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: pygal 3.1.3 | Python 3.13.14
Quality: 77/100 | Updated: 2026-08-04
"""
import os
import sys
import xml.etree.ElementTree as ET
# Avoid naming conflict with pygal.py script name
# Remove current directory from path temporarily
cwd = os.getcwd()
sys.path = [p for p in sys.path if p not in ("", ".", cwd)]
import cairosvg
import numpy as np
import pygal
from pygal.style import Style
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
# Imprint palette (first series = #009E73)
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314")
# Data: DevOps tooling adoption survey - term frequencies from a platform-engineering survey
word_frequencies = {
"Kubernetes": 195,
"Docker": 178,
"Terraform": 162,
"Ansible": 148,
"Helm": 135,
"Jenkins": 122,
"GitLab": 110,
"ArgoCD": 98,
"Prometheus": 90,
"Grafana": 82,
"Vault": 75,
"Consul": 68,
"CircleCI": 60,
"Chef": 54,
"Puppet": 48,
"GitOps": 44,
"Istio": 40,
"Envoy": 36,
"Fluentd": 32,
"Loki": 29,
"Kibana": 26,
"Vagrant": 23,
"Packer": 20,
"Nginx": 18,
"HAProxy": 16,
"Zabbix": 14,
"Nagios": 12,
"PagerDuty": 10,
}
# Canvas dimensions (canonical landscape)
canvas_w = 3200
canvas_h = 1800
# Scale frequencies to font sizes
min_freq = min(word_frequencies.values())
max_freq = max(word_frequencies.values())
min_size = 50
max_size = 187
# Sort by frequency (largest first for better placement)
sorted_words = sorted(word_frequencies.items(), key=lambda x: x[1], reverse=True)
n_words = len(sorted_words)
# Build word positions using spiral algorithm; opacity tiers by frequency add a
# secondary depth cue beyond size alone (top tier fully opaque, tail eases back)
word_data = []
placed_boxes = []
for i, (word, freq) in enumerate(sorted_words):
# Scale frequency to font size
size = int(min_size + (freq - min_freq) / (max_freq - min_freq) * (max_size - min_size))
opacity = round(0.7 + 0.3 * (freq - min_freq) / (max_freq - min_freq), 2)
# Estimate dimensions (generous width factor so bold glyphs keep a visible gap)
w = len(word) * size * 0.62
h = size * 1.2
# Spiral placement - centered with balanced distribution, biased below the title band
cx, cy = canvas_w / 2, canvas_h / 2 + 60
# Ellipse ratio narrows from wide (matches the 16:9 canvas for early, large
# words) toward near-circular for the tail, so late/small words reach the
# top-right/bottom-right voids instead of stacking against the horizontal bound
progress = i / max(n_words - 1, 1)
ellipse_x = 2.8 - 1.2 * progress
ellipse_y = 1.8 - 0.2 * progress
# Stagger each word's starting angle by the golden angle so consecutive
# spirals fan out in different directions instead of retracing the same
# path and piling into whichever gap opens first along it
angle = i * 2.399963
radius = 0
x, y = cx, cy
box = (cx - w / 2, cy - h / 2, w, h)
for _ in range(50000):
# Elliptical spiral
test_x = cx + radius * ellipse_x * np.cos(angle) - w / 2
test_y = cy + radius * ellipse_y * np.sin(angle) - h / 2
# Check bounds with margins for title and edges
if 67 < test_x < canvas_w - w - 67 and 150 < test_y < canvas_h - h - 67:
test_box = (test_x, test_y, w, h)
# Check for overlap with placed words
overlap = False
for pb in placed_boxes:
x1, y1, w1, h1 = test_box
x2, y2, w2, h2 = pb
padding = 40 # Padding to prevent clustering / guarantee a visible gap
if not (
x1 + w1 + padding < x2 or x2 + w2 + padding < x1 or y1 + h1 + padding < y2 or y2 + h2 + padding < y1
):
overlap = True
break
if not overlap:
x = test_x + w / 2
y = test_y + h / 2
box = test_box
break
angle += 0.06 # Slower angle progression for better spacing
radius += 2.33 # Moderate radius growth
placed_boxes.append(box)
word_data.append(
{"word": word, "x": x, "y": y, "size": size, "opacity": opacity, "color": IMPRINT[i % len(IMPRINT)]}
)
# Recenter the whole cloud within the safe canvas area: the spiral settles
# wherever overlap checks first succeed, which tends to drift the bounding
# box off-center (e.g. a large left-side void with words crowded right).
# Shifting every word by the same offset preserves all relative spacing and
# the zero-overlap guarantee while balancing the leftover whitespace.
safe_left, safe_right = 67, canvas_w - 67
safe_top, safe_bottom = 150, canvas_h - 67
box_min_x = min(b[0] for b in placed_boxes)
box_max_x = max(b[0] + b[2] for b in placed_boxes)
box_min_y = min(b[1] for b in placed_boxes)
box_max_y = max(b[1] + b[3] for b in placed_boxes)
shift_x = (safe_left + safe_right) / 2 - (box_min_x + box_max_x) / 2
shift_y = (safe_top + safe_bottom) / 2 - (box_min_y + box_max_y) / 2
for item in word_data:
item["x"] += shift_x
item["y"] += shift_y
# Minimal pygal chart as canvas, using pygal's own title rendering (idiomatic
# high-level API) instead of hand-drawn SVG text; only the word placement below
# needs manual SVG injection since pygal has no word-cloud primitive.
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
title_font_size=66,
title_font_family="sans-serif", # match the bold sans-serif word text below
)
chart = pygal.XY(
style=custom_style,
width=canvas_w,
height=canvas_h,
title="wordcloud-basic · pygal · anyplot.ai",
show_legend=False,
show_x_labels=False,
show_y_labels=False,
show_x_guides=False,
show_y_guides=False,
show_dots=False,
stroke=False,
margin=0,
)
# Add dummy data (required for chart to render)
chart.add("", [(0, 0)])
# Render SVG and manually inject the word cloud text elements (no pygal
# primitive exists for freeform-positioned, variably-sized text)
svg_string = chart.render(is_unicode=True)
root = ET.fromstring(svg_string)
for item in word_data:
text_elem = ET.SubElement(root, "text")
text_elem.set("x", str(int(item["x"])))
text_elem.set("y", str(int(item["y"])))
text_elem.set("font-size", str(item["size"]))
text_elem.set("font-weight", "bold")
text_elem.set("fill", item["color"])
text_elem.set("fill-opacity", str(item["opacity"]))
text_elem.set("text-anchor", "middle")
text_elem.set("dominant-baseline", "middle")
text_elem.set("font-family", "sans-serif")
text_elem.text = item["word"]
# Write modified SVG
modified_svg = ET.tostring(root, encoding="unicode")
with open(f"plot-{THEME}.svg", "w") as f:
f.write(modified_svg)
# Render PNG using cairosvg
cairosvg.svg2png(bytestring=modified_svg.encode(), write_to=f"plot-{THEME}.png")
# Save as HTML for interactive viewing
with open(f"plot-{THEME}.html", "w") as f:
f.write(
f"""<!DOCTYPE html>
<html>
<head>
<title>wordcloud-basic · pygal · anyplot.ai</title>
<style>
body {{ margin: 0; display: flex; justify-content: center; align-items: center; min-height: 100vh; background: {PAGE_BG}; }}
svg {{ max-width: 100%; height: auto; }}
</style>
</head>
<body>
{modified_svg}
</body>
</html>"""
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/wordcloud-basic/pygal/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": "pygal",
"page": "https://anyplot.ai/wordcloud-basic/python/pygal",
"hub": "https://anyplot.ai/wordcloud-basic",
"code_json": "https://api.anyplot.ai/specs/wordcloud-basic/pygal/code",
"spec_json": "https://api.anyplot.ai/specs/wordcloud-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/pygal/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/pygal/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/pygal/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/pygal/plot-dark.html",
"quality_score": 77.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Word Cloud on anyplot.ai.