antononcube

That Python package, "NLPTemplateEngine" has Raku and Wolfram Language counterparts:

- Raku, "ML::NLPTemplateEngine"

  - https://raku.land/zef:antononcube/ML::NLPTemplateEngine

- Wolfram Language, "NLPTemplateEngine"

  - https://resources.wolframcloud.com/PacletRepository/resources/AntonAntonov/NLPTemplateEngine/

The integer 2026 is semiprime and a happy number, with 365 as one of its primitive roots. Although 2026 may not be particularly noteworthy in number theory, this provides a great excuse to create various elaborate visualizations that reveal some interesting aspects of the number.

This document (notebook) shows transformations of a movie dataset into a format more suitable for data analysis and for making a movie recommender system. It is the first of a three-part series of notebooks that showcase Raku packages for doing Data Science (DS).

Yes, Wolfram Language (WL) -- aka Mathematica -- introduced `Tabular` in 2025. It is a new data structure with a constellation of related functions (like `ToTabular`, `PivotToColumns`, etc.) Using it is 10÷100 times faster than using WL's older `Dataset` structure. (In my experience. With both didactic and real life data of 1_000÷100_000 rows and 10÷100 columns.)

Mostly, because Python is not a good a "discovery" and prototyping language. It is like that by design -- Guido Van Rossum decided that TMTOWTDI is counter-productive.

Another point, which could have mentioned in my previous response -- Raku has more elegant and easy to use asynchronous computations framework.

IMO, Python's introspection matches that Raku's introspection.

Some argue that Python's LLM packages are more and better than Raku's. I agree on the "more" part. I am not sure about the "better" part:

- Generally speaking, different people prefer decomposing computations in a different way. - When few years ago I re-implemented Raku's LLM packages in Python, Python did not have equally convenient packages.