bito-code-review[bot] commented on code in PR #44743:
URL: https://github.com/apache/superset/pull/44743#discussion_r4120020725
##########
tests/unit_tests/result_set_test.py:
##########
@@ -621,3 +621,24 @@ def test_empty_result_set_preserves_column_metadata() ->
None:
df = result_set.to_pandas_df()
assert len(df) == 0
assert list(map(str, df.columns)) == ["id", "name", "created_at"]
+
+
+def test_integers_outside_int64_are_stringified() -> None:
+ """
+ Integers PyArrow cannot hold in int64 must not fail the whole result set.
+
+ MySQL-family ``BIGINT UNSIGNED`` columns return Python ints up to 2**64 -
1,
+ and ``pa.array`` raises ``OverflowError`` for anything above 2**63 - 1.
Such a
+ column falls back to strings, which also keeps the exact value that a
+ JavaScript number could not represent.
+ """
+ data = [(1, 18446744073709551615), (2, 0), (3, None)]
+ description = [
+ ("id", "int", None, None, None, None, True),
+ ("ubig", "int", None, None, None, None, True),
+ ]
+ result_set = SupersetResultSet(data, description, BaseEngineSpec) # type:
ignore
+
+ df = result_set.to_pandas_df()
+ assert df["id"].tolist() == [1, 2, 3]
+ assert df["ubig"].tolist() == ["18446744073709551615", "0", None]
Review Comment:
<!-- Bito Reply -->
The suggestion is correct. In the specified environment, `to_pandas_df`
converts null values in string columns to `nan` rather than `None`. Using
`pd.isna` is the appropriate way to assert the presence of a null value in this
context.
**tests/unit_tests/result_set_test.py**
```
assert df["id"].tolist() == [1, 2, 3]
assert df["ubig"].tolist() == ["18446744073709551615", "0", None]
```
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