Use as_column instead of full

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

@@ -16,7 +16,6 @@
     column_empty_like_same_mask,
     concat_columns,
     deserialize_columns,
-    full,
     serialize_columns,
 )
 from cudf.core.column.datetime import DatetimeColumn  # noqa: F401

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

@@ -734,8 +734,8 @@ def normalize_binop_value(self, other: ScalarLike) -> CategoricalColumn:
                 )
             return other
 
-        ary = column.full(
-            len(self), self._encode(other), dtype=self.codes.dtype
+        ary = column.as_column(
+            self._encode(other), length=len(self), dtype=self.codes.dtype
         )
         return column.build_categorical_column(
             categories=self.dtype.categories._values,
@@ -1438,11 +1438,9 @@ def _create_empty_categorical_column(
     return column.build_categorical_column(
         categories=column.as_column(dtype.categories),
         codes=column.as_column(
-            column.full(
-                categorical_column.size,
-                _DEFAULT_CATEGORICAL_VALUE,
-                categorical_column.codes.dtype,
-            )
+            _DEFAULT_CATEGORICAL_VALUE,
+            length=categorical_column.size,
+            dtype=categorical_column.codes.dtype,
         ),
         offset=categorical_column.offset,
         size=categorical_column.size,

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

@@ -58,7 +58,6 @@
     infer_dtype,
     is_bool_dtype,
     is_datetime64_dtype,
-    is_decimal_dtype,
     is_dtype_equal,
     is_integer_dtype,
     is_list_dtype,
@@ -855,7 +854,7 @@ def isin(self, values: Sequence) -> ColumnBase:
         except ValueError:
             # pandas functionally returns all False when cleansing via
             # typecasting fails
-            return full(len(self), False, dtype="bool")
+            return as_column(False, length=len(self), dtype="bool")
 
         return lhs._obtain_isin_result(rhs)
 
@@ -882,9 +881,9 @@ def _isin_earlystop(self, rhs: ColumnBase) -> Union[ColumnBase, None]:
             if self.null_count and rhs.null_count:
                 return self.isnull()
             else:
-                return cudf.core.column.full(len(self), False, dtype="bool")
+                return as_column(False, length=len(self), dtype="bool")
         elif self.null_count == 0 and (rhs.null_count == len(rhs)):
-            return cudf.core.column.full(len(self), False, dtype="bool")
+            return as_column(False, length=len(self), dtype="bool")
         else:
             return None
 
@@ -1345,9 +1344,7 @@ def _label_encoding(
             na_sentinel = cudf.Scalar(-1)
 
         def _return_sentinel_column():
-            return cudf.core.column.full(
-                size=len(self), fill_value=na_sentinel, dtype=dtype
-            )
+            return as_column(na_sentinel, dtype=dtype, length=len(self))
 
         if dtype is None:
             dtype = min_scalar_type(max(len(cats), na_sentinel), 8)
@@ -1444,7 +1441,9 @@ def column_empty(
     elif isinstance(dtype, ListDtype):
         data = None
         children = (
-            full(row_count + 1, 0, dtype=libcudf.types.size_type_dtype),
+            as_column(
+                0, length=row_count + 1, dtype=libcudf.types.size_type_dtype
+            ),
             column_empty(row_count, dtype=dtype.element_type),
         )
     elif isinstance(dtype, CategoricalDtype):
@@ -1463,7 +1462,9 @@ def column_empty(
     elif dtype.kind in "OU" and not isinstance(dtype, DecimalDtype):
         data = as_buffer(rmm.DeviceBuffer(size=0))
         children = (
-            full(row_count + 1, 0, dtype=libcudf.types.size_type_dtype),
+            as_column(
+                0, length=row_count + 1, dtype=libcudf.types.size_type_dtype
+            ),
         )
     else:
         data = as_buffer(rmm.DeviceBuffer(size=row_count * dtype.itemsize))
@@ -2006,33 +2007,32 @@ def as_column(
         if dtype is not None:
             data = data.astype(dtype)
 
-    elif isinstance(arbitrary, (pd.Timestamp, pd.Timedelta)):
-        # This will always treat NaTs as nulls since it's not technically a
-        # discrete value like NaN
-        length = length or 1
-        data = as_column(
-            pa.array(pd.Series([arbitrary] * length), from_pandas=True)
-        )
-        if dtype is not None:
-            data = data.astype(dtype)
-
-    elif np.isscalar(arbitrary) and not isinstance(arbitrary, memoryview):
-        length = length or 1
+    elif is_scalar(arbitrary) and not isinstance(arbitrary, memoryview):
+        if length is None:
+            length = 1
+        elif length < 0:
+            raise ValueError(f"{length=} must be >=0.")
+        if isinstance(arbitrary, pd.Interval):
+            # No cudf.Scalar support yet
+            return as_column(
+                pd.Series([arbitrary] * length),
+                nan_as_null=nan_as_null,
+                dtype=dtype,
+                length=length,
+            )
         if (
-            (nan_as_null is True)
+            nan_as_null is True
             and isinstance(arbitrary, (np.floating, float))
             and np.isnan(arbitrary)
         ):
-            arbitrary = None
             if dtype is None:
-                dtype = cudf.dtype("float64")
-
-        data = as_column(full(length, arbitrary, dtype=dtype))
-        if not nan_as_null and not is_decimal_dtype(data.dtype):
-            if np.issubdtype(data.dtype, np.floating):
-                data = data.fillna(np.nan)
-            elif np.issubdtype(data.dtype, np.datetime64):
-                data = data.fillna(np.datetime64("NaT"))
+                dtype = getattr(arbitrary, "dtype", cudf.dtype("float64"))
+            arbitrary = None
+        arbitrary = cudf.Scalar(arbitrary, dtype=dtype)
+        if length == 0:
+            return column_empty(length, dtype=arbitrary.dtype)
+        else:
+            return ColumnBase.from_scalar(arbitrary, length)
 
     elif hasattr(arbitrary, "__array_interface__"):
         # CUDF assumes values are always contiguous
@@ -2150,8 +2150,6 @@ def as_column(
         return as_column(
             np.asarray(view), dtype=dtype, nan_as_null=nan_as_null
         )
-    elif isinstance(arbitrary, cudf.Scalar):
-        data = ColumnBase.from_scalar(arbitrary, length if length else 1)
     else:
         if dtype is not None:
             # Arrow throws a type error if the input is of
@@ -2494,42 +2492,6 @@ def deserialize_columns(headers: List[dict], frames: List) -> List[ColumnBase]:
     return columns
 
 
-def full(
-    size: int, fill_value: ScalarLike, dtype: Optional[Dtype] = None
-) -> ColumnBase:
-    """
-    Returns a column of given size and dtype, filled with a given value.
-
-    Parameters
-    ----------
-    size : int
-        size of the expected column.
-    fill_value : scalar
-         A scalar value to fill a new array.
-    dtype : default None
-        Data type specifier. It is inferred from other arguments by default.
-
-    Returns
-    -------
-    Column
-
-    Examples
-    --------
-    >>> import cudf
-    >>> col = cudf.core.column.full(size=5, fill_value=7, dtype='int8')
-    >>> col
-    <cudf.core.column.numerical.NumericalColumn object at 0x7fa0912e8b90>
-    >>> cudf.Series(col)
-    0    7
-    1    7
-    2    7
-    3    7
-    4    7
-    dtype: int8
-    """
-    return ColumnBase.from_scalar(cudf.Scalar(fill_value, dtype), size)
-
-
 def concat_columns(objs: "MutableSequence[ColumnBase]") -> ColumnBase:
     """Concatenate a sequence of columns."""
     if len(objs) == 0:

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

@@ -69,8 +69,8 @@ def as_string_column(
     def __pow__(self, other):
         if isinstance(other, int):
             if other == 0:
-                res = cudf.core.column.full(
-                    size=len(self), fill_value=1, dtype=self.dtype
+                res = cudf.core.column.as_column(
+                    1, dtype=self.dtype, length=len(self)
                 )
                 if self.nullable:
                     res = res.set_mask(self.mask)

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

@@ -42,7 +42,6 @@
     as_column,
     build_column,
     column,
-    full,
     string,
 )
 from cudf.core.dtypes import CategoricalDtype
@@ -513,7 +512,7 @@ def find_and_replace(
             )
         if len(replacement_col) == 1 and len(to_replace_col) > 1:
             replacement_col = column.as_column(
-                full(len(to_replace_col), replacement[0], self.dtype)
+                replacement[0], length=len(to_replace_col), dtype=self.dtype
             )
         elif len(replacement_col) == 1 and len(to_replace_col) == 0:
             return self.copy()

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

@@ -5499,7 +5499,9 @@ def __init__(
 
         if len(children) == 0 and size != 0:
             # all nulls-column:
-            offsets = column.full(size + 1, 0, dtype=size_type_dtype)
+            offsets = column.as_column(
+                0, length=size + 1, dtype=size_type_dtype
+            )
 
             children = (offsets,)
 
@@ -5921,8 +5923,8 @@ def _binaryop(
                     "__eq__",
                     "__ne__",
                 }:
-                    return column.full(
-                        len(self), op == "__ne__", dtype="bool"
+                    return column.as_column(
+                        op == "__ne__", length=len(self), dtype="bool"
                     ).set_mask(self.mask)
                 else:
                     return NotImplemented
@@ -5931,7 +5933,9 @@ def _binaryop(
                 if isinstance(other, cudf.Scalar):
                     other = cast(
                         StringColumn,
-                        column.full(len(self), other, dtype="object"),
+                        column.as_column(
+                            other, length=len(self), dtype="object"
+                        ),
                     )
 
                 # Explicit types are necessary because mypy infers ColumnBase

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

@@ -499,7 +499,7 @@ def components(self, index=None) -> "cudf.DataFrame":
                 break
 
         for name in keys_list:
-            res_col = cudf.core.column.full(len(self), 0, dtype="int64")
+            res_col = column.as_column(0, length=len(self), dtype="int64")
             if self.nullable:
                 res_col = res_col.set_mask(self.mask)
             data[name] = res_col
@@ -588,7 +588,7 @@ def nanoseconds(self) -> "cudf.core.column.NumericalColumn":
         # of nanoseconds.
 
         if self._time_unit != "ns":
-            res_col = cudf.core.column.full(len(self), 0, dtype="int64")
+            res_col = column.as_column(0, length=len(self), dtype="int64")
             if self.nullable:
                 res_col = res_col.set_mask(self.mask)
             return cast("cudf.core.column.NumericalColumn", res_col)

python/cudf/cudf/core/dataframe.py

@@ -1407,7 +1407,7 @@ def __setitem__(self, arg, value):
                             allow_non_unique=True,
                         )
                     if is_scalar(value):
-                        self._data[arg] = column.full(len(self), value)
+                        self._data[arg] = as_column(value, length=len(self))
                     else:
                         value = as_column(value)
                         self._data[arg] = value
@@ -1455,8 +1455,8 @@ def __setitem__(self, arg, value):
                 else:
                     for col in arg:
                         if is_scalar(value):
-                            self._data[col] = column.full(
-                                size=len(self), fill_value=value
+                            self._data[col] = as_column(
+                                value, length=len(self)
                             )
                         else:
                             self._data[col] = column.as_column(value)
@@ -3205,10 +3205,16 @@ def _insert(self, loc, name, value, nan_as_null=None, ignore_index=True):
             )
 
         if _is_scalar_or_zero_d_array(value):
-            value = column.full(
-                len(self),
+            dtype = None
+            if isinstance(value, (np.ndarray, cupy.ndarray)):
+                dtype = value.dtype
+                value = value.item()
+            if libcudf.scalar._is_null_host_scalar(value):
+                dtype = "str"
+            value = as_column(
                 value,
-                "str" if libcudf.scalar._is_null_host_scalar(value) else None,
+                length=len(self),
+                dtype=dtype,
             )
 
         if len(self) == 0:
@@ -5898,7 +5904,7 @@ def isin(self, values):
         fill_value = cudf.Scalar(False)
 
         def make_false_column_like_self():
-            return column.full(len(self), fill_value, "bool")
+            return column.as_column(fill_value, length=len(self), dtype="bool")
 
         # Preprocess different input types into a mapping from column names to
         # a list of values to check.
@@ -6017,7 +6023,7 @@ def _prepare_for_rowwise_op(self, method, skipna, numeric_only):
                 {
                     name: filtered._data[name]._get_mask_as_column()
                     if filtered._data[name].nullable
-                    else column.full(len(filtered._data[name]), True)
+                    else as_column(True, length=len(filtered._data[name]))
                     for name in filtered._data.names
                 }
             )
@@ -7808,8 +7814,8 @@ def func(left, right, output):
             return output
 
         for name in uncommon_columns:
-            output._data[name] = column.full(
-                size=len(output), fill_value=value, dtype="bool"
+            output._data[name] = as_column(
+                value, length=len(output), dtype="bool"
             )
         return output
 

python/cudf/cudf/core/index.py

@@ -1227,9 +1227,9 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None):
             )
 
         needle = as_column(target)
-        result = cudf.core.column.full(
-            len(needle),
-            fill_value=-1,
+        result = as_column(
+            -1,
+            length=len(needle),
             dtype=libcudf.types.size_type_dtype,
         )