Groups values by keys and computes aggregations on those groups. More...

#include <groupby.hpp>

Classes
struct	groups
	The grouped data corresponding to a groupby operation on a set of values. More...

Public Member Functions
	groupby (groupby const &)=delete

	groupby (groupby &&)=delete

groupby &	operator= (groupby const &)=delete

groupby &	operator= (groupby &&)=delete

	groupby (table_view const &keys, null_policy null_handling=null_policy::EXCLUDE, sorted keys_are_sorted=sorted::NO, std::vector< order > const &column_order={}, std::vector< null_order > const &null_precedence={})
	Construct a groupby object with the specified `keys` More...

std::pair< std::unique_ptr< table >, std::vector< aggregation_result > >	aggregate (host_span< aggregation_request const > requests, rmm::cuda_stream_view stream=cudf::get_default_stream(), rmm::device_async_resource_ref mr=cudf::get_current_device_resource_ref())
	Performs grouped aggregations on the specified values. More...

std::pair< std::unique_ptr< table >, std::vector< aggregation_result > >	scan (host_span< scan_request const > requests, rmm::cuda_stream_view stream=cudf::get_default_stream(), rmm::device_async_resource_ref mr=cudf::get_current_device_resource_ref())
	Performs grouped scans on the specified values. More...

std::pair< std::unique_ptr< table >, std::unique_ptr< table > >	shift (table_view const &values, host_span< size_type const > offsets, std::vector< std::reference_wrapper< scalar const >> const &fill_values, rmm::cuda_stream_view stream=cudf::get_default_stream(), rmm::device_async_resource_ref mr=cudf::get_current_device_resource_ref())
	Performs grouped shifts for specified values. More...

groups	get_groups (cudf::table_view values={}, rmm::cuda_stream_view stream=cudf::get_default_stream(), rmm::device_async_resource_ref mr=cudf::get_current_device_resource_ref())
	Get the grouped keys and values corresponding to a groupby operation on a set of values. More...

std::pair< std::unique_ptr< table >, std::unique_ptr< table > >	replace_nulls (table_view const &values, host_span< cudf::replace_policy const > replace_policies, rmm::cuda_stream_view stream=cudf::get_default_stream(), rmm::device_async_resource_ref mr=cudf::get_current_device_resource_ref())
	Performs grouped replace nulls on `value`. More...

Detailed Description

Groups values by keys and computes aggregations on those groups.

Definition at line 95 of file groupby.hpp.

Constructor & Destructor Documentation

◆ groupby()

cudf::groupby::groupby::groupby	(	table_view const &	keys,
		null_policy	null_handling = `null_policy::EXCLUDE`,
		sorted	keys_are_sorted = `sorted::NO`,
		std::vector< order > const &	column_order = `{}`,
		std::vector< null_order > const &	null_precedence = `{}`
	)

explicit

Construct a groupby object with the specified keys

If the keys are already sorted, better performance may be achieved by passing keys_are_sorted == true and indicating the ascending/descending order of each column and null order in column_order and null_precedence, respectively.

Note: This object does not maintain the lifetime of keys. It is the user's responsibility to ensure the groupby object does not outlive the data viewed by the keys table_view.

Parameters

keys	Table whose rows act as the groupby keys
null_handling	Indicates whether rows in `keys` that contain NULL values should be included
keys_are_sorted	Indicates whether rows in `keys` are already sorted
column_order	If `keys_are_sorted == YES`, indicates whether each column is ascending/descending. If empty, assumes all columns are ascending. Ignored if `keys_are_sorted == false`.
null_precedence	If `keys_are_sorted == YES`, indicates the ordering of null values in each column. Else, ignored. If empty, assumes all columns use `null_order::AFTER`. Ignored if `keys_are_sorted == false`.

Member Function Documentation

◆ aggregate()

std::pair<std::unique_ptr<table>, std::vector<aggregation_result> > cudf::groupby::groupby::aggregate	(	host_span< aggregation_request const >	requests,
		rmm::cuda_stream_view	stream = `cudf::get_default_stream()`,
		rmm::device_async_resource_ref	mr = `cudf::get_current_device_resource_ref()`
	)

Performs grouped aggregations on the specified values.

The values to aggregate and the aggregations to perform are specified in an aggregation_request. Each request contains a column_view of values to aggregate and a set of aggregations to perform on those elements.

For each aggregation in a request, values[i] is aggregated with all other values[j] where rows i and j in keys are equivalent.

The size() of the request column must equal keys.num_rows().

For every aggregation_request an aggregation_result will be returned. The aggregation_result holds the resulting column(s) for each requested aggregation on the requests values. The order of the columns in each result is the same order as was specified in the request.

The returned table contains the group labels for each group, i.e., the unique rows from keys. Element i across all aggregation results belongs to the group at row i in the group labels table.

The order of the rows in the group labels is arbitrary. Furthermore, successive groupby::aggregate calls may return results in different orders.

Exceptions

cudf::logic_error If requests[i].values.size() != keys.num_rows().

Example:

Input:
keys:     {1 2 1 3 1}
          {1 2 1 4 1}
request:
  values: {3 1 4 9 2}
  aggregations: {{SUM}, {MIN}}
 
result:
 
keys:  {3 1 2}
       {4 1 2}
values:
  SUM: {9 9 1}
  MIN: {9 2 1}

Parameters

requests	The set of columns to aggregate and the aggregations to perform
stream	CUDA stream used for device memory operations and kernel launches.
mr	Device memory resource used to allocate the returned table and columns' device memory

Returns: Pair containing the table with each group's unique key and a vector of aggregation_results for each request in the same order as specified in requests.

◆ get_groups()

groups cudf::groupby::groupby::get_groups	(	cudf::table_view	values = `{}`,
		rmm::cuda_stream_view	stream = `cudf::get_default_stream()`,
		rmm::device_async_resource_ref	mr = `cudf::get_current_device_resource_ref()`
	)

Get the grouped keys and values corresponding to a groupby operation on a set of values.

Returns a groups object representing the grouped keys and values. If values is not provided, only a grouping of the keys is performed, and the values of the groups object will be nullptr.

Parameters

values	Table representing values on which a groupby operation is to be performed
stream	CUDA stream used for device memory operations and kernel launches.
mr	Device memory resource used to allocate the returned tables's device memory in the returned groups

Returns: A groups object representing grouped keys and values

◆ replace_nulls()

std::pair<std::unique_ptr<table>, std::unique_ptr<table> > cudf::groupby::groupby::replace_nulls	(	table_view const &	values,
		host_span< cudf::replace_policy const >	replace_policies,
		rmm::cuda_stream_view	stream = `cudf::get_default_stream()`,
		rmm::device_async_resource_ref	mr = `cudf::get_current_device_resource_ref()`
	)

Performs grouped replace nulls on value.

For each value[i] == NULL in group j, value[i] is replaced with the first non-null value in group j that precedes or follows value[i]. If a non-null value is not found in the specified direction, value[i] is left NULL.

The returned pair contains a column of the sorted keys and the result column. In result column, values of the same group are in contiguous memory. In each group, the order of values maintain their original order. The order of groups are not guaranteed.

Example:

//Inputs:
keys:    {3 3 1 3 1 3 4}
         {2 2 1 2 1 2 5}
values:  {3 4 7 @ @ @ @}
         {@ @ @ "x" "tt" @ @}
replace_policies:    {FORWARD, BACKWARD}
 
//Outputs (group orders may be different):
keys:    {3 3 3 3 1 1 4}
         {2 2 2 2 1 1 5}
result:  {3 4 4 4 7 7 @}
         {"x" "x" "x" @ "tt" "tt" @}

Parameters

[in]	values	A table whose column null values will be replaced
[in]	replace_policies	Specify the position of replacement values relative to null values, one for each column
[in]	stream	CUDA stream used for device memory operations and kernel launches.
[in]	mr	Device memory resource used to allocate device memory of the returned column

Returns: Pair that contains a table with the sorted keys and the result column

◆ scan()

std::pair<std::unique_ptr<table>, std::vector<aggregation_result> > cudf::groupby::groupby::scan	(	host_span< scan_request const >	requests,
		rmm::cuda_stream_view	stream = `cudf::get_default_stream()`,
		rmm::device_async_resource_ref	mr = `cudf::get_current_device_resource_ref()`
	)

Performs grouped scans on the specified values.

The values to aggregate and the aggregations to perform are specified in an aggregation_request. Each request contains a column_view of values to aggregate and a set of aggregations to perform on those elements.

For each aggregation in a request, values[i] is scan aggregated with all previous values[j] where rows i and j in keys are equivalent.

The size() of the request column must equal keys.num_rows().

For every aggregation_request an aggregation_result will be returned. The aggregation_result holds the resulting column(s) for each requested aggregation on the requests values. The order of the columns in each result is the same order as was specified in the request.

The returned table contains the group labels for each row, i.e., the keys given to groupby object. Element i across all aggregation results belongs to the group at row i in the group labels table.

The order of the rows in the group labels is arbitrary. Furthermore, successive groupby::scan calls may return results in different orders.

Exceptions

cudf::logic_error If requests[i].values.size() != keys.num_rows().

Example:

Input:
keys:     {1 2 1 3 1}
          {1 2 1 4 1}
request:
  values: {3 1 4 9 2}
  aggregations: {{SUM}, {MIN}}
 
result:
 
keys:  {3 1 1 1 2}
       {4 1 1 1 2}
values:
  SUM: {9 3 7 9 1}
  MIN: {9 3 3 2 1}

Parameters

requests	The set of columns to scan and the scans to perform
stream	CUDA stream used for device memory operations and kernel launches.
mr	Device memory resource used to allocate the returned table and columns' device memory

Returns: Pair containing the table with each group's key and a vector of aggregation_results for each request in the same order as specified in requests.

◆ shift()

std::pair<std::unique_ptr<table>, std::unique_ptr<table> > cudf::groupby::groupby::shift	(	table_view const &	values,
		host_span< size_type const >	offsets,
		std::vector< std::reference_wrapper< scalar const >> const &	fill_values,
		rmm::cuda_stream_view	stream = `cudf::get_default_stream()`,
		rmm::device_async_resource_ref	mr = `cudf::get_current_device_resource_ref()`
	)

Performs grouped shifts for specified values.

In jth column, for each group, ith element is determined by the i - offsets[j]th element of the group. If i - offsets[j] < 0 or >= group_size, the value is determined by fill_values[j].

Note: The first returned table stores the keys passed to the groupby object. Row i of the key table corresponds to the group labels of row i in the shifted columns. The key order in each group matches the input order. The order of each group is arbitrary. The group order in successive calls to groupby::shifts may be different.

Example:

keys:    {1 4 1 3 4 4 1}
         {1 2 1 3 2 2 1}
values:  {3 9 1 4 2 5 7}
         {"a" "c" "bb" "ee" "z" "x" "d"}
offset:  {2, -1}
fill_value: {@, @}
result (group order maybe different):
   keys:   {3 1 1 1 4 4 4}
           {3 1 1 1 2 2 2}
   values: {@ @ @ 3 @ @ 9}
           {@ "bb" "d" @ "z" "x" @}
 
-------------------------------------------------
keys:    {1 4 1 3 4 4 1}
         {1 2 1 3 2 2 1}
values:  {3 9 1 4 2 5 7}
         {"a" "c" "bb" "ee" "z" "x" "d"}
offset:  {-2, 1}
fill_value: {-1, "42"}
result (group order maybe different):
   keys:   {3 1 1 1 4 4 4}
           {3 1 1 1 2 2 2}
   values: {-1 7 -1 -1 5 -1 -1}
           {"42" "42" "a" "bb" "42" "c" "z"}

Parameters

values	Table whose columns to be shifted
offsets	The offsets by which to shift the input
fill_values	Fill values for indeterminable outputs
stream	CUDA stream used for device memory operations and kernel launches.
mr	Device memory resource used to allocate the returned table and columns' device memory

Returns: Pair containing the tables with each group's key and the columns shifted

Exceptions

cudf::logic_error if fill_value[i] dtype does not match values[i] dtype for ith column

The documentation for this class was generated from the following file:

groupby.hpp

Classes

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ groupby()

Member Function Documentation

◆ aggregate()

◆ get_groups()

◆ replace_nulls()

◆ scan()

◆ shift()