fix: allow for date/datetime subclasses (e.g. pd.Timestamp, FreezeGun…

…) in pl.lit

crates/polars-python/src/functions/lazy.rs

@@ -442,12 +442,24 @@ pub fn lit(value: &Bound<'_, PyAny>, allow_object: bool) -> PyResult<PyExpr> {
         });
         Ok(dsl::lit(s).into())
     } else {
-        Err(PyTypeError::new_err(format!(
-            "cannot create expression literal for value of type {}: {}\
-            \n\nHint: Pass `allow_object=True` to accept any value and create a literal of type Object.",
-            value.get_type().qualname()?,
-            value.repr()?
-        )))
+        Python::with_gil(|py| {
+            // One final attempt before erroring. Do we have a date/datetime subclass?
+            // E.g. pd.Timestamp, or Freezegun.
+            let datetime_module = PyModule::import_bound(py, "datetime")?;
+            let datetime_class = datetime_module.getattr("datetime")?;
+            let date_class = datetime_module.getattr("date")?;
+            if value.is_instance(&datetime_class)? || value.is_instance(&date_class)? {
+                let av = py_object_to_any_value(value, true)?;
+                Ok(Expr::Literal(LiteralValue::try_from(av).unwrap()).into())
+            } else {
+                Err(PyTypeError::new_err(format!(
+                    "cannot create expression literal for value of type {}: {}\
+                    \n\nHint: Pass `allow_object=True` to accept any value and create a literal of type Object.",
+                    value.get_type().name()?,
+                    value.repr()?
+                )))
+            }
+        })
     }
 }
 

py-polars/tests/benchmark/interop/test_numpy.py

@@ -18,19 +18,19 @@ def floats_array() -> np.ndarray[Any, Any]:
     return np.random.randn(n_rows)
 
 
-@pytest.fixture
+@pytest.fixture()
 def floats(floats_array: np.ndarray[Any, Any]) -> pl.Series:
     return pl.Series(floats_array)
 
 
-@pytest.fixture
+@pytest.fixture()
 def floats_with_nulls(floats: pl.Series) -> pl.Series:
     null_probability = 0.1
     validity = pl.Series(np.random.uniform(size=floats.len())) > null_probability
     return pl.select(pl.when(validity).then(floats)).to_series()
 
 
-@pytest.fixture
+@pytest.fixture()
 def floats_chunked(floats_array: np.ndarray[Any, Any]) -> pl.Series:
     n_chunks = 5
     chunk_len = len(floats_array) // n_chunks

py-polars/tests/docs/test_user_guide.py

@@ -29,7 +29,7 @@ def _change_test_dir() -> Iterator[None]:
     os.chdir(current_path)
 
 
-@pytest.mark.docs
+@pytest.mark.docs()
 @pytest.mark.parametrize("path", snippet_paths)
 @pytest.mark.usefixtures("_change_test_dir")
 def test_run_python_snippets(path: Path) -> None:

py-polars/tests/unit/cloud/test_prepare_cloud_plan.py

@@ -94,7 +94,7 @@ def test_prepare_cloud_plan_fail_on_local_data_source(lf: pl.LazyFrame) -> None:
         prepare_cloud_plan(lf)
 
 
-@pytest.mark.write_disk
+@pytest.mark.write_disk()
 def test_prepare_cloud_plan_fail_on_python_scan(tmp_path: Path) -> None:
     tmp_path.mkdir(exist_ok=True)
     data_path = tmp_path / "data.parquet"

py-polars/tests/unit/conftest.py

@@ -32,13 +32,13 @@
 NESTED_DTYPES = [pl.List, pl.Struct, pl.Array]
 
 
-@pytest.fixture
+@pytest.fixture()
 def partition_limit() -> int:
     """The limit at which Polars will start partitioning in debug builds."""
     return 15
 
 
-@pytest.fixture
+@pytest.fixture()
 def df() -> pl.DataFrame:
     df = pl.DataFrame(
         {
@@ -68,14 +68,14 @@ def df() -> pl.DataFrame:
     )
 
 
-@pytest.fixture
+@pytest.fixture()
 def df_no_lists(df: pl.DataFrame) -> pl.DataFrame:
     return df.select(
         pl.all().exclude(["list_str", "list_int", "list_bool", "list_int", "list_flt"])
     )
 
 
-@pytest.fixture
+@pytest.fixture()
 def fruits_cars() -> pl.DataFrame:
     return pl.DataFrame(
         {
@@ -88,7 +88,7 @@ def fruits_cars() -> pl.DataFrame:
     )
 
 
-@pytest.fixture
+@pytest.fixture()
 def str_ints_df() -> pl.DataFrame:
     n = 1000
 
@@ -199,7 +199,7 @@ def get_peak(self) -> int:
         return tracemalloc.get_traced_memory()[1]
 
 
-@pytest.fixture
+@pytest.fixture()
 def memory_usage_without_pyarrow() -> Generator[MemoryUsage, Any, Any]:
     """
     Provide an API for measuring peak memory usage.

py-polars/tests/unit/dataframe/test_df.py

@@ -1555,7 +1555,7 @@ def test_reproducible_hash_with_seeds() -> None:
         assert_series_equal(expected, result, check_names=False, check_exact=True)
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 @pytest.mark.parametrize(
     "e",
     [

py-polars/tests/unit/dataframe/test_from_dict.py

@@ -145,7 +145,7 @@ def test_from_dict_with_scalars() -> None:
     assert df9.rows() == [(0, 2, 0, "x"), (1, 1, 0, "x"), (2, 0, 0, "x")]
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_from_dict_with_values_mixed() -> None:
     # a bit of everything
     mixed_dtype_data: dict[str, Any] = {

py-polars/tests/unit/dataframe/test_partition_by.py

@@ -6,7 +6,7 @@
 import polars.selectors as cs
 
 
-@pytest.fixture
+@pytest.fixture()
 def df() -> pl.DataFrame:
     return pl.DataFrame(
         {
@@ -75,7 +75,7 @@ def test_partition_by_as_dict_include_keys_false_maintain_order_false() -> None:
         df.partition_by(["a"], maintain_order=False, include_key=False, as_dict=True)
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_partition_by_as_dict_include_keys_false_large() -> None:
     # test with both as_dict and include_key=False
     df = pl.DataFrame(

py-polars/tests/unit/dataframe/test_serde.py

@@ -85,7 +85,7 @@ def test_df_serde_to_from_buffer(
     assert_frame_equal(df, read_df, categorical_as_str=True)
 
 
-@pytest.mark.write_disk
+@pytest.mark.write_disk()
 def test_df_serde_to_from_file(df: pl.DataFrame, tmp_path: Path) -> None:
     tmp_path.mkdir(exist_ok=True)
 

py-polars/tests/unit/dataframe/test_vstack.py

@@ -5,12 +5,12 @@
 from polars.testing import assert_frame_equal
 
 
-@pytest.fixture
+@pytest.fixture()
 def df1() -> pl.DataFrame:
     return pl.DataFrame({"foo": [1, 2], "bar": [6, 7], "ham": ["a", "b"]})
 
 
-@pytest.fixture
+@pytest.fixture()
 def df2() -> pl.DataFrame:
     return pl.DataFrame({"foo": [3, 4], "bar": [8, 9], "ham": ["c", "d"]})
 

py-polars/tests/unit/datatypes/test_array.py

@@ -176,7 +176,7 @@ def test_cast_list_to_array(data: Any, inner_type: pl.DataType) -> None:
     assert s.to_list() == data
 
 
-@pytest.fixture
+@pytest.fixture()
 def data_dispersion() -> pl.DataFrame:
     return pl.DataFrame(
         {

py-polars/tests/unit/datatypes/test_bool.py

@@ -6,7 +6,7 @@
 import polars as pl
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_bool_arg_min_max() -> None:
     # masks that ensures we take more than u64 chunks
     # and slicing and dicing to ensure the offsets work

py-polars/tests/unit/datatypes/test_categorical.py

@@ -480,7 +480,7 @@ def test_cast_inner_categorical() -> None:
         )
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_stringcache() -> None:
     N = 1_500
     with pl.StringCache():

py-polars/tests/unit/datatypes/test_decimal.py

@@ -29,7 +29,7 @@ def permutations_int_dec_none() -> list[tuple[D | int | None, ...]]:
     )
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_series_from_pydecimal_and_ints(
     permutations_int_dec_none: list[tuple[D | int | None, ...]],
 ) -> None:
@@ -45,7 +45,7 @@ def test_series_from_pydecimal_and_ints(
         assert s.to_list() == [D(x) if x is not None else None for x in data]
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_frame_from_pydecimal_and_ints(
     permutations_int_dec_none: list[tuple[D | int | None, ...]], monkeypatch: Any
 ) -> None:

py-polars/tests/unit/datatypes/test_list.py

@@ -667,7 +667,7 @@ def test_as_list_logical_type() -> None:
     ).to_dict(as_series=False) == {"literal": [True], "timestamp": [[date(2000, 1, 1)]]}
 
 
-@pytest.fixture
+@pytest.fixture()
 def data_dispersion() -> pl.DataFrame:
     return pl.DataFrame(
         {

py-polars/tests/unit/functions/range/test_date_range.py

@@ -310,3 +310,17 @@ def test_date_ranges_datetime_input() -> None:
         "literal", [[date(2022, 1, 1), date(2022, 1, 2), date(2022, 1, 3)]]
     )
     assert_series_equal(result, expected)
+
+
+def test_date_range_with_subclass_18470_18447() -> None:
+    class MyAmazingDate(date):
+        pass
+
+    class MyAmazingDatetime(datetime):
+        pass
+
+    result = pl.datetime_range(
+        MyAmazingDate(2020, 1, 1), MyAmazingDatetime(2020, 1, 2), eager=True
+    )
+    expected = pl.Series("literal", [datetime(2020, 1, 1), datetime(2020, 1, 2)])
+    assert_series_equal(result, expected)

py-polars/tests/unit/functions/test_concat.py

@@ -3,7 +3,7 @@
 import polars as pl
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_concat_expressions_stack_overflow() -> None:
     n = 10000
     e = pl.concat([pl.lit(x) for x in range(n)])
@@ -12,7 +12,7 @@ def test_concat_expressions_stack_overflow() -> None:
     assert df.shape == (n, 1)
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 def test_concat_lf_stack_overflow() -> None:
     n = 1000
     bar = pl.DataFrame({"a": 0}).lazy()

py-polars/tests/unit/functions/test_when_then.py

@@ -529,7 +529,7 @@ def test_when_then_null_broadcast() -> None:
     )
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 @pytest.mark.parametrize("len", [1, 10, 100, 500])
 @pytest.mark.parametrize(
     ("dtype", "vals"),

py-polars/tests/unit/interchange/test_from_dataframe.py

@@ -406,13 +406,13 @@ def test_construct_offsets_buffer_copy() -> None:
     assert_series_equal(result, expected)
 
 
-@pytest.fixture
+@pytest.fixture()
 def bitmask() -> PolarsBuffer:
     data = pl.Series([False, True, True, False])
     return PolarsBuffer(data)
 
 
-@pytest.fixture
+@pytest.fixture()
 def bytemask() -> PolarsBuffer:
     data = pl.Series([0, 1, 1, 0], dtype=pl.UInt8)
     return PolarsBuffer(data)

py-polars/tests/unit/interop/test_from_pandas.py

@@ -321,7 +321,7 @@ def test_untrusted_categorical_input() -> None:
     assert_frame_equal(result, expected, categorical_as_str=True)
 
 
-@pytest.fixture
+@pytest.fixture()
 def _set_pyarrow_unavailable(monkeypatch: pytest.MonkeyPatch) -> None:
     monkeypatch.setattr(
         "polars._utils.construction.dataframe._PYARROW_AVAILABLE", False

py-polars/tests/unit/io/cloud/test_aws.py

@@ -49,7 +49,7 @@ def s3_base(monkeypatch_module: Any) -> Iterator[str]:
     p.kill()
 
 
-@pytest.fixture
+@pytest.fixture()
 def s3(s3_base: str, io_files_path: Path) -> str:
     region = "us-east-1"
     client = boto3.client("s3", region_name=region, endpoint_url=s3_base)

py-polars/tests/unit/io/cloud/test_cloud.py

@@ -4,7 +4,7 @@
 from polars.exceptions import ComputeError
 
 
-@pytest.mark.slow
+@pytest.mark.slow()
 @pytest.mark.parametrize("format", ["parquet", "csv", "ndjson", "ipc"])
 def test_scan_nonexistent_cloud_path_17444(format: str) -> None:
     # https://github.com/pola-rs/polars/issues/17444

py-polars/tests/unit/io/conftest.py

@@ -5,6 +5,6 @@
 import pytest
 
 
-@pytest.fixture
+@pytest.fixture()
 def io_files_path() -> Path:
     return Path(__file__).parent / "files"

py-polars/tests/unit/io/database/conftest.py

@@ -10,7 +10,7 @@
     from pathlib import Path
 
 
-@pytest.fixture
+@pytest.fixture()
 def tmp_sqlite_db(tmp_path: Path) -> Path:
     test_db = tmp_path / "test.db"
     test_db.unlink(missing_ok=True)
@@ -51,7 +51,7 @@ def convert_date(val: bytes) -> date:
     return test_db
 
 
-@pytest.fixture
+@pytest.fixture()
 def tmp_sqlite_inference_db(tmp_path: Path) -> Path:
     test_db = tmp_path / "test_inference.db"
     test_db.unlink(missing_ok=True)

py-polars/tests/unit/io/database/test_read.py

@@ -150,7 +150,7 @@ class ExceptionTestParams(NamedTuple):
     kwargs: dict[str, Any] | None = None
 
 
-@pytest.mark.write_disk
+@pytest.mark.write_disk()
 @pytest.mark.parametrize(
     (
         "read_method",
@@ -698,7 +698,7 @@ def test_read_database_cx_credentials(uri: str) -> None:
             pl.read_database_uri("SELECT * FROM data", uri=uri, engine="connectorx")
 
 
-@pytest.mark.write_disk
+@pytest.mark.write_disk()
 def test_read_kuzu_graph_database(tmp_path: Path, io_files_path: Path) -> None:
     import kuzu