holoviews.element.tabular module#

class holoviews.element.tabular.ItemTable(data, **params)[source]#

Bases: Element

A tabular element type to allow convenient visualization of either a standard Python dictionary or a list of tuples (i.e. input suitable for an dict constructor). Tables store heterogeneous data with different labels.

Dimension objects are also accepted as keys, allowing dimensional information (e.g. type and units) to be associated per heading.

Attributes:
cols
rows

Methods

cell_type(row, col)

Returns the cell type given a row and column index.

dimension_values(dimension[, expanded, flat])

Return the values along the requested dimension.

hist(*args, **kwargs)

Computes and adjoins histogram along specified dimension(s).

pprint_cell(row, col)

Get the formatted cell value for the given row and column indices.

reduce([dimensions, function])

Applies reduction along the specified dimension(s).

sample([samples])

Samples values at supplied coordinates.

Parameter Definitions

Parameters inherited from:

group = String(constant=True, default='ItemTable', label='Group')

A string describing the data wrapped by the object.

kdims = List(bounds=(0, 0), default=[], label='Kdims')

ItemTables hold an index Dimension for each value they contain, i.e. they are equivalent to the keys.

vdims = List(bounds=(0, None), default=[Dimension('Default')], label='Vdims')

ItemTables should have only index Dimensions.

cell_type(row, col)[source]#

Returns the cell type given a row and column index. The common basic cell types are ‘data’ and ‘heading’.

dimension_values(dimension, expanded=True, flat=True)[source]#

Return the values along the requested dimension.

Parameters:
dimensionstr

The dimension to return values for.

expandedbool, optional

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

flatbool, optional

Whether to flatten array.

Returns:
np.ndarray

Array of values along the requested dimension.

hist(*args, **kwargs)[source]#

Computes and adjoins histogram along specified dimension(s).

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

Parameters:
dimension

Dimension(s) to compute histogram on

num_binsint, optional

Number of bins

bin_rangetuple, optional

Lower and upper bounds of bins

adjoinbool, optional

Whether to adjoin histogram

Returns:
AdjointLayout of element and histogram or just the
histogram
pprint_cell(row, col)[source]#

Get the formatted cell value for the given row and column indices.

reduce(dimensions=None, function=None, **reduce_map)[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)

Parameters:
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 element after reductions have been applied.
sample(samples=None)[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)

Parameters:
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
class holoviews.element.tabular.Table(data=None, kdims=None, vdims=None, **kwargs)[source]#

Bases: SelectionIndexExpr, Dataset, Tabular

Table is a Dataset type, which gets displayed in a tabular format and is convertible to most other Element types.

Parameter Definitions

Parameters inherited from:

group = String(constant=True, default='Table', label='Group')

The group is used to describe the Table.