Area glyphs

Rectangles

Bars

Quads

To draw axis aligned rectangles by specifying the left, right, top, and bottom positions, use the quad() glyph function:

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.quad(top=[2, 3, 4], bottom=[1, 2, 3], left=[1, 2, 3],
       right=[1.2, 2.5, 3.7], color="#B3DE69")

show(p)

Blocks

To draw axis aligned rectangles by specifying the x and y coordinates for a corner, and a width and height, use the block() glyph function:

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.block(x=[1, 2, 3], y=[1, 2, 3], width=[0.2, 0.5, 0.1], height=1.5)

show(p)

Rotatable

To draw arbitrary rectangles by specifying center coordinates, width, height, and angle, use the rect() glyph function:

from math import pi

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.rect(x=[1, 2, 3], y=[1, 2, 3], width=0.2, height=40, color="#CAB2D6",
       angle=pi/3, height_units="screen")

show(p)

Directed areas

Directed areas are filled regions between two series that share a common index. For instance, a vertical directed area has one x coordinate array and two y coordinate arrays, y1 and y2, defining the space for Bokeh to fill.

Single areas

To fill an area in vertical direction, use the varea() method. You can do the same in horizontal direction with harea().

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

p.varea(x=[1, 2, 3, 4, 5],
        y1=[2, 6, 4, 3, 5],
        y2=[1, 4, 2, 2, 3])

show(p)

Stacked areas

To stack directed areas, use the varea_stack() and harea_stack() convenience methods.

from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, show

source = ColumnDataSource(data=dict(
    x=[1, 2, 3, 4, 5],
    y1=[1, 2, 4, 3, 4],
    y2=[1, 4, 2, 2, 3],
))
p = figure(width=400, height=400)

p.varea_stack(['y1', 'y2'], x='x', color=("grey", "lightgrey"), source=source)

show(p)

Patches

Single patches

The following example generates a single polygonal patch from one-dimensional sequences of x and y points using the patch() glyph method:

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

# add a patch renderer with an alpha and line width
p.patch([1, 2, 3, 4, 5], [6, 7, 8, 7, 3], alpha=0.5, line_width=2)

show(p)

Multiple patches

To plot several polygonal patches, use the patches() glyph method:

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

p.patches([[1, 3, 2], [3, 4, 6, 6]], [[2, 1, 4], [4, 7, 8, 5]],
          color=["firebrick", "navy"], alpha=[0.8, 0.3], line_width=2)

show(p)

Note

Unlike many other glyph methods, patches() accepts a list of lists of x and y positions for each line. The patches() method also expects a scalar value or a list of scalars for each patch for parameters such as color, alpha, and line width. You can similarly use a ColumnDataSource consisting of a list of lists of point coordinates and a list of scalar values of matching length.

Missing points

Just as with the line() and multi_line() methods, you can pass NaN values to patch() and patches() glyphs. This produces disjointed patches with gaps for NaN values.

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

# add a patch renderer with a NaN value
nan = float('nan')
p.patch([1, 2, 3, nan, 4, 5, 6], [6, 7, 5, nan, 7, 3, 6], alpha=0.5, line_width=2)

show(p)

Warning

Bokeh doesn’t currently support hit testing on patch objects with NaN values.

Polygons

The multi_polygons() glyph uses nesting to accept a variety of information relevant to polygons. The method duplicates the functionality of patches() but you can also use it to render holes inside polygons.

Note

Unlike many other glyph methods, multi_polygons() accepts a triple-nested lists of x and y positions for the exterior and holes composing each polygon. The multi_polygons() method also expects a scalar value or a list of scalars for each item for parameters such as color, alpha, and line width. You can similarly use a ColumnDataSource consisting of a triple- nested list of point coordinates and a list of scalars, with the top-level list of point coordinates being of equal length with the list of scalars.

Simple polygon

The following example generates a single polygon from a triple-nested list of one-dimensional sequences of x and y points using the multi_polygons() glyph method.

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.multi_polygons(xs=[[[[1, 1, 2, 2]]]],
                 ys=[[[[3, 4, 4, 3]]]])

show(p)

Polygon with holes

The following example generates a single polygon with holes from three sequences of x and y points. The first sequence represents the exterior of the polygon and the following sequences represent the holes.

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.multi_polygons(xs=[[[ [1, 2, 2, 1], [1.2, 1.6, 1.6], [1.8, 1.8, 1.6] ]]],
                 ys=[[[ [3, 3, 4, 4], [3.2, 3.6, 3.2], [3.4, 3.8, 3.8] ]]])

show(p)

Multi-polygon with separate parts

A single polygon concept can comprise multiple polygon geometries. The following example generates a multi-polygon glyph from several sequences of x and y points. Each item in the sequence represents a part of the glyph.

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.multi_polygons(xs=[[[ [1, 1, 2, 2], [1.2, 1.6, 1.6], [1.8, 1.8, 1.6] ], [ [3, 4, 3] ]]],
                 ys=[[[ [4, 3, 3, 4], [3.2, 3.2, 3.6], [3.4, 3.8, 3.8] ], [ [1, 1, 3] ]]])

show(p)

Multiple multi-polygons

The top-level of nesting separates each multi-polygon from the rest. You can think of each multi-polygon as a row in the data source, potentially with a corresponding label or color.

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.multi_polygons(
    xs=[
        [[ [1, 1, 2, 2], [1.2, 1.6, 1.6], [1.8, 1.8, 1.6] ], [ [3, 3, 4] ]],
        [[ [1, 2, 2, 1], [1.3, 1.3, 1.7, 1.7] ]]],
    ys=[
        [[ [4, 3, 3, 4], [3.2, 3.2, 3.6], [3.4, 3.8, 3.8] ], [ [1, 3, 1] ]],
        [[ [1, 1, 2, 2], [1.3, 1.7, 1.7, 1.3] ]]],
    color=['blue', 'red'])

show(p)

Ellipses

The ellipse() glyph method accepts the same properties as rect(), but renders ellipse shapes.

from math import pi

from bokeh.plotting import figure, show

p = figure(width=400, height=400)
p.ellipse(x=[1, 2, 3], y=[1, 2, 3], width=[0.2, 0.3, 0.1], height=0.3,
          angle=pi/3, color="#CAB2D6")

show(p)