ggpubpy 0.2.1

matplotlib Based Publication-Ready Plots

ggpubpy: 'matplotlib' Based Publication-Ready Plots

Matplotlib is an excellent and flexible package for elegant data visualization in Python. However, the default plotting routines often require extensive boilerplate and manual styling before figures are ready for publication. Customizing complex plots can be a barrier for researchers and analysts without advanced plotting expertise.

The ggpubpy library provides a suite of easy-to-use functions for creating and customizing Matplotlib-based, publication-ready plots—complete with built-in statistical tests and automatic p-value or significance star annotations. This project is directly inspired by R's ggpubr package.

📦 PyPI Package: https://pypi.org/project/ggpubpy/
🐙 GitHub Repository: https://github.com/turkalpmd/ggpubpy

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Installation and loading

Install the latest stable release from PyPI (recommended):

pip install ggpubpy

Why install from PyPI?

• ✅ Stable, tested releases
• ✅ Automatic dependency management
• ✅ Easy updates with pip install --upgrade ggpubpy
• ✅ Compatible with virtual environments

Or install the development version directly from GitHub:

pip install git+https://github.com/turkalpmd/ggpubpy.git

Load the package:

import ggpubpy
from ggpubpy import violinggplot, boxggplot, plot_shift
from ggpubpy.datasets import load_iris  # Built-in datasets

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Core Features

• Violin + boxplot + jitter in one call
• Automatic color palettes with ColorBrewer-inspired defaults
• Built-in datasets (iris) for quick testing and examples
• Flexible group comparisons - works with 2-group, 3-group, or more
• Built-in Kruskal–Wallis & Mann–Whitney U tests (or ANOVA & t-tests for parametric option)
• Automatic p-value or "star" annotation with dynamic bracket placement
• Smart p-value formatting - pairwise comparisons show significance stars (*, **, ns), global tests show formatted values (<0.001)
• Parametric and non-parametric statistical tests with parametric=True/False option
• Smart test selection - t-test for 2 groups, ANOVA for 3+ groups (parametric mode)
• Modular, data-driven API: custom labels, ordering, figure sizing

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Quick Examples

🎻 Violin plots with boxplots & jitter + statistical tests

3-Group Comparison (All Species)

import ggpubpy
from ggpubpy.datasets import load_iris

# Load the iris dataset
iris = load_iris()

# Create the plot with default colors (automatic palette)
fig, ax = ggpubpy.violinggplot(
    df=iris, 
    x="species", 
    y="sepal_length",
    x_label="Species", 
    y_label="Sepal Length (cm)"
)

2-Group Comparison (Subset Analysis)

# Filter for 2-group comparison
iris_2groups = iris[iris['species'].isin(['setosa', 'versicolor'])]

# Create 2-group comparison plot
fig, ax = ggpubpy.violinggplot(
    df=iris_2groups, 
    x="species", 
    y="sepal_length",
    x_label="Species", 
    y_label="Sepal Length (cm)"
)

📊 Boxplots with jitter + statistical tests

3-Group Box Plot with Default Colors

# Create boxplot with default automatic colors
fig, ax = ggpubpy.boxggplot(
    df=iris, 
    x="species", 
    y="sepal_length",
    x_label="Species", 
    y_label="Sepal Length (cm)"
)

2-Group Box Plot with Statistical Tests

# 2-group comparison with Mann-Whitney U test (non-parametric default)
iris_2groups = iris[iris['species'].isin(['setosa', 'versicolor'])]

fig, ax = ggpubpy.boxggplot(
    df=iris_2groups, 
    x="species", 
    y="sepal_length",
    x_label="Species", 
    y_label="Sepal Length (cm)",
    parametric=False  # Non-parametric tests (default)
)

📈 Shift plots for distribution comparison

Shift plots provide a powerful visualization for comparing two distributions by showing:

• Half-violin plots showing distribution shapes
• Box plots with quartiles and outliers
• Raw data points for transparency
• Quantile connections (optional) showing how percentiles shift between groups
• Statistical test results in the title
• Quantile difference subplot (optional) for detailed quantile analysis

Basic Shift Plot

# Compare two groups with shift plot
iris_2groups = iris[iris['species'].isin(['setosa', 'versicolor'])]
x = iris_2groups[iris_2groups['species'] == 'setosa']['sepal_length'].values
y = iris_2groups[iris_2groups['species'] == 'versicolor']['sepal_length'].values

fig = ggpubpy.plot_shift(
    x, y, 
    paired=False, 
    n_boot=1000,
    percentiles=[10, 50, 90], 
    confidence=0.95,
    show_quantiles=True,  # Show quantile connection lines
    show_quantile_diff=False,  # Hide quantile difference subplot
    x_name="Setosa", 
    y_name="Versicolor"
)

Shift Plot with Quantile Differences

# Same plot but with quantile difference subplot
fig = ggpubpy.plot_shift(
    x, y,
    paired=False,
    show_quantiles=True,
    show_quantile_diff=True,  # Show quantile difference subplot
    x_name="Setosa",
    y_name="Versicolor"
)

🎨 Advanced Features

# Custom color palette
custom_palette = {
    "setosa": "#FF6B6B", 
    "versicolor": "#4ECDC4", 
    "virginica": "#45B7D1"
}

fig, ax = ggpubpy.violinggplot(
    df=iris, 
    x="species", 
    y="petal_length",
    x_label="Species", 
    y_label="Petal Length (cm)",
    palette=custom_palette
)

# Parametric tests (ANOVA + t-test instead of Kruskal-Wallis + Mann-Whitney)
fig, ax = ggpubpy.violinggplot(
    df=iris, 
    x="species", 
    y="sepal_length",
    x_label="Species", 
    y_label="Sepal Length (cm)",
    parametric=True
)

# Custom ordering
fig, ax = ggpubpy.violinggplot(
    df=iris, 
    x="species",
    y="petal_width",
    order=["virginica", "versicolor", "setosa"]  # Custom order
)

📊 Built-in Datasets

# Load built-in datasets
iris = ggpubpy.datasets.load_iris()
print(f"Available datasets: {ggpubpy.datasets.list_datasets()}")

# Get recommended color palette for iris species
palette = ggpubpy.datasets.get_iris_palette()
print(palette)  # {'setosa': '#00AFBB', 'versicolor': '#E7B800', 'virginica': '#FC4E07'}

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🤝 Contributing

We welcome contributions! This project is designed to be contribution-friendly.

Ways to Contribute:

• 🐛 Bug reports and feature requests
• 📖 Documentation improvements
• 🔧 Code contributions (new features, optimizations, tests)
• 🎨 New plot types and statistical tests
• 📊 Additional datasets and examples

Getting Started:

# Clone and setup development environment
git clone https://github.com/turkalpmd/ggpubpy.git
cd ggpubpy
pip install -e .
pip install -r requirements-dev.txt

# Run tests to verify setup
python final_check.py

Getting Help & Support

• 🐛 GitHub Issues: For bug reports and feature requests.
• 💬 GitHub Discussions: For questions, suggestions, and community discussion.
• 📚 API Reference: Complete function documentation is available in the source code docstrings.

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License

ggpubpy is released under the MIT License. See LICENSE for details.

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📈 Project Status

🎉 PUBLISHED ON PyPI: June 20, 2025
📦 Latest Version: 0.2.0
🌟 Status: Stable and ready for production use
🤝 Contributing: Open for community contributions

Install now: pip install ggpubpy