),
date(2022, 9, 25)]
df_groups = pd.DataFrame({"dates":pd.to_datetime(group_dates), "group":pd.to_datetime(group_dates)})
(
pd.merge(df, df_groups, on="dates", how="left")
.assign(group=lambda x: x.group.bfill())
.groupby("group")
[["values"]].sum()
.reset_index()
)
輸出：
group values
0 2017-04-01 4095
1 2017-07-01 12376
2 2017-09-30 20657
3 2017-12-30 28938
4 2018-03-31 37219
5 2018-06-30 45500
6 2018-09-29 53781
7 2018-12-29 62062
8 2019-03-30 70343
9 2019-06-29 78624
10 2019-09-28 86905
11 2019-12-28 95186
12 2020-03-28 103467
13 2020-06-27 111748
14 2020-09-26 120029
15 2020-12-26 128310
16 2021-03-27 136591
17 2021-06-26 144872
18 2021-09-25 153153
19 2021-12-25 161434
20 2022-03-26 169715
21 2022-06-25 177996
22 2022-09-25 188370
uj5u.com熱心網友回復：
嘗試：
# put the required dates inside a list:
dates = [
date(2017, 4, 1),
date(2017, 7, 1),
date(2017, 9, 30),
date(2017, 12, 30),
date(2018, 3, 31),
date(2018, 6, 30),
date(2018, 9, 29),
date(2018, 12, 29),
date(2019, 3, 30),
date(2019, 6, 29),
date(2019, 9, 28),
date(2019, 12, 28),
date(2020, 3, 28),
date(2020, 6, 27),
date(2020, 9, 26),
date(2020, 12, 26),
date(2021, 3, 27),
date(2021, 6, 26),
date(2021, 9, 25),
date(2021, 12, 25),
date(2022, 3, 26),
date(2022, 6, 25),
date(2022, 9, 25),
]
# create a series by which we group the dataframe:
m = df["dates"].isin(dates).cumsum()
# group the dataframe, print some info:
for _, g in df.groupby(m):
print(f'Group {g["dates"].min().date()} - {g["dates"].max().date()}')
# uncomment to print whole group:
# print(g)
印刷：
Group 2017-01-01 - 2017-03-31
Group 2017-04-01 - 2017-06-30
Group 2017-07-01 - 2017-09-29
Group 2017-09-30 - 2017-12-29
Group 2017-12-30 - 2018-03-30
Group 2018-03-31 - 2018-06-29
Group 2018-06-30 - 2018-09-28
Group 2018-09-29 - 2018-12-28
Group 2018-12-29 - 2019-03-29
Group 2019-03-30 - 2019-06-28
Group 2019-06-29 - 2019-09-27
Group 2019-09-28 - 2019-12-27
Group 2019-12-28 - 2020-03-27
Group 2020-03-28 - 2020-06-26
Group 2020-06-27 - 2020-09-25
Group 2020-09-26 - 2020-12-25
Group 2020-12-26 - 2021-03-26
Group 2021-03-27 - 2021-06-25
Group 2021-06-26 - 2021-09-24
Group 2021-09-25 - 2021-12-24
Group 2021-12-25 - 2022-03-25
Group 2022-03-26 - 2022-06-24
Group 2022-06-25 - 2022-09-24
Group 2022-09-25 - 2022-10-01
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