Bokeh Accessible Plot Examples

Code examples showing how to make Bokeh bar charts, stacked and grouped bars, histograms, line, step and scatter plots, heatmaps and gridplot layouts accessible with py-maidr.

Bokeh Examples

maidr also supports Bokeh figures and layouts. Pass a figure, a gridplot, or a row/column of figures to maidr.show(), maidr.render() or maidr.save_html(), and maidr produces a page where the interactive Bokeh plot keeps its pan, zoom and hover tools and can also be read from the keyboard, as sound, as text and in braille. For the other libraries, see the Matplotlib / Seaborn, Plotly and Altair pages.

Experimental

Bokeh support is experimental. What it reads, and how, may change without a deprecation period, whichever plot type is drawn: see Plot Type Stability. Install it with pip install "maidr[bokeh]".

maidr reads the figure’s own renderers and data sources, not the drawn canvas, so what you hear is the data you plotted. As you move through a chart the mark being read is highlighted in the Bokeh plot itself: a bar, bin, cell or point is selected, dimming the rest, and a line, step or area shows a ring on the current point. A bar, bin, cell or point whose data source another mark also draws from, such as markers on a line built from one ColumnDataSource, gets the ring too, so the other mark is never faded.

Bokeh	read as
`vbar`, `hbar`	bar
`vbar_stack`, `hbar_stack`	stacked bar
`vbar` with `dodge()` or an offset such as `("a", -0.2)`, or on a nested `FactorRange`	dodged bar
`quad`	histogram
`line`, `multi_line`	line, one series per renderer or path
`step`	step
`scatter`, `circle`	point
`rect` coloured through a continuous colour mapper (`linear_cmap`, `log_cmap`)	heatmap
`varea`, `varea_stack`	area [experimental], stacked area [experimental]

Any other glyph is left out with a warning naming it, and a figure with nothing maidr can read is still drawn. A Tabs layout is read one panel at a time: the active one.

Bar Plot

import seaborn as sns
from bokeh.plotting import figure

import maidr  

penguins = sns.load_dataset("penguins")
avg_mass = penguins.groupby("species")["body_mass_g"].mean()
species = avg_mass.index.tolist()

p = figure(
    x_range=species,
    title="Average Body Mass of Penguins by Species",
    x_axis_label="Species",
    y_axis_label="Body Mass (g)",
    height=350,
)
p.vbar(x=species, top=avg_mass.round(1).tolist(), width=0.8, color="#4c72b0")

maidr.show(p)

Horizontal Bar Plot

import seaborn as sns
from bokeh.plotting import figure

import maidr  

penguins = sns.load_dataset("penguins")
counts = penguins["island"].value_counts().sort_values()

p = figure(
    y_range=counts.index.tolist(),
    title="Penguins Counted on Each Island",
    x_axis_label="Penguins",
    y_axis_label="Island",
    height=300,
)
p.hbar(y=counts.index.tolist(), right=counts.tolist(), height=0.7, color="#55a868")

maidr.show(p)

Stacked Bar Plot

import seaborn as sns
from bokeh.plotting import figure

import maidr  

tips = sns.load_dataset("tips")
totals = tips.pivot_table(
    index="day", columns="sex", values="total_bill", aggfunc="sum", observed=True
)
days = [str(day) for day in totals.index]
source = {"day": days, "Male": totals["Male"].round(2), "Female": totals["Female"].round(2)}

p = figure(
    x_range=days,
    title="Total Bill by Day and Sex",
    x_axis_label="Day",
    y_axis_label="Total Bill ($)",
    height=350,
)
p.vbar_stack(
    ["Male", "Female"],
    x="day",
    width=0.8,
    source=source,
    color=["#4c72b0", "#dd8452"],
    legend_label=["Male", "Female"],
)
p.legend.title = "Sex"

maidr.show(p)

Dodged (Grouped) Bar Plot

import seaborn as sns
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from bokeh.transform import dodge

import maidr  

penguins = sns.load_dataset("penguins").dropna()
by_sex = penguins.pivot_table(
    index="species", columns="sex", values="body_mass_g", aggfunc="mean"
).round(1)
species = by_sex.index.tolist()
source = ColumnDataSource(
    {"species": species, "Male": by_sex["Male"], "Female": by_sex["Female"]}
)

p = figure(
    x_range=species,
    title="Average Body Mass by Species and Sex",
    x_axis_label="Species",
    y_axis_label="Body Mass (g)",
    height=350,
)
p.vbar(
    x=dodge("species", -0.2, range=p.x_range), top="Male", width=0.35,
    source=source, color="#4c72b0", legend_label="Male",
)
p.vbar(
    x=dodge("species", 0.2, range=p.x_range), top="Female", width=0.35,
    source=source, color="#dd8452", legend_label="Female",
)
p.legend.title = "Sex"

maidr.show(p)

A vbar on a nested FactorRange – x=[("Adelie", "Male"), ("Adelie", "Female"), ...] – is read the same way: the inner factor names the group, and the outer one the category.

Histogram

import numpy as np
import seaborn as sns
from bokeh.plotting import figure

import maidr  

penguins = sns.load_dataset("penguins").dropna()
counts, edges = np.histogram(penguins["flipper_length_mm"], bins=12)

p = figure(
    title="Distribution of Penguin Flipper Length",
    x_axis_label="Flipper Length (mm)",
    y_axis_label="Count",
    height=350,
)
p.quad(
    top=counts, bottom=0, left=edges[:-1], right=edges[1:],
    fill_color="#8172b3", line_color="white",
)

maidr.show(p)

Line Plot

import seaborn as sns
from bokeh.plotting import figure

import maidr  

flights = sns.load_dataset("flights")
per_year = flights.groupby("year")["passengers"].sum()

p = figure(
    title="Airline Passengers per Year",
    x_axis_label="Year",
    y_axis_label="Passengers",
    height=350,
)
p.line(per_year.index.tolist(), per_year.tolist(), line_width=2)

maidr.show(p)

Multi-Line Plot

import seaborn as sns
from bokeh.plotting import figure

import maidr  

flights = sns.load_dataset("flights")

p = figure(
    title="Monthly Passengers in Three Years",
    x_axis_label="Month",
    y_axis_label="Passengers",
    height=350,
)
for year, color in [(1949, "#4c72b0"), (1954, "#55a868"), (1960, "#c44e52")]:
    rows = flights[flights["year"] == year]
    p.line(
        list(range(1, 13)), rows["passengers"].tolist(),
        line_width=2, color=color, legend_label=str(year),
    )
p.legend.title = "Year"
p.legend.location = "top_left"

maidr.show(p)

Every line on a figure becomes one series of one line layer, named by its legend label; press the up and down arrows to move between them.

Step Plot

import seaborn as sns
from bokeh.plotting import figure

import maidr  

tips = sns.load_dataset("tips").head(20)
running_max = tips["total_bill"].cummax()

p = figure(
    title="Largest Bill So Far",
    x_axis_label="Table",
    y_axis_label="Total Bill ($)",
    height=350,
)
p.step(list(range(1, 21)), running_max.tolist(), mode="after", line_width=2)

maidr.show(p)

Scatter Plot

import seaborn as sns
from bokeh.plotting import figure

import maidr  

penguins = sns.load_dataset("penguins").dropna()

p = figure(
    title="Bill Length vs Bill Depth",
    x_axis_label="Bill Length (mm)",
    y_axis_label="Bill Depth (mm)",
    height=350,
)
p.scatter(
    penguins["bill_length_mm"].tolist(), penguins["bill_depth_mm"].tolist(),
    size=6, alpha=0.7,
)

maidr.show(p)

Heatmap

import seaborn as sns
from bokeh.plotting import figure
from bokeh.transform import linear_cmap

import maidr  

flights = sns.load_dataset("flights")
flights["year"] = flights["year"].astype(str)
flights["month"] = flights["month"].astype(str)
years = sorted(flights["year"].unique())
months = flights["month"].unique().tolist()[::-1]

p = figure(
    x_range=years,
    y_range=months,
    title="Airline Passengers by Month and Year",
    x_axis_label="Year",
    y_axis_label="Month",
    height=450,
)
cells = p.rect(
    x="year", y="month", width=1, height=1, source=flights, line_color=None,
    fill_color=linear_cmap(
        "passengers", "Viridis256",
        flights["passengers"].min(), flights["passengers"].max(),
    ),
)
p.add_layout(cells.construct_color_bar(title="Passengers"), "right")

maidr.show(p)

Stacked Area Plot [experimental]

import seaborn as sns
from bokeh.plotting import figure

import maidr  

flights = sns.load_dataset("flights")
wide = flights.pivot(index="month", columns="year", values="passengers")
source = {
    "month": list(range(1, 13)),
    "1958": wide[1958].tolist(),
    "1959": wide[1959].tolist(),
    "1960": wide[1960].tolist(),
}

p = figure(
    title="Monthly Passengers, 1958 to 1960",
    x_axis_label="Month",
    y_axis_label="Passengers",
    height=350,
)
p.varea_stack(
    ["1958", "1959", "1960"],
    x="month",
    source=source,
    color=["#4c72b0", "#55a868", "#c44e52"],
    legend_label=["1958", "1959", "1960"],
)
p.legend.location = "top_left"

maidr.show(p)

Multi-Panel Layout (gridplot)

import seaborn as sns
from bokeh.layouts import gridplot
from bokeh.plotting import figure

import maidr  

penguins = sns.load_dataset("penguins")
flights = sns.load_dataset("flights")
counts = penguins["species"].value_counts().sort_index()
per_year = flights.groupby("year")["passengers"].sum()

left = figure(
    x_range=counts.index.tolist(), title="Penguins per Species",
    x_axis_label="Species", y_axis_label="Penguins", width=350, height=300,
)
left.vbar(x=counts.index.tolist(), top=counts.tolist(), width=0.8)

right = figure(
    title="Passengers per Year", x_axis_label="Year",
    y_axis_label="Passengers", width=350, height=300,
)
right.line(per_year.index.tolist(), per_year.tolist(), line_width=2)

maidr.show(gridplot([[left, right]]))

Each figure of a gridplot, row or column is a subplot. Press Enter to go into one, Escape to come back out, and the arrow keys between them, in the directions they are laid out on the page. A figure with nothing maidr can read is left out of the grid.

Saving and embedding

maidr.save_html(p, "chart.html") writes the same page to a file. use_cdn decides where maidr.js comes from, exactly as for matplotlib and Plotly; BokehJS itself is always loaded from Bokeh’s CDN at the version you have installed, as plotly.js is from Plotly’s, so opening the page needs a network connection either way.