# Converting to an array that accepts 'missing' type

**URL:** <https://discourse.julialang.org/t/converting-to-an-array-that-accepts-missing-type/94111>\
**Category:** New to Julia\
**Tags:** question, arrays, missing-values\
**Created:** [February 6, 2023, 12:10am UTC](https://discourse.julialang.org/t/converting-to-an-array-that-accepts-missing-type/94111 "2023-02-06T00:10:41Z")\
**Posts on this page:** 1\
**Showing post:** 13

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**Author:** ![aplavin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/aplavin/32/222056_2.png) [@aplavin](https://discourse.julialang.org/u/aplavin)\
**Post date:** [February 7, 2023, 9:08am UTC](https://discourse.julialang.org/t/converting-to-an-array-that-accepts-missing-type/94111/13 "2023-02-07T09:08:30Z")

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> [@hexaeder](#):
>
> According to the [docs](https://docs.julialang.org/en/v1/manual/missing/#Arrays-With-Missing-Values), arrays with missing values are still fast

`missing`s are fast in the simplest cases, but generally they can have huge overhead wrt NaNs — see a simple example at [Is there any reason to use NaN instead of missing? - #4 by aplavin](https://discourse.julialang.org/t/is-there-any-reason-to-use-nan-instead-of-missing/84396/4).  
If you know that you have floating point data, nans are perfectly fine, and they are also convenient to work with in julia.

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