ueshin commented on a change in pull request #33882:
URL: https://github.com/apache/spark/pull/33882#discussion_r700579072
##########
File path: python/pyspark/pandas/namespace.py
##########
@@ -2814,9 +2824,18 @@ def to_numeric(arg):
1.0
"""
if isinstance(arg, Series):
- return arg._with_new_scol(arg.spark.column.cast("float"))
+ if errors == "coerce":
+ return arg._with_new_scol(arg.spark.column.cast("int"))
+ elif errors == "ignore":
+ scol = arg.spark.column
+ casted_scol = scol.cast("int")
+ return arg._with_new_scol(F.when(casted_scol.isNull(),
scol).otherwise(casted_scol))
Review comment:
Actually the case @itholic raised is a bit tricky.
pandas can return numeric type if there is no error.
```py
>>> pd.to_numeric(pd.Series(["1", "2", "3"]), errors="ignore")
0 1
1 2
2 3
dtype: int64
```
whereas the current implementation always returns `StringType`:
```py
>>> ps.to_numeric(ps.Series(["1", "2", "3"]), errors="ignore")
0 1
1 2
2 3
dtype: object
```
As Spark can't change the data type depending on whether there is an error
or not, we have to check it by ourselves beforehand. (or just we don't support
this?)
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