lists

pylibcudf.lists.ConcatenateNullPolicy

See also cudf::concatenate_null_policy.

Enum members

• IGNORE

• NULLIFY_OUTPUT_ROW

pylibcudf.lists.DuplicateFindOption

See also cudf::duplicate_find_option.

Enum members

• FIND_FIRST

• FIND_LAST

pylibcudf.lists.apply_boolean_mask(Column input, Column boolean_mask) → 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.

Returns:

Column

A Column of filtered elements based upon the boolean mask.

pylibcudf.lists.concatenate_list_elements(Column input, concatenate_null_policy null_policy) → 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) → Column

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

For details, see concatenate_list_elements().

Parameters:

inputTable

The input table

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.

Returns:

Column

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

pylibcudf.lists.contains_nulls(Column input) → 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.

Returns:

Column

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

pylibcudf.lists.count_elements(Column input) → 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

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) → 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) → 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) → 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) → 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) → 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) → Column

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

For details, see reverse().

Parameters:

inputColumn

The input column.

Returns:

Column

A new Column with reversed lists.

pylibcudf.lists.segmented_gather(Column input, Column gather_map_list) → 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.

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) → 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) → 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) → 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.