seaborn: statistical data visualization — seaborn 0.13.2 documentation
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.
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Key Features of the Site
Attractive Statistical Plots
Seaborn provides a variety of visualization patterns that are both attractive and informative, making it easier to understand complex datasets.
High-Level Interface
It offers a high-level interface for drawing attractive statistical graphics, simplifying the process of creating complex visualizations.
Integration with Pandas
Seaborn works seamlessly with pandas data structures, making it easier to plot directly from dataframes and arrays.
Customizable Themes
It comes with several built-in themes for styling matplotlib graphics, allowing for easy customization of plot aesthetics.
Statistical Estimation
Seaborn can automatically perform statistical estimation and plot the results, providing insights into the data distribution and relationships.
Multi-plot Grids
Facilitates the creation of multi-plot grids for plotting complex relationships within the dataset.
Color Palettes
Offers tools for choosing color palettes that can reveal patterns in the data, enhancing the visual appeal and interpretability of plots.
Additional information
Creator
Michael Waskom
License
BSD 3-Clause License
Repository
https://github.com/mwaskom/seaborn
Documentation
Extensive documentation is available on the site, including tutorials, API reference, and example galleries.
Community
Seaborn has a vibrant community of users and contributors. Support can be found through various channels including GitHub issues, Stack Overflow, and the Python mailing list.
Dependencies
Seaborn requires Python, NumPy, SciPy, pandas, and matplotlib. It is designed to work well within the PyData stack.
HTTP headers
Security headers report is a very important part of user data protection. Learn more about http headers for seaborn.pydata.org