A streamgraph (also known as a stacked area chart with a centered baseline) displaying the composition of multiple categories over time with smooth, flowing curves. Unlike traditional stacked area charts, streamgraphs use a symmetric baseline centered around the x-axis, creating an organic, river-like appearance that emphasizes the overall shape and relative proportions of each category while minimizing the visual distortion of individual layers.
""" anyplot.ai
streamgraph-basic: Basic Stream Graph
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-05
"""
import os
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline
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 = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
np.random.seed(42)
months = np.arange(24)
month_labels = [
"Jan'23",
"Feb'23",
"Mar'23",
"Apr'23",
"May'23",
"Jun'23",
"Jul'23",
"Aug'23",
"Sep'23",
"Oct'23",
"Nov'23",
"Dec'23",
"Jan'24",
"Feb'24",
"Mar'24",
"Apr'24",
"May'24",
"Jun'24",
"Jul'24",
"Aug'24",
"Sep'24",
"Oct'24",
"Nov'24",
"Dec'24",
]
# Monthly streaming hours by music genre — diverse trend patterns
pop = 50 + 10 * np.sin(months / 6) + np.random.randn(24) * 3 + months * 0.3
rock = 35 + 8 * np.cos(months / 4) + np.random.randn(24) * 2
hiphop = 25 + months * 0.8 + 5 * np.sin(months / 3) + np.random.randn(24) * 3
electronic = 20 + 15 * np.sin((months - 3) / 6 * np.pi) + np.random.randn(24) * 2
jazz = 18 - months * 0.15 + 4 * np.cos(months / 5) + np.random.randn(24) * 1.5
classical = 15 + 8 * np.cos(months / 6 * np.pi) + np.random.randn(24) * 1.5
data_raw = [pop, rock, hiphop, electronic, jazz, classical]
for arr in data_raw:
np.maximum(arr, 5, out=arr)
categories = ["Pop", "Rock", "Hip-Hop", "Electronic", "Jazz", "Classical"]
# Cubic spline interpolation → smooth, flowing curves
months_fine = np.linspace(0, 23, 300)
data_smooth = np.array([np.maximum(make_interp_spline(months, series, k=3)(months_fine), 1.0) for series in data_raw])
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
ax.stackplot(months_fine, data_smooth, labels=categories, colors=IMPRINT, baseline="wiggle", alpha=0.85)
ax.set_xlabel("Month (Jan 2023 – Dec 2024)", fontsize=20, color=INK)
ax.set_title("streamgraph-basic · matplotlib · anyplot.ai", fontsize=24, color=INK, fontweight="medium")
tick_positions = list(range(0, 24, 3))
ax.set_xticks(tick_positions)
ax.set_xticklabels([month_labels[i] for i in tick_positions], fontsize=16)
ax.tick_params(axis="x", colors=INK_SOFT, labelcolor=INK_SOFT)
ax.set_yticks([])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_color(INK_SOFT)
ax.set_xlim(months_fine[0], months_fine[-1])
leg = ax.legend(loc="upper left", fontsize=16, framealpha=0.9)
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Basic Stream Graph on anyplot.ai.