# plotly

This Cursor rule guides Plotly code in Python files, from separating data preparation to styling figures and handling large scatter plots.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/plotly.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/plotly

## What it does

The rule applies to `**/*.py` files. It recommends keeping data processing and metric calculations separate from plotting, then assembling a figure from traces and layout settings. For common chart types, it favors `plotly.express`, whose output is a `go.Figure` that can still be customized.

The guidance covers descriptive titles and axis labels, consistent styling, and ways to handle large datasets: aggregation, sampling, or `go.Scattergl` for large scatter plots. Use `fig.show()` or HTML for interactive output, and `fig.write_image()` for a static image. Image export requires `kaleido`, so that route depends on having the library installed.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/plotly.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/plotly.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/plotly.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/plotly.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/plotly.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/plotly.mdc

## When to use it
- separate data processing from chart code
- build a figure from traces and layout settings
- add clear titles and axis labels
- choose an output format for a report or dashboard
- handle large scatter datasets with sampling, aggregation, or `go.Scattergl`

## Example prompts
- Refactor this chart so data preparation is separate from plotting, then label its axes.
- Use `plotly.express` for this chart and keep the result customizable as a `go.Figure`.
- Prepare this large scatter plot for browser display and choose an appropriate export format.

## Source text · plotly.mdc · CC0-1.0

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BEGIN THIRD-PARTY FILE c11644fdb2fb0d3d
````text
---
description: Definitive guidelines for writing maintainable, performant, and reproducible Plotly visualizations using modern Python best practices, especially for AI/ML workflows.
globs: **/*.py
---
# Plotly Best Practices

Plotly 6.5.0 is a powerful tool for interactive, publication-quality visualizations, crucial for modern AI/ML workflows. These rules ensure your Plotly code is maintainable, reproducible, and production-ready, leveraging the latest features including AI-assisted Studio tools.

## Code Organization and Structure

### 1. Separate Data Preparation from Visualization
Keep data preprocessing, model outputs, and metric calculations distinct from plotting logic. This improves debugging and reusability.

❌ BAD:
```python
import pandas as pd
import plotly.express as px

def analyze_and_plot_data(raw_data: pd.DataFrame):
    # Data processing mixed with plotting
    processed_df = raw_data[raw_data['value'] > 0]
    processed_df['normalized'] = processed_df['value'] / processed_df['value'].max()
    fig = px.scatter(processed_df, x='timestamp', y='normalized', title='Normalized Data')
    fig.show()
```

✅ GOOD:
```python
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

def preprocess_data(raw_data: pd.DataFrame) -> pd.DataFrame:
    """Processes raw data for visualization."""
    processed_df = raw_data[raw_data['value'] > 0]
    processed_df['normalized'] = processed_df['value'] / processed_df['value'].max()
    return processed_df

def create_normalized_scatter_plot(data: pd.DataFrame) -> go.Figure:
    """Creates a scatter plot from processed data."""
    fig = px.scatter(data, x='timestamp', y='normalized', title='Normalized Data Over Time')
    return fig

# Usage example:
# raw_df = pd.read_csv('my_data.csv')
# clean_df = preprocess_data(raw_df)
# plot_fig = create_normalized_scatter_plot(clean_df)
# plot_fig.show()
```

### 2. Modular Figure Construction
Treat the `Figure` object as a modular data structure. Build traces, then define the layout. This mirrors Plotly's internal design and enhances readability.

❌ BAD:
```python
import plotly.graph_objects as go

# Hard-to-read, deeply nested dictionary for figure definition
fig = go.Figure(
    data=[{'type': 'bar', 'x': [1, 2, 3], 'y': [10, 15, 13]}],
    layout={'title': {'text': 'Sales Data'}, 'xaxis': {'title': {'text': 'Month'}}}
)
```

✅ GOOD:
```python
import plotly.graph_objects as go

# Separate data (traces) and layout definition
trace1 = go.Bar(x=[1, 2, 3], y=[10, 15, 13], name='Product A')
trace2 = go.Bar(x=[1, 2, 3], y=[12, 10, 18], name='Product B')

fig = go.Figure(data=[trace1, trace2])
fig.update_layout(
    title_text='Monthly Sales Data',
    xaxis_title_text='Month',
    yaxis_title_text='Revenue ($)',
    legend_title_text='Product'
)
```

## Common Patterns and Anti-patterns

### 3. Prefer Plotly Express for Most Charts
Use `plotly.express` for common chart types. It's terse, consistent, and returns a full `go.Figure` object, allowing easy customization with `graph_objects` methods.

❌ BAD:
```python
import plotly.graph_objects as go
import pandas as pd

df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 1, 2], 'category': ['A', 'B', 'A']})
# Manually creating a scatter plot with graph_objects for a simple case
fig = go.Figure(data=[
    go.Scatter(x=df['x'], y=df['y'], mode='markers', marker=dict(color=df['category'].map({'A': 'red', 'B': 'blue'})))
])
fig.update_layout(title_text='Custom Scatter Plot')
```

✅ GOOD:
```python
import plotly.express as px
import pandas as pd

df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 1, 2], 'category': ['A', 'B', 'A']})
# Plotly Express handles common mappings automatically
fig = px.scatter(df, x='x', y='y', color='category', title='Simple Scatter Plot')
# Further customization is still possible
fig.update_traces(marker_size=10)
```

### 4. Apply Consistent Labeling and Theming
````
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