scispace - formally typeset
Open AccessProceedings ArticleDOI

Data Structures for Statistical Computing in Python

Wes McKinney
- pp 56-61
Reads0
Chats0
TLDR
P pandas is a new library which aims to facilitate working with data sets common to finance, statistics, and other related fields and to provide a set of fundamental building blocks for implementing statistical models.
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.

read more

Content maybe subject to copyright    Report

Citations
More filters
Proceedings ArticleDOI

Vectorized UDFs in Column-Stores

TL;DR: MonetDB/Python is presented, a new system that combines the open-source database MonetDB with the vector-based language Python and demonstrates efficiency gains of orders of magnitude.
Journal ArticleDOI

Many-body localization in a quasiperiodic Fibonacci chain

TL;DR: In this paper, the many-body localization (MBL) properties of a chain of interacting fermions subject to a quasiperiodic potential such that the noninteracting chain is always delocalized and displays multifractality were investigated.
Journal ArticleDOI

Exploring the Role of Osteosarcoma-Derived Extracellular Vesicles in Pre-Metastatic Niche Formation and Metastasis in the 143-B Xenograft Mouse Osteosarcoma Model.

TL;DR: Identification of regulators of cellular and molecular changes in the pre-metastatic lungs might lead to the development of a combination therapies in the future that interrupt PMN formation and combat osteosarcoma metastasis.
Posted Content

Finite Versus Infinite Neural Networks: an Empirical Study

TL;DR: In this article, the authors perform a large-scale empirical study of the correspondence between wide neural networks and kernel methods and show that kernel methods outperform fully-connected finite-width networks, but underperform convolutional finite width networks, and neural network Gaussian process kernels frequently outperform neural tangent (NT) kernels.
Journal ArticleDOI

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

TL;DR: The analyses showed that seed germination increases with increasing warm-cold variety and scarification, and the number of shoots per stump decreases as shoot age increases, and spreading increases with greater light availability and decreasing tillage.
Related Papers (5)