lists#

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, bool dropna) → Column#

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

For details, see concatenate_list_elements().

Parameters:

inputColumn: The input column
dropnabool: If true, null list elements will be ignored from concatenation. Otherwise any input null values will result in the corresponding output row being set to null.

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, bool nulls_equal=True, bool nans_equal=True) → 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_equalbool, default True: If true, null elements are considered equal. Otherwise, unequal.
nans_equalbool, default True: If true, libcudf will treat nan elements from {-nan, +nan} as equal. Otherwise, unequal. Otherwise, unequal.

Returns:

Column: A lists column containing the difference results.

pylibcudf.lists.distinct(Column input, bool nulls_equal, bool nans_equal) → Column#

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

For details, see distinct().

Parameters:

inputColumn: The input column.
nulls_equalbool: If true, null elements are considered equal. Otherwise, unequal.
nans_equalbool: If true, libcudf will treat nan elements from {-nan, +nan} as equal. Otherwise, unequal. Otherwise, unequal.

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, bool nulls_equal=True, bool nans_equal=True) → 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_equalbool, default True: If true, null elements are considered equal. Otherwise, unequal.
nans_equalbool, default True: If true, libcudf will treat nan elements from {-nan, +nan} as equal. Otherwise, unequal. Otherwise, unequal.

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_first_optionbool: If true, index_of returns the first match. Otherwise the last match is returned.

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, bool nulls_equal=True, bool nans_equal=True) → 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_equalbool, default True: If true, null elements are considered equal. Otherwise, unequal.
nans_equalbool, default True: If true, libcudf will treat nan elements from {-nan, +nan} as equal. Otherwise, unequal. Otherwise, unequal.

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, bool ascending, 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.
ascendingbool: If true, the sort order is ascending. Otherwise, the sort order is descending.
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, bool nulls_equal=True, bool nans_equal=True) → 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_equalbool, default True: If true, null elements are considered equal. Otherwise, unequal.
nans_equalbool, default True: If true, libcudf will treat nan elements from {-nan, +nan} as equal. Otherwise, unequal. Otherwise, unequal.

Returns:

Column: A lists column containing the union results.