aggregation#

class cudf._lib.pylibcudf.aggregation.Aggregation#

A type of aggregation to perform.

Aggregations are passed to APIs like aggregate() to indicate what operations to perform. Using a class for aggregations provides a unified API for handling parametrizable aggregations. This class should never be instantiated directly, only via one of the factory functions.

For details, see cudf::aggregation.

Methods

kind(self)

Get the kind of the aggregation.

kind(self)#

Get the kind of the aggregation.

cudf._lib.pylibcudf.aggregation.all() Aggregation#

Create an all aggregation.

For details, see make_all_aggregation().

Returns:
Aggregation

The all aggregation.

cudf._lib.pylibcudf.aggregation.any() Aggregation#

Create an any aggregation.

For details, see make_any_aggregation().

Returns:
Aggregation

The any aggregation.

cudf._lib.pylibcudf.aggregation.argmax() Aggregation#

Create an argmax aggregation.

For details, see make_argmax_aggregation().

Returns:
Aggregation

The argmax aggregation.

cudf._lib.pylibcudf.aggregation.argmin() Aggregation#

Create an argmin aggregation.

For details, see make_argmin_aggregation().

Returns:
Aggregation

The argmin aggregation.

cudf._lib.pylibcudf.aggregation.collect_list(null_policy null_handling=null_policy.INCLUDE) Aggregation#

Create a collect_list aggregation.

For details, see make_collect_list_aggregation().

Parameters:
null_handlingnull_policy, default INCLUDE

Whether or not nulls should be included.

Returns:
Aggregation

The collect_list aggregation.

cudf._lib.pylibcudf.aggregation.collect_set(null_handling=null_policy.INCLUDE, nulls_equal=null_equality.EQUAL, nans_equal=nan_equality.ALL_EQUAL) Aggregation#

Create a collect_set aggregation.

For details, see make_collect_set_aggregation().

Parameters:
null_handlingnull_policy, default INCLUDE

Whether or not nulls should be included.

nulls_equalnull_equality, default EQUAL

Whether or not nulls should be considered equal.

nans_equalnan_equality, default ALL_EQUAL

Whether or not NaNs should be considered equal.

Returns:
Aggregation

The collect_set aggregation.

cudf._lib.pylibcudf.aggregation.correlation(correlation_type type, size_type min_periods) Aggregation#

Create a correlation aggregation.

For details, see make_correlation_aggregation().

Parameters:
typecorrelation_type

The type of correlation to compute.

min_periodsint

The minimum number of observations to consider for computing the correlation.

Returns:
Aggregation

The correlation aggregation.

cudf._lib.pylibcudf.aggregation.count(null_policy null_handling=null_policy.EXCLUDE) Aggregation#

Create a count aggregation.

For details, see make_count_aggregation().

Parameters:
null_handlingnull_policy, default EXCLUDE

Whether or not nulls should be included.

Returns:
Aggregation

The count aggregation.

cudf._lib.pylibcudf.aggregation.covariance(size_type min_periods, size_type ddof) Aggregation#

Create a covariance aggregation.

For details, see make_covariance_aggregation().

Parameters:
min_periodsint

The minimum number of observations to consider for computing the covariance.

ddofint

Delta degrees of freedom.

Returns:
Aggregation

The covariance aggregation.

cudf._lib.pylibcudf.aggregation.ewma(float center_of_mass, ewm_history history) Aggregation#

Create a EWMA aggregation.

For details, see make_ewma_aggregation().

Parameters:
center_of_massfloat

The decay in terms of the center of mass

historyewm_history

Whether or not to treat the history as infinite.

Returns:
Aggregation

The EWMA aggregation.

cudf._lib.pylibcudf.aggregation.max() Aggregation#

Create a max aggregation.

For details, see make_max_aggregation().

Returns:
Aggregation

The max aggregation.

cudf._lib.pylibcudf.aggregation.mean() Aggregation#

Create a mean aggregation.

For details, see make_mean_aggregation().

Returns:
Aggregation

The mean aggregation.

cudf._lib.pylibcudf.aggregation.median() Aggregation#

Create a median aggregation.

For details, see make_median_aggregation().

Returns:
Aggregation

The median aggregation.

cudf._lib.pylibcudf.aggregation.min() Aggregation#

Create a min aggregation.

For details, see make_min_aggregation().

Returns:
Aggregation

The min aggregation.

cudf._lib.pylibcudf.aggregation.nth_element(size_type n, null_policy null_handling=null_policy.INCLUDE) Aggregation#

Create a nth_element aggregation.

For details, see make_nth_element_aggregation().

Parameters:
null_handlingnull_policy, default INCLUDE

Whether or not nulls should be included.

Returns:
Aggregation

The nth_element aggregation.

cudf._lib.pylibcudf.aggregation.nunique(null_policy null_handling=null_policy.EXCLUDE) Aggregation#

Create a nunique aggregation.

For details, see make_nunique_aggregation().

Parameters:
null_handlingnull_policy, default EXCLUDE

Whether or not nulls should be included.

Returns:
Aggregation

The nunique aggregation.

cudf._lib.pylibcudf.aggregation.product() Aggregation#

Create a product aggregation.

For details, see make_product_aggregation().

Returns:
Aggregation

The product aggregation.

cudf._lib.pylibcudf.aggregation.quantile(list quantiles, interpolation interp=interpolation.LINEAR) Aggregation#

Create a quantile aggregation.

For details, see make_quantile_aggregation().

Parameters:
quantileslist

List of quantiles to compute, should be between 0 and 1.

interpinterpolation, default LINEAR

Interpolation technique to use when the desired quantile lies between two data points.

Returns:
Aggregation

The quantile aggregation.

cudf._lib.pylibcudf.aggregation.rank(rank_method method, order column_order=order.ASCENDING, null_policy null_handling=null_policy.EXCLUDE, null_order null_precedence=null_order.AFTER, rank_percentage percentage=rank_percentage.NONE) Aggregation#

Create a rank aggregation.

For details, see make_rank_aggregation().

Parameters:
methodrank_method

The method to use for ranking.

column_orderorder, default ASCENDING

The order in which to sort the column.

null_handlingnull_policy, default EXCLUDE

Whether or not nulls should be included.

null_precedencenull_order, default AFTER

Whether nulls should come before or after non-nulls.

percentagerank_percentage, default NONE

Whether or not ranks should be converted to percentages, and if so, the type of normalization to use.

Returns:
Aggregation

The rank aggregation.

cudf._lib.pylibcudf.aggregation.std(size_type ddof=1) Aggregation#

Create a std aggregation.

For details, see make_std_aggregation().

Parameters:
ddofint, default 1

Delta degrees of freedom. The default value is 1.

Returns:
Aggregation

The std aggregation.

cudf._lib.pylibcudf.aggregation.sum() Aggregation#

Create a sum aggregation.

For details, see make_sum_aggregation().

Returns:
Aggregation

The sum aggregation.

cudf._lib.pylibcudf.aggregation.sum_of_squares() Aggregation#

Create a sum_of_squares aggregation.

For details, see make_sum_of_squares_aggregation().

Returns:
Aggregation

The sum_of_squares aggregation.

cudf._lib.pylibcudf.aggregation.udf(unicode operation, DataType output_type) Aggregation#

Create a udf aggregation.

For details, see make_udf_aggregation().

Parameters:
operationstr

The operation to perform as a string of PTX code.

output_typeDataType

The output type of the aggregation.

Returns:
Aggregation

The udf aggregation.

cudf._lib.pylibcudf.aggregation.variance(size_type ddof=1) Aggregation#

Create a variance aggregation.

For details, see make_variance_aggregation().

Parameters:
ddofint, default 1

Delta degrees of freedom.

Returns:
Aggregation

The variance aggregation.