Styling Visual Attributes¶

Using Palettes¶

Palettes are sequences (lists or tuples) of RGB(A) hex strings that define a colormap and can be set as the color attribute of many plot objects from bokeh.plotting. Bokeh offers many of the standard Brewer palettes, which can be imported from the bokeh.palettes module. For example, importing “Spectral6” gives a six element list of RGB(A) hex strings from the Brewer “Spectral” colormap.

>>> from bokeh.palettes import Spectral6
>>> Spectral6
['#3288bd', '#99d594', '#e6f598', '#fee08b', '#fc8d59', '#d53e4f']

All of the standard palettes included in bokeh can be found at bokeh.palettes. Custom palettes can be made by creating sequences of RGB(A) hex strings.

Using Mappers¶

Color Mappers allow you to encode some data sequence into a palette of colors based on the value in that sequence. The mappers are then set as the color attribute on marker objects. Bokeh includes several types of mappers to encode colors:

bokeh.transform.factor_cmap: Maps colors to specific categorical elements see Handling Categorical Data for more detail.

bokeh.transform.linear_cmap: Maps a range of numeric values across the available colors from high to low. For example, a range of [0,99] given the colors [‘red’, ‘green’, ‘blue’] would be mapped as follows:

    x < 0  : 'red'     # values < low are clamped
>= x < 33 : 'red'
>= x < 66 : 'green'
>= x < 99 : 'blue'
>= x      : 'blue'    # values > high are clamped

bokeh.transform.log_cmap: Similar to linear_cmap but uses a natural log scale to map the colors.

These mapper functions return a DataSpec property that can be passed to the color attribute of the glyph. The returned dataspec includes a bokeh.transform which can be accessed to use the mapper in another context such as to create a ColorBar as in the example below:

from bokeh.models import ColorBar, ColumnDataSource
from bokeh.palettes import Spectral6
from bokeh.plotting import figure, output_file, show
from bokeh.transform import linear_cmap

output_file("styling_linear_mappers.html", title="styling_linear_mappers.py example")

x = [1,2,3,4,5,7,8,9,10]
y = [1,2,3,4,5,7,8,9,10]

#Use the field name of the column source
mapper = linear_cmap(field_name='y', palette=Spectral6 ,low=min(y) ,high=max(y))

source = ColumnDataSource(dict(x=x,y=y))

p = figure(plot_width=300, plot_height=300, title="Linear Color Map Based on Y")

p.circle(x='x', y='y', line_color=mapper,color=mapper, fill_alpha=1, size=12, source=source)

color_bar = ColorBar(color_mapper=mapper['transform'], width=8,  location=(0,0))

p.add_layout(color_bar, 'right')

show(p)

Visual Properties¶

In order to style the visual attributes of Bokeh plots, you need to know what the available properties are. The full Reference will list all the properties of every object individually, though there are three broad groups of properties that show up often. They are:

line properties line color, width, etc.
fill properties fill color, alpha, etc.
text properties font styles, colors, etc.

Below is more detail about each of these.

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)

Hatch Properties¶

hatch_color

color to use to stroke hatch patterns with

hatch_alpha

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

hatch_weight

line stroke width in units of pixels

hatch_scale

a rough measure of the “size” of a pattern. This value has different specific meanings, depending on the pattern.

hatch_pattern

a string name (or abbreviation) for a built-in pattern, or a string name of a pattern provided in hatch_extra. The built-in patterns are:

Built in Hatch Patterns¶
FullName	Abbreviation	Example
`blank`	`" "`
`dot`	`"."`
`ring`	`"o"`
`horizontal_line`	`"-"`
`vertical_line`	`"\|"`
`cross`	`"+"`
`horizontal_dash`	`'"'`
`vertical_dash`	`":"`
`spiral`	`"@"`
`right_diagonal_line`	`"/"`
`left_diagonal_line`	`"\\"`
`diagonal_cross`	`"x"`
`right_diagonal_dash`	`","`
`left_diagonal_dash`	"`"
`horizontal_wave`	`"v"`
`vertical_wave`	`">"`
`criss_cross`	`"*"`

hatch_extra

a dict mapping string names to custom pattern implementations. The name can be referred to by hatch_pattern. For example, if the follwing value is set for hatch_extra:

hatch_extra={ 'mycustom': ImageURLTexture(url=...) }

then the name "mycustom" may be set as a hatch_pattern.

Text Properties¶

text_font

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

text_font_size

font size in px, em, or pt, e.g., '16px', '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'

Note

There is currently only support for filling text. An interface to stroke the outlines of text has not yet been exposed.

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 a Bokeh server or CustomJS.

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

output_file("styling_visible_annotation_with_interaction.html")

p = figure(plot_width=600, plot_height=200, tools='')
p.line([1, 2, 3], [1, 2, 1], line_color="blue")
pink_line = p.line([1, 2, 3], [2, 1, 2], line_color="pink")

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

# Use js_link to connect button active property to glyph visible property

toggle1 = Toggle(label="Green Box", button_type="success", active=True)
toggle1.js_link('active', green_box, 'visible')

toggle2 = Toggle(label="Pink Line", button_type="success", active=True)
toggle2.js_link('active', pink_line, 'visible')

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

Warning

Supplying lists of RGB or RGBA color tuples as color arguments (either directly or as a DataSource column reference) doesn’t work. You may read a discussion of the issue on our project GitHub page. Suggested work-arounds include using lists of:

RGB hexadecimal values
bokeh.colors.RGB objects (i.e. [RGB(255, 0, 0), RGB(0, 255, 0)"])
CSS-format RGB/RGBA strings (i.e. ["rgb(255, 0, 0)", "rgb(0, 255, 0)"])

Color alpha can be specified in multiple ways for the visual properties. This can be done 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:

Note

If using the bokeh.plotting interface, another option is to specify color and/or alpha as a keyword, as well as the demonstrated color properties. These inputs work by applying the provided value to both of the corresponding line and fill properties. However, you can still provide fill|line_alpha or fill|line_color in combination with the color/alpha keywords, and the former will take precedence.

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 arrowhead 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 arrowhead 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.

Selecting Plot Objects¶

As described in Defining Key Concepts, Bokeh plots comprise graphs of objects that represent all the different parts of the plot: grids, axes, glyphs, etc. In order to style Bokeh plots, it is necessary to first find the right object, then set its various attributes. Some objects have convenience methods to help find the objects of interest (see Axes, Grids, and Legends). But there is also a select() method on Plot that can be used to query for Bokeh plot objects more generally.

For example, you can query for objects by type. The following snippet returns all the PanTool objects a plot has:

>>> p.select(type=PanTool)
[<bokeh.models.tools.PanTool at 0x106608b90>]

The select() method can query on other attributes as well:

>>> p.circle(0, 0, name="mycircle")
<bokeh.plotting.Figure at 0x106608810>

>>> p.select(name="mycircle")
[<bokeh.models.renderers.GlyphRenderer at 0x106a4c810>]

This sort of query can be especially useful for styling visual attributes of Glyphs.

Plots¶

Plot objects themselves have many visual characteristics that can be styled: the dimensions of the plot, backgrounds, borders, outlines, etc. This section describes how to change these attributes of a Bokeh plot. The example code primarily uses the bokeh.plotting interface to create plots. However, the instructions apply regardless of how a Bokeh plot was created.

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¶

For 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.

If you set sizing_mode, the plot_width and plot_height may 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.

Title¶

The styling of the plot title is controlled by the properties of Title annotation, which is available as the .title property on the Plot. Most of the standard Text Properties are available, with the exception of text_align and text_baseline which do not apply. For positioning the title relative to the entire plot, use the properties align and offset.

As an example, to set the color and font style 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, which 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)

Glyphs¶

To style the fill, line, or text properties of a glyph, it is first necessary to obtain a specific GlyphRenderer. When using the bokeh.plotting interface, the glyph functions return the renderer:

>>> r = p.circle([1,2,3,4,5], [2,5,8,2,7])
>>> r
<bokeh.models.renderers.GlyphRenderer at 0x106a4c810>

Then, the glyph itself is obtained from the .glyph attribute of a GlyphRenderer:

>>> r.glyph
<bokeh.models.markers.Circle at 0x10799ba10>

This is the object to set fill, line, or text property values for:

from bokeh.plotting import figure, output_file, show

output_file("axes.html")

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

glyph = r.glyph
glyph.size = 60
glyph.fill_alpha = 0.2
glyph.line_color = "firebrick"
glyph.line_dash = [6, 3]
glyph.line_width = 2

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 non-selected 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.models import Circle
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)

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 non-selected glyphs, this can be accomplished even more easily 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)

Note

Only the visual properties of selection_glyph and nonselection_glyph are considered when rendering. Changing positions, sizes, etc. will have no effect.

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

output_file("styling_hover.html")

subset = data.loc['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)

Note

Only the visual properties of hover_glyph are considered when rendering. Changing positions, sizes, etc. will have no effect.

Tool Overlays¶

Some Bokeh tools also have configurable visual attributes. For instance, the various region selection tools and box zoom tool all have an overlay whose line and fill properties may be set:

import numpy as np

from bokeh.models import BoxSelectTool, BoxZoomTool, LassoSelectTool
from bokeh.plotting import figure, output_file, show

output_file("styling_tool_overlays.html")

x = np.random.random(size=200)
y = np.random.random(size=200)

# Basic plot setup
plot = figure(plot_width=400, plot_height=400, title='Select and Zoom',
              tools="box_select,box_zoom,lasso_select,reset")

plot.circle(x, y, size=5)

select_overlay = plot.select_one(BoxSelectTool).overlay

select_overlay.fill_color = "firebrick"
select_overlay.line_color = None

zoom_overlay = plot.select_one(BoxZoomTool).overlay

zoom_overlay.line_color = "olive"
zoom_overlay.line_width = 8
zoom_overlay.line_dash = "solid"
zoom_overlay.fill_color = None

plot.select_one(LassoSelectTool).overlay.line_dash = [10, 10]

show(plot)

Axes¶

In this section, you will learn how to change various visual properties of Bokeh plot axes.

To set style attributes on Axis objects, use the xaxis, yaxis, and axis methods on Plot to first obtain a plot’s Axis objects:

>>> p.xaxis
[<bokeh.models.axes.LinearAxis at 0x106fa2390>]

This returns a list of Axis objects (since there may be more than one). But note that, as a convenience, these lists are splattable, meaning that you can set attributes directly on this result, and the attributes will be applied to all the axes in the list:

p.xaxis.axis_label = "Temperature"

will change the value of axis_label for every x-axis (however many there may be).

Below is code that will set some of the properties of axes. You can execute this code, and try setting other properties as well.

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)

# change just some things about the x-axis
p.xaxis.axis_label = "Temp"
p.xaxis.axis_line_width = 3
p.xaxis.axis_line_color = "red"

# change just some things about the y-axis
p.yaxis.axis_label = "Pressure"
p.yaxis.major_label_text_color = "orange"
p.yaxis.major_label_orientation = "vertical"

# change things on all axes
p.axis.minor_tick_in = -3
p.axis.minor_tick_out = 6

show(p)

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.

`FixedTicker`¶

This ticker model allows users to specify exact tick locations explicitly, e.g.

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])

However, it is also possible to supply the list of ticks directly as a shortcut, e.g. p.xaxis.ticker = [10, 20, 37.4]. The example below demonstrates this method.

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.

Note

To replace a tick formatter on an Axis, you must set the formatter property on an actual Axis object, not on a splattable list. This is why p.yaxis[0].formatter, etc. (with the subscript [0]) is used.

`NumeralTickFormatter`¶

The NumeralTickFormatter has a format property that can be used to control the text formatting of axis ticks.

from bokeh.models import NumeralTickFormatter
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[0].formatter = NumeralTickFormatter(format="0.0%")
p.yaxis[0].formatter = NumeralTickFormatter(format="$0.00")

show(p)

Many additional formats are available. See the full NumeralTickFormatter documentation in the Reference.

`PrintfTickFormatter`¶

The PrintfTickFormatter has a format property that can be used to control the text formatting of axis ticks using printf style format strings.

from bokeh.models import PrintfTickFormatter
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[0].formatter = PrintfTickFormatter(format="%4.1e")
p.yaxis[0].formatter = PrintfTickFormatter(format="%5.3f mu")

show(p)

For full details about formats, see the full PrintfTickFormatter documentation in the Reference.

`FuncTickFormatter`¶

The FuncTickFormatter allows arbitrary tick formatting to be performed by supplying a JavaScript snippet as the code property. The variable tick will contain the unformatted tick value and can be expected to be present in the snippet or function namespace at render time. The example below demonstrates configuring a FuncTickFormatter from pure JavaScript:

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

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)

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.

Grids¶

In this section, you will learn how to set the visual properties of grid lines and grid bands on Bokeh plots.

Similar to the convenience methods for axes, there are xgrid, ygrid, and grid methods on Plot that can be used to obtain a plot’s Grid objects:

>>> p.grid
[<bokeh.models.grids.Grid at 0x106fa2278>,
 <bokeh.models.grids.Grid at 0x106fa22e8>]

These methods also return splattable lists, so that you can set an attribute on the list as if it was a single object, and the attribute is changed for every element of the list:

p.grid.line_dash = [4 2]

Note

The xgrid property provides the grid objects that intersect the x-axis (i.e., are vertical). Correspondingly, ygrid provides the grid objects that intersect the y-axis (i.e., are horizontal).

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 possible to display filled, shaded bands between adjacent grid lines. The visual appearance of these bands is controlled by a collection of Fill Properties, and Hatch 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).

Here is an example demonstrating bands filled with a solid color:

from bokeh.plotting import figure, output_file, show

output_file("grid_band_fill.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)

And here is an example demonstrating bands filled with a hatch pattern:

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

output_file("grid_band_hatch.html")

p = figure(plot_height=250, plot_width=600, x_range=(0, 10), tools="", toolbar_location=None)
p.line(x=[0,1,2,3,4,5,6,7,8,9,10],
       y=[1,3,4,3,1,2,6,5,2,3,4])

p.ygrid.grid_line_color = None

ticks = [0, 2, 4, 6, 8, 10]
p.xaxis[0].ticker = ticks
p.xgrid[0].ticker = ticks

p.xgrid.band_hatch_pattern = "/"
p.xgrid.band_hatch_alpha = 0.6
p.xgrid.band_hatch_color = "lightgrey"
p.xgrid.band_hatch_weight = 0.5
p.xgrid.band_hatch_scale = 10

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.

Legends¶

Similar to the convenience methods for axes and grids, there is a legend method on Plot that can be used to obtain a plot’s Legend objects:

>>> p.legend
[<bokeh.models.annotations.Legend at 0x106fa2278>]

This method also returns a splattable list, so that you can set an attribute on the list as if it was a single object, and the attribute is changed for every element of the list:

p.legend.label_text_font = "times"

Location¶

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

Inside the Plot Area¶

For legends in the central layout area, such as those created automatically by bokeh.plotting, values for location can be:

"top_left"

"top_center"

"top_right" (the default)

"center_right"

"bottom_right"

"bottom_center"

"bottom_left"

"center_left"

"center"

or a (x, y) tuple indicating an absolute location in screen coordinates (pixels from the bottom-left corner).

import numpy as np

from bokeh.plotting import figure, output_file, 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_label="sin(x)")
p.line(x, y, legend_label="sin(x)")

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

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

p.legend.location = "bottom_left"

show(p)

Outside the Plot Area¶

It is also possible to position a legend outside the central area, by using the add_layout method of plots, but doing so requires creating the Legend object directly:

import numpy as np

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

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="center")

p.add_layout(legend, 'right')

show(p)

In this use-case, the location must be specified absolutely. Future releases will add additional options for laying out legend positions.

Title¶

Legends can have a title, specified by the title property:

plot.legend.title = "Division"

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

The distance to separate the title from the rest of the legend (in pixels) is controlled by the title_standoff property.

import pandas as pd

from bokeh.palettes import Spectral4
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT

output_file("styling_legend_title.html", title="styling_legend_title.py example")

p = figure(plot_width=800, plot_height=250, x_axis_type="datetime")

for data, name, color in zip([AAPL, IBM, MSFT, GOOG], ["AAPL", "IBM", "MSFT", "GOOG"], Spectral4):
    df = pd.DataFrame(data)
    df['date'] = pd.to_datetime(df['date'])
    p.line(df['date'], df['close'], line_width=2, color=color, legend_label=name)

p.legend.location = "top_left"
p.legend.title = 'Stock'
p.legend.title_text_font_style = "bold"
p.legend.title_text_font_size = "20px"

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, output_file, 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_label="sin(x)")
p.line(x, y, legend_label="sin(x)")

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

p.square(x, 3*y, legend_label="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend_label="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 figure, output_file, 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_label="sin(x)")
p.line(x, y, legend_label="sin(x)")

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

p.square(x, 3*y, legend_label="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend_label="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 figure, output_file, 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_label="sin(x)")
p.line(x, y, legend_label="sin(x)")

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

p.square(x, 3*y, legend_label="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend_label="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 figure, output_file, 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_label="sin(x)")
p.line(x, y, legend_label="sin(x)")

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

p.square(x, 3*y, legend_label="3*sin(x)", fill_color=None, line_color="green")
p.line(x, 3*y, legend_label="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. Only applicable when when border is visible, otherwise collapses to 0.

spacing¶

property type: Int

Amount of spacing (in pixels) between legend entries.

margin¶

property type: Int

Amount of margin around the legend.

import numpy as np

from bokeh.plotting import figure, output_file, 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_label="sin(x)")
p.line(x, y, legend_label="sin(x)")

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

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

Render Level¶

Bokeh has a notion of render level to specify the order in which things are drawn:

image: “lowest” render level, drawn before anything else
underlay: default render level for grids
glyph: default render level for all glyphs (i.e. above grids)
annotation: default render level for annotation renderers
overlay: “highest” render level, for tool overlays

Within a given level, renderers are drawn in the order that they were added. It is sometimes useful to specify a render level explicitly. This can be done by setting the level parameter on the renderer. For example, to cause an image to show up under the grid lines, you can call:

p.image(..., level="image")

You can see a complete example with output in the section Colormapped Images.

Styling Visual Attributes¶

Using Palettes¶

Using Mappers¶

Visual Properties¶

Line Properties¶

Fill Properties¶

Hatch Properties¶

Text Properties¶

Visible property¶

Specifying Colors¶

Styling Arrow Annotations¶

Screen Units and Data-space Units¶

Selecting Plot Objects¶

Plots¶

Dimensions¶

Responsive Dimensions¶

Title¶

Background¶

Border¶

Outline¶

Glyphs¶

Selected and Unselected Glyphs¶

Hover Inspections¶

Tool Overlays¶

ToolBar Autohide¶

Axes¶

Labels¶

Bounds¶

Tick Locations¶

FixedTicker¶

Tick Lines¶

Tick Label Formats¶

NumeralTickFormatter¶

PrintfTickFormatter¶

FuncTickFormatter¶

Tick Label Orientation¶

Grids¶

Lines¶

Minor Lines¶

Bands¶

Bounds¶

Legends¶

Location¶

Inside the Plot Area¶

Outside the Plot Area¶

Title¶

Orientation¶

Label Text¶

Border¶

Background¶

Dimensions¶

Render Level¶

`FixedTicker`¶

`NumeralTickFormatter`¶

`PrintfTickFormatter`¶

`FuncTickFormatter`¶