A count plot displays the frequency of observations in each category of a categorical variable using vertical bars. Unlike a basic bar chart that requires pre-computed values, a count plot automatically counts occurrences from raw data. This makes it ideal for quick exploratory analysis of categorical distributions without manual aggregation.
""" anyplot.ai
count-basic: Basic Count Plot
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 84/100 | Updated: 2026-05-07
"""
import os
import sys
import time
from pathlib import Path
sys.path = [p for p in sys.path if "implementations" not in p]
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, LabelSet
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens
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"
BRAND = "#009E73" # Okabe-Ito position 1
# Data - Survey responses simulating a customer satisfaction survey
np.random.seed(42)
responses = np.random.choice(
["Very Satisfied", "Satisfied", "Neutral", "Dissatisfied", "Very Dissatisfied"],
size=200,
p=[0.25, 0.35, 0.20, 0.12, 0.08],
)
# Count occurrences
categories, counts = np.unique(responses, return_counts=True)
# Sort by count descending for better readability
sorted_indices = np.argsort(-counts)
categories = categories[sorted_indices]
counts = counts[sorted_indices]
# Create data source
source = ColumnDataSource(
data={"category": categories.tolist(), "count": counts.tolist(), "label": [str(c) for c in counts]}
)
# Create figure with categorical x-axis
p = figure(
x_range=categories.tolist(),
width=4800,
height=2700,
title="count-basic · bokeh · anyplot.ai",
x_axis_label="Response Category",
y_axis_label="Number of Responses",
toolbar_location=None,
)
# Plot bars with Okabe-Ito green
p.vbar(x="category", top="count", source=source, width=0.7, color=BRAND, alpha=0.85, line_color=INK_SOFT, line_width=2)
# Add count labels above bars
labels = LabelSet(
x="category",
y="count",
text="label",
source=source,
text_align="center",
text_baseline="bottom",
y_offset=10,
text_font_size="22pt",
text_color=INK_SOFT,
)
p.add_layout(labels)
# Style the plot
p.title.text_font_size = "28pt"
p.title.align = "center"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = INK
p.ygrid.grid_line_alpha = 0.10
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
# Axis and background
p.xaxis.major_label_orientation = 0.4
p.y_range.start = 0
p.y_range.end = max(counts) * 1.15
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Save files in script directory
script_dir = Path(__file__).parent
html_path = script_dir / f"plot-{THEME}.html"
png_path = script_dir / f"plot-{THEME}.png"
output_file(str(html_path))
save(p)
# Screenshot with headless Chrome
W, H = 4800, 2700
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://{html_path.resolve()}")
time.sleep(3)
driver.save_screenshot(str(png_path))
driver.quit()
Part of Basic Count Plot on anyplot.ai.