根据另一个列条件过滤列值

Question

I have a dataset with multiple columns out of which 2 columns I want to use to filter data as follows:

1. In the Reason column keep all row values that are A3
2. In the Reason column keep all values (even null) that has a date(obj) in Goods_Issue_Date_(GID)

*feel free to assign a value for the nulls in reason that have date in GID

and drop the other values like c,b..

have used this code which works for A3 values fine:

Df = Data[Data["Reason"].isna(['A3'])]....?

df=pd.DataFrame({'Reason':{0: 'b',1: 'c',2: 'a3',3: ' ',4: ' ',5: 'a3',6: 'a3',7: ' ',8: 'b',9: ' ',}, 'Goods_Issue_Date_(GID)':{0: ' -1',1: '2 ',2: ' ',3: ' ',4: '2021-11-03T00:00:00',5: '2021-11-03T00:00:00',6: '',7: '',8: '0.5',9: '2021-11-03T00:00:00'}})

Reason	Goods_Issue_Date_(GID)
b	-1
c	2
a3
	2021-11-03T00:00:00
	2021-11-03T00:00:00
a3
a3
	0
b	0.5
	2021-11-03T00:00:00

Answer 1

This assumes your date formats always end in 00:00

df[(df['Reason']=='a3') | (df['Goods_Issue_Date_(GID)'].str[-5:]=='00:00')]

  Reason Goods_Issue_Date_(GID)
2     a3
4           2021-11-03T00:00:00
5     a3    2021-11-03T00:00:00
6     a3
9           2021-11-03T00:00:00

Answer 2

df[(df['Reason']=='a3') | pd.to_datetime(df['Goods_Issue_Date_(GID)'], errors='coerce').notna()]