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Julia Missing Values "Julia provides support for representing missing values in the statistical sense, that is for situations where no value is available for a
by pella 8y ago
Julia Missing Values
"Julia provides support for representing missing values in the statistical sense, that is for situations where no value is available for a variable in an observation, but a valid value theoretically exists. Missing values are represented via the missing object, which is the singleton instance of the type Missing. missing is equivalent to NULL in SQL and NA in R, and behaves like them in most situations."
https://docs.julialang.org/en/v1/manual/missing/index.html https://docs.julialang.org/en/v1/manual/missing/index.html
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"First-Class Statistical Missing Values Support in Julia 0.7"
https://julialang.org/blog/2018/06/missing https://julialang.org/blog/2018/06/missing
- ChrisRackauckas 8y agoThe nice thing about Julia is that it separates `nothing` from `missing`. nothing<:Nothing is a null that does not propagate, i.e. `1+nothing` is an error. It's an engineering null, providing a type of null where you don't want to silently continue if doing something bad. On the other hand, missing is propagates, so `1+missing` outputs a missing. This is a Data Scientist's null, where you want to calculate as much as possible and see what results are directly known given the data you have. The two are different concepts and when conflated it makes computing more difficult. By separating the two, Julia handles both domains quite elegantly.