sorting

pylibcudf.sorting.is_sorted(

Table tbl,

list column_order,

list null_precedence,

stream=None,

) → bool

Checks if the table is sorted.

For details, see is_sorted().

Parameters:

tblTable

The table to check.

column_orderList[ColumnOrder]

Whether each column is expected to be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls are expected before or after non-nulls.

Returns:

bool

Whether the table is sorted.

pylibcudf.sorting.rank(

Column input_view,

rank_method method,

order column_order,

null_policy null_handling,

null_order null_precedence,

bool percentage,

stream=None,

DeviceMemoryResource mr=None,

) → Column

Computes the rank of each element in the column.

For details, see rank().

Parameters:

input_viewColumn

The column to rank.

methodrank_method

The method to use for ranking ties.

column_orderorder

Whether the column should be sorted in ascending or descending order.

null_handlingnull_policy

Whether or not nulls should be included in the ranking.

null_precedencenull_order

Whether nulls should come before or after non-nulls.

percentagebool

Whether to return the rank as a percentage.

Returns:

Column

The rank of each element in the column.

pylibcudf.sorting.segmented_sort_by_key(

Table values,

Table keys,

Column segment_offsets,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Table

Sorts the table by key, within segments.

For details, see segmented_sort_by_key().

Parameters:

valuesTable

The table to sort.

keysTable

The table to sort by.

segment_offsetsColumn

The offsets of the segments.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Table

The sorted table.

pylibcudf.sorting.sort(

Table source_table,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Table

Sorts the table.

For details, see sort().

Parameters:

source_tableTable

The table to sort.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Table

The sorted table.

pylibcudf.sorting.sort_by_key(

Table values,

Table keys,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Table

Sorts the table by key.

For details, see sort_by_key().

Parameters:

valuesTable

The table to sort.

keysTable

The table to sort by.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Table

The sorted table.

pylibcudf.sorting.sorted_order(

Table source_table,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Column

Computes the row indices required to sort the table.

For details, see sorted_order().

Parameters:

source_tableTable

The table to sort.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Column

The row indices required to sort the table.

pylibcudf.sorting.stable_segmented_sort_by_key(

Table values,

Table keys,

Column segment_offsets,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Table

Sorts the table by key preserving order of equal elements, within segments.

For details, see stable_segmented_sort_by_key().

Parameters:

valuesTable

The table to sort.

keysTable

The table to sort by.

segment_offsetsColumn

The offsets of the segments.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Table

The sorted table.

pylibcudf.sorting.stable_sort(

Table source_table,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Table

Sorts the table preserving order of equal elements.

For details, see stable_sort().

Parameters:

source_tableTable

The table to sort.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Table

The sorted table.

pylibcudf.sorting.stable_sort_by_key(

Table values,

Table keys,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Table

Sorts the table by key preserving order of equal elements.

For details, see stable_sort_by_key().

Parameters:

valuesTable

The table to sort.

keysTable

The table to sort by.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Table

The sorted table.

pylibcudf.sorting.stable_sorted_order(

Table source_table,

list column_order,

list null_precedence,

stream=None,

DeviceMemoryResource mr=None,

) → Column

Computes the row indices required to sort the table, preserving order of equal elements.

For details, see stable_sorted_order().

Parameters:

source_tableTable

The table to sort.

column_orderList[ColumnOrder]

Whether each column should be sorted in ascending or descending order.

null_precedenceList[NullOrder]

Whether nulls should come before or after non-nulls.

Returns:

Column

The row indices required to sort the table.

pylibcudf.sorting.top_k(

Column col,

size_type k,

order sort_order=order.DESCENDING,

stream=None,

DeviceMemoryResource mr=None,

) → Column

Computes the top-k values of a column.

For details, see top_k().

Parameters:

colColumn

The input column.

kint

The number of top values to retrieve.

sort_orderOrder, default DESCENDING

The desired order of the top values. If ASCENDING, the smallest k values are returned. If DESCENDING, the largest k values are returned.

Returns:

Column

A column of the top k elements from the input.

pylibcudf.sorting.top_k_order(

Column col,

size_type k,

order sort_order=order.DESCENDING,

stream=None,

DeviceMemoryResource mr=None,

) → Column

Computes the indices of the top-k values of a column.

This returns the row indices of the top-k elements.

For details, see top_k_order().

Parameters:

colColumn

The input column.

kint

The number of top values to retrieve.

sort_orderOrder, default DESCENDING

The desired order of the top values. If ASCENDING, the indices of the smallest k values are returned. If DESCENDING, the indices of the largest k values are returned.

Returns:

Column

A column of the indices of the top k elements.