hvPlot.kde#

hvPlot.kde(y=None, by=None, **kwds)[source]#

The Kernel density estimate (kde) plot shows the distribution and spread of the data.

The kde and density plots are the same.

Reference: https://hvplot.holoviz.org/reference/tabular/kde.html

Parameters:

ystring or sequence: Field(s) in the data to compute distribution on. If not specified all numerical fields are used.
bystring or sequence: Field(s) in the data to group by.
bandwidthfloat, optional: The bandwidth of the kernel for the density estimate. Default is None.
cut: Draw the estimate to cut * bw from the extreme data points.
n_samplesint, optional: Number of samples to compute the KDE over. Default is 100.
filled: Whether the bivariate contours should be filled. Default is True.
kwdsoptional: Additional keywords arguments are documented in hvplot.help(‘kde’). See Plotting Options for more information.

Returns:

holoviews.element.Distribution / Panel object

You can print the object to study its composition and run:

import holoviews as hv
hv.help(the_holoviews_object)

to learn more about its parameters and options.

References

Examples

Lets display a ‘kde’ plot from wide data

import hvplot.pandas
import numpy as np
import pandas as pd

df = pd.DataFrame({
    'x': [1, 2, 2.5, 3, 3.5, 4, 5],
    'y': [4, 4, 4.5, 5, 5.5, 6, 6],
})
df.hvplot.kde(color=["orange", "green"])

Lets display a ‘kde’ plot from long data using the ‘by’ attribute

import hvplot.pandas # noqa
import pandas as pd
import numpy as np
df = pd.DataFrame({
    'category': list('xxxxxxxyyyyyyy'),
    'value': [1, 2, 2.5, 3, 3.5, 4, 5, 4, 4, 4.5, 5, 5.5, 6, 6],
})
df.hvplot.kde(by='category', filled=False)

Backend-specific styling options#

Bokeh

alpha, color, fill_alpha, fill_color, hover_alpha, hover_color, hover_fill_alpha, hover_fill_color, hover_line_alpha, hover_line_cap, hover_line_color, hover_line_dash, hover_line_dash_offset, hover_line_join, hover_line_width, line_alpha, line_cap, line_color, line_dash, line_dash_offset, line_join, line_width, muted, muted_alpha, muted_color, muted_fill_alpha, muted_fill_color, muted_line_alpha, muted_line_cap, muted_line_color, muted_line_dash, muted_line_dash_offset, muted_line_join, muted_line_width, nonselection_alpha, nonselection_color, nonselection_fill_alpha, nonselection_fill_color, nonselection_line_alpha, nonselection_line_cap, nonselection_line_color, nonselection_line_dash, nonselection_line_dash_offset, nonselection_line_join, nonselection_line_width, selection_alpha, selection_color, selection_fill_alpha, selection_fill_color, selection_line_alpha, selection_line_cap, selection_line_color, selection_line_dash, selection_line_dash_offset, selection_line_join, selection_line_width, visible

Matplotlib

alpha, c, capstyle, color, ec, ecolor, edgecolor, facecolor, fc, fill, hatch, interpolate, joinstyle, linestyle, linewidth, lw, step

Examples#

TBD

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