# seaborn

This rule guides Seaborn charts in Python, from data shape and styling to plotting large datasets. It fits projects that need consistent, reproducible visualizations.

- 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/seaborn.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/seaborn

## What it does

The rule targets `**/*.py` files. It recommends keeping shared plot settings in `styles.py`, using figure-level functions for faceted layouts, and shaping data in long form, with one observation per row. Functions that create plots should also include type hints.

For large datasets, the guidance favors sampling or aggregation and warns that too many `hue` categories can clutter legends and slow rendering. If `pandas` becomes a processing bottleneck, it suggests considering `polars` or `duckdb`. Reproducibility guidance covers virtual environments and pinned dependencies in `requirements.txt`; critical charts can be checked with visual regression tests. The examples use `seaborn`, `matplotlib`, `pandas`, and `numpy`, so account for those libraries in the project environment.

## Install

### Cursor (native platform)

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

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/seaborn.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/seaborn.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/seaborn.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/seaborn.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/seaborn.mdc

## When to use it
- centralize chart styling in `styles.py`
- reshape data into long form for Seaborn
- create faceted layouts with figure-level functions
- reduce overplotting in large datasets
- catch unintended chart changes with visual tests

## Example prompts
- Create a Seaborn chart with facets for `category` and color groups from `group`.
- Reshape this data into long form and plot it as a bar chart.
- Add type hints to the histogram function and group the plot with `hue_col`.

## Source text · seaborn.mdc · CC0-1.0

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````text
---
description: This guide defines best practices for using Seaborn in Python for data visualization, emphasizing modern, reproducible, and performant approaches in AI/ML pipelines.
globs: **/*.py
---
# seaborn Best Practices

Seaborn is the definitive library for statistical data visualization in Python. This guide outlines our team's mandatory best practices to ensure consistent, reproducible, and high-quality plots in all AI/ML projects.

## 1. Code Organization & Project-Wide Styling

Always configure Seaborn's global style settings once per project. Centralize this in a dedicated `styles.py` module and import it. This guarantees a uniform visual language across all outputs.

❌ BAD: Ad-hoc styling in every script/notebook
```python
# my_script.py
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="darkgrid", palette="viridis") # In every file!
# ... plotting code
```

✅ GOOD: Centralized and imported styling
```python
# styles.py
import seaborn as sns
import matplotlib.pyplot as plt

def apply_seaborn_defaults():
    """Applies project-wide Seaborn and Matplotlib styling."""
    sns.set_theme(
        context="talk",      # Readable text for presentations/reports
        style="whitegrid",   # Clean background with light grid
        palette="deep"       # Perceptually uniform, colorblind-friendly
    )
    plt.rcParams["figure.figsize"] = (8, 5)    # Consistent figure size
    plt.rcParams["figure.dpi"] = 150           # On-screen clarity

# my_script.py
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from .styles import apply_seaborn_defaults # Adjust import path as needed

apply_seaborn_defaults()
# ... plotting code
```

## 2. Prefer Figure-Level Functions

For complex layouts, especially with faceting, always use Seaborn's figure-level functions (`relplot`, `catplot`, `pairplot`, `lmplot`). They handle figure creation, axis management, and legends automatically, ensuring consistency.

❌ BAD: Manual `subplots` with axes-level functions for faceting
```python
# Hard to manage multiple subplots, shared axes, and legends manually
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.scatterplot(data=df[df['category'] == 'A'], x='x', y='y', ax=axes[0])
sns.scatterplot(data=df[df['category'] == 'B'], x='x', y='y', ax=axes[1])
plt.tight_layout()
plt.show()
```

✅ GOOD: `relplot` for automatic faceting and consistent sizing
```python
import pandas as pd
import numpy as np

# Example data
data = {
    'x': np.random.rand(100),
    'y': np.random.rand(100) + np.random.rand(100),
    'category': np.random.choice(['A', 'B'], 100),
    'group': np.random.choice(['X', 'Y'], 100)
}
df = pd.DataFrame(data)

g = sns.relplot(
    data=df, x='x', y='y',
    col='category', # Facet by category
    hue='group',    # Color by group
    kind='scatter',
    height=4, aspect=1.2
)
g.set_axis_labels("Feature X", "Feature Y")
g.set_titles("Category: {col_name}")
plt.tight_layout()
plt.show()
```

## 3. Data Format: Long-Form is King

Seaborn thrives on "tidy" or "long-form" data. Ensure your Pandas DataFrames are structured with one observation per row and variables as columns. This allows Seaborn to infer groupings automatically, reducing boilerplate.

❌ BAD: Wide-form data requiring `melt` or manual iteration
```python
# Data where 'value_A' and 'value_B' are separate columns for the same metric
wide_df = pd.DataFrame({
    'id': [1, 2], 'value_A': [10, 20], 'value_B': [15, 25]
})
# To plot, you'd need:
melted_df = wide_df.melt(id_vars=['id'], var_name='metric', value_name='value')
sns.barplot(data=melted_df, x='metric', y='value')
plt.show() # Added plt.show()
```

✅ GOOD: Data already in long-form
```python
long_df = pd.DataFrame({
    'id': [1, 2, 1, 2],
    'metric': ['value_A', 'value_A', 'value_B', 'value_B'],
    'value': [10, 20, 15, 25]
})
sns.barplot(data=long_df, x='metric', y='value')
plt.show()
```
````
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