holoviews.core.data.ibis module

class holoviews.core.data.ibis.IbisInterface(*, name)

Bases: Interface

Methods

add_dimension(dataset, dimension, dim_pos, ...)	Returns a copy of the data with the dimension values added.
applies(obj)	Indicates whether the interface is designed specifically to handle the supplied object's type.
assign(dataset, new_data)	Adds a dictionary containing data for multiple new dimensions to a copy of the dataset.data.
compute(dataset)	Converts a lazy Dataset to a non-lazy, in-memory format.
has_rowid()
histogram(expr, bins[, density, weights])	Computes the histogram on the dimension values with support for specific bins, normalization and weighting.
iloc(dataset, index)	Implements integer indexing on the rows and columns of the data.
loaded()	Indicates whether the required dependencies are loaded.
persist(dataset)	Persists the data backing the Dataset in memory.
redim(dataset, dimensions)	Renames dimensions in the data.
reindex(dataset[, kdims, vdims])	Reindexes data given new key and value dimensions.
select_mask(dataset, selection)	Given a Dataset object and a dictionary with dimension keys and selection keys (i.e. tuple ranges, slices, sets, lists, or literals) return a boolean mask over the rows in the Dataset object that have been selected.
unpack_scalar(dataset, data)	Given a dataset object and data in the appropriate format for the interface, return a simple scalar.
validate(dataset[, vdims])	Validation runs after the Dataset has been constructed and should validate that the Dataset is correctly formed and contains all declared dimensions.

aggregate
dframe
dimension_type
dtype
groupby
init
is_rowid_zero_indexed
isscalar
length
mask
nonzero
range
sample
select
shape
sort
values

Parameter Definitions

---

classmethod add_dimension(dataset, dimension, dim_pos, values, vdim)

Returns a copy of the data with the dimension values added.

Parameters:

datasetDataset

The Dataset to add the dimension to

dimensionDimension

The dimension to add

dim_posint

The position in the data to add it to

valuesarray_like

The array of values to add

vdimbool

Whether the data is a value dimension

Returns:

data

A copy of the data with the new dimension

classmethod applies(obj)

Indicates whether the interface is designed specifically to handle the supplied object’s type. By default simply checks if the object is one of the types declared on the class, however if the type is expensive to import at load time the method may be overridden.

classmethod assign(dataset, new_data)

Adds a dictionary containing data for multiple new dimensions to a copy of the dataset.data.

Parameters:

datasetDataset

The Dataset to add the dimension to

new_datadict

Dictionary containing new data to add to the Dataset

Returns:

data

A copy of the data with the new data dimensions added

classmethod compute(dataset)

Converts a lazy Dataset to a non-lazy, in-memory format.

Parameters:

datasetDataset

The dataset to compute

Returns:

Dataset

Dataset with non-lazy data

Notes

This is a no-op if the data is already non-lazy.

classmethod dframe(*args, **kwargs)

Returns the data as a pandas.DataFrame containing the selected dimensions.

Parameters:

datasetDataset

The dataset to convert

dimensionslist[str]

List of dimensions to include

Returns:

DataFrame

A pandas DataFrame containing the selected dimensions

classmethod dtype(*args, **kwargs)

Returns the dtype for the selected dimension.

Parameters:

datasetDataset

The dataset to query

dimensionstr or Dimension

Dimension to return the dtype for

Returns:

numpy.dtype

The dtype of the selected dimension

classmethod histogram(expr, bins, density=True, weights=None)

Computes the histogram on the dimension values with support for specific bins, normalization and weighting.

Parameters:

arrayarray_like

In memory representation of the dimension values

binsnp.ndarray | int

An array of bins or the number of bins

densitybool, default True

Whether to normalize the histogram

weightsarray_like, optional

In memory representation of the weighting

Returns:

tuple[np.ndarray, np.ndarray]

Tuple of (histogram values, bin edges)

Notes

Usually the dimension_values and weights are assumed to be arrays but each interface should support data stored in whatever format it uses to store dimensions internally.

classmethod iloc(dataset, index)

Implements integer indexing on the rows and columns of the data.

Parameters:

datasetDataset

The dataset to apply the indexing operation on

indextuple or int

Index specification (row_index, col_index) or row_index

Returns:

data

Indexed data

Notes

Only implement for tabular interfaces.

classmethod isscalar(*args, **kwargs)

Whether the selected dimension is a scalar value.

Parameters:

datasetDataset

The dataset to query

dimstr or Dimension

Dimension to check for scalar value

Returns:

bool

Whether the dimension is scalar

classmethod length(*args, **kwargs)

Returns the number of rows in the Dataset.

Parameters:

datasetDataset

The dataset to get the length from

Returns:

int

Length of the data

classmethod loaded()

Indicates whether the required dependencies are loaded.

classmethod nonzero(*args, **kwargs)

Returns a boolean indicating whether the Dataset contains any data.

Parameters:

datasetDataset

The dataset to check

Returns:

bool

Whether the dataset is not empty

classmethod persist(dataset)

Persists the data backing the Dataset in memory.

Parameters:

datasetDataset

The dataset to persist

Returns:

Dataset

Dataset with the data persisted to memory

Notes

This is a no-op if the data is already in memory.

classmethod range(*args, **kwargs)

Computes the minimum and maximum value along a dimension.

Parameters:

datasetDataset

The dataset to query

dimensionstr or Dimension

Dimension to compute the range on

Returns:

tuple[Any, Any]

Tuple of (min, max) values

Notes

In the past categorical and string columns were handled by sorting the values and taking the first and last value. This behavior is deprecated and will be removed in 2.0. In future the range for these columns will be returned as (None, None).

classmethod redim(dataset, dimensions)

Renames dimensions in the data.

Parameters:

datasetDataset

The dataset to transform

dimensionsdict[str, str]

Dictionary mapping from old to new dimension names

Returns:

data

Data after the dimension names have been transformed

Notes

Only meaningful for data formats that store dimension names.

classmethod reindex(dataset, kdims=None, vdims=None)

Reindexes data given new key and value dimensions.

classmethod select_mask(dataset, selection)

Given a Dataset object and a dictionary with dimension keys and selection keys (i.e. tuple ranges, slices, sets, lists, or literals) return a boolean mask over the rows in the Dataset object that have been selected.

Parameters:

datasetDataset

The dataset to select from

selectiondict

Dictionary containing selections for each column

Returns:

ndarray of bool

Boolean array representing the selection mask

classmethod shape(*args, **kwargs)

Returns the shape of the data.

Parameters:

datasetDataset

The dataset to get the shape from

Returns:

tuple[int, int]

The shape of the data (rows, cols)

classmethod unpack_scalar(dataset, data)

Given a dataset object and data in the appropriate format for the interface, return a simple scalar.

classmethod validate(dataset, vdims=True)

Validation runs after the Dataset has been constructed and should validate that the Dataset is correctly formed and contains all declared dimensions.

classmethod values(*args, **kwargs)

Returns the values along a dimension of the dataset.

Parameters:

datasetDataset

The dataset to query

dimensionstr or Dimension

Dimension to return the values for

expandedbool, default True

When false returns unique values along the dimension

flatbool, default True

Whether to flatten the array

computebool, default True

Whether to load lazy data into memory as a NumPy array

keep_indexbool, default False

Whether to return the data with an index (if present)

Returns:

array_like

Dimension values in the requested format

Notes

The expanded keyword has different behavior for gridded interfaces where it determines whether 1D coordinates are expanded into a multi-dimensional array.