-
Notifications
You must be signed in to change notification settings - Fork 1.9k
Open
Labels
Microsoft.Data.AnalysisAll DataFrame related issues and PRsAll DataFrame related issues and PRsP2Priority of the issue for triage purpose: Needs to be fixed at some point.Priority of the issue for triage purpose: Needs to be fixed at some point.enhancementNew feature or requestNew feature or request
Milestone
Description
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.
sfiruch, Nils12345678901234567, gusmally and alexeyshockov
Metadata
Metadata
Assignees
Labels
Microsoft.Data.AnalysisAll DataFrame related issues and PRsAll DataFrame related issues and PRsP2Priority of the issue for triage purpose: Needs to be fixed at some point.Priority of the issue for triage purpose: Needs to be fixed at some point.enhancementNew feature or requestNew feature or request