itholic commented on a change in pull request #33757:
URL: https://github.com/apache/spark/pull/33757#discussion_r690857061
##########
File path: python/pyspark/pandas/tests/test_categorical.py
##########
@@ -243,18 +243,16 @@ def test_astype(self):
self.assert_eq(kcser.astype("category"), pcser.astype("category"))
+ # CategoricalDtype is not updated if the dtype is same from pandas 1.3.
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
- # TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
- pass
- elif LooseVersion(pd.__version__) >= LooseVersion("1.2"):
self.assert_eq(
kcser.astype(CategoricalDtype(["b", "c", "a"])),
pcser.astype(CategoricalDtype(["b", "c", "a"])),
)
else:
self.assert_eq(
kcser.astype(CategoricalDtype(["b", "c", "a"])),
- pser.astype(CategoricalDtype(["b", "c", "a"])),
+ kcser,
Review comment:
Yes, because `kcser` is pandas-on-Spark Series, which always follows
latest pandas(1.3).
Latest pandas doesn't update the `dtype` after `astype`, if the unique
values of target `dtype` are the same as original unique values as described in
the PR description.
```python
>>> pcat1 = pser.astype(CategoricalDtype(["b", "c", "a"]))
>>> pcat2 = pser.astype(CategoricalDtype(["a", "b", "c"]))
>>> pcat1.dtype == pcat2.dtype
True
```
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