A parallel categories plot visualizes categorical data across multiple dimensions, with vertical axes representing each categorical variable and ribbons connecting categories to show observation flow. Unlike parallel coordinates (which use lines for numeric data), parallel categories use width-proportional ribbons to show counts or frequencies, making it ideal for understanding how categorical values co-occur and flow across multiple classification dimensions.
""" anyplot.ai
parallel-categories-basic: Basic Parallel Categories Plot
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-13
"""
import os
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.path import Path
# 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"
# Data: Product purchase flow (Channel -> Category -> Outcome)
np.random.seed(42)
n_samples = 500
channels = np.random.choice(["Online", "Store", "Mobile"], size=n_samples, p=[0.4, 0.35, 0.25])
categories = np.random.choice(["Electronics", "Clothing", "Home", "Sports"], size=n_samples, p=[0.3, 0.25, 0.25, 0.2])
outcomes = np.random.choice(["Purchased", "Returned", "Abandoned"], size=n_samples, p=[0.6, 0.15, 0.25])
df = pd.DataFrame({"Channel": channels, "Category": categories, "Outcome": outcomes})
# Define dimensions and their categories
dimensions = ["Channel", "Category", "Outcome"]
dim_categories = {
"Channel": ["Online", "Store", "Mobile"],
"Category": ["Electronics", "Clothing", "Home", "Sports"],
"Outcome": ["Purchased", "Returned", "Abandoned"],
}
# Okabe-Ito palette for channel colors
colors = {"Online": "#009E73", "Store": "#C475FD", "Mobile": "#4467A3"}
# Create figure
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Calculate positions for each dimension
n_dims = len(dimensions)
x_positions = np.linspace(0, 1, n_dims)
dim_width = 0.08
# Calculate category positions within each dimension
category_positions = {}
category_heights = {}
for dim in dimensions:
cats = dim_categories[dim]
counts = df[dim].value_counts()
total = counts.sum()
heights = {cat: counts.get(cat, 0) / total for cat in cats}
y_start = 0.05
y_end = 0.95
available_height = y_end - y_start
gap = 0.02
total_gap = gap * (len(cats) - 1)
usable_height = available_height - total_gap
positions = {}
current_y = y_start
for cat in cats:
h = heights[cat] * usable_height
positions[cat] = (current_y, current_y + h)
current_y += h + gap
category_positions[dim] = positions
category_heights[dim] = heights
# Draw ribbons between consecutive dimensions
for i in range(n_dims - 1):
dim1 = dimensions[i]
dim2 = dimensions[i + 1]
x1 = x_positions[i]
x2 = x_positions[i + 1]
flow_counts = df.groupby([dim1, dim2]).size().reset_index(name="count")
current_y_left = {cat: category_positions[dim1][cat][0] for cat in dim_categories[dim1]}
current_y_right = {cat: category_positions[dim2][cat][0] for cat in dim_categories[dim2]}
total = len(df)
for _, row in flow_counts.iterrows():
cat1 = row[dim1]
cat2 = row[dim2]
count = row["count"]
y1_top = current_y_left[cat1] + (count / df[dim1].value_counts()[cat1]) * (
category_positions[dim1][cat1][1] - category_positions[dim1][cat1][0]
)
y2_bottom = current_y_right[cat2]
y2_top = current_y_right[cat2] + (count / df[dim2].value_counts()[cat2]) * (
category_positions[dim2][cat2][1] - category_positions[dim2][cat2][0]
)
y1_bottom = current_y_left[cat1]
x_ctrl1 = x1 + dim_width + (x2 - x1 - 2 * dim_width) * 0.4
x_ctrl2 = x1 + dim_width + (x2 - x1 - 2 * dim_width) * 0.6
vertices = [
(x1 + dim_width, y1_bottom),
(x_ctrl1, y1_bottom),
(x_ctrl2, y2_bottom),
(x2 - dim_width, y2_bottom),
(x2 - dim_width, y2_top),
(x_ctrl2, y2_top),
(x_ctrl1, y1_top),
(x1 + dim_width, y1_top),
(x1 + dim_width, y1_bottom),
]
codes = [
Path.MOVETO,
Path.CURVE4,
Path.CURVE4,
Path.CURVE4,
Path.LINETO,
Path.CURVE4,
Path.CURVE4,
Path.CURVE4,
Path.CLOSEPOLY,
]
path = Path(vertices, codes)
if i == 0:
color = colors[cat1]
else:
orig_cat = df[df[dim1] == cat1]["Channel"].mode()
if len(orig_cat) > 0:
color = colors.get(orig_cat.iloc[0], INK_SOFT)
else:
color = INK_SOFT
patch = mpatches.PathPatch(path, facecolor=color, edgecolor=PAGE_BG, linewidth=0.5, alpha=0.6)
ax.add_patch(patch)
current_y_left[cat1] = y1_top
current_y_right[cat2] = y2_top
# Draw category bars
for i, dim in enumerate(dimensions):
x = x_positions[i]
for cat in dim_categories[dim]:
y_start, y_end = category_positions[dim][cat]
rect = mpatches.Rectangle(
(x - dim_width, y_start),
dim_width * 2,
y_end - y_start,
facecolor=ELEVATED_BG,
edgecolor=INK_SOFT,
linewidth=2,
)
ax.add_patch(rect)
ax.text(x, (y_start + y_end) / 2, cat, ha="center", va="center", fontsize=14, fontweight="bold", color=INK)
# Add dimension labels
for i, dim in enumerate(dimensions):
ax.text(x_positions[i], 1.02, dim, ha="center", va="bottom", fontsize=20, fontweight="bold", color=INK)
# Styling
ax.set_xlim(-0.15, 1.15)
ax.set_ylim(-0.05, 1.15)
ax.set_aspect("equal")
ax.axis("off")
# Title
ax.set_title("parallel-categories-basic · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", pad=20, color=INK)
# Legend
legend_patches = [mpatches.Patch(color=colors[ch], alpha=0.6, label=ch) for ch in ["Online", "Store", "Mobile"]]
leg = ax.legend(
handles=legend_patches,
loc="lower right",
fontsize=16,
title="Channel",
title_fontsize=18,
bbox_to_anchor=(1.12, 0.0),
)
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Basic Parallel Categories Plot on anyplot.ai.