tablecloth VS tech.ml.dataset

Another, and definitely better for serious projects, approach is to use https://github.com/scicloj/tablecloth or things that it mentions - tech.ml.dataset and dtype-next.

The forth meeting will be at the end of October and will be dedicated to the Tablecloth dataset manipulation library by generateme, with an intro by Ethan Miller, who is nowadays involved in developing Tablecloth.

I really like geni: it is really idiomatic in its approach to Apache Spark. There are some gaps (no UDFs), and I am not sure that the project is as active as it used to be. But I still use it and find it very nice (I do have Apache Spark background already). tablecloth is an alternative dataframe library that is being used by a lot of folks in the Clojure data science world. For that matter, you should check out scicloj, and also hang out in the data channel in zulip.

At this re:Clojure workshop (Nov. 7th), @ladymeyy taught us about Tablecloth.

In both sessions, we will practice data visualization on a real-world data problem. Among other things, we will try a new data visualization library that Ashima Panjwani is working on. We will assume basic familiarity with Clojure and with Tablecloth. Both sessions will be independent, probably overlapping in content.

We are planning some Scicloj study sessions this weekend about data wrangling with Tablecloth.

And in general for R integration and more data science goodies checkout scicloj and in the vein of dplyr style extremely thought out interfaces I highly recommend tablecloth.

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