A Pareto chart combining descending-sorted bars (by frequency or count) with a cumulative percentage line overlay on a secondary y-axis. This visualization helps identify the most significant factors in a dataset by applying the Pareto principle (80/20 rule), making it one of the "7 Basic Tools of Quality" in Six Sigma and quality management. It reveals which categories contribute the most to an overall effect, enabling data-driven prioritization.
""" anyplot.ai
bar-pareto: Pareto Chart with Cumulative Line
Library: letsplot 4.10.1 | Python 3.13.14
Quality: 91/100 | Updated: 2026-06-20
"""
import os
import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot import ggsave
LetsPlot.setup_html()
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
GRID_COLOR = "#E0DFD8" if THEME == "light" else "#2A2A27"
# Imprint palette — canonical order
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
BRAND = IMPRINT_PALETTE[0] # brand green — bars contributing to 80%
CUMLINE_COLOR = IMPRINT_PALETTE[1] # lavender — cumulative percentage line
THRESHOLD_COLOR = "#DDCC77" # amber — warning/caution semantic anchor
# Data — manufacturing defect types sorted by frequency (descending)
categories = ["Scratches", "Dents", "Misalignment", "Cracks", "Discoloration", "Burrs", "Warping", "Contamination"]
counts = [186, 145, 98, 72, 54, 38, 27, 16]
df = pd.DataFrame({"category": categories, "count": counts})
# Cumulative percentage
total = sum(counts)
cumulative_pct = np.cumsum(counts) / total * 100
# Scale cumulative percentages to share the primary y-axis
max_count = max(counts)
y_max = total
scale_factor = y_max / 100
cumulative_scaled = cumulative_pct * scale_factor
threshold_80_scaled = 80 * scale_factor
# Semantic bar colors: brand green for ≤80% threshold, muted for the tail
bar_colors = [BRAND if cumulative_pct[i] <= 80 else INK_MUTED for i in range(len(categories))]
df["bar_color"] = bar_colors
# Segments to draw cumulative line across discrete x-axis
seg_df = pd.DataFrame(
{
"x": categories[:-1],
"xend": categories[1:],
"y": cumulative_scaled[:-1].tolist(),
"yend": cumulative_scaled[1:].tolist(),
}
)
# Points for cumulative line markers
df_points = pd.DataFrame(
{
"category": categories,
"cumulative_scaled": cumulative_scaled.tolist(),
"cumulative_pct": [f"{p:.0f}%" for p in cumulative_pct],
}
)
# Simulated secondary y-axis tick labels (right of last bar)
sec_ticks = [20, 40, 60, 80, 100]
sec_labels_df = pd.DataFrame(
{
"category": [categories[-1]] * len(sec_ticks),
"y": [t * scale_factor for t in sec_ticks],
"label": [f"{t}%" for t in sec_ticks],
}
)
# Title with dynamic font size (16px baseline for ~67-char title)
title = "bar-pareto · python · letsplot · anyplot.ai"
n = len(title)
title_size = round(16 * 67 / n) if n > 67 else 16
title_size = max(title_size, 11)
# Plot
plot = (
ggplot(df, aes(x="category", y="count"))
+ geom_bar(
aes(fill="bar_color"),
stat="identity",
width=0.72,
tooltips=layer_tooltips()
.title("@category")
.line("Count|@count")
.format("count", "d"),
show_legend=False,
)
+ scale_fill_identity()
# Cumulative percentage line (segments span discrete axis positions)
+ geom_segment(
data=seg_df,
mapping=aes(x="x", y="y", xend="xend", yend="yend"),
color=CUMLINE_COLOR,
size=2.0,
inherit_aes=False,
)
# Cumulative line markers
+ geom_point(
data=df_points,
mapping=aes(x="category", y="cumulative_scaled"),
color=CUMLINE_COLOR,
fill=PAGE_BG,
size=5,
shape=21,
stroke=2.0,
inherit_aes=False,
tooltips=layer_tooltips().line("Cumulative|@cumulative_pct"),
)
# 80% threshold reference line (amber warning anchor)
+ geom_hline(yintercept=threshold_80_scaled, color=THRESHOLD_COLOR, size=1.0, linetype="dashed")
+ geom_text(
data=pd.DataFrame({"category": [categories[0]], "y": [threshold_80_scaled], "label": ["80%"]}),
mapping=aes(x="category", y="y", label="label"),
color=THRESHOLD_COLOR,
size=9,
hjust=0.0,
vjust=-0.7,
fontface="bold",
inherit_aes=False,
)
# Simulated secondary y-axis labels (right of last category)
+ geom_text(
data=sec_labels_df[sec_labels_df["label"] != "100%"],
mapping=aes(x="category", y="y", label="label"),
color=CUMLINE_COLOR,
size=6,
hjust=-1.6,
fontface="bold",
inherit_aes=False,
)
+ geom_text(
data=sec_labels_df[sec_labels_df["label"] == "100%"],
mapping=aes(x="category", y="y", label="label"),
color=CUMLINE_COLOR,
size=6,
hjust=-1.6,
vjust=1.8,
fontface="bold",
inherit_aes=False,
)
# Cumulative percentage annotations on first 3 points
+ geom_text(
data=df_points.iloc[:3],
mapping=aes(x="category", y="cumulative_scaled", label="cumulative_pct"),
color=CUMLINE_COLOR,
size=9,
vjust=-1.5,
fontface="bold",
inherit_aes=False,
)
+ scale_x_discrete(limits=categories)
+ scale_y_continuous(limits=[0, y_max], expand=[0, 0, 0.05, 0])
+ labs(
x="Defect Type", y="Frequency (Count)", title=title, caption="Line: cumulative % · Dashed: 80% threshold"
)
+ theme_minimal()
+ theme(
axis_text_x=element_text(angle=45, hjust=1, size=16, color=INK_SOFT),
axis_text_y=element_text(size=16, color=INK_SOFT),
axis_title=element_text(size=20, color=INK),
plot_title=element_text(size=title_size, hjust=0.5, face="bold", color=INK),
plot_caption=element_text(size=14, color=INK_SOFT, hjust=0.5),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_y=element_line(color=GRID_COLOR, size=0.3),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
axis_line=element_line(color=INK_SOFT),
plot_margin=[20, 90, 10, 10],
)
+ ggsize(800, 450)
)
# Save — scale=4 yields 3200×1800 px from the 800×450 base
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Pareto Chart with Cumulative Line on anyplot.ai.