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For working with datasets (loading and processing), I use Kotlin DataFrame. It is a library designed for working with structured in-memory data, such as tabular or JSON. It offers convenient storage, manipulation, and data analysis with a convenient, typesafe, readable API. With features for data initialization and operations like filtering, sorting, and integration, Kotlin DataFrame is a powerful tool for data analytics. I also use the Kandy - Kotlin plotting library, designed specifically for full compatibility with Kotlin DataFrame. It brings many types of plots (including statistical) with rich customization options via a powerful Kotlin DSL. The best way to run all of this is Kotlin Notebook. It works out of the box, has native rendering of Kandy plots and DataFrame tables, and has IntelliJ IDEA support. It can also be run in Jupyter notebooks with a Kotlin kernel and on Datalore.
For working with datasets (loading and processing), I use Kotlin DataFrame. It is a library designed for working with structured in-memory data, such as tabular or JSON. It offers convenient storage, manipulation, and data analysis with a convenient, typesafe, readable API. With features for data initialization and operations like filtering, sorting, and integration, Kotlin DataFrame is a powerful tool for data analytics. I also use the Kandy - Kotlin plotting library, designed specifically for full compatibility with Kotlin DataFrame. It brings many types of plots (including statistical) with rich customization options via a powerful Kotlin DSL. The best way to run all of this is Kotlin Notebook. It works out of the box, has native rendering of Kandy plots and DataFrame tables, and has IntelliJ IDEA support. It can also be run in Jupyter notebooks with a Kotlin kernel and on Datalore.
For working with datasets (loading and processing), I use Kotlin DataFrame. It is a library designed for working with structured in-memory data, such as tabular or JSON. It offers convenient storage, manipulation, and data analysis with a convenient, typesafe, readable API. With features for data initialization and operations like filtering, sorting, and integration, Kotlin DataFrame is a powerful tool for data analytics. I also use the Kandy - Kotlin plotting library, designed specifically for full compatibility with Kotlin DataFrame. It brings many types of plots (including statistical) with rich customization options via a powerful Kotlin DSL. The best way to run all of this is Kotlin Notebook. It works out of the box, has native rendering of Kandy plots and DataFrame tables, and has IntelliJ IDEA support. It can also be run in Jupyter notebooks with a Kotlin kernel and on Datalore.