An alluvial diagram visualizes how entities flow or transition between discrete categories across multiple time points or ordered stages. Unlike general Sankey diagrams, alluvial diagrams enforce strict vertical ordering where each column represents a specific time step or category dimension. Bands connect related segments to show how proportions shift over time, making it ideal for tracking structural changes, migrations, and transitions in categorical data.
""" anyplot.ai
alluvial-basic: Basic Alluvial Diagram
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-09
"""
import os
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
# 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"
# Okabe-Ito palette - first series always #009E73
IMPRINT = [
"#009E73", # 1. bluish green (brand)
"#C475FD", # 2. vermillion
"#4467A3", # 3. blue
"#BD8233", # 4. reddish purple
]
# Data: Employment sector transitions across 4 years
np.random.seed(42)
years = ["2021", "2022", "2023", "2024"]
sectors = ["Technology", "Finance", "Healthcare", "Manufacturing"]
# Employment counts (thousands) at each time point
sector_counts = np.array(
[
[450, 520, 580, 620], # Technology
[280, 290, 310, 315], # Finance
[320, 340, 360, 380], # Healthcare
[250, 230, 210, 190], # Manufacturing
]
)
# Flow transitions between consecutive years
flows = [
# 2021 -> 2022
{
("Technology", "Technology"): 400,
("Technology", "Finance"): 25,
("Technology", "Healthcare"): 15,
("Technology", "Manufacturing"): 10,
("Finance", "Technology"): 20,
("Finance", "Finance"): 250,
("Finance", "Healthcare"): 5,
("Finance", "Manufacturing"): 5,
("Healthcare", "Technology"): 10,
("Healthcare", "Finance"): 5,
("Healthcare", "Healthcare"): 300,
("Healthcare", "Manufacturing"): 5,
("Manufacturing", "Technology"): 70,
("Manufacturing", "Finance"): 10,
("Manufacturing", "Healthcare"): 40,
("Manufacturing", "Manufacturing"): 130,
},
# 2022 -> 2023
{
("Technology", "Technology"): 480,
("Technology", "Finance"): 15,
("Technology", "Healthcare"): 20,
("Technology", "Manufacturing"): 5,
("Finance", "Technology"): 35,
("Finance", "Finance"): 245,
("Finance", "Healthcare"): 5,
("Finance", "Manufacturing"): 5,
("Healthcare", "Technology"): 30,
("Healthcare", "Finance"): 10,
("Healthcare", "Healthcare"): 295,
("Healthcare", "Manufacturing"): 5,
("Manufacturing", "Technology"): 50,
("Manufacturing", "Finance"): 5,
("Manufacturing", "Healthcare"): 25,
("Manufacturing", "Manufacturing"): 150,
},
# 2023 -> 2024
{
("Technology", "Technology"): 550,
("Technology", "Finance"): 10,
("Technology", "Healthcare"): 15,
("Technology", "Manufacturing"): 5,
("Finance", "Technology"): 30,
("Finance", "Finance"): 280,
("Finance", "Healthcare"): 3,
("Finance", "Manufacturing"): 2,
("Healthcare", "Technology"): 50,
("Healthcare", "Finance"): 8,
("Healthcare", "Healthcare"): 295,
("Healthcare", "Manufacturing"): 7,
("Manufacturing", "Technology"): 25,
("Manufacturing", "Finance"): 3,
("Manufacturing", "Healthcare"): 30,
("Manufacturing", "Manufacturing"): 152,
},
]
# Create mapping from sector to color
sector_colors = {sector: IMPRINT[i] for i, sector in enumerate(sectors)}
# Create figure
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Calculate positions for each time point
n_years = len(years)
x_positions = np.linspace(0, 10, n_years)
bar_width = 0.6
total_height = 100
# Track positions for each node
node_positions = {}
# Draw nodes (stacked bars) at each time point
for year_idx, year in enumerate(years):
x = x_positions[year_idx]
year_total = sector_counts[:, year_idx].sum()
y_bottom = 0
for sector_idx, sector in enumerate(sectors):
height = (sector_counts[sector_idx, year_idx] / year_total) * total_height
y_top = y_bottom + height
node_positions[(year_idx, sector)] = (y_bottom, y_top)
# Draw the bar segment
rect = mpatches.Rectangle(
(x - bar_width / 2, y_bottom),
bar_width,
height,
facecolor=sector_colors[sector],
edgecolor=PAGE_BG,
linewidth=1.5,
)
ax.add_patch(rect)
# Add sector labels on first and last columns
count = sector_counts[sector_idx, year_idx]
label_text = f"{sector} ({count}k)"
font_size = 14
if year_idx == 0:
ax.text(
x - bar_width / 2 - 0.2,
(y_bottom + y_top) / 2,
label_text,
ha="right",
va="center",
fontsize=font_size,
fontweight="bold",
color=sector_colors[sector],
)
elif year_idx == n_years - 1:
ax.text(
x + bar_width / 2 + 0.2,
(y_bottom + y_top) / 2,
label_text,
ha="left",
va="center",
fontsize=font_size,
fontweight="bold",
color=sector_colors[sector],
)
y_bottom = y_top
# Add year labels
year_total_display = sector_counts[:, year_idx].sum()
ax.text(
x,
total_height + 3,
f"{year}\n({year_total_display}k total)",
ha="center",
va="bottom",
fontsize=18,
fontweight="bold",
color=INK,
)
# Draw flows between consecutive time points
for flow_idx, flow_dict in enumerate(flows):
x0 = x_positions[flow_idx]
x1 = x_positions[flow_idx + 1]
year0_total = sector_counts[:, flow_idx].sum()
year1_total = sector_counts[:, flow_idx + 1].sum()
source_offsets = {sector: node_positions[(flow_idx, sector)][0] for sector in sectors}
target_offsets = {sector: node_positions[(flow_idx + 1, sector)][0] for sector in sectors}
for (source_sector, target_sector), flow_value in flow_dict.items():
if flow_value <= 0:
continue
source_height = (flow_value / year0_total) * total_height
target_height = (flow_value / year1_total) * total_height
y0_bot = source_offsets[source_sector]
y0_top = y0_bot + source_height
y1_bot = target_offsets[target_sector]
y1_top = y1_bot + target_height
# Draw curved band
band_x0 = x0 + bar_width / 2
band_x1 = x1 - bar_width / 2
cx0 = band_x0 + 0.4 * (band_x1 - band_x0)
cx1 = band_x0 + 0.6 * (band_x1 - band_x0)
verts = [
(band_x0, y0_bot),
(cx0, y0_bot),
(cx1, y1_bot),
(band_x1, y1_bot),
(band_x1, y1_top),
(cx1, y1_top),
(cx0, y0_top),
(band_x0, y0_top),
(band_x0, y0_bot),
]
codes = [
mpatches.Path.MOVETO,
mpatches.Path.CURVE4,
mpatches.Path.CURVE4,
mpatches.Path.CURVE4,
mpatches.Path.LINETO,
mpatches.Path.CURVE4,
mpatches.Path.CURVE4,
mpatches.Path.CURVE4,
mpatches.Path.CLOSEPOLY,
]
path = mpatches.Path(verts, codes)
min_height = min(source_height, target_height)
alpha = 0.5 if min_height < 3 else 0.35
patch = mpatches.PathPatch(
path,
facecolor=sector_colors[source_sector],
edgecolor=sector_colors[source_sector],
linewidth=0.5,
alpha=alpha,
)
ax.add_patch(patch)
source_offsets[source_sector] = y0_top
target_offsets[target_sector] = y1_top
# Add legend
legend_patches = [
mpatches.Patch(facecolor=sector_colors[sector], edgecolor=PAGE_BG, label=sector) for sector in sectors
]
ax.legend(
handles=legend_patches,
loc="upper right",
fontsize=14,
frameon=True,
facecolor=ELEVATED_BG,
edgecolor=INK_SOFT,
framealpha=0.95,
)
# Styling
ax.set_xlim(-2.8, 13.3)
ax.set_ylim(-8, 120)
ax.set_aspect("auto")
ax.set_xticks([])
ax.set_yticks([])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
# Title
ax.set_title("alluvial-basic · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK, pad=25)
# Subtitle with data context
ax.text(
5,
-5,
"Employment Sector Transitions 2021-2024 | Values in thousands | Flow width proportional to transitions",
ha="center",
va="top",
fontsize=14,
color=INK_SOFT,
style="italic",
)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Basic Alluvial Diagram on anyplot.ai.