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(>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"] -mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"] -output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"] -parquet = ["pyarrow (>=7.0.0)"] -performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"] -plot = ["matplotlib (>=3.6.1)"] -postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"] -spss = ["pyreadstat (>=1.1.2)"] -sql-other = ["SQLAlchemy (>=1.4.16)"] -test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"] -xml = ["lxml (>=4.6.3)"] +all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"] +aws = ["s3fs (>=2022.11.0)"] +clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"] +compression = ["zstandard (>=0.19.0)"] +computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"] +consortium-standard = ["dataframe-api-compat (>=0.1.7)"] +excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"] +feather = ["pyarrow (>=10.0.1)"] +fss = ["fsspec (>=2022.11.0)"] +gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"] +hdf5 = ["tables (>=3.8.0)"] +html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"] +mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"] +output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"] +parquet = ["pyarrow (>=10.0.1)"] +performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"] +plot = ["matplotlib (>=3.6.3)"] +postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"] +pyarrow = ["pyarrow (>=10.0.1)"] +spss = ["pyreadstat (>=1.2.0)"] +sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"] +test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"] +xml = ["lxml (>=4.9.2)"] [[package]] name = "pathable" diff --git a/pyproject.toml b/pyproject.toml index 30290bcffc..fcfbb6c0d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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] diff --git a/tests/integration/test_reads.py b/tests/integration/test_reads.py index 006e1f3af1..f8bc57bb8c 100644 --- a/tests/integration/test_reads.py +++ b/tests/integration/test_reads.py @@ -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 @@ -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())