Categorical Scatter Plot in lets-plot (Python)

The same plot in 14 other libraries — Python: Altair, Bokeh, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Categorical Scatter Plot in Python, R, Julia and JavaScript.

A scatter plot where points are colored according to a categorical variable. Each category has a distinct color, allowing visual comparison of patterns across groups. A legend maps colors to category names.

Categorical Scatter Plot rendered with lets-plot

Renders

Python source (lets-plot)

""" anyplot.ai
scatter-categorical: Categorical Scatter Plot
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 88/100 | Created: 2026-05-12
"""

import os

import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot.export import ggsave as export_ggsave


LetsPlot.setup_html()

# 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"

# Okabe-Ito palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]

# Data: Plant growth by treatment type
np.random.seed(42)
treatment_a = np.random.normal(loc=45, scale=8, size=40)
growth_a = treatment_a * 1.1 + np.random.normal(loc=5, scale=6, size=40)

treatment_b = np.random.normal(loc=52, scale=9, size=35)
growth_b = treatment_b * 0.95 + np.random.normal(loc=3, scale=7, size=35)

treatment_c = np.random.normal(loc=38, scale=7, size=38)
growth_c = treatment_c * 1.25 + np.random.normal(loc=8, scale=5, size=38)

treatment_d = np.random.normal(loc=55, scale=10, size=37)
growth_d = treatment_d * 0.85 + np.random.normal(loc=2, scale=8, size=37)

df = pd.DataFrame(
    {
        "Temperature (°C)": np.concatenate([treatment_a, treatment_b, treatment_c, treatment_d]),
        "Growth Rate (%)": np.concatenate([growth_a, growth_b, growth_c, growth_d]),
        "Treatment": (["Treatment A"] * 40 + ["Treatment B"] * 35 + ["Treatment C"] * 38 + ["Treatment D"] * 37),
    }
)

# Plot with shape variation for visual hierarchy and distinction
plot = (
    ggplot(df, aes(x="Temperature (°C)", y="Growth Rate (%)", color="Treatment", shape="Treatment"))
    + geom_point(size=3.5, alpha=0.8, stroke=0.8)
    + scale_color_manual(values=IMPRINT[:4])
    + scale_shape_manual(values=[21, 22, 23, 24])  # Circle, square, diamond, triangle
    + labs(
        x="Temperature (°C)",
        y="Growth Rate (%)",
        title="scatter-categorical · letsplot · anyplot.ai",
        color="Treatment",
        shape="Treatment",
    )
    + ggsize(1600, 900)
    + theme_minimal()
    + theme(
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG, color=None),
        panel_grid_major=element_line(color=INK_SOFT, size=0.15),
        panel_grid_minor=element_blank(),
        axis_title=element_text(size=20, color=INK, face="bold"),
        axis_text=element_text(size=16, color=INK_SOFT),
        axis_line=element_line(color=INK_SOFT, size=0.5),
        plot_title=element_text(size=24, color=INK, face="bold"),
        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT, size=0.5),
        legend_text=element_text(size=16, color=INK_SOFT),
        legend_title=element_text(size=16, color=INK, face="bold"),
    )
)

# Save
export_ggsave(plot, filename=f"plot-{THEME}.png", path=".", scale=3)
export_ggsave(plot, filename=f"plot-{THEME}.html", path=".")

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/scatter-categorical/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": "scatter-categorical",
  "language": "python",
  "library": "letsplot",
  "page": "https://anyplot.ai/scatter-categorical/python/letsplot",
  "hub": "https://anyplot.ai/scatter-categorical",
  "code_json": "https://api.anyplot.ai/specs/scatter-categorical/letsplot/code",
  "spec_json": "https://api.anyplot.ai/specs/scatter-categorical",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-categorical/python/letsplot/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-categorical/python/letsplot/plot-dark.png",
  "interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/scatter-categorical/python/letsplot/plot-light.html",
  "interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/scatter-categorical/python/letsplot/plot-dark.html",
  "quality_score": 88.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

Part of Categorical Scatter Plot on anyplot.ai.

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