holoviews.core.data.spatialpandas module

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

Bases: MultiInterface

Methods

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.
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.
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.
values(dataset, dimension[, expanded, flat, ...])	Returns a single concatenated array of all subpaths separated by NaN values.

add_dimension
aggregate
array_type
base_interface
data_types
dframe
dimension_type
dtype
frame_type
geo_column
geom_dims
groupby
has_holes
holes
iloc
init
nonzero
range
redim
reindex
sample
series_type
sort
validate

Parameter Definitions

---

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.

base_interface

alias of PandasInterface

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 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.

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 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