I need to convert the following `DataFrame`

```
julia> df = DataFrame(:A=>["", "2", "3"], :B=>[1.1, 2.2, 3.3])

```

which looks like

```
3×2 DataFrame
│ Row │ A      │ B       │
│     │ String │ Float64 │
├─────┼────────┼─────────┤
│ 1   │        │ 1.1     │
│ 2   │ 2      │ 2.2     │
│ 3   │ 3      │ 3.3     │

```

I would like to convert A column from `Array{String,1}` to array of `Int` with missing values.

I tried

```
julia> df.A = tryparse.(Int, df.A)
3-element Array{Union{Nothing, Int64},1}:
  nothing
 2
 3

julia> df
3×2 DataFrame
│ Row │ A      │ B       │
│     │ Union… │ Float64 │
├─────┼────────┼─────────┤
│ 1   │        │ 1.1     │
│ 2   │ 2      │ 2.2     │
│ 3   │ 3      │ 3.3     │

julia> eltype(df.A)
Union{Nothing, Int64}

```

but I'm getting A column with elements of type `Union{Nothing, Int64}`.

`nothing` (of type `Nothing`) and `missing` (of type `Missing`) seems to be 2 differents kind of value.

So I wonder how I can A columns with `missing` values instead?

I also wonder if `missing` and `nothing` leads to different performance.