Pharo DataFrame: Past, Present, and Future

Safina Larisa, Zaitsev Oleksandr, Ferlicot-Delbecque Cyril, Sow Papa Ibrahima. 2023. Pharo DataFrame: Past, Present, and Future. In : Proceedings of the International Workshop on Smalltalk Technologies (IWST 2023) Vol 3627. Stéphane Ducasse (ed.), Gordana Rakić (ed.). Lyon : CEUR-WS, 11 p. International Workshop on Smalltalk Technologies (IWST'2023), Lyon, France, 29 Août 2023/31 Août 2023.

Résumé : DataFrame is a tabular data structure for data analysis. It is a two-dimensional table (similar to a spreadsheet) with an extensive API for querying and manipulating the data. Data frames are available in many programming languages (e.g., pandas in Python or data.frame in R), they are the go-to tools for data scientists and machine learning practitioners. Pharo DataFrame was first released in 2017. Since then, the library underwent many changes and improvements. In this paper, we present the Pharo DataFrame library, show examples of its usage, and compare its API to that of pandas. We overview the changes that have been made since DataFrame v1.0, discuss the limitations of the current implementation, and present the roadmap for future.

Mots-clés libres : Pharo, DataFrame, Data analysis, Data structure

Auteurs et affiliations

• Safina Larisa, Université de Lille 1 (FRA) - auteur correspondant
• Zaitsev Oleksandr, CIRAD-ES-UMR SENS (FRA) ORCID: 0000-0003-0267-2874
• Ferlicot-Delbecque Cyril, Université de Lille 1 (FRA)
• Sow Papa Ibrahima, ESP (SEN)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/608298/)

Métriques

[ Page générée et mise en cache le 2024-02-13 ]