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new 98645a267511 [SPARK-52791][PS] Fix error when inferring a UDT with a
null first element
98645a267511 is described below
commit 98645a2675110d9f4aab3d6dca1fc4e89cdff053
Author: Peter Nguyen <petern0...@gmail.com>
AuthorDate: Wed Jul 23 09:14:42 2025 +0900
[SPARK-52791][PS] Fix error when inferring a UDT with a null first element
I modified the udt condition to check the first non-null element instead of
the first element (which might be null).
```
import pyspark.pandas as ps
from pyspark.ml.linalg import SparseVector
sparse_values = {0: 0.1, 1: 1.1}
ps_series = ps.Series([None, SparseVector(1, \{0: 1.2}), SparseVector(1,
\{0: 3})])
```
Error:
```
pyarrow.lib.ArrowInvalid: Could not convert SparseVector(1, {0: 1.2}) with
type SparseVector: did not recognize Python value type when inferring an Arrow
data type
```
This should work as normal, but it fails because the first element is None
Yes, previously it would error, but now it works properly. This is a
behavior change from all previous spark versions, and should probably be
backported.
Added a test
No
Closes #51475 from petern48/fix_infer_spark_type.
Authored-by: Peter Nguyen <petern0...@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls...@apache.org>
(cherry picked from commit 5182eb4c6a51989b37f054ef07173cd797611d2b)
Signed-off-by: Hyukjin Kwon <gurwls...@apache.org>
---
.../pandas/tests/data_type_ops/test_udt_ops.py | 20 ++++++++++++++++++++
python/pyspark/pandas/typedef/typehints.py | 5 +++--
2 files changed, 23 insertions(+), 2 deletions(-)
diff --git a/python/pyspark/pandas/tests/data_type_ops/test_udt_ops.py
b/python/pyspark/pandas/tests/data_type_ops/test_udt_ops.py
index 45f8cca56ee9..7b264582e044 100644
--- a/python/pyspark/pandas/tests/data_type_ops/test_udt_ops.py
+++ b/python/pyspark/pandas/tests/data_type_ops/test_udt_ops.py
@@ -129,6 +129,26 @@ class UDTOpsTestsMixin:
self.assert_eq(pser, psser._to_pandas())
self.assert_eq(ps.from_pandas(pser), psser)
+ def test_with_first_null(self):
+ lst = [None, None, None, SparseVector(1, {0: 0.1})]
+ pser = pd.Series(lst)
+ psser = ps.Series(lst)
+ self.assert_eq(pser, psser._to_pandas())
+ self.assert_eq(ps.from_pandas(pser), psser)
+
+ lst2 = [SparseVector(1, {0: 0.1}), None, None, None]
+ pdf = pd.DataFrame({"a": lst, "b": lst2})
+ psdf = ps.DataFrame({"a": lst, "b": lst2})
+ self.assert_eq(pdf, psdf._to_pandas())
+ self.assert_eq(ps.from_pandas(pdf), psdf)
+
+ def test_with_all_null(self):
+ lst = [None, None, None, None]
+ pser = pd.Series(lst, dtype=object)
+ psser = ps.Series(lst, dtype=object)
+ self.assert_eq(pser, psser._to_pandas())
+ self.assert_eq(ps.from_pandas(pser), psser)
+
def test_isnull(self):
self.assert_eq(self.pser.isnull(), self.psser.isnull())
diff --git a/python/pyspark/pandas/typedef/typehints.py
b/python/pyspark/pandas/typedef/typehints.py
index 012eabf958eb..5c7b3e01686a 100644
--- a/python/pyspark/pandas/typedef/typehints.py
+++ b/python/pyspark/pandas/typedef/typehints.py
@@ -354,8 +354,9 @@ def infer_pd_series_spark_type(
if dtype == np.dtype("object"):
if len(pser) == 0 or pser.isnull().all():
return types.NullType()
- elif hasattr(pser.iloc[0], "__UDT__"):
- return pser.iloc[0].__UDT__
+ notnull = pser[pser.notnull()]
+ if hasattr(notnull.iloc[0], "__UDT__"):
+ return notnull.iloc[0].__UDT__
else:
return from_arrow_type(pa.Array.from_pandas(pser).type,
prefer_timestamp_ntz)
elif isinstance(dtype, CategoricalDtype):
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