plotly

name	plotly
description	Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.
type	skill
created	2026-02-27T00:00:00.000Z
domain	ai-ml
category	ml-data-science
risk	unknown
source	community
tags	["skill","ai-ml","ml-data-science","plotly"]

Plotly

Python graphing library for creating interactive, publication-quality visualizations with 40+ chart types.

When to Use

• You need interactive charts with hover, zoom, pan, or web embedding.
• You are building dashboards, exploratory analysis notebooks, or presentations that benefit from rich interaction.
• You want to choose between Plotly Express and Graph Objects for the same visualization task.

Quick Start

Install Plotly:

uv pip install plotly

Basic usage with Plotly Express (high-level API):

import plotly.express as px
import pandas as pd

df = pd.DataFrame({
    'x': [1, 2, 3, 4],
    'y': [10, 11, 12, 13]
})

fig = px.scatter(df, x='x', y='y', title='My First Plot')
fig.show()

Choosing Between APIs

Use Plotly Express (px)

For quick, standard visualizations with sensible defaults:

• Working with pandas DataFrames
• Creating common chart types (scatter, line, bar, histogram, etc.)
• Need automatic color encoding and legends
• Want minimal code (1-5 lines)

See reference/plotly-express.md for complete guide.

Use Graph Objects (go)

For fine-grained control and custom visualizations:

• Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)
• Building complex multi-trace figures from scratch
• Need precise control over individual components
• Creating specialized visualizations with custom shapes and annotations

See reference/graph-objects.md for complete guide.

Note: Plotly Express returns graph objects Figure, so you can combine approaches:

fig = px.scatter(df, x='x', y='y')
fig.update_layout(title=)  
fig.add_hline(y=)