Given two DataFame objects it should be easy to perform a simple outer join to achieve these results:
DataFrame1:
| Date |
ClosePrice |
| 3/1/2022 12:00:00 AM |
10.5 |
| 3/2/2022 12:00:00 AM |
12.4 |
| 3/3/2022 12:00:00 AM |
11.3 |
DataFrame2:
| Date |
ShortPercent |
| 3/1/2022 12:00:00 AM |
2.34 |
| 3/2/2022 12:00:00 AM |
2.36 |
| 3/3/2022 12:00:00 AM |
3.01 |
| 3/4/2022 12:00:00 AM |
3.04 |
Resulting DataFrame:
| Date |
ClosePrice |
ShortPercent |
| 3/1/2022 12:00:00 AM |
10.5 |
2.34 |
| 3/2/2022 12:00:00 AM |
12.4 |
2.36 |
| 3/3/2022 12:00:00 AM |
11.3 |
3.01 |
| 3/4/2022 12:00:00 AM |
null |
3.04 |
This does not seem to be possible with the current Merge or Join methods, since they end up with two columns for Date (Date_left and Date_right) in the resulting DataFrame and combining those to a single column seems complex and error prone.
Given two DataFame objects it should be easy to perform a simple outer join to achieve these results:
DataFrame1:
DataFrame2:
Resulting DataFrame:
This does not seem to be possible with the current Merge or Join methods, since they end up with two columns for Date (Date_left and Date_right) in the resulting DataFrame and combining those to a single column seems complex and error prone.