from SO
I didn't find an issue about this, but it has come up some times at stackoverflow: having columns with integers for year, month, day, hour, ..., how do you convert this to a datetime column/index ?
You have the typical solution of adding the columns: pd.to_datetime((df['Y']*10000 + df['M']*100 + df['D']).astype('int'), format='%Y%m%d'), and @unutbu added now a faster solution using numpy's different datetime64 resolutions to that question on SO.
I personally think this would be a nice addition to pandas to have a more native solution for this. But then we need to figure out a nice API. Or we keep it as is, but try to document it more (add as example to docs?)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4