ggpubpy 0.4.3

matplotlib Based Publication-Ready Plots with Statistical Tests

ggpubpy

ggpubpy is a Python library for creating publication-ready plots with built-in statistical tests and automatic p-value annotations. Inspired by R's ggpubr package, ggpubpy provides easy-to-use functions for creating professional visualizations suitable for scientific publications.

Features

• 📊 Publication-ready plots: Clean, professional appearance suitable for scientific publications
• 🔬 Built-in statistical tests: Automatic ANOVA, t-tests, correlation analysis, and more
• ⭐ Automatic annotations: P-values and significance stars added automatically
• 🎨 Flexible customization: Extensive options for colors, styling, and layout
• 📈 Multiple plot types: Box plots, violin plots, correlation matrices, shift plots, and alluvial plots
• 🔗 Easy integration: Works seamlessly with pandas DataFrames and numpy arrays

Installation

pip install ggpubpy

Quick Start

from ggpubpy import plot_boxplot_with_stats, load_iris
import matplotlib.pyplot as plt

# Load sample data
iris = load_iris()

# Create a publication-ready boxplot with statistical annotations
fig, ax = plot_boxplot_with_stats(
    df=iris,
    x="species",
    y="sepal_length",
    title="Sepal Length by Species"
)

plt.show()

Available Plot Types

📊 Box Plots

Create box plots with statistical annotations including ANOVA/Kruskal-Wallis tests and pairwise comparisons.

from ggpubpy import plot_boxplot_with_stats, load_iris

fig, ax = plot_boxplot_with_stats(
    df=load_iris(),
    x="species",
    y="sepal_length",
    parametric=False  # Use non-parametric tests
)

🎻 Violin Plots

Visualize data distributions with violin plots that combine the benefits of box plots and density plots.

from ggpubpy import plot_violin_with_stats, load_iris

fig, ax = plot_violin_with_stats(
    df=load_iris(),
    x="species",
    y="petal_length",
    palette={"setosa": "#FF6B6B", "versicolor": "#4ECDC4", "virginica": "#45B7D1"}
)

📈 Shift Plots

Perfect for before-after comparisons and paired data analysis.

from ggpubpy import plot_shift
import numpy as np

# Create sample paired data
before = np.random.normal(10, 2, 30)
after = before + np.random.normal(1, 1.5, 30)

fig = plot_shift(
    x=before,
    y=after,
    x_label="Before Treatment",
    y_label="After Treatment"
)

🔗 Correlation Matrix

Comprehensive visualization of relationships between multiple variables.

from ggpubpy import plot_correlation_matrix, load_iris

fig, axes = plot_correlation_matrix(
    df=load_iris(),
    columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
    title="Iris Dataset Correlation Matrix"
)

🌊 Alluvial Plots

Flow diagrams showing how data moves between categorical dimensions.

from ggpubpy import plot_alluvial, load_titanic
import pandas as pd
import numpy as np

# Load and prepare data
titanic = load_titanic()
titanic = titanic.dropna(subset=["Age"])
titanic["Class"] = titanic["Pclass"].map({1: "1st", 2: "2nd", 3: "3rd"})
titanic["AgeCat"] = np.where(titanic["Age"] < 18, "Child", "Adult")
titanic["Survived"] = titanic["Survived"].astype(str).replace({"0": "No", "1": "Yes"})

# Create frequency table
titanic_tab = (titanic.groupby(["Class", "Sex", "AgeCat", "Survived"])
                    .size()
                    .reset_index(name="Freq")
                    .rename(columns={"AgeCat": "Age"}))
titanic_tab["alluvium"] = titanic_tab.index

# Create alluvial plot
fig, ax = plot_alluvial(
    titanic_tab,
    dims=["Class", "Sex", "Age"],
    value_col="Freq",
    color_by="Survived",
    id_col="alluvium",
    title="Titanic Survival Analysis"
)

Statistical Tests

ggpubpy automatically performs appropriate statistical tests:

• Global Tests: One-way ANOVA, Kruskal-Wallis
• Pairwise Comparisons: t-tests, Mann-Whitney U tests
• Correlation Analysis: Pearson, Spearman, Kendall
• Significance Levels: *** p < 0.001, ** p < 0.01, * p < 0.05, ns p ≥ 0.05

Documentation

📖 Complete documentation is available at https://ggpubpy.readthedocs.io

The documentation includes:

• Detailed function references
• Comprehensive examples
• Statistical test explanations
• Customization guides
• Best practices

Examples

Check out the examples/ directory for complete working examples:

• basic_usage.py: Introduction to ggpubpy functions
• alluvial_examples.py: Alluvial plot examples
• correlation_matrix_example.py: Correlation matrix examples

Dependencies

• Python 3.8+
• matplotlib
• pandas
• numpy
• scipy (for statistical tests)

Contributing

We welcome contributions! Please see our contributing guidelines for more information.

License

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

Citation

If you use ggpubpy in your research, please cite:

@software{ggpubpy,
  title={ggpubpy: Publication-Ready Plots for Python},
  author={Izzet Turkalp Akbasli},
  year={2024},
  url={https://github.com/yourusername/ggpubpy}
}

Support

For questions, bug reports, or feature requests, please open an issue on our GitHub repository.

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Happy plotting! 📊✨