geoviews.data package

Submodules

• geoviews.data.geom_dict module
  • GeomDictInterface
    • GeomDictInterface.applies()
    • GeomDictInterface.dtype()
    • GeomDictInterface.has_holes()
    • GeomDictInterface.holes()
    • GeomDictInterface.iloc()
    • GeomDictInterface.length()
    • GeomDictInterface.range()
    • GeomDictInterface.shape()
    • GeomDictInterface.validate()
    • GeomDictInterface.values()
• geoviews.data.geopandas module
  • GeoPandasInterface
    • GeoPandasInterface.add_dimension()
    • GeoPandasInterface.applies()
    • GeoPandasInterface.dtype()
    • GeoPandasInterface.has_holes()
    • GeoPandasInterface.holes()
    • GeoPandasInterface.iloc()
    • GeoPandasInterface.isscalar()
    • GeoPandasInterface.length()
    • GeoPandasInterface.loaded()
    • GeoPandasInterface.nonzero()
    • GeoPandasInterface.range()
    • GeoPandasInterface.redim()
    • GeoPandasInterface.reindex()
    • GeoPandasInterface.select()
    • GeoPandasInterface.select_mask()
    • GeoPandasInterface.shape()
    • GeoPandasInterface.split()
    • GeoPandasInterface.validate()
    • GeoPandasInterface.values()
  • from_multi()
  • get_geom_type()
  • to_geopandas()
• geoviews.data.iris module
  • CubeInterface
    • CubeInterface.add_dimension()
    • CubeInterface.aggregate()
    • CubeInterface.applies()
    • CubeInterface.assign()
    • CubeInterface.concat_dim()
    • CubeInterface.coords()
    • CubeInterface.dtype()
    • CubeInterface.groupby()
    • CubeInterface.irregular()
    • CubeInterface.length()
    • CubeInterface.loaded()
    • CubeInterface.range()
    • CubeInterface.redim()
    • CubeInterface.reindex()
    • CubeInterface.sample()
    • CubeInterface.select()
    • CubeInterface.select_to_constraint()
    • CubeInterface.shape()
    • CubeInterface.sort()
    • CubeInterface.validate()
    • CubeInterface.values()
  • coord_to_dimension()
  • sort_coords()

Module contents

class geoviews.data.CubeInterface(*, name)

Bases: GridInterface

The CubeInterface provides allows HoloViews to interact with iris Cube data. When passing an iris Cube to a HoloViews Element the init method will infer the dimensions of the Cube from its coordinates. Currently the interface only provides the basic methods required for HoloViews to work with an object.

Methods

add_dimension(columns, dimension, dim_pos, ...)	Adding value dimensions not currently supported by iris interface.
aggregate(columns, kdims, function, **kwargs)	Aggregation currently not implemented.
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.
concat_dim(datasets, dim, vdims)	Concatenates datasets along one dimension.
coords(dataset, dim[, ordered, expanded])	Returns the coordinates along a dimension.
dtype(dataset, dimension)	Returns the dtype for the selected dimension.
groupby(dataset, dims[, container_type, ...])	Groups the data by one or more dimensions returning a container indexed by the grouped dimensions containing slices of the cube wrapped in the group_type.
irregular(dataset, dim)	CubeInterface does not support irregular data
length(dataset)	Returns the total number of samples in the dataset.
loaded()	Indicates whether the required dependencies are loaded.
range(dataset, dimension)	Computes the range along a particular dimension.
redim(dataset, dimensions)	Rename coords on the Cube.
reindex(dataset[, kdims, vdims])	Reindexes data given new key and value dimensions.
sample(dataset[, samples])	Sampling currently not implemented.
select(dataset[, selection_mask])	Apply a selection to the data.
select_to_constraint(dataset, selection)	Transform a selection dictionary to an iris Constraint.
shape(dataset[, gridded])	Returns the shape of the data.
sort(columns[, by, reverse])	Cubes are assumed to be sorted by default.
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, dim[, expanded, flat, ...])	Returns an array of the values along the supplied dimension.

init
mask
packed

Parameter Definitions

---

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

Adding value dimensions not currently supported by iris interface.

Adding key dimensions not possible on dense interfaces.

classmethod aggregate(columns, kdims, function, **kwargs)

Aggregation currently not implemented.

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_datapython:dict

Dictionary containing new data to add to the Dataset

Returns:

data

A copy of the data with the new data dimensions added

classmethod concat_dim(datasets, dim, vdims)

Concatenates datasets along one dimension.

classmethod coords(dataset, dim, ordered=False, expanded=False)

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 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 groupby(dataset, dims, container_type=<class 'holoviews.core.spaces.HoloMap'>, group_type=None, **kwargs)

Groups the data by one or more dimensions returning a container indexed by the grouped dimensions containing slices of the cube wrapped in the group_type. This makes it very easy to break up a high-dimensional dataset into smaller viewable chunks.

classmethod irregular(dataset, dim)

CubeInterface does not support irregular data

classmethod length(dataset)

Returns the total number of samples in the dataset.

classmethod loaded()

Indicates whether the required dependencies are loaded.

classmethod range(dataset, dimension)

Computes the range along a particular dimension.

classmethod redim(dataset, dimensions)

Rename coords on the Cube.

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

Reindexes data given new key and value dimensions.

classmethod sample(dataset, samples=None)

Sampling currently not implemented.

classmethod select(dataset, selection_mask=None, **selection)

Apply a selection to the data.

classmethod select_to_constraint(dataset, selection)

Transform a selection dictionary to an iris Constraint.

classmethod shape(dataset, gridded=False)

Returns the shape of the data.

Parameters:

datasetDataset

The dataset to get the shape from

Returns:

python:tuple[python:int, python:int]

The shape of the data (rows, cols)

classmethod sort(columns, by=None, reverse=False)

Cubes are assumed to be sorted by default.

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, dim, expanded=True, flat=True, compute=True, keep_index=False)

Returns an array of the values along the supplied dimension.

class geoviews.data.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

---

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.

class geoviews.data.GeomDictInterface(*, name)

Bases: DictInterface

Methods

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.
length(dataset)	Returns the number of rows in the Dataset.
range(dataset, dim)	Computes the minimum and maximum value along a dimension.
shape(dataset)	Returns the shape of the data.
validate(dataset, validate_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, dim[, expanded, flat, ...])	Returns the values along a dimension of the dataset.

aggregate
concat
dimension_type
geo_column
geom_dims
geom_type
init
sample
select
shape_mask

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 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 length(dataset)

Returns the number of rows in the Dataset.

Parameters:

datasetDataset

The dataset to get the length from

Returns:

python:int

Length of the data

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 shape(dataset)

Returns the shape of the data.

Parameters:

datasetDataset

The dataset to get the shape from

Returns:

python:tuple[python:int, python:int]

The shape of the data (rows, cols)

classmethod validate(dataset, validate_vdims)

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, dim, expanded=True, flat=True, compute=True, keep_index=False)

Returns the values along a dimension of the dataset.

Parameters:

datasetDataset

The dataset to query

dimensionpython:str or Dimension

Dimension to return the values for

expandedbool, default python:True

When false returns unique values along the dimension

flatbool, default python:True

Whether to flatten the array

computebool, default python:True

Whether to load lazy data into memory as a NumPy array

keep_indexbool, default python:False

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

Returns:

numpy: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.