Seaborn is a Python library for statistical data visualization that provides a high-level interface to matplotlib and integrates closely with pandas data structures. It offers a declarative, dataset-oriented API that makes it easy to translate data questions into visual answers. Seaborn automatically maps data values to visual attributes such as color, size, or style, computes statistical transformations, and decorates plots with informative labels and legends. It supports multiple panels for comparing conditional subsets of data or across variable pairings. Seaborn is designed to be useful throughout the lifecycle of a scientific project, enabling rapid prototyping and exploratory data analysis. It also allows extensive customization and can produce publication-quality figures. Data visualization is essential in the scientific process, and seaborn provides a balance between efficiency and flexibility. While matplotlib is highly flexible, its low-level API can make common tasks cumbersome. Seaborn offers a more user-friendly interface that retains the flexibility needed for publication-quality graphics. It is domain-general and can visualize a wide range of tabular datasets. Seaborn allows variables in a dataset to be automatically mapped to visual attributes, enabling rapid exploration of multidimensional relationships. It also applies statistical transformations to data before plotting, such as estimating means or fitting regression models, and shows error bars to indicate uncertainty. Seaborn supports both long-form and wide-form data formats and offers multiple built-in themes for customizing visual appearance. Seaborn does not replace matplotlib but complements it, allowing users to create complex figures by combining seaborn and matplotlib functions. It provides parameters that pass through to matplotlib functions, enabling deeper customization. Seaborn aims to facilitate rapid exploration and prototyping through named functions and opinionated defaults while allowing users to leverage matplotlib's flexibility for more specialized graphics. Seaborn is supported by the National Science Foundation and the Simons Foundation, and it has been improved by a community of contributors. It is an important tool for data visualization in the scientific Python ecosystem.Seaborn is a Python library for statistical data visualization that provides a high-level interface to matplotlib and integrates closely with pandas data structures. It offers a declarative, dataset-oriented API that makes it easy to translate data questions into visual answers. Seaborn automatically maps data values to visual attributes such as color, size, or style, computes statistical transformations, and decorates plots with informative labels and legends. It supports multiple panels for comparing conditional subsets of data or across variable pairings. Seaborn is designed to be useful throughout the lifecycle of a scientific project, enabling rapid prototyping and exploratory data analysis. It also allows extensive customization and can produce publication-quality figures. Data visualization is essential in the scientific process, and seaborn provides a balance between efficiency and flexibility. While matplotlib is highly flexible, its low-level API can make common tasks cumbersome. Seaborn offers a more user-friendly interface that retains the flexibility needed for publication-quality graphics. It is domain-general and can visualize a wide range of tabular datasets. Seaborn allows variables in a dataset to be automatically mapped to visual attributes, enabling rapid exploration of multidimensional relationships. It also applies statistical transformations to data before plotting, such as estimating means or fitting regression models, and shows error bars to indicate uncertainty. Seaborn supports both long-form and wide-form data formats and offers multiple built-in themes for customizing visual appearance. Seaborn does not replace matplotlib but complements it, allowing users to create complex figures by combining seaborn and matplotlib functions. It provides parameters that pass through to matplotlib functions, enabling deeper customization. Seaborn aims to facilitate rapid exploration and prototyping through named functions and opinionated defaults while allowing users to leverage matplotlib's flexibility for more specialized graphics. Seaborn is supported by the National Science Foundation and the Simons Foundation, and it has been improved by a community of contributors. It is an important tool for data visualization in the scientific Python ecosystem.