facet.data.partition.CategoryPartitioner#

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

Partition categorical values.

Create one partition each per unique value, considering only the max_partitions most frequent values.

Bases: Partitioner [Any]
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`
`is_fitted`	`True` if this object is fitted, `False` otherwise.
`max_partitions`	The maximum number of partitions to be generated by this partitioner.
`partitions_`	The values representing the partitions.

Definitions

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

Calculate the partitioning for the given observed values.

Parameters

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

Return type

CategoryPartitioner

Returns

self

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

The count of values allocated to each partition.

property is_categorical: bool#

True

property is_fitted: bool#

True if this object is fitted, False otherwise.

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

facet.data.partition.ContinuousRangePartitioner