Seaborn is a Python library designed for statistical data visualization, offering a high-level interface to Matplotlib and integrating closely with Pandas data structures. It provides a declarative, dataset-oriented API that simplifies the process of translating data questions into visual representations. Seaborn automatically maps data values to visual attributes, performs statistical transformations, and decorates plots with informative labels and legends. It supports multiple panels for comparing subsets of data and is useful throughout the lifecycle of a scientific project, facilitating rapid prototyping and exploratory data analysis. Seaborn also offers extensive customization options and can generate publication-quality figures. The library addresses the need for efficient and flexible data visualization tools within the scientific Python ecosystem. While Matplotlib is highly flexible, its low-level API can make common tasks cumbersome. Seaborn aims to bridge this gap by providing a user-friendly interface that retains the flexibility and stability needed for publication-quality graphics. It supports a wide range of datasets, including those in tabular format, and offers built-in themes for customizing the visual appearance of plots. Key features of Seaborn include semantic mappings, which automatically map dataset variables to visual attributes, and statistical transformations, such as mean estimation and regression model fitting. Seaborn supports both long-form and wide-form data formats and provides built-in themes that can be used to modify the visual appearance of plots. While Seaborn does not aim to replace Matplotlib entirely, it complements it by facilitating rapid exploration and prototyping while allowing deeper customization for more advanced applications.Seaborn is a Python library designed for statistical data visualization, offering a high-level interface to Matplotlib and integrating closely with Pandas data structures. It provides a declarative, dataset-oriented API that simplifies the process of translating data questions into visual representations. Seaborn automatically maps data values to visual attributes, performs statistical transformations, and decorates plots with informative labels and legends. It supports multiple panels for comparing subsets of data and is useful throughout the lifecycle of a scientific project, facilitating rapid prototyping and exploratory data analysis. Seaborn also offers extensive customization options and can generate publication-quality figures. The library addresses the need for efficient and flexible data visualization tools within the scientific Python ecosystem. While Matplotlib is highly flexible, its low-level API can make common tasks cumbersome. Seaborn aims to bridge this gap by providing a user-friendly interface that retains the flexibility and stability needed for publication-quality graphics. It supports a wide range of datasets, including those in tabular format, and offers built-in themes for customizing the visual appearance of plots. Key features of Seaborn include semantic mappings, which automatically map dataset variables to visual attributes, and statistical transformations, such as mean estimation and regression model fitting. Seaborn supports both long-form and wide-form data formats and provides built-in themes that can be used to modify the visual appearance of plots. While Seaborn does not aim to replace Matplotlib entirely, it complements it by facilitating rapid exploration and prototyping while allowing deeper customization for more advanced applications.