OpenRefine VS orange

"OpenRefine is a powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data." https://openrefine.org/

Yes, let's hope! The strategy has worked out sometimes - Google shut down 'Google Refine' 10 years ago, it got turned into 'Open Refine', last update 2 months ago. https://github.com/OpenRefine/OpenRefine

It's a hugely useful tool if you're working with messy Excel-scale data, i.e., most biologists or social scientists.

It seems to be pure JS with jQuery: https://github.com/OpenRefine/OpenRefine/blob/master/main/we...

EDIT: trim() does not remove unicode 0x200b (unicode character for zero width space). https://github.com/OpenRefine/OpenRefine/issues/5105 is worth a read.

There were six homework assignments. In order they were Regular Expressions, OpenRefine, Datalog, SQL, Provenance, and Python. None of these assignments took more than two to three hours to complete. They all were basic implementation and programming assignments with autograders.

Open Refine will get you about 70% there. It's FOSS

I’ve heard good things about https://openrefine.org/

It's not suited to SQL, use Open Refine or python fuzzywuzzy.

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.