holoviews.data Package¶

data Package¶

holoviews.core.data.Columns¶

alias of holoviews.core.data.Dataset

class holoviews.core.data.DataConversion(element)[source]¶

Bases: object

DataConversion is a very simple container object which can be given an existing Dataset Element and provides methods to convert the Dataset into most other Element types.

class holoviews.core.data.Dataset(data=None, kdims=None, vdims=None, **kwargs)[source]¶

Bases: holoviews.core.element.Element

Dataset provides a general baseclass for Element types that contain structured data and supports a range of data formats.

The Dataset class supports various methods offering a consistent way of working with the stored data regardless of the storage format used. These operations include indexing, selection and various ways of aggregating or collapsing the data with a supplied function.

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims, kdims, vdims

group = param.String(default=’Dataset’)

A string describing the data wrapped by the object.

datatype = param.List(bounds=(0, None), default=[‘dataframe’, ‘dictionary’, ‘grid’, ‘spatialpandas’, ‘xarray’, ‘cuDF’, ‘dask’, ‘array’, ‘multitabular’])

A priority list of the data types to be used for storage on the .data attribute. If the input supplied to the element constructor cannot be put into the requested format, the next format listed will be used until a suitable format is found (or the data fails to be understood).

add_dimension(**kwargs)[source]¶

Adds a dimension and its values to the Dataset

Requires the dimension name or object, the desired position in the key dimensions and a key value scalar or array of values, matching the length or shape of the Dataset.

Args:

dimension: Dimension or dimension spec to add dim_pos (int): Integer index to insert dimension at dim_val (scalar or ndarray): Dimension value(s) to add vdim: Disabled, this type does not have value dimensions **kwargs: Keyword arguments passed to the cloned element

Returns:

Cloned object containing the new dimension

aggregate(**kwargs)[source]¶

Aggregates data on the supplied dimensions.

Aggregates over the supplied key dimensions with the defined function or dim_transform specified as a tuple of the transformed dimension name and dim transform.

Args:

dimensions: Dimension(s) to aggregate on

Default to all key dimensions

function: Aggregation function or transform to apply

Supports both simple functions and dimension transforms

spreadfn: Secondary reduction to compute value spread

Useful for computing a confidence interval, spread, or standard deviation.

**kwargs: Keyword arguments either passed to the aggregation function

or to create new names for the transformed variables

Returns:

Returns the aggregated Dataset

array(dimensions=None)¶

Convert dimension values to columnar array.

Args:

dimensions: List of dimensions to return

Returns:

Array of columns corresponding to each dimension

clone(data=None, shared_data=True, new_type=None, link=True, *args, **overrides)[source]¶

Clones the object, overriding data and parameters.

Args:

data: New data replacing the existing data shared_data (bool, optional): Whether to use existing data new_type (optional): Type to cast object to link (bool, optional): Whether clone should be linked

Determines whether Streams and Links attached to original object will be inherited.

*args: Additional arguments to pass to constructor **overrides: New keyword arguments to pass to constructor

Returns:

Cloned object

closest(**kwargs)[source]¶

Snaps coordinate(s) to closest coordinate in Dataset

Args:

coords: List of coordinates expressed as tuples **kwargs: Coordinates defined as keyword pairs

Returns:

List of tuples of the snapped coordinates

Raises:

NotImplementedError: Raised if snapping is not supported

classmethod collapse_data(data, function=None, kdims=None, **kwargs)¶

Deprecated method to perform collapse operations, which may now be performed through concatenation and aggregation.

columns(**kwargs)[source]¶

Convert dimension values to a dictionary.

Returns a dictionary of column arrays along each dimension of the element.

Args:

dimensions: Dimensions to return as columns

Returns:

Dictionary of arrays for each dimension

property dataset¶

The Dataset that this object was created from

property ddims¶

The list of deep dimensions

dframe(**kwargs)[source]¶

Convert dimension values to DataFrame.

Returns a pandas dataframe of columns along each dimension, either completely flat or indexed by key dimensions.

Args:

dimensions: Dimensions to return as columns multi_index: Convert key dimensions to (multi-)index

Returns:

DataFrame of columns corresponding to each dimension

dimension_values(**kwargs)[source]¶

Return the values along the requested dimension.

Args:

dimension: The dimension to return values for expanded (bool, optional): Whether to expand values

Whether to return the expanded values, behavior depends on the type of data:

• Columnar: If false returns unique values

• Geometry: If false returns scalar values per geometry

• Gridded: If false returns 1D coordinates

flat (bool, optional): Whether to flatten array

Returns:

NumPy array of values along the requested dimension

dimensions(selection='all', label=False)¶

Lists the available dimensions on the object

Provides convenient access to Dimensions on nested Dimensioned objects. Dimensions can be selected by their type, i.e. ‘key’ or ‘value’ dimensions. By default ‘all’ dimensions are returned.

Args:

selection: Type of dimensions to return

The type of dimension, i.e. one of ‘key’, ‘value’, ‘constant’ or ‘all’.

label: Whether to return the name, label or Dimension

Whether to return the Dimension objects (False), the Dimension names (True/’name’) or labels (‘label’).

Returns:

List of Dimension objects or their names or labels

get_dimension(dimension, default=None, strict=False)¶

Get a Dimension object by name or index.

Args:

dimension: Dimension to look up by name or integer index default (optional): Value returned if Dimension not found strict (bool, optional): Raise a KeyError if not found

Returns:

Dimension object for the requested dimension or default

get_dimension_index(dimension)¶

Get the index of the requested dimension.

Args:

dimension: Dimension to look up by name or by index

Returns:

Integer index of the requested dimension

get_dimension_type(**kwargs)[source]¶

Get the type of the requested dimension.

Type is determined by Dimension.type attribute or common type of the dimension values, otherwise None.

Args:

dimension: Dimension to look up by name or by index

Returns:

Declared type of values along the dimension

groupby(**kwargs)[source]¶

Groups object by one or more dimensions

Applies groupby operation over the specified dimensions returning an object of type container_type (expected to be dictionary-like) containing the groups.

Args:

dimensions: Dimension(s) to group by container_type: Type to cast group container to group_type: Type to cast each group to dynamic: Whether to return a DynamicMap **kwargs: Keyword arguments to pass to each group

Returns:

Returns object of supplied container_type containing the groups. If dynamic=True returns a DynamicMap instead.

hist(dimension=None, num_bins=20, bin_range=None, adjoin=True, **kwargs)¶

Computes and adjoins histogram along specified dimension(s).

Defaults to first value dimension if present otherwise falls back to first key dimension.

Args:

dimension: Dimension(s) to compute histogram on num_bins (int, optional): Number of bins bin_range (tuple optional): Lower and upper bounds of bins adjoin (bool, optional): Whether to adjoin histogram

Returns:

AdjointLayout of element and histogram or just the histogram

property iloc¶

Returns iloc indexer with support for columnar indexing.

Returns an iloc object providing a convenient interface to slice and index into the Dataset using row and column indices. Allow selection by integer index, slice and list of integer indices and boolean arrays.

Examples:

• Index the first row and column:

  dataset.iloc[0, 0]

• Select rows 1 and 2 with a slice:

  dataset.iloc[1:3, :]

• Select with a list of integer coordinates:

  dataset.iloc[[0, 2, 3]]

map(**kwargs)[source]¶

Map a function to all objects matching the specs

Recursively replaces elements using a map function when the specs apply, by default applies to all objects, e.g. to apply the function to all contained Curve objects:

dmap.map(fn, hv.Curve)

Args:

map_fn: Function to apply to each object specs: List of specs to match

List of types, functions or type[.group][.label] specs to select objects to return, by default applies to all objects.

clone: Whether to clone the object or transform inplace

Returns:

Returns the object after the map_fn has been applied

mapping(kdims=None, vdims=None, **kwargs)¶

Deprecated method to convert data to dictionary

matches(spec)¶

Whether the spec applies to this object.

Args:

spec: A function, spec or type to check for a match

• A ‘type[[.group].label]’ string which is compared against the type, group and label of this object

• A function which is given the object and returns a boolean.

• An object type matched using isinstance.

Returns:

bool: Whether the spec matched this object.

property ndloc¶

Returns ndloc indexer with support for gridded indexing.

Returns an ndloc object providing nd-array like indexing for gridded datasets. Follows NumPy array indexing conventions, allowing for indexing, slicing and selecting a list of indices on multi-dimensional arrays using integer indices. The order of array indices is inverted relative to the Dataset key dimensions, e.g. an Image with key dimensions ‘x’ and ‘y’ can be indexed with image.ndloc[iy, ix], where iy and ix are integer indices along the y and x dimensions.

Examples:

• Index value in 2D array:

  dataset.ndloc[3, 1]

• Slice along y-axis of 2D array:

  dataset.ndloc[2:5, :]

• Vectorized (non-orthogonal) indexing along x- and y-axes:

  dataset.ndloc[[1, 2, 3], [0, 2, 3]]

options(**kwargs)[source]¶

Applies simplified option definition returning a new object.

Applies options on an object or nested group of objects in a flat format returning a new object with the options applied. If the options are to be set directly on the object a simple format may be used, e.g.:

obj.options(cmap=’viridis’, show_title=False)

If the object is nested the options must be qualified using a type[.group][.label] specification, e.g.:

obj.options(‘Image’, cmap=’viridis’, show_title=False)

or using:

obj.options({‘Image’: dict(cmap=’viridis’, show_title=False)})

Identical to the .opts method but returns a clone of the object by default.

Args:

*args: Sets of options to apply to object

Supports a number of formats including lists of Options objects, a type[.group][.label] followed by a set of keyword options to apply and a dictionary indexed by type[.group][.label] specs.

backend (optional): Backend to apply options to

Defaults to current selected backend

clone (bool, optional): Whether to clone object

Options can be applied inplace with clone=False

**kwargs: Keywords of options

Set of options to apply to the object

Returns:

Returns the cloned object with the options applied

property pipeline¶

Chain operation that evaluates the sequence of operations that was used to create this object, starting with the Dataset stored in dataset property

range(**kwargs)[source]¶

Return the lower and upper bounds of values along dimension.

Args:

dimension: The dimension to compute the range on. data_range (bool): Compute range from data values dimension_range (bool): Include Dimension ranges

Whether to include Dimension range and soft_range in range calculation

Returns:

Tuple containing the lower and upper bound

reduce(**kwargs)[source]¶

Applies reduction along the specified dimension(s).

Allows reducing the values along one or more key dimension with the supplied function. Supports two signatures:

Reducing with a list of dimensions, e.g.:

ds.reduce([‘x’], np.mean)

Defining a reduction using keywords, e.g.:

ds.reduce(x=np.mean)

Args:

dimensions: Dimension(s) to apply reduction on

Defaults to all key dimensions

function: Reduction operation to apply, e.g. numpy.mean spreadfn: Secondary reduction to compute value spread

Useful for computing a confidence interval, spread, or standard deviation.

**reductions: Keyword argument defining reduction

Allows reduction to be defined as keyword pair of dimension and function

Returns:

The Dataset after reductions have been applied.

reindex(**kwargs)[source]¶

Reindexes Dataset dropping static or supplied kdims

Creates a new object with a reordered or reduced set of key dimensions. By default drops all non-varying key dimensions.x

Args:

kdims (optional): New list of key dimensionsx vdims (optional): New list of value dimensions

Returns:

Reindexed object

relabel(**kwargs)[source]¶

Clone object and apply new group and/or label.

Applies relabeling to children up to the supplied depth.

Args:

label (str, optional): New label to apply to returned object group (str, optional): New group to apply to returned object depth (int, optional): Depth to which relabel will be applied

If applied to container allows applying relabeling to contained objects up to the specified depth

Returns:

Returns relabelled object

sample(**kwargs)[source]¶

Samples values at supplied coordinates.

Allows sampling of element with a list of coordinates matching the key dimensions, returning a new object containing just the selected samples. Supports multiple signatures:

Sampling with a list of coordinates, e.g.:

ds.sample([(0, 0), (0.1, 0.2), …])

Sampling a range or grid of coordinates, e.g.:

1D: ds.sample(3) 2D: ds.sample((3, 3))

Sampling by keyword, e.g.:

ds.sample(x=0)

Args:

samples: List of nd-coordinates to sample bounds: Bounds of the region to sample

Defined as two-tuple for 1D sampling and four-tuple for 2D sampling.

closest: Whether to snap to closest coordinates **kwargs: Coordinates specified as keyword pairs

Keywords of dimensions and scalar coordinates

Returns:

Element containing the sampled coordinates

select(**kwargs)[source]¶

Applies selection by dimension name

Applies a selection along the dimensions of the object using keyword arguments. The selection may be narrowed to certain objects using selection_specs. For container objects the selection will be applied to all children as well.

Selections may select a specific value, slice or set of values:

• value: Scalar values will select rows along with an exact

  match, e.g.:

  ds.select(x=3)

• slice: Slices may be declared as tuples of the upper and

  lower bound, e.g.:

  ds.select(x=(0, 3))

• values: A list of values may be selected using a list or

  set, e.g.:

  ds.select(x=[0, 1, 2])

• predicate expression: A holoviews.dim expression, e.g.:

  from holoviews import dim ds.select(selection_expr=dim(‘x’) % 2 == 0)

Args:

selection_expr: holoviews.dim predicate expression

specifying selection.

selection_specs: List of specs to match on

A list of types, functions, or type[.group][.label] strings specifying which objects to apply the selection on.

**selection: Dictionary declaring selections by dimension

Selections can be scalar values, tuple ranges, lists of discrete values and boolean arrays

Returns:

Returns an Dimensioned object containing the selected data or a scalar if a single value was selected

property shape¶

Returns the shape of the data.

sort(**kwargs)[source]¶

Sorts the data by the values along the supplied dimensions.

Args:

by: Dimension(s) to sort by reverse (bool, optional): Reverse sort order

Returns:

Sorted Dataset

table(datatype=None)¶

Deprecated method to convert any Element to a Table.

property to¶

Returns the conversion interface with methods to convert Dataset

transform(**kwargs)[source]¶

Transforms the Dataset according to a dimension transform.

Transforms may be supplied as tuples consisting of the dimension(s) and the dim transform to apply or keyword arguments mapping from dimension(s) to dim transforms. If the arg or kwarg declares multiple dimensions the dim transform should return a tuple of values for each.

A transform may override an existing dimension or add a new one in which case it will be added as an additional value dimension.

Args:

args: Specify the output arguments and transforms as a

tuple of dimension specs and dim transforms

drop (bool): Whether to drop all variables not part of the transform keep_index (bool): Whether to keep indexes

Whether to apply transform on datastructure with index, e.g. pandas.Series or xarray.DataArray, (important for dask datastructures where index may be required to align datasets).

kwargs: Specify new dimensions in the form new_dim=dim_transform

Returns:

Transformed dataset with new dimensions

traverse(fn=None, specs=None, full_breadth=True)¶

Traverses object returning matching items Traverses the set of children of the object, collecting the all objects matching the defined specs. Each object can be processed with the supplied function. Args:

fn (function, optional): Function applied to matched objects specs: List of specs to match

Specs must be types, functions or type[.group][.label] specs to select objects to return, by default applies to all objects.

full_breadth: Whether to traverse all objects

Whether to traverse the full set of objects on each container or only the first.

Returns:

list: List of objects that matched

class holoviews.core.data.PipelineMeta(classname, bases, classdict)[source]¶

Bases: param.parameterized.ParameterizedMetaclass

property abstract¶

Return True if the class has an attribute __abstract set to True. Subclasses will return False unless they themselves have __abstract set to true. This mechanism allows a class to declare itself to be abstract (e.g. to avoid it being offered as an option in a GUI), without the “abstract” property being inherited by its subclasses (at least one of which is presumably not abstract).

get_param_descriptor(param_name)¶

Goes up the class hierarchy (starting from the current class) looking for a Parameter class attribute param_name. As soon as one is found as a class attribute, that Parameter is returned along with the class in which it is declared.

mro()¶

Return a type’s method resolution order.

holoviews.core.data.concat(datasets, datatype=None)[source]¶

Concatenates collection of datasets along NdMapping dimensions.

Concatenates multiple datasets wrapped in an NdMapping type along all of its dimensions. Before concatenation all datasets are cast to the same datatype, which may be explicitly defined or implicitly derived from the first datatype that is encountered. For columnar data concatenation adds the columns for the dimensions being concatenated along and then concatenates all the old and new columns. For gridded data a new axis is created for each dimension being concatenated along and then hierarchically concatenates along each dimension.

Args:

datasets: NdMapping of Datasets to concatenate datatype: Datatype to cast data to before concatenation

Returns:

Concatenated dataset

array Module¶

class holoviews.core.data.array.ArrayInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.interface.Interface

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()¶

Indicates whether the required dependencies are loaded.

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.

classmethod unpack_scalar(dataset, data)[source]¶

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

cudf Module¶

class holoviews.core.data.cudf.cuDFInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.pandas.PandasInterface

The cuDFInterface allows a Dataset objects to wrap a cuDF DataFrame object. Using cuDF allows working with columnar data on a GPU. Most operations leave the data in GPU memory, however to plot the data it has to be loaded into memory.

The cuDFInterface covers almost the complete API exposed by the PandasInterface with two notable exceptions:

1. Aggregation and groupby do not have a consistent sort order (see https://github.com/rapidsai/cudf/issues/4237)

3. Not all functions can be easily applied to a cuDF so some functions applied with aggregate and reduce will not work.

classmethod applies(obj)[source]¶

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()[source]¶

Indicates whether the required dependencies are loaded.

classmethod select_mask(dataset, selection)[source]¶

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.

classmethod unpack_scalar(dataset, data)¶

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

dask Module¶

class holoviews.core.data.dask.DaskInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.pandas.PandasInterface

The DaskInterface allows a Dataset objects to wrap a dask DataFrame object. Using dask allows loading data lazily and performing out-of-core operations on the data, making it possible to work on datasets larger than memory.

The DaskInterface covers almost the complete API exposed by the PandasInterface with two notable exceptions:

1. Sorting is not supported and any attempt at sorting will be ignored with an warning.

2. Dask does not easily support adding a new column to an existing dataframe unless it is a scalar, add_dimension will therefore error when supplied a non-scalar value.

4. Not all functions can be easily applied to a dask dataframe so some functions applied with aggregate and reduce will not work.

classmethod applies(obj)[source]¶

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod iloc(dataset, index)[source]¶

Dask does not support iloc, therefore iloc will execute the call graph and lose the laziness of the operation.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()[source]¶

Indicates whether the required dependencies are loaded.

classmethod select_mask(dataset, selection)[source]¶

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.

classmethod unpack_scalar(dataset, data)[source]¶

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

dictionary Module¶

class holoviews.core.data.dictionary.DictInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.interface.Interface

Interface for simple dictionary-based dataset format. The dictionary keys correspond to the column (i.e dimension) names and the values are collections representing the values in that column.

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()¶

Indicates whether the required dependencies are loaded.

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.

classmethod unpack_scalar(dataset, data)[source]¶

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

grid Module¶

class holoviews.core.data.grid.GridInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.dictionary.DictInterface

Interface for simple dictionary-based dataset format using a compressed representation that uses the cartesian product between key dimensions. As with DictInterface, the dictionary keys correspond to the column (i.e dimension) names and the values are NumPy arrays representing the values in that column.

To use this compressed format, the key dimensions must be orthogonal to one another with each key dimension specifying an axis of the multidimensional space occupied by the value dimension data. For instance, given an temperature recordings sampled regularly across the earth surface, a list of N unique latitudes and M unique longitudes can specify the position of NxM temperature samples.

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod canonicalize(dataset, data, data_coords=None, virtual_coords=[])[source]¶

Canonicalize takes an array of values as input and reorients and transposes it to match the canonical format expected by plotting functions. In certain cases the dimensions defined via the kdims of an Element may not match the dimensions of the underlying data. A set of data_coords may be passed in to define the dimensionality of the data, which can then be used to np.squeeze the data to remove any constant dimensions. If the data is also irregular, i.e. contains multi-dimensional coordinates, a set of virtual_coords can be supplied, required by some interfaces (e.g. xarray) to index irregular datasets with a virtual integer index. This ensures these coordinates are not simply dropped.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod coords(dataset, dim, ordered=False, expanded=False, edges=False)[source]¶

Returns the coordinates along a dimension. Ordered ensures coordinates are in ascending order and expanded creates ND-array matching the dimensionality of the dataset.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()¶

Indicates whether the required dependencies are loaded.

classmethod sample(dataset, samples=[])[source]¶

Samples the gridded data into dataset of samples.

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.

classmethod unpack_scalar(dataset, data)¶

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

image Module¶

class holoviews.core.data.image.ImageInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.grid.GridInterface

Interface for 2 or 3D arrays representing images of raw luminance values, RGB values or HSV values.

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod canonicalize(dataset, data, data_coords=None, virtual_coords=[])¶

Canonicalize takes an array of values as input and reorients and transposes it to match the canonical format expected by plotting functions. In certain cases the dimensions defined via the kdims of an Element may not match the dimensions of the underlying data. A set of data_coords may be passed in to define the dimensionality of the data, which can then be used to np.squeeze the data to remove any constant dimensions. If the data is also irregular, i.e. contains multi-dimensional coordinates, a set of virtual_coords can be supplied, required by some interfaces (e.g. xarray) to index irregular datasets with a virtual integer index. This ensures these coordinates are not simply dropped.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod coords(dataset, dim, ordered=False, expanded=False, edges=False)[source]¶

Returns the coordinates along a dimension. Ordered ensures coordinates are in ascending order and expanded creates ND-array matching the dimensionality of the dataset.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod irregular(dataset, dim)[source]¶

ImageInterface does not support irregular data

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()¶

Indicates whether the required dependencies are loaded.

classmethod sample(dataset, samples=[])[source]¶

Sample the Raster along one or both of its dimensions, returning a reduced dimensionality type, which is either a ItemTable, Curve or Scatter. If two dimension samples and a new_xaxis is provided the sample will be the value of the sampled unit indexed by the value in the new_xaxis tuple.

classmethod select(dataset, selection_mask=None, **selection)[source]¶

Slice the underlying numpy array in sheet coordinates.

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.

classmethod unpack_scalar(dataset, data)[source]¶

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

classmethod values(dataset, dim, expanded=True, flat=True, compute=True, keep_index=False)[source]¶

The set of samples available along a particular dimension.

interface Module¶

exception holoviews.core.data.interface.DataError(msg, interface=None)[source]¶

Bases: ValueError

DataError is raised when the data cannot be interpreted

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class holoviews.core.data.interface.Interface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: param.parameterized.Parameterized

classmethod applies(obj)[source]¶

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 as_dframe(dataset)[source]¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)[source]¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)[source]¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod indexed(dataset, selection)[source]¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)[source]¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()[source]¶

Indicates whether the required dependencies are loaded.

classmethod select_mask(dataset, selection)[source]¶

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.

class holoviews.core.data.interface.iloc(dataset)[source]¶

Bases: holoviews.core.data.interface.Accessor

iloc is small wrapper object that allows row, column based indexing into a Dataset using the .iloc property. It supports the usual numpy and pandas iloc indexing semantics including integer indices, slices, lists and arrays of values. For more information see the Dataset.iloc property docstring.

class holoviews.core.data.interface.ndloc(dataset)[source]¶

Bases: holoviews.core.data.interface.Accessor

ndloc is a small wrapper object that allows ndarray-like indexing for gridded Datasets using the .ndloc property. It supports the standard NumPy ndarray indexing semantics including integer indices, slices, lists and arrays of values. For more information see the Dataset.ndloc property docstring.

multipath Module¶

class holoviews.core.data.multipath.MultiInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.interface.Interface

MultiInterface allows wrapping around a list of tabular datasets including dataframes, the columnar dictionary format or 2D tabular NumPy arrays. Using the split method the list of tabular data can be split into individual datasets.

The interface makes the data appear a list of tabular datasets as a single dataset. The interface may be used to represent geometries so the behavior depends on the type of geometry being represented.

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isscalar(dataset, dim, per_geom=False)[source]¶

Tests if dimension is scalar in each subpath.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod length(dataset)[source]¶

Returns the length of the multi-tabular dataset making it appear like a single array of concatenated subpaths separated by NaN values.

classmethod loaded()¶

Indicates whether the required dependencies are loaded.

classmethod select(dataset, selection_mask=None, **selection)[source]¶

Applies selectiong on all the subpaths.

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.

classmethod select_paths(dataset, index)[source]¶

Allows selecting paths with usual NumPy slicing index.

classmethod shape(dataset)[source]¶

Returns the shape of all subpaths, making it appear like a single array of concatenated subpaths separated by NaN values.

classmethod split(dataset, start, end, datatype, **kwargs)[source]¶

Splits a multi-interface Dataset into regular Datasets using regular tabular interfaces.

classmethod values(dataset, dimension, expanded=True, flat=True, compute=True, keep_index=False)[source]¶

Returns a single concatenated array of all subpaths separated by NaN values. If expanded keyword is False an array of arrays is returned.

holoviews.core.data.multipath.ensure_ring(geom, values=None)[source]¶

Ensure the (multi-)geometry forms a ring.

Checks the start- and end-point of each geometry to ensure they form a ring, if not the start point is inserted at the end point. If a values array is provided (which must match the geometry in length) then the insertion will occur on the values instead, ensuring that they will match the ring geometry.

Args:

geom: 2-D array of geometry coordinates values: Optional array of values

Returns:

Array where values have been inserted and ring closing indexes

pandas Module¶

class holoviews.core.data.pandas.PandasInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.interface.Interface

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 as_dframe(dataset)[source]¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()¶

Indicates whether the required dependencies are loaded.

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.

classmethod unpack_scalar(dataset, data)[source]¶

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

spatialpandas Module¶

class holoviews.core.data.spatialpandas.SpatialPandasInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.multipath.MultiInterface

classmethod applies(obj)[source]¶

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isscalar(dataset, dim, per_geom=False)[source]¶

Tests if dimension is scalar in each subpath.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod length(dataset)[source]¶

Returns the length of the multi-tabular dataset making it appear like a single array of concatenated subpaths separated by NaN values.

classmethod loaded()[source]¶

Indicates whether the required dependencies are loaded.

classmethod select(dataset, selection_mask=None, **selection)[source]¶

Applies selectiong on all the subpaths.

classmethod select_mask(dataset, selection)[source]¶

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.

classmethod select_paths(dataset, index)¶

Allows selecting paths with usual NumPy slicing index.

classmethod shape(dataset)[source]¶

Returns the shape of all subpaths, making it appear like a single array of concatenated subpaths separated by NaN values.

classmethod split(dataset, start, end, datatype, **kwargs)[source]¶

Splits a multi-interface Dataset into regular Datasets using regular tabular interfaces.

classmethod values(dataset, dimension, expanded=True, flat=True, compute=True, keep_index=False)[source]¶

Returns a single concatenated array of all subpaths separated by NaN values. If expanded keyword is False an array of arrays is returned.

holoviews.core.data.spatialpandas.from_multi(eltype, data, kdims, vdims)[source]¶

Converts list formats into spatialpandas.GeoDataFrame.

Args:

eltype: Element type to convert data: The original data kdims: The declared key dimensions vdims: The declared value dimensions

Returns:

A GeoDataFrame containing in the list based format.

holoviews.core.data.spatialpandas.from_shapely(data)[source]¶

Converts shapely based data formats to spatialpandas.GeoDataFrame.

Args:

data: A list of shapely objects or dictionaries containing

shapely objects

Returns:

A GeoDataFrame containing the shapely geometry data.

holoviews.core.data.spatialpandas.geom_array_to_array(geom_array, index, expand=False, geom_type=None)[source]¶

Converts spatialpandas extension arrays to a flattened array.

Args:

geom: spatialpandas geometry index: The column index to return

Returns:

Flattened array

holoviews.core.data.spatialpandas.geom_to_array(geom, index=None, multi=False, geom_type=None)[source]¶

Converts spatialpandas geometry to an array.

Args:

geom: spatialpandas geometry index: The column index to return multi: Whether to concatenate multiple arrays or not

Returns:

Array or list of arrays.

holoviews.core.data.spatialpandas.geom_to_holes(geom)[source]¶

Extracts holes from spatialpandas Polygon geometries.

Args:

geom: spatialpandas geometry

Returns:

List of arrays representing holes

holoviews.core.data.spatialpandas.get_geom_type(gdf, col)[source]¶

Return the HoloViews geometry type string for the geometry column.

Args:

gdf: The GeoDataFrame to get the geometry from col: The geometry column

Returns:

A string representing the type of geometry

holoviews.core.data.spatialpandas.get_value_array(data, dimension, expanded, keep_index, geom_col, is_points, geom_length=<function geom_length>)[source]¶

Returns an array of values from a GeoDataFrame.

Args:

data: GeoDataFrame dimension: The dimension to get the values from expanded: Whether to expand the value array keep_index: Whether to return a Series geom_col: The column in the data that contains the geometries is_points: Whether the geometries are points geom_length: The function used to compute the length of each geometry

Returns:

An array containing the values along a dimension

holoviews.core.data.spatialpandas.to_geom_dict(eltype, data, kdims, vdims, interface=None)[source]¶

Converts data from any list format to a dictionary based format.

Args:

eltype: Element type to convert data: The original data kdims: The declared key dimensions vdims: The declared value dimensions

Returns:

A list of dictionaries containing geometry coordinates and values.

holoviews.core.data.spatialpandas.to_spatialpandas(data, xdim, ydim, columns=[], geom='point')[source]¶

Converts list of dictionary format geometries to spatialpandas line geometries.

Args:

data: List of dictionaries representing individual geometries xdim: Name of x-coordinates column ydim: Name of y-coordinates column columns: List of columns to add geom: The type of geometry

Returns:

A spatialpandas.GeoDataFrame version of the data

xarray Module¶

class holoviews.core.data.xarray.XArrayInterface(*, fig, holomap, widgets, fps, max_frames, size, dpi, filename, info, css, widget_location)[source]¶

Bases: holoviews.core.data.grid.GridInterface

classmethod applies(obj)[source]¶

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 as_dframe(dataset)¶

Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.

classmethod canonicalize(dataset, data, data_coords=None, virtual_coords=[])¶

Canonicalize takes an array of values as input and reorients and transposes it to match the canonical format expected by plotting functions. In certain cases the dimensions defined via the kdims of an Element may not match the dimensions of the underlying data. A set of data_coords may be passed in to define the dimensionality of the data, which can then be used to np.squeeze the data to remove any constant dimensions. If the data is also irregular, i.e. contains multi-dimensional coordinates, a set of virtual_coords can be supplied, required by some interfaces (e.g. xarray) to index irregular datasets with a virtual integer index. This ensures these coordinates are not simply dropped.

classmethod cast(datasets, datatype=None, cast_type=None)¶

Given a list of Dataset objects, cast them to the specified datatype (by default the format matching the current interface) with the given cast_type (if specified).

classmethod concatenate(datasets, datatype=None, new_type=None)¶

Utility function to concatenate an NdMapping of Dataset objects.

classmethod coords(dataset, dimension, ordered=False, expanded=False, edges=False)[source]¶

Returns the coordinates along a dimension. Ordered ensures coordinates are in ascending order and expanded creates ND-array matching the dimensionality of the dataset.

classmethod indexed(dataset, selection)¶

Given a Dataset object and selection to be applied returns boolean to indicate whether a scalar value has been indexed.

classmethod isunique(dataset, dim, per_geom=False)¶

Compatibility method introduced for v1.13.0 to smooth over addition of per_geom kwarg for isscalar method.

classmethod loaded()[source]¶

Indicates whether the required dependencies are loaded.

classmethod sample(dataset, samples=[])[source]¶

Samples the gridded data into dataset of samples.

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.

classmethod unpack_scalar(dataset, data)[source]¶

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