pralabhkumar commented on a change in pull request #35191:
URL: https://github.com/apache/spark/pull/35191#discussion_r799222433
##########
File path: python/pyspark/pandas/series.py
##########
@@ -5228,22 +5228,128 @@ def asof(self, where: Union[Any, List]) ->
Union[Scalar, "Series"]:
where = [where]
index_scol = self._internal.index_spark_columns[0]
index_type = self._internal.spark_type_for(index_scol)
+
+ # e.g where = [10, 20]
+ # In the comments below , will explain how the dataframe will look
after transformations.
+ # e.g pd.Series([2, 1, np.nan, 4], index=[10, 20, 30, 40],
name="Koalas")
+
+ column_prefix_constant = "col_"
cond = [
- F.max(F.when(index_scol <= SF.lit(index).cast(index_type),
self.spark.column))
- for index in where
+ F.when(
+ index_scol <= SF.lit(index).cast(index_type),
+ F.struct(
+ F.lit(column_prefix_constant + str(index) + "_" +
str(idx)).alias("identifier"),
+ self.spark.column.alias("col_value"),
+ ),
+ ).alias(column_prefix_constant + str(index) + "_" + str(idx))
Review comment:
Yes @itholic , this is working (since __index_level_0__) is sorted.
However , test case with psser.asof([25, 25]) , ambiguous of duplicate cols in
psdf = ps.DataFrame(sdf) . Therefore , in order to pass above test case ,
below is the change.
```python
cond = [
F.last(
F.when(index_scol <= SF.lit(index).cast(index_type),
self.spark.column),
ignorenulls=True,
).alias(column_prefix_constant + str(index) + "_" + str(idx))
for idx, index in enumerate(where)
]
```
Then
```python
with ps.option_context("compute.default_index_type", "distributed",
"compute.max_rows", 1):
psdf = ps.DataFrame(sdf) # type: DataFrame
df = pd.DataFrame(psdf.transpose().values, columns=[self.name],
index=where)
return df[df.columns[0]]
```
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