holoviews.plotting.plotly.stats module#

class holoviews.plotting.plotly.stats.BivariatePlot(element, plot=None, **params)[source]#

Bases: ChartPlot, ColorbarPlot

Parameter Definitions

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

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

ncontours = Integer(allow_None=True, inclusive_bounds=(True, True), label='Ncontours')

class holoviews.plotting.plotly.stats.BoxWhiskerPlot(element, plot=None, **params)[source]#

Bases: MultiDistributionPlot

Parameter Definitions

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

boxpoints = Selector(default='outliers', label='Boxpoints', names={}, objects=['all', 'outliers', 'suspectedoutliers', False])

Which points to show, valid options are ‘all’, ‘outliers’, ‘suspectedoutliers’ and False

jitter = Number(default=0, inclusive_bounds=(True, True), label='Jitter')

Sets the amount of jitter in the sample points drawn. If “0”, the sample points align along the distribution axis. If “1”, the sample points are drawn in a random jitter of width equal to the width of the box(es).

mean = Selector(default=False, label='Mean', names={}, objects=[True, False, 'sd'])

If “True”, the mean of the box(es)’ underlying distribution is drawn as a dashed line inside the box(es). If “sd” the standard deviation is also drawn.

class holoviews.plotting.plotly.stats.DistributionPlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameter Definitions

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

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

The bandwidth of the kernel for the density estimate.

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

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

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

Whether the bivariate contours should be filled.

class holoviews.plotting.plotly.stats.MultiDistributionPlot(element, plot=None, **params)[source]#

Bases: MultiDistributionMixin, ElementPlot

Parameter Definitions

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

class holoviews.plotting.plotly.stats.ViolinPlot(element, plot=None, **params)[source]#

Bases: MultiDistributionPlot

Parameter Definitions

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

box = Boolean(default=True, label='Box')

Whether to draw a boxplot inside the violin

meanline = Boolean(default=False, label='Meanline')

If “True”, the mean of the box(es)’ underlying distribution is drawn as a dashed line inside the box(es). If “sd” the standard deviation is also drawn.