paper.bib

@misc{charlierTrevismdStatannotationsV02022,
  title = {Trevismd/Statannotations: V0.5},
  shorttitle = {Trevismd/Statannotations},
  author = {Charlier, Florian and Weber, Marc and Izak, Dariusz and Harkin, Emerson and Magnus, Marcin and Lalli, Joseph and Fresnais, Louison and Chan, Matt and Markov, Nikolay and Amsalem, Oren and Proost, Sebastian and {Agamemnon Krasoulis} and {Getzze} and Repplinger, Stefan},
  year = {2022},
  month = oct,
  doi = {10.5281/ZENODO.7213391},
  url = {https://zenodo.org/record/7213391},
  urldate = {2023-11-16},
  abstract = {Add scipy's Brunner-Munzel test Fix applying statannotations for non-string group labels (Issue \#65) Get Zenodo DOI},
  copyright = {Open Access},
  howpublished = {Zenodo}
}

@article{hunterMatplotlib2DGraphics2007,
  title = {Matplotlib: {{A 2D Graphics Environment}}},
  shorttitle = {Matplotlib},
  author = {Hunter, John D.},
  year = {2007},
  month = may,
  journal = {Computing in Science \& Engineering},
  volume = {9},
  number = {3},
  pages = {90--95},
  issn = {1558-366X},
  doi = {10.1109/MCSE.2007.55},
  url = {https://ieeexplore.ieee.org/document/4160265},
  urldate = {2023-11-15},
  abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems},
  file = {/Users/martinkuric/Zotero/storage/W4FJZDNY/§-hunterMatplotlib2DGraphics2007.pdf;/Users/martinkuric/Zotero/storage/GW3HZZHR/4160265.html}
}

@inproceedings{mckinneyDataStructuresStatistical2010,
  title = {Data {{Structures}} for {{Statistical Computing}} in {{Python}}},
  author = {McKinney, Wes},
  year = {2010},
  month = jan,
  pages = {56--61},
  doi = {10.25080/Majora-92bf1922-00a}
}

@article{mckinneyPandasFoundationalPython2011,
  title = {Pandas: A {{Foundational Python Library}} for {{Data Analysis}} and {{Statistics}}},
  shorttitle = {Pandas},
  author = {Mckinney, Wes},
  year = {2011},
  month = jan,
  journal = {Python High Performance Science Computer},
  abstract = {---In this paper we will discuss pandas, a Python library of rich data structures and tools for working with structured data sets common to statistics, finance, social sciences, and many other fields. The library provides integrated, intuitive routines for performing common data manipulations and analysis on such data sets. It aims to be the foundational layer for the future of statistical computing in Python. It serves as a strong complement to the existing scientific Python stack while implementing and improving upon the kinds of data manipulation tools found in other statistical programming languages such as R. In addition to detailing its design and features of pandas, we will discuss future avenues of work and growth opportunities for statistics and data analysis applications in the Python language.},
  file = {/Users/martinkuric/Zotero/storage/IH5C5UZ3/§-mckinneyPandasFoundationalPython2011.pdf}
}

@misc{reback2020pandas,
  title = {Pandas-Dev/Pandas: {{Pandas}}},
  author = {The Pandas Development Team},
  year = {2020},
  month = feb,
  doi = {10.5281/zenodo.3509134},
  url = {https://doi.org/10.5281/zenodo.3509134},
  howpublished = {Zenodo}
}

@article{vallatPingouinStatisticsPython2018,
  title = {Pingouin: Statistics in {{Python}}},
  shorttitle = {Pingouin},
  author = {Vallat, Raphael},
  year = {2018},
  month = nov,
  journal = {Journal of Open Source Software},
  volume = {3},
  number = {31},
  pages = {1026},
  issn = {2475-9066},
  doi = {10.21105/joss.01026},
  url = {https://joss.theoj.org/papers/10.21105/joss.01026},
  urldate = {2023-05-29},
  abstract = {Vallat, (2018). Pingouin: statistics in Python. Journal of Open Source Software, 3(31), 1026, https://doi.org/10.21105/joss.01026},
  langid = {english},
  file = {/Users/martinkuric/Zotero/storage/ECARCXLJ/§-vallatPingouinStatisticsPython2018.pdf}
}

@article{waskomSeabornStatisticalData2021,
  title = {Seaborn: Statistical Data Visualization},
  shorttitle = {Seaborn},
  author = {Waskom, Michael L.},
  year = {2021},
  month = apr,
  journal = {Journal of Open Source Software},
  volume = {6},
  number = {60},
  pages = {3021},
  issn = {2475-9066},
  doi = {10.21105/joss.03021},
  url = {https://joss.theoj.org/papers/10.21105/joss.03021},
  urldate = {2023-03-26},
  abstract = {Waskom, M. L., (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021, https://doi.org/10.21105/joss.03021},
  langid = {english},
  file = {/Users/martinkuric/Zotero/storage/2ZWPNQDG/§-waskomSeabornStatisticalData2021.pdf}
}

@article{wickhamTidyData2014a,
  title = {Tidy {{Data}}},
  author = {Wickham, Hadley},
  year = {2014},
  month = sep,
  journal = {Journal of Statistical Software},
  volume = {59},
  pages = {1--23},
  issn = {1548-7660},
  doi = {10.18637/jss.v059.i10},
  url = {https://doi.org/10.18637/jss.v059.i10},
  urldate = {2023-11-15},
  abstract = {A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.},
  copyright = {Copyright (c) 2013 Hadley  Wickham},
  langid = {english}
}