Column refactoring 2

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

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import pickle
+from collections.abc import MutableSequence
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -35,6 +36,7 @@
     min_signed_type,
     min_unsigned_type,
 )
+from cudf.utils.utils import cached_property
 
 if TYPE_CHECKING:
     from cudf.core.column import (
@@ -819,7 +821,7 @@ def __contains__(self, item: ScalarLike) -> bool:
         return self._encode(item) in self.as_numerical
 
     def serialize(self) -> Tuple[dict, list]:
-        header = {}  # type: Dict[Any, Any]
+        header: Dict[Any, Any] = {}
         frames = []
         header["type-serialized"] = pickle.dumps(type(self))
         header["dtype"], dtype_frames = self.dtype.serialize()
@@ -1341,23 +1343,13 @@ def find_last_value(self, value: ScalarLike, closest: bool = False) -> int:
         """
         return self.as_numerical.find_last_value(self._encode(value))
 
-    @property
+    @cached_property
     def is_monotonic_increasing(self) -> bool:
-        if not hasattr(self, "_is_monotonic_increasing"):
-            self._is_monotonic_increasing = (
-                bool(self.ordered)
-                and self.as_numerical.is_monotonic_increasing
-            )
-        return self._is_monotonic_increasing
+        return bool(self.ordered) and self.as_numerical.is_monotonic_increasing
 
-    @property
+    @cached_property
     def is_monotonic_decreasing(self) -> bool:
-        if not hasattr(self, "_is_monotonic_decreasing"):
-            self._is_monotonic_decreasing = (
-                bool(self.ordered)
-                and self.as_numerical.is_monotonic_decreasing
-            )
-        return self._is_monotonic_decreasing
+        return bool(self.ordered) and self.as_numerical.is_monotonic_decreasing
 
     def as_categorical_column(
         self, dtype: Dtype, **kwargs
@@ -1472,6 +1464,52 @@ def view(self, dtype: Dtype) -> ColumnBase:
             "Categorical column views are not currently supported"
         )
 
+    @staticmethod
+    def _concat(objs: MutableSequence[CategoricalColumn]) -> CategoricalColumn:
+        # TODO: This function currently assumes it is being called from
+        # column._concat_columns, at least to the extent that all the
+        # preprocessing in that function has already been done. That should be
+        # improved as the concatenation API is solidified.
+        # Find the first non-null column:
+        head = next((obj for obj in objs if obj.valid_count), objs[0])
+
+        # Combine and de-dupe the categories
+        cats = (
+            cudf.concat([o.cat().categories for o in objs])
+            .to_series()
+            .drop_duplicates(ignore_index=True)
+            ._column
+        )
+        objs = [
+            o.cat()._set_categories(o.cat().categories, cats, is_unique=True)
+            for o in objs
+        ]
+        # Map `objs` into a list of the codes until we port Categorical to
+        # use the libcudf++ Category data type.
+        objs = [o.cat().codes._column for o in objs]
+        head = head.cat().codes._column
+
+        newsize = sum(map(len, objs))
+        if newsize > libcudf.MAX_COLUMN_SIZE:
+            raise MemoryError(
+                f"Result of concat cannot have "
+                f"size > {libcudf.MAX_COLUMN_SIZE_STR}"
+            )
+        elif newsize == 0:
+            col = column.column_empty(0, head.dtype, masked=True)
+        else:
+            # Filter out inputs that have 0 length, then concatenate.
+            objs = [o for o in objs if len(o) > 0]
+            col = libcudf.concat.concat_columns(objs)
+
+        return column.build_categorical_column(
+            categories=column.as_column(cats),
+            codes=column.as_column(col.base_data, dtype=col.dtype),
+            mask=col.base_mask,
+            size=col.size,
+            offset=col.offset,
+        )
+
 
 def _create_empty_categorical_column(
     categorical_column: CategoricalColumn, dtype: "CategoricalDtype"

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

@@ -56,7 +56,7 @@
     min_unsigned_type,
     np_to_pa_dtype,
 )
-from cudf.utils.utils import mask_dtype
+from cudf.utils.utils import cached_property, mask_dtype
 
 T = TypeVar("T", bound="ColumnBase")
 
@@ -189,114 +189,6 @@ def __sizeof__(self) -> int:
             n += bitmask_allocation_size_bytes(self.size)
         return n
 
-    def cat(
-        self, parent=None
-    ) -> "cudf.core.column.categorical.CategoricalAccessor":
-        raise NotImplementedError()
-
-    def str(self, parent=None) -> "cudf.core.column.string.StringMethods":
-        raise NotImplementedError()
-
-    @classmethod
-    def _concat(
-        cls, objs: "MutableSequence[ColumnBase]", dtype: Dtype = None
-    ) -> ColumnBase:
-        if len(objs) == 0:
-            dtype = pd.api.types.pandas_dtype(dtype)
-            if is_categorical_dtype(dtype):
-                dtype = CategoricalDtype()
-            return column_empty(0, dtype=dtype, masked=True)
-
-        # If all columns are `NumericalColumn` with different dtypes,
-        # we cast them to a common dtype.
-        # Notice, we can always cast pure null columns
-        not_null_cols = list(filter(lambda o: o.valid_count > 0, objs))
-        if len(not_null_cols) > 0 and (
-            len(
-                [
-                    o
-                    for o in not_null_cols
-                    if not is_numerical_dtype(o.dtype)
-                    or np.issubdtype(o.dtype, np.datetime64)
-                ]
-            )
-            == 0
-        ):
-            col_dtypes = [o.dtype for o in not_null_cols]
-            # Use NumPy to find a common dtype
-            common_dtype = np.find_common_type(col_dtypes, [])
-            # Cast all columns to the common dtype
-            for i in range(len(objs)):
-                objs[i] = objs[i].astype(common_dtype)
-
-        # Find the first non-null column:
-        head = objs[0]
-        for i, obj in enumerate(objs):
-            if obj.valid_count > 0:
-                head = obj
-                break
-
-        for i, obj in enumerate(objs):
-            # Check that all columns are the same type:
-            if not pd.api.types.is_dtype_equal(obj.dtype, head.dtype):
-                # if all null, cast to appropriate dtype
-                if obj.valid_count == 0:
-                    objs[i] = column_empty_like(
-                        head, dtype=head.dtype, masked=True, newsize=len(obj)
-                    )
-                else:
-                    raise ValueError("All columns must be the same type")
-
-        cats = None
-        is_categorical = all(is_categorical_dtype(o.dtype) for o in objs)
-
-        # Combine CategoricalColumn categories
-        if is_categorical:
-            # Combine and de-dupe the categories
-            cats = (
-                cudf.concat([o.cat().categories for o in objs])
-                .to_series()
-                .drop_duplicates(ignore_index=True)
-                ._column
-            )
-            objs = [
-                o.cat()._set_categories(
-                    o.cat().categories, cats, is_unique=True
-                )
-                for o in objs
-            ]
-            # Map `objs` into a list of the codes until we port Categorical to
-            # use the libcudf++ Category data type.
-            objs = [o.cat().codes._column for o in objs]
-            head = head.cat().codes._column
-
-        newsize = sum(map(len, objs))
-        if newsize > libcudf.MAX_COLUMN_SIZE:
-            raise MemoryError(
-                f"Result of concat cannot have "
-                f"size > {libcudf.MAX_COLUMN_SIZE_STR}"
-            )
-
-        # Filter out inputs that have 0 length
-        objs = [o for o in objs if len(o) > 0]
-
-        # Perform the actual concatenation
-        if newsize > 0:
-            col = libcudf.concat.concat_columns(objs)
-        else:
-            col = column_empty(0, head.dtype, masked=True)
-
-        if is_categorical:
-            col = build_categorical_column(
-                categories=as_column(cats),
-                codes=as_column(col.base_data, dtype=col.dtype),
-                mask=col.base_mask,
-                size=col.size,
-                offset=col.offset,
-            )
-
-        return col
-
     def dropna(self, drop_nan: bool = False) -> ColumnBase:
         if drop_nan:
             col = self.nans_to_nulls()
@@ -796,7 +688,7 @@ def find_last_value(self, value: ScalarLike, closest: bool = False) -> int:
         return indices[-1]
 
     def append(self, other: ColumnBase) -> ColumnBase:
-        return self.__class__._concat([self, as_column(other)])
+        return _concat_columns([self, as_column(other)])
 
     def quantile(
         self,
@@ -932,27 +824,17 @@ def is_unique(self) -> bool:
     def is_monotonic(self) -> bool:
         return self.is_monotonic_increasing
 
-    @property
+    @cached_property
     def is_monotonic_increasing(self) -> bool:
-        if not hasattr(self, "_is_monotonic_increasing"):
-            if self.has_nulls:
-                self._is_monotonic_increasing = False
-            else:
-                self._is_monotonic_increasing = self.as_frame()._is_sorted(
-                    ascending=None, null_position=None
-                )
-        return self._is_monotonic_increasing
+        return not self.has_nulls and self.as_frame()._is_sorted(
+            ascending=None, null_position=None
+        )
 
-    @property
+    @cached_property
     def is_monotonic_decreasing(self) -> bool:
-        if not hasattr(self, "_is_monotonic_decreasing"):
-            if self.has_nulls:
-                self._is_monotonic_decreasing = False
-            else:
-                self._is_monotonic_decreasing = self.as_frame()._is_sorted(
-                    ascending=[False], null_position=None
-                )
-        return self._is_monotonic_decreasing
+        return not self.has_nulls and self.as_frame()._is_sorted(
+            ascending=[False], null_position=None
+        )
 
     def get_slice_bound(
         self, label: ScalarLike, side: builtins.str, kind: builtins.str
@@ -1211,7 +1093,7 @@ def unique(self) -> ColumnBase:
         )
 
     def serialize(self) -> Tuple[dict, list]:
-        header = {}  # type: Dict[Any, Any]
+        header: Dict[Any, Any] = {}
         frames = []
         header["type-serialized"] = pickle.dumps(type(self))
         header["dtype"] = self.dtype.str
@@ -2226,7 +2108,7 @@ def serialize_columns(columns) -> Tuple[List[dict], List]:
     frames : list
         list of frames
     """
-    headers = []  # type List[Dict[Any, Any], ...]
+    headers: List[Dict[Any, Any]] = []
     frames = []
 
     if len(columns) > 0:
@@ -2346,3 +2228,67 @@ def full(size: int, fill_value: ScalarLike, dtype: Dtype = None) -> ColumnBase:
     dtype: int8
     """
     return ColumnBase.from_scalar(cudf.Scalar(fill_value, dtype), size)
+
+
+def _concat_columns(objs: "MutableSequence[ColumnBase]") -> ColumnBase:
+    """Concatenate a sequence of columns."""
+    # TODO: This function currently assumes that len(objs) > 0. Concatenation
+    # is only accessible via cudf.concat (which calls through to this from
+    # methods like Index._concat or Series._concat), and that method
+    # pre-filters on zero objects being provided. We may need to add logic for
+    # handling an empty input sequence as more Frame logic becomes centralized.
+
+    # If all columns are `NumericalColumn` with different dtypes,
+    # we cast them to a common dtype.
+    # Notice, we can always cast pure null columns
+    not_null_col_dtypes = [o.dtype for o in objs if o.valid_count > 0]
+    if len(not_null_col_dtypes) and all(
+        is_numerical_dtype(dtyp) and np.issubdtype(dtyp, np.datetime64)
+        for dtyp in not_null_col_dtypes
+    ):
+        # Use NumPy to find a common dtype
+        common_dtype = np.find_common_type(not_null_col_dtypes, [])
+        # Cast all columns to the common dtype
+        objs = [obj.astype(common_dtype) for obj in objs]
+
+    # Find the first non-null column:
+    head = next((obj for obj in objs if obj.valid_count), objs[0])
+
+    for i, obj in enumerate(objs):
+        # Check that all columns are the same type:
+        if not pd.api.types.is_dtype_equal(obj.dtype, head.dtype):
+            # if all null, cast to appropriate dtype
+            if obj.valid_count == 0:
+                objs[i] = column_empty_like(
+                    head, dtype=head.dtype, masked=True, newsize=len(obj)
+                )
+            else:
+                raise ValueError("All columns must be the same type")
+
+    # TODO: This logic should be generalized to a dispatch to
+    # ColumnBase._concat so that all subclasses can override necessary
+    # behavior. However, at the moment it's not clear what that API should look
+    # like, so CategoricalColumn simply implements a minimal working API.
+    if all(is_categorical_dtype(o.dtype) for o in objs):
+        return cudf.core.column.categorical.CategoricalColumn._concat(
+            cast(
+                MutableSequence[
+                    cudf.core.column.categorical.CategoricalColumn
+                ],
+                objs,
+            )
+        )
+
+    newsize = sum(map(len, objs))
+    if newsize > libcudf.MAX_COLUMN_SIZE:
+        raise MemoryError(
+            f"Result of concat cannot have "
+            f"size > {libcudf.MAX_COLUMN_SIZE_STR}"
+        )
+    elif newsize == 0:
+        col = column_empty(0, head.dtype, masked=True)
+    else:
+        # Filter out inputs that have 0 length, then concatenate.
+        objs = [o for o in objs if len(o) > 0]
+        col = libcudf.concat.concat_columns(objs)
+    return col