Scatter Plot with Text Labels Instead of Points — Matplotlib

A scatter plot where data points are represented by text labels instead of markers. Each label is positioned at its corresponding coordinates, making the text itself the visual element. This visualization is particularly useful for displaying named entities in 2D space, such as word embeddings, dimensionality reduction outputs, or any scenario where identifying individual items by name is more important than seeing their relative density.

Scatter Plot with Text Labels Instead of Points rendered with Matplotlib

Python source (Matplotlib)

""" anyplot.ai
scatter-text: Scatter Plot with Text Labels Instead of Points
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-17
"""

import os

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Patch


# 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"

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

# Data: Programming languages positioned by paradigm similarity
# (simulating dimensionality reduction output)
languages = [
    "Python",
    "JavaScript",
    "Java",
    "C++",
    "Ruby",
    "Go",
    "Rust",
    "Swift",
    "Kotlin",
    "TypeScript",
    "Scala",
    "Haskell",
    "Clojure",
    "Elixir",
    "Julia",
    "R",
    "MATLAB",
    "Perl",
    "PHP",
    "C#",
    "F#",
    "OCaml",
    "Erlang",
    "Lua",
    "Dart",
    "Zig",
    "Nim",
    "Crystal",
    "Groovy",
    "Fortran",
]

# Improved coordinates to reduce overlap while maintaining logical clusters
x = np.array(
    [
        1.5,
        3.2,
        4.0,
        5.8,
        2.5,
        6.2,
        6.8,
        5.0,
        4.8,
        3.5,
        3.0,
        0.5,
        1.0,
        0.8,
        2.2,
        1.2,
        2.3,
        3.5,
        2.5,
        4.2,
        2.8,
        0.3,
        1.8,
        4.5,
        5.8,
        6.5,
        5.5,
        3.2,
        4.5,
        7.0,
    ]
)

y = np.array(
    [
        5.8,
        4.8,
        3.2,
        2.0,
        5.5,
        3.5,
        2.5,
        3.8,
        3.2,
        4.3,
        4.0,
        6.8,
        6.5,
        5.8,
        4.8,
        4.2,
        3.8,
        3.2,
        2.8,
        3.0,
        6.2,
        6.2,
        5.5,
        2.5,
        4.5,
        1.8,
        2.2,
        5.8,
        3.2,
        1.2,
    ]
)

# Category assignments
categories = [
    "dynamic",
    "dynamic",
    "jvm",
    "systems",
    "dynamic",
    "systems",
    "systems",
    "systems",
    "jvm",
    "dynamic",
    "jvm",
    "functional",
    "functional",
    "functional",
    "dynamic",
    "dynamic",
    "dynamic",
    "dynamic",
    "dynamic",
    "jvm",
    "functional",
    "functional",
    "functional",
    "dynamic",
    "dynamic",
    "systems",
    "systems",
    "functional",
    "jvm",
    "systems",
]

category_to_color = {"dynamic": IMPRINT[0], "functional": IMPRINT[1], "systems": IMPRINT[2], "jvm": IMPRINT[3]}

color_list = [category_to_color[cat] for cat in categories]

# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Plot text labels at each coordinate
for xi, yi, label, color in zip(x, y, languages, color_list, strict=True):
    ax.text(
        xi, yi, label, fontsize=18, ha="center", va="center", color=color, fontweight="bold", alpha=0.9
    )

# Set axis limits with padding
ax.set_xlim(-0.5, 7.5)
ax.set_ylim(0.5, 7.5)

# Labels and styling
ax.set_xlabel("Embedding Dimension 1", fontsize=20, color=INK)
ax.set_ylabel("Embedding Dimension 2", fontsize=20, color=INK)
ax.set_title("scatter-text · Python · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)

# Grid
ax.grid(True, alpha=0.1, linewidth=0.8, color=INK)

# Legend
legend_elements = [
    Patch(facecolor=IMPRINT[0], label="Dynamic/Scripting"),
    Patch(facecolor=IMPRINT[1], label="Functional"),
    Patch(facecolor=IMPRINT[2], label="Systems"),
    Patch(facecolor=IMPRINT[3], label="JVM-based"),
]
leg = ax.legend(handles=legend_elements, loc="upper right", fontsize=16, framealpha=0.9)
if leg:
    leg.get_frame().set_facecolor(PAGE_BG)
    leg.get_frame().set_edgecolor(INK_SOFT)
    for text in leg.get_texts():
        text.set_color(INK_SOFT)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Scatter Plot with Text Labels Instead of Points on anyplot.ai.

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