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.
""" anyplot.ai
strip-basic: Basic Strip Plot
Library: letsplot 4.11.0 | Python 3.13.14
Quality: 90/100 | Updated: 2026-08-05
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_jitter,
ggplot,
ggsave,
ggsize,
labs,
scale_color_manual,
stat_summary,
theme,
theme_minimal,
)
LetsPlot.setup_html()
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Imprint categorical palette - position 1 is always #009E73
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
# Data - Survey response scores by department
np.random.seed(42)
departments = ["Marketing", "Engineering", "Sales", "Support"]
data = []
distributions = {"Marketing": (72, 12), "Engineering": (78, 8), "Sales": (68, 15), "Support": (75, 10)}
for dept in departments:
n_points = np.random.randint(25, 45)
mean, std = distributions[dept]
scores = np.clip(np.random.normal(mean, std, n_points), 40, 100)
for score in scores:
data.append({"Department": dept, "Score": score})
df = pd.DataFrame(data)
# Plot - individual points per department, jittered to reveal every observation
plot = (
ggplot(df, aes(x="Department", y="Score"))
+ geom_jitter(aes(color="Department"), size=4, alpha=0.65, width=0.25, height=0, seed=42, show_legend=False)
# Mean + interquartile reference per department - draws the eye to the
# Engineering-vs-Sales spread/level contrast that the raw jitter alone hides
+ stat_summary(
aes(x="Department", y="Score"), fun="mean", fun_min="lq", fun_max="uq", geom="pointrange", color=INK, size=1.1
)
+ scale_color_manual(values=IMPRINT_PALETTE)
+ labs(x="Department", y="Survey Score (points)", title="strip-basic · python · letsplot · anyplot.ai")
+ ggsize(800, 450)
+ theme_minimal()
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
axis_title=element_text(color=INK, size=12),
axis_text=element_text(color=INK_SOFT, size=10),
plot_title=element_text(color=INK, size=16),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_y=element_line(color=INK_SOFT, size=0.3),
panel_border=element_blank(),
axis_line=element_line(color=INK_SOFT, size=0.5),
)
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/strip-basic/letsplot/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "strip-basic",
"language": "python",
"library": "letsplot",
"page": "https://anyplot.ai/strip-basic/python/letsplot",
"hub": "https://anyplot.ai/strip-basic",
"code_json": "https://api.anyplot.ai/specs/strip-basic/letsplot/code",
"spec_json": "https://api.anyplot.ai/specs/strip-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/strip-basic/python/letsplot/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/strip-basic/python/letsplot/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/strip-basic/python/letsplot/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/strip-basic/python/letsplot/plot-dark.html",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Strip Plot on anyplot.ai.