I have a Pandas df which contains daily average tempreature of 3 countries:
| date | country | temp |
|---|---|---|
| 1/1/19 | US | 18 |
| 1/1/19 | GB | 21 |
| 1/1/19 | AU | 26 |
| 2/1/19 | US | 19 |
| 2/1/19 | GB | 19 |
| 2/1/19 | AU | 30 |
| 3/1/19 | GB | 16 |
| 3/1/19 | AU | 30 |
| 4/1/19 | GB | 20 |
| 4/1/19 | US | 20 |
I am trying to create a transformation whereby I can arrnage all the countries in a separate row based on the date, taking into account missing values like so:
| date | US | GB | AU |
|---|---|---|---|
| 1/1/19 | 18 | 21 | 26 |
| 2/1/19 | 19 | 19 | 30 |
| 3/1/19 | 16 | 30 | |
| 4/1/19 | 20 | 20 |
What would be the fastest way to acheive this? Is there a fatser way with numpy?
