Learning Julia

NA

In the real world, every data that we get or apply our manipulations and transformations to can always have missing data. So the immediate question that follows is how do we handle that missing value?

In Julia, when we create an Array, suppose of length 5 with some random values, if we try having any array position as nothing, we can get something like:

julia> a = [1,2,3,nothing,5,6]
6-element Array{Any,1}:
 1       
 2       
 3       
  nothing
 5       
 6       

# try to access the element
julia> a[4]

# check the data type
julia> typeof(a[4])
Void

But we might not want to have this kind of empty data in our result set. After all, we might need to classify the data as Not Available or NA to make more sense in the result set. Hence, the NA data of the DataFrames structure can ...