Pandas：使用 groupby 和 nunique 考虑时间

Question

I have a dataframe in this form:

A    B    time
1    2    2019-01-03
1    3    2018-04-05
1    4    2020-01-01
1    4    2020-02-02

where A and B contain some integer identifiers. I want to measure the number of different identifiers each A has interacted with. To do this I usually simply do

df.groupby('A')['B'].nunique()   

I now have to do a slightly different thing: each identifier has a date assigned (different for each identifier), that splits its interactions in 2 parts: the ones happening before that date, and the ones happening after that date. The same operation previously done (counting number of unique B interacted with ) needs to be done for both parts separately.

For example, if the date for A=1 was 2018-07-01, the output would be

A    before    after
1    1         2

In the real data, A contains millions of different identifiers, each with its unique date assigned.

EDITED To be more clear I added a line to df. I want to count the number of different values of B each A interacts with before and after the date

Answer 1

I would convert A into dates, compare those with df['time'] and then groupby().value_counts() :

(df['A'].map(date_dict)
    .gt(df['time'])
    .groupby(df['A'])
    .value_counts()
    .unstack()
    .rename({False:'after',True:'before'}, axis=1)
)

Output:

   after  before
A               
1      2       1