Basic Strip Plot — Bokeh

A strip plot displays individual data points for each category along a single axis, with random horizontal jitter applied to reduce overplotting. Unlike box plots or violin plots that show summary statistics, strip plots reveal every observation, making them ideal for small to medium datasets where individual values matter. The random jitter spreads points horizontally within each category to show density through point accumulation.

Basic Strip Plot rendered with Bokeh

Python source (Bokeh)

""" anyplot.ai
strip-basic: Basic Strip Plot
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-04
"""

import os
import time
from pathlib import Path

import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"

IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]

# Data - Survey response scores by department
np.random.seed(42)

categories = ["Engineering", "Marketing", "Sales", "HR"]
n_per_category = [45, 38, 52, 30]

data = {
    "Engineering": np.clip(np.random.normal(7.2, 1.5, n_per_category[0]), 1, 10),
    "Marketing": np.clip(np.random.normal(6.8, 1.8, n_per_category[1]), 1, 10),
    "Sales": np.clip(np.random.normal(7.5, 1.2, n_per_category[2]), 1, 10),
    "HR": np.clip(np.random.normal(8.0, 1.0, n_per_category[3]), 1, 10),
}

# Build arrays for plotting with jitter
x_values = []
y_values = []
colors = []
color_map = dict(zip(categories, IMPRINT, strict=True))

jitter_width = 0.25

for i, cat in enumerate(categories):
    values = data[cat]
    n = len(values)
    jittered_x = i + np.random.uniform(-jitter_width, jitter_width, n)
    x_values.extend(jittered_x)
    y_values.extend(values)
    colors.extend([color_map[cat]] * n)

source = ColumnDataSource(data={"x": x_values, "y": y_values, "color": colors})

# Plot
p = figure(
    width=4800,
    height=2700,
    title="strip-basic · bokeh · anyplot.ai",
    x_axis_label="Department",
    y_axis_label="Survey Score (1–10)",
    x_range=(-0.5, len(categories) - 0.5),
    y_range=(0, 11),
)

p.scatter(x="x", y="y", source=source, size=28, color="color", alpha=0.6, line_color=PAGE_BG, line_width=2)

# Mean reference lines — one legend entry shared across all categories
for i, cat in enumerate(categories):
    mean_val = float(np.mean(data[cat]))
    legend_kw = {"legend_label": "Group Mean"} if i == 0 else {}
    p.line(x=[i - 0.35, i + 0.35], y=[mean_val, mean_val], line_color=INK_SOFT, line_width=5, **legend_kw)

# Text sizes for 4800×2700 px
p.title.text_font_size = "42pt"
p.xaxis.axis_label_text_font_size = "32pt"
p.yaxis.axis_label_text_font_size = "32pt"
p.xaxis.major_label_text_font_size = "26pt"
p.yaxis.major_label_text_font_size = "26pt"

# Categorical tick labels on x-axis
p.xaxis.ticker = list(range(len(categories)))
p.xaxis.major_label_overrides = dict(enumerate(categories))

# Theme-adaptive chrome
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT

p.title.text_color = INK
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.axis_line_width = 2
p.yaxis.axis_line_width = 2
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT

p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = INK
p.ygrid.grid_line_alpha = 0.10

if p.legend:
    p.legend.background_fill_color = ELEVATED_BG
    p.legend.border_line_color = INK_SOFT
    p.legend.label_text_color = INK_SOFT
    p.legend.label_text_font_size = "22pt"
    p.legend.location = "top_right"

# Save HTML
output_file(f"plot-{THEME}.html")
save(p)

# Screenshot with headless Chrome (use taller window to capture x-axis labels)
W, H = 4800, 3000
opts = Options()
for arg in (
    "--headless=new",
    "--no-sandbox",
    "--disable-dev-shm-usage",
    "--disable-gpu",
    f"--window-size={W},{H}",
    "--hide-scrollbars",
):
    opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()

Part of Basic Strip Plot on anyplot.ai.

Other implementations