Stream Graph
Chart overview
Stream graphs are a variation of stacked area charts where series flow around a central baseline rather than stacking from zero.
Key points
- This creates an organic, river-like appearance that emphasizes changes in composition over time while de-emphasizing absolute values.
Python Tutorial
How to create a stream graph in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Complete Guide to Scientific Data VisualizationExample Visualization
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View example prompt
"Create a stream graph showing 'Music Genre Popularity' on streaming platforms over the past decade (2014-2024). Generate data for 6 genres with realistic trends: Pop (stable 25-30%), Hip-Hop (rising from 15% to 30%), Rock (declining 20% to 12%), Electronic/EDM (peaked 2016-2018 at 18%, now 12%), R&B (steady 10-12%), Country (growing 8% to 14%). Use smooth asymmetric streamlines centered on a baseline. Apply distinct, harmonious colors per genre. Add subtle year markers on X-axis. Include interactive hover showing exact percentages. Add annotations for key moments: 'Streaming revolution 2015', 'Hip-Hop becomes #1 (2018)'. Legend on right side. Title: 'Decade of Streaming: Genre Evolution 2014-2024'."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import altair as alt
# === USER-EDITABLE PARAMETERS ===
title = "Decade of Streaming: Genre Evolution 2014-2024"
figsize = (14, 6)
# === EXAMPLE DATASET ===
years = list(range(2014, 2025))
genres = ['Pop', 'Hip-Hop', 'Rock', 'Electronic', 'R&B', 'Country']
# Genre percentages over time
data = {
'Pop': [28, 27, 26, 25, 26, 27, 28, 27, 26, 25, 26],
'Hip-Hop': [15, 17, 20, 23, 26, 28, 29, 30, 30, 30, 30],
'Rock': [20, 19, 18, 17, 16, 15, 14, 13, 13, 12, 12],
'Electronic': [14, 16, 18, 17, 15, 13, 12, 12, 12, 12, 12],
'R&B': [11, 11, 10, 10, 10, 10, 10, 11, 12, 12, 12],
'Country': [8, 8, 8, 8, 8, 9, 10, 11, 12, 13, 14],
}
# Create DataFrame
rows = []
for year_idx, year in enumerate(years):
for genre in genres:
rows.append({
'Year': year,
'Genre': genre,
'Percentage': data[genre][year_idx]
})
df = pd.DataFrame(rows)
# Print summary
print("=== Streaming Genre Evolution ===")
print(f"\nYears covered: {min(years)} - {max(years)}")
print(f"\nGenre percentages in 2024:")
for genre in genres:
val_2014 = data[genre][0]
val_2024 = data[genre][-1]
change = val_2024 - val_2014
print(f" {genre}: {val_2024}% ({change:+d}% from 2014)")
# === CREATE STREAM GRAPH ===
# Using matplotlib stackplot with wiggle baseline for stream effect
fig, ax = plt.subplots(figsize=figsize)
# Compute baseline for centered stream
y_values = np.array([data[genre] for genre in genres])
# Colors for genres
colors = ['#e74c3c', '#3498db', '#95a5a6', '#9b59b6', '#f39c12', '#27ae60']
# Create stackplot with 'wiggle' baseline for stream effect
ax.stackplot(years, y_values, labels=genres, colors=colors, alpha=0.8, baseline='wiggle')
# Styling
ax.set_xlabel('Year', fontsize=12, fontweight='bold')
ax.set_ylabel('Relative Share (%)', fontsize=12, fontweight='bold')
ax.set_title(title, fontsize=16, fontweight='bold', pad=20)
# Set x-axis ticks
ax.set_xticks(years)
ax.set_xticklabels([str(y) for y in years], rotation=45)
# Legend
ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1), framealpha=0.9)
# Annotations
ax.annotate('Streaming\nRevolution', xy=(2015, 0), fontsize=10, ha='center',
bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
ax.annotate('Hip-Hop\nbecomes #1', xy=(2018, 10), fontsize=10, ha='center',
bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
ax.set_xlim(2014, 2024)
ax.grid(True, alpha=0.3, axis='x')
plt.tight_layout()
plt.show()
# END-OF-CODE
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
=== Streaming Genre Evolution === Years covered: 2014 - 2024 Genre percentages in 2024: Pop: 26% (-2% from 2014) Hip-Hop: 30% (+15% from 2014) Rock: 12% (-8% from 2014) Electronic: 12% (-2% from 2014) R&B: 12% (+1% from 2014) Country: 14% (+6% from 2014)
Common Use Cases
- 1Trend evolution visualization
- 2Content popularity over time
- 3Genre/category shifts
- 4Artistic data presentations
Pro Tips
Order streams by peak timing
Use smooth interpolation
Add interactive tooltips for values
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
altair
Useful in specialized workflows that complement core Python plotting libraries for stream-graph analysis tasks.
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for stream-graph.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.