geoviews.data.geopandas module

class geoviews.data.geopandas.GeoPandasInterface(*, name)

Bases: PandasAPI, 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.
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
dimension_type
geo_column
geom_dims
groupby
init
sample
shape_mask
sort

Parameter Definitions

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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_pospython:int

The position in the data to add it to

valuesnumpy:array_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 dtype(dataset, dimension)

Returns the dtype for the selected dimension.

Parameters:

datasetDataset

The dataset to query

dimensionpython:str 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:

python:list[python: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

indexpython:tuple or python: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

dimensionpython:str or Dimension

Dimension to compute the range on

Returns:

python: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

dimensionspython:dict[python:str, python: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

selectionpython:dict

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.

geoviews.data.geopandas.from_multi(eltype, data, kdims, vdims)

Converts list formats into geopandas.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 the data in the python:list based format.

geoviews.data.geopandas.get_geom_type(geom)

Returns the HoloViews geometry type.

Parameters:

geom

A shapely geometry

Returns:

python:str

A string representing type of the geometry.

geoviews.data.geopandas.to_geopandas(data, xdim, ydim, columns=None, geom='point')

Converts a list of geometry dictionaries into a GeoPandas GeoDataFrame.

Parameters:

datapython:list of python:dict

List of dictionaries representing individual geometries

xdimpython:str

Name of x-coordinates column

ydimpython:str

Name of y-coordinates column

columnspython:list of python:str, optional

List of additional column names to include in the resulting GeoDataFrame, apart from the geometry. Defaults to an empty list.

geom{‘point’, ‘Line’, ‘Polygon’}, default=’point’

Specifies the geometry type to construct. Supports: - ‘point’ : Point or MultiPoint - ‘Line’ : LineString or MultiLineString - ‘Polygon’ : Polygon or MultiPolygon

Returns:

geopandas.GeoDataFrame

A GeoDataFrame containing the parsed geometries in a ‘geometry’ column along with any specified attribute columns.