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seaborn: statistical data visualization
Open accessAbstract
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 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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- Wickham, Hadley, ggplot2: Elegant Graphics for Data Analysis, New York, Springer-Verlag, 2016, 978-3-319-24277-4, https://ggplot2.tidyverse.org
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Metadata
- Authors
- Waskom, M.
- Affiliations
- New York University (US)
- Corresponding author
- Waskom, Michael
- ORCID
- Waskom, Michael
- Venue
- Journal of Open Source Software
- Publisher
- The Open Journal
- 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
- FWCI
- 349.32
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- 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 › Software › Computational Physics and Python Applications
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Mathematics › Statistics and Probability › Data Analysis with R
- Keywords
- seaborn, statistical graphics, Python library, exploratory data analysis, data visualization, publication-quality figures
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