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.
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#
- dimension
The dimension to return values for
- expandedbool, optional
Whether to expand values 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#
NumPy 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
- 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:
holoviews.core.dimension.LabelledData
: labelholoviews.core.dimension.Dimensioned
: cdims, kdims, vdimsholoviews.core.data.Dataset
: datatypegroup = String(constant=True, default='Table', label='Group')
The group is used to describe the Table.