如何从 dataframe 中获取 map 值并获取最后更新

Question

I'm stuck with a problem because map() don't let me map values if they are repeated. The scenario is this.

I have this df1 of daily observations

   Obvserved_Today  A   B  
0  192.168.1.1      3   2
1  192.168.1.3      1   3
2  192.168.1.4      1   5
3  192.168.1.5      2   9

I want to update that df by adding two columns of historical data, so I already have this second dataframe df2 of historics

   IP_Historical    Ticket  Date  
0  192.168.1.1      3001    11/01/2020
1  192.168.1.1      3002    11/02/2020
2  192.168.1.3      3003    11/03/2020
3  192.168.1.5      3004    11/04/2020
4  192.168.1.5      3005    11/05/2020

I want an output like the one below, I want to add two more columns, the first one containing the last ticket seen in the historical df2 and a second one, also from the historical df2, with the last seen date if there's no match let it as NaN or 0.

   Obvserved_Today  A   B   Last Ticket  Last update
0  192.168.1.1      3   2   3002         11/02/2020
1  192.168.1.3      1   3   3003         11/03/2020
2  192.168.1.4      1   5   0            0
3  192.168.1.5      2   9   3005         11/05/2020

Answer 1

df1.merge(df2[~df2.duplicated('Obvserved_Today',keep='last')], how='left', on='Obvserved_Today')

This will have the columns named "Ticket" instead of "Last Ticket" and "Date" instead of "Last update". If you want you can rename after the merge.

What is this code doing?

It is merging the two dataframe (looking at the on= column and matching up same values) but only looking at the rows of the second dataframe that are the last of the observation.