🌄 Day 43: Ridge Plot in Python

On Day 43 of our Data Visualization journey, we created a beautiful and modern Ridge Plot (Joy Plot) using Plotly in Python.

Ridge plots are perfect when you want to compare distributions across multiple categories — while keeping the visualization smooth and visually engaging.

Today’s example visualizes Sales Distribution by Month from January to May.

🎯 What is a Ridge Plot?

A Ridge Plot is a series of overlapping density plots stacked vertically.

It helps you:

• Compare distributions across categories

• Identify trends over time

• Spot shifts in data patterns

• Understand spread and concentration

It’s especially popular in:

• Time-series distribution analysis

• Financial data

• Sales performance tracking

• Experimental comparisons

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📊 What We’re Visualizing

We simulated monthly sales data for:

• Jan

• Feb

• Mar

• Apr

• May

Each month has its own distribution curve, showing how sales values are spread.

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🧑‍💻 Python Implementation (Plotly)

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✅ Step 1: Import Libraries

import numpy as np
import plotly.graph_objects as go
 

• NumPy → Generate sample distribution data

• Plotly → Create smooth violin-based ridge effect

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✅ Step 2: Set Random Seed

np.random.seed(42)

This ensures reproducible results.

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✅ Step 3: Define Months & Colors

months = ["Jan", "Feb", "Mar", "Apr", "May"]
colors = ["#A3B18A", "#588157", "#3A5A40", "#BC6C25", "#DDA15E"]

We use earthy, muted tones for a clean aesthetic look.

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✅ Step 4: Create Ridge Plot Using Violin Traces

fig = go.Figure()

for i, month in enumerate(months):
   data = np.random.normal(loc=i*5, scale=2, size=200)    
    fig.add_trace(go.Violin(
        x=data,
        y=[month]*len(data),
        orientation='h',
        line_color=colors[i], 
        fillcolor=colors[i],        
        opacity=0.6,
        showlegend=False 
    ))

How This Works:

• np.random.normal() generates distribution data

• Each month shifts slightly using loc=i*5

• Horizontal violins mimic ridge effect

• Transparency creates layered visual flow

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✅ Step 5: Layout Styling

fig.update_layout(
 title="Sales Distribution by Month (Ridge Plot)",
 paper_bgcolor="#FAF9F6", 
 plot_bgcolor="#FAF9F6", 
 font_family="serif", 
  width=900, 
  height=500 )

✨ Design Highlights:

• Soft linen background

• Serif typography

• Horizontal layout

• Clean spacing

• Modern pastel-earth palette

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📈 What the Ridge Plot Shows

• January has lower average sales

• Sales gradually increase toward May

• May shows the highest concentration of values

• Each month’s distribution spreads differently

Instead of just showing averages, the ridge plot shows:

✔ Shape of distribution
✔ Spread of values
✔ Density concentration
✔ Trend shifts over time

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💡 Why Use a Ridge Plot?

✔ Compare multiple distributions at once
✔ Visually appealing and modern
✔ Better than stacked histograms
✔ Ideal for storytelling dashboards
✔ Great for trend-based analysis

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🔥 When to Use Ridge Plots

• Monthly revenue distribution

• Customer spending patterns

• Test score distributions by class

• Stock returns over time

• Performance metrics comparison

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🚀 Day 43 Key Takeaway

Averages don’t tell the full story.

Ridge plots show:

• Variation

• Patterns

• Trends

• Distribution shape