Data Visualization Using Plotly in Python

Quick answer: Learn how to create interactive charts in Python using Plotly, including line charts, bar charts, scatter plots and dashboards, with example code.

Why Use Plotly Instead of Matplotlib?

Plotly creates interactive charts by default, letting viewers hover for details, zoom, and pan, directly in a web browser or Jupyter notebook, without any extra code. Matplotlib produces static images, which are simpler but offer none of this interactivity out of the box.

Installation

pip install plotly

A Basic Line Chart

import plotly.express as px
import pandas as pd

df = pd.DataFrame({
    'month': ['Jan', 'Feb', 'Mar', 'Apr'],
    'sales': [12000, 15000, 11000, 18000]
})

fig = px.line(df, x='month', y='sales', title='Monthly Sales')
fig.show()

Bar Chart

fig = px.bar(df, x='month', y='sales', color='month', title='Sales by Month')
fig.show()

Scatter Plot

fig = px.scatter(df, x='marketing_spend', y='sales', size='sales', color='region',
                  hover_data=['month'])
fig.show()

hover_data adds extra fields visible only when hovering over a point, a genuinely useful feature for exploring a dataset interactively that a static chart cannot replicate.

Pie Chart

fig = px.pie(df, names='month', values='sales', title='Sales Distribution')
fig.show()

Combining Multiple Charts

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(rows=1, cols=2, subplot_titles=('Sales Trend', 'Sales by Region'))
fig.add_trace(go.Scatter(x=df['month'], y=df['sales']), row=1, col=1)
fig.add_trace(go.Bar(x=df['region'], y=df['sales']), row=1, col=2)

fig.show()

Exporting Charts

fig.write_html('sales_chart.html')     # interactive HTML file
fig.write_image('sales_chart.png')     # static image, requires the kaleido package

Plotly vs Matplotlib vs Seaborn

Common Interview Questions

What is the main advantage of Plotly over Matplotlib?

Plotly charts are interactive by default, allowing hovering, zooming and panning without extra code, which is particularly valuable for dashboards and reports shared with non-technical stakeholders.

When might you still choose Matplotlib over Plotly?

For quick, simple exploratory plots during initial analysis, or when producing static, publication-quality figures where interactivity is not needed.