PerilousApricot commented on pull request #34505:
URL: https://github.com/apache/spark/pull/34505#issuecomment-964650255
Hi @HyukjinKwon - thanks for this PR! I had a question about the
python-facing API. Is it possible to have something similar to how current
python/pandas UDFs' withColumn() can tell spark which columns are necessary for
the inputs and pass the remainder of the columns through to the resulting DF
without round-tripping them through python? For example (open-coding this,
might be missing some syntax):
```python
df = spark.createDataFrame(
[(1, "foo"), (2, None), (3, "bar"), (4, "bar")], "a int, b string")
@udf(IntType())
def squared(x):
return x * x
df = df.withColumn("squared", squared["a"]))
```
Would only ser/de the "a" column through the function, but the remaining
column "b" would be in the resulting dataframe.
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