Kotlin for data analysis

Mobile usability of the docs / notebooks

• Several people struggle to zoom on the Kotlin data-analysis pages on mobile, making notebook content hard to read.
• Others report that enabling “always allow zoom” / accessibility zoom overrides in Safari and Firefox Android fixes this.
• Some recall legacy HTML5 boilerplate patterns that disabled zoom and suspect such patterns are still in use.

Kotlin vs Python for data work

• Strong sentiment that Python dominates data science, LLMs, and analytics because of its ecosystem and “path of least resistance,” not because the language is particularly good.
• Some describe Python as dreary, slow, and with messy package management and bolted‑on typing; they still use it pragmatically for data work.
• Others defend Python as clearly useful and mature; criticisms like “slow” or “bad packaging” are seen as underspecified and applicable to many languages.
• A recurring theme: Kotlin is a joy to write and preferred for many tasks, but its data‑science ecosystem is niche, so most people still reach for Python or sometimes Julia.

Expressiveness: collections, comprehensions, and generators

• Advocates argue Kotlin is more expressive due to: rich, consistent collections APIs; powerful lambdas; scope functions; and DSL-style APIs (e.g., charting).
• Python supporters highlight list comprehensions, simple generators, and yield as elegant tools, especially for those used to math/SQL notation.
• Others counter that comprehensions are special syntax compensating for weak lambdas, and that chained map/filter/sequence operations in Kotlin (or Rust-style iterators) scale better in complexity.
• It is noted that Kotlin has generator-like sequence {} with yield(), matching Python’s capabilities in practice.

Typing, tooling, and ecosystem

• Static typing in Kotlin is seen as a major advantage for large codebases; some feel Python’s gradual typing has made it less pleasant.
• IntelliJ’s Kotlin support is praised as a key part of the value proposition; VS Code / Positron support is viewed as bare‑bones.
• Kotlin notebooks in IntelliJ require the Ultimate edition, but the Kotlin Jupyter kernel can be installed separately via pip/conda.
• Some see a need for “stubborn” adopters to grow the Kotlin data ecosystem, but acknowledge the current dominance of Python tools.

JVM and platform concerns

• Several commenters like Kotlin as a language but dislike the JVM’s complexity and startup overhead, and note clients prefer simpler stacks (Python, Go).
• Kotlin’s reliance on JVM/Android is seen as both strength (ecosystem) and burden; multiplatform/Native/WASM are mentioned but not yet seen as game‑changers.
• There is interest in using Kotlin for Spark and in better desktop/data tooling, but support is currently limited or unclear.