Python in Science Conferences

Multidimensional categorical data is widespread but not easily visualized using standard methods. For example, questionnaire data generally consists of questions with categorical responses. Popular methods of handling categorical data include one-hot encoding and enumeration, which applies an unwarranted and potentially misleading notional order to the data. To address this, we introduce a novel visualization method named Data Reduction Network.

In the era of exascale computing, storage and analysis of large scale data have become more important and difficult. We present libyt, an open source C++ library, that allows researchers to analyze and visualize data using yt or other Python packages in parallel during simulation runtime.

Data quality remains a core concern for practitioners in machine learning, data science, and data engineering, and many specialized packages have emerged to fulfill the need of validating and monitoring data and models. This paper outlines pandera’s motivation and challenges that took it from being a pandas-only data validation framework to one that is extensible to other non-pandas-compliant dataframe-like libraries.

The gene sequencing data, along with the associated lineage tracing and research data generated throughout the Coronavirus disease 2019 (COVID-19) pandemic, constitute invaluable resources that profoundly empower phylogeography research. To optimize the utilization of these resources, we have developed an interactive analysis platform called aPhyloGeo-Covid.