# Is there a strong case that Julia is a more composable language?

**URL:** <https://discourse.julialang.org/t/is-there-a-strong-case-that-julia-is-a-more-composable-language/118554>\
**Category:** General Usage\
**Tags:** question, python\
**Created:** [August 24, 2024, 10:26am UTC](https://discourse.julialang.org/t/is-there-a-strong-case-that-julia-is-a-more-composable-language/118554 "2024-08-24T10:26:33Z")\
**Posts on this page:** 1\
**Showing post:** 14

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**Author:** ![Tamas\_Papp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tamas_papp/32/25949_2.png) [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)\
**Post date:** [August 30, 2024, 11:47am UTC](https://discourse.julialang.org/t/is-there-a-strong-case-that-julia-is-a-more-composable-language/118554/14 "2024-08-30T11:47:17Z")

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> [@Satvik](#):
>
> Another big reason is that almost everything in Julia is built around the native `Array` type.

I have totally different impression: I think that whenever arrays are involved, idiomatic Julia code tries very hard to stick to the `AbstractArray` interface, and provides special casing (eg for `Array`) as optimizations when applicable.

In other words, most generic Julia methods that take `Array`s will be perfectly happy with `UnitRange`s or whatever.

That said, I would shy away from statements like

> [@alex-s-gardner](#):
>
> Julia inherently provides a more composable eco system relative to python and other major science ML languages

Not because I don’t think it is true, but because it is hard to quantify or measure. Most people who like Julia have just tried it and it worked for them. (Yes, I understand that you want something for the application, but if writing it in Julia vs Python is the only selling point, I am not sure that is a strong argument).

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