Basic Area Chart — plotnine

An area chart showing quantitative data over a continuous axis with the area below the line filled. It emphasizes the magnitude of values over time by filling the space between the line and axis, creating visual weight that helps readers understand volume and trends. Particularly effective for showing cumulative totals, resource consumption, or any data where the "amount" is as important as the trend.

Basic Area Chart rendered with plotnine

Python source (plotnine)

""" anyplot.ai
area-basic: Basic Area Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-28
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    annotate,
    element_blank,
    element_line,
    element_rect,
    element_text,
    geom_line,
    geom_ribbon,
    geom_smooth,
    ggplot,
    labs,
    scale_x_datetime,
    scale_y_continuous,
    theme,
    theme_minimal,
)


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"]
BRAND = IMPRINT_PALETTE[0]

# Data
np.random.seed(42)
dates = pd.date_range(start="2024-01-01", periods=31, freq="D")
base_traffic = 4800
trend = np.linspace(0, 2200, 31)
weekly_pattern = 1000 * np.sin(np.arange(31) * 2 * np.pi / 7)
amplitude_growth = np.linspace(1.0, 1.8, 31)
noise = np.random.normal(0, 500, 31) * amplitude_growth
visitors = base_traffic + trend + weekly_pattern * amplitude_growth + noise
visitors[14:16] -= np.array([1400, 600])
visitors[19:23] = np.mean(visitors[19:23]) * np.ones(4) + np.random.normal(0, 100, 4)
visitors = np.maximum(visitors, 1000)

df = pd.DataFrame({"date": dates, "visitors": visitors})

peak_idx = int(df["visitors"].idxmax())
peak_val = int(df["visitors"].max())
dip_idx = 14
dip_val = int(df.loc[dip_idx, "visitors"])

y_min = int(np.floor(df["visitors"].min() / 500) * 500)
y_max = int(np.ceil(df["visitors"].max() / 500) * 500) + 500
df["y_floor"] = y_min

title = "area-basic · python · plotnine · anyplot.ai"
n = len(title)
title_fontsize = max(8, round(12 * (67 / n if n > 67 else 1.0)))

# Plot
plot = (
    ggplot(df, aes(x="date", y="visitors"))
    + geom_ribbon(aes(ymin="y_floor", ymax="visitors"), fill=BRAND, alpha=0.35)
    + geom_line(color=BRAND, size=1.5)
    + geom_smooth(method="lowess", color=IMPRINT_PALETTE[2], size=1.5, se=False, span=0.5)
    + annotate(
        "text",
        x=dates[peak_idx],
        y=peak_val + 300,
        label=f"Peak: {peak_val:,}",
        size=4.0,
        color=INK,
        fontweight="bold",
        ha="right",
    )
    + annotate(
        "text",
        x=dates[dip_idx + 1],
        y=dip_val + 350,
        label=f"Maintenance: {dip_val:,}",
        size=3.5,
        color=INK_MUTED,
        fontstyle="italic",
        ha="left",
    )
    + annotate(
        "text",
        x=dates[25],
        y=df.loc[25, "visitors"] + 500,
        label="Trend (LOWESS)",
        size=3.5,
        color=IMPRINT_PALETTE[2],
        fontweight="bold",
    )
    + labs(
        x="Date (January 2024)",
        y="Daily Visitors (count)",
        title=title,
        subtitle="Upward trend with weekly cycles, a mid-month maintenance dip, and a brief plateau",
    )
    + scale_x_datetime(date_labels="%b %d")
    + scale_y_continuous(labels=lambda lst: [f"{int(v):,}" for v in lst], limits=(y_min, y_max))
    + theme_minimal()
    + theme(
        figure_size=(8, 4.5),
        text=element_text(size=7, color=INK_SOFT),
        axis_title=element_text(size=10, color=INK),
        axis_text=element_text(size=8, color=INK_SOFT),
        plot_title=element_text(size=title_fontsize, weight="bold", color=INK),
        plot_subtitle=element_text(size=8, color=INK_MUTED, style="italic"),
        panel_background=element_rect(fill=PAGE_BG, color="none"),
        plot_background=element_rect(fill=PAGE_BG, color="none"),
        panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.15),
        panel_grid_major_x=element_line(color=INK, size=0.2, alpha=0.08),
        panel_grid_minor=element_blank(),
        axis_line_x=element_line(color=INK_SOFT, size=0.6),
        axis_ticks_major_x=element_line(color=INK_SOFT, size=0.4),
        axis_ticks_major_y=element_blank(),
        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
        legend_text=element_text(color=INK_SOFT),
        plot_margin=0.04,
    )
)

plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)

Part of Basic Area Chart on anyplot.ai.

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