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new 0abfcf53e76b [MINOR][PYTHON] Fix wrong expected_type in DataFrame.sort
ascending type error
0abfcf53e76b is described below
commit 0abfcf53e76b9aa85d9a46bbe6905451817cabe1
Author: Nikolaus Schuetz <[email protected]>
AuthorDate: Sun Aug 2 17:27:52 2026 +0000
[MINOR][PYTHON] Fix wrong expected_type in DataFrame.sort ascending type
error
### What changes were proposed in this pull request?
`DataFrame.sort` / `orderBy` / `sortWithinPartitions` raise
`NOT_EXPECTED_TYPE` for an invalid `ascending` argument with `expected_type` =
`"Column, int or str"`. But `ascending` accepts `bool`, `int`, or `list` —
never `Column` or `str`. The literal was copied from the column-argument error
a few lines above in `_preapare_cols_for_sort`. This corrects it to `"bool, int
or list"`.
### Why are the changes needed?
The message names types `ascending` never accepts (`Column`, `str`) and
omits the ones it does (`bool`, `list`), misleading users about how to fix the
call. The `ascending` parameter is documented as `bool or list`.
### Does this PR introduce _any_ user-facing change?
Yes, the error message changes.
Before:
```
[NOT_EXPECTED_TYPE] Argument `ascending` should be Column, int or str, got
str.
```
After:
```
[NOT_EXPECTED_TYPE] Argument `ascending` should be bool, int or list, got
str.
```
### How was this patch tested?
Added `test_sort_ascending_invalid_type` in
`python/pyspark/sql/tests/test_dataframe.py` asserting the corrected
`expected_type`. Verified against a released `pyspark` session: `df.sort("age",
ascending="asc")` reports `bool, int or list` with the fix (previously `Column,
int or str`); valid `ascending` values (`False`, `[True]`) are unaffected.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code (Opus 4.8)
Closes #57522 from nikolauspschuetz/minor-sort-ascending-expected-type.
Authored-by: Nikolaus Schuetz <[email protected]>
Signed-off-by: Uros Bojanic <[email protected]>
(cherry picked from commit 1222a59fa61965f50f35443f70db8d6821ea0bc1)
Signed-off-by: Uros Bojanic <[email protected]>
---
python/pyspark/sql/dataframe.py | 2 +-
python/pyspark/sql/tests/test_dataframe.py | 14 ++++++++++++++
2 files changed, 15 insertions(+), 1 deletion(-)
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 6c4d32ea1797..40c719a99105 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -3351,7 +3351,7 @@ class DataFrame:
raise PySparkTypeError(
errorClass="NOT_EXPECTED_TYPE",
messageParameters={
- "expected_type": "Column, int or str",
+ "expected_type": "bool, int or list",
"arg_name": "ascending",
"arg_type": type(ascending).__name__,
},
diff --git a/python/pyspark/sql/tests/test_dataframe.py
b/python/pyspark/sql/tests/test_dataframe.py
index e66750f78f27..4ac5ef46df0d 100644
--- a/python/pyspark/sql/tests/test_dataframe.py
+++ b/python/pyspark/sql/tests/test_dataframe.py
@@ -261,6 +261,20 @@ class DataFrameTestsMixin:
messageParameters={"expected_type": "dict", "arg_name": "colsMap",
"arg_type": "tuple"},
)
+ def test_sort_ascending_invalid_type(self):
+ df = self.spark.createDataFrame([("Alice", 10)], ["name", "age"])
+ with self.assertRaises(PySparkTypeError) as pe:
+ df.sort("age", ascending="asc")
+ self.check_error(
+ exception=pe.exception,
+ errorClass="NOT_EXPECTED_TYPE",
+ messageParameters={
+ "expected_type": "bool, int or list",
+ "arg_name": "ascending",
+ "arg_type": "str",
+ },
+ )
+
def test_with_columns_renamed_with_duplicated_names(self):
df1 = self.spark.createDataFrame([(1, "v1")], ["id", "value"])
df2 = self.spark.createDataFrame([(1, "x", "v2")], ["id", "a",
"value"])
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