cudf-polars string/numeric casting

cpp/include/cudf/utilities/traits.hpp

@@ -303,6 +303,30 @@ constexpr inline bool is_integral_not_bool()
  */
 bool is_integral_not_bool(data_type type);
 
+/**
+ * @brief Indicates whether the type `T` is a numeric type but not bool type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is numeric but not bool
+ * @return false  `T` is not numeric or is bool
+ */
+template <typename T>
+constexpr inline bool is_numeric_not_bool()
+{
+  return cudf::is_numeric<T>() and not std::is_same_v<T, bool>;
+}
+
+/**
+ * @brief Indicates whether `type` is a numeric `data_type` but not BOOL8
+ *
+ * "Numeric" types are integral/floating point types such as `INT*` or `FLOAT*`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is numeric but not bool
+ * @return false `type` is not numeric or is bool
+ */
+bool is_numeric_not_bool(data_type type);
+
 /**
  * @brief Indicates whether the type `T` is a floating point type.
  *

cpp/src/utilities/traits.cpp

@@ -171,6 +171,19 @@ bool is_integral_not_bool(data_type type)
   return cudf::type_dispatcher(type, is_integral_not_bool_impl{});
 }
 
+struct is_numeric_not_bool_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_numeric_not_bool<T>();
+  }
+};
+
+bool is_numeric_not_bool(data_type type)
+{
+  return cudf::type_dispatcher(type, is_numeric_not_bool_impl{});
+}
+
 struct is_floating_point_impl {
   template <typename T>
   constexpr bool operator()()

python/cudf_polars/cudf_polars/containers/column.py

@@ -9,6 +9,17 @@
 from typing import TYPE_CHECKING
 
 import pylibcudf as plc
+from pylibcudf.strings.convert.convert_floats import from_floats, is_float, to_floats
+from pylibcudf.strings.convert.convert_integers import (
+    from_integers,
+    is_integer,
+    to_integers,
+)
+from pylibcudf.traits import is_floating_point
+
+from polars.exceptions import InvalidOperationError
+
+from cudf_polars.utils.dtypes import is_order_preserving_cast
 
 if TYPE_CHECKING:
     from typing_extensions import Self
@@ -129,11 +140,46 @@ def astype(self, dtype: plc.DataType) -> Column:
         This only produces a copy if the requested dtype doesn't match
         the current one.
         """
-        if self.obj.type() != dtype:
-            return Column(plc.unary.cast(self.obj, dtype), name=self.name).sorted_like(
-                self
-            )
-        return self
+        if self.obj.type() == dtype:
+            return self
+
+        if dtype.id() == plc.TypeId.STRING or self.obj.type().id() == plc.TypeId.STRING:
+            return Column(self._handle_string_cast(dtype))
+        else:
+            result = Column(plc.unary.cast(self.obj, dtype))
+            if is_order_preserving_cast(self.obj.type(), dtype):
+                return result.sorted_like(self)
+            return result
+
+    def _handle_string_cast(self, dtype: plc.DataType) -> plc.Column:
+        if dtype.id() == plc.TypeId.STRING:
+            if is_floating_point(self.obj.type()):
+                return from_floats(self.obj)
+            else:
+                return from_integers(self.obj)
+        else:
+            if is_floating_point(dtype):
+                floats = is_float(self.obj)
+                if not plc.interop.to_arrow(
+                    plc.reduce.reduce(
+                        floats,
+                        plc.aggregation.all(),
+                        plc.DataType(plc.TypeId.BOOL8),
+                    )
+                ).as_py():
+                    raise InvalidOperationError("Conversion from `str` failed.")
+                return to_floats(self.obj, dtype)
+            else:
+                integers = is_integer(self.obj)
+                if not plc.interop.to_arrow(
+                    plc.reduce.reduce(
+                        integers,
+                        plc.aggregation.all(),
+                        plc.DataType(plc.TypeId.BOOL8),
+                    )
+                ).as_py():
+                    raise InvalidOperationError("Conversion from `str` failed.")
+                return to_integers(self.obj, dtype)
 
     def copy_metadata(self, from_: pl.Series, /) -> Self:
         """

python/cudf_polars/cudf_polars/dsl/expressions/unary.py

@@ -34,7 +34,7 @@ def __init__(self, dtype: plc.DataType, value: Expr) -> None:
         self.children = (value,)
         if not dtypes.can_cast(value.dtype, self.dtype):
             raise NotImplementedError(
-                f"Can't cast {self.dtype.id().name} to {value.dtype.id().name}"
+                f"Can't cast {value.dtype.id().name} to {self.dtype.id().name}"
             )
 
     def do_evaluate(
@@ -47,7 +47,7 @@ def do_evaluate(
         """Evaluate this expression given a dataframe for context."""
         (child,) = self.children
         column = child.evaluate(df, context=context, mapping=mapping)
-        return Column(plc.unary.cast(column.obj, self.dtype)).sorted_like(column)
+        return column.astype(self.dtype)
 
     def collect_agg(self, *, depth: int) -> AggInfo:
         """Collect information about aggregations in groupbys."""

python/cudf_polars/cudf_polars/testing/plugin.py

@@ -158,6 +158,7 @@ def pytest_configure(config: pytest.Config):
     "tests/unit/sql/test_cast.py::test_cast_errors[values0-values::uint8-conversion from `f64` to `u64` failed]": "Casting that raises not supported on GPU",
     "tests/unit/sql/test_cast.py::test_cast_errors[values1-values::uint4-conversion from `i64` to `u32` failed]": "Casting that raises not supported on GPU",
     "tests/unit/sql/test_cast.py::test_cast_errors[values2-values::int1-conversion from `i64` to `i8` failed]": "Casting that raises not supported on GPU",
+    "tests/unit/sql/test_cast.py::test_cast_errors[values5-values::int4-conversion from `str` to `i32` failed]": "Cast raises, but error user recieves is wrong",
     "tests/unit/sql/test_miscellaneous.py::test_read_csv": "Incorrect handling of missing_is_null in read_csv",
     "tests/unit/sql/test_wildcard_opts.py::test_select_wildcard_errors": "Raises correctly but with different exception",
     "tests/unit/streaming/test_streaming_io.py::test_parquet_eq_statistics": "Debug output on stderr doesn't match",

python/cudf_polars/cudf_polars/utils/dtypes.py

@@ -9,11 +9,21 @@
 
 import pyarrow as pa
 import pylibcudf as plc
+from pylibcudf.traits import (
+    is_floating_point,
+    is_integral_not_bool,
+    is_numeric_not_bool,
+)
 from typing_extensions import assert_never
 
 import polars as pl
 
-__all__ = ["from_polars", "downcast_arrow_lists", "can_cast"]
+__all__ = [
+    "from_polars",
+    "downcast_arrow_lists",
+    "can_cast",
+    "is_order_preserving_cast",
+]
 
 
 def downcast_arrow_lists(typ: pa.DataType) -> pa.DataType:
@@ -61,12 +71,57 @@ def can_cast(from_: plc.DataType, to: plc.DataType) -> bool:
     True if casting is supported, False otherwise
     """
     return (
-        plc.traits.is_fixed_width(to)
-        and plc.traits.is_fixed_width(from_)
-        and plc.unary.is_supported_cast(from_, to)
+        (
+            plc.traits.is_fixed_width(to)
+            and plc.traits.is_fixed_width(from_)
+            and plc.unary.is_supported_cast(from_, to)
+        )
+        or (from_.id() == plc.TypeId.STRING and is_numeric_not_bool(to))
+        or (to.id() == plc.TypeId.STRING and is_numeric_not_bool(from_))
     )
 
 
+def is_order_preserving_cast(from_: plc.DataType, to: plc.DataType) -> bool:
+    """
+    Determine if a cast would preserve the order of the source data.
+
+    Parameters
+    ----------
+    from_
+        Source datatype
+    to
+        Target datatype
+
+    Returns
+    -------
+    True if the cast is order-preserving, False otherwise
+    """
+    if from_.id() == to.id():
+        return True
+
+    if is_integral_not_bool(from_) and is_integral_not_bool(to):
+        # True if signedness is the same and the target is larger
+        if plc.traits.is_unsigned(from_) == plc.traits.is_unsigned(to):
+            if plc.types.size_of(to) >= plc.types.size_of(from_):
+                return True
+        elif (plc.traits.is_unsigned(from_) and not plc.traits.is_unsigned(to)) and (
+            plc.types.size_of(to) > plc.types.size_of(from_)
+        ):
+            # Unsigned to signed is order preserving if target is large enough
+            # But signed to unsigned is never order preserving due to negative values
+            return True
+    elif is_floating_point(from_) and is_floating_point(to):
+        # True if the target is larger
+        if plc.types.size_of(to) > plc.types.size_of(from_):
+            return True
+    elif (is_integral_not_bool(from_) and is_floating_point(to)) and (
+        plc.types.size_of(to) > plc.types.size_of(from_)
+    ):
+        # Int64 fits in float64, but not in float32
+        return True
+    return False
+
+
 @cache
 def from_polars(dtype: pl.DataType) -> plc.DataType:
     """

python/cudf_polars/tests/expressions/test_casting.py

@@ -14,7 +14,7 @@
 _supported_dtypes = [(pl.Int8(), pl.Int64())]
 
 _unsupported_dtypes = [
-    (pl.String(), pl.Int64()),
+    (pl.Datetime("ns"), pl.Int64()),
 ]
 
 

python/cudf_polars/tests/expressions/test_numeric_binops.py

@@ -8,7 +8,6 @@
 
 from cudf_polars.testing.asserts import (
     assert_gpu_result_equal,
-    assert_ir_translation_raises,
 )
 
 dtypes = [
@@ -114,12 +113,3 @@ def test_binop_with_scalar(left_scalar, right_scalar):
     q = df.select(lop / rop)
 
     assert_gpu_result_equal(q)
-
-
-def test_numeric_to_string_cast_fails():
-    df = pl.DataFrame(
-        {"a": [1, 1, 2, 3, 3, 4, 1], "b": [None, 2, 3, 4, 5, 6, 7]}
-    ).lazy()
-    q = df.select(pl.col("a").cast(pl.String))
-
-    assert_ir_translation_raises(q, NotImplementedError)

python/cudf_polars/tests/expressions/test_stringfunction.py

@@ -40,6 +40,79 @@ def ldf(with_nulls):
     )
 
 
+@pytest.fixture(params=[pl.Int8, pl.Int16, pl.Int32, pl.Int64])
+def integer_type(request):
+    return request.param
+
+
+@pytest.fixture(params=[pl.Float32, pl.Float64])
+def floating_type(request):
+    return request.param
+
+
+@pytest.fixture(params=[pl.Int8, pl.Int16, pl.Int32, pl.Int64, pl.Float32, pl.Float64])
+def numeric_type(request):
+    return request.param
+
+
+@pytest.fixture
+def str_to_integer_data(with_nulls):
+    a = ["1", "2", "3", "4", "5", "6"]
+    if with_nulls:
+        a[4] = None
+    return pl.LazyFrame({"a": a})
+
+
+@pytest.fixture
+def str_to_float_data(with_nulls):
+    a = [
+        "1.1",
+        "2.2",
+        "3.3",
+        "4.4",
+        "5.5",
+        "6.6",
+        "inf",
+        "+inf",
+        "-inf",
+        "Inf",
+        "-Inf",
+        "nan",
+        "-1.234",
+        "2e2",
+    ]
+    if with_nulls:
+        a[4] = None
+    return pl.LazyFrame({"a": a})
+
+
+@pytest.fixture
+def str_from_integer_data(with_nulls, integer_type):
+    a = [1, 2, 3, 4, 5, 6]
+    if with_nulls:
+        a[4] = None
+    return pl.LazyFrame({"a": pl.Series(a, dtype=integer_type)})
+
+
+@pytest.fixture
+def str_from_float_data(with_nulls, floating_type):
+    a = [
+        1.1,
+        2.2,
+        3.3,
+        4.4,
+        5.5,
+        6.6,
+        float("inf"),
+        float("+inf"),
+        float("-inf"),
+        float("nan"),
+    ]
+    if with_nulls:
+        a[4] = None
+    return pl.LazyFrame({"a": pl.Series(a, dtype=floating_type)})
+
+
 slice_cases = [
     (1, 3),
     (0, 3),
@@ -337,3 +410,47 @@ def test_unsupported_regex_raises(pattern):
 
     q = df.select(pl.col("a").str.contains(pattern, strict=True))
     assert_ir_translation_raises(q, NotImplementedError)
+
+
+def test_string_to_integer(str_to_integer_data, integer_type):
+    query = str_to_integer_data.select(pl.col("a").cast(integer_type))
+    assert_gpu_result_equal(query)
+
+
+def test_string_from_integer(str_from_integer_data):
+    query = str_from_integer_data.select(pl.col("a").cast(pl.String))
+    assert_gpu_result_equal(query)
+
+
+def test_string_to_float(str_to_float_data, floating_type):
+    query = str_to_float_data.select(pl.col("a").cast(floating_type))
+    assert_gpu_result_equal(query)
+
+
+def test_string_from_float(request, str_from_float_data):
+    if str_from_float_data.collect_schema()["a"] == pl.Float32:
+        # libcudf will return a string representing the precision out to
+        # a certain number of hardcoded decimal places. This results in
+        # the fractional part being thrown away which causes discrepancies
+        # for certain numbers. For instance, the float32 representation of
+        # 1.1 is 1.100000023841858. When cast to a string, this will become
+        # 1.100000024. But the float64 representation of 1.1 is
+        # 1.1000000000000000888 which will result in libcudf truncating the
+        # final value to 1.1.
+        request.applymarker(pytest.mark.xfail(reason="libcudf truncation"))
+    query = str_from_float_data.select(pl.col("a").cast(pl.String))
+
+    # libcudf reads float('inf') -> "inf"
+    # but polars reads float('inf') -> "Inf"
+    query = query.select(pl.col("a").str.to_lowercase())
+    assert_gpu_result_equal(query)
+
+
+def test_string_to_numeric_invalid(numeric_type):
+    df = pl.LazyFrame({"a": ["a", "b", "c"]})
+    q = df.select(pl.col("a").cast(numeric_type))
+    assert_collect_raises(
+        q,
+        polars_except=pl.exceptions.InvalidOperationError,
+        cudf_except=pl.exceptions.ComputeError,
+    )