A box plot (box-and-whisker plot) showing the distribution of numerical data through quartiles. Displays the median, first and third quartiles as a box, with whiskers extending to show the data range. Essential for comparing distributions across categories and identifying outliers.
""" anyplot.ai
box-basic: Basic Box Plot
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-28
"""
import os
import sys
# Prevent this file (named matplotlib.py) from shadowing the installed matplotlib package
_here = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.realpath(p or ".") != _here]
del _here
import matplotlib.patheffects as pe
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data — annual salary distributions (USD thousands) across five job sectors
np.random.seed(42)
sectors = ["Technology", "Finance", "Healthcare", "Education", "Retail"]
salary_data = [
np.clip(np.random.normal(118, 28, 80), 55, 260), # Technology: high, moderate spread
np.clip(np.random.normal(108, 35, 75), 45, 300), # Finance: high, wide spread
np.clip(np.random.normal(94, 20, 85), 42, 200), # Healthcare: moderate, tight
np.clip(np.random.normal(66, 14, 70), 35, 120), # Education: lower, narrow
np.clip(np.random.normal(50, 11, 65), 28, 95), # Retail: lowest, tight
]
# Inject deliberate outliers for feature coverage
salary_data[0] = np.append(salary_data[0], [228, 246]) # Tech: senior engineers
salary_data[1] = np.append(salary_data[1], [268, 290, 47]) # Finance: top earners + entry-level
# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
bp = ax.boxplot(
salary_data,
tick_labels=sectors,
patch_artist=True,
widths=0.55,
showmeans=True,
notch=True,
meanprops={"marker": "D", "markerfacecolor": PAGE_BG, "markeredgecolor": INK, "markersize": 6, "zorder": 5},
flierprops={
"marker": "o",
"markerfacecolor": INK_MUTED,
"markersize": 5,
"alpha": 0.65,
"markeredgecolor": PAGE_BG,
"markeredgewidth": 0.5,
},
medianprops={"color": INK, "linewidth": 2.5},
whiskerprops={"linewidth": 1.8, "color": INK_SOFT, "linestyle": "--"},
capprops={"linewidth": 2.0, "color": INK_SOFT},
)
# Apply Imprint palette colors to boxes with theme-adaptive path effect depth
for patch, color in zip(bp["boxes"], IMPRINT_PALETTE[:5], strict=False):
patch.set_facecolor(color)
patch.set_alpha(0.75)
patch.set_edgecolor(color)
patch.set_linewidth(2.0)
patch.set_path_effects([pe.withStroke(linewidth=3.5, foreground=INK_SOFT)])
# Compute stats for data-driven annotations
iqrs = [np.percentile(d, 75) - np.percentile(d, 25) for d in salary_data]
medians = [np.median(d) for d in salary_data]
q3s = [np.percentile(d, 75) for d in salary_data]
tightest_idx = int(np.argmin(iqrs))
widest_idx = int(np.argmax(iqrs))
best_idx = int(np.argmax(medians))
# Annotation — highest median salary (Technology, directly above box)
ax.annotate(
f"Highest median\n${medians[best_idx]:.0f}K",
xy=(best_idx + 1, q3s[best_idx] + 1),
xytext=(best_idx + 1, q3s[best_idx] + 52),
fontsize=8,
fontweight="bold",
color=IMPRINT_PALETTE[best_idx],
ha="center",
va="bottom",
arrowprops={"arrowstyle": "->", "color": IMPRINT_PALETTE[best_idx], "lw": 1.5},
bbox={"boxstyle": "round,pad=0.3", "facecolor": ELEVATED_BG, "edgecolor": IMPRINT_PALETTE[best_idx], "alpha": 0.9},
)
# Annotation — widest IQR (Finance, placed higher to clear Healthcare box region)
ax.annotate(
f"Widest spread\nIQR=${iqrs[widest_idx]:.0f}K",
xy=(widest_idx + 1, medians[widest_idx]),
xytext=(widest_idx + 1.7, medians[widest_idx] + 65),
fontsize=8,
fontweight="bold",
color=IMPRINT_PALETTE[widest_idx],
ha="left",
arrowprops={
"arrowstyle": "->",
"connectionstyle": "arc3,rad=-0.3",
"color": IMPRINT_PALETTE[widest_idx],
"lw": 1.5,
},
bbox={
"boxstyle": "round,pad=0.3",
"facecolor": ELEVATED_BG,
"edgecolor": IMPRINT_PALETTE[widest_idx],
"alpha": 0.9,
},
)
# Annotation — tightest IQR (Retail, placed directly above Retail's box)
# Placed at x=tightest_idx+1 (same column as Retail) so it cannot overlap Healthcare (x=3).
# Use INK text color in dark theme: #AE3030 on dark elevated bg has marginal contrast.
_tight_text_color = INK if THEME == "dark" else IMPRINT_PALETTE[tightest_idx]
ax.annotate(
f"Tightest spread\nIQR=${iqrs[tightest_idx]:.0f}K",
xy=(tightest_idx + 1, q3s[tightest_idx] + 1),
xytext=(tightest_idx + 1, q3s[tightest_idx] + 42),
fontsize=8,
fontweight="bold",
color=_tight_text_color,
ha="center",
va="bottom",
arrowprops={"arrowstyle": "->", "color": IMPRINT_PALETTE[tightest_idx], "lw": 1.5},
bbox={
"boxstyle": "round,pad=0.3",
"facecolor": ELEVATED_BG,
"edgecolor": IMPRINT_PALETTE[tightest_idx],
"alpha": 0.9,
},
)
# Style
title = "box-basic · python · matplotlib · anyplot.ai"
title_fontsize = max(8, round(12 * 67 / len(title))) if len(title) > 67 else 12
ax.set_xlabel("Sector", fontsize=10, color=INK)
ax.set_ylabel("Annual Salary (USD thousands)", fontsize=10, color=INK)
ax.set_title(
title,
fontsize=title_fontsize,
fontweight="medium",
color=INK,
pad=15,
path_effects=[pe.withStroke(linewidth=3, foreground=PAGE_BG)],
)
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT, labelcolor=INK_SOFT)
ax.tick_params(axis="x", length=0, pad=6)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
ax.set_axisbelow(True)
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)
fig.subplots_adjust(left=0.12, right=0.97, top=0.88, bottom=0.13)
# Save
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Part of Basic Box Plot on anyplot.ai.