holoviews.operation.stats module#

class holoviews.operation.stats.bivariate_kde(*, bandwidth, bw_method, contours, cut, filled, levels, n_samples, x_range, y_range, dynamic, group, input_ranges, link_inputs, streams, name)[source]#

Bases: Operation

Computes a 2D kernel density estimate (KDE) of the first two dimensions in the input data. Kernel density estimation is a non-parametric way to estimate the probability density function of a random variable.

The KDE works by placing 2D Gaussian kernel at each sample with the supplied bandwidth. These kernels are then summed to produce the density estimate. By default a good bandwidth is determined using the bw_method but it may be overridden by an explicit value.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.operation.Operation: group, dynamic, input_ranges, link_inputs, streams

contours = Boolean(default=True, label='Contours')

Whether to compute contours from the KDE, determines whether to return an Image or Contours/Polygons.

bw_method = Selector(default='scott', label='Bw method', names={}, objects=['scott', 'silverman'])

Method of automatically determining KDE bandwidth

bandwidth = Number(allow_None=True, inclusive_bounds=(True, True), label='Bandwidth')

Allows supplying explicit bandwidth value rather than relying on scott or silverman method.

cut = Number(default=3, inclusive_bounds=(True, True), label='Cut')

Draw the estimate to cut * bw from the extreme data points.

filled = Boolean(default=False, label='Filled')

Controls whether to return filled or unfilled contours.

levels = ClassSelector(class_=(<class 'list'>, <class 'int'>), default=10, label='Levels')

A list of scalar values used to specify the contour levels.

n_samples = Integer(default=100, inclusive_bounds=(True, True), label='N samples')

Number of samples to compute the KDE over.

x_range = NumericTuple(allow_None=True, label='X range', length=2)

The x_range as a tuple of min and max x-value. Auto-ranges if set to None.

y_range = NumericTuple(allow_None=True, label='Y range', length=2)

The x_range as a tuple of min and max y-value. Auto-ranges if set to None.

class holoviews.operation.stats.univariate_kde(*, bandwidth, bin_range, bw_method, cut, dimension, filled, groupby, n_samples, dynamic, group, input_ranges, link_inputs, streams, name)[source]#

Bases: Operation

Computes a 1D kernel density estimate (KDE) along the supplied dimension. Kernel density estimation is a non-parametric way to estimate the probability density function of a random variable.

The KDE works by placing a Gaussian kernel at each sample with the supplied bandwidth. These kernels are then summed to produce the density estimate. By default a good bandwidth is determined using the bw_method but it may be overridden by an explicit value.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.operation.Operation: group, dynamic, input_ranges, link_inputs, streams

bw_method = Selector(default='scott', label='Bw method', names={}, objects=['scott', 'silverman'])

Method of automatically determining KDE bandwidth

bandwidth = Number(allow_None=True, inclusive_bounds=(True, True), label='Bandwidth')

Allows supplying explicit bandwidth value rather than relying on scott or silverman method.

cut = Number(default=3, inclusive_bounds=(True, True), label='Cut')

Draw the estimate to cut * bw from the extreme data points.

bin_range = NumericTuple(allow_None=True, label='Bin range', length=2)

Specifies the range within which to compute the KDE.

dimension = String(allow_None=True, label='Dimension')

Along which dimension of the Element to compute the KDE.

filled = Boolean(default=True, label='Filled')

Controls whether to return filled or unfilled KDE.

n_samples = Integer(default=100, inclusive_bounds=(True, True), label='N samples')

Number of samples to compute the KDE over.

groupby = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'holoviews.core.dimension.Dimension'>), label='Groupby')

Defines a dimension to group the Histogram returning an NdOverlay of Histograms.