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Posted to reviews@spark.apache.org by "Spenserrrr (via GitHub)" <gi...@apache.org> on 2026/08/04 22:08:16 UTC
[PR] [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls [spark]
Spenserrrr opened a new pull request, #57774:
URL: https://github.com/apache/spark/pull/57774
### What changes were proposed in this pull request?
Add `PyArrowArrayToPandasIntegerObjectNullsTests` to the existing `test_pyarrow_arrow_to_pandas_non_default.py`, pinning the behavior of `pa.Array.to_pandas(integer_object_nulls=True)` in a golden file. Sub-task of SPARK-54936.
numpy integer dtypes cannot represent a null, so PyArrow must choose a representation when an integer array contains one. By default it widens to `float64` with `NaN`; with `integer_object_nulls=True` the result stays `object` dtype holding Python `int` and `None`.
The row set reuses all 121 rows of the sibling `test_pyarrow_arrow_to_pandas_default.py` via `_build_source_arrays`, so both golden files pin the same types, plus 18 appended integer rows that the shared rows do not reach:
- **Width extremes with a null** (8 rows) — the shared nullable rows use small values that `float64` represents exactly. At 64 bits the range exceeds float64's 53-bit mantissa and the value itself changes.
- **`int64:all-null`, `int64:multi-chunk-nullable`** — no integer left to convert, and values split across buffers with the null in only one chunk. The latter is reachable in production: cogrouped `applyInPandas` is the one UDF path that does not call `combine_chunks()` first.
- **Nested types with a null integer element** (7 rows) — `list`, `large_list`, `fixed_size_list`, `list<list>`, `struct`, `map`. The shared nested rows hold a missing *sub-list*, which this argument does not affect; a missing *element inside* one is what it changes. These are also the types `convert_legacy` still serves.
- **`dictionary<int64>:nullable`** — control. Dictionary encoding stores the distinct values once plus an index per row, so the null lives in the indices and the int64 values hold none; with no null to represent, the argument has nothing to decide.
Three output columns per row: the argument off, on, and the full `pandas_options` dict `convert_legacy` passes. The last is not a duplicate of the second — `coerce_temporal_nanoseconds` shifts the temporal rows to `ns` — and pinning the call as Spark makes it also catches PyArrow changing the `date_as_object=True` default it relies on.
No `dev/sparktestsupport/modules.py` line is needed; the test file is already registered.
### Why are the changes needed?
`integer_object_nulls=True` is passed unconditionally at `python/pyspark/sql/conversion.py:1839`, and again to `_create_converter_to_pandas` at `:1850` so the fix-up stage knows what it received. The object Series is an intermediate, not the result: it is narrowed to a nullable extension dtype (`Int8Dtype` .. `Int64Dtype`) via `_to_corrected_pandas_ext_type`, and it is the only bridge from Arrow to those dtypes that avoids `float64`.
It was added by SPARK-54962 to fix a correctness bug where an identity pandas UDF returned a different number for a large nullable long, and its behavior has never been pinned by a test. Unlike `types_mapper` on the sibling `convert_numpy` path, which is gated by `spark.sql.execution.pythonUDF.pandas.preferIntExtensionDtype` (default false), this argument has no config to fall back on.
Scope is worth being precise about: this argument serves the pandas UDF path over the complex types `convert_numpy` does not handle yet (`TODO(SPARK-55324)` at `conversion.py:1999-2007`). `df.toPandas()` builds its own options at `python/pyspark/sql/pandas/conversion.py:249-251` and does not pass it. That the covered type region is scheduled to move is itself an argument for recording the current semantics now.
### Does this PR introduce _any_ user-facing change?
No. Tests only.
### How was this patch tested?
New golden-file test, 139 rows x 4 columns. The argument changes 25 rows; the remaining 114 pin that it does not affect them.
Validated across **16/16** environments — PyArrow 18/19/20/21/22/23/24/25 x pandas 2 and 3 — running the committed test file against the committed golden in a fresh venv per combination. Golden regeneration is byte-identical.
Two `overrides` tiers were needed and both were verified load-bearing by disabling each and observing the failure: pandas 3 renders non-empty Arrow string arrays with its dedicated string dtype, while empty ones only gain it from PyArrow 24.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code (Opus 5)
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Re: [PR] [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls [spark]
Posted by "Spenserrrr (via GitHub)" <gi...@apache.org>.
Spenserrrr commented on code in PR #57774:
URL: https://github.com/apache/spark/pull/57774#discussion_r3716889184
##########
python/pyspark/tests/upstream/pyarrow/test_pyarrow_arrow_to_pandas_non_default.py:
##########
@@ -493,6 +493,178 @@ def compute_cell(row_name, col_name):
)
+@unittest.skipIf(
+ not have_pyarrow or not have_pandas or not have_numpy,
+ pyarrow_requirement_message or pandas_requirement_message or numpy_requirement_message,
+)
+class PyArrowArrayToPandasIntegerObjectNullsTests(_PyArrowToPandasTestBase):
+ """
+ Tests pa.Array.to_pandas(integer_object_nulls=True) via golden file comparison.
+
+ numpy integers have no null, so by default PyArrow widens a null-bearing integer
+ array to ``float64`` with ``NaN`` -- which cannot hold every int64 exactly, so a
+ large value silently changes. ``integer_object_nulls=True`` keeps ``object``
+ dtype (Python ``int`` and ``None``) instead, preserving the values.
+
+ PySpark passes it in ``ArrowArrayToPandasConversion.convert_legacy``
+ (``python/pyspark/sql/conversion.py``), bundled with ``date_as_object`` and
+ ``coerce_temporal_nanoseconds``, then narrows the object Series to a nullable
+ extension dtype (``Int8Dtype`` .. ``Int64Dtype``) -- the only bridge from Arrow to
+ those dtypes that avoids ``float64``.
+
+ Three output columns are recorded per source array: the argument off, on, and the
+ full ``pandas_options`` dict ``convert_legacy`` passes. The last is not a
+ duplicate of the second -- ``coerce_temporal_nanoseconds`` shifts the temporal
+ rows to ``ns`` -- and pinning the call as Spark makes it also catches PyArrow
+ changing the ``date_as_object=True`` default it relies on.
+
+ The row set mirrors ``test_pyarrow_arrow_to_pandas_default.py``'s rows so both
+ golden files pin the same types, and appends the integer variants those rows do
+ not reach (see ``_build_source_arrays``).
+ """
+
+ def _build_source_arrays(self):
+ """
+ Reuse every row of the sibling default test, then add the integer variants
+ that only matter for this argument: the shared rows' nullable values are
+ small enough to survive ``float64``, and their nested rows are missing whole
+ sub-lists rather than a single integer inside one.
+ """
+ default_tests = test_pyarrow_arrow_to_pandas_default.PyArrowArrayToPandasDefaultTests
+ sources = default_tests._build_source_arrays(self)
+
+ # Each width's min and max alongside a null. The shared rows use small values,
+ # which float64 represents exactly; only at 64 bits does the range exceed its
+ # 53-bit mantissa and the value itself change.
+ for pa_type in [
+ pa.int8(),
+ pa.int16(),
+ pa.int32(),
+ pa.int64(),
+ pa.uint8(),
+ pa.uint16(),
+ pa.uint32(),
+ pa.uint64(),
+ ]:
+ if pa.types.is_signed_integer(pa_type):
+ bounds = [2 ** (pa_type.bit_width - 1) - 1, -(2 ** (pa_type.bit_width - 1))]
+ else:
+ bounds = [2**pa_type.bit_width - 1, 0]
+ sources[f"{pa_type}:extremes-nullable"] = pa.array(bounds + [None], pa_type)
+
+ # Every value is null, so there is no integer left to convert.
+ sources["int64:all-null"] = pa.array([None, None], pa.int64())
+ # Values split across buffers, with the null in one chunk and not the other.
+ # Reachable in production: cogrouped applyInPandas is the one UDF path that does
+ # not call combine_chunks() first.
+ sources["int64:multi-chunk-nullable"] = pa.chunked_array(
+ [pa.array([1, None], pa.int64()), pa.array([2], pa.int64())]
+ )
+
+ # Nested types whose null is an integer ELEMENT, not a missing sub-list: the
+ # shared rows only cover the latter, which this argument does not affect.
+ # These are also the types convert_legacy still serves.
+ sources["list<int64>:null-element"] = pa.array([[1, None], [2, 3]], pa.list_(pa.int64()))
+ sources["list<int64>:null-element-extreme"] = pa.array(
+ [[2**63 - 1, None]], pa.list_(pa.int64())
+ )
+ sources["large_list<int64>:null-element"] = pa.array([[1, None]], pa.large_list(pa.int64()))
+ sources["fixed_size_list<int64>[3]:null-element"] = pa.array(
+ [[1, None, 3]], pa.list_(pa.int64(), 3)
+ )
+ sources["list<list<int64>>:null-element"] = pa.array(
+ [[[1, None], [2]]], pa.list_(pa.list_(pa.int64()))
+ )
+ sources["struct:null-int-field"] = pa.array(
+ [{"x": 1, "y": "a"}, {"x": None, "y": "b"}],
+ pa.struct([("x", pa.int64()), ("y", pa.string())]),
+ )
+ sources["map<string,int64>:null-value"] = pa.array(
+ [[("a", 1), ("b", None)]], pa.map_(pa.string(), pa.int64())
+ )
+ # Control: dictionary encoding stores the distinct values once plus an index per
+ # row, so the null lives in the indices and the int64 values hold none. With no
+ # null to represent there, the argument has nothing to decide -- both columns
+ # stay category, whose own values also remain int64.
+ sources["dictionary<int64>:nullable"] = pa.array(
+ [1, None, 1], pa.int64()
+ ).dictionary_encode()
+
+ return sources
+
+ # Output column for the default: a null-bearing integer array widens to float64.
+ COL_INTEGER_OBJECT_NULLS_OFF = "integer_object_nulls=False"
+ # Output column for the argument on: the result stays object dtype.
+ COL_INTEGER_OBJECT_NULLS_ON = "integer_object_nulls=True"
+ # Output column for all three arguments as convert_legacy passes them together.
+ COL_SPARK_PANDAS_OPTIONS = "spark pandas_options"
+
+ # Kept as one dict so the column cannot drift from the call site it mirrors.
+ SPARK_PANDAS_OPTIONS = {
+ "date_as_object": True,
+ "coerce_temporal_nanoseconds": True,
+ "integer_object_nulls": True,
+ }
+
+ def test_to_pandas_integer_object_nulls(self):
+ """Test pa.Array.to_pandas(integer_object_nulls=True/False) against golden file."""
+ sources = self._build_source_arrays()
+ row_names = list(sources.keys())
+ col_names = [
+ "pyarrow array",
+ self.COL_INTEGER_OBJECT_NULLS_OFF,
+ self.COL_INTEGER_OBJECT_NULLS_ON,
+ self.COL_SPARK_PANDAS_OPTIONS,
+ ]
+
+ # Version-specific expected values go here, keyed by (row, col), when a
+ # newer pandas/PyArrow/NumPy legitimately changes a cell's output.
+ overrides: dict[tuple[str, str], str] = {}
+ # Pandas 3 renders Arrow string arrays with its dedicated string dtype. This
+ # argument does not touch strings, so every output column shifts together.
+ if LooseVersion(pd.__version__) >= LooseVersion("3.0.0"):
+
+ def override_all_outputs(row, expected):
+ for col in col_names[1:]:
+ overrides[(row, col)] = expected
+
+ for row, expected in [
+ ("string:standard", "['hello', 'world', '']@Series[str]"),
+ ("string:nullable", "['hello', nan, 'world']@Series[str]"),
+ ("large_string:standard", "['hello', 'world']@Series[str]"),
+ ("large_string:nullable", "['hello', nan]@Series[str]"),
+ ]:
+ override_all_outputs(row, expected)
+
+ # Empty string arrays only gain that dtype from PyArrow 24; before that
+ # they stay object even on pandas 3, so the baseline still holds there.
+ if LooseVersion(pa.__version__) >= LooseVersion("24.0.0"):
+ for row in ["string:empty", "large_string:empty"]:
+ override_all_outputs(row, "[]@Series[str]")
Review Comment:
Thanks for the review, @Yicong-Huang! I've just revised according to the suggestion.
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Re: [PR] [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls [spark]
Posted by "uros-b (via GitHub)" <gi...@apache.org>.
uros-b closed pull request #57774: [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls
URL: https://github.com/apache/spark/pull/57774
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Re: [PR] [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls [spark]
Posted by "Yicong-Huang (via GitHub)" <gi...@apache.org>.
Yicong-Huang commented on code in PR #57774:
URL: https://github.com/apache/spark/pull/57774#discussion_r3716745889
##########
python/pyspark/tests/upstream/pyarrow/test_pyarrow_arrow_to_pandas_non_default.py:
##########
@@ -493,6 +493,178 @@ def compute_cell(row_name, col_name):
)
+@unittest.skipIf(
+ not have_pyarrow or not have_pandas or not have_numpy,
+ pyarrow_requirement_message or pandas_requirement_message or numpy_requirement_message,
+)
+class PyArrowArrayToPandasIntegerObjectNullsTests(_PyArrowToPandasTestBase):
+ """
+ Tests pa.Array.to_pandas(integer_object_nulls=True) via golden file comparison.
+
+ numpy integers have no null, so by default PyArrow widens a null-bearing integer
+ array to ``float64`` with ``NaN`` -- which cannot hold every int64 exactly, so a
+ large value silently changes. ``integer_object_nulls=True`` keeps ``object``
+ dtype (Python ``int`` and ``None``) instead, preserving the values.
+
+ PySpark passes it in ``ArrowArrayToPandasConversion.convert_legacy``
+ (``python/pyspark/sql/conversion.py``), bundled with ``date_as_object`` and
+ ``coerce_temporal_nanoseconds``, then narrows the object Series to a nullable
+ extension dtype (``Int8Dtype`` .. ``Int64Dtype``) -- the only bridge from Arrow to
+ those dtypes that avoids ``float64``.
+
+ Three output columns are recorded per source array: the argument off, on, and the
+ full ``pandas_options`` dict ``convert_legacy`` passes. The last is not a
+ duplicate of the second -- ``coerce_temporal_nanoseconds`` shifts the temporal
+ rows to ``ns`` -- and pinning the call as Spark makes it also catches PyArrow
+ changing the ``date_as_object=True`` default it relies on.
+
+ The row set mirrors ``test_pyarrow_arrow_to_pandas_default.py``'s rows so both
+ golden files pin the same types, and appends the integer variants those rows do
+ not reach (see ``_build_source_arrays``).
+ """
+
+ def _build_source_arrays(self):
+ """
+ Reuse every row of the sibling default test, then add the integer variants
+ that only matter for this argument: the shared rows' nullable values are
+ small enough to survive ``float64``, and their nested rows are missing whole
+ sub-lists rather than a single integer inside one.
+ """
+ default_tests = test_pyarrow_arrow_to_pandas_default.PyArrowArrayToPandasDefaultTests
+ sources = default_tests._build_source_arrays(self)
+
+ # Each width's min and max alongside a null. The shared rows use small values,
+ # which float64 represents exactly; only at 64 bits does the range exceed its
+ # 53-bit mantissa and the value itself change.
+ for pa_type in [
+ pa.int8(),
+ pa.int16(),
+ pa.int32(),
+ pa.int64(),
+ pa.uint8(),
+ pa.uint16(),
+ pa.uint32(),
+ pa.uint64(),
+ ]:
+ if pa.types.is_signed_integer(pa_type):
+ bounds = [2 ** (pa_type.bit_width - 1) - 1, -(2 ** (pa_type.bit_width - 1))]
+ else:
+ bounds = [2**pa_type.bit_width - 1, 0]
+ sources[f"{pa_type}:extremes-nullable"] = pa.array(bounds + [None], pa_type)
+
+ # Every value is null, so there is no integer left to convert.
+ sources["int64:all-null"] = pa.array([None, None], pa.int64())
+ # Values split across buffers, with the null in one chunk and not the other.
+ # Reachable in production: cogrouped applyInPandas is the one UDF path that does
+ # not call combine_chunks() first.
+ sources["int64:multi-chunk-nullable"] = pa.chunked_array(
+ [pa.array([1, None], pa.int64()), pa.array([2], pa.int64())]
+ )
+
+ # Nested types whose null is an integer ELEMENT, not a missing sub-list: the
+ # shared rows only cover the latter, which this argument does not affect.
+ # These are also the types convert_legacy still serves.
+ sources["list<int64>:null-element"] = pa.array([[1, None], [2, 3]], pa.list_(pa.int64()))
+ sources["list<int64>:null-element-extreme"] = pa.array(
+ [[2**63 - 1, None]], pa.list_(pa.int64())
+ )
+ sources["large_list<int64>:null-element"] = pa.array([[1, None]], pa.large_list(pa.int64()))
+ sources["fixed_size_list<int64>[3]:null-element"] = pa.array(
+ [[1, None, 3]], pa.list_(pa.int64(), 3)
+ )
+ sources["list<list<int64>>:null-element"] = pa.array(
+ [[[1, None], [2]]], pa.list_(pa.list_(pa.int64()))
+ )
+ sources["struct:null-int-field"] = pa.array(
+ [{"x": 1, "y": "a"}, {"x": None, "y": "b"}],
+ pa.struct([("x", pa.int64()), ("y", pa.string())]),
+ )
+ sources["map<string,int64>:null-value"] = pa.array(
+ [[("a", 1), ("b", None)]], pa.map_(pa.string(), pa.int64())
+ )
+ # Control: dictionary encoding stores the distinct values once plus an index per
+ # row, so the null lives in the indices and the int64 values hold none. With no
+ # null to represent there, the argument has nothing to decide -- both columns
+ # stay category, whose own values also remain int64.
+ sources["dictionary<int64>:nullable"] = pa.array(
+ [1, None, 1], pa.int64()
+ ).dictionary_encode()
+
+ return sources
+
+ # Output column for the default: a null-bearing integer array widens to float64.
+ COL_INTEGER_OBJECT_NULLS_OFF = "integer_object_nulls=False"
+ # Output column for the argument on: the result stays object dtype.
+ COL_INTEGER_OBJECT_NULLS_ON = "integer_object_nulls=True"
+ # Output column for all three arguments as convert_legacy passes them together.
+ COL_SPARK_PANDAS_OPTIONS = "spark pandas_options"
+
+ # Kept as one dict so the column cannot drift from the call site it mirrors.
+ SPARK_PANDAS_OPTIONS = {
+ "date_as_object": True,
+ "coerce_temporal_nanoseconds": True,
+ "integer_object_nulls": True,
+ }
+
+ def test_to_pandas_integer_object_nulls(self):
+ """Test pa.Array.to_pandas(integer_object_nulls=True/False) against golden file."""
+ sources = self._build_source_arrays()
+ row_names = list(sources.keys())
+ col_names = [
+ "pyarrow array",
+ self.COL_INTEGER_OBJECT_NULLS_OFF,
+ self.COL_INTEGER_OBJECT_NULLS_ON,
+ self.COL_SPARK_PANDAS_OPTIONS,
+ ]
+
+ # Version-specific expected values go here, keyed by (row, col), when a
+ # newer pandas/PyArrow/NumPy legitimately changes a cell's output.
+ overrides: dict[tuple[str, str], str] = {}
+ # Pandas 3 renders Arrow string arrays with its dedicated string dtype. This
+ # argument does not touch strings, so every output column shifts together.
+ if LooseVersion(pd.__version__) >= LooseVersion("3.0.0"):
+
+ def override_all_outputs(row, expected):
+ for col in col_names[1:]:
+ overrides[(row, col)] = expected
+
+ for row, expected in [
+ ("string:standard", "['hello', 'world', '']@Series[str]"),
+ ("string:nullable", "['hello', nan, 'world']@Series[str]"),
+ ("large_string:standard", "['hello', 'world']@Series[str]"),
+ ("large_string:nullable", "['hello', nan]@Series[str]"),
+ ]:
+ override_all_outputs(row, expected)
+
+ # Empty string arrays only gain that dtype from PyArrow 24; before that
+ # they stay object even on pandas 3, so the baseline still holds there.
+ if LooseVersion(pa.__version__) >= LooseVersion("24.0.0"):
+ for row in ["string:empty", "large_string:empty"]:
+ override_all_outputs(row, "[]@Series[str]")
Review Comment:
nit: I think it might be better to write this format for readability? I want to avoid nested if and loops.
if pandas > 3 and 18 <= arrow < 24:
overrides[(r1, c1)] = "xxx"
overrides[(r2, c2)] = "yyy"
if pandas > 3 and arrow > 24:
overrides[(r2, c2)] = "zzz"
...
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Re: [PR] [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls [spark]
Posted by "Spenserrrr (via GitHub)" <gi...@apache.org>.
Spenserrrr commented on PR #57774:
URL: https://github.com/apache/spark/pull/57774#issuecomment-5185215283
Hi @Yicong-Huang @zhengruifeng! Could you take a look when you have a moment? Thank you!
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Re: [PR] [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls [spark]
Posted by "uros-b (via GitHub)" <gi...@apache.org>.
uros-b commented on PR #57774:
URL: https://github.com/apache/spark/pull/57774#issuecomment-5189623026
**Merge Summary:**
- merged into master https://github.com/apache/spark/commit/a8dcc97d1382fd9405b5b363e33d0065b8f7db31
- merged into branch-4.x https://github.com/apache/spark/commit/083a70d57c59184de1b37ef4fea58ba5d44b1ad8
*Posted by `merge_spark_pr.py`*
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Re: [PR] [SPARK-58576][PYTHON][TESTS] Add tests for pa.Array.to_pandas with integer_object_nulls [spark]
Posted by "uros-b (via GitHub)" <gi...@apache.org>.
uros-b commented on PR #57774:
URL: https://github.com/apache/spark/pull/57774#issuecomment-5189616848
Thank you @Spenserrrr and @Yicong-Huang @zhengruifeng!
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