Data Structures for Statistical Computing in Python
Wes McKinney low
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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What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| The R Project in Statistical Computing | 2001 | 2,682 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Array programming with NumPy | 2020 | 23,533 |
| Array programming with NumPy | 2020 | 18,812 |
| Definitions, methods, and applications in interpretable machine learning | 2019 | 2,161 |
| Pingouin: statistics in Python | 2018 | 1,913 |
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| Computational Physics and Python Applications | Computer Science |
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