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Data Structures for Statistical Computing in Python
Open accessAbstract
In this paper we are concerned with the practical issues of working with data sets common to finance, statistics, and other related fields.pandas is a new library which aims to facilitate working with these data sets and to provide a set of fundamental building blocks for implementing statistical models.We will discuss specific design issues encountered in the course of developing pandas with relevant examples and some comparisons with the R language.We conclude by discussing possible future directions for statistical computing and data analysis using Python.
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Cited by
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
“Now, fifteen years later, NumPy underpins almost every Python library that does scientific or numerical computation including SciPy [13], Matplotlib [14], pandas [15], scikit-learn [16], and scikit-image [17].”
BackgroundDiet rapidly and reproducibly alters the human gut microbiome
David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., et al. (2014) · Nature · doi:10.1038/nature12820 · Cited by 10,100
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
seaborn: statistical data visualization
Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310
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
“Examples include interactive environments (89–91), tools for visualization (92–94), and data exploration tools (95, 96).”
Background
Metadata
- Authors
- McKinney, W.
- Venue
- Proceedings of the Python in Science Conference
- Publisher
- SciPy
- Published
- 2010
- Pages
- 56-61
- ISSN
- 2575-9752 (ISSN-L)
- OpenAlex
- W2342249984
- Cited by
- 11,032
- References
- 0
- FWCI
- 0.26
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the Python in Science Conference. https://doi.org/10.25080/majora-92bf1922-00a
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
