I think piping and method chaining are a little bit different.
Piping generally chains functions, by passing the result of one call into the next (eg result is first argument to the next).
Method chaining, like in Python, can't do this via syntax. Methods live on an object. Pipes work on any function, not just an object's methods (which can only chain to other object methods, not any function whose eg first argument can take that object).
For example, if you access Polars.DataFrame.style it returns a great_tables.GT object. But in a piping world, we wouldn't have had to add a style property that just calls GT() on the data. With a pipe, people would just be able to pipe their DataFrame to GT().
I think it's often a syntax convenience. For example, Polars and Pandas both have DataFrame.pipe(...) methods, that create the same effect. But it's a bit cumbersome to write.
Piping generally chains functions, by passing the result of one call into the next (eg result is first argument to the next).
Method chaining, like in Python, can't do this via syntax. Methods live on an object. Pipes work on any function, not just an object's methods (which can only chain to other object methods, not any function whose eg first argument can take that object).
For example, if you access Polars.DataFrame.style it returns a great_tables.GT object. But in a piping world, we wouldn't have had to add a style property that just calls GT() on the data. With a pipe, people would just be able to pipe their DataFrame to GT().