Basic Error Bar Plot — Seaborn

An error bar plot displays data points with associated uncertainty or variability represented by bars extending above and below (or left and right of) each point. Error bars commonly represent standard deviation, standard error, confidence intervals, or min/max ranges. This visualization is essential for communicating the reliability and precision of measurements or statistical estimates.

Basic Error Bar Plot rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
errorbar-basic: Basic Error Bar Plot
Library: seaborn 0.13.2 | Python 3.13.14
Quality: 88/100 | Updated: 2026-06-30
"""

import os

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns


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"

# Imprint palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
BRAND = IMPRINT[0]
ACCENT = IMPRINT[1]

# Clinical trial: symptom reduction (%) by dose group (n=30 per group)
np.random.seed(42)
categories = ["Control", "Placebo", "10 mg", "25 mg", "50 mg", "100 mg"]
means = [45.2, 46.8, 52.3, 57.4, 61.3, 58.9]
stds = [4.5, 5.1, 6.2, 4.9, 5.8, 7.1]
n_per_group = 30

records = [
    {"Dose": cat, "Symptom Reduction (%)": value}
    for cat, mu, sigma in zip(categories, means, stds, strict=True)
    for value in np.random.normal(mu, sigma, n_per_group)
]
df = pd.DataFrame(records)

top_performer = categories[int(np.argmax(means))]
palette = {c: (ACCENT if c == top_performer else BRAND) for c in categories}

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": 0.15,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400)

sns.barplot(
    data=df,
    x="Dose",
    y="Symptom Reduction (%)",
    hue="Dose",
    palette=palette,
    legend=False,
    errorbar="sd",
    capsize=0.25,
    err_kws={"color": INK, "linewidth": 1.5},
    edgecolor=INK_SOFT,
    linewidth=0.6,
    ax=ax,
)

ax.set_xlabel("Dose Group", fontsize=10)
ax.set_ylabel("Symptom Reduction (%)", fontsize=10)
ax.tick_params(axis="both", labelsize=8)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8)
ax.xaxis.grid(False)
ax.set_axisbelow(True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

# Legend explaining the two-color strategy: all groups vs. top performer
brand_patch = mpatches.Patch(color=BRAND, label="Dose group")
accent_patch = mpatches.Patch(color=ACCENT, label=f"Top performer ({top_performer})")
ax.legend(handles=[brand_patch, accent_patch], fontsize=8, framealpha=0.9, loc="lower right")

# Title and subtitle placed in figure coordinates to control vertical spacing precisely
fig.text(
    0.5,
    0.96,
    "errorbar-basic · python · seaborn · anyplot.ai",
    ha="center",
    va="top",
    fontsize=12,
    fontweight="medium",
    color=INK,
)
fig.text(
    0.5,
    0.90,
    f"Bars show mean ± 1 SD — {top_performer} achieves the highest mean symptom reduction",
    ha="center",
    va="top",
    fontsize=9,
    color=INK_MUTED,
)

fig.subplots_adjust(top=0.81, bottom=0.13, left=0.11, right=0.97)
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)

Part of Basic Error Bar Plot on anyplot.ai.

Other implementations