marcuslin123 opened a new pull request, #56635:
URL: https://github.com/apache/spark/pull/56635
### What changes were proposed in this pull request?
Add support for `TimeType` columns in pandas API on Spark (`pyspark.pandas`):
- Map `datetime.time` to `TimeType` in the dtype translation layer
(`typehints.py`)
- Map `TimeType` to `np.dtype("object")` for pandas representation
- Create `TimeOps` class for column operations (comparisons supported,
arithmetic rejected)
- Register `TimeOps` in the dispatch system (`base.py`)
### Why are the changes needed?
`pyspark.pandas` does not handle `TimeType` — the Spark-to-pandas dtype
machinery treats `datetime.time` as a generic object with no explicit mapping.
Without these changes, creating a pandas-on-Spark DataFrame with
`datetime.time` values fails, and column operations on TIME columns crash with
`TypeError`.
The underlying Arrow conversion already supports TIME (SPARK-53263 /
SPARK-53305), so this wires up the remaining pyspark.pandas layer.
### Does this PR introduce _any_ user-facing change?
Yes. Users can now work with `TimeType` columns in `pyspark.pandas`:
```python
import pyspark.pandas as ps
import datetime
df = ps.DataFrame({"shift_start": [datetime.time(8, 0), datetime.time(14,
0)]})
df["shift_start"].dtype # returns object
afternoon = df[df["shift_start"] > datetime.time(12, 0)] # comparisons work
```
Previously this would fail with a TypeError or produce incorrect results.
### How was this patch tested?
- Added `datetime.time` mapping to `test_typedef.py`
- Added new `test_time_ops.py` covering arithmetic rejection and comparison
operations
- All tests pass locally: `python/run-tests --testnames
pyspark.pandas.tests.test_typedef` and `python/run-tests --testnames
pyspark.pandas.tests.data_type_ops.test_time_ops`
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code
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