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seaborn: statistical data visualization

Michael Waskom


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  <identifier identifierType="DOI">10.5281/zenodo.4645478</identifier>
  <creators>
    <creator>
      <creatorName>Michael Waskom</creatorName>
      <affiliation>Center for Neural Science, NYU</affiliation>
    </creator>
  </creators>
  <titles>
    <title>seaborn: statistical data visualization</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Python</subject>
    <subject>data science</subject>
    <subject>data visualization</subject>
    <subject>statistical graphics</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-03-29</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4645478</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/mwaskom/seaborn/tree/joss_paper</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.592845</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/zenodo</relatedIdentifier>
  </relatedIdentifiers>
  <version>JOSS paper</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
    <description descriptionType="Other">This DOI points to the commit representing the v0.11.1 release.</description>
  </descriptions>
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