Abstract
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.
Keywords
Python (programming language)Computer scienceData scienceData explorationStatistical analysisData structureComputational statisticsTheoretical computer scienceData miningSoftware engineeringProgramming languageMachine learningStatisticsVisualizationMathematics
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Publication Info
- Year
- 2010
- Type
- article
- Pages
- 56-61
- Citations
- 10212
- Access
- Closed
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Cite This
Wes McKinney
(2010).
Data Structures for Statistical Computing in Python.
Proceedings of the Python in Science Conferences
, 56-61.
https://doi.org/10.25080/majora-92bf1922-00a
Identifiers
- DOI
- 10.25080/majora-92bf1922-00a