itholic commented on code in PR #41514:
URL: https://github.com/apache/spark/pull/41514#discussion_r1228833815
##########
python/pyspark/pandas/data_type_ops/num_ops.py:
##########
@@ -213,37 +214,31 @@ def abs(self, operand: IndexOpsLike) -> IndexOpsLike:
F.abs(operand.spark.column), field=operand._internal.data_fields[0]
)
+ def eq(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
+ # We can directly use `super().eq` when given object is list, tuple,
dict or set.
+ if not isinstance(right, IndexOpsMixin) and is_list_like(right):
+ return super().eq(left, right)
+ return pyspark_column_op("__eq__", left, right, fillna=False)
+
+ def ne(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
+ _sanitize_list_like(right)
+ return pyspark_column_op("__ne__", left, right, fillna=True)
Review Comment:
For `ne`:
```python
>>> pser = pd.Series([1.0, 2.0, np.nan])
>>> psser = ps.from_pandas(pser)
>>> pser.ne(pser)
0 False
1 False
2 True
dtype: bool
>>> psser.ne(psser)
0 False
1 False
2 None
dtype: bool
```
We expect `True` for non-equal case, but it returns `None` in Spark Connect.
So we cast `None` to `True` for `ne`.
For `eq`:
```python
>>> pser = pd.Series([1.0, 2.0, np.nan])
>>> psser = ps.from_pandas(pser)
>>> pser.eq(pser)
0 True
1 True
2 False
dtype: bool
>>> psser.eq(psser)
0 True
1 True
2 None
dtype: bool
```
We expect `False` for non-equal case, but it returns `None` in Spark
Connect. So we cast `None` to `False` for `eq`.
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