lists#

pylibcudf.lists.ConcatenateNullPolicy#

See also duplicate_find_option.

Enum members

FIND_FIRST
FIND_LAST

pylibcudf.lists.apply_boolean_mask( Column input, Column boolean_mask, stream=None, DeviceMemoryResource mr=None, ) → Column#

Filters elements in each row of the input lists column using a boolean mask

For details, see apply_boolean_mask().

Parameters:

inputColumn: The input column.
boolean_maskColumn: The boolean mask.
streamStream | None: CUDA stream on which to perform the operation.

Returns:

Column: A Column of filtered elements based upon the boolean mask.

pylibcudf.lists.apply_deletion_mask( Column input, Column deletion_mask, stream=None, DeviceMemoryResource mr=None, ) → Column#

Filters elements in each row of the input lists column using a deletion mask.

For details, see apply_deletion_mask().

Parameters:

inputColumn: The input lists column.
deletion_maskColumn: A lists-of-bools column used as a deletion mask.

Returns:

Column: Lists column with elements removed where deletion_mask is true.

pylibcudf.lists.concatenate_list_elements( Column input, concatenate_null_policy null_policy, stream=None, DeviceMemoryResource mr=None, ) → Column#

Concatenate multiple lists on the same row into a single list.

For details, see concatenate_list_elements().

Parameters:

inputColumn: The input column
null_policyConcatenateNullPolicy: How to treat null list elements.

Returns:

Column: A new Column of concatenated list elements

pylibcudf.lists.concatenate_rows( Table input, stream=None, DeviceMemoryResource mr=None, ) → Column#

Concatenate multiple lists columns into a single lists column row-wise.

For details, see concatenate_list_elements().

Parameters:

inputTable: The input table
streamStream | None: CUDA stream on which to perform the operation.

Returns:

Table: A new Column of concatenated rows

pylibcudf.lists.contains(signatures, args, kwargs, defaults, _fused_sigindex={})#

Create a column of bool values indicating whether the search_key is contained in the input.

search_key may be a Column or a Scalar.

For details, see contains().

Parameters:

inputColumn: The input column.
search_keyUnion[Column, Scalar]: The search key.
streamStream | None: CUDA stream on which to perform the operation.

Returns:

Column: A new Column of bools indicating if the search_key was found in the list column.

pylibcudf.lists.contains_nulls( Column input, stream=None, DeviceMemoryResource mr=None, ) → Column#

Create a column of bool values indicating whether each row in the lists column contains a null value.

For details, see contains_nulls().

Parameters:

inputColumn: The input column.
streamStream | None: CUDA stream on which to perform the operation.

Returns:

Column: A new Column of bools indicating if the list column contains a null value.

pylibcudf.lists.count_elements( Column input, stream=None, DeviceMemoryResource mr=None, ) → Column#

Count the number of rows in each list element in the given lists column. For details, see count_elements().

For details, see count_elements().

Parameters:

inputColumn: The input column
streamStream | None: CUDA stream on which to perform the operation.

Returns:

Column: A new Column of the lengths of each list element

pylibcudf.lists.difference_distinct( Column lhs, Column rhs, null_equality nulls_equal=null_equality.EQUAL, nan_equality nans_equal=nan_equality.ALL_EQUAL, stream=None, DeviceMemoryResource mr=None, ) → Column#

Create a column of index values indicating the position of a search key row within the corresponding list row in the lists column.

For details, see difference_distinct().

Parameters:

lhsColumn: The input lists column of elements that may be included.
rhsColumn: The input lists column of elements to exclude.
nulls_equalNullEquality, default EQUAL: Are nulls considered equal.
nans_equalNanEquality, default ALL_EQUAL: Are nans considered equal.

Returns:

Column: A lists column containing the difference results.

pylibcudf.lists.distinct( Column input, null_equality nulls_equal, nan_equality nans_equal, stream=None, DeviceMemoryResource mr=None, ) → Column#

Create a new list column without duplicate elements in each list.

For details, see distinct().

Parameters:

inputColumn: The input column.
nulls_equalNullEquality: Are nulls considered equal.
nans_equalNanEquality: Are nans considered equal.

Returns:

Column: A new list column without duplicate elements in each list.

pylibcudf.lists.explode_outer( Table input, size_type explode_column_idx, stream=None, DeviceMemoryResource mr=None, ) → Table#

Explode a column of lists into rows.

All other columns will be duplicated for each element in the list.

For details, see explode_outer().

Parameters:

inputTable: The input table
explode_column_idxint: The index of the column to explode

Returns:

Table: A new table with the exploded column

pylibcudf.lists.extract_list_element( signatures, args, kwargs, defaults, _fused_sigindex={}, )#

Create a column of extracted list elements.

For details, see extract_list_element().

Parameters:

inputColumn: The input column.
indexUnion[Column, size_type]: The selection index or indices.

Returns:

Column: A new Column with elements extracted.

pylibcudf.lists.have_overlap( Column lhs, Column rhs, null_equality nulls_equal=null_equality.EQUAL, nan_equality nans_equal=nan_equality.ALL_EQUAL, stream=None, DeviceMemoryResource mr=None, ) → Column#

Check if lists at each row of the given lists columns overlap.

For details, see have_overlap().

Parameters:

lhsColumn: The input lists column for one side.
rhsColumn: The input lists column for the other side.
nulls_equalNullEquality, default EQUAL: Are nulls considered equal.
nans_equalNanEquality, default ALL_EQUAL: Are nans considered equal.

Returns:

Column: A column containing the check results.

pylibcudf.lists.index_of(signatures, args, kwargs, defaults, _fused_sigindex={})#

Create a column of index values indicating the position of a search key row within the corresponding list row in the lists column.

search_key may be a Column or a Scalar.

For details, see index_of().

Parameters:

inputColumn: The input column.
search_keyUnion[Column, Scalar]: The search key.
find_optionDuplicateFindOption: Which match to return if there are duplicates.

Returns:

Column: A new Column of index values that indicate where in the list column tthe search_key was found. An index value of -1 indicates that the search_key was not found.

pylibcudf.lists.intersect_distinct( Column lhs, Column rhs, null_equality nulls_equal=null_equality.EQUAL, nan_equality nans_equal=nan_equality.ALL_EQUAL, stream=None, DeviceMemoryResource mr=None, ) → Column#

Create a lists column of distinct elements common to two input lists columns.

For details, see intersect_distinct().

Parameters:

lhsColumn: The input lists column of elements that may be included.
rhsColumn: The input lists column of elements to exclude.
nulls_equalNullEquality, default EQUAL: Are nulls considered equal.
nans_equalNanEquality, default ALL_EQUAL: Are nans considered equal.

Returns:

Column: A lists column containing the intersection results.

pylibcudf.lists.reverse( Column input, stream=None, DeviceMemoryResource mr=None, ) → Column#

Reverse the element order within each list of the input column.

For details, see reverse().

Parameters:

inputColumn: The input column.
streamStream | None: CUDA stream on which to perform the operation.

Returns:

Column: A new Column with reversed lists.

pylibcudf.lists.segmented_gather( Column input, Column gather_map_list, out_of_bounds_policy bounds_policy=out_of_bounds_policy.DONT_CHECK, stream=None, DeviceMemoryResource mr=None, ) → Column#

Create a column with elements gathered based on the indices in gather_map_list

For details, see segmented_gather().

Parameters:

inputColumn

The input column.

gather_map_listColumn

The indices of the lists column to gather.

bounds_policyOutOfBoundsPolicy

Can be DONT_CHECK or NULLIFY. Selects whether or not to nullify the output list row’s element, when the gather index falls outside the range [-n, n), where n is the number of elements in list row corresponding to the gather-map row.

When bounds_policy is DONT_CHECK, it’s the caller’s responsibility to ensure that the indices in gather_map_list are in-bounds for the lists in input before calling this function. The behavior with out-of-bounds indices and DONT_CHECK is undefined and maybe produce invalid results or crash.

Returns:

Column: A new Column with elements in list of rows gathered based on gather_map_list

pylibcudf.lists.sequences( Column starts, Column sizes, Column steps=None, stream=None, DeviceMemoryResource mr=None, ) → Column#

Create a lists column in which each row contains a sequence of values specified by a tuple of (start, step, size) parameters.

For details, see sequences().

Parameters:

startsColumn: First values in the result sequences.
sizesColumn: Numbers of values in the result sequences.
stepsOptional[Column]: Increment values for the result sequences.

Returns:

Column: The result column containing generated sequences.

pylibcudf.lists.sort_lists( Column input, order sort_order, null_order na_position, bool stable=False, stream=None, DeviceMemoryResource mr=None, ) → Column#

Sort the elements within a list in each row of a list column.

For details, see sort_lists().

Parameters:

inputColumn: The input column.
ascendingOrder: Sort order in the list.
na_positionNullOrder: If na_position equals NullOrder.FIRST, then the null values in the output column are placed first. Otherwise, they are be placed after.
stable: bool: If true stable_sort_lists() is used, Otherwise, sort_lists() is used.

Returns:

Column: A new Column with elements in each list sorted.

pylibcudf.lists.union_distinct( Column lhs, Column rhs, null_equality nulls_equal=null_equality.EQUAL, nan_equality nans_equal=nan_equality.ALL_EQUAL, stream=None, DeviceMemoryResource mr=None, ) → Column#

Create a lists column of distinct elements found in either of two input lists columns.

For details, see union_distinct().

Parameters:

lhsColumn: The input lists column of elements that may be included.
rhsColumn: The input lists column of elements to exclude.
nulls_equalNullEquality, default EQUAL: Are nulls considered equal.
nans_equalNanEquality, default ALL_EQUAL: Are nans considered equal.

Returns:

Column: A lists column containing the union results.