tech.ml.dataset VS tmdjs

Tablecloth by generateme is a friendly & expressive table-processing library built on top of tech.ml.dataset & dtype-next, Chris Nuernberger's high-performance data libraries.

For 1: This ns of tech.ml.dataset supports reading of multiple worksheets per file https://github.com/techascent/tech.ml.dataset/blob/master/src/tech/v3/libs/fastexcel.clj

After building tech.ml.dataset and charred I wanted to take the lessons learned there and apply them back into the base Clojure substrate of persistent maps, persistent vectors, and algorithmic primitives like group-by and frequencies.

Did you try tech.ml.dataset?

Checkout babashka for scritping and clj-python to use numpy from clojure, or https://github.com/techascent/tech.ml.dataset for pure clojure

Really high quality libraries for deep learning, dataset manipulation, and more

tech.ml.dataset has seen some major upgrades for discoverability - specifically the tech.ml.dataset main namespace has been revamped. If you use Cursive or Calva your intellisense will now work with the main namespaces.

It is based on a state-of-the art , high performance tabular dataset implementation, tech.ml.dataset and combines it with a innovative pipeline approach build with idiomatic and functional Clojure concepts in mind. Machine Learning models get pulled in as plugins from existing ecosystems, so are available from the start. Please find user guides and example code in GitHub at https://github.com/scicloj/scicloj.ml

tmdjs - dataframe-style processing for ClojureScript - is now version 1.001. tmdjs brings some serious memory and performance enhancements as compared to pure cljs sequence-of-maps datasets. To this end there is now a very efficient transit-json based dataset serialization format support both client and server side.