Any cider users? Iām curious how people view tablecloth or dataset tables in emacs
C-c C-p
I also really like cider-pprint-eval-last-sexp as otform proposed.
Sometimes I use clay-make-last-sexp or clay-make-defun-at-point with https://github.com/scicloj/clay.el to send datasets to the browser. Then they can be styled with zebra colours etc.,
Then they can also be turned into interactive tables and hold some visualizations inside.
https://scicloj.github.io/clay/clay_book.examples.html#datasets
https://scicloj.github.io/clay/clay_book.examples.html#tables
I have sometimes used clerk for this as well (tho not usually via tap>) but cider-pprint-eval-last-sexp is probably what I use most often
hello, couple of stupid questions about tech.ml.dataset: 1- how to append new rows into a dataset? 2- is there a way to return json str instead of writing directly into a file? I'd like to use this to preview data in my web app, so datasets are really small.
re: 1 - I create a new dataset with the data and then concat the two usually. This from the Tablecloth documentation might help: https://scicloj.github.io/tablecloth/#joinconcat-datasets
tablecloth will also let you get the rows back as maps using (tc/rows ds :as-maps) IIRC which can then easily be turned into json using whatever json tooling you have
I am not using tablecloth though but I'll consider it further.
Thanks man
there will be tmd things that are being called underneath, I just don't know what they are off the top of my head. Tablecloth is very helpful though
I drop into tmd for things like group-by-column-agg and even into hamfisted for some of the parallel reducing there
yea, I like tc api better but i feel like using tmd will force me (in a away) into writing things that are more performant
just a hunch, I'll most likely end up using tc
tc sugar helps me go faster and then I can drop down to the stuff underneath when I need to
but YMMV
I think this is exactly the use case. If you need a performance - go for TMD and it's reducing pathways, if you work on small/mid-sized dataset TC should be enough.