krangl

> And what's the alternative, Excel? Take this with a grain of salt from someone who needs data manipulation occasionally every now and then (as opposed to being a full time number-cruncher, data-scientist, statistician etc.), using krangl[1] for Kotlin has been a great experience. I was drawn to this library because I use Kotlin in my dayjob for backend development, but I love how well Kotlin's succinct syntax & features like extension functions lends itself to data transformation & ETL kind of use cases. Also it doesn't hurt that JVM has a plethora of libraries available for things like DB access, plotting, etc. I am sure that Pandas has many features I am unaware of, and for a lot of people the high-ish startup time can be a deterrant, but for most of my day to day data munging the combination of jbang, krangl & kravis has been a prett

The tooling is ahead in some ways and behind in others. As a statically typed language, Kotlin's autocompletion and static analysis is already miles ahead. More broadly, the JVM has a rich ecosystem of libraries for data processing, from natural language processing (CoreNLP) to big data analytics (Spark). If you're looking for something similar to Pandas/Numpy/Matplotlib, maybe check out Krangl [1], EJML-Kotlin [3] or lets-plot [4]. There are also efforts to port TensorFlow [5] and PyTorch [6] to the JVM. While not all libraries as mature as their Python cousins, there are a growing number of native libraries and JVM wrappers for scientific computing, which provide most of the functionality and are very ergonomic to use in Kotlin. [1]: https://github.com/holgerbrandl/krangl [2]: https://github.com/mipt-npm/kmath [3]: https://github.com/le