比较两个数据框并根据多个条件删除行

Question

If I have two date-frames in the following format. df-a:

         ID         Start_Date     End_Date 
1        cd2        2020-06-01     2020-06-09              
2        cd2        2020-06-24     2020-07-21             
3        cd56       2020-06-10     2020-07-03              
4        cd915      2020-04-28     2020-07-21              
5        cd103      2020-04-13     2020-04-24

and df-b:

         ID         Date
1        cd2        2020-05-12                   
2        cd2        2020-04-12                  
3        cd2        2020-06-29                  
4        cd15       2020-04-28                   
5        cd193      2020-04-13     

        

I need to discard all rows for all IDs in df-b where they fall in various date ranges for the same ID in df-a. Ie ANSWER

         ID         Date
1        cd2        2020-05-12                   
2        cd2        2020-04-12                  
                
4        cd15       2020-04-28                   
5        cd193      2020-04-13   

as ID cd2 is the only ID that matches in df-a with one date that fall within cd2's date ranges from df-a.

Sorry for the long-winded question. First time posting.

Answer 1

I tried my best to understand your question, however I am confused by your sample answer.
None of the IDs in df-b should be removed. Even for row 3 of df-b, the date (2020-06-10) does not fall in the range of any start/end dates for ID cd2 in df-a.

I did set up a similar example to what you provided with df-a being:

 ID Start_Date End_Date 0 cd2 2020-06-01 2020-06-11 1 cd2 2020-06-24 2020-07-21 2 cd56 2020-06-10 2020-07-03 3 cd915 2020-04-28 2020-07-21 4 cd103 2020-04-13 2020-04-24

and df-b being:

 ID Date 0 cd2 2020-05-12 1 cd2 2020-04-12 2 cd2 2020-06-10 3 cd15 2020-04-28 4 cd193 2020-04-13

With this example, row 2 (0-based) of df-b should be removed since 2020-06-10 falls between 2020-06-01 and 2020-06-11 in row 0 of df-a.

Here's my code for doing the row deletions

df_c = df_b.copy() for i in range(df_c.shape[0]): currentID = df_c.ID[i] currentDate = df_c.Date[i] df_a_entriesForCurrentID = df_a.loc[df_a.ID == currentID] for j in range(df_a_entriesForCurrentID.shape[0]): startDate = df_a_entriesForCurrentID.iloc[j,:].Start_Date endDate = df_a_entriesForCurrentID.iloc[j,:].End_Date if (startDate <= currentDate <= endDate): df_c = df_c.drop(i) print('dropped')

where df_c is the output DataFrame.

After running this, df_c should look like:

 ID Date 0 cd2 2020-05-12 1 cd2 2020-04-12 3 cd15 2020-04-28 4 cd193 2020-04-13