Python pandas creator Wes McKinney has joined data science company Posit as a principal architect, signaling the company's efforts to play a bigger role in the Python universe as well as the R ecosystem. Credit: eamesBot/Shutterstock Python pandas creator Wes McKinney has joined Posit as a principal architect, a move that signals the company’s increasing seriousness about being a key player in the Python universe as well as the R ecosystem. “I will advocate for the needs of the PyData ecosystem in Posit’s work as well as continue advancing critical open-source initiatives,” McKinney wrote in a blog post Monday. Along with being known for the pandas data analysis library, McKinney has worked on other open-source projects including Apache Arrow, Apache Parquet, and Ibis. He is also a co-founder of Voltron Data, which focuses on composable enterprise data systems. McKinney’s move highlights a continued expansion of mission for Posit, a company once known as RStudio with a primary focus on the R programming language and its popular RStudio IDE. The company changed its name last year, pledging not to forsake R but to help data science practitioners and teams who use both R and Python. In the last few years Posit has launched a Python version of its Shiny Web framework, unveiled the Quarto technical publishing platform that is equally friendly to R, Python, and Observable JavaScript, and promoted its Posit Connect enterprise data platform for collaboration and communication work in both R and Python. Related content feature Dataframes explained: The modern in-memory data science format Dataframes are a staple element of data science libraries and frameworks. Here's why many developers prefer them for working with in-memory data. By Serdar Yegulalp Nov 06, 2024 6 mins Data Science Data Management analysis How to support accurate revenue forecasting with data science and dataops Data science and dataops have a critical role to play in developing revenue forecasts business leaders can count on. By Isaac Sacolick Nov 05, 2024 8 mins Data Science Machine Learning Artificial Intelligence feature The best Python libraries for parallel processing Do you need to distribute a heavy Python workload across multiple CPUs or a compute cluster? These seven frameworks are up to the task. By Serdar Yegulalp Oct 23, 2024 11 mins Python Data Science Machine Learning news Julia language adds lower-overhead Memory type Dynamic language built for fast numerical computing introduces lower-level alternative to Array that delivers significant speedups and more maintainable code. By Paul Krill Oct 08, 2024 3 mins Julia Data Science Programming Languages Resources Videos