MCPInstall

plotly

by davila7Media & Creative

Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/davila7/claude-code-templates --skill plotly
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads plotly automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Plotly

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

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='Custom Title')  # Use go methods on px figure
fig.add_hline(y=10)                     # Add shapes

Core Capabilities

1. Chart Types

Plotly supports 40+ chart types organized into categories:

Basic Charts: scatter, line, bar, pie, area, bubble

Statistical Charts: histogram, box plot, violin, distribution, error bars

Scientific Charts: heatmap, contour, ternary, image display

Financial Charts: candlestick, OHLC, waterfall, funnel, time series

Maps: scatter maps, choropleth, density maps (geographic visualization)

3D Charts: scatter3d, surface, mesh, cone, volume

Specialized: sunburst, treemap, sankey, parallel coordinates, gauge

For detailed examples and usage of all chart types, see reference/chart-types.md.

2. Layouts and Styling

Subplots: Create multi-plot figures with shared axes:

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

fig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))
fig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)

Templates: Apply coordinated styling:

fig = px.scatter(df, x='x', y='y', template='plotly_dark')
# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white

Customization: Control every aspect of appearance:

  • Colors (discrete sequences, continuous scales)
  • Fonts and text
  • Axes (ranges, ticks, grids)
  • Legends
  • Margins and sizing
  • Annotations and shapes

For complete layout and styling options, see reference/layouts-styling.md.

3. Interactivity

Built-in interactive features:

  • Hover tooltips with customizable data
  • Pan and zoom
  • Legend toggling
  • Box/lasso selection
  • Rangesliders for time series
  • Buttons and dropdowns
  • Animations
# Custom hover template
fig.update_traces(
    hovertemplate='<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>'
)

# Add rangeslider
fig.update_xaxes(rangeslider_visible=True)

# Animations
fig = px.scatter(df, x='x', y='y', animation_frame='year')

For complete interactivity guide, see reference/export-interactivity.md.

4. Export Options

Interactive HTML:

fig.write_html('chart.html')                       # Full standalone
fig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file

Static Images (requires kaleido):

uv pip install kaleido

fig.write_image('chart.png')   # PNG
fig.write_image('chart.pdf')   # PDF
fig.write_image('chart.svg')   # SVG

For complete export options, see reference/export-interactivity.md.

Common Workflows

Scientific Data Visualization

import plotly.express as px

# Scatter plot with trendline
fig = px.scatter(df, x='temperature', y='yield', trendline='ols')

# Heatmap from matrix
fig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')

# 3D surface plot
import plotly.graph_objects as go
fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])

Statistical Analysis

# Distribution comparison
fig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)

# Box plot with all points
fig = px.box(df, x='category', y='value', points='all')

# Violin plot
fig = px.violin(df, x='group', y='measurement', box=True)

Time Series and Financial

# Time series with rangeslider
fig = px.line(df, x='date', y='price')
fig.update_xaxes(rangeslider_visible=True)

# Candlestick chart
import plotly.graph_objects as go
fig = go.Figure(data=[go.Candlestick(
    x=df['date'],
    open=df['open'],
    high=df['high'],
    low=df['low'],
    close=df['close']
)])

Multi-Plot Dashboards

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

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),
    specs=[[{'type': 'scatter'}, {'type': 'bar'}],
           [{'type': 'histogram'}, {'type': 'box'}]]
)

fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)
fig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)
fig.add_trace(go.Histogram(x=data), row=2, col=1)
fig.add_trace(go.Box(y=data), row=2, col=2)

fig.update_layout(height=800, showlegend=False)

Integration with Dash

For interactive web applications, use Dash (Plotly's web app framework):

uv pip install dash

import dash
from dash import dcc, html
import plotly.express as px

app = dash.Dash(__name__)

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

app.layout = html.Div([
    html.H1('Dashboard'),
    dcc.Graph(figure=fig)
])

app.run_server(debug=True)

Reference Files

  • plotly-express.md - High-level API for quick visualizations
  • graph-objects.md - Low-level API for fine-grained control
  • chart-types.md - Complete catalog of 40+ chart types with examples
  • layouts-styling.md - Subplots, templates, colors, customization
  • export-interactivity.md - Export options and interactive features

Additional Resources

FAQ

What does the plotly skill do?

Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).

How do I install plotly?

Run npx skills add https://github.com/davila7/claude-code-templates --skill plotly in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/plotly/ (personal) or .claude/skills/plotly/ (project).

Which AI agents can use plotly?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is plotly safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains plotly?

It is published by davila7 in davila7/claude-code-templates. It has 1,045 recorded installs.

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