facet.data.partition.Partitioner#

class facet.data.partition.Partitioner(max_partitions=None)[source]#

Abstract base class of all partitioners.

Bases: FittableMixin [ndarray [Any, dtype [~T_Values]]]
Generic types: ~T_Values(bound= generic)
Metaclasses: ABCMeta
Parameters: max_partitions (Optional[int]) – the maximum number of partitions to generate; must be at least 2 (default: 20)

Method summary

Calculate the partitioning for the given observed values.

Attribute summary

`DEFAULT_MAX_PARTITIONS`
`frequencies_`	The count of values allocated to each partition.
`is_categorical`	`True` if this is partitioner handles categorical values, `False` otherwise.
`is_fitted`	See `pytools.fit.FittableMixin.is_fitted`
`max_partitions`	The maximum number of partitions to be generated by this partitioner.
`partitions_`	The values representing the partitions.

Definitions

abstract fit(values, **fit_params)[source]#

Calculate the partitioning for the given observed values.

Parameters

values (ndarray[Any, dtype[TypeVar(T_Values, bound= generic)]]) – a sequence of observed values as the empirical basis for calculating the partitions
fit_params (Any) – optional fitting parameters

Return type

Partitioner

Returns

self

property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#

The count of values allocated to each partition.

abstract property is_categorical: bool#

True if this is partitioner handles categorical values, False otherwise.

abstract property is_fitted: bool#: See pytools.fit.FittableMixin.is_fitted

property max_partitions: int#

The maximum number of partitions to be generated by this partitioner.

property partitions_: Sequence[T_Values]#

The values representing the partitions.

facet.data.partition.IntegerRangePartitioner

facet.data.partition.RangePartitioner