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fix warnings from newer versions of dependencies (#1414)
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* filter pyspark warning

* daft error
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kevinjqliu authored Dec 7, 2024
1 parent 8cbaa2b commit 1c73b7c
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Showing 3 changed files with 86 additions and 60 deletions.
137 changes: 78 additions & 59 deletions poetry.lock

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2 changes: 2 additions & 0 deletions pyproject.toml
Original file line number Diff line number Diff line change
Expand Up @@ -907,6 +907,8 @@ filterwarnings = [
"ignore:datetime.datetime.utcfromtimestamp\\(\\) is deprecated and scheduled for removal in a future version.",
# Remove this once https://github.com/boto/boto3/issues/3889 is fixed.
"ignore:datetime.datetime.utcnow\\(\\) is deprecated and scheduled for removal in a future version.",
# Latest PySpark version (v3.5.3) throws this error, remove in a future release of PySpark (possibly v4.0.0).
"ignore:is_datetime64tz_dtype is deprecated and will be removed in a future version.",
]

[tool.black]
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7 changes: 6 additions & 1 deletion tests/integration/test_reads.py
Original file line number Diff line number Diff line change
Expand Up @@ -294,9 +294,11 @@ def test_pyarrow_limit_with_multiple_files(catalog: Catalog) -> None:


@pytest.mark.integration
@pytest.mark.filterwarnings("ignore")
@pytest.mark.parametrize("catalog", [pytest.lazy_fixture("session_catalog_hive"), pytest.lazy_fixture("session_catalog")])
def test_daft_nan(catalog: Catalog) -> None:
import daft

daft.context.set_runner_native()
table_test_null_nan_rewritten = catalog.load_table("default.test_null_nan_rewritten")
df = table_test_null_nan_rewritten.to_daft()
assert df.count_rows() == 3
Expand All @@ -306,6 +308,9 @@ def test_daft_nan(catalog: Catalog) -> None:
@pytest.mark.integration
@pytest.mark.parametrize("catalog", [pytest.lazy_fixture("session_catalog_hive"), pytest.lazy_fixture("session_catalog")])
def test_daft_nan_rewritten(catalog: Catalog) -> None:
import daft

daft.context.set_runner_native()
table_test_null_nan_rewritten = catalog.load_table("default.test_null_nan_rewritten")
df = table_test_null_nan_rewritten.to_daft()
df = df.where(df["col_numeric"].float.is_nan())
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