DataFrame: Kotlin's Innovative Approach to Data Structures | Roman Belov

Roman Belov introduces Kotlin Dataframe, demonstrating how to read, transform, and organize hierarchical data structures in Kotlin Notebook.

Recording brought to you by American Express. americanexpress.io/kotlin-jobs Enjoyed this talk? Experience it live at KotlinConf 2025—the biggest in history! Grab your tickets now: kotl.in/kotlinconf25 We'll talk about Dataframe — a library and a data structure that can help to read, write, generate, transform and organize data for displaying or plotting. It supports not only flat data structures but also hierarchical ones and thus can represent CSV, JSON or even subgraphs of objects in memory. While dataframes are usually mentioned in the context of data analytics, Kotlin Dataframe is also focused on general data cases and perfect for both professional and personal projects. We'll start with how it all works in Kotlin Notebook — an environment where you can write and execute fragments of code. It’s a good fit for prototyping and trying out ideas. The IDE provides beautiful interactive tables that can display hierarchical data. Also, in the notebooks, the types of dataframe variables are updated after each fragment execution. It means that columns of the dataframe will appear in completion together with their content type. Kotlin Dataframe’s new compiler plugin takes this idea even further and infers on-the-fly the types of columns while data wrangling. Come over, and we’ll show it in action and share what Kotlin language features make it possible