holoviews.core.data.spatialpandas module

class holoviews.core.data.spatialpandas.SpatialPandasInterface(*, name)

Bases: MultiInterface

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.
as_dframe(dataset)	Returns the data of a Dataset as a dataframe avoiding copying if it already a dataframe type.
dframe(dataset, dimensions)	Returns the data as a pandas.DataFrame containing the selected dimensions.
dtype(dataset, dimension)	Returns the dtype for the selected dimension.
has_holes(dataset)	Whether the Dataset contains geometries with holes.
holes(dataset)	Returns a list of lists of arrays containing the holes for each geometry in the Dataset.
iloc(dataset, index)	Implements integer indexing on the rows and columns of the data.
isscalar(dataset, dim[, per_geom])	Tests if dimension is scalar in each subpath.
length(dataset)	Returns the length of the multi-tabular dataset making it appear like a single array of concatenated subpaths separated by NaN values.
loaded()	Indicates whether the required dependencies are loaded.
nonzero(dataset)	Returns a boolean indicating whether the Dataset contains any data.
range(dataset, dim)	Computes the minimum and maximum value along a dimension.
redim(dataset, dimensions)	Renames dimensions in the data.
reindex(dataset[, kdims, vdims])	Reindexes data given new key and value dimensions.
select(dataset[, selection_mask])	Applies selectiong on all the subpaths.
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.
shape(dataset)	Returns the shape of all subpaths, making it appear like a single array of concatenated subpaths separated by NaN values.
split(dataset, start, end, datatype, **kwargs)	Splits a multi-interface Dataset into regular Datasets using regular tabular interfaces.
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.
values(dataset, dimension[, expanded, flat, ...])	Returns a single concatenated array of all subpaths separated by NaN values.

aggregate
array_type
base_interface
data_types
dimension_type
frame_type
geo_column
geom_dims
groupby
init
sample
series_type
sort

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 as_dframe(dataset)

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

Parameters:

datasetDataset

The dataset to convert

Returns:

DataFrame

DataFrame representation of the data

base_interface

alias of PandasInterface

classmethod dframe(dataset, dimensions)

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(dataset, dimension)

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 has_holes(dataset)

Whether the Dataset contains geometries with holes.

Parameters:

datasetDataset

The dataset to check

Returns:

bool

Whether the Dataset contains geometries with holes

Notes

Only meaningful to implement on Interfaces that support geometry data.

classmethod holes(dataset)

Returns a list of lists of arrays containing the holes for each geometry in the Dataset.

Parameters:

datasetDataset

The dataset to extract holes from

Returns:

list[list[np.ndarray]]

List of list of arrays representing geometry holes

Notes

Only meaningful to implement on Interfaces that support geometry data.

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(dataset, dim, per_geom=False)

Tests if dimension is scalar in each subpath.

classmethod length(dataset)

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 nonzero(dataset)

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 range(dataset, dim)

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(dataset, selection_mask=None, **selection)

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.

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(dataset)

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)

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

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(dataset, dimension, expanded=True, flat=True, compute=True, keep_index=False)

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)

Converts list formats into spatialpandas.GeoDataFrame.

Parameters:

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)

Converts shapely based data formats to spatialpandas.GeoDataFrame.

Parameters:

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)

Converts spatialpandas extension arrays to a flattened array.

Parameters:

geomspatialpandas 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)

Converts spatialpandas geometry to an array.

Parameters:

geomspatialpandas 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)

Extracts holes from spatialpandas Polygon geometries.

Parameters:

geomspatialpandas geometry

Returns:

List of arrays representing holes

holoviews.core.data.spatialpandas.get_geom_type(gdf, col)

Return the HoloViews geometry type string for the geometry column.

Parameters:

gdf

The GeoDataFrame to get the geometry from

col

The geometry column

Returns:

A str 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>)

Returns an array of values from a GeoDataFrame.

Parameters:

dataGeoDataFrame

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)

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

Parameters:

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=None, geom='point')

Converts list of dictionary format geometries to spatialpandas line geometries.

Parameters:

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