Thanks for the explanation. In your example, my naive expectation would be that df[!,:col] .= 1 creates a column of Int64[] called col with a single element of 1:
df
1×1 DataFrame
│ Row │ a │
│ │ Int64 │
├─────┼───────┤
│ 1 │ 1 │
It is somewhat of an edge case. Although I would expect it to create a new column and assign 1, that may not be consistent with the current behavior, where it would create a column of size R where R is the number of rows. For example:
using DataFrames
df = DataFrame()
R = 10
df[!,:a] = rand( R)
df[!,:b] .= .4
df
10×2 DataFrame
│ Row │ a │ b │
│ │ Float64 │ Float64 │
├─────┼──────────┼─────────┤
│ 1 │ 0.859254 │ 0.4 │
│ 2 │ 0.931467 │ 0.4 │
│ 3 │ 0.276189 │ 0.4 │
│ 4 │ 0.695778 │ 0.4 │
│ 5 │ 0.436747 │ 0.4 │
│ 6 │ 0.478671 │ 0.4 │
│ 7 │ 0.220407 │ 0.4 │
│ 8 │ 0.778795 │ 0.4 │
│ 9 │ 0.614729 │ 0.4 │
│ 10 │ 0.825508 │ 0.4 │
I’m not entirely certain what should be done when R = 0 or R is undefined because the dataframe is empty On one hand, it seems odd to create an empty column when a specific value is being supplied, but assigning the value does not seem entirely consistent with the behavior above either. I guess, between the two options, I might choose to have it create the column and assign the value and use something like df[!,:a] .= Int64[ ] to create an empty column. Of course, that is my somewhat under-informed assessment, and I have not thought about all of the ramifications.
The current behavior is slightly inconvenient because I have to wrap scalars in an array, at least when creating the initial column, e.g. df = DataFrame(); df[!,:a] = [.3]. After that, as you pointed out in your second comment to me, it is possible to assign values subsequently. That’s not a big deal if I always know where the first column will be created. What is more problematic is if v could be a scalar or vector, and the DataFrame is empty. As far as I know, this would require two sets of syntax to handle both cases, whereas before, it handled the assignment of both scalars and vectors.
I agree with you. I’m not sure how often this problem arises in actual code. I have encountered it from time to time in my work. This occurs when I need to extract data from various sources and integrate it with some container iteratively and flexibly (i.e. I may not know what the columns will be or what their sizes will ultimately be).
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