在 pandas 专栏中查找下一个工作日 [英] find next working day in a pandas column
本文介绍了在 pandas 专栏中查找下一个工作日的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个df熊猫，它代表着一家商店的营业日，看起来像:
I have a pandas df that represents the open days of a shop looking like:
Dates Open
0 2016-01-01 0
1 2016-01-02 0
2 2016-01-03 0
3 2016-01-04 1
4 2016-01-05 1
5 2016-01-06 1
6 2016-01-07 1
7 2016-01-08 1
8 2016-01-09 0
9 2016-01-10 0
10 2016-01-11 1
11 2016-01-12 1
12 2016-01-13 1
13 2016-01-14 1
14 2016-01-15 1
15 2016-01-16 0
16 2016-01-17 0
17 2016-01-18 1
18 2016-01-19 1
19 2016-01-20 1
20 2016-01-21 1
21 2016-01-22 1
22 2016-01-23 0
23 2016-01-24 0
24 2016-01-25 1
25 2016-01-26 1
26 2016-01-27 1
27 2016-01-28 1
28 2016-01-29 1
这可以通过以下方式重新创建:
this can be recreated by:
Dates =['2016-01-01',
'2016-01-02',
'2016-01-03',
'2016-01-04',
'2016-01-05',
'2016-01-06',
'2016-01-07',
'2016-01-08',
'2016-01-09',
'2016-01-10',
'2016-01-11',
'2016-01-12',
'2016-01-13',
'2016-01-14',
'2016-01-15',
'2016-01-16',
'2016-01-17',
'2016-01-18',
'2016-01-19',
'2016-01-20',
'2016-01-21',
'2016-01-22',
'2016-01-23',
'2016-01-24',
'2016-01-25',
'2016-01-26',
'2016-01-27',
'2016-01-28',
'2016-01-29']
Open = [0,0,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1]
df = DataFrame({'Dates':Dates, 'Open':Open})
打开的列表示开店交货的天数.我想在最左边的列中为每个日期的下一个营业日创建一个新列.我无法使用预定义的工作日功能，但必须使用打开"列来确定商店是否营业.理想的结果将是:
the column open represents the days in which the shop is open for deliveries. I want to create a new column with the next open day for each date in the leftmost column. I cannot use predefined workingdays functions but I have to use the Open column to determine whether or not the shop is open. The desired outcome would be:
Dates Open Desired
0 2016-01-01 0 2016-01-04
1 2016-01-02 0 2016-01-04
2 2016-01-03 0 2016-01-04
3 2016-01-04 1 2016-01-05
4 2016-01-05 1 2016-01-06
5 2016-01-06 1 2016-01-07
6 2016-01-07 1 2016-01-08
7 2016-01-08 1 2016-01-11
8 2016-01-09 0 2016-01-11
9 2016-01-10 0 2016-01-11
10 2016-01-11 1 2016-01-12
11 2016-01-12 1 2016-01-13
12 2016-01-13 1 2016-01-14
13 2016-01-14 1 2016-01-15
14 2016-01-15 1 2016-01-18
15 2016-01-16 0 2016-01-18
16 2016-01-17 0 2016-01-18
17 2016-01-18 1 2016-01-19
18 2016-01-19 1 2016-01-20
19 2016-01-20 1 2016-01-21
20 2016-01-21 1 2016-01-22
21 2016-01-22 1 2016-01-25
22 2016-01-23 0 2016-01-25
23 2016-01-24 0 2016-01-25
24 2016-01-25 1 2016-01-26
25 2016-01-26 1 2016-01-27
26 2016-01-27 1 2016-01-28
27 2016-01-28 1 2016-01-29
28 2016-01-29 1
推荐答案
IIUC then you can add Business Day offset after converting your strings to datetime dtype using to_datetime:
In [141]:
df['Dates'] = pd.to_datetime(df['Dates'])
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 29 entries, 0 to 28
Data columns (total 2 columns):
Dates 29 non-null datetime64[ns]
Open 29 non-null int64
dtypes: datetime64[ns](1), int64(1)
memory usage: 696.0 bytes
In [146]:
from pandas.tseries.offsets import *
df['Desired'] = df['Dates'] + BDay()
df
Out[146]:
Dates Open Desired
0 2016-01-01 0 2016-01-04
1 2016-01-02 0 2016-01-04
2 2016-01-03 0 2016-01-04
3 2016-01-04 1 2016-01-05
4 2016-01-05 1 2016-01-06
5 2016-01-06 1 2016-01-07
6 2016-01-07 1 2016-01-08
7 2016-01-08 1 2016-01-11
8 2016-01-09 0 2016-01-11
9 2016-01-10 0 2016-01-11
10 2016-01-11 1 2016-01-12
11 2016-01-12 1 2016-01-13
12 2016-01-13 1 2016-01-14
13 2016-01-14 1 2016-01-15
14 2016-01-15 1 2016-01-18
15 2016-01-16 0 2016-01-18
16 2016-01-17 0 2016-01-18
17 2016-01-18 1 2016-01-19
18 2016-01-19 1 2016-01-20
19 2016-01-20 1 2016-01-21
20 2016-01-21 1 2016-01-22
21 2016-01-22 1 2016-01-25
22 2016-01-23 0 2016-01-25
23 2016-01-24 0 2016-01-25
24 2016-01-25 1 2016-01-26
25 2016-01-26 1 2016-01-27
26 2016-01-27 1 2016-01-28
27 2016-01-28 1 2016-01-29
28 2016-01-29 1 2016-02-01
这篇关于在 pandas 专栏中查找下一个工作日的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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