Extend DatetimeAccessor with `snap`-method
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- Dominant language
- Python
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Description
Is your feature request related to a problem?
With satellite remote sensing data, you sometimes end up with a blown up DataArray/Dataset because individual acquisitions have been saved in slices:
One could then aggregate these slices with something like this:
ds.coords['time'] = ds.time.dt.floor('1H') # or .ceil
ds = ds_copy.groupby('time').mean()
However, this would miss cases where one slice has been acquired before and the other after a specific hour. The pandas.DatetimeIndex.snap method could be a good alternative for such cases.
Describe the solution you'd like
In addition to the floor, ceil and round methods, it would be great to also implement pandas.DatetimeIndex.snap.
Describe alternatives you've considered
No response
Additional context
No response
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the existing DatetimeAccessor floor, ceil, and round methods, then compare their behavior with pandas.DatetimeIndex.snap. Implement the corresponding dt.snap entry point so slices can be grouped using snap, and verify that its behavior matches the pandas method for the described acquisition times.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- pandas, python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100