Styling Visual Attributes¶

Line Properties¶

line_color

color to use to stroke lines with

line_width

line stroke width in units of pixels

line_alpha

floating point between 0 (transparent) and 1 (opaque)

line_join

how path segments should be joined together

• 'miter'
• 'round'
• 'bevel'

line_cap

how path segments should be terminated

• 'butt'
• 'round'
• 'square'

line_dash

a line style to use

• 'solid'
• 'dashed'
• 'dotted'
• 'dotdash'
• 'dashdot'
• an array of integer pixel distances that describe the on-off pattern of dashing to use
• a string of spaced integers matching the regular expression ‘^(\d+(\s+\d+)*)?$’ that describe the on-off pattern of dashing to use

line_dash_offset

the distance in pixels into the line_dash that the pattern should start from

Fill Properties¶

fill_color

color to use to fill paths with

fill_alpha

floating point between 0 (transparent) and 1 (opaque)

Text Properties¶

text_font

font name, e.g., 'times', 'helvetica'

text_font_size

font size in px, em, or pt, e.g., '12pt', '1.5em'

text_font_style

font style to use

• 'normal' normal text
• 'italic' italic text
• 'bold' bold text

text_color

color to use to render text with

text_alpha

floating point between 0 (transparent) and 1 (opaque)

text_align

horizontal anchor point for text: 'left', 'right', 'center'

text_baseline

vertical anchor point for text

• 'top'
• 'middle'
• 'bottom'
• 'alphabetic'
• 'hanging'

Visible property¶

Glyph renderers, axes, grids, and annotations all have a visible property that can be used to turn them on and off.

from bokeh.io import output_file, show
from bokeh.plotting import figure

# We set-up a standard figure with two lines
p = figure(plot_width=500, plot_height=200, tools='')
visible_line = p.line([1, 2, 3], [1, 2, 1], line_color="blue")
invisible_line = p.line([1, 2, 3], [2, 1, 2], line_color="pink")

# We hide the xaxis, the xgrid lines, and the pink line
invisible_line.visible = False
p.xaxis.visible = False
p.xgrid.visible = False

output_file("styling_visible_property.html")

show(p)

This can be particularly useful in interactive examples with bokeh server or CustomJS.

from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.layouts import layout
from bokeh.models import Toggle, BoxAnnotation, CustomJS

# We set-up the same standard figure with two lines and now a box over top
p = figure(plot_width=600, plot_height=200, tools='')
visible_line = p.line([1, 2, 3], [1, 2, 1], line_color="blue")
invisible_line = p.line([1, 2, 3], [2, 1, 2], line_color="pink")

box = BoxAnnotation(left=1.5, right=2.5, fill_color='green', fill_alpha=0.1)
p.add_layout(box)

# We write coffeescript to link toggle with visible property of box and line
code = '''\
object.visible = toggle.active
'''

callback1 = CustomJS.from_coffeescript(code=code, args={})
toggle1 = Toggle(label="Green Box", button_type="success", callback=callback1)
callback1.args = {'toggle': toggle1, 'object': box}

callback2 = CustomJS.from_coffeescript(code=code, args={})
toggle2 = Toggle(label="Pink Line", button_type="success", callback=callback2)
callback2.args = {'toggle': toggle2, 'object': invisible_line}

output_file("styling_visible_annotation_with_interaction.html")

show(layout([p], [toggle1, toggle2]))

Specifying Colors¶

Colors properties are used in many places in Bokeh, to specify the colors to use for lines, fills or text. Color values can be provided in any of the following ways:

• any of the 147 named CSS colors, e.g 'green', 'indigo'
• an RGB(A) hex value, e.g., '#FF0000', '#44444444'
• a 3-tuple of integers (r,g,b) between 0 and 255
• a 4-tuple of (r,g,b,a) where r, g, b are integers between 0 and 255 and a is a floating point value between 0 and 1

Color alpha can be specified in multiple ways for the visual properties. This can be by specifying the alpha directly with line|fill_alpha, or by providing the alpha through the RGBA 4-tuple for the line|fill_color.

Additionally, there is also the freedom to use a combination of the two, or no alpha at all. The following figure demonstrates each possible combination of the inputs for line and fill alphas:

Styling Arrow Annotations¶

There are several ArrowHead subtypes that can be applied to Arrow annotations. Setting the start or end property to None will cause no arrow head to be applied at the specified arrow end. Double-sided arrows can be created by setting both start and end styles. Setting visible to false on an arrow will also make the corresponding arrow head invisible.

Screen Units and Data-space Units¶

Screen units use raw numbers of pixels to specify height or width, while data-space units are relative to the data and the axes of the plot. For example, in a 400 pixel by 400 pixel graph with x and y axes ranging from 0 through 10, a glyph one fifth as wide and tall as the graph would be 80 screen units or 2 data-space units.

Dimensions¶

The dimensions (width and height) of a Plot are controlled by plot_width and plot_height attributes. These values are in screen units, and they control the size of the entire canvas area, including any axes or titles (but not the toolbar). If you are using the bokeh.plotting interface, then these values can be passed to figure() as a convenience:

from bokeh.plotting import figure, output_file, show

output_file("dimensions.html")

# create a new plot with specific dimensions
p = figure(plot_width=700)
p.plot_height = 300

p.circle([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=10)

show(p)

Responsive Dimensions¶

In addition, you can use the responsive attribute. The responsive attribute causes the plot to fill the container it’s sitting in, and to respond to changes in browser size. Responsive web elements are common-place in web development and the responsive flag may be useful if you are trying to present your plot on a website where you want it to conform to a number of browsers. If you set the responsive flag, the plot_width and plot_height will immediately change when a plot is rendered to fill the container. However, those parameters will be used to calculate the initial aspect ratio for your plot, so you may want to keep them. Plots will only resize down to a minimum of 100px (height or width) to prevent problems in displaying your plot.

For more precise control over how the plot scales to fill its container, see the documentation for bokeh.models.layouts, in particular the sizing_mode property of LayoutDOM.

Title¶

The styling of the plot title is controlled by a set of Text Properties on a Title annotation available as the .title property of the Plot. For instance, to set the color of the title text, use plot.title.text_color:

from bokeh.plotting import figure, output_file, show

output_file("title.html")

p = figure(plot_width=400, plot_height=400, title="Some Title")
p.title.text_color = "olive"
p.title.text_font = "times"
p.title.text_font_style = "italic"

p.circle([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=10)

show(p)

Background¶

The background fill style is controlled by the background_fill_color and background_fill_alpha properties of the Plot object:

from bokeh.plotting import figure, output_file, show

output_file("background.html")

p = figure(plot_width=400, plot_height=400)
p.background_fill_color = "beige"
p.background_fill_alpha = 0.5

p.circle([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=10)

show(p)

Border¶

The border fill style is controlled by the border_fill_color and border_fill_alpha properties of the Plot object. You can also set the minimum border on each side (in screen units) with the properties

min_border_left

min_border_right

min_border_top

min_border_bottom

Additionally, setting min_border will apply a minimum border setting to all sides as a convenience. The min_border default value is 40px.

from bokeh.plotting import figure, output_file, show

output_file("border.html")

p = figure(plot_width=400, plot_height=400)
p.border_fill_color = "whitesmoke"
p.min_border_left = 80

p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

show(p)

Outline¶

The styling of the outline of the plotting area is controlled by a set of Line Properties on the Plot, that are prefixed with outline_. For instance, to set the color of the outline, use outline_line_color:

from bokeh.plotting import figure, output_file, show

output_file("outline.html")

p = figure(plot_width=400, plot_height=400)
p.outline_line_width = 7
p.outline_line_alpha = 0.3
p.outline_line_color = "navy"

p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

show(p)

Selected and Unselected Glyphs¶

The styling of selected and non-selected glyphs can be customized by setting the selection_glyph and/or nonselection_glyph attributes of the GlyphRenderer either manually or by passing them to add_glyph().

The plot below demonstrates how to set these attributes using the bokeh.plotting interface. Click or tap circles on the plot to see the effect on the selected and nonselected glyphs. To clear the selection and restore the original state, click anywhere in the plot outside of a circle.

from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.models import Circle

output_file("styling_selections.html")

plot = figure(plot_width=400, plot_height=400, tools="tap", title="Select a circle")
renderer = plot.circle([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=50)

selected_circle = Circle(fill_alpha=1, fill_color="firebrick", line_color=None)
nonselected_circle = Circle(fill_alpha=0.2, fill_color="blue", line_color="firebrick")

renderer.selection_glyph = selected_circle
renderer.nonselection_glyph = nonselected_circle

show(plot)

If you just need to set the color or alpha parameters of the selected or nonselected glyphs, this can be accomplished even more simply by providing color and alpha arguments to the glyph function, prefixed by "selection_" or "nonselection_". The plot below demonstrates this technique:

from bokeh.io import output_file, show
from bokeh.plotting import figure

output_file("styling_selections.html")

plot = figure(plot_width=400, plot_height=400, tools="tap", title="Select a circle")
renderer = plot.circle([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=50,

                       # set visual properties for selected glyphs
                       selection_color="firebrick",

                       # set visual properties for non-selected glyphs
                       nonselection_fill_alpha=0.2,
                       nonselection_fill_color="blue",
                       nonselection_line_color="firebrick",
                       nonselection_line_alpha=1.0)

show(plot)

The same could also be achieved with the models interface as follows:

p = Plot()
source = ColumnDataSource(dict(x=[1, 2, 3], y=[1, 2, 3]))

initial_circle = Circle(x='x', y='y', fill_color='blue', size=50)
selected_circle = Circle(fill_alpha=1, fill_color="firebrick", line_color=None)
nonselected_circle = Circle(fill_alpha=0.2, fill_color="blue", line_color="firebrick")

p.add_glyph(source,
            initial_circle,
            selection_glyph=selected_circle,
            nonselection_glyph=nonselected_circle)

Hover Inspections¶

Setting the highlight policy for glyphs that are hovered over is completely analogous to setting the selection_glyph or nonselection_glyph, or by passing color or alpha parameters prefixed with "hover_". The example below demonstrates the latter method:

from bokeh.plotting import figure, output_file, show
from bokeh.models import HoverTool
from bokeh.sampledata.glucose import data

output_file("styling_hover.html")

subset = data.ix['2010-10-06']

x, y = subset.index.to_series(), subset['glucose']

# Basic plot setup
plot = figure(plot_width=600, plot_height=300, x_axis_type="datetime", tools="",
              toolbar_location=None, title='Hover over points')

plot.line(x, y, line_dash="4 4", line_width=1, color='gray')

cr = plot.circle(x, y, size=20,
                fill_color="grey", hover_fill_color="firebrick",
                fill_alpha=0.05, hover_alpha=0.3,
                line_color=None, hover_line_color="white")

plot.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))

show(plot)

Labels¶

The text of an overall label for an axis is controlled by the axis_label property. Additionally, there are Text Properties prefixed with axis_label_ that control the visual appearance of the label. For instance to set the color of the label, set axis_label_text_color. Finally, to change the distance between the axis label and the major tick labels, set the axis_label_standoff property:

from bokeh.plotting import figure, output_file, show

output_file("bounds.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis.axis_label = "Lot Number"
p.xaxis.axis_label_text_color = "#aa6666"
p.xaxis.axis_label_standoff = 30

p.yaxis.axis_label = "Bin Count"
p.yaxis.axis_label_text_font_style = "italic"

show(p)

Bounds¶

Sometimes it is useful to limit the bounds where axes are drawn. This can be accomplished by setting the bounds property of an axis object to a 2-tuple of (start, end):

from bokeh.plotting import figure, output_file, show

output_file("bounds.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis.bounds = (2, 4)

show(p)

Tick Locations¶

Bokeh has several “ticker” models that can choose nice locations for ticks. These are configured on the .ticker property of an axis. With the bokeh.plotting interface, choosing an appropriate ticker type (categorical, datetime, mercator, linear or log scale) normally happens automatically. However, there are cases when more explicit control is useful.

from bokeh.plotting import figure
from bokeh.models.tickers import FixedTicker

p = figure()

# no additional tick locations will be displayed on the x-axis
p.xaxis.ticker = FixedTicker(ticks=[10, 20, 37.4])

from bokeh.plotting import figure, output_file, show

output_file("fixed_ticks.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis.ticker = [2, 3.5, 4]

show(p)

Tick Lines¶

The visual appearance of the major and minor ticks is controlled by a collection of Line Properties, prefixed with major_tick_ and minor_tick_, respectively. For instance, to set the color of the major ticks, use major_tick_line_color. To hide either set of ticks, set the color to None. Additionally, you can control how far in and out of the plotting area the ticks extend, with the properties major_tick_in/major_tick_out and minor_tick_in/minor_tick_out. These values are in screen units, and negative values are acceptable.

from bokeh.plotting import figure, output_file, show

output_file("axes.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis.major_tick_line_color = "firebrick"
p.xaxis.major_tick_line_width = 3
p.xaxis.minor_tick_line_color = "orange"

p.yaxis.minor_tick_line_color = None

p.axis.major_tick_out = 10
p.axis.minor_tick_in = -3
p.axis.minor_tick_out = 8

show(p)

Tick Label Formats¶

The text styling of axis labels is controlled by a TickFormatter object configured on the axis’ formatter property. Bokeh uses a number of ticker formatters by default in different situations:

• BasicTickFormatter — Default formatter for linear axes.
• CategoricalTickFormatter — Default formatter for categorical axes.
• DatetimeTickFormatter — Default formatter for datetime axes.
• LogTickFormatter — Default formatter for log axes.

These default tick formatters do not expose many configurable properties. To control tick formatting at a finer grained level, use one of the NumeralTickFormatter or PrintfTickFormatter described below.

from bokeh.plotting import figure, output_file, show
from bokeh.models import NumeralTickFormatter

output_file("gridlines.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p.yaxis[0].formatter = NumeralTickFormatter(format="$0.00")

show(p)

from bokeh.plotting import figure, output_file, show
from bokeh.models import PrintfTickFormatter

output_file("gridlines.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis[0].formatter = PrintfTickFormatter(format="%4.1e")
p.yaxis[0].formatter = PrintfTickFormatter(format="%5.3f mu")

show(p)

from bokeh.models import FuncTickFormatter
from bokeh.plotting import figure, show, output_file

output_file("formatter.html")

p = figure(plot_width=500, plot_height=500)
p.circle([0, 2, 4, 6, 8, 10], [6, 2, 4, 10, 8, 0], size=30)

p.yaxis.formatter = FuncTickFormatter(code="""
    return Math.floor(tick) + " + " + (tick % 1).toFixed(2)
""")

show(p)

def ticker():
    return "{:.0f} + {:.2f}".format(tick, tick % 1)

p.yaxis.formatter = FuncTickFormatter.from_py_func(ticker)

Tick Label Orientation¶

The orientation of major tick labels can be controlled with the major_label_orientation property. This property accepts the values "horizontal" or "vertical" or a floating point number that gives the angle (in radians) to rotate from the horizontal:

from math import pi
from bokeh.plotting import figure, output_file, show

output_file("gridlines.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis.major_label_orientation = pi/4
p.yaxis.major_label_orientation = "vertical"

show(p)

There are more properties that Bokeh axes support configuring. For a complete listing of all the various attributes that can be set on different types of Bokeh axes, consult the bokeh.models.axes section of the Reference Guide.

Lines¶

The visual appearance of grid lines is controlled by a collection of Line Properties, prefixed with grid_. For instance, to set the color of grid lines, use grid_line_color. To hide grid lines, set their line color to None.

from bokeh.plotting import figure, output_file, show

output_file("gridlines.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

# change just some things about the x-grid
p.xgrid.grid_line_color = None

# change just some things about the y-grid
p.ygrid.grid_line_alpha = 0.5
p.ygrid.grid_line_dash = [6, 4]

show(p)

Minor Lines¶

The visual appearance of minor grid lines is controlled by a collection of Line Properties, prefixed with minor_grid_. For instance, to set the color of grid lines, use minor_grid_line_color. By default, minor grid lines are hidden (i.e., their line color is set to None).

from bokeh.plotting import figure, output_file, show

output_file("minorgridlines.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

# change just some things about the y-grid
p.ygrid.minor_grid_line_color = 'navy'
p.ygrid.minor_grid_line_alpha = 0.1

show(p)

Bands¶

It is also possible to display filled, shaded bands between adjacent grid lines. The visual appearance of these bands is controlled by a collection of Fill Properties, prefixed with band_. For instance, to set the color of grid bands, use band_fill_color. To hide grid bands, set their fill color to None (this is the default).

from bokeh.plotting import figure, output_file, show

output_file("gridbands.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

# change just some things about the x-grid
p.xgrid.grid_line_color = None

# change just some things about the y-grid
p.ygrid.band_fill_alpha = 0.1
p.ygrid.band_fill_color = "navy"

show(p)

Bounds¶

Grids also support setting explicit bounds between which they are drawn. They are set in an identical fashion to axes bounds, with a 2-tuple of (start, end):

from bokeh.plotting import figure, output_file, show

output_file("bounds.html")

p = figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.grid.bounds = (2, 4)

show(p)

There are other properties that Bokeh grids support configuring. For a complete listing of all the various attributes that can be set on Bokeh plot grids, consult the bokeh.models.grids section of the Reference Guide.

Location¶

The location of the legend labels is controlled by the location property.

import numpy as np
from bokeh.plotting import figure, show, output_file

x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)

output_file("legend_labels.html")

p = figure()

p.circle(x, y, legend="sin(x)")
p.line(x, y, legend="sin(x)")

p.line(x, 2*y, legend="2*sin(x)",
       line_dash=[4, 4], line_color="orange", line_width=2)

p.square(x, 3*y, legend="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend="3*sin(x)", line_color="green")

p.legend.location = "bottom_left"

show(p)

import numpy as np
from bokeh.models import Legend
from bokeh.plotting import figure, show, output_file

x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)

output_file("legend_labels.html")

p = figure(toolbar_location="above")

r0 = p.circle(x, y)
r1 = p.line(x, y)

r2 = p.line(x, 2*y, line_dash=[4, 4], line_color="orange", line_width=2)

r3 = p.square(x, 3*y, fill_color=None, line_color="green")
r4 = p.line(x, 3*y, line_color="green")

legend = Legend(items=[
    ("sin(x)"   , [r0, r1]),
    ("2*sin(x)" , [r2]),
    ("3*sin(x)" , [r3, r4]),
], location=(0, -30))

p.add_layout(legend, 'right')

show(p)

Orientation¶

The orientation of the legend is controlled by the orientation property. Valid values for this property are:

"vertical"

"horizontal"

The default orientation is "vertical".

import numpy as np
from bokeh.plotting import figure, show, output_file

x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)

output_file("legend_labels.html")

p = figure()

p.circle(x, y, legend="sin(x)")
p.line(x, y, legend="sin(x)")

p.line(x, 2*y, legend="2*sin(x)",
       line_dash=[4, 4], line_color="orange", line_width=2)

p.square(x, 3*y, legend="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend="3*sin(x)", line_color="green")

p.legend.orientation = "horizontal"

show(p)

Label Text¶

The visual appearance of the legend labels is controlled by a collection of Text Properties, prefixed with label_. For instance, to set the font style of the labels, use label_text_font_style.

import numpy as np
from bokeh.plotting import output_file, figure, show

x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)

output_file("legend_labels.html")

p = figure()

p.circle(x, y, legend="sin(x)")
p.line(x, y, legend="sin(x)")

p.line(x, 2*y, legend="2*sin(x)",
       line_dash=[4, 4], line_color="orange", line_width=2)

p.square(x, 3*y, legend="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend="3*sin(x)", line_color="green")

p.legend.label_text_font = "times"
p.legend.label_text_font_style = "italic"
p.legend.label_text_color = "navy"

show(p)

Border¶

The visual appearance of the legend border is controlled by a collection of Line Properties, prefixed with border_. For instance, to set the color of the border, use border_line_color. To make the border invisible, set the border line color to None.

import numpy as np
from bokeh.plotting import output_file, figure, show

x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)

output_file("legend_border.html")

p = figure()

p.circle(x, y, legend="sin(x)")
p.line(x, y, legend="sin(x)")

p.line(x, 2*y, legend="2*sin(x)",
       line_dash=[4, 4], line_color="orange", line_width=2)

p.square(x, 3*y, legend="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend="3*sin(x)", line_color="green")

p.legend.border_line_width = 3
p.legend.border_line_color = "navy"
p.legend.border_line_alpha = 0.5

show(p)

Background¶

The visual appearance of the legend background is controlled by a collection of Fill Properties, prefixed with background_. For instance, to set the color of the background, use background_fill_color. To make the background transparent, set the background_fill_alpha to 0.

import numpy as np
from bokeh.plotting import output_file, figure, show

x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)

output_file("legend_background.html")

p = figure()

p.circle(x, y, legend="sin(x)")
p.line(x, y, legend="sin(x)")

p.line(x, 2*y, legend="2*sin(x)",
       line_dash=[4, 4], line_color="orange", line_width=2)

p.square(x, 3*y, legend="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend="3*sin(x)", line_color="green")

#  3*sin(x) curve should be under this legend at initial viewing, so
# we can see that the legend is transparent
p.legend.location = "bottom_right"
p.legend.background_fill_color = "navy"
p.legend.background_fill_alpha = 0.5

show(p)

Dimensions¶

There are several properties that can be used to control the layout, spacing, etc. of the legend components:

label_standoff¶

property type: Int

The distance (in pixels) to separate the label from its associated glyph.

label_width¶

property type: Int

The minimum width (in pixels) of the area that legend labels should occupy.

label_height¶

property type: Int

The minimum height (in pixels) of the area that legend labels should occupy.

glyph_width¶

property type: Int

The width (in pixels) that the rendered legend glyph should occupy.

glyph_height¶

property type: Int

The height (in pixels) that the rendered legend glyph should occupy.

padding¶

property type: Int

Amount of padding around the contents of the legend.

spacing¶

property type: Int

Amount of spacing (in pixles) between legend entries.

margin¶

property type: Int

Amount of margin around the legend.

import numpy as np
from bokeh.plotting import output_file, figure, show

x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)

output_file("legend_labels.html")

p = figure()

p.circle(x, y, legend="sin(x)")
p.line(x, y, legend="sin(x)")

p.line(x, 2*y, legend="2*sin(x)",
       line_dash=[4, 4], line_color="orange", line_width=2)

p.square(x, 3*y, legend="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend="3*sin(x)", line_color="green")

p.legend.label_standoff = 5
p.legend.glyph_width = 50
p.legend.spacing = 10
p.legend.padding = 50
p.legend.margin = 50

show(p)