Bubble Chart in Plotly

Last Updated: 08 Nov 2025

Bubble charts are enhanced scatter plots where marker size = third variable — perfect for showing 3D relationships like GDP vs Life Expectancy vs Population, Sales vs Profit vs Region, or Risk vs Return vs Investment.

Hinglish Tip: Bubble = "teen cheez ek saath dikhao" — x, y, aur size! Bada bubble = zyada impact.

Basic Bubble Chart

import plotly.express as px

# Sample data
df = px.data.gapminder().query("year == 2007")

fig = px.scatter(
    df,
    x="gdpPercap",
    y="lifeExp",
    size="pop",
    color="continent",
    hover_name="country",
    title="GDP vs Life Expectancy (Bubble = Population)"
)
fig.show()

Key Parameters in `px.scatter()` for Bubble

Parameter	Values / Examples	Purpose
size	Column name	Bubble size (larger = bigger value)
size_max	60	Max bubble size (pixels)
color	Column	Color by category
hover_name	Column	Bold label on hover
hover_data	['pop']	Extra info on hover
log_x, log_y	True	Log scale (for skewed data)
labels	'gdpPercap': 'GDP per Capita'	Rename axes
title	String	Chart title
opacity	0.7	Transparency (avoid overlap)

Full Example: Company Performance (Revenue, Profit, Employees)

import plotly.express as px
import pandas as pd

# Sample business data
df = pd.DataFrame({
    'Company': ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon', 'Zeta'],
    'Revenue (Cr)': [120, 80, 150, 60, 95, 200],
    'Profit (Cr)': [25, 18, 35, 12, 22, 45],
    'Employees': [800, 500, 1200, 400, 700, 1500],
    'Sector': ['Tech', 'Retail', 'Tech', 'Retail', 'Finance', 'Tech']
})

fig = px.scatter(
    df,
    x='Revenue (Cr)',
    y='Profit (Cr)',
    size='Employees',
    color='Sector',
    hover_name='Company',
    size_max=70,
    log_x=True,  # Better spread for revenue
    opacity=0.8,
    title='Company Analysis: Revenue vs Profit (Bubble = Headcount)',
    labels={
        'Revenue (Cr)': 'Annual Revenue (₹ Crore)',
        'Profit (Cr)': 'Net Profit (₹ Crore)'
    }
)

fig.update_layout(
    legend_title="Sector",
    font=dict(size=12),
    xaxis_title="Revenue (log scale)",
    yaxis_title="Profit"
)

fig.show()

Hinglish Tip: log_x=True → chhote aur bade companies dono dikhe — no overlap!

Animated Bubble Chart (Over Time)

df = px.data.gapminder()

fig = px.scatter(
    df,
    x="gdpPercap", y="lifeExp",
    size="pop", color="continent",
    hover_name="country",
    animation_frame="year",
    animation_group="country",
    size_max=55,
    range_x=[100,100000], range_y=[25,90],
    log_x=True,
    title="World Development: 1952–2007 (Animated)"
)

fig.show()

Hinglish Tip: animation_frame="year" → time travel — dekho kaise countries improve hote hain!

Bubble with Custom Size Scaling

fig = px.scatter(
    df, x='Revenue (Cr)', y='Profit (Cr)',
    size='Employees',
    size_max=60
)

# Custom size scaling (sqrt for better perception)
import numpy as np
sizes = np.sqrt(df['Employees']) * 2  # Adjust multiplier
fig.update_traces(marker=dict(sizemode='area', sizeref=2.*max(sizes)/(60.**2), sizemin=4))
fig.show()

Common Mistakes to Avoid

Mistake	Fix
Bubbles too big → hide others	Use size_max=50 + opacity=0.7
Linear scale → small bubbles invisible	Use log_x or log_y
No hover info	Use hover_name + hover_data
Animation jumps	Use animation_group="country"
Can't export	Install kaleido

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