Elevator Pitch:
The explosion in Data Analytics and Data Science applications has been driven by Python and its library modules such as Pandas and Polars. This talk is about re-inventing DataFrames using a raku-centric approach to fundamentally improve architecture, code concision and manipulexity.
Description:
As raku matures, we need useful basic eco-system modules to bring the unique strengths and cultural style of raku to real-world use-cases.
raku::Dan (Data ANalytics) is a new raku module family (also Dan::Pandas and Dan::Polars) that offers top level Raku-oriented data structures for Series and DataFrames. The thinking is:
- we need a Raku-esque way to do the analytics basics - Series and DataFrames
- much has been inspired by Python Pandas (not least for good interworking)
- we can draw from many Raku native capabilities (accessors, types, hypers, laziness, pipes, sort, grep, splice, etc.)
- Pandas suffers from featuritis (Pandas has 422 object methods, plus 50 or so module methods and raku offers a fresh start
This talk will describe the raku::Dan family - design considerations, focus & roadmap and unique differentiators - with hands on examples of Data Munging in Raku and interworking with Python Pandas via raku Inline::Python.
