Cumulative Flow Diagram for Workflow Analytics — Seaborn

A cumulative flow diagram (CFD) displays the cumulative count of items in each workflow stage as stacked areas over time. Each band represents a stage (e.g., Backlog, In Progress, Done), and the vertical distance between two adjacent band boundaries shows the number of items currently in that stage (work-in-progress). This is a key visualization in Lean and Agile project management for identifying bottlenecks, monitoring throughput, and assessing flow efficiency across a delivery pipeline.

Cumulative Flow Diagram for Workflow Analytics rendered with Seaborn

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Python source (Seaborn)

""" anyplot.ai
area-cumulative-flow: Cumulative Flow Diagram for Workflow Analytics
Library: seaborn 0.13.2 | Python 3.13.15
Quality: 91/100 | Updated: 2026-08-18
"""

import os

import matplotlib.dates as mdates
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn.objects as so


# 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"

# Imprint palette — canonical order, first series always #009E73
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]

sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Data — 90-day Kanban board simulation
np.random.seed(42)
n_days = 90
dates = pd.date_range("2024-01-15", periods=n_days, freq="D")

# Cumulative items entering each stage (each stage lags and is capped by upstream)
arrivals = np.random.poisson(6, n_days)
backlog_cum = np.cumsum(arrivals).astype(float)

analysis_cum = np.minimum(backlog_cum, np.cumsum(np.random.poisson(5, n_days)).astype(float))
dev_cum = np.minimum(analysis_cum, np.cumsum(np.random.poisson(4.3, n_days)).astype(float))
testing_cum = np.minimum(dev_cum, np.cumsum(np.random.poisson(3.6, n_days)).astype(float))
done_cum = np.minimum(testing_cum, np.cumsum(np.random.poisson(3.0, n_days)).astype(float))

# WIP per stage: vertical band height = items currently in that stage
done_wip = done_cum
testing_wip = testing_cum - done_cum
dev_wip = dev_cum - testing_cum
analysis_wip = analysis_cum - dev_cum
backlog_wip = backlog_cum - analysis_cum

# Long-form frame for seaborn's objects interface — "stage" is an ordered
# category so.Stack() reads bottom-up, giving the CFD convention directly:
# Done (bottom) ... Backlog (top)
stage_labels = ["Done", "Testing", "Development", "Analysis", "Backlog"]
wip_by_stage = {
    "Done": done_wip,
    "Testing": testing_wip,
    "Development": dev_wip,
    "Analysis": analysis_wip,
    "Backlog": backlog_wip,
}
cfd = pd.DataFrame(
    {
        "date": np.tile(dates, len(stage_labels)),
        "stage": pd.Categorical(np.repeat(stage_labels, n_days), categories=stage_labels, ordered=True),
        "wip": np.concatenate([wip_by_stage[stage] for stage in stage_labels]),
    }
)

# Plot — seaborn's objects interface stacks Area marks bottom-up per CFD
# convention; the custom legend below (not the built-in one) mirrors the
# visual stack order
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

(
    so.Plot(cfd, x="date", y="wip", color="stage")
    .add(so.Area(alpha=0.85, edgewidth=0), so.Stack(), legend=False)
    .scale(color=so.Nominal(IMPRINT, order=stage_labels))
    .on(ax)
    .plot()
)

# Annotate the widening Backlog band — intake (rate 6/day) outpaces Analysis
# throughput (rate 5/day), the CFD's key bottleneck signal in this dataset
annot_day = int(n_days * 0.83)
annot_y = analysis_cum[annot_day] + backlog_wip[annot_day] / 2
ax.annotate(
    "Backlog bottleneck",
    xy=(dates[annot_day], annot_y),
    xytext=(-95, 18),
    textcoords="offset points",
    fontsize=9,
    color=INK,
    arrowprops={"arrowstyle": "->", "color": INK, "lw": 1.2},
)

# Style
ax.set_xlabel("Date", fontsize=11, color=INK)
ax.set_ylabel("Cumulative Items", fontsize=11, color=INK)
ax.set_title("area-cumulative-flow · python · seaborn · anyplot.ai", fontsize=13, fontweight="medium", color=INK)

ax.tick_params(axis="both", labelsize=9, colors=INK_SOFT)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))
ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=0, interval=2))
plt.setp(ax.xaxis.get_majorticklabels(), rotation=30, ha="right")

ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)

ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)

# Legend in visual order — Backlog at top matches its position in the chart
legend_handles = [
    mpatches.Patch(facecolor=color, alpha=0.85, label=stage) for stage, color in zip(stage_labels, IMPRINT, strict=True)
][::-1]
ax.legend(
    handles=legend_handles,
    loc="upper left",
    fontsize=9,
    framealpha=0.9,
    facecolor=ELEVATED_BG,
    edgecolor=INK_SOFT,
    labelcolor=INK,
)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)

Part of Cumulative Flow Diagram for Workflow Analytics on anyplot.ai.

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