Statistical plots#


Use quad() glyphs to create a histogram plotted from np.histogram output

import numpy as np

from bokeh.plotting import figure, show

rng = np.random.default_rng()
x = rng.normal(loc=0, scale=1, size=1000)

p = figure(width=670, height=400, toolbar_location=None,
           title="Normal (Gaussian) Distribution")

# Histogram
bins = np.linspace(-3, 3, 40)
hist, edges = np.histogram(x, density=True, bins=bins)
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
         fill_color="skyblue", line_color="white",
         legend_label="1000 random samples")

# Probability density function
x = np.linspace(-3.0, 3.0, 100)
pdf = np.exp(-0.5*x**2) / np.sqrt(2.0*np.pi)
p.line(x, pdf, line_width=2, line_color="navy",
       legend_label="Probability Density Function")

p.y_range.start = 0
p.xaxis.axis_label = "x"
p.yaxis.axis_label = "PDF(x)"


A population pyramid plot is a divergent horizontal bar plot that can be used to compare distributions between two groups. In Bokeh they can be created using hbar() glyphs.

import numpy as np

from bokeh.models import CustomJSTickFormatter, Label
from bokeh.palettes import DarkText, Vibrant3 as colors
from bokeh.plotting import figure, show
from bokeh.sampledata.titanic import data as df

sex_group = df.groupby("sex")

female_ages = sex_group.get_group("female")["age"].dropna()
male_ages = sex_group.get_group("male")["age"].dropna()

bin_width = 5
bins = np.arange(0, 72, bin_width)
m_hist, edges = np.histogram(male_ages, bins=bins)
f_hist, edges = np.histogram(female_ages, bins=bins)

p = figure(title="Age population pyramid of titanic passengers, by gender", height=400, width=600,
           x_range=(-90, 90), x_axis_label="count")

p.hbar(right=f_hist, y=edges[1:], height=bin_width*0.8, color=colors[0], line_width=0)

p.hbar(right=m_hist * -1, y=edges[1:], height=bin_width*0.8, color=colors[1], line_width=0)

# add text to every other bar
for i, (count, age) in enumerate(zip(f_hist, edges[1:])):
    if i % 2 == 1:
    p.text(x=count, y=edges[1:][i], text=[f"{age-bin_width}-{age}yrs"],
           x_offset=5, y_offset=7, text_font_size="12px", text_color=DarkText[5])

# customise x-axis and y-axis
p.xaxis.ticker = (-80, -60, -40, -20, 0, 20, 40, 60, 80)
p.xaxis.major_tick_out = 0
p.y_range.start = 3
p.ygrid.grid_line_color = None
p.yaxis.visible = False

# format tick labels as absolute values for the two-sided plot
p.xaxis.formatter = CustomJSTickFormatter(code="return Math.abs(tick);")

# add labels
p.add_layout(Label(x=-40, y=70, text="Men", text_color=colors[1], x_offset=5))
p.add_layout(Label(x=20, y=70, text="Women", text_color=colors[0], x_offset=5))



Box plots can be assembled using Whisker annotations, vbar() and scatter() glyphs:

import pandas as pd

from bokeh.models import ColumnDataSource, Whisker
from bokeh.plotting import figure, show
from bokeh.sampledata.autompg2 import autompg2
from bokeh.transform import factor_cmap

df = autompg2[["class", "hwy"]].rename(columns={"class": "kind"})

kinds = df.kind.unique()

# compute quantiles
qs = df.groupby("kind").hwy.quantile([0.25, 0.5, 0.75])
qs = qs.unstack().reset_index()
qs.columns = ["kind", "q1", "q2", "q3"]
df = pd.merge(df, qs, on="kind", how="left")

# compute IQR outlier bounds
iqr = df.q3 - df.q1
df["upper"] = df.q3 + 1.5*iqr
df["lower"] = df.q1 - 1.5*iqr

source = ColumnDataSource(df)

p = figure(x_range=kinds, tools="", toolbar_location=None,
           title="Highway MPG distribution by vehicle class",
           background_fill_color="#eaefef", y_axis_label="MPG")

# outlier range
whisker = Whisker(base="kind", upper="upper", lower="lower", source=source)
whisker.upper_head.size = whisker.lower_head.size = 20

# quantile boxes
cmap = factor_cmap("kind", "TolRainbow7", kinds)
p.vbar("kind", 0.7, "q2", "q3", source=source, color=cmap, line_color="black")
p.vbar("kind", 0.7, "q1", "q2", source=source, color=cmap, line_color="black")

# outliers
outliers = df[~df.hwy.between(df.lower, df.upper)]
p.scatter("kind", "hwy", source=outliers, size=6, color="black", alpha=0.3)

p.xgrid.grid_line_color = None


Kernel density estimation#

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)


Kernel density estimates can also be plotted using varea() glyphs:

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(

# 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



SinaPlots can be assembled using the harea() and scatter() glyphs:

import numpy as np
import pandas as pd
from sklearn.neighbors import KernelDensity

from bokeh.plotting import figure, show
from import data as df

df["DATE"] = pd.to_datetime(df["DATE"])
df["TAVG"] = (df["TMAX"] + df["TMIN"]) / 2
df["MONTH"] = df.DATE.dt.strftime("%b")

months = list(df.MONTH.unique())

p = figure(
    y_axis_label="mean temperature (F)",

# add a non-uniform categorical offset to a given category
def offset(category, data, scale=7):
    return list(zip([category] * len(data), scale * data))

for month in months:
    month_df = df[df.MONTH == month].dropna()
    tavg = month_df.TAVG.values
    temps = np.linspace(tavg.min(), tavg.max(), 50)

    kde = KernelDensity(kernel="gaussian", bandwidth=3).fit(tavg[:, np.newaxis])
    density = np.exp(kde.score_samples(temps[:, np.newaxis]))
    x1, x2 = offset(month, density), offset(month, -density)

    p.harea(x1=x1, x2=x2, y=temps, alpha=0.8, color="#E0E0E0")

    # pre-compute jitter in Python, this case is too complex for BokehJS
    tavg_density = np.exp(kde.score_samples(tavg[:, np.newaxis]))
    jitter = (np.random.random(len(tavg)) * 2 - 1) * tavg_density

    p.scatter(x=offset(month, jitter), y=tavg, color="black")

p.y_range.start = -10
p.yaxis.ticker = [0, 25, 50, 75]
p.grid.grid_line_color = None



A SPLOM is “scatter plot matrix” that arranges multiple scatter plots in a grid fashion in order to highlight correlations between dimensions. Key components of a SPLOM are Linked panning and Linked brushing as demonstrated in this example:

from itertools import product

from import show
from bokeh.layouts import gridplot
from bokeh.models import (BasicTicker, ColumnDataSource, DataRange1d,
                          Grid, LassoSelectTool, LinearAxis, PanTool,
                          Plot, ResetTool, Scatter, WheelZoomTool)
from bokeh.sampledata.penguins import data
from bokeh.transform import factor_cmap

df = data.copy()
df["body_mass_kg"] = df["body_mass_g"] / 1000

SPECIES = sorted(df.species.unique())
ATTRS = ("bill_length_mm", "bill_depth_mm", "body_mass_kg")
N = len(ATTRS)

source = ColumnDataSource(data=df)

xdrs = [DataRange1d(bounds=None) for _ in range(N)]
ydrs = [DataRange1d(bounds=None) for _ in range(N)]

plots = []

for i, (y, x) in enumerate(product(ATTRS, reversed(ATTRS))):
    p = Plot(x_range=xdrs[i%N], y_range=ydrs[i//N],
             border_fill_color="white", width=200, height=200, min_border=5)

    if i % N == 0:  # first column
        p.min_border_left = p.min_border + 4
        p.width += 40
        yaxis = LinearAxis(axis_label=y)
        yaxis.major_label_orientation = "vertical"
        p.add_layout(yaxis, "left")
        yticker = yaxis.ticker
        yticker = BasicTicker()
    p.add_layout(Grid(dimension=1, ticker=yticker))

    if i >= N*(N-1):  # last row
        p.min_border_bottom = p.min_border + 40
        p.height += 40
        xaxis = LinearAxis(axis_label=x)
        p.add_layout(xaxis, "below")
        xticker = xaxis.ticker
        xticker = BasicTicker()
    p.add_layout(Grid(dimension=0, ticker=xticker))

    scatter = Scatter(x=x, y=y, fill_alpha=0.6, size=5, line_color=None,
                      fill_color=factor_cmap('species', 'Category10_3', SPECIES))
    r = p.add_glyph(source, scatter)

    # suppress the diagonal
    if (i%N) + (i//N) == N-1:
        r.visible = False
        p.grid.grid_line_color = None

    p.add_tools(PanTool(), WheelZoomTool(), ResetTool(), LassoSelectTool())


show(gridplot(plots, ncols=N))