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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2021/10/01 05:16:26 UTC
[GitHub] [spark] itholic commented on a change in pull request #34114: [SPARK-36438][PYTHON] Support list-like Python objects for Series comparison
itholic commented on a change in pull request #34114:
URL: https://github.com/apache/spark/pull/34114#discussion_r719942176
##########
File path: python/pyspark/pandas/data_type_ops/base.py
##########
@@ -349,11 +349,86 @@ def ge(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
raise TypeError(">= can not be applied to %s." % self.pretty_name)
def eq(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
- from pyspark.pandas.base import column_op
-
- _sanitize_list_like(right)
+ if isinstance(right, (list, tuple)):
+ from pyspark.pandas.series import first_series, scol_for
+ from pyspark.pandas.frame import DataFrame
+ from pyspark.pandas.internal import NATURAL_ORDER_COLUMN_NAME, InternalField
+
+ sdf = left._internal.spark_frame
+ structed_scol = F.struct(
+ sdf[NATURAL_ORDER_COLUMN_NAME],
+ *left._internal.index_spark_columns,
+ left.spark.column
+ )
+ # The size of the list is expected to be small.
+ collected_structed_scol = F.collect_list(structed_scol)
+ # Sort the array by NATURAL_ORDER_COLUMN so that we can guarantee the order.
+ collected_structed_scol = F.array_sort(collected_structed_scol)
+ right_values_scol = F.array([F.lit(x) for x in right]) # type: ignore
+ index_scol_names = left._internal.index_spark_column_names
+ scol_name = left._internal.spark_column_name_for(left._internal.column_labels[0])
+ # Compare the values of left and right by using zip_with function.
+ cond = F.zip_with(
+ collected_structed_scol,
+ right_values_scol,
+ lambda x, y: F.struct(
+ *[
+ x[index_scol_name].alias(index_scol_name)
+ for index_scol_name in index_scol_names
+ ],
+ F.when(
+ F.assert_true(
+ # If the comparing result is null,
+ # that means the length of `left` and `right` is not the same.
Review comment:
Oh, then they should return `False`. Let me address this case.
```python
>>> pd.Series([1, 2, None]) == [1, 2, None]
0 True
1 True
2 False
dtype: bool
>>> pd.Series([1, 2, None]) == [1, 2, 3]
0 True
1 True
2 False
dtype: bool
```
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