You can write
df[isnull.(df[:A]), :A] = newValue to avoid accessing private fields.
But since there won’t be any missing values left in the column, you can also convert it to a standard
Array. The conversion method accepts a second argument giving the value to replace nulls with:
df[:A] = convert(Array, df[:A], newValue).
We could provide a more discoverable and shorter function for that. dplyr uses
coalesce (inspired from SQL), which can be passed either arrays or scalars. In the present case, passing a vector and a scalar would replace the nulls with the scalar.