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seaborn: statistical data visualization

Michael Waskom


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    <dct:title>seaborn: statistical data visualization</dct:title>
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    <dcat:keyword>data science</dcat:keyword>
    <dcat:keyword>data visualization</dcat:keyword>
    <dcat:keyword>statistical graphics</dcat:keyword>
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    <dct:description>&lt;p&gt;Seaborn&amp;nbsp;is a library for making statistical graphics in Python. It provides a high-level interface to matplotlib&amp;nbsp;and integrates closely with pandas&amp;nbsp;data structures. Functions in the seaborn&amp;nbsp;library expose a declarative, dataset-oriented API that makes it easy to translate questions about data into graphics that can answer them. When given a dataset and a specification of the plot to make, seaborn&amp;nbsp;automatically maps the data values to visual attributes such as color, size, or style, internally computes statistical transformations, and decorates the plot with informative axis labels and a legend. Many seaborn&amp;nbsp;functions can generate figures with multiple panels that elicit comparisons between conditional subsets of data or across different pairings of variables in a dataset. seaborn&amp;nbsp;is designed to be useful throughout the lifecycle of a scientific project. By producing complete graphics from a single function call with minimal arguments, seaborn&amp;nbsp;facilitates rapid prototyping and exploratory data analysis. And by offering extensive options for customization, along with exposing the underlying matplotlib&amp;nbsp;objects, it can be used to create polished, publication-quality figures.&lt;/p&gt;</dct:description>
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