raincloudpy 0.4.4

Beautiful raincloud plots for Python

raincloudpy 🌧️☁️

Beautiful raincloud plots for Python, combining boxplots, half-violin plots, and density-aligned scatter plots to create comprehensive and visually appealing data visualizations.

What is a Raincloud Plot?

Raincloud plots are a hybrid visualization that combines:

• 📊 Box plots - showing quartiles and outliers
• 🎻 Half-violin plots - displaying the probability density
• 🔴 Scatter plots - showing individual data points aligned by density

This creates a "rain cloud" effect that provides maximum information about your data distribution.

Example

Installation

pip install raincloudpy

Quick Start

basic

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from raincloudpy import raincloudplot
import seaborn as sns

# Set random seed for reproducibility
np.random.seed(42)

# Create sample data
df = pd.DataFrame({
    'group': ['A'] * 50 + ['B'] * 50 + ['C'] * 50,
    'value': np.concatenate([
        np.random.randn(50) + 2,
        np.random.randn(50) + 3,
        np.random.randn(50) + 2.5
    ])
})

# Create raincloud plot
# fig, ax = plt.subplots(figsize=(10, 6))
plt.figure(figsize=(10, 6))
sns.set_context('talk')
raincloudplot(data=df, x='group', y='value', palette='Set2')
plt.title('basic raincloud plot example')
plt.ylabel('value')
plt.xlabel('group')
plt.tight_layout()
plt.show()

advanced

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import seaborn as sns

# Set random seed for reproducibility
np.random.seed(42)

# Create sample data mimicking value comparison
N = 150
df_fit = pd.DataFrame({
    'group': ['group 1'] * N + ['group 2'] * N + ['group 3'] * N,
    'value': np.concatenate([
        np.random.normal(loc=300, scale=20, size=N),
        np.random.normal(loc=250, scale=20, size=N),
        np.random.normal(loc=200, scale=20, size=N)
    ])
})

sns.set_style("ticks")
sns.set_context("notebook")

# Create customized raincloud plot
plt.figure(figsize=(6, 5), dpi=300)
raincloudplot(
    data=df_fit, 
    x='group', 
    y='value',
    order=['group 1', 'group 2', 'group 3'],
    palette=['#3498db', '#e74c3c', '#2ecc71'],
    box_width=0.14,
    violin_width=0.4,
    dot_size=10,
    dot_spacing=0.04,
    y_threshold=3,
    box_kwargs={'linewidth': 3},
    scatter_kwargs={'alpha': 0.9, 'edgecolor': 'black', 'linewidth': 0.5},
    violin_kwargs={'alpha': 0.25},
)


plt.title('group comparison using raincloudpy', fontweight='bold')
plt.ylabel('value')
plt.xlabel('')
plt.tight_layout()
sns.despine(offset=10, trim=True, bottom=True)
plt.tick_params(axis='x', which='both', bottom=False, top=False)
plt.show()

Hiding Components

# Only show violin and scatter
raincloudplot(
    data=df, 
    x='group', 
    y='value',
    show_box=False,
    show_violin=True,
    show_scatter=True
)

Features

• 🎨 Customizable colors - Use any seaborn color palette or custom colors
• 📏 Flexible sizing - Control box width, violin width, and dot sizes
• 🎯 Density-aligned scatter - Points are intelligently positioned based on data density
• 🔧 Component control - Show/hide individual components (box, violin, scatter)
• 📊 Works with pandas - Seamless integration with pandas DataFrames
• 🎭 Matplotlib-based - Fully compatible with matplotlib customization

Parameters

Parameter	Type	Default	Description
data	DataFrame	None	Input data (required)
x	str	None	Column name for categorical variable
y	str	None	Column name for continuous variable
order	list	None	Order to plot categorical levels
palette	str/list	None	Colors for different groups
ax	Axes	None	Matplotlib axes object
box_width	float	0.15	Width of boxplot boxes
violin_width	float	0.3	Maximum width of violin plot
dot_size	float	7	Size of scatter points
dot_spacing	float	0.03	Horizontal spacing between dots
box_dots_spacing	float	0.05	Gap between boxplot and scatter points
y_threshold	float/str/None	"5%"	Threshold for grouping y-values: None=stripplot (3% jitter), "5%"=percentage, number=absolute
n_bins	int	40	Number of bins for density estimation
box_kwargs	dict	None	Additional boxplot arguments
violin_kwargs	dict	None	Additional violin plot arguments
scatter_kwargs	dict	None	Additional scatter plot arguments
show_box	bool	True	Whether to show boxplot
show_violin	bool	True	Whether to show violin plot
show_scatter	bool	True	Whether to show scatter plot
orient	str	'v'	Plot orientation ('v' or 'h')

Requirements

• Python >= 3.7
• numpy >= 1.19.0
• matplotlib >= 3.3.0
• scipy >= 1.5.0
• pandas >= 1.1.0
• seaborn >= 0.11.0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this package in your research, please cite:

@software{raincloudpy2025,
  author = {Basile Garcia},
  title = {raincloudpy: Beautiful raincloud plots for Python},
  year = {2025},
  url = {https://github.com/bsgarcia/raincloudpy}
}

Acknowledgments

Inspired by the original raincloud plots concept and various implementations in R and Python.

References

• Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., & Kievit, R. A. (2019). Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Research, 4.