Cited by 7,310Computational Physics and Python ApplicationsData Visualization and AnalyticsData Analysis with R

seaborn: statistical data visualization

Open access
Waskom, Michael·2021·Journal of Open Source Software

Abstract

seaborn is a library for making statistical graphics in Python.It provides a high-level interface to matplotlib and integrates closely with pandas data structures.Functions in the seaborn 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 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 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 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 facilitates rapid prototyping and exploratory data analysis.And by offering extensive options for customization, along with exposing the underlying matplotlib objects, it can be used to create polished, publication-quality figures.

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  • All graphs of experimental results plot the mean and standard deviation over 3 runs as computed by Seaborn.

    “All graphs of our experimental results plot the mean and standard deviation over 3 runs as computed by Seaborn (Waskom, 2021).”

    From The Power of Scale for Parameter-Efficient Prompt Tuning (2021) · cites this paper as Waskom, 2021

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Research works printed but not linked: 2

  1. Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley. ISBN: 978-0201076165
  2. Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag. ISBN: 978-3-319-24277-4

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Authors
Waskom, M.
Affiliations
New York University (US)
Corresponding author
Waskom, Michael
Venue
Journal of Open Source Software
Published
2021-04-06
Volume
6
Issue
60
Pages
3021
ISSN
2475-9066 (ISSN-L)
OpenAlex
W3128261063
Cited by
7,310
References
6
Citations by year
2026: 1498 · 2025: 1881 · 2024: 1517 · 2023: 1172 · 2022: 805 · 2021: 380 · 2020: 34 · 2019: 8 · 2018: 6 · 2017: 5 · 2012: 2
FWCI
349.32
Type
journal article
Language
en
Open access
Locations
  • The Journal of Open Source Software (publishedVersion, cc-by)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
  • Astrophysics Source Code Library
  • Zenodo (CERN European Organization for Nuclear Research) (other-oa)
Funders
  • National Science Foundation (0801700)
  • Simons Foundation (527794)
  • Simons Society of Fellows
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Computational Physics and Python Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
Keywords
seaborn, statistical graphics, Python library, exploratory data analysis, data visualization, publication-quality figures

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