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: bokeh 3.9.2 | Python 3.13.14
Quality: 93/100 | Updated: 2026-08-04
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, LabelSet
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
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"
# Color encodes topical category (a second, independent dimension) rather than
# frequency, which is already shown by size - a size+color double-encoding of
# the same variable was flagged as redundant in review. Each category gets one
# fixed Imprint categorical color (see prompts/library/bokeh.md IMPRINT_PALETTE).
CATEGORY_COLORS = {
"ML & Algorithms": "#009E73",
"Data & Analytics": "#C475FD",
"Tools & Libraries": "#4467A3",
"Infra & Pipeline": "#BD8233",
}
# Data: Technology terms with frequencies and topical category
words_data = [
("Python", 100, "Tools & Libraries"),
("Data", 95, "Data & Analytics"),
("Machine", 92, "ML & Algorithms"),
("Learning", 88, "ML & Algorithms"),
("Analytics", 85, "Data & Analytics"),
("Visualization", 82, "Infra & Pipeline"),
("Statistics", 78, "Data & Analytics"),
("Algorithm", 75, "ML & Algorithms"),
("Model", 72, "ML & Algorithms"),
("Neural", 70, "ML & Algorithms"),
("Network", 68, "ML & Algorithms"),
("Cloud", 65, "Infra & Pipeline"),
("API", 62, "Tools & Libraries"),
("Framework", 60, "Tools & Libraries"),
("Library", 58, "Tools & Libraries"),
("Code", 55, "Tools & Libraries"),
("Science", 52, "Data & Analytics"),
("Analysis", 50, "Data & Analytics"),
("Deep", 48, "ML & Algorithms"),
("Tensor", 46, "ML & Algorithms"),
("Deploy", 44, "Infra & Pipeline"),
("Pipeline", 42, "Infra & Pipeline"),
("Training", 40, "Infra & Pipeline"),
("Metrics", 38, "Data & Analytics"),
("Dataset", 36, "Data & Analytics"),
("Vector", 34, "ML & Algorithms"),
("Graph", 32, "Infra & Pipeline"),
("Batch", 30, "Infra & Pipeline"),
("Query", 28, "Tools & Libraries"),
("Cache", 26, "Tools & Libraries"),
("Index", 24, "Tools & Libraries"),
("Schema", 22, "Tools & Libraries"),
("Token", 20, "Tools & Libraries"),
("Epoch", 18, "Infra & Pipeline"),
("Layer", 16, "Infra & Pipeline"),
("Cluster", 14, "Infra & Pipeline"),
("Stream", 12, "Infra & Pipeline"),
("Config", 10, "Tools & Libraries"),
]
canvas_width = 3200
canvas_height = 1800
min_freq = min(f for _, f, _ in words_data)
max_freq = max(f for _, f, _ in words_data)
min_size, max_size = 42, 150
rotations = [0, 0, 90, -90]
words = []
x_pos = []
y_pos = []
sizes = []
colors = []
angles = []
frequencies = []
categories = []
placed_boxes = []
for i, (word, freq, category) in enumerate(words_data):
# Sub-linear (power 0.6) curve on the normalized frequency raises the
# size floor for the lowest-frequency words so they stay legible once
# downscaled to a gallery thumbnail, while the highest-frequency words
# still top out at max_size.
freq_normalized = (freq - min_freq) / (max_freq - min_freq)
size = int(min_size + freq_normalized**0.6 * (max_size - min_size))
if i < 5:
angle_deg = 0
else:
angle_deg = rotations[i % len(rotations)]
angle_rad = np.radians(angle_deg)
# bokeh text_font_size is set in CSS pt; headless Chrome renders pt at
# 1pt ~= 1.333 source-px, so the pt value must be converted to px before
# it's usable as a collision-box dimension.
px_size = size * 1.333
base_width = len(word) * px_size * 0.58
base_height = px_size * 1.2
if angle_deg != 0:
word_width = base_height
word_height = base_width
else:
word_width = base_width
word_height = base_height
cx, cy = canvas_width / 2, canvas_height / 2
spiral_angle = 0
radius = 0
padding = 7
found_x, found_y = cx, cy
found_box = (cx - word_width / 2, cy - word_height / 2, word_width, word_height)
for _ in range(25000):
test_x = cx + radius * 2.0 * np.cos(spiral_angle) - word_width / 2
test_y = cy + radius * np.sin(spiral_angle) - word_height / 2
margin_x = 20
margin_y = 35
if (
margin_x < test_x < canvas_width - word_width - margin_x
and margin_y < test_y < canvas_height - word_height - margin_y
):
test_box = (test_x, test_y, word_width, word_height)
overlap = False
for pb in placed_boxes:
px, py, pw, ph = pb
if not (
test_x + word_width + padding < px
or px + pw + padding < test_x
or test_y + word_height + padding < py
or py + ph + padding < test_y
):
overlap = True
break
if not overlap:
found_x = test_x + word_width / 2
found_y = test_y + word_height / 2
found_box = test_box
break
spiral_angle += 0.08
radius += 0.8
placed_boxes.append(found_box)
words.append(word)
x_pos.append(found_x)
y_pos.append(found_y)
sizes.append(size)
angles.append(angle_rad)
frequencies.append(freq)
categories.append(category)
colors.append(CATEGORY_COLORS[category])
p = figure(
width=canvas_width,
height=canvas_height,
title="wordcloud-basic · bokeh · anyplot.ai",
x_range=(0, canvas_width),
y_range=(0, canvas_height),
tools="hover",
toolbar_location=None,
)
hit_sizes = [s * 0.8 for s in sizes]
source = ColumnDataSource(
data={
"x": x_pos,
"y": y_pos,
"text": words,
"size": sizes,
"hit_size": hit_sizes,
"color": colors,
"angle": angles,
"frequency": frequencies,
"category": categories,
}
)
p.scatter(x="x", y="y", size="hit_size", source=source, fill_alpha=0, line_alpha=0)
hover = p.select_one(HoverTool)
hover.tooltips = [("Word", "@text"), ("Frequency", "@frequency"), ("Category", "@category")]
hover.mode = "mouse"
source.data["size"] = [f"{s}pt" for s in sizes]
labels = LabelSet(
x="x",
y="y",
text="text",
text_font_size="size",
text_color="color",
text_align="center",
text_baseline="middle",
text_font_style="bold",
angle="angle",
source=source,
)
p.add_layout(labels)
p.axis.visible = False
p.grid.visible = False
p.outline_line_color = None
p.title.text_font_size = "50pt"
p.title.text_color = INK
p.title.align = "center"
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
output_file(f"plot-{THEME}.html")
save(p)
W, H = canvas_width, canvas_height
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
driver.execute_cdp_cmd(
"Emulation.setDeviceMetricsOverride", {"width": W, "height": H, "deviceScaleFactor": 1, "mobile": False}
)
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/wordcloud-basic/bokeh/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": "bokeh",
"page": "https://anyplot.ai/wordcloud-basic/python/bokeh",
"hub": "https://anyplot.ai/wordcloud-basic",
"code_json": "https://api.anyplot.ai/specs/wordcloud-basic/bokeh/code",
"spec_json": "https://api.anyplot.ai/specs/wordcloud-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/bokeh/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/bokeh/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/bokeh/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/wordcloud-basic/python/bokeh/plot-dark.html",
"quality_score": 93.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Word Cloud on anyplot.ai.