kde2d

A bivariate kernel density estimation plot of the “autompg” data using the scipy.stats.gaussian_kde funtion and Bokeh contour renderer.s

Details

Sampledata:

bokeh.sampledata.autompg

Bokeh APIs:

figure.contour

More info:

Kernel density estimation

Keywords:

kde, contour

import numpy as np
from scipy.stats import gaussian_kde

from bokeh.palettes import Blues9
from bokeh.plotting import figure, show
from bokeh.sampledata.autompg import autompg as df


def kde(x, y, N):
    xmin, xmax = x.min(), x.max()
    ymin, ymax = y.min(), y.max()

    X, Y = np.mgrid[xmin:xmax:N*1j, ymin:ymax:N*1j]
    positions = np.vstack([X.ravel(), Y.ravel()])
    values = np.vstack([x, y])
    kernel = gaussian_kde(values)
    Z = np.reshape(kernel(positions).T, X.shape)

    return X, Y, Z

x, y, z = kde(df.hp, df.mpg, 300)

p = figure(height=400, x_axis_label="hp", y_axis_label="mpg",
           background_fill_color="#fafafa", tools="", toolbar_location=None,
           title="Kernel density estimation plot of HP vs MPG")
p.grid.level = "overlay"
p.grid.grid_line_color = "black"
p.grid.grid_line_alpha = 0.05

palette = Blues9[::-1]
levels = np.linspace(np.min(z), np.max(z), 10)
p.contour(x, y, z, levels[1:], fill_color=palette, line_color=palette)

show(p)