This post has a reference to one of my post in SO.
Just to reiterate, I have a dataframe df as
Date Group Value Duration
2018-01-01 A 20 30
2018-02-01 A 10 60
2018-03-01 A 25 88 <-----Last row for Group A
2018-01-01 B 15 180
2018-02-01 B 30 210
2018-03-01 B 25 238 <-----Last row of Group B
Considering the last row of each Group, if the Duration value is less than 90, we omit that group. So my resultant data frame df_final should look like
Date Group Value Duration
2018-01-01 B 15 180
2018-02-01 B 30 210
2018-03-01 B 25 240
我们有两种方法可以解决此问题。
First is filter method:
df.groupby('Group').filter(lambda x: x.Duration.max()>=90)
Second is groupby.transform method:
df = df[df.groupby('Group')['Duration'].transform('last') >= 90]
But I want to filter this by the Date column and NOT by Duration. I am getting the correct result by the following code:
df_interim = df.loc[(df['Date']=='2019-03-01')&(df['Duration'] >=90)]
df_final = df.merge(df_interim[['Group','Date']],on='Group',how='right').reset_index()
In the above code, I have hard coded the Date.
My question is : How can I dynamically select the last date in the data frame? And then perform the filter or groupby.transform on Group?
有什么线索吗？
We can select the last date by use
transformas well