Matplotlib-Bubble-Scatter-Plots

Scatter & Bubble Plots in Matplotlib — Baby Steps

Keyword: matplotlib scatter • Difficulty: Beginner • Author: Champak Roy

Scatter plots show relationships between two numeric variables. Bubble plots are an extension where the marker size encodes a third variable (like population, sales, or magnitude). Below you'll find short theory, code examples, a static preview, and an interactive Pyodide practice area.

💡 Step-by-Step Tutorial

1) Import Matplotlib & NumPy

import matplotlib.pyplot as plt
import numpy as np

2) Prepare sample data

x = np.random.rand(50) * 10
y = np.random.rand(50) * 8
sizes = np.random.rand(50) * 300 + 30   # marker sizes for bubble plot
colors = np.random.rand(50)             # color mapping

3) Basic scatter plot

plt.scatter(x, y)
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Simple Scatter Plot')
plt.show()

4) Scatter with color & size

plt.scatter(x, y, s=sizes, c=colors, cmap='viridis', alpha=0.7, edgecolor='k')
plt.colorbar(label='color scale')
plt.title('Bubble plot — size & color')
plt.show()

5) Annotate & highlight

# highlight the largest bubble
idx = np.argmax(sizes)
plt.scatter(x, y, s=sizes, c=colors, cmap='plasma', alpha=0.7, edgecolors='w')
plt.scatter(x[idx], y[idx], s=sizes[idx]*1.2, facecolors='none', edgecolor='red', linewidth=1.8)
plt.text(x[idx]+0.2, y[idx], 'Largest', color='red')

💬 Tip: use alpha to avoid overplotting, and cmap to pick appealing color maps (eg. viridis, plasma, magma).

📊 Sample static output (Scatter + Bubble):

🧪 Practice: Scatter & Bubble with Pyodide

Click Preload Pyodide once to install matplotlib and numpy. Then press Run & Render to display your chart as an image!

# Scatter + Bubble example for Pyodide import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import io, base64 import numpy as np np.random.seed(7) n = 80 x = np.random.rand(n) * 10 y = np.random.rand(n) * 8 sizes = (np.random.rand(n) * 200) + 40 # bubble sizes colors = np.random.rand(n) # color by value plt.figure(figsize=(6,3.5)) sc = plt.scatter(x, y, s=sizes, c=colors, cmap='viridis', alpha=0.75, edgecolors='w', linewidth=0.5) plt.colorbar(sc, label='color scale') plt.title('Scatter & Bubble Plot — size & color') plt.xlabel('X value'); plt.ylabel('Y value') plt.grid(alpha=0.25) # annotate a couple of points max_idx = np.argmax(sizes) plt.scatter(x[max_idx], y[max_idx], s=sizes[max_idx]*1.3, facecolors='none', edgecolor='red', linewidth=1.6) plt.text(x[max_idx]+0.15, y[max_idx], 'Largest', color='red', fontsize=9) buf = io.BytesIO() plt.tight_layout() plt.savefig(buf, format='png') buf.seek(0) base64.b64encode(buf.read()).decode('ascii')

Idle

Console output:

Rendered Chart

No chart yet — run code to render a PNG here.

🧠 Tips & Notes

• Use alpha to reduce overplotting (transparency).
• The s parameter controls marker area (not diameter) — larger values create much bigger bubbles.
• c + cmap gives a meaningful color dimension; add plt.colorbar() to show the scale.
• Always call matplotlib.use('Agg') in Pyodide and return the image as base64 (as in the practice runner).

© Learning Sutras — Champak Roy | Made with 💙 in Varanasi