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: plotnine 0.15.7 | Python 3.13.14
Quality: 85/100 | Updated: 2026-08-04
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
annotate,
coord_cartesian,
element_blank,
element_rect,
element_text,
geom_text,
ggplot,
labs,
scale_alpha_identity,
scale_color_identity,
scale_size_identity,
theme,
)
# 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 palette for frequency tiers
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
# Word frequency data - technology survey responses
words_data = {
"word": [
"Python",
"Data",
"Machine",
"Learning",
"AI",
"Cloud",
"API",
"Database",
"Security",
"DevOps",
"Analytics",
"Automation",
"Software",
"Code",
"Development",
"Integration",
"Platform",
"Infrastructure",
"Testing",
"Deployment",
"Monitoring",
"Framework",
"Docker",
"AWS",
"Azure",
"Kubernetes",
"Terraform",
"GraphQL",
"Redis",
],
"frequency": [
95,
88,
82,
78,
75,
70,
65,
62,
58,
55,
52,
48,
45,
42,
38,
35,
32,
30,
28,
26,
24,
22,
20,
18,
16,
14,
11,
8,
6,
],
}
df = pd.DataFrame(words_data)
# Calculate font sizes (6-25 mm) for emphasis, and a subtle alpha ramp for depth
min_freq, max_freq = df["frequency"].min(), df["frequency"].max()
freq_norm = (df["frequency"] - min_freq) / (max_freq - min_freq)
df["size"] = 6 + freq_norm * 19
df["alpha"] = 0.6 + freq_norm * 0.4
# Sort by frequency descending
df = df.sort_values("frequency", ascending=False).reset_index(drop=True)
# Hand-crafted positions to ensure no overlap
positions = [
(45, 28), # Python (largest) - center
(70, 36), # Data
(22, 24), # Machine
(72, 22), # Learning
(30, 36), # AI
(55, 16), # Cloud
(18, 42), # API
(45, 42), # Database
(68, 46), # Security
(25, 10), # DevOps
(50, 6), # Analytics
(78, 8), # Automation
(6, 28), # Software
(88, 28), # Code
(35, 50), # Development
(60, 50), # Integration
(12, 50), # Platform
(82, 50), # Infrastructure
(6, 16), # Testing
(6, 40), # Deployment
(55, 36), # Monitoring
(88, 40), # Framework
(30, 6), # Docker
(6, 6), # AWS
(75, 6), # Azure
(94, 10), # Kubernetes - fills empty right margin below the legend
(94, 24), # Terraform - fills empty right margin below the legend
(78, 53), # GraphQL - fills empty upper-right quadrant
(42, 20), # Redis - fills gap between the mid-canvas clusters
]
df["x"] = [p[0] for p in positions]
df["y"] = [p[1] for p in positions]
# AWS sits alone in the bottom-left corner - rotate it for an organic word-cloud feel
df["angle"] = np.where(df["word"] == "AWS", 90, 0)
# Assign Imprint colors based on frequency tiers
colors = []
for freq in df["frequency"]:
if freq >= 65:
colors.append(IMPRINT[0]) # Brand green - highest
elif freq >= 35:
colors.append(IMPRINT[1]) # Lavender - medium-high
elif freq >= 15:
colors.append(IMPRINT[2]) # Blue - medium-low
else:
colors.append(IMPRINT[3]) # Ochre - lowest
df["color"] = colors
# Create legend using colored text labels instead of bullets
legend_df = pd.DataFrame(
{
"x": [92, 92, 92, 92],
"y": [46, 42, 38, 34],
"label": ["High (65+)", "Medium (35-64)", "Low-Med (15-34)", "Low (<15)"],
"color": IMPRINT,
}
)
# Create plot
plot = (
ggplot(df, aes(x="x", y="y", label="word", size="size", color="color"))
+ geom_text(aes(alpha="alpha", angle="angle"), family="sans-serif", fontweight="normal", show_legend=False)
+ geom_text(
data=legend_df, mapping=aes(x="x", y="y", label="label", color="color"), size=4, ha="left", show_legend=False
)
+ annotate("text", x=92, y=50, label="Frequency", size=5.5, ha="left", fontweight="bold", color=INK)
+ scale_size_identity()
+ scale_color_identity()
+ scale_alpha_identity()
+ coord_cartesian(xlim=(0, 100), ylim=(0, 56.25), expand=False)
+ labs(title="wordcloud-basic · plotnine · anyplot.ai")
+ theme(
figure_size=(8, 4.5),
plot_title=element_text(size=12, ha="center", weight="bold", color=INK, margin={"b": 8}),
panel_background=element_rect(fill=PAGE_BG, color=None),
plot_background=element_rect(fill=PAGE_BG, color=None),
panel_grid_major=element_blank(),
panel_grid_minor=element_blank(),
axis_text=element_blank(),
axis_title=element_blank(),
axis_ticks=element_blank(),
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/wordcloud-basic/plotnine/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": "plotnine",
"page": "https://anyplot.ai/wordcloud-basic/python/plotnine",
"hub": "https://anyplot.ai/wordcloud-basic",
"code_json": "https://api.anyplot.ai/specs/wordcloud-basic/plotnine/code",
"spec_json": "https://api.anyplot.ai/specs/wordcloud-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/plotnine/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/plotnine/plot-dark.png",
"quality_score": 85.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Word Cloud on anyplot.ai.