The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
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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Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826
David, F. N., & Tukey, J. W. (1977) · Biometrics · doi:10.2307/2529486 · Cited by 12,707
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 12,469
Inference by Eye: Confidence Intervals and How to Read Pictures of Data.
Cumming, G., & Finch, S. (2005) · American Psychologist · doi:10.1037/0003-066x.60.2.170 · Cited by 1,449 · cited 1×
“The confidence intervals are computed with a bootstrap algorithm, allowing them to generalize over many different statistics, and the default level allows the user to perform “inference by eye” (Cumming & Finch, 2005).”
MethodWickham, H. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i10 · Cited by 939
Distributed representation of context by intrinsic subnetworks in prefrontal cortex
Waskom, M., & Wagner, A. D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615269114 · Cited by 29
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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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gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS
Tresanco, M. S. V., Valdés-Tresanco, M. E., Valiente, P. A., & Moreno, E. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00645 · Cited by 3,468
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167
Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adhikari, N., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., et al. (2023) · Physical Review X · doi:10.1103/physrevx.13.041039 · Cited by 3,333
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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