Seaborn

Seaborn: statistical data visualization. Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.


References in zbMATH (referenced in 24 articles )

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  1. Alfredo Mejia-Narvaez, Gustavo Bruzual, Sebastian F. Sanchez, Leticia Carigi, Jorge Barrera-Ballesteros, Mabel Valerdi, Renbin Yan, Niv Drory: CoSHA: Code for Stellar properties Heuristic Assignment - for the MaStar stellar library (2021) arXiv
  2. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  3. Carmichael, Iain; Calhoun, Benjamin C.; Hoadley, Katherine A.; Troester, Melissa A.; Geradts, Joseph; Couture, Heather D.; Olsson, Linnea; Perou, Charles M.; Niethammer, Marc; Hannig, Jan; Marron, J. S.: Joint and individual analysis of breast cancer histologic images and genomic covariates (2021)
  4. Eshin Jolly: Pymer4: Connecting R and Python for Linear Mixed Modeling (2021) not zbMATH
  5. Justin Shenk, Wolf Byttner, Saranraj Nambusubramaniyan, Alexander Zoeller: Traja: A Python toolbox for animal trajectory analysis (2021) not zbMATH
  6. Kelley, Luke Zoltan: kalepy: a Python package for kernel density estimation, sampling and plotting (2021) not zbMATH
  7. Konstantin Stadler: Pymrio - A Python Based Multi-Regional Input-Output Analysis Toolbox (2021) not zbMATH
  8. Mehta, Viraf M.; Demirtas, Mehmet; Long, Cody; Marsh, David J. E.; McAllister, Liam; Stott, Matthew J.: Superradiance in string theory (2021)
  9. Nelly Barret, Fabien Duchateau, Franck Favetta: Predihood: an open-source tool for predicting neighbourhoods’ information (2021) not zbMATH
  10. Oseledets, I. V.; Kharyuk, P. V.: Structuring data with block term decomposition: decomposition of joint tensors and variational block term decomposition as a parametrized mixture distribution model (2021)
  11. Petricek, Tomas: Composable data visualizations (2021)
  12. Arora, Rajat; Zhang, Xiaohan; Acharya, Amit: Finite element approximation of finite deformation dislocation mechanics (2020)
  13. Neal W Morton: Psifr: Analysis and visualization of free recall data (2020) not zbMATH
  14. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  15. Hsieh-Fu Tsai, Joanna Gajda, Tyler F.W. Sloan, Andrei Rares, Jason Ting-Chun Chou, Amy Q. Shen: Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning (2019) not zbMATH
  16. Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Joshua T. Vogelstein: GraSPy: Graph Statistics in Python (2019) arXiv
  17. Liao, Chunxiao; Rosner, Austin O.; Maron, Jill L.; Song, Dongli; Barlow, Steven M.: Automatic nonnutritive suck waveform discrimination and feature extraction in preterm infants (2019)
  18. Michael E.Rose; John R.Kitchin: pybliometrics: Scriptable bibliometrics using a Python interface to Scopus (2019) not zbMATH
  19. Tai Sakuma: AlphaTwirl: A Python library for summarizing event data into multivariate categorical data (2019) arXiv
  20. Aleksey Bilogur: Missingno: a missing data visualization suite (2018) not zbMATH

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