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seaborn: statistical data visualization

Michael Waskom


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4645478", 
  "title": "seaborn: statistical data visualization", 
  "issued": {
    "date-parts": [
      [
        2021, 
        3, 
        29
      ]
    ]
  }, 
  "abstract": "<p>Seaborn&nbsp;is a library for making statistical graphics in Python. It provides a high-level interface to matplotlib&nbsp;and integrates closely with pandas&nbsp;data structures. Functions in the seaborn&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&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&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&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&nbsp;facilitates rapid prototyping and exploratory data analysis. And by offering extensive options for customization, along with exposing the underlying matplotlib&nbsp;objects, it can be used to create polished, publication-quality figures.</p>", 
  "author": [
    {
      "family": "Michael Waskom"
    }
  ], 
  "note": "This DOI points to the commit representing the v0.11.1 release.", 
  "version": "JOSS paper", 
  "type": "article", 
  "id": "4645478"
}
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