zhengruifeng opened a new pull request, #57076:
URL: https://github.com/apache/spark/pull/57076
### What changes were proposed in this pull request?
Fix an incorrect pandas-3 resolution assumption in
`test_pandas_series_numpy_backed` (in
`test_pyarrow_array_type_inference.py`).
The test used a single `ts_unit = "us" if pandas >= 3 else "ns"` for all
numpy-backed temporal cases. Empirically, pandas 3 only produces microsecond
resolution for parse/construct/tz-aware inputs (`pd.to_datetime`,
`pd.Timestamp("...")`, `pd.to_timedelta`, tz-aware Series). The boundary
constants `pd.Timestamp.min/max` and `pd.Timedelta.min/max`, and the
`pd.Timedelta(0)` scalar, remain nanosecond-backed in both pandas 2 and 3,
because those values only fit in nanosecond resolution.
This PR pins those boundary/scalar cases to `"ns"` and keeps `ts_unit` for
the remaining cases, and clarifies the comment.
### Why are the changes needed?
The scheduled `Build / Python-only (Python 3.12, Pandas 3)` job fails on
master:
```
File ".../test_pyarrow_array_type_inference.py", line 348, in
test_pandas_series_numpy_backed
self._run_inference_tests(cases)
AssertionError: TimestampType(timestamp[ns]) != TimestampType(timestamp[us])
```
The test loop stops at the first mismatch (`pd.Timestamp.min`), which is why
only one failure surfaced. The wrong branch was never exercised until the
Pandas-3 job ran.
### Does this PR introduce _any_ user-facing change?
No. Test-only change.
### How was this patch tested?
Ran the suite in two local conda environments (with `SPARK_TESTING=1`):
- pandas 3.0.2 / pyarrow 23.0.1: 10/10 pass
- pandas 2.3.3: 10/10 pass (no regression)
`ruff format --check` and `ruff check` both pass on the changed file.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code
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