I have a one dimensional array of the following type:
5001-element Array{Union{Float64, Missings.Missing},1}
I want to convert it into a pure Float64 array to be able to process it with the function Statsbase.crosscor . How can I achieve that?
Missing values should be replaced with NaN.
Thanks for your replies!
Something like
v = [rand() > 0.90 ? missing : rand() for i in 1:10^4]
g(x) = x === missing ? NaN: x
g.(v)
perhaps?
In 0.7 you can just do
replace(df[col], missing=>NaN)
It’ll even know to change the array type to Vector{Float64}
, which I was impressed with.
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Fixed a typo:
Works like a charm!
Thank you!
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