density#
A multiple kernel density estimation plot of the “cows” data using the sklearn.neighbors.KernelDensity function and Bokeh varea glyph
Details
- Sampledata:
- Bokeh APIs:
- More info:
- Keywords:
density, varea
import numpy as np
from sklearn.neighbors import KernelDensity
from bokeh.models import ColumnDataSource, Label, PrintfTickFormatter
from bokeh.palettes import Dark2_5 as colors
from bokeh.plotting import figure, show
from bokeh.sampledata.cows import data as df
breed_groups = df.groupby('breed')
x = np.linspace(2, 8, 1000)
source = ColumnDataSource(dict(x=x))
p = figure(title="Multiple density estimates", height=300, x_range=(2.5, 7.5), x_axis_label="butterfat contents", y_axis_label="density")
for (breed, breed_df), color in zip(breed_groups, colors):
data = breed_df['butterfat'].values
kde = KernelDensity(kernel="gaussian", bandwidth=0.2).fit(data[:, np.newaxis])
log_density = kde.score_samples(x[:, np.newaxis])
y = np.exp(log_density)
source.add(y, breed)
p.varea(x="x", y1=breed, y2=0, source=source, fill_alpha=0.3, fill_color=color)
# Find the highest point and annotate with a label
max_idx = np.argmax(y)
highest_point_label = Label(
x=x[max_idx],
y=y[max_idx],
text=breed,
text_font_size="10pt",
x_offset=10,
y_offset=-5,
text_color=color,
)
p.add_layout(highest_point_label)
# Display x-axis labels as percentages
p.xaxis.formatter = PrintfTickFormatter(format="%d%%")
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.minor_tick_line_color = None
p.xgrid.grid_line_color = None
p.yaxis.ticker = (0, 0.5, 1, 1.5)
p.y_range.start = 0
show(p)