Use List of Columns as Input for drop_nulls, gather and drop_duplicates

python/cudf/cudf/_lib/copying.pyx

@@ -37,7 +37,12 @@ from cudf._lib.cpp.scalar.scalar cimport scalar
 from cudf._lib.cpp.table.table cimport table
 from cudf._lib.cpp.table.table_view cimport table_view
 from cudf._lib.cpp.types cimport size_type
-from cudf._lib.utils cimport data_from_table_view, data_from_unique_ptr
+from cudf._lib.utils cimport (
+    columns_from_unique_ptr,
+    data_from_table_view,
+    data_from_unique_ptr,
+    table_view_from_columns,
+)
 
 # workaround for https://github.com/cython/cython/issues/3885
 ctypedef const scalar constscalar
@@ -144,26 +149,12 @@ def copy_range(Column input_column,
 
 
 def gather(
-    source_table,
+    data: list,
     Column gather_map,
-    bool keep_index=True,
     bool nullify=False
 ):
-    if not pd.api.types.is_integer_dtype(gather_map.dtype):
-        raise ValueError("Gather map is not integer dtype.")
-
-    if len(gather_map) > 0 and not nullify:
-        gm_min, gm_max = minmax(gather_map)
-        if gm_min < -len(source_table) or gm_max >= len(source_table):
-            raise IndexError(f"Gather map index with min {gm_min},"
-                             f" max {gm_max} is out of bounds in"
-                             f" {type(source_table)} with {len(source_table)}"
-                             f" rows.")
-
     cdef unique_ptr[table] c_result
-    cdef table_view source_table_view = table_view_from_table(
-        source_table, not keep_index
-    )
+    cdef table_view source_table_view = table_view_from_columns(data)
     cdef column_view gather_map_view = gather_map.view()
     cdef cpp_copying.out_of_bounds_policy policy = (
         cpp_copying.out_of_bounds_policy.NULLIFY if nullify
@@ -179,16 +170,7 @@ def gather(
             )
         )
 
-    return data_from_unique_ptr(
-        move(c_result),
-        column_names=source_table._column_names,
-        index_names=(
-            None if (
-                source_table._index is None)
-            or keep_index is False
-            else source_table._index_names
-        )
-    )
+    return columns_from_unique_ptr(move(c_result))
 
 
 def scatter(object source, Column scatter_map, Column target_column,

python/cudf/cudf/_lib/stream_compaction.pyx

@@ -24,40 +24,34 @@ from cudf._lib.cpp.types cimport (
     null_policy,
     size_type,
 )
-from cudf._lib.utils cimport data_from_unique_ptr, table_view_from_table
+from cudf._lib.utils cimport (
+    columns_from_unique_ptr,
+    data_from_unique_ptr,
+    table_view_from_columns,
+    table_view_from_table,
+)
 
 
-def drop_nulls(source_table, how="any", keys=None, thresh=None):
+def drop_nulls(data: list, how="any", keys=None, thresh=None):
     """
     Drops null rows from cols depending on key columns.
 
     Parameters
     ----------
-    source_table : source table whose null rows are dropped to form new table
+    data : list of columns
     how  : "any" or "all". If thresh is None, drops rows of cols that have any
            nulls or all nulls (respectively) in subset (default: "any")
-    keys : List of Column names. If set, then these columns are checked for
+    keys : List of column indices. If set, then these columns are checked for
            nulls rather than all of cols (optional)
     thresh : Minimum number of non-nulls required to keep a row (optional)
 
     Returns
     -------
-    Frame with null rows dropped
+    list of columns with null rows dropped
     """
 
-    num_index_columns = (
-        0 if source_table._index is None else
-        source_table._index._num_columns)
-    # shifting the index number by number of index columns
     cdef vector[size_type] cpp_keys = (
-        [
-            num_index_columns + source_table._column_names.index(name)
-            for name in keys
-        ]
-        if keys is not None
-        else range(
-            num_index_columns, num_index_columns + source_table._num_columns
-        )
+        keys if keys is not None else range(len(data))
     )
 
     cdef size_type c_keep_threshold = cpp_keys.size()
@@ -67,7 +61,7 @@ def drop_nulls(source_table, how="any", keys=None, thresh=None):
         c_keep_threshold = 1
 
     cdef unique_ptr[table] c_result
-    cdef table_view source_table_view = table_view_from_table(source_table)
+    cdef table_view source_table_view = table_view_from_columns(data)
 
     with nogil:
         c_result = move(
@@ -78,13 +72,7 @@ def drop_nulls(source_table, how="any", keys=None, thresh=None):
             )
         )
 
-    return data_from_unique_ptr(
-        move(c_result),
-        column_names=source_table._column_names,
-        index_names=(
-            None if source_table._index is None
-            else source_table._index_names)
-    )
+    return columns_from_unique_ptr(move(c_result))
 
 
 def apply_boolean_mask(source_table, Column boolean_mask):
@@ -124,26 +112,29 @@ def apply_boolean_mask(source_table, Column boolean_mask):
     )
 
 
-def drop_duplicates(source_table,
+def drop_duplicates(data: list,
                     object keys=None,
                     object keep='first',
-                    bool nulls_are_equal=True,
-                    bool ignore_index=False):
+                    bool nulls_are_equal=True):
     """
     Drops rows in source_table as per duplicate rows in keys.
 
     Parameters
     ----------
-    source_table : source_table whose rows gets dropped
-    keys : List of Column names belong to source_table
+    data : List of columns
+    keys : List of column indices. If set, then these columns are checked for
+           duplicates rather than all of cols (optional)
     keep : keep 'first' or 'last' or none of the duplicate rows
     nulls_are_equal : if True, nulls are treated equal else not.
 
     Returns
     -------
-    Frame with duplicate dropped
+    list of columns with duplicate dropped
     """
 
+    cdef vector[size_type] cpp_keys = (
+        keys if keys is not None else range(len(data))
+    )
     cdef duplicate_keep_option cpp_keep_option
 
     if keep == 'first':
@@ -155,30 +146,14 @@ def drop_duplicates(source_table,
     else:
         raise ValueError('keep must be either "first", "last" or False')
 
-    num_index_columns =(
-        0 if (source_table._index is None or ignore_index)
-        else source_table._index._num_columns)
     # shifting the index number by number of index columns
-    cdef vector[size_type] cpp_keys = (
-        [
-            num_index_columns + source_table._column_names.index(name)
-            for name in keys
-        ]
-        if keys is not None
-        else range(
-            num_index_columns, num_index_columns + source_table._num_columns
-        )
-    )
-
     cdef null_equality cpp_nulls_equal = (
         null_equality.EQUAL
         if nulls_are_equal
         else null_equality.UNEQUAL
     )
     cdef unique_ptr[table] c_result
-    cdef table_view source_table_view = table_view_from_table(
-        source_table, ignore_index
-    )
+    cdef table_view source_table_view = table_view_from_columns(data)
 
     with nogil:
         c_result = move(
@@ -190,13 +165,7 @@ def drop_duplicates(source_table,
             )
         )
 
-    return data_from_unique_ptr(
-        move(c_result),
-        column_names=source_table._column_names,
-        index_names=(
-            None if (source_table._index is None or ignore_index)
-            else source_table._index_names)
-    )
+    return columns_from_unique_ptr(move(c_result))
 
 
 def distinct_count(Column source_column, ignore_nulls=True, nan_as_null=False):

python/cudf/cudf/_lib/utils.pxd

@@ -14,5 +14,7 @@ cdef data_from_unique_ptr(
     unique_ptr[table] c_tbl, column_names, index_names=*)
 cdef data_from_table_view(
     table_view tv, object owner, object column_names, object index_names=*)
-cdef table_view table_view_from_columns(columns) except *
+cdef table_view table_view_from_columns(
+    columns) except *
 cdef table_view table_view_from_table(tbl, ignore_index=*) except*
+cdef columns_from_unique_ptr(unique_ptr[table] c_tbl)

python/cudf/cudf/_lib/utils.pyx

@@ -36,7 +36,6 @@ PARQUET_META_TYPE_MAP = {
     for cudf_dtype, pandas_dtype in np_dtypes_to_pandas_dtypes.items()
 }
 
-
 cdef table_view table_view_from_columns(columns) except*:
     """Create a cudf::table_view from an iterable of Columns."""
     cdef vector[column_view] column_views
@@ -290,6 +289,20 @@ cdef data_from_unique_ptr(
     }
     return data, index
 
+cdef columns_from_unique_ptr(
+    unique_ptr[table] c_tbl
+):
+    """
+    """
+    cdef vector[unique_ptr[column]] c_columns = move(c_tbl.get().release())
+    cdef vector[unique_ptr[column]].iterator it = c_columns.begin()
+
+    cdef size_t i
+
+    columns = [Column.from_unique_ptr(move(dereference(it+i)))
+               for i in range(c_columns.size())]
+
+    return columns
 
 cdef data_from_table_view(
     table_view tv,

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

@@ -34,7 +34,11 @@
     create_null_mask,
 )
 from cudf._lib.scalar import as_device_scalar
-from cudf._lib.stream_compaction import distinct_count as cpp_distinct_count
+from cudf._lib.stream_compaction import (
+    distinct_count as cpp_distinct_count,
+    drop_duplicates,
+    drop_nulls,
+)
 from cudf._lib.transform import bools_to_mask
 from cudf._typing import BinaryOperand, ColumnLike, Dtype, ScalarLike
 from cudf.api.types import (
@@ -71,7 +75,7 @@
     pandas_dtypes_alias_to_cudf_alias,
     pandas_dtypes_to_np_dtypes,
 )
-from cudf.utils.utils import mask_dtype
+from cudf.utils.utils import _gather_map_is_valid, mask_dtype
 
 T = TypeVar("T", bound="ColumnBase")
 
@@ -208,11 +212,8 @@ def __sizeof__(self) -> int:
         return n
 
     def dropna(self, drop_nan: bool = False) -> ColumnBase:
-        if drop_nan:
-            col = self.nans_to_nulls()
-        else:
-            col = self
-        return col.as_frame()._drop_na_rows(drop_nan=drop_nan)._as_column()
+        col = self.nans_to_nulls() if drop_nan else self
+        return next(iter(drop_nulls([col])))
 
     def to_arrow(self) -> pa.Array:
         """Convert to PyArrow Array
@@ -688,29 +689,17 @@ def quantile(
     def median(self, skipna: bool = None) -> ScalarLike:
         raise TypeError(f"cannot perform median with type {self.dtype}")
 
-    def take(
-        self: T,
-        indices: ColumnBase,
-        keep_index: bool = True,
-        nullify: bool = False,
-    ) -> T:
+    def take(self: T, indices: ColumnBase, nullify: bool = False,) -> T:
         """Return Column by taking values from the corresponding *indices*."""
         # Handle zero size
         if indices.size == 0:
             return cast(T, column_empty_like(self, newsize=0))
-        try:
-            return (
-                self.as_frame()
-                ._gather(indices, keep_index=keep_index, nullify=nullify)
-                ._as_column()
-                ._with_type_metadata(self.dtype)
-            )
-        except RuntimeError as e:
-            if "out of bounds" in str(e):
-                raise IndexError(
-                    f"index out of bounds for column of size {len(self)}"
-                ) from e
-            raise
+        if not _gather_map_is_valid(indices, len(self), nullify):
+            raise IndexError("Gather map index is out of bounds.")
+
+        return next(
+            iter(libcudf.copying.gather([self], indices, nullify=nullify,))
+        )._with_type_metadata(self.dtype)
 
     def isin(self, values: Sequence) -> ColumnBase:
         """Check whether values are contained in the Column.
@@ -1099,11 +1088,7 @@ def unique(self) -> ColumnBase:
         # the following issue resolved:
         # https://github.com/rapidsai/cudf/issues/5286
 
-        return (
-            self.as_frame()
-            .drop_duplicates(keep="first", ignore_index=True)
-            ._as_column()
-        )
+        return next(iter(drop_duplicates([self], keep="first")))
 
     def serialize(self) -> Tuple[dict, list]:
         header: Dict[Any, Any] = {}