Legend Options#
Parameters |
Description |
|---|---|
legend (bool or str or None, default=None) |
Whether to show a legend, or a legend position. A cardinal position
( |
legend_cols (int or None, default=None) |
Number of columns in the legend. |
legend_opts (dict or None, default=None) |
Allows setting specific styling options for the legend. They keys
should be attributes of the |
legend#
Controls whether and where the legend appears.
Set
legend=Falseto hide the legend.Use
legend=True(orlegend='right') to show it in the default position.Specify one of the cardinal positions
'top','bottom','left', or'right'.Choose a corner placement with
'top_left','top_right','bottom_left', or'bottom_right'.
import hvplot.pandas # noqa
df = hvplot.sampledata.penguins("pandas")
opts = dict(
x='flipper_length_mm', y='body_mass_g', by='species',
frame_width=200, aspect='square'
)
plot1 = df.hvplot.scatter(title="Default: legend=right", **opts)
plot2 = df.hvplot.scatter(legend='left', title="legend=left", **opts)
plot3 = df.hvplot.scatter(legend='top', title="legend=top", **opts)
plot4 = df.hvplot.scatter(legend='bottom', title="legend=bottom", **opts)
plot5 = df.hvplot.scatter(legend='bottom_left', title="legend=bottom_left", **opts)
plot6 = df.hvplot.scatter(legend='bottom_right', title="legend=bottom_right", **opts)
plot7 = df.hvplot.scatter(legend='top_left', title="legend=top_left", **opts)
plot8 = df.hvplot.scatter(legend='top_right', title="legend=top_right", **opts)
plot1 + plot2
plot3 + plot4
plot5 + plot6
plot7 + plot8
legend_cols#
The legend_cols option allows to define the number of columns of the legend grid.
import hvplot.pandas # noqa
import numpy as np
import pandas as pd
df = pd.DataFrame({"y": np.random.random(20), "cat": list(map(chr, range(97, 117)))})
df.hvplot.scatter(by="cat", height=250, legend_cols=3, title="legend_cols=3")
legend_opts#
The legend_opts option allows to customize the legend styling. For the Bokeh plotting backend, the dictionary keys should be properties of its Legend model, such as background_fill_alpha, background_fill_color, etc.
import hvplot.pandas # noqa
df = hvplot.sampledata.penguins("pandas")
df.hvplot.scatter(
x="bill_length_mm", y="bill_depth_mm", by="species",
legend_opts={"background_fill_alpha": 0.2, "background_fill_color": "grey", "spacing": 20}
)
For the Matplotlib plotting backend, the keys should be keyword arguments of its Axes.legend method, such as framealpha, facecolor, etc.
import hvplot.pandas # noqa
hvplot.extension("matplotlib")
df = hvplot.sampledata.penguins("pandas")
df.hvplot.scatter(
x="bill_length_mm", y="bill_depth_mm", by="species",
legend_opts={"framealpha": 0.2, "facecolor": "grey", "labelspacing": 2}
)
See also
The backend_opts option to customize even more the styling of a plot.