ridgeplot.py

A ridgeline plot using the Perceptions of Probability dataset. This example demonstrates the uses of categorical offsets to position categorical values explicitly. This chart shows the distributrion of responses to the prompt What probability would you assign to the phrase “Highly likely”.

Details

Sampledata

bokeh.sampledata.perceptions

Bokeh APIs

Figure.patch, bokeh.models.ColumnDataSource

More info

Handling categorical data > Categorical offsets

Keywords

patch, alpha, categorical, palette, patch, ridgeline

import colorcet as cc
from numpy import linspace
from scipy.stats.kde import gaussian_kde

from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter
from bokeh.plotting import figure, show
from bokeh.sampledata.perceptions import probly


def ridge(category, data, scale=20):
    return list(zip([category]*len(data), scale*data))

cats = list(reversed(probly.keys()))

palette = [cc.rainbow[i*15] for i in range(17)]

x = linspace(-20,110, 500)

source = ColumnDataSource(data=dict(x=x))

p = figure(y_range=cats, width=900, x_range=(-5, 105), toolbar_location=None)

for i, cat in enumerate(reversed(cats)):
    pdf = gaussian_kde(probly[cat])
    y = ridge(cat, pdf(x))
    source.add(y, cat)
    p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source)

p.outline_line_color = None
p.background_fill_color = "#efefef"

p.xaxis.ticker = FixedTicker(ticks=list(range(0, 101, 10)))
p.xaxis.formatter = PrintfTickFormatter(format="%d%%")

p.ygrid.grid_line_color = None
p.xgrid.grid_line_color = "#dddddd"
p.xgrid.ticker = p.xaxis.ticker

p.axis.minor_tick_line_color = None
p.axis.major_tick_line_color = None
p.axis.axis_line_color = None

p.y_range.range_padding = 0.12

show(p)