A word cloud displays text data where word size represents frequency or importance. Words are arranged to fill available space, creating a visual summary of text content that highlights prominent terms and patterns. This visualization is ideal for quickly identifying the most common themes or keywords in a body of text.
""" anyplot.ai
wordcloud-basic: Basic Word Cloud
Library: matplotlib 3.11.1 | Python 3.13.14
Quality: 89/100 | Updated: 2026-08-04
"""
import os
import matplotlib.pyplot as plt
from wordcloud import WordCloud
# Theme tokens (see prompts/default-style-guide.md "Background" + "Theme-adaptive Chrome")
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
# Imprint palette — 8 hues, canonical order (see prompts/default-style-guide.md)
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data - Tech industry survey responses about most valued skills
word_frequencies = {
"Python": 150,
"JavaScript": 120,
"Data": 110,
"Machine Learning": 100,
"Cloud": 95,
"API": 90,
"Database": 85,
"Security": 80,
"DevOps": 75,
"Java": 72,
"Docker": 70,
"Kubernetes": 65,
"React": 60,
"SQL": 58,
"AWS": 55,
"Golang": 53,
"Git": 52,
"Agile": 50,
"Testing": 48,
"Linux": 45,
"TypeScript": 42,
"Node": 40,
"REST": 38,
"Rust": 36,
"CI/CD": 35,
"Microservices": 32,
"Serverless": 31,
"Azure": 30,
"MongoDB": 28,
"Encryption": 27,
"Redis": 26,
"GraphQL": 24,
"Compliance": 23,
"Terraform": 22,
"Blockchain": 20,
"Spark": 20,
"IoT": 18,
"Analytics": 18,
"Frontend": 16,
"Mentoring": 16,
"Backend": 15,
"Code Review": 15,
"Scalability": 14,
"Refactoring": 14,
"Automation": 13,
"Onboarding": 13,
"Architecture": 12,
"Networking": 11,
}
# Assign Imprint hues by frequency rank (not by word hash) so color carries
# meaning: the most-valued skill is always brand green, the runner-up
# lavender, and so on — a deliberate hierarchy instead of an arbitrary bucket.
ranked_words = sorted(word_frequencies, key=word_frequencies.get, reverse=True)
rank_color = {word: IMPRINT[i % len(IMPRINT)] for i, word in enumerate(ranked_words)}
def color_func(word, font_size, position, orientation, random_state=None, **kwargs):
return rank_color[word]
wc = WordCloud(
width=3200,
height=1800,
background_color=PAGE_BG,
max_words=100,
min_font_size=18,
max_font_size=200,
random_state=42,
prefer_horizontal=0.6,
relative_scaling=0.5,
margin=4,
).generate_from_frequencies(word_frequencies)
wc.recolor(color_func=color_func)
# Plot — see default-style-guide.md "Visual Sizing Defaults" for the canvas + sizing values
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
ax.imshow(wc, interpolation="bilinear")
ax.axis("off")
# Title
title = "wordcloud-basic · python · matplotlib · anyplot.ai"
title_fontsize = round(12 * 67 / len(title)) if len(title) > 67 else 12
fig.suptitle(title, fontsize=title_fontsize, fontweight="medium", color=INK, y=0.97)
fig.tight_layout(rect=[0, 0, 1, 0.94])
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG) # bbox_inches MUST stay default (None)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/wordcloud-basic/matplotlib/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": "wordcloud-basic",
"language": "python",
"library": "matplotlib",
"page": "https://anyplot.ai/wordcloud-basic/python/matplotlib",
"hub": "https://anyplot.ai/wordcloud-basic",
"code_json": "https://api.anyplot.ai/specs/wordcloud-basic/matplotlib/code",
"spec_json": "https://api.anyplot.ai/specs/wordcloud-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/matplotlib/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/matplotlib/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Word Cloud on anyplot.ai.