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.