Dataset.from_dataframe will produce a FutureWarning for DatetimeTZ data
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Description
This appears with the development version of pandas; see https://github.com/pandas-dev/pandas/issues/24716 for details.
Example:
In [16]: df = pd.DataFrame({"A": pd.date_range('2000', periods=12, tz='US/Central')})
In [17]: df.to_xarray()
/Users/taugspurger/Envs/pandas-dev/lib/python3.7/site-packages/xarray/core/dataset.py:3111: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
To accept the future behavior, pass 'dtype=object'.
To keep the old behavior, pass 'dtype="datetime64[ns]"'.
data = np.asarray(series).reshape(shape)
Out[17]:
<xarray.Dataset>
Dimensions: (index: 12)
Coordinates:
* index (index) int64 0 1 2 3 4 5 6 7 8 9 10 11
Data variables:
A (index) datetime64[ns] 2000-01-01T06:00:00 ... 2000-01-12T06:00:00
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 Dataset.from_dataframe entry point and reproduce the df.to_xarray example using timezone-aware DatetimeTZ data and the development version of pandas. Check the linked pandas issue for the intended conversion behavior; done means the example no longer emits the FutureWarning while preserving the expected dataset values.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- pandas, python
- Domain
- data
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100