zhengruifeng opened a new pull request, #41180:
URL: https://github.com/apache/spark/pull/41180
### What changes were proposed in this pull request?
Make `DataFrame.drop` support empty column
### Why are the changes needed?
to be consistent with vanilla PySpark
### Does this PR introduce _any_ user-facing change?
yes
```
In [1]: df = spark.createDataFrame([(1, 21), (2, 30)], ("id", "age"))
In [2]: df.drop()
```
before:
```
In [2]: df.drop()
---------------------------------------------------------------------------
PySparkValueError Traceback (most recent call last)
Cell In[2], line 1
----> 1 df.drop()
File ~/Dev/spark/python/pyspark/sql/connect/dataframe.py:449, in
DataFrame.drop(self, *cols)
444 raise PySparkTypeError(
445 error_class="NOT_COLUMN_OR_STR",
446 message_parameters={"arg_name": "cols", "arg_type":
type(cols).__name__},
447 )
448 if len(_cols) == 0:
--> 449 raise PySparkValueError(
450 error_class="CANNOT_BE_EMPTY",
451 message_parameters={"item": "cols"},
452 )
454 return DataFrame.withPlan(
455 plan.Drop(
456 child=self._plan,
(...)
459 session=self._session,
460 )
PySparkValueError: [CANNOT_BE_EMPTY] At least one cols must be specified.
```
after
```
In [2]: df.drop()
Out[2]: DataFrame[id: bigint, age: bigint]
```
### How was this patch tested?
enabled UT
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