aggregation

aggregation#

2026-03-11

29 min read time

Applies to Linux

class pylibhipdf.aggregation.Aggregation#

Bases: object

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.

__init__(*args, **kwargs)#

kind(self)#: Get the kind of the aggregation.

pylibhipdf.aggregation.BitwiseOp#: alias of bitwise_op

pylibhipdf.aggregation.CorrelationType#: alias of correlation_type

pylibhipdf.aggregation.EWMHistory#: alias of ewm_history

class pylibhipdf.aggregation.Kind(*values)#

Bases: IntEnum

conjugate()#: Returns self, the complex conjugate of any int.

bit_length()#

Number of bits necessary to represent self in binary.

>>> bin(37)
'0b100101'
>>> (37).bit_length()
6

bit_count()#

Number of ones in the binary representation of the absolute value of self.

Also known as the population count.

>>> bin(13)
'0b1101'
>>> (13).bit_count()
3

to_bytes(length=1, byteorder='big', *, signed=False)#

Return an array of bytes representing an integer.

length: Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. Default is length 1.
byteorder: The byte order used to represent the integer. If byteorder is ‘big’, the most significant byte is at the beginning of the byte array. If byteorder is ‘little’, the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder’ as the byte order value. Default is to use ‘big’.
signed: Determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.

classmethod from_bytes(bytes, byteorder='big', *, signed=False)#

Return the integer represented by the given array of bytes.

bytes: Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol.
byteorder: The byte order used to represent the integer. If byteorder is ‘big’, the most significant byte is at the beginning of the byte array. If byteorder is ‘little’, the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder’ as the byte order value. Default is to use ‘big’.
signed: Indicates whether two’s complement is used to represent the integer.

as_integer_ratio()#

Return a pair of integers, whose ratio is equal to the original int.

The ratio is in lowest terms and has a positive denominator.

>>> (10).as_integer_ratio()
(10, 1)
>>> (-10).as_integer_ratio()
(-10, 1)
>>> (0).as_integer_ratio()
(0, 1)

is_integer()#: Returns True. Exists for duck type compatibility with float.is_integer.

real#: the real part of a complex number

imag#: the imaginary part of a complex number

numerator#: the numerator of a rational number in lowest terms

denominator#: the denominator of a rational number in lowest terms

SUM = 0#

PRODUCT = 2#

MIN = 3#

MAX = 4#

COUNT_VALID = 5#

COUNT_ALL = 6#

ANY = 7#

ALL = 8#

SUM_OF_SQUARES = 9#

MEAN = 10#

M2 = 11#

VARIANCE = 12#

STD = 13#

MEDIAN = 14#

QUANTILE = 15#

ARGMAX = 16#

ARGMIN = 17#

NUNIQUE = 18#

NTH_ELEMENT = 19#

ROW_NUMBER = 20#

EWMA = 21#

RANK = 22#

COLLECT_LIST = 23#

COLLECT_SET = 24#

LEAD = 25#

LAG = 26#

PTX = 27#

CUDA = 28#

HOST_UDF = 29#

MERGE_LISTS = 30#

MERGE_SETS = 31#

MERGE_M2 = 32#

COVARIANCE = 33#

CORRELATION = 34#

TDIGEST = 35#

MERGE_TDIGEST = 36#

HISTOGRAM = 37#

MERGE_HISTOGRAM = 38#

BITWISE_AGG = 39#

classmethod __getitem__(name)#: Return the member matching name.

__init__(*args, **kwds)#

pylibhipdf.aggregation.RankMethod#: alias of rank_method

pylibhipdf.aggregation.RankPercentage#: alias of rank_percentage

pylibhipdf.aggregation.UdfType#: alias of udf_type

pylibhipdf.aggregation.all() → Aggregation#

Create an all aggregation.

For details, see make_all_aggregation().

Returns#

Aggregation: The all aggregation.

pylibhipdf.aggregation.any() → Aggregation#

Create an any aggregation.

For details, see make_any_aggregation().

Returns#

Aggregation: The any aggregation.

pylibhipdf.aggregation.argmax() → Aggregation#

Create an argmax aggregation.

For details, see make_argmax_aggregation().

Returns#

Aggregation: The argmax aggregation.

pylibhipdf.aggregation.argmin() → Aggregation#

Create an argmin aggregation.

For details, see make_argmin_aggregation().

Returns#

Aggregation: The argmin aggregation.

pylibhipdf.aggregation.bitwise(bitwise_op op) → Aggregation#

Create a bitwise aggregation.

For details, see make_bitwise_aggregation().

Parameters#

opBitwiseOp: The bitwise operation to perform on the input column

Returns#

Aggregation: The bitwise aggregation.

pylibhipdf.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.

pylibhipdf.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.

pylibhipdf.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.

pylibhipdf.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.

pylibhipdf.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.

pylibhipdf.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.

pylibhipdf.aggregation.histogram() → Aggregation#

Create a histogram aggregation.

For details, see make_histogram_aggregation().

Returns#

Aggregation: The histogram aggregation.

pylibhipdf.aggregation.is_valid_aggregation(DataType source, Aggregation agg) → bool#

Return if an aggregation is supported for a given datatype.

Parameters#

source: The type of the column the aggregation is being performed on.
agg: The aggregation.

Returns#

True if the aggregation is supported.

pylibhipdf.aggregation.lag(size_type offset) → Aggregation#

Create a lag aggregation.

For details, see make_lag_aggregation().

Parameters#

offsetint: The number of rows to lag the input

Returns#

Aggregation: The lag aggregation.

pylibhipdf.aggregation.lead(size_type offset) → Aggregation#

Create a lead aggregation.

For details, see make_lead_aggregation().

Parameters#

offsetint: The number of rows to lead the input

Returns#

Aggregation: The lead aggregation.

pylibhipdf.aggregation.m2() → Aggregation#

Create a M2 aggregation.

For details, see make_m2_aggregation().

Returns#

Aggregation: The M2 aggregation.

pylibhipdf.aggregation.max() → Aggregation#

Create a max aggregation.

For details, see make_max_aggregation().

Returns#

Aggregation: The max aggregation.

pylibhipdf.aggregation.mean() → Aggregation#

Create a mean aggregation.

For details, see make_mean_aggregation().

Returns#

Aggregation: The mean aggregation.

pylibhipdf.aggregation.median() → Aggregation#

Create a median aggregation.

For details, see make_median_aggregation().

Returns#

Aggregation: The median aggregation.

pylibhipdf.aggregation.merge_histogram() → Aggregation#

Create a merge histogram aggregation.

For details, see make_merge_histogram_aggregation().

Returns#

Aggregation: The merge histogram aggregation.

pylibhipdf.aggregation.merge_lists() → Aggregation#

Create a merge lists aggregation.

For details, see make_merge_lists_aggregation().

Returns#

Aggregation: The merge lists aggregation.

pylibhipdf.aggregation.merge_m2() → Aggregation#

Create a merge M2 aggregation.

For details, see make_merge_m2_aggregation().

Returns#

Aggregation: The merge M2 aggregation.

pylibhipdf.aggregation.merge_sets(null_equality nulls_equal=null_equality.EQUAL, nan_equality nans_equal=nan_equality.ALL_EQUAL) → Aggregation#

Create a merge sets aggregation.

For details, see make_merge_sets_aggregation().

Parameters#

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 merge sets aggregation.

pylibhipdf.aggregation.merge_tdigest(int max_centroids) → Aggregation#

Create a merge TDIGEST aggregation.

For details, see make_merge_tdigest_aggregation().

Parameters#

max_centroidsint: Parameter controlling compression level and accuracy on subsequent queries on the output tdigest data.

Returns#

Aggregation: The merge TDIGEST aggregation.

pylibhipdf.aggregation.min() → Aggregation#

Create a min aggregation.

For details, see make_min_aggregation().

Returns#

Aggregation: The min aggregation.

pylibhipdf.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.

pylibhipdf.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.

pylibhipdf.aggregation.product() → Aggregation#

Create a product aggregation.

For details, see make_product_aggregation().

Returns#

Aggregation: The product aggregation.

pylibhipdf.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.

pylibhipdf.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.

pylibhipdf.aggregation.row_number() → Aggregation#

Create a row_number aggregation.

For details, see make_row_number_aggregation().

Returns#

Aggregation: The row_number aggregation.

pylibhipdf.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.

pylibhipdf.aggregation.sum() → Aggregation#

Create a sum aggregation.

For details, see make_sum_aggregation().

Returns#

Aggregation: The sum aggregation.

pylibhipdf.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.

pylibhipdf.aggregation.tdigest(int max_centroids) → Aggregation#

Create a TDIGEST aggregation.

For details, see make_tdigest_aggregation().

Parameters#

max_centroidsint: Parameter controlling compression level and accuracy on subsequent queries on the output tdigest data.

Returns#

Aggregation: The TDIGEST aggregation.

pylibhipdf.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.

pylibhipdf.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.

aggregation

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