Convert missing to a numerical value in Julia 1 - dataframe

Question

I am trying to convert all missing values in a df to a numerical value, e.g. 0 (yes, knowing what I am doing..).
In Julia 0.6 I can write:
julia> df = DataFrame(
cat = ["green","blue","white"],
v1 = [1.0,missing,2.0],
v2 = [1,2,missing]
)
julia> [df[ismissing.(df[i]), i] = 0 for i in names(df)]
And get:
julia> df
3×3 DataFrames.DataFrame
│ Row │ cat │ v1 │ v2 │
├─────┼───────┼─────┼────┤
│ 1 │ green │ 1.0 │ 1 │
│ 2 │ blue │ 0.0 │ 2 │
│ 3 │ white │ 2.0 │ 0 │
If I try it in Julia 0.7 I get instead a very weird error:
MethodError: Cannot convert an object of type Float64 to an object
of type String
I can't get what I am trying to convert to a string ??? Any explanation (and workaround) ?

Answer 1

The reason for this problem is that broadcasting mechanism has changed between Julia 0.6 and Julia 1.0 (and it is used in insert_multiple_entries! function in DataFrames.jl). In the end fill! is called and it tries to do a conversion before checking if the collection is empty.
Actually if you want to do a fully general replacement in place (and I understand you want to) this is a bit complex and less efficient than what you have in Base (the reason is that you cannot rely on checking types of elements in vectors as e.g. you can assign Int to vector of Float64 and they have different types):
function myreplacemissing!(vec, val)
for i in eachindex(vec)
ismissing(vec[i]) && (vec[i] = val)
end
end
And now you are good to go:
foreach(col -> myreplacemissing!(col[2], 0), eachcol(df))

Answer 2

While I appreciate the answer of Bogumil Kaminski (also because now I understood the reasons behind the failure), its proposed solution fails if it happens to exists missing elements in non-numeric columns, e.g.:
df = DataFrame(
cat = ["green","blue",missing],
v1 = [1.0,missing,2.0],
v2 = [1,2,missing]
)
What I can instead do is to use (either or only one, depending on my needs):
[df[ismissing.(df[i]), i] = 0 for i in names(df) if typeintersect(Number, eltype(df[i])) != Union{}]
[df[ismissing.(df[i]), i] = "" for i in names(df) if typeintersect(String, eltype(df[i])) != Union{}]
The advantage is that I can select the type of value I need as "missing replacement" for different type of column (e.g. 0 for a number or "" for a string).
EDIT:
Maybe more readable, thanks again to Begumil's answer:
[df[ismissing.(df[i]), i] = 0 for i in names(df) if Base.nonmissingtype(eltype(df[i])) <: Number]
[df[ismissing.(df[i]), i] = "" for i in names(df) if Base.nonmissingtype(eltype(df[i])) <: String]