Ensure cudf objects can astype to any type when empty (rapidsai#16106)

pandas allows objects to `astype` to any other type if the object is empty. The PR mirrors that behavior for cudf.

This PR also more consistently uses `astype` instead of `as_*_column` and fixes a bug in `IntervalDtype.__eq__` discovered when writing a unit test for this bug.

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: rapidsai#16106

python/cudf/cudf/core/column/column.py

@@ -959,6 +959,15 @@ def can_cast_safely(self, to_dtype: Dtype) -> bool:
         raise NotImplementedError()
 
     def astype(self, dtype: Dtype, copy: bool = False) -> ColumnBase:
+        if len(self) == 0:
+            dtype = cudf.dtype(dtype)
+            if self.dtype == dtype:
+                if copy:
+                    return self.copy()
+                else:
+                    return self
+            else:
+                return column_empty(0, dtype=dtype, masked=self.nullable)
         if copy:
             col = self.copy()
         else:

python/cudf/cudf/core/column/datetime.py

@@ -280,8 +280,8 @@ def __contains__(self, item: ScalarLike) -> bool:
             return False
         elif ts.tzinfo is not None:
             ts = ts.tz_convert(None)
-        return ts.to_numpy().astype("int64") in self.as_numerical_column(
-            "int64"
+        return ts.to_numpy().astype("int64") in cast(
+            "cudf.core.column.NumericalColumn", self.astype("int64")
         )
 
     @functools.cached_property
@@ -503,9 +503,9 @@ def mean(
         self, skipna=None, min_count: int = 0, dtype=np.float64
     ) -> ScalarLike:
         return pd.Timestamp(
-            self.as_numerical_column("int64").mean(
-                skipna=skipna, min_count=min_count, dtype=dtype
-            ),
+            cast(
+                "cudf.core.column.NumericalColumn", self.astype("int64")
+            ).mean(skipna=skipna, min_count=min_count, dtype=dtype),
             unit=self.time_unit,
         ).as_unit(self.time_unit)
 
@@ -517,15 +517,17 @@ def std(
         ddof: int = 1,
     ) -> pd.Timedelta:
         return pd.Timedelta(
-            self.as_numerical_column("int64").std(
+            cast("cudf.core.column.NumericalColumn", self.astype("int64")).std(
                 skipna=skipna, min_count=min_count, dtype=dtype, ddof=ddof
             )
             * _unit_to_nanoseconds_conversion[self.time_unit],
         ).as_unit(self.time_unit)
 
     def median(self, skipna: bool | None = None) -> pd.Timestamp:
         return pd.Timestamp(
-            self.as_numerical_column("int64").median(skipna=skipna),
+            cast(
+                "cudf.core.column.NumericalColumn", self.astype("int64")
+            ).median(skipna=skipna),
             unit=self.time_unit,
         ).as_unit(self.time_unit)
 
@@ -534,18 +536,18 @@ def cov(self, other: DatetimeColumn) -> float:
             raise TypeError(
                 f"cannot perform cov with types {self.dtype}, {other.dtype}"
             )
-        return self.as_numerical_column("int64").cov(
-            other.as_numerical_column("int64")
-        )
+        return cast(
+            "cudf.core.column.NumericalColumn", self.astype("int64")
+        ).cov(cast("cudf.core.column.NumericalColumn", other.astype("int64")))
 
     def corr(self, other: DatetimeColumn) -> float:
         if not isinstance(other, DatetimeColumn):
             raise TypeError(
                 f"cannot perform corr with types {self.dtype}, {other.dtype}"
             )
-        return self.as_numerical_column("int64").corr(
-            other.as_numerical_column("int64")
-        )
+        return cast(
+            "cudf.core.column.NumericalColumn", self.astype("int64")
+        ).corr(cast("cudf.core.column.NumericalColumn", other.astype("int64")))
 
     def quantile(
         self,
@@ -554,7 +556,7 @@ def quantile(
         exact: bool,
         return_scalar: bool,
     ) -> ColumnBase:
-        result = self.as_numerical_column("int64").quantile(
+        result = self.astype("int64").quantile(
             q=q,
             interpolation=interpolation,
             exact=exact,
@@ -645,12 +647,12 @@ def indices_of(
     ) -> cudf.core.column.NumericalColumn:
         value = column.as_column(
             pd.to_datetime(value), dtype=self.dtype
-        ).as_numerical_column("int64")
-        return self.as_numerical_column("int64").indices_of(value)
+        ).astype("int64")
+        return self.astype("int64").indices_of(value)
 
     @property
     def is_unique(self) -> bool:
-        return self.as_numerical_column("int64").is_unique
+        return self.astype("int64").is_unique
 
     def isin(self, values: Sequence) -> ColumnBase:
         return cudf.core.tools.datetimes._isin_datetimelike(self, values)

python/cudf/cudf/core/column/decimal.py

@@ -157,7 +157,7 @@ def normalize_binop_value(self, other):
                         "Decimal columns only support binary operations with "
                         "integer numerical columns."
                     )
-                other = other.as_decimal_column(
+                other = other.astype(
                     self.dtype.__class__(self.dtype.__class__.MAX_PRECISION, 0)
                 )
             elif not isinstance(other, DecimalBaseColumn):

python/cudf/cudf/core/column/interval.py

@@ -4,7 +4,7 @@
 
 import cudf
 from cudf.core.column import StructColumn
-from cudf.core.dtypes import CategoricalDtype, IntervalDtype
+from cudf.core.dtypes import IntervalDtype
 
 
 class IntervalColumn(StructColumn):
@@ -87,20 +87,16 @@ def copy(self, deep=True):
 
     def as_interval_column(self, dtype):
         if isinstance(dtype, IntervalDtype):
-            if isinstance(self.dtype, CategoricalDtype):
-                new_struct = self._get_decategorized_column()
-                return IntervalColumn.from_struct_column(new_struct)
-            else:
-                return IntervalColumn(
-                    size=self.size,
-                    dtype=dtype,
-                    mask=self.mask,
-                    offset=self.offset,
-                    null_count=self.null_count,
-                    children=tuple(
-                        child.astype(dtype.subtype) for child in self.children
-                    ),
-                )
+            return IntervalColumn(
+                size=self.size,
+                dtype=dtype,
+                mask=self.mask,
+                offset=self.offset,
+                null_count=self.null_count,
+                children=tuple(
+                    child.astype(dtype.subtype) for child in self.children
+                ),
+            )
         else:
             raise ValueError("dtype must be IntervalDtype")
 

python/cudf/cudf/core/column/timedelta.py

@@ -107,7 +107,9 @@ def __contains__(self, item: DatetimeLikeScalar) -> bool:
             # np.timedelta64 raises ValueError, hence `item`
             # cannot exist in `self`.
             return False
-        return item.view("int64") in self.as_numerical_column("int64")
+        return item.view("int64") in cast(
+            "cudf.core.column.NumericalColumn", self.astype("int64")
+        )
 
     @property
     def values(self):
@@ -132,9 +134,7 @@ def to_arrow(self) -> pa.Array:
                 self.mask_array_view(mode="read").copy_to_host()
             )
         data = pa.py_buffer(
-            self.as_numerical_column("int64")
-            .data_array_view(mode="read")
-            .copy_to_host()
+            self.astype("int64").data_array_view(mode="read").copy_to_host()
         )
         pa_dtype = np_to_pa_dtype(self.dtype)
         return pa.Array.from_buffers(
@@ -295,13 +295,17 @@ def as_timedelta_column(
 
     def mean(self, skipna=None, dtype: Dtype = np.float64) -> pd.Timedelta:
         return pd.Timedelta(
-            self.as_numerical_column("int64").mean(skipna=skipna, dtype=dtype),
+            cast(
+                "cudf.core.column.NumericalColumn", self.astype("int64")
+            ).mean(skipna=skipna, dtype=dtype),
             unit=self.time_unit,
         ).as_unit(self.time_unit)
 
     def median(self, skipna: bool | None = None) -> pd.Timedelta:
         return pd.Timedelta(
-            self.as_numerical_column("int64").median(skipna=skipna),
+            cast(
+                "cudf.core.column.NumericalColumn", self.astype("int64")
+            ).median(skipna=skipna),
             unit=self.time_unit,
         ).as_unit(self.time_unit)
 
@@ -315,7 +319,7 @@ def quantile(
         exact: bool,
         return_scalar: bool,
     ) -> ColumnBase:
-        result = self.as_numerical_column("int64").quantile(
+        result = self.astype("int64").quantile(
             q=q,
             interpolation=interpolation,
             exact=exact,
@@ -337,7 +341,7 @@ def sum(
             # Since sum isn't overridden in Numerical[Base]Column, mypy only
             # sees the signature from Reducible (which doesn't have the extra
             # parameters from ColumnBase._reduce) so we have to ignore this.
-            self.as_numerical_column("int64").sum(  # type: ignore
+            self.astype("int64").sum(  # type: ignore
                 skipna=skipna, min_count=min_count, dtype=dtype
             ),
             unit=self.time_unit,
@@ -351,7 +355,7 @@ def std(
         ddof: int = 1,
     ) -> pd.Timedelta:
         return pd.Timedelta(
-            self.as_numerical_column("int64").std(
+            cast("cudf.core.column.NumericalColumn", self.astype("int64")).std(
                 skipna=skipna, min_count=min_count, ddof=ddof, dtype=dtype
             ),
             unit=self.time_unit,
@@ -362,18 +366,18 @@ def cov(self, other: TimeDeltaColumn) -> float:
             raise TypeError(
                 f"cannot perform cov with types {self.dtype}, {other.dtype}"
             )
-        return self.as_numerical_column("int64").cov(
-            other.as_numerical_column("int64")
-        )
+        return cast(
+            "cudf.core.column.NumericalColumn", self.astype("int64")
+        ).cov(cast("cudf.core.column.NumericalColumn", other.astype("int64")))
 
     def corr(self, other: TimeDeltaColumn) -> float:
         if not isinstance(other, TimeDeltaColumn):
             raise TypeError(
                 f"cannot perform corr with types {self.dtype}, {other.dtype}"
             )
-        return self.as_numerical_column("int64").corr(
-            other.as_numerical_column("int64")
-        )
+        return cast(
+            "cudf.core.column.NumericalColumn", self.astype("int64")
+        ).corr(cast("cudf.core.column.NumericalColumn", other.astype("int64")))
 
     def components(self) -> dict[str, ColumnBase]:
         """

python/cudf/cudf/core/dataframe.py

@@ -2404,7 +2404,7 @@ def scatter_by_map(
         if isinstance(map_index, cudf.core.column.StringColumn):
             cat_index = cast(
                 cudf.core.column.CategoricalColumn,
-                map_index.as_categorical_column("category"),
+                map_index.astype("category"),
             )
             map_index = cat_index.codes
             warnings.warn(

python/cudf/cudf/core/dtypes.py

@@ -937,7 +937,7 @@ def to_pandas(self) -> pd.IntervalDtype:
     def __eq__(self, other):
         if isinstance(other, str):
             # This means equality isn't transitive but mimics pandas
-            return other == self.name
+            return other in (self.name, str(self))
         return (
             type(self) == type(other)
             and self.subtype == other.subtype

python/cudf/cudf/core/frame.py

@@ -927,7 +927,7 @@ def from_arrow(cls, data: pa.Table) -> Self:
                 # of column is 0 (i.e., empty) then we will have an
                 # int8 column in result._data[name] returned by libcudf,
                 # which needs to be type-casted to 'category' dtype.
-                result[name] = result[name].as_categorical_column("category")
+                result[name] = result[name].astype("category")
             elif (
                 pandas_dtypes.get(name) == "empty"
                 and np_dtypes.get(name) == "object"
@@ -936,7 +936,7 @@ def from_arrow(cls, data: pa.Table) -> Self:
                 # is specified as 'empty' and np_dtypes as 'object',
                 # hence handling this special case to type-cast the empty
                 # float column to str column.
-                result[name] = result[name].as_string_column(cudf.dtype("str"))
+                result[name] = result[name].astype(cudf.dtype("str"))
             elif name in data.column_names and isinstance(
                 data[name].type,
                 (

python/cudf/cudf/core/indexing_utils.py

@@ -229,7 +229,7 @@ def parse_row_iloc_indexer(key: Any, n: int) -> IndexingSpec:
     else:
         key = cudf.core.column.as_column(key)
         if isinstance(key, cudf.core.column.CategoricalColumn):
-            key = key.as_numerical_column(key.codes.dtype)
+            key = key.astype(key.codes.dtype)
         if is_bool_dtype(key.dtype):
             return MaskIndexer(BooleanMask(key, n))
         elif len(key) == 0:

python/cudf/cudf/core/series.py

@@ -3107,10 +3107,12 @@ def value_counts(
         # Pandas returns an IntervalIndex as the index of res
         # this condition makes sure we do too if bins is given
         if bins is not None and len(res) == len(res.index.categories):
-            int_index = IntervalColumn.as_interval_column(
-                res.index._column, res.index.categories.dtype
+            interval_col = IntervalColumn.from_struct_column(
+                res.index._column._get_decategorized_column()
+            )
+            res.index = cudf.IntervalIndex._from_data(
+                {res.index.name: interval_col}
             )
-            res.index = int_index
         res.name = result_name
         return res
 

python/cudf/cudf/core/tools/numeric.py

@@ -115,11 +115,11 @@ def to_numeric(arg, errors="raise", downcast=None):
     dtype = col.dtype
 
     if is_datetime_dtype(dtype) or is_timedelta_dtype(dtype):
-        col = col.as_numerical_column(cudf.dtype("int64"))
+        col = col.astype(cudf.dtype("int64"))
     elif isinstance(dtype, CategoricalDtype):
         cat_dtype = col.dtype.type
         if _is_non_decimal_numeric_dtype(cat_dtype):
-            col = col.as_numerical_column(cat_dtype)
+            col = col.astype(cat_dtype)
         else:
             try:
                 col = _convert_str_col(
@@ -146,8 +146,8 @@ def to_numeric(arg, errors="raise", downcast=None):
         raise ValueError("Unrecognized datatype")
 
     # str->float conversion may require lower precision
-    if col.dtype == cudf.dtype("f"):
-        col = col.as_numerical_column("d")
+    if col.dtype == cudf.dtype("float32"):
+        col = col.astype("float64")
 
     if downcast:
         if downcast == "float":
@@ -205,7 +205,7 @@ def _convert_str_col(col, errors, _downcast=None):
 
     is_integer = libstrings.is_integer(col)
     if is_integer.all():
-        return col.as_numerical_column(dtype=cudf.dtype("i8"))
+        return col.astype(dtype=cudf.dtype("i8"))
 
     col = _proc_inf_empty_strings(col)
 
@@ -218,9 +218,9 @@ def _convert_str_col(col, errors, _downcast=None):
                     "limited by float32 precision."
                 )
             )
-            return col.as_numerical_column(dtype=cudf.dtype("f"))
+            return col.astype(dtype=cudf.dtype("float32"))
         else:
-            return col.as_numerical_column(dtype=cudf.dtype("d"))
+            return col.astype(dtype=cudf.dtype("float64"))
     else:
         if errors == "coerce":
             col = libcudf.string_casting.stod(col)

python/cudf/cudf/tests/test_interval.py

@@ -188,3 +188,9 @@ def test_from_pandas_intervaldtype():
     result = cudf.from_pandas(dtype)
     expected = cudf.IntervalDtype("int64", closed="left")
     assert_eq(result, expected)
+
+
+def test_intervaldtype_eq_string_with_attributes():
+    dtype = cudf.IntervalDtype("int64", closed="left")
+    assert dtype == "interval"
+    assert dtype == "interval[int64, left]"