graph LR
Visualization_Plotting["Visualization & Plotting"]
qqplot["qqplot"]
plot_shift["plot_shift"]
plot_paired["plot_paired"]
plot_circmean["plot_circmean"]
plot_rm_corr["plot_rm_corr"]
plot_blandaltman["plot_blandaltman"]
Visualization_Plotting -- "exposes" --> qqplot
Visualization_Plotting -- "exposes" --> plot_shift
Visualization_Plotting -- "exposes" --> plot_paired
Visualization_Plotting -- "exposes" --> plot_circmean
Visualization_Plotting -- "exposes" --> plot_rm_corr
Visualization_Plotting -- "exposes" --> plot_blandaltman
qqplot -- "utilizes" --> _ppoints
plot_shift -- "utilizes" --> adjacent_values
plot_paired -- "utilizes" --> seaborn
plot_circmean -- "utilizes" --> matplotlib
plot_rm_corr -- "utilizes" --> statsmodels
plot_rm_corr -- "utilizes" --> seaborn
plot_blandaltman -- "utilizes" --> matplotlib
plot_blandaltman -- "utilizes" --> scipy_stats
click Visualization_Plotting href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pingouin/Visualization_Plotting.md" "Details"
The pingouin.plotting subsystem serves as the primary visualization interface within the Pingouin library, offering a suite of specialized statistical plots. It acts as a facade, exposing various plotting functions that encapsulate the logic for generating specific types of visualizations. Each plotting function (qqplot, plot_shift, plot_paired, plot_circmean, plot_rm_corr, plot_blandaltman) is a self-contained component responsible for its specific plot type, handling data preparation, statistical calculations, and rendering using underlying libraries like Matplotlib and Seaborn. The Visualization & Plotting component orchestrates access to these individual plotting functionalities, providing a unified entry point for users.
Visualization & Plotting [Expand]
The overarching component providing a comprehensive set of utilities for generating various statistical plots within the pingouin library. It serves as the entry point for users to access visualization functionalities.
Related Classes/Methods:
pingouin.plotting.qqplot:219-407pingouin.plotting.plot_shift:665-898pingouin.plotting.plot_paired:410-662pingouin.plotting.plot_circmean:1033-1160pingouin.plotting.plot_rm_corr:901-1030pingouin.plotting.plot_blandaltman:29-193
Generates Quantile-Quantile (Q-Q) plots to assess if a dataset's distribution matches a theoretical distribution, a fundamental tool for normality testing and distribution comparison. It relies on scipy.stats.probplot for core calculations and matplotlib.pyplot for rendering.
Related Classes/Methods:
Creates plots to visualize shifts or changes in data distributions, useful for comparing groups or pre-post measurements, often employed in effect size visualization. It utilizes seaborn.pointplot and seaborn.boxplot for visualization.
Related Classes/Methods:
Visualizes paired observations, typically displaying individual data points and connecting them to show trends or differences, crucial for repeated measures designs. It leverages seaborn.lineplot and seaborn.scatterplot for individual data points and lines, and seaborn.boxplot for summary statistics.
Related Classes/Methods:
Plots circular data, specifically the circular mean and potentially its confidence intervals or measures of dispersion, catering to specialized statistical domains. It uses matplotlib.patches.Circle and matplotlib.axes.Axes.arrow for drawing the circular plot and mean vector.
Related Classes/Methods:
Generates plots to visualize repeated measures correlations, often showing individual subject trends alongside the overall correlation, vital for longitudinal studies. It integrates with statsmodels.formula.api.ols for statistical modeling and seaborn.FacetGrid, seaborn.regplot, and seaborn.scatterplot for visualization.
Related Classes/Methods:
Generates Bland-Altman plots to assess the agreement between two different measurements or methods. It calculates mean differences and limits of agreement, and visualizes them with scatter plots and horizontal lines.
Related Classes/Methods: